paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
91dbe15b-b799-4ba5-9666-98507123f2ab | exploring-adversarial-learning-for-deep-semi | 2106.02258 | null | https://arxiv.org/abs/2106.02258v1 | https://arxiv.org/pdf/2106.02258v1.pdf | Exploring Adversarial Learning for Deep Semi-Supervised Facial Action Unit Recognition | Current works formulate facial action unit (AU) recognition as a supervised learning problem, requiring fully AU-labeled facial images during training. It is challenging if not impossible to provide AU annotations for large numbers of facial images. Fortunately, AUs appear on all facial images, whether manually labeled or not, satisfy the underlying anatomic mechanisms and human behavioral habits. In this paper, we propose a deep semi-supervised framework for facial action unit recognition from partially AU-labeled facial images. Specifically, the proposed deep semi-supervised AU recognition approach consists of a deep recognition network and a discriminator D. The deep recognition network R learns facial representations from large-scale facial images and AU classifiers from limited ground truth AU labels. The discriminator D is introduced to enforce statistical similarity between the AU distribution inherent in ground truth AU labels and the distribution of the predicted AU labels from labeled and unlabeled facial images. The deep recognition network aims to minimize recognition loss from the labeled facial images, to faithfully represent inherent AU distribution for both labeled and unlabeled facial images, and to confuse the discriminator. During training, the deep recognition network R and the discriminator D are optimized alternately. Thus, the inherent AU distributions caused by underlying anatomic mechanisms are leveraged to construct better feature representations and AU classifiers from partially AU-labeled data during training. Experiments on two benchmark databases demonstrate that the proposed approach successfully captures AU distributions through adversarial learning and outperforms state-of-the-art AU recognition work. | ['Bowen Pan', 'Guozhu Peng', 'Yanan Chang', 'Shangfei Wang'] | 2021-06-04 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 4.40141171e-01 6.40832007e-01 -5.50433159e-01 -5.60875714e-01
-8.73503268e-01 -4.64792490e-01 6.90384954e-02 -6.03158832e-01
-1.01359271e-01 6.04300380e-01 -5.34800962e-02 3.08349341e-01
4.93156463e-01 -7.85731614e-01 -8.48541915e-01 -1.03178942e+00
2.28447974e-01 5.40121734e-01 -4.67275441e-01 1.65720090e-01
-4.26239043e-01 8.87945890e-01 -1.66981816e+00 3.85242432e-01
5.10763049e-01 1.71504998e+00 -4.26886380e-01 2.29132682e-01
7.20798373e-02 8.90797973e-01 -3.78438652e-01 -2.95053810e-01
5.93165636e-01 -7.56818354e-01 -6.48330986e-01 6.12226784e-01
8.12996328e-01 -8.11160386e-01 -6.53680205e-01 9.47610140e-01
5.01095772e-01 -9.08798724e-02 1.18516064e+00 -1.43277979e+00
-8.92998517e-01 1.96510166e-01 -6.74086034e-01 -1.56880885e-01
1.23619676e-01 2.87666202e-01 8.57146919e-01 -7.55266428e-01
5.30135632e-01 9.86083984e-01 3.23416203e-01 1.31675780e+00
-1.21100843e+00 -1.23861575e+00 -1.35023460e-01 -2.24828944e-01
-1.70411420e+00 -6.08265281e-01 8.75035882e-01 -5.13494015e-01
3.58924150e-01 -5.39420135e-02 4.62334216e-01 1.21846902e+00
1.02856077e-01 8.46068084e-01 9.35680091e-01 -4.78078537e-02
9.99142751e-02 1.69321168e-02 -2.30487525e-01 1.17826235e+00
-1.82089046e-01 4.65081573e-01 -2.29219288e-01 -3.00533444e-01
9.21862781e-01 3.26820254e-01 -1.80648461e-01 -6.96460009e-02
-3.89783531e-01 5.71257055e-01 5.00525653e-01 7.98477698e-03
-4.33027178e-01 -3.00616864e-02 3.07163388e-01 1.94726661e-01
2.87344873e-01 -9.67366248e-02 -1.26628965e-01 3.36339951e-01
-7.25327730e-01 -8.68252739e-02 3.35216373e-01 8.44555020e-01
1.14656150e+00 3.65665108e-01 -2.44827166e-01 9.05298591e-01
4.27762866e-01 8.28021109e-01 6.49752557e-01 -8.44981074e-01
-3.08864824e-02 9.03184354e-01 -2.28666201e-01 -9.16903436e-01
-1.60477608e-01 2.10831314e-02 -8.36905003e-01 3.63613933e-01
6.42853081e-01 -2.20464855e-01 -1.18447995e+00 1.92140412e+00
2.70257711e-01 3.04868728e-01 5.04024178e-02 9.01637495e-01
1.02882290e+00 3.28458607e-01 1.79271773e-01 -2.59596288e-01
9.19271886e-01 -6.75503314e-01 -4.96531963e-01 -9.51695591e-02
8.48828018e-01 -4.36817229e-01 7.83645570e-01 1.23647086e-01
-8.32232177e-01 -4.14050311e-01 -7.79459596e-01 1.83632731e-01
2.14382391e-02 6.76939428e-01 3.62471491e-01 5.72823644e-01
-6.67956769e-01 3.89976799e-01 -8.13978791e-01 8.09368342e-02
1.20069289e+00 6.88758135e-01 -8.32501590e-01 -2.24001125e-01
-1.05912483e+00 4.29049939e-01 -4.02034819e-02 2.99188852e-01
-1.63848448e+00 -6.06509447e-01 -1.15983975e+00 -1.33631691e-01
2.44837821e-01 -9.37309638e-02 1.01873589e+00 -1.75648916e+00
-1.40808785e+00 1.59002829e+00 6.79034516e-02 -1.23201944e-01
3.21432084e-01 2.14693382e-01 -3.16430986e-01 4.24411923e-01
-1.21057257e-01 7.70539880e-01 1.15576363e+00 -1.25669026e+00
-2.40528226e-01 -7.18008161e-01 -2.61596978e-01 -1.35372952e-01
-2.03108445e-01 -2.33564034e-01 2.03638464e-01 -5.49799562e-01
2.14928072e-02 -9.51924264e-01 1.52258635e-01 6.62751794e-01
-3.55372638e-01 -1.45224333e-01 7.67175853e-01 -4.58136082e-01
6.98475182e-01 -2.23580146e+00 -8.50052908e-02 4.47880626e-01
3.94976795e-01 3.05049986e-01 -4.54458833e-01 -2.19697595e-01
-3.31959724e-01 -2.64842987e-01 -7.15795085e-02 -3.64140391e-01
-2.92729169e-01 6.93970263e-01 -2.27679491e-01 9.60315883e-01
3.61761540e-01 9.47634101e-01 -7.77553976e-01 -5.40342689e-01
5.66125065e-02 4.29151952e-01 -4.14869368e-01 6.34313345e-01
-1.41519830e-01 6.84199214e-01 -4.86799449e-01 1.33841455e+00
6.42948747e-01 -2.78986841e-02 4.08264659e-02 -3.98811519e-01
7.13789821e-01 -3.33053976e-01 -7.09798694e-01 1.49030578e+00
-2.99695134e-01 2.44518265e-01 1.64937735e-01 -1.10908902e+00
1.30994773e+00 4.56974149e-01 8.23443770e-01 -4.83812958e-01
8.97830248e-01 2.81144023e-01 -4.65824530e-02 -2.71265179e-01
-3.32876384e-01 -5.37773073e-01 2.94905931e-01 6.92434609e-01
3.96863610e-01 1.74262151e-01 -3.71596873e-01 -2.58896679e-01
7.34896064e-01 -9.70366076e-02 2.23935977e-01 -3.00792661e-02
6.25770569e-01 -2.46393442e-01 7.23835766e-01 2.50783831e-01
-6.09755158e-01 6.94026291e-01 5.32184958e-01 -7.00372398e-01
-6.86806262e-01 -1.27714372e+00 -4.97747123e-01 8.76934588e-01
-7.29080066e-02 1.97684675e-01 -8.31589103e-01 -1.31688333e+00
1.52821718e-02 -4.34831530e-02 -1.33307779e+00 -6.21204019e-01
-1.37407020e-01 -2.94197977e-01 9.21260536e-01 7.45203197e-01
2.64979154e-01 -1.08363032e+00 -1.24066599e-01 -1.48801431e-01
3.07320476e-01 -9.70420897e-01 -7.23499656e-01 -9.63549837e-02
-4.59733874e-01 -1.49740648e+00 -8.04876685e-01 -8.84159744e-01
1.32057250e+00 -1.03344791e-01 6.56880498e-01 1.01636618e-01
-7.50045300e-01 3.94265115e-01 -2.75170416e-01 -1.90358654e-01
-5.87153137e-01 -6.50050879e-01 4.09846127e-01 8.38371217e-01
6.30728424e-01 -5.86826742e-01 -7.48899400e-01 6.80594981e-01
-9.94607925e-01 -4.73280191e-01 6.32284939e-01 1.19298077e+00
1.03327584e+00 -3.68565142e-01 4.95028853e-01 -9.51134443e-01
-6.83929622e-02 -4.64444190e-01 -3.47838730e-01 3.03473741e-01
-3.58674794e-01 -1.83120757e-01 6.58788621e-01 -8.11461687e-01
-8.33513796e-01 3.92169446e-01 -2.48029292e-01 -1.13767695e+00
-4.60001081e-01 4.18832079e-02 -5.39438426e-01 -3.85147572e-01
8.98549020e-01 3.13112408e-01 6.60258949e-01 3.62541266e-02
1.95723742e-01 8.66574645e-01 4.78678882e-01 -7.54157424e-01
4.45975393e-01 8.96966875e-01 2.40658913e-02 -6.48580372e-01
-1.06024289e+00 -5.75298816e-02 -7.57653713e-01 -3.49857152e-01
8.47291887e-01 -9.53211248e-01 -4.45856273e-01 5.90768397e-01
-7.91115522e-01 -3.85621458e-01 -5.54946005e-01 2.93887377e-01
-6.79157317e-01 1.27690017e-01 -5.35453856e-01 -5.57496548e-01
-4.17046607e-01 -1.29304802e+00 1.28078902e+00 3.61156911e-01
-1.92788288e-01 -8.45866859e-01 8.24406222e-02 4.47044343e-01
-1.79851189e-01 6.26246274e-01 6.11997128e-01 -7.37979710e-01
-2.21374616e-01 -7.71915853e-01 -2.91896909e-01 9.68288004e-01
8.05432856e-01 6.87818527e-02 -1.20275772e+00 -1.02638863e-01
-3.05324167e-01 -1.16219997e+00 4.72791970e-01 1.82669818e-01
1.23260641e+00 -4.85710174e-01 -1.68973580e-01 8.64777327e-01
9.71774042e-01 1.27550751e-01 6.63130522e-01 -5.77879429e-01
7.92461514e-01 6.76177859e-01 5.03032446e-01 5.71573853e-01
-1.94490731e-01 3.51486355e-01 6.17010295e-01 -1.66078866e-01
-1.89491034e-01 -4.24017668e-01 3.12279403e-01 -4.86861058e-02
7.13668903e-03 2.06214100e-01 -5.87155104e-01 2.40213782e-01
-1.46481204e+00 -7.71540105e-01 6.11560464e-01 2.06035280e+00
8.92065644e-01 -2.38632873e-01 1.61251009e-01 -1.23154216e-01
4.61060822e-01 6.52856976e-02 -9.91001964e-01 -2.34707206e-01
-9.65308845e-02 2.99891114e-01 1.60299629e-01 1.47502780e-01
-9.51219022e-01 8.85981679e-01 6.14885378e+00 7.84919262e-01
-1.53356659e+00 1.80754706e-01 1.06207502e+00 -1.46378160e-01
-1.22681245e-01 -6.70793235e-01 -6.73218489e-01 2.23551780e-01
6.41821206e-01 1.10174432e-01 1.55327648e-01 1.23174298e+00
3.42244771e-03 3.23070973e-01 -1.37246978e+00 1.24922633e+00
5.19803822e-01 -1.11806989e+00 4.06510055e-01 2.17518076e-01
8.72092247e-01 -2.47081175e-01 3.17283928e-01 1.66410848e-01
2.39033580e-01 -1.44559336e+00 2.88627028e-01 3.83501261e-01
1.49776018e+00 -6.41807020e-01 6.83749020e-01 4.47551869e-02
-9.93592858e-01 1.07394241e-01 -3.51496071e-01 1.34130090e-01
-2.38227040e-01 -8.78953636e-02 -6.06584430e-01 3.59439775e-02
3.67534578e-01 9.17760611e-01 -8.00135955e-02 9.53770354e-02
-3.95896196e-01 4.51875180e-01 -3.04298639e-01 3.39996934e-01
2.82736391e-01 -1.86139151e-01 4.40722518e-02 6.54558182e-01
-8.44142362e-02 2.40570098e-01 3.07985634e-01 1.04883635e+00
-4.70714509e-01 2.56086200e-01 -7.80784249e-01 -3.28754753e-01
1.15175694e-01 1.16854894e+00 -2.22786650e-01 -2.69358363e-02
-4.93125886e-01 1.05205810e+00 5.52371562e-01 2.75137186e-01
-6.74996376e-01 3.57920319e-01 9.65903878e-01 2.65666008e-01
-2.84510255e-01 3.53777558e-01 -2.97778044e-02 -1.00717139e+00
-3.46434653e-01 -7.60560930e-01 5.94560683e-01 -6.30049467e-01
-1.46048236e+00 8.74253392e-01 -3.10076267e-01 -1.41403258e+00
-2.88215548e-01 -1.01472855e+00 -5.58517098e-01 8.47815871e-01
-1.21027339e+00 -1.50549209e+00 -4.16949600e-01 1.03780854e+00
1.57427311e-01 -4.81979638e-01 1.04244149e+00 3.15277934e-01
-7.38653362e-01 1.22752857e+00 -1.67115241e-01 7.81046331e-01
6.61303580e-01 -7.33919024e-01 -5.96404195e-01 5.04362345e-01
6.00377023e-02 2.18486443e-01 1.91674501e-01 -5.46360552e-01
-1.07086647e+00 -1.31698966e+00 1.77429512e-01 -2.78337836e-01
6.58425212e-01 -1.04343630e-01 -7.85357654e-01 9.31406736e-01
-4.45137978e-01 9.72431779e-01 1.27185333e+00 -4.19174403e-01
-7.00962722e-01 -4.95132297e-01 -1.44118130e+00 3.18159252e-01
8.34642649e-01 -6.88527822e-01 -2.73515761e-01 5.38897395e-01
-7.94476047e-02 -4.42925513e-01 -1.00711095e+00 4.70203459e-01
8.89706135e-01 -6.03876472e-01 6.25593722e-01 -9.99357760e-01
5.54083586e-01 -2.79414877e-02 -2.45462090e-01 -1.01858246e+00
2.37155974e-01 -4.24757004e-01 -4.18622606e-02 1.04553115e+00
2.67667592e-01 -5.14745593e-01 1.41618896e+00 8.89640689e-01
5.60832918e-02 -1.00761342e+00 -1.05853629e+00 -5.81867933e-01
1.32506490e-01 -1.19621135e-01 3.72481108e-01 9.50101256e-01
-2.07928017e-01 -7.88878649e-02 -4.93711561e-01 1.51120752e-01
7.23078847e-01 1.91714808e-01 6.93129182e-01 -7.74738610e-01
-1.51911467e-01 -2.94018775e-01 -7.67577887e-01 -7.40765810e-01
9.37569737e-01 -1.06950641e+00 1.00938238e-01 -9.33910847e-01
4.75470088e-02 -5.23819625e-01 -2.47052237e-01 9.99250174e-01
8.56617540e-02 6.43095732e-01 -4.02361721e-01 3.09200287e-01
-1.09892428e-01 9.52491343e-01 1.57429862e+00 -5.19481719e-01
-2.39634812e-02 1.58211872e-01 -4.36421275e-01 7.13409722e-01
5.73692560e-01 -4.12628233e-01 -4.34402734e-01 -9.99697894e-02
-4.78567570e-01 3.08233611e-02 2.70225257e-01 -6.27938569e-01
-2.23355979e-01 -2.43544891e-01 4.59743589e-01 1.91286411e-02
3.38030607e-01 -1.05028093e+00 -1.97843120e-01 3.68809700e-01
-2.04040006e-01 -6.07653975e-01 1.05700672e-01 4.03992742e-01
-3.50805730e-01 -1.69835817e-02 1.25588548e+00 -2.16977760e-01
-5.09918630e-01 1.21951199e+00 -1.26351476e-01 -1.65109634e-02
1.38329232e+00 -3.22870970e-01 -1.34085476e-01 -4.02313083e-01
-1.08869231e+00 7.00183064e-02 3.64340246e-01 2.91677713e-01
9.44192648e-01 -1.49513555e+00 -6.13142431e-01 6.99855328e-01
4.90769416e-01 9.80413035e-02 4.73318726e-01 7.12943375e-01
-5.14602482e-01 2.67332084e-02 -6.82674170e-01 -5.69369435e-01
-1.21326637e+00 4.98162389e-01 9.19363022e-01 2.13006228e-01
-2.63293713e-01 1.04986143e+00 7.75996089e-01 -5.22562087e-01
3.03564042e-01 1.34546980e-01 -6.47615716e-02 -8.98605138e-02
8.36145461e-01 -1.21948188e-02 -1.04850709e-01 -1.24077570e+00
-1.88568920e-01 5.79964280e-01 -2.48784229e-01 4.49609995e-01
8.67403150e-01 1.69317722e-01 5.57751916e-02 5.20773698e-03
1.56942117e+00 -2.25620911e-01 -1.60428011e+00 -4.09071058e-01
-7.30023980e-01 -3.95845354e-01 -9.92511287e-02 -5.10383725e-01
-1.71004927e+00 9.33336794e-01 7.45579541e-01 -6.95870996e-01
1.18666327e+00 2.89003611e-01 6.93469882e-01 6.04584403e-02
3.79460603e-01 -6.73985898e-01 3.47332597e-01 -1.34964034e-01
7.62417972e-01 -1.41744137e+00 -3.50486755e-01 -4.21124160e-01
-7.79668152e-01 9.95692909e-01 1.20596707e+00 -3.99157256e-01
9.12131011e-01 2.92102665e-01 5.78007698e-01 -1.99910894e-01
-2.77009130e-01 -1.74614966e-01 4.14119333e-01 8.79878581e-01
1.25166059e-01 2.41207108e-02 1.11445166e-01 8.72334957e-01
1.28561318e-01 1.09841758e-02 4.21084538e-02 6.32571042e-01
-2.06878319e-01 -1.02631187e+00 -2.09694818e-01 6.23421669e-01
-4.35036987e-01 2.68140465e-01 -7.17433274e-01 7.28999615e-01
3.17194223e-01 4.69126701e-01 2.01934531e-01 -5.91675341e-01
2.35830888e-01 -3.85654415e-03 7.06112921e-01 -7.83503115e-01
-1.40870869e-01 -6.70495331e-02 -4.11867052e-01 -8.11214447e-01
-1.80171624e-01 -5.28787494e-01 -1.43836927e+00 -2.73496695e-02
-4.23604660e-02 -7.80404881e-02 8.11651573e-02 9.94705856e-01
1.66622385e-01 3.97232845e-02 9.26895797e-01 -7.29331970e-01
-5.83091080e-01 -9.85535502e-01 -1.17440867e+00 5.94142020e-01
3.95022362e-01 -1.08480871e+00 -4.63219136e-01 2.37652227e-01] | [13.65263843536377, 1.5549930334091187] |
59532dcf-9909-4520-9c21-ea2ea007a3dc | scireviewgen-a-large-scale-dataset-for | 2305.15186 | null | https://arxiv.org/abs/2305.15186v1 | https://arxiv.org/pdf/2305.15186v1.pdf | SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation | Automatic literature review generation is one of the most challenging tasks in natural language processing. Although large language models have tackled literature review generation, the absence of large-scale datasets has been a stumbling block to the progress. We release SciReviewGen, consisting of over 10,000 literature reviews and 690,000 papers cited in the reviews. Based on the dataset, we evaluate recent transformer-based summarization models on the literature review generation task, including Fusion-in-Decoder extended for literature review generation. Human evaluation results show that some machine-generated summaries are comparable to human-written reviews, while revealing the challenges of automatic literature review generation such as hallucinations and a lack of detailed information. Our dataset and code are available at https://github.com/tetsu9923/SciReviewGen. | ['Ichiro Sakata', 'Junichiro Mori', 'Masaru Isonuma', 'Tetsu Kasanishi'] | 2023-05-24 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [ 5.56331612e-02 3.17867488e-01 -8.32222283e-01 1.30640000e-01
-1.52642024e+00 -7.02754259e-01 9.00321841e-01 4.01442260e-01
-1.28871009e-01 1.31223404e+00 7.39647627e-01 -5.50309479e-01
2.89835066e-01 -3.68663907e-01 -4.81478006e-01 -1.15211710e-01
6.79837406e-01 2.41730437e-01 -2.77971447e-01 -1.54530900e-02
9.59812164e-01 -9.42287669e-02 -1.20766675e+00 4.00190085e-01
1.13128722e+00 2.98492759e-01 4.91658092e-01 8.67712438e-01
-4.04722124e-01 8.37606728e-01 -9.93449390e-01 -7.18148470e-01
-4.04082090e-01 -6.84558511e-01 -6.97044373e-01 -2.36685440e-01
2.65977591e-01 -1.30832754e-03 -2.51727641e-01 9.67794538e-01
7.93342829e-01 -1.33313626e-01 9.79172587e-01 -1.12522769e+00
-1.19269490e+00 1.07393205e+00 -8.45240295e-01 4.19334918e-01
8.00172091e-01 2.04307120e-02 1.27118063e+00 -1.21565914e+00
1.02318394e+00 1.30894601e+00 2.27701306e-01 7.47436881e-01
-7.41988838e-01 -9.72411573e-01 -8.43843073e-03 2.52876967e-01
-1.30421209e+00 -8.40570331e-01 6.37158096e-01 -5.23101151e-01
1.26859701e+00 -1.00347459e-01 4.61776018e-01 1.46541762e+00
7.57211506e-01 7.71266937e-01 6.10007405e-01 -4.61035103e-01
3.21950048e-01 2.65999168e-01 2.30351061e-01 2.70079911e-01
9.84699488e-01 -6.37106895e-01 -7.63923585e-01 -2.90377766e-01
9.70025510e-02 -5.25809288e-01 -2.67253160e-01 4.32530791e-01
-1.27792156e+00 6.86731875e-01 -1.54979095e-01 -6.93042949e-02
-5.26623905e-01 5.89528382e-02 8.72551024e-01 3.96830440e-02
5.69165945e-01 6.63811684e-01 -2.86518335e-01 -3.10092121e-01
-1.19638348e+00 4.97682869e-01 9.32195961e-01 1.38669038e+00
3.07274282e-01 1.02588125e-01 -2.24232957e-01 9.63997006e-01
7.08264783e-02 6.35196388e-01 5.50747871e-01 -8.20593655e-01
8.21691573e-01 5.32944858e-01 -4.95141335e-02 -1.00395596e+00
-3.24691921e-01 -3.70807022e-01 -8.10523748e-01 -5.59373140e-01
-3.18832934e-01 -5.10660231e-01 -5.46275496e-01 1.31043553e+00
-3.37449789e-01 -4.17821407e-01 2.76881933e-01 3.86578858e-01
1.77529347e+00 8.66604567e-01 1.81876823e-01 -5.17265201e-01
1.55359030e+00 -9.78766739e-01 -1.16769588e+00 -3.31869125e-01
6.24371231e-01 -9.61141527e-01 7.99106777e-01 4.77471948e-01
-1.50698233e+00 -2.40195885e-01 -1.14322066e+00 -4.69862163e-01
-6.12232447e-01 5.70311606e-01 4.54141021e-01 2.68907309e-01
-1.02210605e+00 5.84047809e-02 -1.63472414e-01 -5.44371188e-01
7.14419961e-01 -1.74038291e-01 -2.44529366e-01 -7.02661574e-02
-1.20742977e+00 1.13934922e+00 1.68636352e-01 -2.99944319e-02
-7.58899033e-01 -7.47838557e-01 -9.60485160e-01 -6.92987517e-02
4.54299390e-01 -1.13040507e+00 1.47185516e+00 1.04118017e-02
-1.08666217e+00 9.42691088e-01 -6.18538857e-01 -2.96129853e-01
6.78612068e-02 -1.31529436e-01 -4.83634800e-01 3.44543666e-01
6.55570924e-01 7.69140542e-01 3.08615476e-01 -1.02736378e+00
-4.39603716e-01 -5.82014723e-03 -3.65747929e-01 2.21691653e-01
9.50233079e-03 4.87031370e-01 -4.61296767e-01 -7.77875543e-01
-8.72636497e-01 -6.19070351e-01 -3.44124347e-01 -5.47416270e-01
-7.87271261e-01 -5.55206418e-01 2.80277461e-01 -7.97155440e-01
1.71869147e+00 -1.45273101e+00 8.40416178e-03 -4.96480137e-01
1.97040901e-01 1.40955836e-01 -3.58209103e-01 9.72321212e-01
-1.97921306e-01 7.96506524e-01 -4.43405300e-01 -5.50185800e-01
-2.24261098e-02 -4.34294760e-01 -7.24820614e-01 2.31688276e-01
5.39790452e-01 1.32702911e+00 -1.22514176e+00 -4.26428258e-01
-1.02591828e-01 1.40616015e-01 4.98057716e-02 -1.49567962e-01
-1.73667207e-01 9.76733491e-02 -5.98786831e-01 7.59412766e-01
4.02762681e-01 -3.17007154e-01 -2.30827779e-01 1.22624256e-01
-2.20646024e-01 1.00045538e+00 -5.33381879e-01 1.77071857e+00
-4.12210107e-01 8.87151420e-01 -7.89550319e-02 -6.90736651e-01
9.90745425e-01 7.80012786e-01 9.07695591e-02 -5.12102485e-01
2.67196774e-01 5.36832392e-01 -3.12025733e-02 -3.20381761e-01
9.01366830e-01 -1.35384232e-01 -2.70543993e-01 7.31623888e-01
1.11207649e-01 -9.75780308e-01 9.71746325e-01 8.67572248e-01
1.18204570e+00 -1.61080077e-01 7.94756293e-01 -1.53648630e-01
4.96723026e-01 2.34434411e-01 2.41151258e-01 8.52155685e-01
1.15379289e-01 8.64689648e-01 7.98345089e-01 9.82830226e-02
-1.07174516e+00 -7.66507149e-01 -1.52692581e-02 1.59300476e-01
-4.07442182e-01 -1.15565252e+00 -5.42461574e-01 -3.81782293e-01
-1.86932102e-01 1.30048943e+00 -5.41492224e-01 -3.11995596e-01
-1.65682748e-01 -7.63197064e-01 7.08443463e-01 2.86747187e-01
4.51402813e-02 -1.42738807e+00 -3.14188421e-01 8.28123316e-02
-3.34594011e-01 -1.20128489e+00 -4.54256326e-01 -3.22700962e-02
-5.67325771e-01 -1.14363611e+00 -1.05125892e+00 -4.71769989e-01
3.82677615e-01 1.19942375e-01 1.51800120e+00 -2.45711431e-02
-4.46560562e-01 4.48218793e-01 -3.06259781e-01 -1.06906962e+00
-6.09686613e-01 5.70088148e-01 -5.87734692e-02 -1.09163690e+00
6.11393273e-01 -2.75367141e-01 -1.84112653e-01 -4.89859045e-01
-7.36516595e-01 2.37059388e-02 6.55893445e-01 5.21127880e-01
3.33833784e-01 -3.71638954e-01 1.67849660e+00 -8.20013463e-01
1.60493338e+00 -8.76133502e-01 -3.39961678e-01 2.32185453e-01
-7.21773744e-01 -2.05885977e-01 6.05782211e-01 -1.69676110e-01
-9.05218065e-01 -5.74937880e-01 -3.59678678e-02 1.85014561e-01
3.13908234e-02 9.73201215e-01 1.96074039e-01 7.29363739e-01
6.67723238e-01 2.61810571e-01 -1.52532697e-01 -2.38724932e-01
5.43745220e-01 7.41347551e-01 3.46273899e-01 -4.89723712e-01
4.74247962e-01 -1.18547484e-01 -1.66950434e-01 -9.35540736e-01
-8.28492820e-01 -3.23431075e-01 -3.89806986e-01 -4.48736958e-02
6.44324303e-01 -1.18472064e+00 -2.67210990e-01 -1.28547460e-01
-1.62591207e+00 6.37155324e-02 -3.82632822e-01 2.45280698e-01
-1.93239555e-01 4.47086662e-01 -5.54247379e-01 -8.07953000e-01
-1.10390115e+00 -1.25438929e+00 1.06963468e+00 3.86494100e-01
-8.53388965e-01 -9.40052450e-01 2.36986876e-01 2.66144067e-01
1.39261276e-01 -8.22571293e-02 9.31668460e-01 -5.75967252e-01
-1.75872773e-01 -2.90260971e-01 -2.78734297e-01 1.02208197e-01
2.36868724e-01 3.98071736e-01 -7.25574732e-01 2.67031401e-01
-2.39748850e-01 -3.95514846e-01 1.11368644e+00 7.49651313e-01
9.94203091e-01 -3.50530535e-01 -4.36351895e-01 -1.00040868e-01
7.77194798e-01 4.50724483e-01 6.59993351e-01 1.93353772e-01
5.59642196e-01 5.25017917e-01 3.58523041e-01 5.55886626e-01
8.98024857e-01 7.54957348e-02 -2.73056507e-01 2.05237240e-01
-3.37181509e-01 -2.67260998e-01 4.05889988e-01 1.38942313e+00
4.81102765e-01 -6.99947178e-01 -9.41190124e-01 1.03350174e+00
-1.44901025e+00 -8.95619929e-01 -2.50768751e-01 1.74851298e+00
1.22548306e+00 6.33912310e-02 2.07159892e-02 -2.10691899e-01
7.02614844e-01 3.08067799e-01 -5.07525444e-01 -1.09504867e+00
-4.04786319e-01 4.65943187e-01 1.19668275e-01 4.71057862e-01
-4.74684507e-01 8.88629794e-01 6.87650299e+00 8.66550267e-01
-7.64741540e-01 -2.28814170e-01 5.68057179e-01 -1.62316248e-01
-7.28736997e-01 1.06936112e-01 -9.09608662e-01 4.98581558e-01
1.05422354e+00 -1.19244480e+00 -5.14241718e-02 4.54894483e-01
5.07604301e-01 -4.18645203e-01 -1.06116867e+00 8.80262554e-01
3.08117896e-01 -1.69140387e+00 6.04959190e-01 -2.05489933e-01
9.06667233e-01 7.17149675e-02 -7.23526552e-02 4.26599503e-01
2.68177271e-01 -1.15478659e+00 4.83729839e-01 4.44258004e-01
8.50049198e-01 -5.94523847e-01 6.73906982e-01 1.33431897e-01
-8.54820490e-01 3.60215127e-01 -4.17940259e-01 -1.71887890e-01
4.81923401e-01 9.49224174e-01 -9.73254502e-01 6.66118205e-01
2.92593151e-01 1.37007415e+00 -8.87810528e-01 9.19541776e-01
-7.55900264e-01 7.90857971e-01 1.73979044e-01 -4.93662953e-01
-4.96822521e-02 -1.28844902e-01 7.82148004e-01 1.58175874e+00
3.65110546e-01 1.52471706e-01 -2.86554515e-01 1.22545218e+00
-6.19657516e-01 2.17648968e-01 -9.07277465e-01 -7.23706067e-01
6.03754103e-01 1.30935276e+00 -5.39381862e-01 -6.13993227e-01
-3.68279964e-01 4.76520509e-01 3.62752415e-02 4.55503643e-01
-4.31987435e-01 -8.14082563e-01 1.60687983e-01 -4.37775284e-01
4.40146811e-02 6.42718002e-02 -7.80096650e-01 -1.36806381e+00
1.52192786e-01 -9.24008906e-01 2.94865847e-01 -1.33164108e+00
-1.20106733e+00 3.61099333e-01 -1.49452239e-02 -9.80802834e-01
-3.32575858e-01 -2.95206428e-01 -7.69684672e-01 1.03067517e+00
-1.46890426e+00 -8.69651139e-01 3.17160964e-01 -2.65942603e-01
1.10378563e+00 -4.21066105e-01 7.30715513e-01 1.99387640e-01
-7.70289838e-01 4.28508729e-01 -2.94843554e-01 -3.29973519e-01
9.45307910e-01 -1.22880316e+00 8.46017897e-01 9.08775806e-01
-3.13636094e-01 1.09166157e+00 5.94521821e-01 -1.21854889e+00
-1.42303646e+00 -7.72837102e-01 1.36970842e+00 -7.71249831e-01
9.61617708e-01 -2.24166512e-01 -8.43050063e-01 3.70686859e-01
9.17818129e-01 -8.65601301e-01 6.85523510e-01 1.85705312e-02
-1.61199495e-01 3.32075208e-01 -7.30116546e-01 1.11093140e+00
7.04065740e-01 -5.26243627e-01 -9.37178910e-01 2.40812093e-01
7.63576686e-01 -2.36733437e-01 -6.29509926e-01 2.29255632e-01
5.75300753e-01 -8.70711207e-02 7.15003967e-01 -2.84382194e-01
1.57596779e+00 -2.70185798e-01 4.95295972e-01 -1.53638017e+00
-2.12873384e-01 -8.12422872e-01 -1.36708453e-01 1.52205801e+00
8.53725195e-01 -3.43616068e-01 1.86259702e-01 4.98018742e-01
-2.55997717e-01 -9.11686659e-01 -4.99115348e-01 -2.18673930e-01
5.82825243e-01 -4.35457319e-01 2.59647697e-01 7.37278223e-01
5.90187371e-01 1.18126702e+00 -1.10747851e-01 -5.46517015e-01
3.28082383e-01 -1.65728420e-01 7.12845027e-01 -8.32814932e-01
3.23972940e-01 -9.83331144e-01 3.06922436e-01 -6.20438755e-01
5.57684660e-01 -1.10388601e+00 -1.98828697e-01 -2.53631902e+00
6.12994611e-01 2.14752987e-01 6.54110238e-02 5.28218806e-01
-5.77334642e-01 -1.10541498e-02 3.64560001e-02 -1.54448420e-01
-7.05785334e-01 7.07228959e-01 1.44142771e+00 -2.66398221e-01
4.32732813e-02 -3.04141670e-01 -1.57938111e+00 3.68320405e-01
8.17114294e-01 -3.90977591e-01 -4.31476176e-01 -2.73784041e-01
6.48206174e-01 9.45897475e-02 -2.45707296e-02 -4.89034653e-01
4.24400568e-01 -1.26471221e-01 2.07934022e-01 -1.15040040e+00
1.31723702e-01 4.21976186e-02 -2.06512019e-01 5.60567044e-02
-8.12770307e-01 5.51055014e-01 7.63417959e-01 4.44537662e-02
-1.61694229e-01 -5.73597491e-01 7.18862936e-02 -4.01992172e-01
-1.20197453e-01 -1.11209556e-01 -1.04776871e+00 4.55546170e-01
3.60720575e-01 2.20261335e-01 -7.91061103e-01 -6.55734360e-01
2.04011366e-01 5.42132497e-01 2.76433378e-01 7.40365744e-01
7.51526713e-01 -7.79402137e-01 -1.15515959e+00 -5.90447009e-01
2.85674453e-01 5.71023561e-02 3.13405469e-02 6.69866145e-01
-3.57395671e-02 9.73044693e-01 1.68832600e-01 1.17783606e-01
-9.77908134e-01 5.87606549e-01 -3.22350800e-01 -3.53926152e-01
-5.84046483e-01 6.10923648e-01 2.40272924e-01 -2.99107820e-01
1.53323840e-02 -5.12962043e-01 -4.37998116e-01 3.20638120e-01
9.47947502e-01 4.64388609e-01 1.41011611e-01 -3.05649519e-01
-4.34720457e-01 3.72396886e-01 -4.27863359e-01 -3.02197397e-01
1.30100691e+00 -2.36002117e-01 -4.41972077e-01 7.16741323e-01
9.20937717e-01 3.66899312e-01 -4.32378277e-02 8.34658146e-02
2.32557133e-01 2.31617093e-01 2.11246703e-02 -1.26651025e+00
-5.77180862e-01 8.37268293e-01 -5.80107212e-01 -1.36820093e-01
8.84657085e-01 -1.26139345e-02 7.20580995e-01 5.59194326e-01
-1.42297402e-01 -1.12665451e+00 -3.81700039e-01 7.51326859e-01
1.30574930e+00 -1.14981067e+00 8.96284282e-01 -1.92812636e-01
-1.01353061e+00 1.02501667e+00 3.12815368e-01 8.58544838e-03
2.46223196e-01 3.96120697e-01 -2.55325064e-03 -4.04151678e-01
-1.42861152e+00 2.25941539e-01 3.59754741e-01 3.92585844e-01
9.32587564e-01 -1.48631647e-01 -9.66830015e-01 1.08740854e+00
-4.29067403e-01 2.34944403e-01 1.27230763e+00 7.20300913e-01
1.65145993e-01 -1.09527171e+00 -2.14765474e-01 7.93589652e-01
-7.90409982e-01 -5.66991210e-01 -7.93878496e-01 4.69792843e-01
-6.14822924e-01 1.27104282e+00 -3.68103534e-01 8.67540836e-02
2.33081385e-01 1.54329501e-02 2.56194293e-01 -1.09365666e+00
-6.68088913e-01 8.29309896e-02 5.37036717e-01 2.04637066e-01
-3.59400123e-01 -6.56044841e-01 -1.13299286e+00 -6.93264157e-02
-1.93006918e-01 4.86527205e-01 5.73132455e-01 8.33936334e-01
7.76439250e-01 7.44038999e-01 1.17365278e-01 -6.64014101e-01
-1.03903346e-01 -1.13245535e+00 -2.84598410e-01 -2.44024903e-01
3.10018063e-01 -4.54336256e-01 -4.10915136e-01 3.38965207e-01] | [12.370509147644043, 9.561125755310059] |
bf5722e0-4d3d-477d-9ec0-4af30c6f54ec | radnet-incident-prediction-in-spatio-temporal | 2206.05602 | null | https://arxiv.org/abs/2206.05602v1 | https://arxiv.org/pdf/2206.05602v1.pdf | RadNet: Incident Prediction in Spatio-Temporal Road Graph Networks Using Traffic Forecasting | Efficient and accurate incident prediction in spatio-temporal systems is critical to minimize service downtime and optimize performance. This work aims to utilize historic data to predict and diagnose incidents using spatio-temporal forecasting. We consider the specific use case of road traffic systems where incidents take the form of anomalous events, such as accidents or broken-down vehicles. To tackle this, we develop a neural model, called RadNet, which forecasts system parameters such as average vehicle speeds for a future timestep. As such systems largely follow daily or weekly periodicity, we compare RadNet's predictions against historical averages to label incidents. Unlike prior work, RadNet infers spatial and temporal trends in both permutations, finally combining the dense representations before forecasting. This facilitates informed inference and more accurate incident detection. Experiments with two publicly available and a new road traffic dataset demonstrate that the proposed model gives up to 8% higher prediction F1 scores compared to the state-of-the-art methods. | ['Chris Kettell', 'Matthew R. Wilkinson', 'Shreshth Tuli'] | 2022-06-11 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [ 4.79825586e-02 -1.00732274e-01 -2.99998254e-01 -5.90348303e-01
-5.78497589e-01 5.16803935e-02 8.08221400e-01 2.73774147e-01
-1.79094374e-01 7.76804507e-01 6.67723954e-01 -6.04970694e-01
-4.30584431e-01 -1.13174117e+00 -6.49106145e-01 -4.92014617e-01
-4.45892930e-01 5.51047087e-01 2.36102015e-01 -1.64012700e-01
1.88826323e-01 6.12725258e-01 -1.76318073e+00 4.70719814e-01
6.58345163e-01 9.73957956e-01 -2.91268170e-01 5.22138119e-01
-1.93265229e-01 1.18708766e+00 -5.67298234e-01 -1.73143685e-01
7.82974511e-02 1.02873981e-01 -6.00652933e-01 -7.86729231e-02
8.58891085e-02 -5.66090047e-01 -8.94416392e-01 3.51948291e-01
2.08540142e-01 3.74239415e-01 8.09611201e-01 -1.45862985e+00
-5.68802476e-01 3.77196521e-01 -3.59241039e-01 8.30947220e-01
9.24323946e-02 2.12271348e-01 5.38213968e-01 -5.68401575e-01
1.31253287e-01 1.00544190e+00 9.95393097e-01 2.13732064e-01
-1.09906554e+00 -6.34117901e-01 4.92657602e-01 5.77498972e-01
-1.62126791e+00 -4.31952983e-01 5.08519888e-01 -5.85755110e-01
1.47065163e+00 2.23148942e-01 3.64912927e-01 1.23731291e+00
5.08109748e-01 7.99552202e-01 3.92063618e-01 4.70459051e-02
2.30995923e-01 -2.64619619e-01 3.48484874e-01 1.79080710e-01
1.88107640e-02 3.48363042e-01 -1.44654930e-01 -8.63649398e-02
3.57636034e-01 7.06535280e-01 1.41259983e-01 4.81770784e-01
-9.34284508e-01 6.48235857e-01 4.22269136e-01 3.32177937e-01
-1.15053165e+00 1.72340930e-01 5.84177792e-01 5.76252490e-02
9.94939089e-01 2.03473326e-02 -4.03345406e-01 -1.76969349e-01
-1.14027643e+00 3.09912503e-01 4.74670023e-01 7.61835396e-01
5.72974920e-01 3.19377363e-01 -5.75150490e-01 6.58105016e-01
-3.57046306e-01 3.83835614e-01 -3.88916507e-02 -6.79620028e-01
6.49004698e-01 4.81368631e-01 1.86981946e-01 -1.26268268e+00
-9.47325230e-01 -4.83226001e-01 -1.14866138e+00 -3.23652357e-01
3.20880339e-02 -1.44580513e-01 -1.17380905e+00 1.34611940e+00
2.09230125e-01 1.05662239e+00 -1.93582907e-01 7.25337684e-01
4.13519740e-01 1.21762621e+00 4.84102398e-01 -3.81740421e-01
8.31327140e-01 -7.00380445e-01 -7.46818125e-01 -5.31543866e-02
7.03857183e-01 -4.29400414e-01 2.60009557e-01 1.97017118e-01
-8.31459999e-01 -5.12789369e-01 -2.18323246e-01 3.54088932e-01
-5.95279753e-01 1.07563354e-01 4.67533231e-01 3.80706072e-01
-7.75116682e-01 3.66940320e-01 -9.85029221e-01 -5.40116549e-01
3.70831996e-01 1.56003088e-01 9.91130695e-02 -1.53754190e-01
-1.30365932e+00 8.63457620e-01 3.30842078e-01 6.16617054e-02
-7.79016495e-01 -1.23612082e+00 -7.03773499e-01 5.42445630e-02
2.99685538e-01 -6.11974597e-01 1.17478263e+00 -7.73354769e-02
-7.83734500e-01 2.28393316e-01 -4.72273141e-01 -8.74295950e-01
2.51140296e-01 -1.57900736e-01 -1.18615222e+00 -1.90017134e-01
2.82874316e-01 8.56608003e-02 4.01087731e-01 -9.53391135e-01
-1.16844487e+00 -8.01577196e-02 7.06634223e-02 -2.74713695e-01
-2.03321010e-01 -5.01002707e-02 -4.48748112e-01 -5.17951190e-01
-2.24610716e-01 -7.07437277e-01 -4.43185359e-01 -8.25504601e-01
-5.74144721e-01 -5.10163546e-01 7.82548666e-01 -7.46363699e-01
1.84833503e+00 -1.82160616e+00 -7.20587015e-01 4.15496737e-01
3.81702781e-02 1.96941152e-01 6.56350031e-02 8.53968024e-01
-1.99400440e-01 -2.01019794e-01 8.41131732e-02 -3.98829579e-01
-6.27506897e-02 4.80345219e-01 -6.56980991e-01 5.04368246e-01
2.57549316e-01 7.48266160e-01 -8.27750385e-01 -1.84933886e-01
7.29384422e-01 6.07958138e-01 -3.93531203e-01 1.14706464e-01
1.63708985e-01 6.00906432e-01 -3.38320702e-01 4.39935267e-01
5.49060881e-01 -3.53597105e-01 -1.32556424e-01 -6.85123652e-02
-5.38606644e-01 6.68416142e-01 -7.25373626e-01 8.99943888e-01
-9.24605250e-01 6.48766637e-01 -6.53366745e-01 -1.24154139e+00
8.55412304e-01 5.89758277e-01 9.40688431e-01 -1.28302407e+00
-1.03409290e-02 -3.73331338e-01 -6.87087834e-01 -6.90763354e-01
6.50455296e-01 1.80682167e-01 -1.83586314e-01 4.13899064e-01
-4.18079495e-01 7.84675360e-01 3.52476656e-01 3.76816988e-02
1.40688992e+00 -5.25396526e-01 1.08910412e-01 1.48236930e-01
1.51398897e-01 2.63236403e-01 7.10821033e-01 7.73619354e-01
5.35133407e-02 3.34538698e-01 1.82846338e-01 -1.33843839e+00
-1.13599896e+00 -1.10206366e+00 -2.53225416e-01 9.92021680e-01
-1.88498855e-01 -1.33908659e-01 -3.45928788e-01 -5.93172789e-01
7.82744512e-02 1.34260154e+00 -6.64553404e-01 -1.56407610e-01
-8.53834808e-01 -1.10128212e+00 3.54457945e-01 8.61478746e-01
2.09995031e-01 -8.55077684e-01 -3.19340765e-01 5.88650286e-01
-3.41664016e-01 -1.11700237e+00 -1.61362961e-01 -2.77439356e-01
-3.14819813e-01 -1.00499558e+00 -3.35212618e-01 -4.00209337e-01
6.07320487e-01 2.08911777e-01 1.25471890e+00 -6.54403716e-02
-3.80214363e-01 2.74137467e-01 -2.90792584e-01 -2.85685748e-01
-1.37604997e-01 2.21454546e-01 4.52569813e-01 1.71149537e-01
8.10064018e-01 -7.08597898e-01 -9.36682463e-01 2.91647136e-01
-7.12790012e-01 -1.51164632e-03 4.53090012e-01 4.57798362e-01
5.71617663e-01 6.00426614e-01 1.02776361e+00 -8.88976395e-01
4.62547153e-01 -1.26213956e+00 -4.92896646e-01 6.20610341e-02
-7.45054603e-01 -4.29502040e-01 8.29913557e-01 -1.75598338e-01
-1.06111908e+00 -1.43535808e-01 -3.86922896e-01 -4.31773782e-01
-6.60875082e-01 4.62019652e-01 6.34140790e-01 7.68459499e-01
5.47236085e-01 3.88191551e-01 -7.79645324e-01 -3.91273648e-01
5.90387220e-03 7.12291777e-01 7.36976862e-01 -8.11066851e-02
5.36348701e-01 5.43543458e-01 -2.08643407e-01 -7.28523314e-01
-9.53290641e-01 -9.70045447e-01 -4.73558903e-01 -4.90023851e-01
8.28220427e-01 -9.01050448e-01 -1.03599489e+00 1.37048110e-01
-1.33090079e+00 -1.33622587e-01 -1.00468211e-01 5.25837064e-01
-3.98354590e-01 -3.57356042e-01 -7.61077940e-01 -1.15799606e+00
-3.87947075e-02 -6.27823174e-01 1.15660143e+00 -1.46523431e-01
-3.18191588e-01 -1.20773947e+00 2.20749095e-01 8.07678476e-02
5.54603815e-01 3.46497416e-01 8.83135676e-01 -6.47582591e-01
-5.11306107e-01 -4.21018392e-01 -4.49568093e-01 -6.27313405e-02
1.37819558e-01 -1.07904397e-01 -8.60562801e-01 -3.90019305e-02
-5.58388233e-01 4.98277396e-01 9.04221356e-01 6.63606942e-01
1.45487571e+00 -7.92897880e-01 -7.85673559e-01 2.63124734e-01
1.12951851e+00 3.80451471e-01 7.90773988e-01 3.15731525e-01
5.70202827e-01 7.40043581e-01 6.38046563e-01 7.29837656e-01
7.72748828e-01 6.78005099e-01 4.25064325e-01 -2.07721353e-01
1.58302754e-01 -3.65771770e-01 -2.77252328e-02 7.68843055e-01
-9.07150358e-02 -5.10049939e-01 -1.52395153e+00 1.33832586e+00
-1.98355794e+00 -1.46715021e+00 -5.56234598e-01 1.94293070e+00
1.30709469e-01 1.11799814e-01 3.53171557e-01 3.40124190e-01
7.19458342e-01 1.67614773e-01 -3.03815693e-01 -4.39666659e-01
3.71962875e-01 -1.00986943e-01 9.15451109e-01 2.61193961e-01
-1.31364095e+00 6.19977355e-01 7.06427574e+00 6.75350249e-01
-9.79436040e-01 1.71781719e-01 9.31789577e-01 -3.35588157e-01
2.78991219e-02 -5.17132342e-01 -8.86311173e-01 7.69696116e-01
1.86763334e+00 -1.26114607e-01 2.43113548e-01 7.86429644e-01
8.54351819e-01 3.01270187e-01 -8.56836855e-01 6.78235471e-01
-2.62191862e-01 -1.77396035e+00 -2.08935812e-02 6.73385803e-03
1.00840700e+00 2.90977746e-01 3.73965874e-02 7.36293018e-01
4.51782137e-01 -1.01103222e+00 1.47946060e-01 1.14557219e+00
6.03599250e-01 -1.33303487e+00 8.46401751e-01 5.02776563e-01
-1.32143164e+00 -3.35643500e-01 -1.18325718e-01 -2.04316720e-01
8.75274956e-01 9.98609304e-01 -1.16650653e+00 4.54400808e-01
7.91906953e-01 7.93721914e-01 -3.13669927e-02 1.12756395e+00
2.36434981e-01 1.07263148e+00 -2.42296785e-01 4.21378821e-01
4.22184438e-01 2.42339894e-01 2.91205496e-01 1.45299459e+00
5.76712608e-01 2.31186032e-01 5.18625498e-01 4.53168094e-01
2.59752512e-01 -2.90952116e-01 -8.86473835e-01 3.96767139e-01
5.66507101e-01 8.22435975e-01 -3.56807947e-01 -3.64939898e-01
-6.04485810e-01 5.71884990e-01 6.92856982e-02 6.43993199e-01
-1.24709702e+00 -8.71756822e-02 1.02956235e+00 5.75210273e-01
2.72768050e-01 -2.26278752e-01 -2.33314708e-01 -5.77171147e-01
-2.24369124e-01 -5.94018623e-02 3.93697947e-01 -5.06831229e-01
-1.62524700e+00 7.46066272e-01 1.80738896e-01 -1.42813098e+00
-7.20847666e-01 -3.24405909e-01 -8.87854218e-01 6.06327176e-01
-1.76338136e+00 -1.13884974e+00 -6.71855435e-02 6.42123818e-01
7.03790665e-01 -6.45780787e-02 5.68660378e-01 9.84994411e-01
-8.71928751e-01 2.59811759e-01 2.16645584e-01 1.37018830e-01
2.54276365e-01 -8.45893800e-01 9.86803353e-01 8.04645479e-01
-1.95149511e-01 2.99872637e-01 8.77776384e-01 -7.75708675e-01
-7.91552901e-01 -2.08862567e+00 1.27389622e+00 -4.83821601e-01
7.22365379e-01 9.29278731e-02 -9.49960768e-01 8.22847486e-01
-1.69790015e-01 4.48304750e-02 5.98933578e-01 4.09121245e-01
-1.04184346e-02 -2.19567090e-01 -9.77753580e-01 3.20762932e-01
1.03789234e+00 -5.06637216e-01 -3.28949481e-01 6.43587649e-01
7.65432715e-01 -1.05163440e-01 -8.04013908e-01 4.61796463e-01
2.00596452e-01 -8.57561171e-01 1.02088356e+00 -8.21852088e-01
2.55122304e-01 -1.48755252e-01 -9.77427233e-03 -1.33982670e+00
-6.97722733e-01 -1.12183459e-01 -4.06600714e-01 1.16633987e+00
4.24335212e-01 -5.72532296e-01 6.54447436e-01 6.60388768e-01
-4.51561093e-01 -7.21981406e-01 -1.11361647e+00 -8.50971818e-01
-2.54847109e-01 -1.15701079e+00 1.15201783e+00 9.54329491e-01
-3.81447315e-01 -3.94645445e-02 -8.79755318e-01 8.74817967e-01
4.59990829e-01 -6.52166754e-02 5.65916121e-01 -1.27512300e+00
2.82402128e-01 -2.53963619e-01 -5.93972206e-01 -8.77523184e-01
2.79152185e-01 -5.18176615e-01 -4.16039117e-03 -1.67641187e+00
-2.03026325e-01 -5.22631407e-01 -5.46312213e-01 4.41703409e-01
1.36464059e-01 3.45506936e-01 -4.77215081e-01 -1.18145803e-02
-8.00997019e-01 4.94859308e-01 4.10217673e-01 -1.22858025e-01
-2.15084270e-01 3.72407228e-01 -1.42287210e-01 5.00998497e-01
8.93708706e-01 -5.00070691e-01 -2.94306248e-01 -4.45234776e-01
1.63655072e-01 2.26682127e-01 5.47239423e-01 -1.23707008e+00
5.85342944e-01 -5.17355621e-01 1.76307380e-01 -1.18847919e+00
4.34738308e-01 -9.41580713e-01 3.56880695e-01 1.69767320e-01
-2.37081349e-01 3.36443007e-01 2.11786821e-01 9.27715242e-01
-2.71124899e-01 6.54464483e-01 1.67365581e-01 2.76552469e-01
-1.02799058e+00 7.41175830e-01 -9.19885039e-01 -3.45214456e-01
1.28549790e+00 -3.32705863e-02 -6.61573589e-01 -5.58151484e-01
-8.14702272e-01 6.56733572e-01 -2.52177417e-01 7.36447334e-01
6.06989801e-01 -1.47885096e+00 -7.62255430e-01 1.83830187e-01
3.11854661e-01 -4.07180637e-01 8.96747231e-01 1.11960566e+00
-2.52165228e-01 9.32498515e-01 1.40640527e-01 -6.82417631e-01
-7.10437894e-01 7.73729920e-01 1.43069938e-01 -3.19346726e-01
-8.10265124e-01 3.19499701e-01 1.24620028e-01 -4.50236440e-01
2.52863597e-02 -3.57475668e-01 -3.11719835e-01 -4.62737586e-03
9.58835900e-01 8.97071123e-01 3.81819427e-01 -8.05902958e-01
-3.87496084e-01 2.54907012e-02 -4.32610102e-02 4.24609989e-01
1.61841226e+00 -3.97386402e-01 3.65795106e-01 6.30196691e-01
1.18218935e+00 -5.97227037e-01 -1.13627279e+00 -3.10866326e-01
-1.74499881e-02 -5.27760863e-01 3.69197994e-01 -5.89273274e-01
-1.11337721e+00 6.63665950e-01 5.92023075e-01 8.21245790e-01
1.18330908e+00 2.03904901e-02 1.38563895e+00 1.93940938e-01
5.64445972e-01 -1.02434373e+00 -6.49033368e-01 6.86694264e-01
5.22448897e-01 -1.17767203e+00 -3.54274988e-01 -1.84971854e-01
-6.10535741e-01 6.99671745e-01 3.85679275e-01 -1.67080402e-01
8.53926063e-01 2.27498785e-01 -7.47349039e-02 -2.30119646e-01
-1.33286417e+00 -4.53009069e-01 5.35346508e-01 5.16885161e-01
2.88166136e-01 3.26132596e-01 1.43256694e-01 3.12266946e-01
-9.47402865e-02 8.77198651e-02 9.11194831e-02 5.62039852e-01
-2.89216936e-01 -4.81124431e-01 -2.73289710e-01 9.03936327e-01
-4.45373207e-01 3.18365395e-02 3.89728874e-01 6.81947112e-01
1.98763460e-01 1.29130268e+00 8.00003111e-01 -5.57895720e-01
5.65118670e-01 2.59679761e-02 -4.57504600e-01 -3.16750646e-01
-4.75246966e-01 -6.06804132e-01 2.70467669e-01 -8.87833595e-01
-2.24936500e-01 -6.62671268e-01 -1.18454981e+00 -1.01186943e+00
1.92083031e-01 1.22464849e-02 5.12918353e-01 1.20928764e+00
6.12682045e-01 1.11793256e+00 8.20211172e-01 -8.15340638e-01
2.33412296e-01 -8.56359124e-01 -3.56353879e-01 3.88022542e-01
7.43459344e-01 -7.67653644e-01 -1.58634305e-01 -6.04371279e-02] | [6.577518463134766, 2.457261085510254] |
e2224c51-4d23-4ff9-9a8c-3d7cfe7d87be | mavd-the-first-open-large-scale-mandarin | 2306.02263 | null | https://arxiv.org/abs/2306.02263v1 | https://arxiv.org/pdf/2306.02263v1.pdf | MAVD: The First Open Large-Scale Mandarin Audio-Visual Dataset with Depth Information | Audio-visual speech recognition (AVSR) gains increasing attention from researchers as an important part of human-computer interaction. However, the existing available Mandarin audio-visual datasets are limited and lack the depth information. To address this issue, this work establishes the MAVD, a new large-scale Mandarin multimodal corpus comprising 12,484 utterances spoken by 64 native Chinese speakers. To ensure the dataset covers diverse real-world scenarios, a pipeline for cleaning and filtering the raw text material has been developed to create a well-balanced reading material. In particular, the latest data acquisition device of Microsoft, Azure Kinect is used to capture depth information in addition to the traditional audio signals and RGB images during data acquisition. We also provide a baseline experiment, which could be used to evaluate the effectiveness of the dataset. The dataset and code will be released at https://github.com/SpringHuo/MAVD. | ['Sen Li', 'Qi Li', 'Tianyi Xu', 'Li Liu', 'Yuchen Huo', 'Jianrong Wang'] | 2023-06-04 | null | null | null | null | ['visual-speech-recognition', 'audio-visual-speech-recognition'] | ['speech', 'speech'] | [ 1.58755165e-02 -2.98768818e-01 2.65088957e-02 -5.32244086e-01
-9.88013923e-01 -5.06650567e-01 6.22759581e-01 -1.69977799e-01
-4.30836737e-01 2.47500554e-01 4.23963785e-01 -1.15371421e-01
4.94248033e-01 -3.95493746e-01 -5.68754077e-01 -6.89373493e-01
3.04508269e-01 2.23096050e-02 1.32088020e-01 -1.30022010e-02
9.83538777e-02 2.07691476e-01 -1.92030454e+00 2.71592855e-01
6.06240332e-01 1.02486169e+00 4.73014474e-01 7.23903775e-01
-4.13983688e-02 4.33810651e-01 -8.20694864e-01 -9.61078852e-02
1.75784752e-01 -4.30144876e-01 -5.76687455e-01 1.57590076e-01
4.82297450e-01 -5.82236946e-01 -5.56887150e-01 1.03588855e+00
9.17032599e-01 2.74037153e-01 9.35567766e-02 -1.31289029e+00
-6.43436074e-01 5.70218682e-01 -3.02172273e-01 -7.11937025e-02
6.64210260e-01 3.42157722e-01 7.27254152e-01 -1.03621042e+00
4.49666381e-01 1.27393842e+00 1.40402764e-01 9.50922370e-01
-6.17579937e-01 -7.14709222e-01 -3.61348763e-02 6.08823717e-01
-1.50089753e+00 -8.98363233e-01 8.54460418e-01 -1.26908362e-01
8.93118501e-01 5.26152849e-01 7.70326972e-01 1.54856277e+00
-3.86520386e-01 1.06737983e+00 1.00484574e+00 -5.27531326e-01
2.71515906e-01 -7.18277618e-02 1.28932819e-01 7.38844797e-02
-1.46039099e-01 9.82736470e-04 -1.07228649e+00 2.19243050e-01
4.54952866e-01 9.15474817e-02 -5.03056407e-01 -7.93935508e-02
-1.23103809e+00 5.09592712e-01 1.58176854e-01 3.29417109e-01
-1.79184496e-01 -2.11731955e-01 2.16641396e-01 4.05155957e-01
9.83006135e-02 -1.84098393e-01 -2.63396263e-01 -8.51705194e-01
-7.31859148e-01 -3.52693684e-02 3.99876058e-01 1.04348660e+00
4.68133092e-01 7.42640160e-03 8.51986259e-02 9.63645160e-01
7.29703426e-01 8.48215938e-01 4.72571075e-01 -9.62343931e-01
6.72419667e-01 6.32288575e-01 -1.92954257e-01 -4.48501021e-01
-1.03335738e-01 3.82143676e-01 -5.52494287e-01 2.06880242e-01
3.84506911e-01 -5.64694218e-02 -9.49317336e-01 1.32058513e+00
5.22514403e-01 -1.33438349e-01 3.33423913e-02 1.35222435e+00
1.31802523e+00 5.13291419e-01 -3.45681489e-01 -1.06857017e-01
1.39257777e+00 -7.62592912e-01 -1.08751130e+00 -1.70327157e-01
8.04693848e-02 -1.00276721e+00 1.64096153e+00 6.49552345e-01
-9.14963961e-01 -4.88328040e-01 -1.10354185e+00 -3.26290309e-01
-3.60902727e-01 1.37294754e-01 2.69345134e-01 9.41496193e-01
-8.29228580e-01 -1.95726767e-01 -1.12160861e+00 -4.47101623e-01
3.49729657e-01 -2.02079862e-02 -5.06905138e-01 -1.97918192e-01
-1.07861829e+00 4.25647914e-01 4.32356484e-02 3.20860833e-01
-1.07427657e+00 -1.42966568e-01 -1.04705727e+00 -4.37454224e-01
3.09314281e-01 1.00320615e-01 1.59314764e+00 -7.96540320e-01
-1.86175573e+00 6.88977003e-01 -4.58514631e-01 -2.61483043e-02
4.96158272e-01 -4.74888921e-01 -3.75458956e-01 2.43502006e-01
-1.99652001e-01 6.65600419e-01 7.93044329e-01 -1.02897274e+00
-4.04710531e-01 -6.49889946e-01 -1.37859225e-01 3.04918408e-01
-3.29532564e-01 2.59165376e-01 -9.82536733e-01 -7.93241978e-01
4.88192774e-02 -7.38251746e-01 3.55112910e-01 -1.92054868e-01
-3.17607850e-01 -5.74177094e-02 9.95689452e-01 -8.63372505e-01
1.12322283e+00 -2.47882748e+00 1.47726893e-01 -1.54986642e-02
1.22854643e-01 2.65715986e-01 6.97196880e-03 3.59619290e-01
2.77312756e-01 -7.60609731e-02 -1.89491138e-01 -6.82697415e-01
-9.17988736e-03 1.61342666e-01 -1.00197410e-02 4.62513596e-01
-9.32779238e-02 7.03345835e-01 -6.08035743e-01 -4.43805426e-01
5.03921509e-01 7.28373110e-01 -1.83454722e-01 4.08959836e-01
-7.77056292e-02 7.47723341e-01 -2.49875054e-01 1.17340875e+00
8.19489002e-01 3.09346706e-01 -2.27346465e-01 1.43070206e-01
-1.36315495e-01 2.90311217e-01 -1.22549784e+00 2.12322092e+00
-5.18560447e-02 1.06048036e+00 6.03442192e-01 -3.87703896e-01
7.80690312e-01 5.12512624e-01 3.35959077e-01 -1.00781024e+00
4.64779168e-01 6.48494139e-02 -4.17239606e-01 -8.53305459e-01
4.81027305e-01 2.02957228e-01 1.15005650e-01 2.72875428e-01
-1.82796836e-01 -3.48943055e-01 -8.26031864e-02 -3.29590701e-02
9.23297524e-01 -1.36464387e-01 -1.33779302e-01 4.57262456e-01
5.63262761e-01 5.88367172e-02 4.81966436e-01 3.77362430e-01
-6.12147152e-01 1.09167087e+00 2.35403217e-02 -4.66345489e-04
-7.39555657e-01 -1.17970312e+00 -1.30698666e-01 8.37889493e-01
3.55818421e-02 -6.35628581e-01 -1.03491569e+00 -3.56040627e-01
-3.86055350e-01 5.24142385e-01 -3.48964900e-01 1.97446853e-01
-3.49920332e-01 -1.74056500e-01 8.16509604e-01 4.44363445e-01
8.27731371e-01 -1.28291988e+00 -7.95205772e-01 -2.88057536e-01
-5.77805281e-01 -1.21519971e+00 -5.67581713e-01 -2.06892520e-01
-4.44219500e-01 -1.28843069e+00 -7.33907342e-01 -6.70206130e-01
3.27567816e-01 3.93386692e-01 6.82791054e-01 -1.96242243e-01
-2.32096791e-01 5.65692127e-01 -8.60385060e-01 -6.14482701e-01
-2.68829316e-01 -1.25721902e-01 1.82231203e-01 -1.10201910e-01
9.31650221e-01 -6.03118502e-02 -4.90523726e-01 4.58037734e-01
-8.36393356e-01 -3.86174694e-02 3.60394448e-01 4.85630125e-01
7.32505620e-01 -2.72725999e-01 1.85249403e-01 -1.79710239e-02
6.81753337e-01 -7.39168897e-02 -6.74402118e-01 6.51335865e-02
-1.76428303e-01 -3.75243664e-01 1.27088558e-02 -4.36555117e-01
-9.53359723e-01 2.24653870e-01 -4.49606776e-01 -5.90217710e-01
-6.20061517e-01 4.09512430e-01 -7.71190882e-01 2.88877904e-01
3.90906930e-01 3.34304154e-01 2.62489051e-01 -8.34156692e-01
2.64145017e-01 1.69436789e+00 7.23682642e-01 -2.12539837e-01
4.00893450e-01 3.94877493e-01 -6.41955256e-01 -1.35739541e+00
-1.44007266e-01 -5.44908226e-01 -5.43152332e-01 -4.37223434e-01
9.64512587e-01 -1.04287171e+00 -8.06672096e-01 1.00903833e+00
-1.02235878e+00 -4.68964815e-01 3.57240140e-02 8.25907588e-01
-2.49728024e-01 4.63386655e-01 -4.63684410e-01 -1.09650683e+00
-3.02071363e-01 -1.39648724e+00 1.22031856e+00 2.14792982e-01
-2.01680392e-01 -2.58688927e-01 5.36866523e-02 9.43495810e-01
1.67441219e-01 -5.60329407e-02 2.14267410e-02 -2.62159377e-01
-5.51453650e-01 -1.01648144e-01 1.17642298e-01 5.06407082e-01
4.06745434e-01 2.14225963e-01 -1.37216747e+00 -2.60545313e-01
2.19267398e-01 -5.70301473e-01 6.11741602e-01 3.99147302e-01
8.54601264e-01 1.41992336e-02 1.41064465e-01 3.98930043e-01
7.68762708e-01 4.46578473e-01 7.36674368e-01 4.04854387e-01
7.88975418e-01 6.04836166e-01 8.37420106e-01 5.40858805e-01
5.62751949e-01 7.82635570e-01 5.19546807e-01 3.22919428e-01
-2.12291241e-01 -1.87313393e-01 6.84652984e-01 1.17131829e+00
-3.34979557e-02 -8.66068620e-03 -1.16306400e+00 4.48939979e-01
-1.54416990e+00 -8.18377435e-01 -9.74768177e-02 2.32088590e+00
1.01286292e+00 -1.07452020e-01 2.68257588e-01 5.56251228e-01
6.10446274e-01 4.38785367e-02 -3.64023030e-01 -1.98141281e-02
-2.80966192e-01 1.41861401e-02 4.04990017e-02 5.35195291e-01
-1.04742002e+00 8.69936466e-01 6.01089048e+00 6.40337646e-01
-1.41693950e+00 2.27321029e-01 1.73016578e-01 -4.36962664e-01
-1.82420075e-01 -3.83483559e-01 -5.81738710e-01 3.51480246e-01
1.01823735e+00 3.55738521e-01 6.69477463e-01 7.46050298e-01
6.61992729e-01 -2.29434207e-01 -8.20844412e-01 1.49593425e+00
1.97170556e-01 -9.39946353e-01 -4.09009606e-01 9.23984796e-02
1.74502954e-01 5.02595663e-01 -1.98554829e-01 1.31844625e-01
-1.26208022e-01 -9.99997258e-01 1.02619255e+00 3.87209624e-01
9.94500756e-01 -6.84810460e-01 5.75290620e-01 3.99940223e-01
-1.26950157e+00 1.78947017e-01 -6.29567131e-02 -9.29778963e-02
1.28051594e-01 1.89854205e-01 -6.14552796e-01 3.16155225e-01
1.21811187e+00 6.53146982e-01 -6.46654248e-01 9.51854527e-01
-4.20634300e-01 8.07033122e-01 -3.96585673e-01 -9.37306285e-02
-1.95699260e-01 -1.29267760e-02 5.07254899e-01 9.35442388e-01
2.32762963e-01 -9.72696468e-02 -1.98453829e-01 5.12168467e-01
5.05152531e-03 1.08346790e-02 -5.81817567e-01 -2.87030578e-01
6.69461250e-01 9.56996322e-01 -3.55347723e-01 1.74819923e-03
-7.76264429e-01 9.18066323e-01 -1.77038059e-01 4.71555382e-01
-8.33567977e-01 -4.45341229e-01 8.88008952e-01 -3.48193049e-02
2.27214068e-01 -6.56927407e-01 -3.88280928e-01 -1.35730445e+00
3.85446906e-01 -1.31152856e+00 1.99362546e-01 -8.95071387e-01
-9.61929739e-01 4.65452462e-01 -6.22479022e-02 -1.28939593e+00
-2.17753425e-01 -6.07907057e-01 -3.33754867e-01 7.44857252e-01
-1.18526471e+00 -1.04506290e+00 -8.21318150e-01 9.14244890e-01
9.19172466e-01 -3.13616365e-01 7.14035094e-01 5.36902070e-01
-7.78818369e-01 7.33363688e-01 -8.31920430e-02 3.30891579e-01
8.29038680e-01 -9.09718454e-01 2.62557447e-01 9.45519567e-01
2.47841686e-01 6.46780908e-01 6.49229348e-01 -5.93642533e-01
-1.86487615e+00 -7.19484031e-01 6.58528268e-01 -6.49036586e-01
3.18262756e-01 -7.08768904e-01 -9.45974290e-01 5.50613701e-01
5.08644938e-01 -2.22631007e-01 6.51087344e-01 -4.60948527e-01
-1.30771860e-01 -1.20047644e-01 -1.08591866e+00 4.69133317e-01
8.55104625e-01 -8.39362204e-01 -6.26034737e-01 -2.94705629e-01
6.39708936e-01 -7.22724319e-01 -7.41735160e-01 1.46902099e-01
6.18739367e-01 -8.74799490e-01 7.37786114e-01 1.61945775e-01
1.11094140e-01 -5.42847872e-01 -4.16682571e-01 -9.75917161e-01
5.49317062e-01 -6.64137959e-01 -2.92972744e-01 1.55783844e+00
1.74850300e-01 -5.12526572e-01 7.66318679e-01 6.77343071e-01
-7.81364366e-02 -3.29014689e-01 -1.30147898e+00 -4.97791976e-01
-2.43043289e-01 -1.14204824e+00 7.90652215e-01 7.26585686e-01
1.62921712e-01 2.55335927e-01 -3.46317947e-01 1.96424440e-01
5.32859981e-01 -2.81893611e-01 1.12736857e+00 -8.23457062e-01
-1.36642633e-02 -3.19790095e-01 -4.60743278e-01 -1.07585025e+00
-6.62073642e-02 -4.51878667e-01 1.55113965e-01 -1.59968793e+00
-8.56398344e-02 2.95182746e-02 1.56760827e-01 5.39246142e-01
-3.02517582e-02 2.03367665e-01 2.27549091e-01 3.54402870e-01
-3.84218395e-01 8.98969173e-01 1.10926425e+00 -3.36816907e-01
-2.85209060e-01 4.48896810e-02 -2.62906671e-01 3.86880517e-01
1.07547975e+00 -1.34355918e-01 -4.46190953e-01 -6.16251945e-01
-2.10704297e-01 -1.16847575e-01 3.00715864e-01 -9.71146643e-01
3.08845371e-01 -1.48334146e-01 2.14184344e-01 -9.26938057e-01
7.16457963e-01 -1.00701022e+00 -5.56195341e-02 2.51840591e-01
-4.45130095e-02 3.06400582e-02 2.64003217e-01 2.34386981e-01
-6.52237594e-01 4.92531508e-02 4.26945537e-01 9.18649510e-02
-8.88985813e-01 1.06822012e-03 -5.39679170e-01 3.84822302e-02
8.98276448e-01 -3.73939484e-01 -5.09025395e-01 -5.89700997e-01
-4.36784208e-01 3.54957312e-01 6.33594990e-01 8.12160015e-01
1.07153332e+00 -1.31332994e+00 -5.22406220e-01 4.67947900e-01
2.96507180e-01 2.86208570e-01 3.18415910e-01 7.45261133e-01
-6.91688001e-01 4.12498236e-01 -2.08766401e-01 -7.23287284e-01
-1.70361459e+00 2.00083345e-01 1.16768822e-01 7.90812373e-01
-5.80027163e-01 7.18771338e-01 -5.13628006e-01 -4.21028137e-01
8.04776430e-01 -5.36732554e-01 -1.94989622e-01 1.84956610e-01
8.55508685e-01 3.90118331e-01 2.11211056e-01 -9.55638945e-01
-6.11824989e-01 3.54700208e-01 4.36341703e-01 -6.45438254e-01
1.15414107e+00 -5.43942213e-01 6.16686558e-03 7.74392247e-01
9.04532850e-01 2.82036066e-01 -1.30445099e+00 -6.43815622e-02
-2.53008991e-01 -7.15265393e-01 5.73282242e-02 -4.97101068e-01
-1.05706692e+00 1.19596970e+00 9.51039433e-01 4.25317958e-02
1.24917161e+00 8.96232575e-02 8.08941841e-01 3.52019221e-01
3.56418759e-01 -1.28210545e+00 5.40664373e-03 5.02437472e-01
1.11091459e+00 -1.57394707e+00 -3.53245527e-01 -2.05115631e-01
-9.58496988e-01 1.01597667e+00 6.65980339e-01 7.10812211e-01
4.42727566e-01 3.18313390e-01 9.45018172e-01 4.20936681e-02
-4.02794152e-01 -3.18982154e-01 2.26795301e-01 8.60166788e-01
5.46405196e-01 1.30969405e-01 2.30796374e-02 7.09144771e-01
-5.89197874e-01 1.13951549e-01 2.83631176e-01 1.27255023e+00
-2.42531657e-01 -1.12702107e+00 -8.33476365e-01 -1.17285274e-01
-3.45368803e-01 7.53729779e-04 -7.24639893e-01 5.92476249e-01
-9.99088511e-02 1.50323522e+00 1.97286736e-02 -6.97863221e-01
4.06878084e-01 5.70130274e-02 3.50460351e-01 -3.72099310e-01
-4.18729663e-01 4.00343895e-01 -3.28723453e-02 -6.35511160e-01
-4.81524885e-01 -8.70889902e-01 -1.50275207e+00 -4.56826806e-01
-5.91737404e-02 -1.70823976e-01 9.46669996e-01 7.03982055e-01
2.72097886e-01 1.91470638e-01 3.73988926e-01 -9.49768841e-01
-2.42589220e-01 -1.32303667e+00 -3.49447280e-01 3.32276464e-01
5.76633692e-01 -4.77703363e-01 -4.09437805e-01 1.45886406e-01] | [14.316630363464355, 5.084782123565674] |
7731a82c-a63f-4344-b543-31471036dce7 | dynamic-inference-with-grounding-based-vision | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Uzkent_Dynamic_Inference_With_Grounding_Based_Vision_and_Language_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Uzkent_Dynamic_Inference_With_Grounding_Based_Vision_and_Language_Models_CVPR_2023_paper.pdf | Dynamic Inference With Grounding Based Vision and Language Models | Transformers have been recently utilized for vision and language tasks successfully. For example, recent image and language models with more than 200M parameters have been proposed to learn visual grounding in the pre-training step and show impressive results on downstream vision and language tasks. On the other hand, there exists a large amount of computational redundancy in these large models which skips their run-time efficiency. To address this problem, we propose dynamic inference for grounding based vision and language models conditioned on the input image-text pair. We first design an approach to dynamically skip multihead self-attention and feed forward network layers across two backbones and multimodal network. Additionally, we propose dynamic token pruning and fusion for two backbones. In particular, we remove redundant tokens at different levels of the backbones and fuse the image tokens with the language tokens in an adaptive manner. To learn policies for dynamic inference, we train agents using reinforcement learning. In this direction, we replace the CNN backbone in a recent grounding-based vision and language model, MDETR, with a vision transformer and call it ViTMDETR. Then, we apply our dynamic inference method to ViTMDETR, called D-ViTDMETR, and perform experiments on image-language tasks. Our results show that we can improve the run-time efficiency of the state-of-the-art models MDETR and GLIP by up to 50% on Referring Expression Comprehension and Segmentation, and VQA with only maximum 0.3% accuracy drop. | ['Mohamed Omar', 'Xiaolong Wang', 'Jingru Yi', 'Keval Doshi', 'Wentao Zhu', 'Amanmeet Garg', 'Burak Uzkent'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-grounding', 'referring-expression'] | ['computer-vision', 'computer-vision'] | [ 1.45649672e-01 2.99818218e-01 3.41208726e-02 -3.32464367e-01
-7.44181991e-01 -4.60927218e-01 6.63467467e-01 -8.42214301e-02
-7.69053519e-01 3.27237189e-01 -1.95826799e-01 -4.67979759e-01
3.42191875e-01 -7.76849926e-01 -1.17743790e+00 -5.89222491e-01
4.50932771e-01 5.17415762e-01 4.14991051e-01 -2.33283043e-01
1.25617683e-01 6.47207275e-02 -1.40539980e+00 5.12849391e-01
9.93957102e-01 1.02932644e+00 6.41069889e-01 6.37750030e-01
-6.19097471e-01 9.97697771e-01 -5.66378832e-01 -6.43946707e-01
1.77945569e-02 -3.15018147e-01 -1.01445198e+00 1.80967987e-01
5.96817195e-01 -6.13873959e-01 -3.61369222e-01 1.11957943e+00
2.17277810e-01 -1.23971492e-01 3.38479578e-01 -1.40481138e+00
-9.16893959e-01 9.38270092e-01 -8.30367565e-01 -1.08831197e-01
2.48488206e-02 5.15305519e-01 9.21474159e-01 -8.41773152e-01
4.48516846e-01 1.52229500e+00 3.48441035e-01 7.51166761e-01
-8.79054725e-01 -5.80119133e-01 6.70280457e-01 3.46529573e-01
-1.15521860e+00 -4.01546866e-01 4.42829609e-01 -3.13659668e-01
1.35648441e+00 -3.05227220e-01 7.98610449e-01 9.82161224e-01
7.89477155e-02 1.08162296e+00 9.13301051e-01 -4.37819183e-01
8.92994627e-02 -1.40755372e-02 1.32576421e-01 1.41358030e+00
1.63331050e-02 -2.36615047e-01 -5.27563989e-01 3.85720730e-01
6.41727448e-01 -3.07983551e-02 -6.59957249e-03 2.38915663e-02
-1.07980537e+00 8.68711472e-01 5.95359921e-01 1.23946466e-01
-2.85030007e-01 6.48262262e-01 4.57454473e-01 2.12274805e-01
1.54757112e-01 2.04706844e-02 -4.21103299e-01 1.05000407e-01
-6.98939681e-01 4.23507486e-03 4.97059017e-01 1.02091014e+00
9.34723377e-01 1.15214534e-01 -3.64862084e-01 8.20471048e-01
5.26521802e-01 6.43049061e-01 4.69327211e-01 -1.07003689e+00
7.14083970e-01 6.28009379e-01 -3.11380118e-01 -6.13579452e-01
-1.71902195e-01 -2.55145252e-01 -8.19678962e-01 1.75552845e-01
3.18547398e-01 -6.67473823e-02 -1.46300995e+00 1.90405798e+00
2.16704547e-01 1.22228198e-01 2.52842933e-01 7.49137878e-01
8.95247936e-01 7.76551306e-01 3.99821788e-01 1.01490552e-02
1.60050738e+00 -1.52116477e+00 -5.04026830e-01 -4.59494770e-01
7.22459972e-01 -4.47162181e-01 1.34663558e+00 3.77860934e-01
-1.40744293e+00 -5.74061275e-01 -8.13096285e-01 -4.41566378e-01
-3.92757058e-01 1.59252763e-01 5.82563698e-01 1.52322352e-01
-1.30869281e+00 3.68718326e-01 -9.48719501e-01 -2.04102173e-01
5.79580188e-01 4.29742754e-01 -1.59963444e-01 -2.09427446e-01
-1.04216766e+00 8.99003744e-01 3.94103557e-01 2.82925725e-01
-1.34086549e+00 -4.48835611e-01 -1.02533531e+00 1.68704331e-01
6.15306675e-01 -1.13686657e+00 1.36789596e+00 -1.11817312e+00
-1.68211520e+00 9.32897091e-01 -2.19194770e-01 -7.21523345e-01
4.26453441e-01 -1.82180852e-01 1.00789286e-01 3.01064789e-01
4.95659448e-02 1.24270177e+00 9.51588452e-01 -1.17683542e+00
-8.24310005e-01 -3.24430168e-01 5.62469304e-01 2.05357283e-01
-2.05535203e-01 -1.46540955e-01 -9.78879809e-01 -2.20395938e-01
-5.31241968e-02 -8.15304220e-01 -1.15087278e-01 7.22752558e-03
-2.66552985e-01 -5.12958646e-01 6.68944359e-01 -6.93927526e-01
7.48625576e-01 -2.08031487e+00 3.62800121e-01 -1.91739723e-01
4.17821705e-01 4.03771222e-01 -4.69278663e-01 5.45607097e-02
4.14664656e-01 1.64805025e-01 -2.48016253e-01 -8.90707374e-01
1.11273676e-01 7.40076542e-01 -4.66457963e-01 9.95321497e-02
4.15416300e-01 1.31338382e+00 -7.32292652e-01 -7.92736828e-01
2.67147601e-01 3.96800220e-01 -7.03760922e-01 1.56026185e-01
-7.37905860e-01 2.87333637e-01 -3.48379165e-01 6.58137023e-01
4.90634233e-01 -4.38793659e-01 5.00613172e-03 -2.81848401e-01
-1.22570641e-01 1.72983125e-01 -5.46941161e-01 2.00077438e+00
-7.30569661e-01 5.56670249e-01 1.63516581e-01 -1.22627866e+00
5.49637258e-01 4.03990597e-02 3.08640581e-02 -1.10213482e+00
1.45759657e-01 -2.84166937e-03 3.74612212e-02 -5.90948105e-01
5.29454827e-01 4.25291993e-02 7.33721908e-03 9.57006663e-02
3.62017781e-01 -1.29262343e-01 2.20796242e-01 4.93658274e-01
7.92931080e-01 3.04379910e-01 -1.46669388e-01 2.47716069e-01
5.11826932e-01 9.03292522e-02 4.03521180e-01 9.06889379e-01
-3.23845856e-02 2.83276409e-01 5.53925395e-01 -3.94869111e-02
-8.75758231e-01 -8.87054265e-01 4.50449169e-01 1.41271567e+00
2.30071038e-01 -5.27732015e-01 -1.06336272e+00 -6.05552971e-01
-2.18621179e-01 6.90934658e-01 -4.63891059e-01 -2.51096487e-01
-6.06013119e-01 -4.60265309e-01 7.88443804e-01 7.45109856e-01
9.50245142e-01 -1.25139248e+00 -6.19181395e-01 1.48771450e-01
-2.84483999e-01 -1.52745438e+00 -2.15611354e-01 1.36693791e-01
-6.81056380e-01 -7.88278043e-01 -5.98676920e-01 -9.11007941e-01
6.49028599e-01 2.35395625e-01 1.13542950e+00 3.15455645e-01
-7.14661777e-02 5.31307280e-01 -3.02064657e-01 -3.32213938e-01
-3.34375829e-01 5.55082001e-02 -4.12111640e-01 -1.45604193e-01
1.73463766e-02 -1.78220794e-01 -5.91568530e-01 -6.08095340e-03
-8.27026784e-01 4.36988652e-01 7.15809762e-01 9.65224087e-01
7.35101581e-01 -3.32933277e-01 3.78213972e-01 -5.82958877e-01
3.41212660e-01 -1.87155500e-01 -9.27332222e-01 5.40196657e-01
-4.17113334e-01 1.95543617e-01 6.27885222e-01 -3.95547003e-01
-9.56583440e-01 -6.67086691e-02 -3.04698586e-01 -7.82821894e-01
-8.76101386e-03 5.88548303e-01 -1.48516268e-01 -3.58082093e-02
1.28132880e-01 3.38490039e-01 -6.23133332e-02 -1.86808944e-01
6.96059108e-01 4.88819689e-01 7.30253160e-01 -6.74897730e-01
5.35376549e-01 5.29743552e-01 -1.99795485e-01 -6.04200304e-01
-9.32577908e-01 -1.22575969e-01 -2.50159949e-01 3.91047895e-02
1.27343929e+00 -9.82019782e-01 -1.09536302e+00 6.89607501e-01
-1.47190630e+00 -8.12560201e-01 -1.30631521e-01 2.18945742e-01
-5.60352504e-01 3.71132642e-01 -7.27125585e-01 -6.80359066e-01
-5.60876548e-01 -1.49521506e+00 1.23698151e+00 2.52263516e-01
3.20469677e-01 -8.69257271e-01 -3.32190365e-01 6.14369571e-01
2.46558160e-01 -2.34554172e-01 1.11710787e+00 -3.44238341e-01
-8.89331162e-01 4.04714406e-01 -5.33371031e-01 4.29911584e-01
-4.03317928e-01 -4.45030816e-03 -9.05932724e-01 -2.29277804e-01
-2.58850962e-01 -6.52375042e-01 1.33298361e+00 3.84742945e-01
1.34717727e+00 -2.14658752e-01 -2.35576645e-01 7.83178031e-01
1.40519714e+00 1.52038604e-01 6.28762066e-01 2.75003374e-01
1.03945041e+00 5.75175047e-01 4.55863386e-01 1.33357182e-01
7.69053578e-01 4.97394413e-01 7.91945994e-01 -2.84558386e-01
-2.87278265e-01 -3.26389581e-01 6.38512850e-01 8.21144998e-01
8.44469592e-02 -4.00426954e-01 -9.05170918e-01 6.24480128e-01
-2.00898051e+00 -6.84288979e-01 1.33290187e-01 1.77578115e+00
7.62497067e-01 1.53504893e-01 -2.12823525e-01 -3.56512904e-01
4.86252844e-01 1.97245367e-02 -6.14331663e-01 -5.16131103e-01
-4.35853675e-02 2.01455578e-01 3.75885516e-01 5.73098361e-01
-7.51977801e-01 1.80084813e+00 5.42037678e+00 7.92003274e-01
-1.21084034e+00 2.07031801e-01 4.95198965e-01 6.74213171e-02
-3.13095152e-01 1.69351935e-01 -9.60887849e-01 1.16952673e-01
7.39058316e-01 2.34217882e-01 6.59094393e-01 6.80895925e-01
9.48095992e-02 -2.79625893e-01 -1.02776790e+00 1.13741899e+00
1.11055695e-01 -1.39851665e+00 3.79307032e-01 -1.45981545e-02
4.53738123e-01 2.92904496e-01 9.44493636e-02 6.37912750e-01
4.88504201e-01 -1.09624910e+00 9.57927823e-01 2.59371966e-01
6.54997289e-01 -5.32575667e-01 4.89401340e-01 3.83275211e-01
-1.09028101e+00 -1.19495206e-01 -4.18096960e-01 2.01092869e-01
2.41195887e-01 1.06399611e-01 -6.39542997e-01 4.71278727e-01
7.09076524e-01 5.23224890e-01 -5.21836519e-01 5.54660439e-01
-4.04146641e-01 5.26706934e-01 -2.42543712e-01 1.51207000e-01
7.27323532e-01 -2.09664240e-01 1.58651993e-01 1.03397107e+00
1.79942906e-01 -7.44217541e-03 2.89893895e-01 9.72953081e-01
-3.27025980e-01 -2.72325933e-01 -4.38449472e-01 -7.53908381e-02
3.32615525e-01 1.22039378e+00 -6.10461712e-01 -7.67763853e-01
-5.95730603e-01 1.23800933e+00 6.06319487e-01 4.73476499e-01
-1.14912999e+00 -2.11064354e-01 4.31270033e-01 -2.41408616e-01
6.97902441e-01 -3.85277748e-01 5.49850389e-02 -1.27751374e+00
-8.29588845e-02 -8.69260490e-01 2.20884472e-01 -9.99003887e-01
-9.17766929e-01 5.81830740e-01 2.36967411e-02 -4.22731668e-01
-2.92134136e-01 -7.20792651e-01 -4.14974719e-01 6.39951646e-01
-1.86193728e+00 -1.55241883e+00 -3.66602957e-01 9.31334853e-01
7.68940866e-01 -1.91300839e-01 5.35614431e-01 9.17401686e-02
-6.47924423e-01 6.34828746e-01 -2.76320606e-01 3.40873808e-01
3.89391005e-01 -1.25515962e+00 5.08667350e-01 7.89819002e-01
3.93956244e-01 5.67916155e-01 3.38487446e-01 -4.65120226e-01
-1.62921190e+00 -1.28640127e+00 5.91956437e-01 -1.10655315e-01
6.77976727e-01 -4.69310105e-01 -9.05239820e-01 9.23590004e-01
6.62619829e-01 -1.19812362e-01 8.03610831e-02 -9.37097818e-02
-4.70619440e-01 -3.73963378e-02 -8.21138263e-01 8.71762097e-01
1.14606059e+00 -5.90279281e-01 -6.80809498e-01 3.38958025e-01
1.14976943e+00 -4.62189138e-01 -5.58610082e-01 2.08526984e-01
3.34774345e-01 -7.42736697e-01 8.42823267e-01 -4.69707519e-01
4.74295437e-01 -3.44097286e-01 -2.18850717e-01 -1.02119505e+00
-3.83665301e-02 -5.00578046e-01 -3.36880870e-02 1.30925524e+00
3.40360314e-01 -5.20219505e-01 6.36851370e-01 3.23970884e-01
-3.77094388e-01 -7.97589183e-01 -8.34707201e-01 -4.32134986e-01
1.75208822e-01 -4.89667028e-01 2.92021155e-01 6.19694054e-01
-3.90234679e-01 6.73805475e-01 -2.52086073e-01 1.55033097e-01
6.14693105e-01 1.37486652e-01 7.40570664e-01 -6.90357804e-01
-4.80370253e-01 -4.33281988e-01 1.98044572e-02 -1.47612131e+00
5.20290434e-01 -9.74314749e-01 1.45921126e-01 -1.89352989e+00
2.84089357e-01 -2.12397605e-01 -1.51622027e-01 9.83154714e-01
-2.21709423e-02 5.92375770e-02 5.00605702e-01 1.47709874e-02
-8.55272532e-01 6.59412146e-01 1.35902333e+00 -5.42726636e-01
-1.05604656e-01 -5.17532825e-01 -5.68312705e-01 8.54269683e-01
7.28290498e-01 -2.71710753e-01 -6.45325005e-01 -1.10363197e+00
2.77469128e-01 -3.10723926e-03 8.45677137e-01 -6.84234142e-01
4.14751172e-01 -4.67355214e-02 1.12919800e-01 -6.87744737e-01
4.03006375e-01 -5.80417812e-01 -4.65772867e-01 4.06138986e-01
-3.37507099e-01 2.33697012e-01 2.83345640e-01 4.44943279e-01
-2.54214197e-01 -2.70690173e-01 6.97361171e-01 -4.63784993e-01
-9.77148354e-01 3.58176947e-01 -3.40189040e-01 1.10371709e-01
7.53798962e-01 -6.34316802e-02 -6.62822485e-01 -1.72790006e-01
-6.59996569e-01 6.22256160e-01 3.54791790e-01 3.07889283e-01
8.27932656e-01 -8.19534957e-01 -5.32264829e-01 2.99891829e-02
2.24151518e-02 3.64874274e-01 2.90757865e-01 1.02603054e+00
-3.81195992e-01 4.03296381e-01 -1.13859095e-01 -8.32432091e-01
-1.19415629e+00 6.03434980e-01 5.51839828e-01 -3.09331656e-01
-6.13479733e-01 8.85350585e-01 6.17381155e-01 -1.70092732e-01
2.47706547e-01 -7.68662035e-01 -1.92001089e-01 -5.68802282e-02
3.20714772e-01 -9.36580151e-02 -1.12328961e-01 -4.91072655e-01
-3.39858592e-01 7.92756021e-01 -4.12580818e-01 -3.65353376e-01
1.07444763e+00 -2.63091475e-01 -2.07517818e-01 2.20190778e-01
1.08400416e+00 -3.55044097e-01 -1.39064479e+00 -2.60168374e-01
-2.08661124e-01 3.96775082e-02 2.03125417e-01 -7.64783859e-01
-1.29540718e+00 1.26232123e+00 4.26855624e-01 -3.22254114e-02
1.16876137e+00 2.90858954e-01 9.32477415e-01 5.99769950e-01
2.56321102e-01 -1.05805135e+00 3.37397426e-01 8.23412120e-01
7.41384149e-01 -1.23778129e+00 -3.99278790e-01 -1.52011424e-01
-8.21960986e-01 8.08729291e-01 9.11356509e-01 -1.08612981e-02
6.89379945e-02 2.07323909e-01 -7.39776716e-02 -1.55372903e-01
-1.06836903e+00 -5.17565906e-01 -1.11835171e-02 5.82384765e-01
1.44660294e-01 -1.71558335e-01 5.25569618e-02 4.13884997e-01
2.29812730e-02 1.44498749e-02 2.20216557e-01 7.70142794e-01
-5.04613519e-01 -1.04997051e+00 -8.94106030e-02 2.04164609e-02
-3.09532911e-01 -5.04447341e-01 -2.76747674e-01 8.69680285e-01
2.54367501e-01 9.13704932e-01 2.21505448e-01 -3.00558358e-01
1.98378906e-01 2.31941864e-02 7.88983703e-01 -4.67097670e-01
-7.16310382e-01 2.34325871e-01 2.09444389e-02 -6.38945103e-01
-4.76139218e-01 -2.62167573e-01 -1.77159309e+00 -1.95830941e-01
-2.30432793e-01 -1.47604987e-01 6.45152092e-01 1.28241718e+00
3.95529717e-01 7.90794671e-01 1.96469314e-02 -6.43782556e-01
-4.92772728e-01 -8.15401256e-01 -4.26328033e-02 7.94457197e-02
3.01406950e-01 -5.24362624e-01 3.71802710e-02 2.22352013e-01] | [10.8071870803833, 1.514342188835144] |
26deb221-baad-4749-8635-f62278a0db86 | multi-view-self-supervised-deep-learning-for | 1609.09475 | null | http://arxiv.org/abs/1609.09475v3 | http://arxiv.org/pdf/1609.09475v3.pdf | Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge | Robot warehouse automation has attracted significant interest in recent
years, perhaps most visibly in the Amazon Picking Challenge (APC). A fully
autonomous warehouse pick-and-place system requires robust vision that reliably
recognizes and locates objects amid cluttered environments, self-occlusions,
sensor noise, and a large variety of objects. In this paper we present an
approach that leverages multi-view RGB-D data and self-supervised, data-driven
learning to overcome those difficulties. The approach was part of the
MIT-Princeton Team system that took 3rd- and 4th- place in the stowing and
picking tasks, respectively at APC 2016. In the proposed approach, we segment
and label multiple views of a scene with a fully convolutional neural network,
and then fit pre-scanned 3D object models to the resulting segmentation to get
the 6D object pose. Training a deep neural network for segmentation typically
requires a large amount of training data. We propose a self-supervised method
to generate a large labeled dataset without tedious manual segmentation. We
demonstrate that our system can reliably estimate the 6D pose of objects under
a variety of scenarios. All code, data, and benchmarks are available at
http://apc.cs.princeton.edu/ | ['Ed Walker Jr.', 'Kuan-Ting Yu', 'Alberto Rodriguez', 'Jianxiong Xiao', 'Daniel Suo', 'Shuran Song', 'Andy Zeng'] | 2016-09-29 | null | null | null | null | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [-2.39794273e-02 -2.55757719e-01 1.64049640e-01 -7.58683026e-01
-7.83973932e-01 -1.08691216e+00 2.35541508e-01 1.86370477e-01
-3.33317339e-01 7.32125342e-02 -3.80193949e-01 -9.65044349e-02
1.17389046e-01 -6.36066020e-01 -1.05273771e+00 -3.91708344e-01
-3.20579745e-02 1.01982510e+00 2.39510462e-01 -1.62231475e-01
3.67753655e-01 7.46772468e-01 -1.36162770e+00 -6.94362298e-02
6.52604997e-01 1.34167254e+00 6.73619747e-01 7.80664802e-01
-3.14042531e-02 2.15600982e-01 -2.30643615e-01 -1.42535985e-01
9.65206444e-01 1.44004256e-01 -6.59759641e-01 6.99569106e-01
3.66323799e-01 -6.36136949e-01 3.46311703e-02 8.42002153e-01
2.25026816e-01 -6.95928037e-02 6.45360410e-01 -1.53360963e+00
-2.28429273e-01 3.61367166e-01 -7.55416751e-01 -1.87743023e-01
2.74560302e-01 4.46929574e-01 6.96224332e-01 -9.39216256e-01
5.20709097e-01 1.16030633e+00 6.42437458e-01 1.46845773e-01
-1.06403339e+00 -4.78595972e-01 2.85528541e-01 -4.79658805e-02
-1.07940090e+00 -2.47281164e-01 8.89591277e-01 -4.32597071e-01
5.54506123e-01 -1.72959253e-01 8.13407660e-01 9.86576736e-01
1.38187960e-01 1.16081464e+00 1.02922249e+00 -3.05996060e-01
4.57324326e-01 1.61474664e-02 2.01931238e-01 9.05340672e-01
3.82687479e-01 -2.69996285e-01 -2.90639460e-01 1.16408607e-02
8.66609335e-01 3.53254765e-01 2.28525743e-01 -1.25572228e+00
-1.38968694e+00 6.36986613e-01 5.84599316e-01 -2.16063276e-01
-5.54675996e-01 1.82411924e-01 1.97723478e-01 -7.36370832e-02
7.41126090e-02 4.68353391e-01 -7.88244367e-01 5.14904819e-02
-5.58215797e-01 2.44905695e-01 1.03138447e+00 1.56421602e+00
6.92438424e-01 -3.28079969e-01 4.92533296e-01 4.56050277e-01
4.55548674e-01 6.44479573e-01 8.71963501e-02 -1.30003846e+00
5.49004078e-01 4.79434162e-01 5.82435012e-01 -8.69643569e-01
-6.62864566e-01 -3.74029428e-02 -5.07054567e-01 2.90876120e-01
5.81037343e-01 -1.68089211e-01 -1.21026695e+00 1.09694564e+00
6.32556319e-01 -3.20909202e-01 -1.13520488e-01 1.21406770e+00
4.82755810e-01 2.33818948e-01 -1.87309504e-01 2.88373560e-01
1.28813004e+00 -1.10869241e+00 -4.73688245e-01 -5.90252221e-01
2.24851698e-01 -9.09540713e-01 8.65679324e-01 5.51929891e-01
-1.10070574e+00 -5.34325778e-01 -1.26704872e+00 -1.67210862e-01
-5.49613118e-01 1.91100523e-01 7.95109808e-01 2.89474100e-01
-7.64706135e-01 3.98049206e-01 -1.19665945e+00 -7.80524611e-01
5.71098804e-01 3.96847606e-01 -4.18724835e-01 -4.56570894e-01
-3.81803542e-01 9.88131404e-01 4.10630494e-01 3.55589658e-01
-1.16654134e+00 -1.58753425e-01 -9.53328669e-01 -3.09382617e-01
7.07023501e-01 -5.78812420e-01 1.57921171e+00 -4.07727152e-01
-1.42402458e+00 7.72514105e-01 2.32934579e-03 -3.49940807e-01
7.27073789e-01 -6.29968822e-01 2.03684703e-01 3.64862308e-02
1.95972174e-01 8.45504344e-01 6.83510303e-01 -1.86784053e+00
-7.29230762e-01 -9.45733249e-01 4.04305160e-02 4.04311955e-01
4.46646959e-01 -4.00155365e-01 -6.63929403e-01 -8.49872455e-02
8.07417691e-01 -1.08848882e+00 -5.83628535e-01 6.15667924e-02
-6.22622669e-01 3.93812954e-02 9.93001759e-01 -4.88903582e-01
-1.02365308e-01 -1.98174345e+00 1.60014331e-02 5.46241775e-02
-3.07386145e-02 -2.15471387e-01 -7.94689953e-02 2.52964050e-01
4.35765773e-01 -3.05243164e-01 -1.45678058e-01 -5.35875201e-01
2.61809468e-01 3.77124101e-01 -2.27267161e-01 7.29308367e-01
1.70704693e-01 9.70087111e-01 -8.89074564e-01 -3.14943522e-01
4.90295291e-01 8.61280635e-02 -2.00080052e-01 3.00524354e-01
-4.43034530e-01 5.43351948e-01 -5.55295825e-01 1.13731623e+00
9.02547419e-01 -1.69662729e-01 1.79456156e-02 -2.82548010e-01
-1.50568232e-01 -1.41616836e-01 -1.43196058e+00 2.18695378e+00
-2.93389469e-01 1.36220321e-01 5.19468725e-01 -9.91363823e-01
1.06917048e+00 -2.28794888e-01 6.40268922e-01 -3.59176576e-01
3.03609937e-01 4.38490361e-01 -3.43606979e-01 -6.77018642e-01
6.98522806e-01 2.20443666e-01 -4.65408683e-01 3.51862103e-01
1.03323245e-02 -6.95679605e-01 1.58376634e-01 4.84934300e-02
1.00362062e+00 5.96551478e-01 1.00012898e-01 6.89286087e-03
-6.44211918e-02 6.47010326e-01 4.41350251e-01 7.06957161e-01
-4.98954922e-01 7.74422109e-01 1.19031303e-01 -6.36337996e-01
-1.12927103e+00 -1.06984389e+00 1.18760847e-01 8.74795437e-01
8.64394784e-01 1.60274893e-01 -5.86159468e-01 -7.81434298e-01
4.18397874e-01 6.59600616e-01 -1.59226015e-01 1.04933307e-01
-5.43926358e-01 -3.43622655e-01 -5.22220209e-02 7.80066729e-01
5.85152090e-01 -1.04598200e+00 -1.16885877e+00 1.71104386e-01
-4.81374636e-02 -1.47629249e+00 -3.11952293e-01 7.37700343e-01
-7.66736448e-01 -1.31099093e+00 -4.69275594e-01 -9.69657123e-01
8.76391947e-01 7.51853466e-01 9.61153686e-01 -5.31057060e-01
-5.26053667e-01 6.16391718e-01 -2.85629034e-01 -7.94872046e-01
-8.95954743e-02 1.40781432e-01 2.02392578e-01 -3.21683496e-01
4.49508071e-01 -1.59246713e-01 -5.64617395e-01 4.76817846e-01
-5.57316422e-01 9.47266631e-03 8.06952655e-01 4.25498605e-01
8.02876592e-01 -1.31472901e-01 2.34882563e-01 -6.16926551e-01
2.56501913e-01 -3.13472271e-01 -9.43187237e-01 6.13678582e-02
-3.28553140e-01 -2.35661253e-01 3.58989149e-01 -1.52477190e-01
-7.11398721e-01 7.92248547e-01 2.03173831e-01 -6.65620446e-01
-7.16184974e-01 1.89911157e-01 -3.50712091e-01 -2.63492134e-03
2.86565989e-01 -3.23362984e-02 1.29090166e-02 -4.53540981e-01
5.14156938e-01 6.84506893e-01 7.46542215e-01 -5.33384204e-01
7.80513585e-01 6.27339423e-01 -5.32699842e-03 -3.85970831e-01
-9.86431777e-01 -7.19786167e-01 -1.25128615e+00 -2.37894729e-01
8.63873005e-01 -9.86546695e-01 -8.06626558e-01 6.81483805e-01
-1.23878670e+00 -5.44024408e-01 -2.69102186e-01 4.43582356e-01
-7.58539498e-01 7.35766217e-02 -3.65591198e-01 -8.21189344e-01
-2.53794432e-01 -1.35729468e+00 1.51328313e+00 3.64730388e-01
1.15262441e-01 -3.48382801e-01 -4.50936109e-01 6.24461889e-01
3.14869970e-01 4.83641684e-01 3.39245737e-01 -8.39965761e-01
-9.32008386e-01 -5.61642587e-01 -2.29479447e-01 9.47967544e-02
1.46379590e-01 -2.57672995e-01 -7.58296788e-01 -2.52387196e-01
6.21593520e-02 -5.14495075e-01 5.28828681e-01 4.69059587e-01
1.12940383e+00 1.08001687e-01 -5.15596569e-01 5.38462281e-01
1.33223534e+00 3.45433384e-01 -1.47338077e-01 4.25872743e-01
7.57618010e-01 5.78492403e-01 1.01558197e+00 4.23138797e-01
7.82636106e-01 5.56505501e-01 9.28845823e-01 -4.22199927e-02
4.31406826e-01 -1.47046372e-01 -1.84383672e-02 5.19345939e-01
2.19845027e-01 -2.13344023e-01 -1.09488177e+00 6.08609438e-01
-1.92508364e+00 -4.22655642e-01 -5.68096228e-02 1.95872402e+00
3.18423897e-01 2.64559984e-01 1.01871841e-01 -1.44336313e-01
5.79270780e-01 -1.30987093e-01 -1.01612544e+00 -1.02399759e-01
2.47883931e-01 -2.55187273e-01 9.57191169e-01 3.03170770e-01
-1.42792976e+00 9.97347534e-01 5.40003681e+00 3.80890705e-02
-8.92802536e-01 -6.85744509e-02 5.48013866e-01 -3.03761959e-02
2.73072571e-01 -1.23841256e-01 -8.16064417e-01 1.43383101e-01
4.42265421e-01 2.47855350e-01 5.14434457e-01 1.27695405e+00
-3.83587107e-02 -5.06586552e-01 -1.31377566e+00 9.24237430e-01
2.25038469e-01 -9.47991908e-01 -6.18987381e-01 1.13469094e-01
5.81873119e-01 4.08267409e-01 -1.06975526e-01 1.83831826e-01
7.01445937e-01 -7.63637841e-01 1.00755954e+00 3.87485385e-01
2.08190590e-01 -5.57877183e-01 8.04480314e-01 7.18520284e-01
-1.10327113e+00 -1.54026031e-01 -3.42742980e-01 2.22683713e-01
1.69592753e-01 5.28319359e-01 -1.13267863e+00 5.36766887e-01
9.07652736e-01 4.23988551e-01 -3.57520401e-01 1.10060751e+00
-7.87941888e-02 1.31708039e-02 -6.66363418e-01 2.49900166e-02
2.69725621e-01 -3.29983979e-01 4.09613520e-01 9.00408864e-01
1.86350420e-01 1.34338036e-01 8.65836442e-01 8.32032204e-01
5.38706593e-02 -4.05889541e-01 -6.71605408e-01 1.89744428e-01
4.80141848e-01 1.64086652e+00 -1.19432974e+00 -1.05807610e-01
-1.03603803e-01 1.19145989e+00 1.91831782e-01 2.50452399e-01
-7.65926600e-01 -2.28729755e-01 2.79700547e-01 -1.19022615e-01
5.91893971e-01 -8.18966091e-01 -5.85130274e-01 -9.60926056e-01
2.01624885e-01 -6.08311057e-01 1.12726010e-01 -9.14606273e-01
-1.35449708e+00 2.87606031e-01 1.76196285e-02 -1.05006945e+00
-1.45766452e-01 -8.25957596e-01 -1.65388852e-01 6.11260772e-01
-1.48122287e+00 -1.30783486e+00 -8.74795735e-01 2.41684034e-01
9.25862134e-01 1.30748689e-01 6.63735509e-01 -2.46248573e-01
-4.10773247e-01 -6.81510940e-02 -3.75033878e-02 2.24375904e-01
6.06355727e-01 -1.45617712e+00 4.75174308e-01 5.54141164e-01
-2.10642442e-02 4.07504380e-01 6.98716879e-01 -6.18587732e-01
-2.14832711e+00 -1.17197013e+00 3.27111185e-01 -6.29134774e-01
3.99115741e-01 -7.03463972e-01 -4.43305671e-01 1.04589891e+00
2.15624020e-01 3.82810622e-01 2.49323949e-01 -2.41161063e-01
-8.28073397e-02 -1.33371294e-01 -1.43274546e+00 3.31768274e-01
1.13950574e+00 3.13792303e-02 -4.69799042e-01 5.46223402e-01
5.26507914e-01 -1.08966649e+00 -6.51882887e-01 5.21082938e-01
5.58785915e-01 -7.14710295e-01 9.26728964e-01 -2.68459201e-01
2.40947083e-01 -4.25475270e-01 -4.61393774e-01 -1.23945034e+00
-3.57089378e-02 -4.75848824e-01 -1.23598494e-01 9.31046963e-01
2.11329013e-01 -3.94157171e-01 9.40892160e-01 8.29281747e-01
-2.44988978e-01 -6.48659527e-01 -6.44811988e-01 -6.74305737e-01
-3.18416595e-01 -3.86952281e-01 5.27598619e-01 5.99983692e-01
-3.63154322e-01 2.22850412e-01 1.83749631e-01 5.94143331e-01
9.94728029e-01 5.65001786e-01 1.26084137e+00 -1.25227332e+00
1.94200620e-01 -9.85757485e-02 -3.38128924e-01 -1.15020919e+00
-1.09932441e-02 -7.08738685e-01 6.05390906e-01 -1.95677555e+00
-1.31249540e-02 -7.05547273e-01 1.61833659e-01 5.69982827e-01
2.15681985e-01 -1.34047074e-02 4.22319621e-01 2.50075907e-01
-8.31239998e-01 4.36994284e-01 1.20334148e+00 -2.40047693e-01
-1.78306505e-01 1.25797018e-01 -4.83366609e-01 8.97368193e-01
9.17214394e-01 -1.48184285e-01 -7.58682042e-02 -6.66425943e-01
-3.72903556e-01 -7.27225095e-02 4.67511624e-01 -1.05408025e+00
3.95695865e-01 -2.40853041e-01 9.33839619e-01 -1.08147216e+00
6.04380548e-01 -1.35693216e+00 -1.09775878e-01 4.88518745e-01
-7.87836835e-02 2.45272323e-01 3.07322741e-01 6.30824983e-01
2.14430302e-01 -2.54145175e-01 5.13216913e-01 -6.75146401e-01
-8.60245347e-01 2.53537297e-01 -8.43484979e-03 -3.66678685e-01
1.43627250e+00 -2.53025979e-01 -1.82038367e-01 -1.49800286e-01
-7.08936334e-01 7.91402400e-01 6.15646243e-01 5.67182004e-01
5.69927871e-01 -1.02266216e+00 -4.22572792e-01 4.32057232e-01
1.64802186e-02 9.38793480e-01 -1.00244015e-01 7.14802086e-01
-7.53643692e-01 3.30239445e-01 -1.28947601e-01 -1.09825253e+00
-7.36250818e-01 5.99535882e-01 1.33687750e-01 -6.55031353e-02
-6.44159257e-01 7.96297193e-01 -2.55796701e-01 -8.49443614e-01
4.31326330e-01 -5.05059540e-01 2.62244612e-01 -1.76727638e-01
2.85918713e-02 2.62455702e-01 7.64564127e-02 -4.53910887e-01
-3.51944745e-01 5.03305912e-01 -1.38587847e-01 -1.61758080e-01
1.63414764e+00 -1.83227196e-01 1.63942724e-01 5.87783813e-01
9.53806221e-01 -3.27749133e-01 -1.73433018e+00 -1.66155651e-01
1.94144815e-01 -4.32630330e-01 -1.37387127e-01 -8.50826502e-01
-1.11310947e+00 6.34729445e-01 5.21041870e-01 3.01945657e-01
8.51682127e-01 1.55842200e-01 9.36823547e-01 6.09871030e-01
1.04399586e+00 -1.14939177e+00 1.96395844e-01 5.73342204e-01
8.32243860e-01 -1.60038638e+00 -8.33852440e-02 -4.81392443e-01
-8.67809474e-01 1.18205023e+00 9.99593139e-01 -2.94829726e-01
3.50771278e-01 6.43980145e-01 3.01510394e-01 -3.75610352e-01
-2.53446758e-01 -1.85316145e-01 -1.71030685e-01 6.07069969e-01
-1.28438979e-01 1.55351549e-01 4.37462002e-01 4.94305879e-01
-2.22057492e-01 2.51252148e-02 3.70294511e-01 1.59253657e+00
-6.04514003e-01 -5.44504344e-01 -5.63926816e-01 2.44951859e-01
9.36608836e-02 5.11315584e-01 -4.17452872e-01 8.26490760e-01
1.28649026e-01 9.21776891e-01 2.11431682e-01 -2.72609651e-01
6.64264619e-01 1.63430035e-01 6.40651703e-01 -5.44658780e-01
-3.61145556e-01 2.57035881e-01 -1.78759858e-01 -8.22091341e-01
-3.65237772e-01 -1.01903880e+00 -1.48016405e+00 7.09971860e-02
-3.94778281e-01 -3.49568874e-01 1.22916949e+00 9.04031932e-01
4.94601458e-01 3.20885420e-01 6.86928749e-01 -1.57971704e+00
-7.94257224e-01 -9.05513585e-01 -5.56665182e-01 2.75880873e-01
2.70884246e-01 -6.14993811e-01 -7.88972676e-02 1.40623674e-01] | [6.094333171844482, -1.016058087348938] |
fe2b095b-4e9e-4870-9b29-f039810b333c | a-novel-embedding-architecture-and-score | null | null | https://ieeexplore.ieee.org/document/10068032 | https://ieeexplore.ieee.org/document/10068032 | A Novel Embedding Architecture and Score Level Fusion Scheme for Occluded Image Acquisition in Ear Biometrics System | Abstract:
Significant progress in the field of ear-based biometrics has been made in recent studies. But methods to deal with degradation caused by hair occlusions during ear image acquisition have not been addressed yet. Use of occluded ear images from both sides can give better or comparable results to that of clean image acquisition from a single side. To test this, in this work we introduce a novel embedding generation network using conventional CNNs along with parallel paths of Learnable Scattering Wavelet Networks. We have shown results of experiments for augmented training of proposed embedding network and score level fusion of both side-ear images to mitigate the effects of hair occlusions in verification and identification tasks. We also show extensive simulation results over closed and open testing sets to analyse the one-shot learning capabilities of our proposed embedding network. The reported performance metrics show the improvement achieved by using our proposed embedding network and fusing both sides of occluded ear images. | ['Vikram M. Gadre', 'Satish Mulleti', 'Divyang Sureshbhai Jadav', 'Saket Pateriya', 'A P Goutham', 'Archishman Biswas'] | 2023-03-21 | null | null | null | national-conference-on-communications-ncc | ['one-shot-learning'] | ['methodology'] | [ 2.60451108e-01 1.67085975e-01 6.81666791e-01 -4.48046654e-01
-7.87257910e-01 -1.77459657e-01 7.66324028e-02 6.69393223e-04
-3.17357063e-01 6.26304150e-01 3.16129386e-01 1.25771075e-01
-2.15823665e-01 -5.58296800e-01 -5.81563115e-01 -1.00238955e+00
-2.89261073e-01 1.67100206e-02 3.51753645e-02 -2.64337242e-01
-2.19123274e-01 5.95650196e-01 -1.82650685e+00 1.06577314e-01
2.69544303e-01 9.54274297e-01 -4.49070483e-01 7.49423325e-01
1.91719264e-01 2.93273246e-03 -7.33442485e-01 -5.75202107e-01
6.31637514e-01 -8.40057060e-02 -4.51245338e-01 1.72101036e-01
6.90243840e-01 -4.52080190e-01 -2.75847554e-01 8.18955779e-01
1.51755559e+00 -3.67830664e-01 3.25882584e-01 -1.22806025e+00
-5.91932535e-01 6.33905470e-01 -7.47936606e-01 -1.96935236e-02
4.77705836e-01 3.02150428e-01 6.19611084e-01 -5.79725683e-01
4.64760453e-01 1.10983086e+00 9.95936215e-01 6.06737375e-01
-1.50518334e+00 -1.02477443e+00 -5.86716890e-01 1.33002847e-01
-1.54438663e+00 -4.69380617e-01 1.01742005e+00 -6.71216398e-02
6.78930461e-01 1.28209814e-01 7.34059870e-01 1.02454495e+00
2.07367316e-01 6.69532299e-01 1.70293307e+00 -7.82591283e-01
-1.61960438e-01 4.86199260e-01 3.55550885e-01 6.78674459e-01
5.40346622e-01 3.74531418e-01 -6.87660813e-01 -2.19248399e-01
4.33359146e-01 -3.47891808e-01 -1.46728233e-01 -3.74525100e-01
-6.25429988e-01 5.44910371e-01 5.12985528e-01 4.89666432e-01
-3.84525716e-01 2.46927217e-01 1.67756170e-01 4.84746695e-01
3.65067035e-01 8.86464119e-02 -3.29237223e-01 3.46007645e-01
-1.06052244e+00 2.28934377e-01 7.35902965e-01 5.06102800e-01
4.47997481e-01 2.37914756e-01 -9.16546732e-02 7.04325557e-01
4.14342046e-01 5.35270035e-01 1.82687610e-01 -5.68294108e-01
1.00620463e-01 3.71451765e-01 1.22770974e-02 -7.90669322e-01
-4.79618520e-01 -7.38986969e-01 -8.09302270e-01 5.40322602e-01
4.44612950e-01 -3.24719042e-01 -1.32049024e+00 1.67615581e+00
4.38093811e-01 5.25752604e-01 2.25699171e-01 6.50573850e-01
8.32646549e-01 9.37175974e-02 -8.42817128e-03 8.22672695e-02
1.48173952e+00 -4.65162307e-01 -9.11826074e-01 5.33879161e-01
3.40132415e-01 -1.12236249e+00 4.39502627e-01 5.48157334e-01
-9.15609598e-01 -7.34605551e-01 -1.31637883e+00 7.99471214e-02
-5.13779700e-01 1.31360799e-01 3.42800975e-01 1.19644916e+00
-1.21727824e+00 3.21552575e-01 -8.11046422e-01 -3.99361730e-01
3.40945810e-01 1.01717114e+00 -6.97808981e-01 4.41944897e-02
-1.08770120e+00 8.82220864e-01 -8.11900198e-02 5.02557695e-01
-5.29175162e-01 -6.73088431e-01 -7.43564248e-01 -5.63260121e-03
-3.07951808e-01 -5.68758607e-01 8.13099265e-01 -4.89959210e-01
-1.21571040e+00 8.69351447e-01 -1.71125191e-03 -7.65115142e-01
3.12698931e-01 -4.98442203e-01 -4.92089391e-01 -1.35769313e-02
-3.47652882e-01 8.22798431e-01 1.03114879e+00 -1.26131511e+00
-1.53288379e-01 -7.32125163e-01 -2.61400580e-01 -2.69140154e-01
-4.83318508e-01 1.70299783e-02 2.47653708e-01 -6.28658056e-01
1.99214444e-01 -9.74246323e-01 2.11064622e-01 1.70791373e-01
-2.62780368e-01 1.76265970e-01 1.07624555e+00 -1.04332745e+00
9.31670368e-01 -2.09782982e+00 -3.13952327e-01 3.29279870e-01
2.63981819e-02 4.95337516e-01 -4.45752263e-01 5.54030597e-01
-4.62843925e-01 -1.58924460e-01 -7.21913651e-02 -5.20205796e-01
-6.80503175e-02 6.33701459e-02 -1.60373580e-02 5.91616273e-01
1.98977351e-01 5.36821663e-01 -2.03526333e-01 -4.76858228e-01
2.16463134e-01 1.38136494e+00 -2.74060488e-01 -1.82980299e-03
5.55115521e-01 4.63531405e-01 -4.63387556e-02 9.34892058e-01
1.11308384e+00 4.97695506e-01 -2.59410828e-01 -6.35472238e-01
3.56410772e-01 -1.90938205e-01 -1.57237327e+00 1.57114542e+00
-6.14572585e-01 6.23812735e-01 2.06071287e-01 -7.22458541e-01
1.06196749e+00 8.04438710e-01 5.13461709e-01 -5.60827732e-01
2.84190685e-01 2.03383818e-01 3.64727110e-01 -6.06965780e-01
-1.46035865e-01 -2.08470628e-01 3.28077137e-01 4.94882822e-01
5.60910881e-01 2.48799443e-01 -3.90340418e-01 -2.85654455e-01
1.01652098e+00 4.45029028e-02 -1.62223261e-02 -5.17646074e-02
6.87410295e-01 -6.84950352e-01 2.14380994e-01 4.34627354e-01
-4.91019636e-01 5.97967744e-01 -8.79432112e-02 -6.47276521e-01
-1.00329661e+00 -1.13669324e+00 -5.26592433e-01 6.35731816e-01
-1.56740487e-01 1.55407455e-04 -9.49989974e-01 -3.53929937e-01
2.48109773e-01 1.05315402e-01 -8.00957978e-01 6.68670833e-02
-4.92556453e-01 -8.61058772e-01 9.86398339e-01 4.44176495e-01
7.75251746e-01 -9.30851638e-01 -7.29169786e-01 2.06982091e-01
2.53035545e-01 -8.98658395e-01 9.23111513e-02 1.61327690e-01
-7.78703451e-01 -1.05080533e+00 -9.62327898e-01 -8.99815083e-01
5.30103624e-01 -5.67317940e-02 6.55477047e-01 1.76268041e-01
-1.06088161e+00 4.84190226e-01 -1.75163731e-01 -9.11843121e-01
-2.82754749e-02 6.85362816e-02 1.39249861e-01 3.05801570e-01
6.82915747e-01 -8.07680905e-01 -8.59916270e-01 2.35915303e-01
-8.25343847e-01 -4.77923423e-01 6.07465684e-01 1.15274036e+00
1.34880051e-01 1.71047464e-01 4.05267209e-01 -5.13023853e-01
7.15731919e-01 9.61420983e-02 -4.64121610e-01 2.00178355e-01
-5.54528832e-01 3.43762875e-01 -2.57654369e-01 -3.10978234e-01
-9.36535895e-01 2.43281901e-01 -4.19775009e-01 -4.34752591e-02
-3.20874006e-01 -1.92979388e-02 9.01025683e-02 -5.79486847e-01
5.37321329e-01 3.97337899e-02 3.71722966e-01 -4.84183520e-01
1.18684128e-01 9.24523413e-01 4.48638082e-01 -5.23170292e-01
1.09647119e+00 5.50055861e-01 2.49075681e-01 -8.56174111e-01
-3.98681730e-01 -5.70260227e-01 -7.86085188e-01 -1.86785415e-01
7.52549708e-01 -8.02576244e-01 -9.38222289e-01 7.45102108e-01
-1.03038311e+00 4.04156893e-01 -1.59989849e-01 3.31764460e-01
-2.42018759e-01 1.85283169e-01 -5.24785757e-01 -1.40206993e+00
-7.20317602e-01 -1.13303828e+00 1.26026881e+00 3.20247859e-01
-8.81493539e-02 -8.42267096e-01 1.97557539e-01 3.90686035e-01
6.32657170e-01 4.82004583e-01 4.46832746e-01 -5.11630476e-01
-4.66540247e-01 -7.66882956e-01 8.07710290e-02 5.49560487e-01
2.36834854e-01 -1.26645923e-01 -1.77663684e+00 -7.19537973e-01
4.53667939e-02 -2.37266868e-01 8.74812663e-01 4.01869118e-01
5.57386994e-01 -3.55622582e-02 -1.79751724e-01 4.66703296e-01
1.72914708e+00 4.34223711e-02 8.52424979e-01 2.29320943e-01
8.04964900e-02 9.11404133e-01 1.99920192e-01 5.62099740e-02
-1.99807838e-01 6.59841299e-01 2.31188133e-01 -3.25273454e-01
-5.66338480e-01 -3.61535512e-02 2.17512175e-01 5.67679286e-01
-2.73762316e-01 6.07262887e-02 -5.79779685e-01 7.06354618e-01
-1.28435588e+00 -8.17797303e-01 1.04827158e-01 2.18426514e+00
4.29402858e-01 3.25250849e-02 2.62945414e-01 7.69862950e-01
6.20634854e-01 -1.95230111e-01 -2.30247751e-01 -3.10613275e-01
-2.12890103e-01 1.12021244e+00 4.43419337e-01 5.82737744e-01
-9.26158547e-01 3.54980886e-01 6.62330484e+00 3.11677098e-01
-1.17767811e+00 2.90216863e-01 1.17770359e-01 1.21752145e-02
1.13494977e-01 -1.74531356e-01 -8.18442643e-01 2.29695812e-01
8.15048456e-01 4.40031916e-01 -3.68677638e-02 3.80214572e-01
-1.75871477e-01 -8.07157978e-02 -6.81009352e-01 9.82430816e-01
1.79604813e-01 -7.43469834e-01 -1.76644489e-01 2.43752971e-01
4.40067738e-01 -4.03652817e-01 4.40491229e-01 -1.52858004e-01
-8.94830562e-03 -1.04962122e+00 4.41993438e-02 4.71368015e-01
4.25331652e-01 -8.04535568e-01 1.49181163e+00 -2.17458308e-01
-1.32927871e+00 -1.21118963e-01 -1.74198359e-01 2.59112120e-01
2.69320738e-02 3.46811682e-01 -1.16802979e+00 5.37881255e-01
7.38811493e-01 -9.05343518e-02 -7.86761761e-01 1.32980430e+00
3.94224413e-02 7.28498697e-01 -6.68392360e-01 1.56882137e-01
-1.68734774e-01 2.99653977e-01 5.05947053e-01 1.09788775e+00
4.81575102e-01 -1.24893807e-01 -5.67704678e-01 5.83311260e-01
2.17422202e-01 1.09086253e-01 -8.36537421e-01 2.08970696e-01
3.01289350e-01 1.25345206e+00 -4.73892450e-01 1.03437565e-01
-2.51094937e-01 6.77629828e-01 -3.00743878e-01 2.66244411e-01
-4.25639391e-01 -6.03303730e-01 6.33084655e-01 4.66522127e-01
4.79190499e-01 -5.68169467e-02 -2.48478025e-01 -7.10993290e-01
-5.00747841e-03 -4.71113861e-01 2.21384913e-01 -7.78521001e-01
-1.13731134e+00 6.72753155e-01 -4.26028930e-02 -1.05315959e+00
9.83920321e-02 -7.37607181e-01 -5.05493104e-01 1.09420550e+00
-1.42687309e+00 -1.86957622e+00 -3.45367014e-01 4.46210653e-01
1.95783243e-01 -4.80859190e-01 1.14254010e+00 3.62621933e-01
-2.01929510e-01 1.13024139e+00 -2.49037996e-01 2.63192177e-01
8.54122162e-01 -1.09547520e+00 3.70995067e-02 7.11567402e-01
3.49114239e-01 8.79880309e-01 1.08757102e+00 -3.59920681e-01
-1.24430120e+00 -5.12106717e-01 6.67612255e-01 -2.63819873e-01
3.08999091e-01 -2.96824545e-01 -8.50448847e-01 3.63040090e-01
6.99279368e-01 3.17915119e-02 1.09692502e+00 2.27281839e-01
-5.20221651e-01 -5.58732212e-01 -1.65064204e+00 -5.96690066e-02
6.11863196e-01 -6.51737988e-01 -6.10845089e-01 3.71207818e-02
2.33904302e-01 -1.65000275e-01 -9.93843257e-01 5.46779811e-01
1.18618512e+00 -9.94230330e-01 1.15025914e+00 -3.11747909e-01
-1.54247195e-01 -3.01687598e-01 -2.64757842e-01 -1.06967556e+00
1.84623227e-01 -5.65528095e-01 2.38740761e-02 1.35816538e+00
1.81570590e-01 -7.37888992e-01 9.61523473e-01 2.90755481e-01
3.81992102e-01 -6.26848161e-01 -8.61075997e-01 -6.49801612e-01
-3.64365280e-02 -2.17704609e-01 6.16485357e-01 3.20108950e-01
-4.97825950e-01 8.90229344e-02 -4.33614969e-01 4.28933382e-01
1.24420881e+00 -2.48814002e-01 8.30260217e-01 -1.47666860e+00
-2.64025867e-01 3.47787142e-01 -1.19389498e+00 -1.29401674e-02
-5.77650703e-02 -4.59343612e-01 -2.41457462e-01 -1.11726344e+00
1.42666534e-01 -1.04155600e-01 -5.96246302e-01 4.96712565e-01
1.38716966e-01 9.19964910e-01 1.24982722e-01 -2.69270122e-01
2.72809952e-01 -3.94701995e-02 1.05659699e+00 -2.13518739e-01
-2.08724383e-02 -4.27471511e-02 -5.46114862e-01 3.92901719e-01
7.65061498e-01 -2.79434741e-01 -3.06234390e-01 -1.00233071e-01
3.73277664e-02 -9.39484388e-02 6.16267562e-01 -1.40383685e+00
2.69653708e-01 8.25759411e-01 5.91346979e-01 -6.56559944e-01
7.03081310e-01 -1.10430837e+00 4.44050789e-01 6.10413253e-01
-1.71987519e-01 3.37432921e-02 4.40408140e-01 4.83410120e-01
-4.42614019e-01 -2.21610582e-03 9.23543096e-01 2.07024798e-01
-1.81502298e-01 -1.19023487e-01 4.53438535e-02 -9.01242793e-01
9.68659043e-01 -5.86423814e-01 8.30593929e-02 -4.23150033e-01
-1.41801405e+00 -1.73038363e-01 6.84991181e-02 2.27171808e-01
7.34566212e-01 -1.27451742e+00 -7.39429832e-01 7.11870372e-01
-4.21143770e-02 -5.33788383e-01 3.08762580e-01 8.70955586e-01
-5.09729147e-01 3.83700877e-01 -6.83764815e-01 -5.04840672e-01
-2.12445903e+00 2.76525497e-01 4.58127350e-01 -1.60617474e-02
-4.56330180e-01 9.26055431e-01 -2.52738714e-01 -3.29827338e-01
5.92580855e-01 -1.79354072e-01 -1.14483006e-01 -2.37521790e-02
4.54708666e-01 2.33112067e-01 3.20556134e-01 -5.03844559e-01
-2.04319321e-02 9.09927607e-01 -2.80330449e-01 -2.90637344e-01
1.50083232e+00 7.96685368e-03 6.23647030e-03 2.09260508e-01
1.25740123e+00 -6.51030988e-02 -6.09396696e-01 -1.22549079e-01
-2.67564118e-01 -4.18699265e-01 -1.46680037e-02 -9.23116922e-01
-1.14798474e+00 1.26372254e+00 1.78701317e+00 1.60429001e-01
1.26918590e+00 -3.22675109e-01 9.20038164e-01 2.15601057e-01
4.97955948e-01 -9.41391826e-01 -2.20123865e-02 -3.56824309e-01
8.12382221e-01 -1.11595941e+00 -1.34307742e-01 -4.56814468e-01
-8.81052092e-02 1.21933103e+00 3.14158946e-01 -2.84385532e-01
1.16587305e+00 5.52761555e-01 4.34722692e-01 -3.12591344e-01
-4.30003822e-01 -3.94598573e-01 2.53833055e-01 1.04177511e+00
6.85046554e-01 -7.34973699e-02 -3.14727038e-01 1.02857210e-01
-2.22727135e-01 2.05510467e-01 2.78217643e-01 1.19715452e+00
-2.53303736e-01 -1.46977222e+00 -7.74155140e-01 3.29817384e-01
-6.97068393e-01 2.63976127e-01 -3.04630518e-01 8.94152701e-01
5.62681437e-01 6.70779765e-01 -2.59344667e-01 -5.63714147e-01
1.46223083e-01 5.39425135e-01 9.45329249e-01 -3.27562094e-01
-6.89981461e-01 1.68205053e-01 -9.78778526e-02 -1.67286694e-01
-9.18054760e-01 -5.79601169e-01 -6.05804384e-01 -1.53472289e-01
-7.92554855e-01 -1.86026450e-02 9.27504718e-01 6.47576332e-01
1.96318209e-01 4.69593138e-01 4.03297454e-01 -6.06378376e-01
-6.47460997e-01 -1.28832853e+00 -9.33604181e-01 3.95003945e-01
6.81582332e-01 -9.16037619e-01 -3.15643579e-01 2.48002693e-01] | [13.368940353393555, 0.9990918636322021] |
20fdf060-f696-4015-93af-c53fc2f224ca | weakly-supervised-temporal-action-7 | 2304.12616 | null | https://arxiv.org/abs/2304.12616v1 | https://arxiv.org/pdf/2304.12616v1.pdf | Weakly-Supervised Temporal Action Localization with Bidirectional Semantic Consistency Constraint | Weakly Supervised Temporal Action Localization (WTAL) aims to classify and localize temporal boundaries of actions for the video, given only video-level category labels in the training datasets. Due to the lack of boundary information during training, existing approaches formulate WTAL as a classificationproblem, i.e., generating the temporal class activation map (T-CAM) for localization. However, with only classification loss, the model would be sub-optimized, i.e., the action-related scenes are enough to distinguish different class labels. Regarding other actions in the action-related scene ( i.e., the scene same as positive actions) as co-scene actions, this sub-optimized model would misclassify the co-scene actions as positive actions. To address this misclassification, we propose a simple yet efficient method, named bidirectional semantic consistency constraint (Bi-SCC), to discriminate the positive actions from co-scene actions. The proposed Bi-SCC firstly adopts a temporal context augmentation to generate an augmented video that breaks the correlation between positive actions and their co-scene actions in the inter-video; Then, a semantic consistency constraint (SCC) is used to enforce the predictions of the original video and augmented video to be consistent, hence suppressing the co-scene actions. However, we find that this augmented video would destroy the original temporal context. Simply applying the consistency constraint would affect the completeness of localized positive actions. Hence, we boost the SCC in a bidirectional way to suppress co-scene actions while ensuring the integrity of positive actions, by cross-supervising the original and augmented videos. Finally, our proposed Bi-SCC can be applied to current WTAL approaches, and improve their performance. Experimental results show that our approach outperforms the state-of-the-art methods on THUMOS14 and ActivityNet. | ['Xinbo Gao', 'Jie Li', 'Nannan Wang', 'Xinpeng Ding', 'De Cheng', 'Guozhang Li'] | 2023-04-25 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.15991449e-01 -5.42406328e-02 -6.15539908e-01 -2.35510603e-01
-3.31906527e-01 -4.82434690e-01 4.74424571e-01 -3.50750804e-01
-2.28781953e-01 5.69832981e-01 2.23430678e-01 -4.91143316e-02
-1.20633356e-01 -4.52241689e-01 -7.90336967e-01 -8.83596838e-01
1.92099102e-02 -5.62701486e-02 8.16094756e-01 2.39719406e-01
9.78565663e-02 2.82572359e-02 -1.48108852e+00 8.63921642e-01
6.24205649e-01 1.16389954e+00 4.01149839e-01 1.42441437e-01
-6.45022318e-02 1.38302660e+00 -5.23005605e-01 1.20504824e-02
2.04573095e-01 -9.44204390e-01 -9.65673685e-01 5.47761738e-01
4.35327351e-01 -3.31626058e-01 -3.85650307e-01 1.15791476e+00
-2.08953708e-01 2.78841227e-01 2.34863654e-01 -1.68029583e+00
-2.83430815e-01 2.74129629e-01 -6.30473614e-01 2.45095745e-01
3.23116273e-01 1.89019397e-01 9.66477513e-01 -8.47411633e-01
7.48970270e-01 1.17200816e+00 3.45596343e-01 5.04684091e-01
-1.00319135e+00 -7.58066952e-01 8.34996521e-01 6.49524629e-01
-1.31794453e+00 -3.71170372e-01 9.13300455e-01 -3.88431966e-01
6.73337758e-01 3.39636147e-01 8.95709813e-01 1.08489692e+00
4.93679754e-02 9.39018905e-01 1.05751789e+00 -9.44816247e-02
3.58995020e-01 -1.68164507e-01 -7.57046491e-02 6.69991732e-01
-1.96905121e-01 -8.41932371e-02 -6.98757350e-01 1.54401585e-01
6.84553564e-01 1.98862284e-01 -3.98056597e-01 -5.82126141e-01
-1.57763767e+00 3.47472280e-01 2.38749608e-01 5.67486703e-01
-3.99434388e-01 1.56060219e-01 6.17045283e-01 4.65839952e-02
4.66204673e-01 -4.70158905e-02 -6.29970729e-01 -1.25905082e-01
-7.35870361e-01 -3.18027698e-02 2.67143339e-01 9.46667552e-01
7.87884176e-01 -9.47214514e-02 -4.01070297e-01 7.47417092e-01
1.57339200e-01 1.40394256e-01 5.20716608e-01 -1.10522008e+00
7.04760194e-01 7.50236154e-01 4.06365842e-02 -1.06343222e+00
-8.07918310e-02 -2.04726949e-01 -7.04279542e-01 -1.27149403e-01
4.13604736e-01 2.08995238e-01 -1.01471162e+00 1.83685374e+00
4.42266285e-01 7.96205044e-01 -9.69441142e-03 1.11670148e+00
5.36247253e-01 7.79756069e-01 2.23616794e-01 -6.20102167e-01
1.08455932e+00 -1.22555590e+00 -9.03561473e-01 -3.96090001e-01
9.25900102e-01 -5.64282119e-01 9.84837472e-01 2.83282459e-01
-8.15254688e-01 -6.00945294e-01 -8.96757543e-01 3.26881379e-01
-5.43698221e-02 2.28642374e-01 5.40802181e-01 3.06438562e-03
-6.36982203e-01 3.95713061e-01 -9.99935806e-01 -3.10937315e-01
4.74632025e-01 7.74709135e-02 -5.63001215e-01 -2.53826648e-01
-1.10658276e+00 5.46105027e-01 7.59247839e-01 1.18965976e-01
-1.14490819e+00 -4.77665693e-01 -1.00407481e+00 -1.28975242e-01
1.04919028e+00 -1.63563460e-01 1.05149746e+00 -1.68562698e+00
-1.06101334e+00 5.03769636e-01 -4.96747553e-01 -2.04780906e-01
4.48872447e-01 -2.30792090e-02 -6.10889494e-01 3.07163268e-01
3.88944805e-01 6.38511181e-01 8.05986345e-01 -1.30491328e+00
-1.04856229e+00 -1.25752404e-01 2.79667854e-01 3.76081288e-01
-3.94377500e-01 -4.53417823e-02 -9.92745459e-01 -8.72434378e-01
6.41124368e-01 -1.03029037e+00 3.86814773e-02 3.40704396e-02
-3.52436393e-01 -1.95686832e-01 1.22915995e+00 -7.77724385e-01
1.39770603e+00 -2.30548477e+00 1.87100038e-01 1.18126333e-01
-1.31464720e-01 7.84061700e-02 -2.92260259e-01 -7.34562334e-03
-3.93273383e-01 7.83526003e-02 -4.10149068e-01 -2.18137652e-01
-4.15889502e-01 5.70419669e-01 -2.36130595e-01 4.55427170e-01
1.71941265e-01 6.16594732e-01 -1.12696826e+00 -7.64453948e-01
4.73442405e-01 1.02644078e-01 -5.11084795e-01 6.98034167e-02
-3.46687764e-01 6.76003695e-01 -4.78451610e-01 7.03604996e-01
5.59272528e-01 -8.10096115e-02 3.50451440e-01 -4.39108133e-01
-4.73023690e-02 3.32655489e-01 -1.38876975e+00 1.57465279e+00
-2.92086303e-01 3.38800520e-01 -4.88548987e-02 -1.35030723e+00
4.14947271e-01 3.59232664e-01 9.25069392e-01 -7.74849117e-01
-2.88518190e-01 8.25544670e-02 2.15651523e-02 -6.02848887e-01
3.46317329e-02 3.36334743e-02 1.73962131e-01 2.37535730e-01
3.16912942e-02 3.59172791e-01 3.16392988e-01 2.13028759e-01
8.92653108e-01 6.17336631e-01 5.62120527e-02 2.38925475e-03
9.07152951e-01 -6.97963163e-02 1.26492202e+00 4.99756247e-01
-5.13193727e-01 5.00242174e-01 5.08886337e-01 -2.60202080e-01
-5.54169476e-01 -8.22882533e-01 2.73608387e-01 1.06525970e+00
5.91800392e-01 -5.56131065e-01 -7.46816039e-01 -1.32606411e+00
-3.69311363e-01 5.03244042e-01 -6.06989026e-01 -3.14301789e-01
-7.60157049e-01 -3.73184294e-01 2.03833312e-01 6.74337983e-01
9.13620234e-01 -1.11656988e+00 -3.83622557e-01 1.41328275e-01
-6.38776362e-01 -1.32049191e+00 -7.51045585e-01 7.51485601e-02
-7.42147684e-01 -1.32513690e+00 -4.45149869e-01 -9.28071201e-01
8.14597905e-01 5.66184402e-01 5.86740196e-01 3.08954298e-01
3.07322323e-01 9.94560197e-02 -5.16698718e-01 3.40294808e-01
-1.45298392e-01 -5.48239768e-01 -7.96759501e-03 4.86068934e-01
2.13454992e-01 -3.40149671e-01 -5.23253858e-01 8.06125581e-01
-8.74132574e-01 3.76383245e-01 4.31879282e-01 8.59206498e-01
9.10573542e-01 4.94036138e-01 3.76339197e-01 -5.98099530e-01
-2.81826317e-01 -3.86711121e-01 -3.17317009e-01 4.90025312e-01
-2.55881220e-01 -2.05919310e-01 7.02173233e-01 -7.89518297e-01
-1.18426824e+00 2.91203588e-01 1.54748484e-01 -8.14790070e-01
-2.00544477e-01 4.58564937e-01 -6.35748923e-01 1.10917009e-01
5.40684685e-02 5.23256779e-01 -5.16621411e-01 -1.94759846e-01
4.71346416e-02 3.10673267e-01 5.37886798e-01 -4.03284162e-01
5.26768386e-01 7.37928987e-01 -1.11887664e-01 -6.17420852e-01
-1.12469256e+00 -6.98596597e-01 -6.86592281e-01 -5.89645028e-01
1.13310933e+00 -8.36921871e-01 -2.19174057e-01 5.94559908e-01
-1.07545471e+00 -3.32044214e-01 -8.90374631e-02 7.16722727e-01
-5.12457669e-01 6.89203262e-01 -4.28164035e-01 -7.20360935e-01
3.01161617e-01 -1.12988353e+00 1.03791225e+00 -2.87701339e-02
-1.85686395e-01 -7.80466497e-01 -2.83470333e-01 5.39734185e-01
-2.66323268e-01 1.80289187e-02 7.87944794e-01 -4.28579420e-01
-6.54588282e-01 1.15331009e-01 -7.90356472e-02 5.69313288e-01
3.14524949e-01 -2.52339486e-02 -7.89596081e-01 -8.53109136e-02
6.32051304e-02 -1.28636643e-01 8.72005522e-01 2.91791528e-01
1.44973302e+00 -4.70097542e-01 -5.14317393e-01 4.56211299e-01
1.01281548e+00 6.88554347e-01 6.47501230e-01 2.90853173e-01
9.47754920e-01 6.12263799e-01 1.23102474e+00 2.20299348e-01
1.32796675e-01 9.05429482e-01 3.82341713e-01 -1.12236455e-01
-2.12850079e-01 -4.24259216e-01 7.37032354e-01 6.23997629e-01
-6.17531277e-02 -2.14543909e-01 -6.01116598e-01 5.74311018e-01
-2.23272467e+00 -1.26347721e+00 -2.82537282e-01 2.27657461e+00
6.69952214e-01 2.81785101e-01 2.24245079e-02 3.21548074e-01
9.97316957e-01 4.57085073e-01 -5.81493556e-01 1.16301559e-01
-1.04485959e-01 -2.38698676e-01 2.71720380e-01 2.39687860e-01
-1.32848155e+00 8.84703457e-01 4.56772661e+00 1.00632215e+00
-1.04752195e+00 3.36947322e-01 6.33807898e-01 -1.17114641e-01
2.39328034e-02 3.34503800e-01 -4.46419328e-01 7.59406209e-01
2.49916166e-01 9.35380980e-02 3.63576889e-01 8.58336747e-01
5.60321987e-01 -3.46033335e-01 -1.25645077e+00 7.72478104e-01
3.19023907e-01 -9.61205363e-01 6.03569709e-02 -1.65157005e-01
8.20566177e-01 -4.63341802e-01 -1.71180934e-01 4.56959933e-01
-1.31269619e-01 -3.90280932e-01 1.09824657e+00 1.92592502e-01
5.12348771e-01 -5.57051778e-01 6.96435869e-01 5.29747665e-01
-1.57262623e+00 -1.81953713e-01 -4.15899903e-02 1.02212960e-02
1.59728333e-01 3.54138166e-01 -1.71623722e-01 6.62820458e-01
9.34912145e-01 1.27475667e+00 -4.06446546e-01 7.59105742e-01
-3.81633103e-01 6.18893981e-01 -1.65394805e-02 5.18765092e-01
3.02095115e-01 -3.47590446e-01 5.01175404e-01 8.32251847e-01
1.89446911e-01 1.31114155e-01 5.82377613e-01 5.69254160e-01
2.39172563e-01 -1.29171357e-01 -3.74620199e-01 6.09169379e-02
3.02200884e-01 8.86725605e-01 -7.98760772e-01 -6.98603332e-01
-7.20625579e-01 1.33830035e+00 2.84251515e-02 6.24015927e-01
-1.23627293e+00 1.42507881e-01 6.20036602e-01 -3.51681858e-02
2.73337930e-01 -4.14587520e-02 -1.70403808e-01 -1.29059637e+00
4.86601293e-01 -7.81404436e-01 6.70397878e-01 -8.12627792e-01
-8.93732131e-01 2.30763316e-01 1.45413131e-01 -1.74338520e+00
6.97345957e-02 -1.46633625e-01 -5.51383197e-01 3.57346147e-01
-1.27561879e+00 -1.19082177e+00 -3.83974731e-01 8.35754752e-01
8.60296249e-01 2.62980998e-01 2.37634197e-01 4.21309859e-01
-7.19922841e-01 3.46982449e-01 -3.22229087e-01 1.48485765e-01
6.51835263e-01 -8.33258033e-01 -3.48016351e-01 1.30174124e+00
1.08823858e-01 3.47425461e-01 2.82379329e-01 -9.55235839e-01
-1.02437472e+00 -1.56154466e+00 8.88582289e-01 -1.00173078e-01
7.15944052e-01 -3.05480778e-01 -1.03031254e+00 7.88554311e-01
-1.34953186e-01 1.83104470e-01 3.04409921e-01 -3.10212791e-01
-2.70191550e-01 -2.17242301e-01 -8.48297536e-01 6.46629751e-01
1.52822363e+00 -6.29442394e-01 -6.61372066e-01 5.18179953e-01
7.60597587e-01 -1.99229196e-01 -4.82001722e-01 7.31546164e-01
3.75099689e-01 -9.55147684e-01 8.99955273e-01 -4.52990323e-01
5.72950006e-01 -8.30303192e-01 -3.09530571e-02 -8.89807642e-01
-2.72185743e-01 -1.98035777e-01 -3.37150812e-01 1.39529240e+00
7.41637573e-02 -3.21004301e-01 7.51299143e-01 3.46010447e-01
-4.91383135e-01 -6.75190032e-01 -1.16934371e+00 -1.10305476e+00
-5.00607252e-01 -5.67854166e-01 2.30219930e-01 1.30898499e+00
4.28363718e-02 -4.23044851e-03 -5.74951947e-01 3.49751502e-01
2.84245461e-01 1.03794344e-01 4.28926378e-01 -5.64995706e-01
-3.17696542e-01 -2.45229721e-01 -4.20645922e-01 -1.17025387e+00
3.86981249e-01 -6.50611937e-01 3.50835681e-01 -1.39818239e+00
3.21133703e-01 -4.60135013e-01 -5.21075606e-01 9.88783956e-01
-2.62244731e-01 3.07302773e-01 2.60096133e-01 3.64427239e-01
-9.20645535e-01 6.20001018e-01 1.38275814e+00 -2.76416391e-01
-3.09155643e-01 -2.13029906e-01 -6.20770603e-02 8.70074868e-01
5.20239770e-01 -4.35160637e-01 -6.64895236e-01 -1.93722397e-01
-3.07836145e-01 1.62696570e-01 5.20082295e-01 -1.12470043e+00
1.42131612e-01 -6.44862473e-01 2.85701782e-01 -5.94768167e-01
3.19482386e-01 -1.07284451e+00 2.23701835e-01 6.16876245e-01
-3.00907552e-01 -3.32558513e-01 -1.00103855e-01 7.50022054e-01
-5.47986269e-01 2.88060959e-03 7.05175579e-01 -1.85195189e-02
-1.20451152e+00 3.40158463e-01 -3.68854105e-01 -8.65018442e-02
1.54800582e+00 -5.81253290e-01 -2.83922195e-01 -1.48020104e-01
-7.69867301e-01 4.61684316e-01 4.79142725e-01 6.26362264e-01
6.19766235e-01 -1.41093242e+00 -3.13674033e-01 1.76077798e-01
2.88191855e-01 1.42440926e-02 5.52835941e-01 1.18390501e+00
-3.23693641e-02 3.03790599e-01 4.28985730e-02 -7.81170666e-01
-1.58863366e+00 8.07804763e-01 3.30933392e-01 -1.01656735e-01
-5.92094600e-01 5.92555404e-01 7.71368921e-01 6.52147159e-02
3.51652473e-01 -4.97550249e-01 -2.68491000e-01 -5.88524714e-03
4.30359185e-01 7.47828931e-02 -9.65989381e-02 -9.87430394e-01
-6.51550949e-01 5.22194684e-01 9.35451984e-02 3.29638608e-02
9.64378536e-01 -9.23006833e-02 -1.08411916e-01 4.49239761e-01
1.14492416e+00 -2.20572561e-01 -1.53552032e+00 -1.47911802e-01
1.14265899e-03 -6.88076258e-01 2.48841252e-02 -6.53565466e-01
-1.37635219e+00 5.48612058e-01 4.82830793e-01 -2.66747773e-01
1.45380008e+00 1.42874613e-01 5.91525793e-01 1.54978663e-01
4.16258097e-01 -1.20449102e+00 4.45676476e-01 4.63542104e-01
6.68697119e-01 -1.02578247e+00 -9.39796269e-02 -9.08465445e-01
-8.95405948e-01 7.16876149e-01 1.10444558e+00 2.63108075e-01
3.94268155e-01 -7.43894801e-02 -2.81233132e-01 3.90061224e-03
-6.92208588e-01 -8.37986395e-02 3.42409164e-01 3.50099057e-01
1.03169128e-01 -1.52791351e-01 -4.45645303e-01 5.17227769e-01
5.58539331e-01 1.02967426e-01 2.57710725e-01 1.18450832e+00
-1.38228372e-01 -8.46464753e-01 -2.47392237e-01 3.55518311e-01
-2.01994210e-01 7.66844600e-02 -4.90894496e-01 6.25142276e-01
7.28151143e-01 1.09863877e+00 1.73622966e-01 -6.48234308e-01
2.54870534e-01 4.19417322e-02 2.37206146e-01 -6.46596372e-01
-2.30632409e-01 3.77078593e-01 1.17084928e-01 -8.97570252e-01
-8.89490902e-01 -7.15633571e-01 -1.57937753e+00 9.38603804e-02
-4.32342023e-01 4.00204621e-02 8.99781734e-02 1.19988608e+00
1.04467332e-01 5.51598012e-01 7.35420167e-01 -5.53402305e-01
1.39393583e-02 -7.18279481e-01 -4.61876869e-01 7.96450496e-01
1.51064932e-01 -9.20431137e-01 -5.39999127e-01 4.95250881e-01] | [8.562241554260254, 0.6454331278800964] |
46a0228b-38b4-4a3e-83a6-c13976e9ee1d | unsupervised-learning-with-contrastive-latent | 1811.06094 | null | http://arxiv.org/abs/1811.06094v1 | http://arxiv.org/pdf/1811.06094v1.pdf | Unsupervised learning with contrastive latent variable models | In unsupervised learning, dimensionality reduction is an important tool for
data exploration and visualization. Because these aims are typically
open-ended, it can be useful to frame the problem as looking for patterns that
are enriched in one dataset relative to another. These pairs of datasets occur
commonly, for instance a population of interest vs. control or signal vs.
signal free recordings.However, there are few methods that work on sets of data
as opposed to data points or sequences. Here, we present a probabilistic model
for dimensionality reduction to discover signal that is enriched in the target
dataset relative to the background dataset. The data in these sets do not need
to be paired or grouped beyond set membership. By using a probabilistic model
where some structure is shared amongst the two datasets and some is unique to
the target dataset, we are able to recover interesting structure in the latent
space of the target dataset. The method also has the advantages of a
probabilistic model, namely that it allows for the incorporation of prior
information, handles missing data, and can be generalized to different
distributional assumptions. We describe several possible variations of the
model and demonstrate the application of the technique to de-noising, feature
selection, and subgroup discovery settings. | ['Kristen Severson', 'Kenney Ng', 'Soumya Ghosh'] | 2018-11-14 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 5.19118726e-01 -1.34860501e-01 -1.55374691e-01 -6.86104238e-01
-5.31035244e-01 -5.11291921e-01 7.68411577e-01 3.94220829e-01
-2.61182636e-01 5.61377466e-01 4.58908409e-01 -1.57334022e-02
-1.05788338e+00 -6.36073530e-01 -2.27068469e-01 -1.03085625e+00
-4.22630489e-01 5.13247430e-01 2.05406725e-01 1.36578828e-01
1.78537875e-01 6.26899302e-01 -1.95444560e+00 4.39050466e-01
2.92116106e-01 6.20640457e-01 1.74693421e-01 3.87074172e-01
-1.45159349e-01 2.57027000e-01 -6.66861832e-01 1.98568702e-01
1.91304564e-01 -4.91107732e-01 -4.40653592e-01 4.55167070e-02
2.55126983e-01 3.33424896e-01 -6.48499057e-02 1.03844965e+00
7.16084898e-01 3.46311741e-02 8.72115493e-01 -1.35697913e+00
-3.57251950e-02 5.23486257e-01 -5.11009455e-01 2.85406470e-01
2.19919667e-01 -2.42795005e-01 8.01366806e-01 -6.56050742e-01
7.61569202e-01 1.40442848e+00 5.27251661e-01 2.22080737e-01
-1.98030043e+00 -5.03709137e-01 -3.88954766e-02 -6.67572469e-02
-1.30363047e+00 -6.12240195e-01 8.78390968e-01 -7.86956549e-01
2.83770382e-01 6.74981534e-01 3.43875825e-01 1.02676785e+00
-1.01643503e-01 5.10949433e-01 1.25406039e+00 -5.41845858e-01
4.83759791e-01 3.38490337e-01 6.18391335e-01 1.42783195e-01
1.97775900e-01 2.27160528e-01 -5.86568594e-01 -6.14619195e-01
1.92597494e-01 1.83165386e-01 -2.05069914e-01 -8.64771843e-01
-1.34583640e+00 8.87741625e-01 -1.71444356e-01 5.15967906e-01
-3.64499837e-01 -2.22810179e-01 5.00162661e-01 2.37421647e-01
4.13426369e-01 5.72856307e-01 -5.09657681e-01 -5.11532202e-02
-1.08055770e+00 4.16331261e-01 5.92349052e-01 4.04673249e-01
6.87757015e-01 -4.33708489e-01 -2.44138330e-01 7.43173778e-01
4.08285260e-01 5.91298041e-04 8.50330174e-01 -7.47909188e-01
4.57097776e-02 5.29314756e-01 -1.06439933e-01 -1.05598044e+00
-7.69984007e-01 -1.72013640e-01 -7.64741421e-01 2.04127356e-01
5.49333632e-01 1.39055774e-02 -6.89766884e-01 1.93546796e+00
3.91900092e-01 1.21314898e-01 2.56336089e-02 5.87982833e-01
7.38631129e-01 2.93244511e-01 -8.21493641e-02 -6.54973626e-01
1.43531656e+00 1.59207270e-01 -8.96935523e-01 8.43385532e-02
6.54398501e-01 -4.04419661e-01 9.20603871e-01 4.67049360e-01
-4.01370823e-01 -3.01242203e-01 -8.76375973e-01 1.61973104e-01
-4.53713536e-01 2.23335475e-02 5.50996125e-01 8.30644608e-01
-6.09866738e-01 6.87003136e-01 -8.01247180e-01 -4.59261894e-01
2.64747411e-01 4.27806497e-01 -4.79967326e-01 1.54883593e-01
-1.15358424e+00 5.06534040e-01 5.46214461e-01 -2.33864293e-01
-4.01669174e-01 -7.86329329e-01 -6.80983305e-01 2.95913331e-02
1.41399831e-01 -2.88172007e-01 5.07707000e-01 -7.34021604e-01
-9.30373251e-01 8.09471488e-01 -2.46531948e-01 -2.12960020e-01
4.21843201e-01 1.34087861e-01 -5.14104307e-01 -1.71227440e-01
1.38421386e-01 2.34661967e-01 8.44316304e-01 -9.48275924e-01
-5.14431596e-01 -6.62315845e-01 -5.60567021e-01 -1.12559475e-01
-2.18930826e-01 3.77425581e-01 -2.02950254e-01 -8.77633989e-01
3.69344413e-01 -9.17234898e-01 -1.99344337e-01 -4.60404605e-02
-5.02461314e-01 -9.40198600e-02 1.03342366e+00 -3.55784208e-01
1.23850441e+00 -2.68081474e+00 1.24809876e-01 6.15947247e-01
3.60229433e-01 -1.67219996e-01 1.76480442e-01 5.51091552e-01
-7.01995194e-01 1.30182415e-01 -4.53066170e-01 4.87427339e-02
5.47284074e-03 2.45685667e-01 -2.19190195e-01 7.25008011e-01
1.57164782e-01 3.16885203e-01 -6.71336651e-01 -3.17543834e-01
1.52695507e-01 3.58556002e-01 -3.55574995e-01 -5.65985739e-02
1.14097998e-01 5.86606979e-01 -3.18400800e-01 1.46076307e-01
5.32308936e-01 4.57484536e-02 4.65705454e-01 -1.69603199e-01
-3.46722394e-01 2.11444169e-01 -1.79676688e+00 1.30920327e+00
2.47541100e-01 8.68104994e-01 -1.76025257e-01 -1.24171054e+00
1.14869666e+00 2.25464791e-01 7.08846211e-01 -2.48727113e-01
-6.02419376e-02 3.17872725e-02 3.50380123e-01 -6.27571464e-01
4.40629125e-02 -3.20291966e-01 -6.60907179e-02 5.56684196e-01
5.66737726e-02 2.21449465e-01 2.71630615e-01 -1.85096011e-01
1.09849441e+00 -1.83227405e-01 4.64011937e-01 -4.77706730e-01
2.33809844e-01 -2.59634972e-01 9.11951065e-01 7.48185456e-01
1.54757962e-01 6.06380343e-01 9.75420535e-01 -1.82124242e-01
-7.59821951e-01 -1.03615069e+00 -7.26332843e-01 8.65203738e-01
-2.30622590e-01 -5.63309431e-01 -2.18393341e-01 -3.48312676e-01
9.52877253e-02 6.84828997e-01 -7.81417608e-01 -2.20404044e-01
-2.42920145e-01 -1.33017325e+00 4.45020318e-01 9.50499400e-02
-2.12268066e-02 -6.46459222e-01 -6.81305051e-01 -4.90201544e-03
1.28355414e-01 -6.50284886e-01 4.31506410e-02 5.86070061e-01
-1.12448549e+00 -1.18361628e+00 -3.03458810e-01 -4.80687201e-01
5.31089485e-01 -1.27171353e-01 8.46583784e-01 -5.77067494e-01
-5.45526564e-01 2.39461035e-01 -3.20723116e-01 -6.64843678e-01
-3.57745379e-01 -3.15050423e-01 4.57884908e-01 4.02693868e-01
6.48204625e-01 -6.34296358e-01 -1.73384562e-01 3.98762673e-01
-9.78246093e-01 -3.55491430e-01 3.63942116e-01 8.20409954e-01
7.16293514e-01 6.80244923e-01 5.23285985e-01 -1.03818977e+00
7.32330024e-01 -7.19363809e-01 -4.86259609e-01 5.23319803e-02
-5.53154647e-01 3.17185640e-01 1.28156990e-01 -6.01463020e-01
-6.41759396e-01 3.92605931e-01 3.00342739e-01 -4.05239522e-01
-5.70626080e-01 6.31984591e-01 -5.41879475e-01 3.74468625e-01
9.00941968e-01 8.93799439e-02 4.11498398e-01 -9.67823625e-01
1.96757451e-01 8.29126358e-01 2.19633445e-01 -2.50922769e-01
3.62717509e-01 5.10693729e-01 1.79930165e-01 -1.28531110e+00
-1.39839634e-01 -5.90802312e-01 -1.00302720e+00 8.55470970e-02
6.72565818e-01 -4.34630007e-01 -4.92664605e-01 2.96521354e-02
-5.80526590e-01 1.46705210e-01 -6.98255599e-01 9.12498653e-01
-5.05391538e-01 4.10983026e-01 2.63978332e-01 -8.94664288e-01
2.94644922e-01 -1.13964915e+00 8.37409437e-01 -2.51661777e-01
-5.70179701e-01 -1.01561356e+00 3.76717716e-01 -1.88077435e-01
-8.43528435e-02 5.33852279e-01 1.21941781e+00 -1.34386110e+00
1.29775703e-01 -4.27151233e-01 1.72343761e-01 5.80227338e-02
7.02231228e-01 -1.16990739e-02 -1.20823121e+00 -2.15065897e-01
2.92714983e-01 2.50718802e-01 6.75273955e-01 6.29841328e-01
1.18319762e+00 -3.45149666e-01 -6.77626848e-01 3.00668806e-01
8.24733913e-01 2.58609444e-01 4.00937200e-01 1.16124436e-01
3.58766615e-01 1.28052545e+00 4.27823752e-01 4.23971355e-01
-1.26551330e-01 9.42458510e-01 9.01440382e-02 -2.37065367e-02
2.05960765e-01 7.84236342e-02 1.81939989e-01 1.35954261e-01
4.99145597e-01 1.81653589e-01 -8.54883790e-01 5.59743822e-01
-1.75601208e+00 -1.08841157e+00 -4.78205353e-01 2.61950755e+00
6.07190609e-01 -3.53456996e-02 5.56275487e-01 5.92302263e-01
9.25405264e-01 -6.47819787e-02 -5.04037023e-01 -9.91272479e-02
-2.21043095e-01 -1.24335639e-01 2.69477099e-01 9.61045399e-02
-1.23264802e+00 -1.01641426e-03 6.93350983e+00 5.47189355e-01
-9.75948930e-01 -2.61770636e-01 4.11580116e-01 -3.12700659e-01
-1.19171254e-01 1.18051358e-02 -5.77523470e-01 6.64716542e-01
1.08228040e+00 -1.19730771e-01 -2.81544458e-02 5.38227618e-01
6.42355800e-01 -1.97941199e-01 -1.50229263e+00 1.04196811e+00
-1.38894722e-01 -1.01034880e+00 -2.65843004e-01 4.65986937e-01
1.73452914e-01 -3.49719763e-01 6.50054589e-02 -1.71836212e-01
-2.48272661e-02 -8.98198783e-01 3.16213399e-01 8.23249459e-01
5.69916904e-01 -6.20961547e-01 5.53486824e-01 5.31311333e-01
-5.98689497e-01 -8.80471244e-02 -2.08705515e-01 1.10745974e-01
-3.65213044e-02 9.31284189e-01 -8.90797377e-01 4.67170238e-01
8.20045114e-01 7.39082694e-01 -5.90576053e-01 1.20060241e+00
2.66419798e-01 6.08834267e-01 -4.73092020e-01 1.36936873e-01
-2.95802981e-01 -1.72466353e-01 9.41913784e-01 1.10479593e+00
2.16309965e-01 -1.30241007e-01 1.65018857e-01 8.02305996e-01
5.17167151e-01 1.24444179e-01 -8.50340605e-01 -6.78934529e-02
4.08004463e-01 1.09039903e+00 -8.11938465e-01 -7.15504363e-02
-3.99040133e-01 5.68698764e-01 -3.04860443e-01 9.62497294e-02
-1.03591360e-01 -5.54624140e-01 8.79373968e-01 2.32600659e-01
1.40257314e-01 -6.99009048e-03 -7.75091276e-02 -8.47920656e-01
-1.88347146e-01 -9.72307503e-01 7.75898576e-01 -3.61229002e-01
-1.42762077e+00 2.00064763e-01 5.31116664e-01 -1.32548213e+00
-4.54132885e-01 -6.03222787e-01 -3.31549674e-01 9.99540508e-01
-7.61812150e-01 -6.39206648e-01 1.52871180e-02 4.85805988e-01
2.05847561e-01 -2.93398947e-01 8.44356120e-01 2.87960768e-01
-4.92041320e-01 2.38462433e-01 4.86417115e-01 -6.95082843e-02
8.03369701e-01 -1.39387012e+00 -1.09788425e-01 6.55184448e-01
3.23146909e-01 8.13996196e-01 1.07688296e+00 -6.82885587e-01
-1.01406062e+00 -8.48972857e-01 7.49756753e-01 -4.88695383e-01
6.24459565e-01 -5.59779406e-01 -1.12957513e+00 4.69037861e-01
-3.43955398e-01 -1.30162865e-01 1.32715178e+00 4.62615013e-01
-1.19901910e-01 -2.06532013e-02 -1.22751153e+00 4.37981129e-01
6.32171631e-01 -3.07674140e-01 -8.54869723e-01 2.82755435e-01
2.79323250e-01 -4.23604362e-02 -9.83950973e-01 3.12179506e-01
5.93538165e-01 -9.11673963e-01 8.85511756e-01 -8.42172801e-01
-2.62095809e-01 -6.67579412e-01 -3.58376026e-01 -1.26013410e+00
-4.10447061e-01 -5.75933218e-01 5.72278425e-02 1.50944924e+00
3.06242943e-01 -4.79866654e-01 6.36819541e-01 7.01479375e-01
2.25260139e-01 -2.07902059e-01 -1.01311493e+00 -8.31952512e-01
-1.97652385e-01 -5.38165212e-01 6.11329377e-01 1.11855602e+00
8.64152834e-02 2.49841601e-01 -3.67874652e-01 1.88476488e-01
5.39773881e-01 2.20090538e-01 6.74495578e-01 -1.94271207e+00
-2.52500594e-01 -3.51534963e-01 -9.00072575e-01 -4.14964110e-01
-6.24768324e-02 -1.06525540e+00 -9.87000912e-02 -1.21418309e+00
1.15517251e-01 -5.89801967e-01 -2.69494265e-01 5.04397154e-01
4.35549207e-02 -3.03844325e-02 -1.14897907e-01 4.02390748e-01
4.46224622e-02 2.62277216e-01 4.59512740e-01 -6.45466596e-02
-7.63202846e-01 3.47284049e-01 -8.26739013e-01 6.71569169e-01
6.71501279e-01 -7.82455266e-01 -5.01287580e-01 3.46917301e-01
1.14064932e-01 -1.85892120e-01 3.90759528e-01 -7.27047563e-01
1.19759077e-02 -7.70640466e-03 5.33265591e-01 -5.79208076e-01
1.42778814e-01 -8.49557400e-01 6.99582219e-01 3.52463692e-01
-5.58864236e-01 -1.56456441e-01 1.16410077e-01 7.48071969e-01
-1.62627801e-01 -4.72530425e-01 7.63661265e-01 1.20258555e-01
-5.84125698e-01 -2.03895181e-01 -5.04435122e-01 -8.14588889e-02
8.84165466e-01 -3.00420731e-01 6.60370439e-02 -1.40950292e-01
-1.13705635e+00 6.33355379e-02 1.57904372e-01 5.29748738e-01
4.54492927e-01 -1.28582072e+00 -6.96954191e-01 4.50799733e-01
3.31948102e-01 -3.29460979e-01 2.11376131e-01 1.00343621e+00
8.96032155e-02 3.32460165e-01 -1.72233418e-01 -8.87108207e-01
-1.65844905e+00 8.66045535e-01 1.93256646e-01 1.54468998e-01
-7.09364235e-01 4.13577795e-01 3.72559577e-01 -5.13175309e-01
9.35340449e-02 -1.97542742e-01 -6.23802662e-01 8.88147950e-01
7.62291133e-01 5.99042594e-01 8.45493302e-02 -6.62394762e-01
-6.08207464e-01 4.18140650e-01 3.52958106e-02 -1.49310961e-01
1.43712890e+00 -8.11472535e-02 -3.08509797e-01 1.12325931e+00
1.16303432e+00 1.00262880e-01 -8.13382447e-01 -2.72553921e-01
4.91362244e-01 -5.13057292e-01 9.46583822e-02 -5.44129312e-01
-6.55416489e-01 5.97162962e-01 1.05528617e+00 4.83700961e-01
1.15224385e+00 2.81358600e-01 -5.20931304e-01 1.87838241e-01
-1.03594907e-01 -8.70522022e-01 -4.89400506e-01 1.79689452e-01
8.93294692e-01 -1.04431069e+00 2.34879926e-01 -1.87345117e-01
-5.08702874e-01 1.12253892e+00 -1.13971584e-01 2.84194201e-02
9.43395793e-01 9.44680572e-02 -1.01177037e-01 -5.65689743e-01
-6.40667200e-01 -2.93904126e-01 5.00439882e-01 8.90323400e-01
4.43474561e-01 2.26765987e-03 -4.56780404e-01 5.92518628e-01
-1.95335969e-01 -1.70166776e-01 3.65395904e-01 8.50033402e-01
-2.44800329e-01 -1.21797800e+00 -6.18550420e-01 9.70016837e-01
-6.17055535e-01 5.03529571e-02 -5.51968753e-01 8.90687644e-01
1.15156054e-01 8.32433283e-01 3.25775325e-01 -1.78775296e-01
4.66360599e-01 4.66634154e-01 4.69674245e-02 -7.28657246e-01
-2.05451086e-01 4.83284056e-01 6.67930245e-02 -4.78874803e-01
-5.72935104e-01 -1.24174750e+00 -8.59952271e-01 2.92252153e-01
-3.60105336e-01 3.54367048e-01 6.31644607e-01 8.92915606e-01
4.86477017e-01 5.26939809e-01 6.49086058e-01 -5.95040679e-01
-7.19162226e-02 -9.05939221e-01 -1.08509064e+00 3.78893495e-01
5.16814828e-01 -9.88136828e-01 -4.80026573e-01 1.66262865e-01] | [7.7718634605407715, 4.502693176269531] |
0c432d79-2d9b-4ff6-be4c-28433abceea3 | a-fast-attention-network-for-joint-intent | 2205.07646 | null | https://arxiv.org/abs/2205.07646v1 | https://arxiv.org/pdf/2205.07646v1.pdf | A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices | Intent detection and slot filling are two main tasks in natural language understanding and play an essential role in task-oriented dialogue systems. The joint learning of both tasks can improve inference accuracy and is popular in recent works. However, most joint models ignore the inference latency and cannot meet the need to deploy dialogue systems at the edge. In this paper, we propose a Fast Attention Network (FAN) for joint intent detection and slot filling tasks, guaranteeing both accuracy and latency. Specifically, we introduce a clean and parameter-refined attention module to enhance the information exchange between intent and slot, improving semantic accuracy by more than 2%. FAN can be implemented on different encoders and delivers more accurate models at every speed level. Our experiments on the Jetson Nano platform show that FAN inferences fifteen utterances per second with a small accuracy drop, showing its effectiveness and efficiency on edge devices. | ['Nan Gao', 'Feiyang Ye', 'Senjie Liang', 'Liang Huang'] | 2022-05-16 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [-7.54188001e-03 4.00173873e-01 -4.28060532e-01 -5.54436147e-01
-6.77230358e-01 -2.96635926e-01 4.48349744e-01 3.38629261e-02
-6.30949974e-01 8.79251420e-01 3.32470775e-01 -6.88993573e-01
1.27485171e-01 -7.10593760e-01 -3.80123168e-01 1.65221374e-02
5.68922043e-01 6.57279491e-01 4.19950873e-01 -2.96704918e-01
1.45769387e-01 5.23313954e-02 -1.16680956e+00 3.24526012e-01
1.11796546e+00 1.12936068e+00 3.83541912e-01 9.49225366e-01
-7.09181726e-01 1.08563542e+00 -7.99302459e-01 -4.22813386e-01
-2.50124097e-01 -1.37384593e-01 -1.00010240e+00 -3.95171940e-01
-9.01759416e-02 -7.65334129e-01 -4.49900955e-01 7.29645014e-01
5.45451283e-01 -1.47619709e-01 3.08196321e-02 -1.31658709e+00
-1.63935810e-01 8.48329842e-01 -2.41385624e-01 2.92113304e-01
3.63758892e-01 2.87270267e-02 1.00768709e+00 -5.04480779e-01
1.46947220e-01 1.18512702e+00 6.98399842e-01 6.81867361e-01
-7.63846934e-01 -8.00106049e-01 1.76506326e-01 3.12372327e-01
-1.09497046e+00 -8.62476647e-01 4.53471631e-01 -4.98597659e-02
1.47600687e+00 4.09852982e-01 3.40226740e-01 9.82895494e-01
1.63507015e-01 1.00002205e+00 6.19770348e-01 -1.21727929e-01
3.75224739e-01 2.36545369e-01 3.87057096e-01 6.56083524e-01
-5.20011447e-02 -5.54662108e-01 -7.51786590e-01 3.74217890e-02
7.94143736e-01 6.57113791e-02 -1.06333487e-01 5.44267535e-01
-9.73710299e-01 7.30813920e-01 1.76010072e-01 2.88764179e-01
-3.80417138e-01 4.21641678e-01 8.45180333e-01 8.81168768e-02
6.46560907e-01 4.69158471e-01 -5.49049795e-01 -9.18636501e-01
-6.68501675e-01 -3.30089889e-02 1.06842792e+00 1.25112057e+00
4.87779528e-01 -2.06476137e-01 -5.69103420e-01 8.60696316e-01
2.79223293e-01 3.40836346e-01 3.07528555e-01 -9.71495271e-01
5.21481216e-01 5.68349481e-01 2.25717857e-01 -6.38057709e-01
-7.14938462e-01 -3.29581887e-01 -7.90535569e-01 -3.78253907e-01
1.36433274e-01 -4.35512960e-01 -7.28251576e-01 1.65588784e+00
2.40640953e-01 2.33176231e-01 -3.80556323e-02 8.78583610e-01
9.39301431e-01 6.76386178e-01 3.53216559e-01 -1.05745979e-01
1.82255268e+00 -1.40455055e+00 -1.34182525e+00 -5.45068800e-01
6.02107704e-01 -7.14414716e-01 1.00334907e+00 1.38551131e-01
-1.17359900e+00 -6.97372198e-01 -1.14436758e+00 -7.18949258e-01
-1.29705980e-01 1.24149092e-01 1.29328787e+00 7.39378572e-01
-9.48173106e-01 3.98317873e-01 -9.57285523e-01 -1.19075187e-01
3.95092875e-01 5.86454093e-01 7.56177604e-02 1.44889563e-01
-1.37958336e+00 5.42671084e-01 6.94481358e-02 2.04462260e-01
-2.89962322e-01 -9.20919895e-01 -7.41105795e-01 4.12636966e-01
5.76053023e-01 -8.77570808e-01 1.77397966e+00 -3.92672300e-01
-2.16717625e+00 2.36095339e-01 -4.71882582e-01 -6.83085084e-01
5.46462357e-01 -6.79678023e-01 -4.38124478e-01 -8.53537582e-03
2.38158956e-01 7.34096169e-01 5.83666027e-01 -3.54296654e-01
-8.66658568e-01 -1.78432524e-01 5.12451410e-01 3.44160974e-01
-4.79532152e-01 -1.87894195e-01 -9.73826528e-01 -7.92066604e-02
-1.22762822e-01 -5.77237666e-01 -2.60355860e-01 7.43593574e-02
-3.73506814e-01 -4.71070737e-01 7.74613023e-01 -8.54154944e-01
1.32481360e+00 -2.13211417e+00 -3.01094979e-01 -3.79877567e-01
4.00194228e-01 4.56028134e-01 1.91373751e-01 1.86342075e-01
5.14656305e-01 4.99348901e-02 -1.41475266e-02 -7.73006439e-01
2.99247712e-01 2.35624209e-01 -3.01030397e-01 -2.17450000e-02
9.21841636e-02 1.23550975e+00 -7.68762529e-01 -2.54395545e-01
3.33327651e-01 3.54364216e-01 -8.78164768e-01 4.19413894e-01
-5.84634423e-01 2.78950900e-01 -5.41286588e-01 3.62300426e-01
5.40332317e-01 -5.46943724e-01 3.20451587e-01 -3.57554466e-01
-1.73883542e-01 9.81137872e-01 -7.48165727e-01 2.07701302e+00
-9.86987770e-01 7.47399390e-01 4.42316055e-01 -7.01327324e-01
6.90455616e-01 3.78609955e-01 3.50844502e-01 -1.32493913e+00
1.26602575e-01 1.20071582e-01 -2.06712469e-01 -3.12779039e-01
9.55059826e-01 1.48518190e-01 -2.03714728e-01 6.27022207e-01
-3.75715597e-03 1.01061568e-01 -5.42639848e-03 1.97529718e-01
1.03910875e+00 -3.07613969e-01 9.65711996e-02 -1.11365750e-01
2.78242916e-01 -1.98529273e-01 6.27367616e-01 9.89369571e-01
-1.70154616e-01 1.37956545e-01 5.78619301e-01 -4.10004795e-01
-1.00412810e+00 -5.86772382e-01 -1.14135087e-01 1.36818886e+00
3.71615589e-01 -7.58704901e-01 -7.43766904e-01 -8.37686479e-01
-2.50350386e-01 8.41743529e-01 -7.30338320e-02 -1.96072251e-01
-3.95122468e-01 -5.31010330e-01 5.32880366e-01 8.35978746e-01
1.13015723e+00 -7.20681369e-01 -6.66703045e-01 4.84981865e-01
-4.77482051e-01 -1.63900125e+00 -4.28852707e-01 3.79606560e-02
-4.66514289e-01 -6.66405201e-01 -3.49849671e-01 -3.04273933e-01
3.32196265e-01 1.62284613e-01 1.14394462e+00 1.49754003e-01
-1.66023999e-01 1.28423542e-01 -2.54406989e-01 -3.58917445e-01
-3.47302854e-01 4.05741632e-01 8.57563503e-03 -2.80558735e-01
3.89278650e-01 -4.11085367e-01 -6.88589990e-01 4.12021041e-01
-4.83736932e-01 6.39701366e-01 4.59048420e-01 8.31769526e-01
-3.89314368e-02 -4.20430601e-02 7.53203988e-01 -9.52998936e-01
6.56839371e-01 -2.85216540e-01 -5.40613770e-01 -2.17800424e-03
-5.82728982e-01 1.51443049e-01 5.41211486e-01 9.03939828e-02
-1.29953003e+00 -1.59480706e-01 -9.04537678e-01 1.02079280e-01
-1.93017945e-02 2.22268283e-01 -1.13400228e-01 1.99459031e-01
1.83043033e-01 5.77145889e-02 7.30677396e-02 -3.62767637e-01
3.19301814e-01 1.11290026e+00 2.30615184e-01 -3.52970481e-01
-1.76230166e-02 3.50872368e-01 -4.57596511e-01 -9.05592382e-01
-9.18141246e-01 -5.49863636e-01 -1.53631479e-01 1.61429286e-01
8.05214584e-01 -1.13137937e+00 -1.30407929e+00 3.72501820e-01
-1.36614811e+00 -5.75164974e-01 -8.63099471e-03 3.43116283e-01
-4.30933058e-01 2.10427001e-01 -9.63505507e-01 -9.08799171e-01
-8.92090440e-01 -1.21114612e+00 1.44408798e+00 5.49842894e-01
-3.59167844e-01 -9.10450816e-01 -5.03553629e-01 6.72787130e-01
9.58017230e-01 -8.64646316e-01 6.82132065e-01 -6.81624651e-01
-7.17770398e-01 6.31708726e-02 -8.37452114e-01 -1.98595300e-01
2.13139981e-01 -5.95085561e-01 -1.19501984e+00 -4.10836078e-02
7.46114627e-02 -2.77897090e-01 7.32771158e-01 2.55570322e-01
1.34658182e+00 -2.10829034e-01 -5.37555933e-01 3.73910248e-01
8.62447500e-01 3.00092041e-01 6.51040614e-01 -1.45929769e-01
5.72827101e-01 6.70551974e-03 5.86404860e-01 6.08396232e-01
5.81462681e-01 9.83905733e-01 1.07799746e-01 -3.25532496e-01
-1.64791673e-01 -1.47839293e-01 1.43009990e-01 8.77869844e-01
4.26090121e-01 -3.21322054e-01 -7.04964995e-01 2.88074702e-01
-2.07681823e+00 -4.97929305e-01 8.09303150e-02 1.89149749e+00
9.74852979e-01 5.34068048e-01 -6.29611090e-02 -1.09413259e-01
5.22999167e-01 1.20859429e-01 -6.68683171e-01 -5.63870788e-01
5.74480832e-01 3.09149832e-01 4.08588707e-01 8.51152003e-01
-9.38260674e-01 1.15843666e+00 6.49692678e+00 9.51350689e-01
-1.06189764e+00 2.42645204e-01 7.54198194e-01 -8.26104265e-03
-3.24966520e-01 -2.29422584e-01 -1.08615351e+00 5.77393651e-01
1.23554862e+00 -2.63597239e-02 2.38600641e-01 9.57878828e-01
5.11067733e-02 -2.30829865e-01 -1.07354999e+00 1.08235705e+00
-2.84920782e-01 -1.57600522e+00 -3.62919778e-01 -2.05108419e-01
2.20390365e-01 -1.39785752e-01 -2.50952423e-01 8.32914174e-01
2.17985511e-01 -8.72727156e-01 2.99809992e-01 3.66501868e-01
8.76314938e-01 -7.43517518e-01 9.46001351e-01 3.53467584e-01
-1.08897030e+00 1.66870337e-02 -2.78568506e-01 -3.64796013e-01
6.45059824e-01 8.74406457e-01 -1.10462058e+00 4.20687199e-01
3.89483303e-01 4.77537483e-01 -1.44515604e-01 6.24776900e-01
-8.75335932e-02 5.64837933e-01 -5.33895969e-01 -4.10501093e-01
1.45583391e-01 3.60523760e-02 2.59309858e-01 1.18405855e+00
5.69431623e-03 8.01235437e-02 5.97233698e-02 9.43724513e-01
-2.10016787e-01 -3.71214032e-01 -1.12470597e-01 -3.06860775e-01
6.73931003e-01 1.25681555e+00 -6.90394282e-01 -4.20923471e-01
-5.19304991e-01 1.56693220e+00 3.08834821e-01 4.45587784e-02
-1.31038880e+00 -4.22511160e-01 1.10828042e+00 -3.82962912e-01
3.59198041e-02 -3.91142040e-01 -5.79602599e-01 -8.71063948e-01
7.94954449e-02 -4.85367775e-01 -2.26020012e-02 -4.32960719e-01
-7.79048920e-01 5.78007340e-01 -5.68847835e-01 -4.19688940e-01
-2.23807111e-01 -5.95846117e-01 -3.96212280e-01 7.43766069e-01
-1.53170013e+00 -7.21386373e-01 -4.97151047e-01 1.17633559e-01
1.04527187e+00 2.18102336e-01 9.63108778e-01 7.43375957e-01
-7.81624675e-01 7.62311399e-01 -3.57364535e-01 7.23984763e-02
3.61191154e-01 -1.21142614e+00 1.10440373e+00 3.39925587e-01
-1.15236275e-01 5.81138372e-01 5.83553553e-01 -4.54703540e-01
-1.70436347e+00 -9.80929375e-01 9.94909465e-01 -1.67516977e-01
3.72795135e-01 -7.96632886e-01 -6.46747887e-01 5.61002493e-01
3.20722580e-01 -2.07715929e-01 6.45112932e-01 4.99872833e-01
-2.23947186e-02 6.03124360e-03 -8.00977111e-01 7.58378923e-01
1.17542255e+00 -9.34896946e-01 -1.05887115e-01 5.15143275e-01
1.24171758e+00 -7.99808025e-01 -6.93397045e-01 3.34968209e-01
4.88693804e-01 -1.11970353e+00 7.56865382e-01 -4.35665995e-01
2.82301039e-01 -3.75986239e-03 4.53755185e-02 -8.77171993e-01
-1.86157182e-01 -7.97499835e-01 -3.63132596e-01 1.25579429e+00
5.48180461e-01 -6.30911589e-01 9.77144361e-01 9.71272409e-01
-3.24344188e-01 -7.83078432e-01 -9.47257102e-01 -3.56519580e-01
-4.76126522e-01 -6.89944983e-01 6.76942825e-01 4.94085610e-01
3.50589722e-01 9.61051345e-01 -4.96455342e-01 4.99177240e-02
6.86400682e-02 1.45334139e-01 7.52954304e-01 -8.89492333e-01
-4.30804968e-01 -4.01434094e-01 -3.60135660e-02 -1.91871047e+00
1.50500417e-01 -4.70104724e-01 2.30162218e-01 -1.69368005e+00
6.40394688e-02 -7.19486535e-01 1.07972421e-01 4.63742077e-01
-3.55778664e-01 4.36415942e-03 -1.01912871e-01 -2.89739549e-01
-1.17857289e+00 8.09938610e-01 8.65669012e-01 -1.51380196e-01
-2.05910295e-01 -4.53968532e-02 -7.63934135e-01 5.44456422e-01
7.70309448e-01 1.49944853e-02 -5.14465749e-01 -6.01325333e-01
1.93349019e-01 3.45295310e-01 -6.01953827e-02 -1.02777982e+00
5.61287642e-01 2.14334056e-01 2.25410536e-02 -4.12138134e-01
6.95495546e-01 -7.22504854e-01 -1.26126543e-01 4.17601615e-01
-3.36732924e-01 -3.35727841e-01 5.22792637e-01 4.24025387e-01
1.67593323e-02 -6.89967200e-02 4.54102248e-01 1.59034327e-01
-8.57432246e-01 3.34239542e-01 -3.68350953e-01 -6.38567135e-02
7.39727020e-01 2.98123181e-01 -5.20882249e-01 -6.18160129e-01
-3.90154421e-01 5.44731855e-01 -3.64458263e-02 5.35683036e-01
3.90140474e-01 -1.06999755e+00 -3.23498309e-01 4.49508756e-01
-1.50934055e-01 1.04370467e-01 4.96675253e-01 9.46878850e-01
-4.75140959e-01 9.14521337e-01 7.46745989e-02 -6.00223780e-01
-8.87563646e-01 1.36877209e-01 4.31177676e-01 -3.83072495e-01
-5.44924498e-01 9.78051960e-01 9.89479497e-02 -3.81919682e-01
3.18274766e-01 -3.37305158e-01 -8.82108063e-02 -2.47388482e-01
9.00016665e-01 2.71149814e-01 2.43366703e-01 1.74200445e-01
-2.38426030e-01 -2.56654583e-02 -4.13339108e-01 -1.85300354e-02
8.20176899e-01 -4.43697870e-01 -3.94210368e-02 3.39369237e-01
9.88647282e-01 -8.39976966e-02 -1.25144446e+00 -1.71094611e-01
-1.04000323e-01 -2.49013156e-01 3.22345704e-01 -8.85446966e-01
-7.58389473e-01 9.15646970e-01 3.59395087e-01 3.79368991e-01
7.98948824e-01 -9.47515070e-02 1.36563110e+00 3.26123089e-01
4.85141158e-01 -1.19365549e+00 6.37251139e-02 7.89279878e-01
5.11533439e-01 -1.30280066e+00 -4.13387179e-01 -7.78230488e-01
-5.65382063e-01 9.31868196e-01 7.85115242e-01 5.21078229e-01
3.44337046e-01 6.95181608e-01 -1.07127436e-01 -1.32071286e-01
-9.46508110e-01 -1.20639510e-01 -3.83887365e-02 3.18548799e-01
6.72400832e-01 5.61346970e-02 -3.57812077e-01 9.49766040e-01
1.84799824e-02 1.62093356e-01 1.15410395e-01 6.87953174e-01
-4.66024876e-01 -1.09640884e+00 2.96309888e-01 6.27688885e-01
-4.22845811e-01 -2.98679471e-01 -8.61340016e-02 2.68167228e-01
-3.68532807e-01 1.17042267e+00 4.21874553e-01 -4.65876192e-01
1.50752991e-01 1.02118030e-01 1.75987452e-01 -5.19632161e-01
-3.83549452e-01 -1.78790629e-01 7.81289577e-01 -1.04899514e+00
1.26677126e-01 -2.03526095e-02 -1.54515350e+00 -5.36336362e-01
-4.41271454e-01 3.30462605e-01 8.17950904e-01 1.28109169e+00
1.01575327e+00 1.13637507e+00 3.81486803e-01 -5.84957123e-01
-3.28803271e-01 -9.66401994e-01 -2.47163564e-01 -9.30561796e-02
2.32365787e-01 -6.04343772e-01 2.92854197e-02 -5.41211724e-01] | [12.485857009887695, 7.430184364318848] |
cde5f6c4-33bc-4b18-b447-8831b22f0dc9 | computer-aided-arrhythmia-diagnosis-by | 1810.04123 | null | http://arxiv.org/abs/1810.04123v1 | http://arxiv.org/pdf/1810.04123v1.pdf | Computer-Aided Arrhythmia Diagnosis by Learning ECG Signal | Electrocardiogram (ECG) is one of the non-invasive and low-risk methods to
monitor the condition of the human heart. Any abnormal pattern(s) in the ECG
signal is an indicative measure of malfunctioning of the heart, termed as
arrhythmia. Due to the lack of human expertise and high probability to
misdiagnose, computer-aided diagnosis and analysis are preferred. In this work,
we perform arrhythmia detection with an optimized neural network having
piecewise linear approximation based activation function to alleviate the
complex computations in the traditional activation functions. Further, we
propose a self-learning method for arrhythmia detection by learning and
analyzing the characteristics (period) of the ECG signal. Self-learning based
approach achieves 97.28% of arrhythmia detection accuracy, and neural network
with optimized activation functions achieve an arrhythmia detection accuracy of
99.56%. | [] | 2018-09-28 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 1.90056920e-01 -2.35175639e-01 8.57651606e-02 -1.46509215e-01
-4.41765457e-01 -5.17872214e-01 -5.28385043e-01 2.92592585e-01
-2.63080776e-01 7.68990993e-01 -5.15660107e-01 -4.50674415e-01
-1.34445980e-01 -5.09025335e-01 -1.12607680e-01 -6.19375944e-01
-3.96333873e-01 6.91331103e-02 -2.49999091e-01 2.05369174e-01
2.26911366e-01 6.57149076e-01 -9.50927734e-01 -3.64221036e-02
1.02981222e+00 1.16157854e+00 -2.97767967e-01 8.15351129e-01
4.37984139e-01 2.61172533e-01 -8.87463093e-01 2.39093423e-01
1.96649626e-01 -7.03446686e-01 -4.31928635e-01 -2.07852095e-01
-3.37017000e-01 -3.50310504e-02 1.75349377e-02 7.89094627e-01
9.37010646e-01 -3.01346868e-01 7.40327835e-01 -9.97553885e-01
1.06283918e-01 3.07487905e-01 -4.68102008e-01 6.29538953e-01
2.28384420e-01 -4.21837009e-02 1.96686223e-01 -8.02095950e-01
-1.80199463e-02 3.61546278e-01 1.30053043e+00 2.25747749e-01
-1.01436925e+00 -6.06797934e-01 -5.21193624e-01 9.66487527e-02
-1.78226542e+00 1.24476710e-02 1.08167922e+00 -3.93066913e-01
5.85501552e-01 3.93850386e-01 1.10892165e+00 3.40033412e-01
5.20127714e-01 2.17310548e-01 9.79879260e-01 -4.29354429e-01
1.18757896e-02 2.00521603e-01 1.65521547e-01 8.02175403e-01
3.92562956e-01 9.87345576e-02 -2.35794187e-02 -5.69639027e-01
1.06403816e+00 2.89312959e-01 -2.79470026e-01 1.53691426e-01
-8.91796589e-01 3.43536198e-01 -9.88815818e-03 4.80151862e-01
-7.90101290e-01 -1.22643992e-01 6.48465574e-01 3.95021617e-01
1.48350403e-01 7.54985690e-01 -5.46518505e-01 -4.04597700e-01
-1.00534296e+00 -5.63930199e-02 6.10503614e-01 2.07672566e-01
5.16091287e-01 6.95831478e-01 -1.90404341e-01 6.11562431e-01
4.71425727e-02 3.56117606e-01 8.35158825e-01 -8.28659177e-01
-2.68800408e-01 9.30678964e-01 -2.02199817e-02 -1.11136162e+00
-6.02991462e-01 -1.03050780e+00 -1.26822686e+00 7.20728561e-02
2.86732048e-01 -5.73619783e-01 -4.92701113e-01 1.22775710e+00
2.41366431e-01 4.81246263e-01 3.60933840e-02 6.49029911e-01
4.78485525e-01 8.27960968e-01 -1.39554709e-01 -9.35537815e-01
1.18870616e+00 -2.95043528e-01 -7.90832341e-01 2.17167675e-01
5.14775217e-01 -4.90283877e-01 8.32828403e-01 7.90986717e-01
-9.29532826e-01 -7.12114573e-01 -1.09498072e+00 7.69803166e-01
9.22157094e-02 4.18255299e-01 4.45836425e-01 7.74291098e-01
-5.72686493e-01 6.87900841e-01 -7.36991346e-01 -1.15494179e-02
1.93493024e-01 3.95689607e-01 4.48839106e-02 5.83387911e-01
-1.12711573e+00 7.33263671e-01 5.12484133e-01 3.23476374e-01
-5.54522634e-01 -7.89833426e-01 -4.39744681e-01 -3.16121764e-02
2.58554309e-03 -2.90625781e-01 8.80924702e-01 -1.03939962e+00
-1.29665196e+00 7.87631989e-01 5.08449622e-04 -6.30590856e-01
4.44131523e-01 -9.78256762e-02 -6.47850335e-01 2.63708621e-01
-4.10380721e-01 -1.66175440e-01 1.17771053e+00 -8.20265710e-01
-4.33677226e-01 -3.08130533e-01 -7.77255177e-01 6.38229726e-03
-3.20066690e-01 -6.65308461e-02 1.04349695e-01 -7.94960141e-01
4.85239834e-01 -7.39027679e-01 -4.77285683e-02 -3.13424528e-01
-1.71490982e-01 -1.68941900e-01 8.08556855e-01 -1.00450623e+00
1.90067840e+00 -2.38696146e+00 -3.86943370e-01 6.00366056e-01
1.80714682e-01 6.93607986e-01 5.72620749e-01 1.41174689e-01
-2.47240022e-01 4.09410475e-03 -6.07048512e-01 6.09796166e-01
-7.63992429e-01 1.92180052e-02 -3.62986512e-02 5.54689646e-01
1.74437925e-01 7.12667346e-01 -7.01591790e-01 -6.08252823e-01
1.33752272e-01 4.68360960e-01 -7.12726712e-02 4.69190598e-01
4.75384206e-01 6.78111374e-01 -4.21205372e-01 8.12951565e-01
3.28906268e-01 -1.02923393e-01 2.04346925e-01 -4.61214334e-01
-1.12809855e-02 -2.71765381e-01 -1.19593596e+00 1.17616546e+00
-1.70467883e-01 4.07659858e-01 -2.68386960e-01 -1.33781028e+00
1.26725221e+00 8.98234785e-01 7.92892635e-01 -1.58010796e-01
2.75621712e-01 4.08790261e-01 3.88514936e-01 -8.94631505e-01
-4.54055697e-01 -1.72507316e-01 1.23752341e-01 5.83996117e-01
-2.85733640e-01 1.49477407e-01 -3.32525760e-01 -3.17845106e-01
1.02160573e+00 -1.06221423e-01 8.45056891e-01 -3.10333967e-01
7.03156948e-01 -2.32027456e-01 9.71984744e-01 6.04892194e-01
-4.61631685e-01 4.26413774e-01 3.25661212e-01 -9.79797900e-01
-7.99366176e-01 -7.98552752e-01 -3.46392691e-01 2.35471845e-01
-2.80944437e-01 7.49608576e-02 -7.69936144e-01 -3.34922791e-01
-3.10948715e-02 2.51370281e-01 -2.62777209e-01 -5.64241052e-01
-8.13077629e-01 -9.79093075e-01 9.99376595e-01 6.66387141e-01
5.81381679e-01 -1.14653528e+00 -1.16077864e+00 5.99491954e-01
-8.68730173e-02 -5.70681095e-01 -1.07036546e-01 1.65360928e-01
-1.45995116e+00 -1.06186402e+00 -7.57229328e-01 -7.71016061e-01
7.28261292e-01 -4.53031272e-01 7.95113683e-01 3.89243126e-01
-8.81233573e-01 1.77007750e-01 -2.83527803e-02 -6.93434596e-01
-3.97843271e-01 -1.81650892e-01 2.16804415e-01 3.45599383e-01
2.60794386e-02 -7.20579386e-01 -8.60726416e-01 1.80102229e-01
-3.86955261e-01 -4.00286436e-01 6.06626987e-01 8.28463793e-01
6.68003559e-01 2.95675576e-01 1.11986327e+00 -7.60093153e-01
9.31331336e-01 -3.02126169e-01 -4.26823765e-01 2.07665220e-01
-1.14007199e+00 -3.71637911e-01 6.10061347e-01 -5.50802946e-01
-6.83088541e-01 3.27824682e-01 1.42818645e-01 -4.03487027e-01
-1.64844945e-01 5.11084437e-01 1.96836784e-01 -1.94031194e-01
7.60552883e-01 4.14015532e-01 1.51454538e-01 -1.91675916e-01
-5.24428785e-01 6.41930640e-01 8.60704720e-01 -3.60460997e-01
4.95419413e-01 -1.17340125e-01 1.88606173e-01 -8.03253531e-01
-5.20667791e-01 -4.67419565e-01 -3.91680270e-01 -5.12323380e-01
6.05132937e-01 -6.17602050e-01 -9.10723209e-01 5.64334750e-01
-1.05792606e+00 5.88730052e-02 -7.96265155e-02 6.82195306e-01
-3.39677632e-01 4.45681483e-01 -5.69921851e-01 -1.44851887e+00
-1.03997862e+00 -5.51154077e-01 3.35769743e-01 2.65694559e-01
-5.92488050e-01 -7.70667553e-01 -7.54352883e-02 -1.89843148e-01
3.36520463e-01 8.40275884e-01 9.16732430e-01 -6.42619133e-01
6.21292070e-02 -8.14053297e-01 9.32430848e-02 6.37904942e-01
4.03975755e-01 1.40014756e-02 -1.00268960e+00 -1.95631027e-01
5.70832908e-01 2.07677886e-01 2.61230409e-01 6.18290365e-01
1.41933191e+00 -1.85495883e-01 -2.08777651e-01 6.74377322e-01
1.47140944e+00 9.12033439e-01 5.94191790e-01 -1.50239065e-01
4.72638994e-01 1.94168448e-01 3.98087144e-01 6.42315626e-01
-1.14348471e-01 1.60315007e-01 -2.37872582e-02 -3.60687971e-01
4.77947146e-01 -4.98782061e-02 -2.42534913e-02 9.60224450e-01
-6.43043995e-01 2.12150410e-01 -1.09907186e+00 2.56005049e-01
-1.72976136e+00 -8.73328149e-01 -3.06807101e-01 2.49176836e+00
8.26055527e-01 2.34686732e-01 4.57404494e-01 9.49790776e-01
9.66836631e-01 -6.67167187e-01 -7.36450851e-01 -5.87429702e-01
3.10056470e-02 3.43087733e-01 2.01970488e-01 1.19789258e-01
-9.61656868e-01 1.74800500e-01 7.19745445e+00 3.58703762e-01
-1.51522028e+00 -1.25596493e-01 8.11118722e-01 4.63773042e-01
3.42807621e-01 -2.04563871e-01 -3.23617458e-01 5.78280389e-01
8.05365324e-01 -1.48025021e-01 2.59899586e-01 7.08666265e-01
3.47002059e-01 1.13008976e-01 -9.03158545e-01 1.40837038e+00
9.73823387e-03 -9.92569864e-01 -4.82644171e-01 -2.51480192e-01
3.50714594e-01 -7.47593820e-01 -3.25480789e-01 1.41636923e-01
-8.97694468e-01 -9.32501137e-01 1.21348642e-01 9.33231175e-01
9.70538378e-01 -8.24022651e-01 9.48142290e-01 4.82986450e-01
-1.20273733e+00 -3.14361185e-01 9.11266953e-02 -2.47146904e-01
2.89689694e-02 8.18729401e-01 -9.27739382e-01 2.65343517e-01
5.12402058e-01 5.33605099e-01 -2.69040197e-01 1.33706105e+00
5.36164716e-02 1.17712533e+00 -4.48923647e-01 8.81327242e-02
-2.98139393e-01 -1.61620826e-01 7.11645663e-01 1.00720108e+00
4.47324306e-01 3.43619198e-01 1.76598519e-01 6.78604245e-01
4.53199029e-01 2.55982906e-01 -5.66071868e-01 1.40089005e-01
5.99199116e-01 1.14983654e+00 -6.77610099e-01 -4.60952759e-01
1.24973059e-01 7.98359096e-01 -2.71213651e-01 2.72642434e-01
-6.99629366e-01 -7.08854318e-01 -2.12412655e-01 2.56636798e-01
-4.16758299e-01 1.81874782e-01 -7.77820230e-01 -8.40644479e-01
1.16564058e-01 -9.69821751e-01 4.98347282e-01 -4.53903466e-01
-8.69746268e-01 6.04534447e-01 -2.28185058e-01 -1.51896346e+00
-3.80787402e-01 -1.91316336e-01 -1.11478913e+00 1.01114106e+00
-8.61872017e-01 -5.24351597e-01 -3.76263082e-01 4.71700430e-01
2.75335252e-01 -2.85469770e-01 1.15858233e+00 3.34354311e-01
-6.37133360e-01 6.42076135e-01 -4.83115911e-01 5.40948808e-02
4.47675347e-01 -1.24001014e+00 -2.30260447e-01 7.38484502e-01
-4.27098542e-01 5.47359407e-01 6.01668298e-01 -6.99135184e-01
-1.23096120e+00 -7.61524081e-01 8.34106982e-01 1.16594776e-03
3.78298648e-02 2.68563271e-01 -1.21148384e+00 8.80229175e-02
-1.41477183e-01 2.27413580e-01 8.15604806e-01 -2.86378235e-01
2.88364410e-01 -6.64874375e-01 -1.35810530e+00 2.16986060e-01
3.41831833e-01 -3.10804904e-01 -5.62024891e-01 -6.75732153e-04
-8.21029320e-02 -2.08255216e-01 -1.27130342e+00 7.61654675e-01
1.09366941e+00 -6.66124046e-01 8.79547477e-01 -2.69274950e-01
-2.08919704e-01 -2.90501326e-01 4.30532962e-01 -9.61323142e-01
-3.94236207e-01 -9.95509207e-01 -3.99332851e-01 9.15650845e-01
3.78333002e-01 -8.90386105e-01 7.76708901e-01 3.56224269e-01
1.03325322e-01 -1.14211524e+00 -6.45316958e-01 -6.73861206e-01
-4.53833938e-01 -1.52402759e-01 3.03852558e-01 1.08094037e+00
1.17538773e-01 1.50637358e-01 -4.44493800e-01 8.17010552e-02
6.72080636e-01 -1.11223049e-01 2.81984597e-01 -1.55576539e+00
-2.79303104e-01 -1.88328296e-01 -4.28155273e-01 -2.52700783e-02
-3.49385411e-01 -5.66934168e-01 -1.70625389e-01 -1.12254858e+00
-2.17785016e-01 -4.66548830e-01 -9.06032205e-01 4.06374514e-01
-3.13255847e-01 3.31746221e-01 -4.32053804e-01 1.95816860e-01
1.11834638e-01 -1.41837314e-01 9.45085227e-01 2.29730099e-01
-7.65523732e-01 5.41030586e-01 -3.17632914e-01 9.44424033e-01
1.22354376e+00 -5.45018315e-01 -4.61972386e-01 1.31364882e-01
1.45003632e-01 6.41982138e-01 1.59180090e-01 -1.22373712e+00
-5.29458106e-04 6.33473694e-02 8.30641627e-01 -6.46753490e-01
-3.50917317e-03 -8.38248670e-01 3.28849882e-01 1.15687144e+00
-1.37663528e-01 4.61939394e-01 1.18605658e-01 3.21444780e-01
-2.97043473e-01 -3.48038137e-01 7.64106095e-01 -1.23774074e-01
-1.24150187e-01 2.40190551e-01 -7.12713778e-01 4.45093447e-03
1.06814563e+00 -5.37685394e-01 4.77923065e-01 -2.14952499e-01
-7.61101663e-01 -1.08529828e-01 -2.70890415e-01 -2.55390763e-01
9.52102363e-01 -1.19409204e+00 -6.69710338e-01 5.38876593e-01
-2.95730326e-02 -2.41078675e-01 2.13446915e-01 1.05048203e+00
-8.48316908e-01 9.45795849e-02 -2.94939309e-01 -6.11429691e-01
-1.26796579e+00 1.22669697e-01 7.27207899e-01 -8.72530490e-02
-6.91895723e-01 5.24092376e-01 -5.04026949e-01 1.63811728e-01
3.29480112e-01 -1.06977597e-01 -3.76135081e-01 -1.36392206e-01
4.26832199e-01 8.28381300e-01 3.75292003e-02 -6.49352968e-02
-4.77036804e-01 7.53273129e-01 3.27783883e-01 9.05862451e-02
9.31557775e-01 2.23107427e-01 -2.49304324e-01 6.26472533e-01
8.18686008e-01 -2.51720428e-01 -6.24683201e-01 1.42593503e-01
-1.06136546e-01 -2.45315265e-02 1.94675922e-01 -9.21472669e-01
-8.03623676e-01 8.43844652e-01 1.26799905e+00 3.27876508e-01
1.52284169e+00 -7.62361825e-01 8.44595432e-01 4.82755750e-01
1.28732115e-01 -1.10507536e+00 -1.39301598e-01 -8.29405338e-02
5.98056257e-01 -9.41699862e-01 2.00404134e-02 -1.83735281e-01
-4.76706445e-01 1.30782187e+00 4.91521567e-01 -5.45317948e-01
1.04365158e+00 2.87982166e-01 3.85798603e-01 4.07698043e-02
-4.02721316e-01 5.82269430e-01 2.75799513e-01 5.95924556e-01
5.64926445e-01 1.43467873e-01 -8.91330183e-01 8.79196525e-01
2.06074834e-01 3.85957003e-01 1.74999252e-01 1.06683946e+00
-4.53362793e-01 -7.70939946e-01 -4.58613068e-01 7.56316662e-01
-8.16250384e-01 2.12861206e-02 -1.12149172e-01 2.64289230e-01
2.64418781e-01 7.40410447e-01 -2.70950943e-02 -1.73455894e-01
2.71262258e-01 5.46213806e-01 5.09007752e-01 -3.80936205e-01
-8.38849545e-01 3.74917030e-01 -2.47514680e-01 -8.02919939e-02
-1.19363956e-01 -4.15554911e-01 -1.44179046e+00 3.34083766e-01
-2.63035506e-01 3.96301985e-01 6.82865143e-01 7.55191386e-01
2.67118275e-01 6.12095594e-01 9.55403090e-01 -1.76500693e-01
-6.44164622e-01 -1.02038050e+00 -8.33185315e-01 1.39378518e-01
4.32880670e-01 -2.69260079e-01 -2.67104536e-01 2.55494654e-01] | [14.225159645080566, 3.217386484146118] |
c3d608e5-7892-4dd5-b84f-ac2408ccdbb9 | can-dnns-learn-to-lipread-full-sentences | 1805.11685 | null | http://arxiv.org/abs/1805.11685v1 | http://arxiv.org/pdf/1805.11685v1.pdf | Can DNNs Learn to Lipread Full Sentences? | Finding visual features and suitable models for lipreading tasks that are
more complex than a well-constrained vocabulary has proven challenging. This
paper explores state-of-the-art Deep Neural Network architectures for
lipreading based on a Sequence to Sequence Recurrent Neural Network. We report
results for both hand-crafted and 2D/3D Convolutional Neural Network visual
front-ends, online monotonic attention, and a joint Connectionist Temporal
Classification-Sequence-to-Sequence loss. The system is evaluated on the
publicly available TCD-TIMIT dataset, with 59 speakers and a vocabulary of over
6000 words. Results show a major improvement on a Hidden Markov Model
framework. A fuller analysis of performance across visemes demonstrates that
the network is not only learning the language model, but actually learning to
lipread. | ['Naomi Harte', 'George Sterpu', 'Christian Saam'] | 2018-05-29 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.69682497e-01 -1.15794584e-01 -6.14719868e-01 -3.58565629e-01
-1.16282594e+00 -2.64875084e-01 6.89586937e-01 -5.42847455e-01
-5.81038833e-01 3.61467123e-01 6.24603629e-01 -7.34502494e-01
6.23226583e-01 3.06327492e-01 -6.48176193e-01 -4.25201476e-01
3.12329471e-01 2.32261494e-01 -7.77979800e-03 4.89905998e-02
3.45343977e-01 3.53984684e-01 -1.79848289e+00 3.91897500e-01
3.08426648e-01 1.03346312e+00 3.51678312e-01 1.28728330e+00
-1.35477245e-01 9.49447036e-01 -2.06695944e-01 -4.55584854e-01
-9.09907371e-02 -3.55259687e-01 -7.39470661e-01 -2.67934464e-02
1.00349557e+00 -3.34627628e-01 -6.29887044e-01 7.49429405e-01
1.20568442e+00 1.45451143e-01 8.77664566e-01 -1.14868963e+00
-1.03558660e+00 2.34144643e-01 -2.04612702e-01 2.67164856e-01
3.59679401e-01 8.74471366e-01 1.02359009e+00 -1.21504474e+00
3.52358133e-01 1.49873471e+00 8.14436555e-01 1.11280954e+00
-1.09344625e+00 -6.10612273e-01 8.79036188e-02 5.96051216e-01
-1.22755015e+00 -1.59389079e+00 5.19960165e-01 -5.78195691e-01
1.75156665e+00 -5.47555089e-02 4.06648219e-01 1.54494321e+00
-3.16238999e-02 1.21198392e+00 9.03801918e-01 -6.31589592e-01
-1.71970755e-01 6.15308806e-02 1.87710091e-01 7.16940522e-01
-2.44717598e-01 3.74944389e-01 -1.12332654e+00 2.26693392e-01
2.88527668e-01 -6.51784241e-01 -3.55537444e-01 -4.07075018e-01
-8.30313683e-01 8.03628623e-01 -1.96127787e-01 -2.19575211e-01
-1.90663546e-01 3.68142098e-01 8.00548911e-01 1.42477065e-01
4.07076597e-01 -1.77653834e-01 -6.01447105e-01 -5.18748105e-01
-1.30517590e+00 -3.48809481e-01 6.40583158e-01 1.04078305e+00
2.72198498e-01 5.37343621e-01 -3.11880589e-01 1.12883258e+00
6.95983112e-01 6.25969410e-01 7.77804732e-01 -6.24236703e-01
5.15034556e-01 -3.08493078e-01 -1.82334542e-01 -2.26702571e-01
-3.58544558e-01 1.94100067e-01 -5.42531312e-01 4.84861225e-01
5.12017727e-01 3.28458138e-02 -1.32003140e+00 1.89074194e+00
-3.50889832e-01 8.68774801e-02 2.29575589e-01 7.40908921e-01
1.11590838e+00 4.18843657e-01 3.78453523e-01 -1.29458874e-01
1.34094834e+00 -1.30066609e+00 -8.92485023e-01 -2.84416348e-01
2.00624466e-01 -8.57366204e-01 1.28993893e+00 3.74857128e-01
-1.56991744e+00 -6.71001554e-01 -9.22911644e-01 -8.22156906e-01
-2.78438419e-01 3.80478948e-01 2.84920394e-01 7.97404110e-01
-1.63093066e+00 1.34434566e-01 -4.98606265e-01 -5.22840500e-01
5.24464190e-01 2.45801598e-01 -2.53883809e-01 1.62421286e-01
-1.07930410e+00 1.21747005e+00 7.88188875e-02 -1.49456367e-01
-1.36030710e+00 -6.23081505e-01 -1.05976427e+00 1.53523475e-01
-1.66112155e-01 -6.03749871e-01 1.62455869e+00 -1.09432828e+00
-1.97971892e+00 1.21060824e+00 -7.74388015e-01 -6.51440322e-01
6.53967738e-01 -3.36698703e-02 -4.25142229e-01 1.79164454e-01
-3.32960784e-01 1.12241197e+00 1.32651174e+00 -9.49127555e-01
-4.01154995e-01 1.06740855e-01 -6.20548904e-01 4.67270851e-01
-1.27838254e-01 3.49700332e-01 -5.80547273e-01 -5.99896550e-01
-4.07864332e-01 -7.65177131e-01 5.27765393e-01 4.18019146e-01
-3.09570432e-01 -7.08949387e-01 4.47526932e-01 -1.24750447e+00
8.84249032e-01 -2.17783046e+00 -7.80386850e-02 -4.64395374e-01
2.33824048e-02 4.93382365e-01 -3.53610605e-01 1.05831429e-01
-1.21232867e-01 1.81880131e-01 1.76013663e-01 -9.78098512e-01
2.53135443e-01 -2.21795648e-01 -3.07854563e-01 5.84676623e-01
1.14360690e-01 1.16410041e+00 -4.15761292e-01 -5.37652135e-01
2.94979990e-01 7.26047873e-01 -3.19138139e-01 2.31572777e-01
-3.62771213e-01 -2.90821135e-01 5.11516333e-01 7.10377336e-01
5.26557088e-01 -1.05759367e-01 -1.24346636e-01 3.41164693e-02
-9.87811089e-02 7.62242973e-01 -3.97775114e-01 1.87923312e+00
-5.95918655e-01 1.65591359e+00 3.20665278e-02 -5.54289281e-01
7.07068264e-01 6.89772010e-01 -2.86756754e-02 -7.52847373e-01
1.21436909e-01 2.78452672e-02 -1.85525462e-01 -8.40398431e-01
4.01739299e-01 -2.89490014e-01 3.97598088e-01 4.16548908e-01
2.61373460e-01 1.12219892e-01 -2.49688223e-01 -1.26887351e-01
5.89319348e-01 6.90096691e-02 1.03157029e-01 -1.21439420e-01
4.57117409e-01 -5.65302372e-01 8.26936960e-03 6.81241035e-01
-7.51245975e-01 8.01183999e-01 3.92836392e-01 -1.10792853e-01
-1.32094574e+00 -1.13879108e+00 -1.60011016e-02 1.50665629e+00
-2.82722890e-01 -3.81096229e-02 -5.79529762e-01 -3.84181350e-01
5.52804470e-02 8.64519894e-01 -6.28843844e-01 -1.69918135e-01
-1.31279022e-01 1.01921819e-01 1.08903539e+00 6.09126806e-01
3.10076326e-01 -1.46214509e+00 -4.01382148e-01 1.69484261e-02
-3.96741062e-01 -1.05728245e+00 -1.16995299e+00 2.08506249e-02
-3.30052167e-01 -6.41149819e-01 -1.23889387e+00 -1.23445261e+00
-2.02094354e-02 1.99813932e-01 1.16923225e+00 -2.01635599e-01
-2.19644457e-01 5.14908314e-01 1.60955191e-01 -7.02518642e-01
-5.92693329e-01 1.84580192e-01 2.41638899e-01 -9.66034383e-02
7.23837674e-01 -6.39505386e-02 -4.35222566e-01 9.00948048e-02
-2.17286542e-01 8.13852027e-02 6.03092968e-01 1.03108132e+00
1.81349739e-01 -7.69048154e-01 5.10908723e-01 3.92676920e-01
7.92072237e-01 -7.69728273e-02 -5.39915800e-01 5.01135230e-01
-5.84467828e-01 3.24313372e-01 -4.09511365e-02 -9.50591385e-01
-1.03743994e+00 8.67102519e-02 -2.85425007e-01 -9.58582103e-01
-1.26135543e-01 -8.30250531e-02 -1.72617719e-01 1.45550042e-01
4.01540846e-01 6.67570353e-01 5.81843734e-01 -5.56302607e-01
5.43598533e-01 1.15771282e+00 5.62910855e-01 -1.06641233e-01
1.00053586e-01 1.32856160e-01 -5.23363531e-01 -1.24929786e+00
-2.64013112e-01 -5.78672588e-01 -5.23553073e-01 -2.62409270e-01
1.12826431e+00 -1.13618875e+00 -1.32348669e+00 9.65202689e-01
-1.37223732e+00 -8.96435916e-01 -1.51839361e-01 5.70057690e-01
-1.04385757e+00 2.83757389e-01 -5.17407596e-01 -1.02480066e+00
-3.95078361e-01 -1.21414304e+00 9.16420162e-01 4.10856009e-02
-2.29963377e-01 -9.33935285e-01 1.04144812e-01 4.53344494e-01
5.93417048e-01 -7.46509492e-01 7.64976025e-01 -5.96419513e-01
-4.78267670e-01 2.73884475e-01 -4.85726207e-01 5.40484071e-01
-1.55861810e-01 -1.70255452e-03 -1.63389397e+00 -4.17590231e-01
-4.74620253e-01 -8.59648883e-01 1.28421414e+00 8.30854774e-01
8.71502101e-01 -5.45507967e-01 -2.08511114e-01 7.17433095e-01
1.02530265e+00 9.48842615e-02 7.26508439e-01 -3.92661132e-02
4.40938085e-01 5.55184364e-01 -1.61605775e-01 2.00970888e-01
5.95858574e-01 7.82335103e-01 1.48028165e-01 -9.21036452e-02
-1.01473212e+00 -6.56364501e-01 7.11931705e-01 6.76891446e-01
4.25170511e-01 -5.03763855e-01 -1.09134865e+00 1.02221978e+00
-1.30792844e+00 -1.18518281e+00 4.39406574e-01 1.91990495e+00
1.02201545e+00 7.78975040e-02 4.07310337e-01 -2.18300764e-02
8.22270215e-01 2.46936411e-01 -7.74313748e-01 -9.74784374e-01
-2.34667867e-01 7.41050020e-02 4.06558454e-01 1.05835557e+00
-8.48299205e-01 1.40290475e+00 7.46248484e+00 9.28378463e-01
-1.53865683e+00 1.74735457e-01 4.38691705e-01 -4.18355763e-01
-1.12333789e-01 -4.37032163e-01 -1.16156936e+00 3.96513134e-01
1.28038085e+00 3.14420573e-02 6.35592937e-01 6.40967250e-01
4.10673916e-01 1.39594883e-01 -1.05112863e+00 1.36407709e+00
6.09955907e-01 -1.27918553e+00 1.68693930e-01 5.07604480e-02
2.68265247e-01 5.13296723e-01 6.72383785e-01 2.78980881e-01
2.57412612e-01 -1.60504365e+00 1.16506314e+00 7.09743381e-01
1.72464502e+00 -4.53729540e-01 9.78342742e-02 6.57800585e-02
-1.08301198e+00 -3.52659523e-02 -2.12531835e-01 3.06641102e-01
1.08673140e-01 -3.47889930e-01 -1.31353915e+00 -4.37848896e-01
5.93466640e-01 7.48794675e-01 -4.47406858e-01 1.06843376e+00
-1.26267225e-01 7.56559730e-01 -7.70739093e-02 -4.00623977e-01
1.79814413e-01 6.70138538e-01 5.30240059e-01 1.56215346e+00
-4.81376238e-02 -2.47326523e-01 -4.84682471e-01 5.38323402e-01
-2.49671072e-01 9.96906236e-02 -6.42943382e-01 -6.52773231e-02
3.39368284e-01 5.49211144e-01 5.00252433e-02 -2.71817207e-01
-4.86734480e-01 9.50130343e-01 4.55673873e-01 7.46069908e-01
-6.66927993e-01 -4.87192690e-01 1.06291175e+00 -7.46229738e-02
6.00052655e-01 -1.76313668e-01 -3.99538726e-01 -1.02305508e+00
-1.19172290e-01 -1.03651369e+00 1.31959066e-01 -1.29284632e+00
-1.12737739e+00 5.27926326e-01 -4.17999268e-01 -7.58244395e-01
-5.05057573e-01 -8.32351983e-01 -3.62803757e-01 1.28930604e+00
-2.04031539e+00 -1.29880619e+00 -1.52012529e-02 8.86130691e-01
1.11735272e+00 -6.90655172e-01 6.52992725e-01 -7.69883320e-02
-2.90390104e-01 1.35491157e+00 7.71684125e-02 2.71673828e-01
8.93747449e-01 -8.24347377e-01 7.32895255e-01 7.34002233e-01
1.32912606e-01 3.03482354e-01 4.21885639e-01 -3.72855157e-01
-1.09999490e+00 -6.70212150e-01 1.43178904e+00 -4.51724529e-01
4.35698360e-01 -5.69512963e-01 -7.11351216e-01 7.19560921e-01
6.51767671e-01 -1.23771384e-01 4.79836464e-01 4.64582071e-02
-7.35285461e-01 1.15881562e-01 -9.54273403e-01 7.28060365e-01
1.09300160e+00 -1.32270885e+00 -7.09456325e-01 4.00957651e-02
6.80038631e-01 -1.69417977e-01 -2.19600394e-01 4.76040803e-02
1.09257150e+00 -7.46988416e-01 9.55041528e-01 -9.91584122e-01
8.13518185e-03 2.82970965e-01 -1.54114634e-01 -9.38354135e-01
7.50958920e-03 -1.07379067e+00 -1.06406190e-01 9.83018994e-01
5.91207325e-01 -4.06376749e-01 6.97454989e-01 3.90922695e-01
-1.18438639e-01 -5.31463802e-01 -1.37311339e+00 -8.85140598e-01
4.65268612e-01 -7.36110389e-01 2.36427382e-01 4.86441433e-01
1.60233855e-01 4.59580898e-01 -5.00089765e-01 -1.38641983e-01
5.37809253e-01 -5.84718168e-01 5.07523775e-01 -8.03936601e-01
-1.48101198e-02 -1.05093324e+00 -4.06411946e-01 -1.49985349e+00
8.04119110e-01 -9.18579102e-01 3.30439031e-01 -1.48794222e+00
2.21022934e-01 -6.90900832e-02 -1.54532626e-01 7.27863610e-01
-5.64992442e-05 6.65313825e-02 3.16319138e-01 6.61158934e-02
-2.89429247e-01 5.41615129e-01 8.94217312e-01 -5.33722520e-01
-4.03836258e-02 6.06420413e-02 -3.35139364e-01 4.12050664e-01
7.97666073e-01 -5.24318330e-02 -4.63564485e-01 -5.45047879e-01
-1.75648898e-01 1.58257902e-01 4.50529754e-01 -8.44966233e-01
4.91604388e-01 1.30265698e-01 2.32725382e-01 -8.37280273e-01
9.14390564e-01 -1.82320058e-01 -5.40289760e-01 2.56613910e-01
-1.05782461e+00 -1.56204030e-01 6.92758858e-01 3.64620298e-01
9.95207876e-02 -7.99633339e-02 1.16563630e+00 1.40129209e-01
-8.69779766e-01 2.37591118e-01 -7.18371689e-01 4.98066515e-01
5.91735899e-01 -3.32747638e-01 -2.65230238e-01 -9.55169857e-01
-8.65296364e-01 -7.81474337e-02 3.79013747e-01 7.13389695e-01
9.86649692e-01 -1.15049601e+00 -1.01447463e+00 4.86585528e-01
2.04999804e-01 -8.59541774e-01 1.19376764e-01 5.81874490e-01
-8.00548866e-02 9.44492698e-01 -2.85794079e-01 -7.52741814e-01
-1.81147552e+00 8.02119851e-01 7.37138867e-01 2.21374363e-01
-5.54674506e-01 1.37977076e+00 -2.82710157e-02 -4.55329902e-02
9.86095071e-01 -1.91581160e-01 -1.05510786e-01 3.31191093e-01
4.84302819e-01 3.10427427e-01 -1.07889734e-01 -8.73313725e-01
-4.10916805e-01 6.55802190e-01 -1.13396943e-01 -6.72855258e-01
6.99345112e-01 -4.96445507e-01 7.29108989e-01 5.75178742e-01
1.48375964e+00 -1.98118210e-01 -1.66151011e+00 -2.17123941e-01
-1.90549448e-01 -8.94771591e-02 1.73628122e-01 -1.10238218e+00
-6.96319878e-01 1.78017771e+00 8.29247117e-01 -2.94461071e-01
6.79895043e-01 -4.29037213e-02 6.56963527e-01 1.93631023e-01
-3.32273245e-01 -1.07700026e+00 2.73203671e-01 7.89457142e-01
1.00576389e+00 -1.35803854e+00 -4.12161648e-01 4.08534825e-01
-1.04271424e+00 1.11953580e+00 2.64597267e-01 4.83703852e-01
5.38949370e-01 2.09632605e-01 4.24089670e-01 2.84806728e-01
-1.16612029e+00 -5.25691807e-01 5.59098125e-01 9.05914128e-01
2.75587976e-01 -1.79498449e-01 5.94342828e-01 1.31252170e-01
-1.04406402e-01 2.30010629e-01 2.53054172e-01 3.33218038e-01
-3.79383504e-01 -5.13215721e-01 -1.35114538e-02 7.70600587e-02
-3.23675066e-01 -7.82005012e-01 -2.03989223e-01 6.53611839e-01
-2.69640625e-01 1.03611624e+00 2.74428219e-01 -3.38941552e-02
-1.94808841e-02 8.61060917e-01 4.56613451e-01 -1.35870129e-01
-2.23327547e-01 3.95703390e-02 2.22492546e-01 -3.96630049e-01
1.68717019e-02 -1.03728366e+00 -7.95149446e-01 -2.74951309e-01
-3.75727266e-01 -4.78717327e-01 1.09838426e+00 6.05965793e-01
3.46236169e-01 1.64352849e-01 2.99457222e-01 -8.82552505e-01
-8.17026317e-01 -1.22051311e+00 -1.13924287e-01 3.92254926e-02
1.20623720e+00 -5.26085675e-01 -6.33255720e-01 2.88004547e-01] | [14.329901695251465, 5.022734642028809] |
996b762b-68a5-4daf-b0fa-82eddaba0121 | on-explaining-multimodal-hateful-meme | 2204.01734 | null | https://arxiv.org/abs/2204.01734v2 | https://arxiv.org/pdf/2204.01734v2.pdf | On Explaining Multimodal Hateful Meme Detection Models | Hateful meme detection is a new multimodal task that has gained significant traction in academic and industry research communities. Recently, researchers have applied pre-trained visual-linguistic models to perform the multimodal classification task, and some of these solutions have yielded promising results. However, what these visual-linguistic models learn for the hateful meme classification task remains unclear. For instance, it is unclear if these models are able to capture the derogatory or slurs references in multimodality (i.e., image and text) of the hateful memes. To fill this research gap, this paper propose three research questions to improve our understanding of these visual-linguistic models performing the hateful meme classification task. We found that the image modality contributes more to the hateful meme classification task, and the visual-linguistic models are able to perform visual-text slurs grounding to a certain extent. Our error analysis also shows that the visual-linguistic models have acquired biases, which resulted in false-positive predictions. | ['Wen-Haw Chong', 'Roy Ka-Wei Lee', 'Ming Shan Hee'] | 2022-04-04 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [-1.80652827e-01 -1.38035029e-01 -7.17747677e-03 -7.26166591e-02
-2.67451376e-01 -7.12116241e-01 9.02349055e-01 1.84797525e-01
-2.07914203e-01 5.17868936e-01 3.42916161e-01 -8.41682553e-02
3.83170903e-01 -3.42816651e-01 -4.79766786e-01 -6.79899275e-01
3.28534395e-01 4.26139757e-02 -4.31396961e-02 -2.65057087e-01
8.96285295e-01 2.40517512e-01 -1.68954694e+00 8.89846563e-01
6.39301658e-01 6.59921050e-01 3.68764102e-02 7.04577327e-01
-4.14327979e-01 1.53790450e+00 -8.08956146e-01 -7.57160425e-01
-4.30691779e-01 -4.77582574e-01 -7.44135439e-01 1.85995996e-01
9.33334231e-01 -3.33017468e-01 -2.36607000e-01 1.27310431e+00
3.66135538e-01 -2.31663883e-01 8.48303795e-01 -1.56978452e+00
-1.26846373e+00 2.19650671e-01 -7.23505437e-01 2.76008219e-01
4.88906920e-01 1.60515413e-01 6.71734214e-01 -1.19914269e+00
8.12426269e-01 1.65977633e+00 4.55408067e-01 5.66209078e-01
-1.24507296e+00 -7.15034902e-01 -2.70050168e-01 2.99744278e-01
-1.50536621e+00 -2.62660027e-01 9.75290537e-01 -1.22777915e+00
6.63622200e-01 2.65055746e-01 5.27620256e-01 1.53429747e+00
4.21070665e-01 1.04735076e+00 1.62040854e+00 -3.96216094e-01
-2.09260657e-01 9.92778480e-01 1.03802882e-01 8.07431757e-01
1.26125425e-01 -1.28762886e-01 -8.95530105e-01 -1.21028006e-01
4.52761412e-01 9.79084522e-02 -1.84742615e-01 -2.07813289e-02
-1.12961483e+00 9.53438461e-01 3.37066144e-01 6.68794453e-01
3.03839706e-02 -6.29603490e-02 6.40997410e-01 2.91226864e-01
7.56585896e-01 5.65042198e-01 3.10734212e-01 -9.00396705e-02
-1.16035223e+00 -2.03444824e-01 5.62350273e-01 4.82924879e-01
8.93085122e-01 -5.27777225e-02 -2.25898281e-01 9.71111238e-01
3.03852588e-01 9.16640997e-01 2.08291516e-01 -6.64128006e-01
3.40155274e-01 8.15743208e-01 9.82183218e-02 -1.80187285e+00
-3.83019149e-01 2.05957383e-01 -6.13384008e-01 3.89765978e-01
3.79809976e-01 2.67459989e-01 -8.02220762e-01 1.48205733e+00
-1.07693747e-01 -4.82341468e-01 -3.15544695e-01 1.20218229e+00
8.85145962e-01 8.83752346e-01 5.41408539e-01 -2.72968076e-02
1.45625913e+00 -8.10525417e-01 -1.13013685e+00 -5.02848864e-01
7.50728846e-01 -1.17502344e+00 1.36376882e+00 1.29559919e-01
-9.75957930e-01 -5.79591155e-01 -8.74313712e-01 -2.27054954e-01
-7.07907200e-01 3.19609225e-01 2.19801992e-01 6.35638833e-01
-9.43637908e-01 2.23558828e-01 -3.07700694e-01 -8.98276687e-01
3.67053330e-01 -2.24282205e-01 -5.37158668e-01 -9.24017727e-02
-1.09848046e+00 1.38129354e+00 2.95823038e-01 1.59633428e-01
-8.75204265e-01 -1.05520681e-01 -8.10399771e-01 -4.25762922e-01
1.90683872e-01 -3.12769651e-01 6.01653934e-01 -1.61949170e+00
-6.17348135e-01 1.64112973e+00 -2.65972346e-01 2.40986705e-01
1.52864993e-01 -4.60029878e-02 -4.70058024e-01 4.73290771e-01
1.73180938e-01 7.81542778e-01 1.17148352e+00 -1.87183630e+00
-3.10687661e-01 -5.35499632e-01 -1.72574207e-01 -1.20455831e-01
-8.82014632e-01 3.88312489e-01 -5.40960208e-03 -8.04883420e-01
-1.84432402e-01 -9.63737071e-01 7.04568684e-01 -6.25949055e-02
-5.18640637e-01 -2.24444225e-01 1.13750672e+00 -1.00207818e+00
1.59328735e+00 -2.37169337e+00 8.34242478e-02 -1.28537323e-02
5.99270821e-01 4.21923220e-01 -1.08034134e-01 8.84932339e-01
1.08374394e-01 7.33975589e-01 -1.10515609e-01 -3.22599292e-01
6.94509521e-02 1.00963198e-01 -3.59714001e-01 6.53409004e-01
3.10584515e-01 7.93884039e-01 -7.35988736e-01 -9.20905590e-01
1.90639496e-01 7.13345170e-01 -2.00904645e-02 3.72713596e-01
9.69977677e-02 3.17374319e-01 -2.10440516e-01 8.43040168e-01
7.44944096e-01 -9.51486006e-02 -1.39260627e-02 -3.73472840e-01
-3.87540638e-01 -1.95885763e-01 -4.43837136e-01 1.01712263e+00
-4.41717505e-02 1.52427876e+00 1.88626662e-01 -6.20968580e-01
8.22460055e-01 1.05983272e-01 -5.31134382e-02 -9.88057971e-01
3.20846558e-01 2.31091782e-01 -2.12044299e-01 -1.25009143e+00
7.92475581e-01 -5.96298814e-01 -3.16294096e-02 3.09471786e-01
-1.83363676e-01 1.14780717e-01 1.48185030e-01 3.86896998e-01
5.96106708e-01 -3.39802094e-02 5.66000752e-02 -1.76947355e-01
4.92040336e-01 3.50650817e-01 1.14512920e-01 6.34441376e-01
-5.90129733e-01 5.92496276e-01 6.90723360e-01 -3.89728189e-01
-1.18328035e+00 -7.55788207e-01 1.63584240e-02 1.43850422e+00
2.55704045e-01 -2.31248945e-01 -8.09027493e-01 -6.05838537e-01
-1.59401707e-02 8.95653546e-01 -8.62690032e-01 -1.71573699e-01
-2.46720240e-01 -5.33688724e-01 6.99376523e-01 1.76874965e-01
2.58060068e-01 -1.28691292e+00 -6.30462766e-01 -3.44438016e-01
-3.58881205e-01 -9.83763039e-01 -3.08909893e-01 -1.42724037e-01
-5.24400592e-01 -1.05598450e+00 -7.57434726e-01 -9.21814263e-01
7.29295492e-01 5.58598697e-01 1.04946613e+00 6.04792178e-01
-1.86447933e-01 6.19600415e-01 -5.19681096e-01 -2.94301987e-01
-8.10900331e-01 -1.87553570e-01 -3.11375022e-01 1.03479542e-01
6.77556574e-01 6.63855374e-02 -3.02819192e-01 3.74426246e-01
-1.25277078e+00 1.82971418e-01 3.66654843e-01 6.63459718e-01
-6.40092641e-02 -2.16481328e-01 6.49628043e-02 -6.27311468e-01
7.61252701e-01 -7.62397647e-01 6.06463738e-02 3.09647262e-01
-3.10314804e-01 -2.52967954e-01 2.10940853e-01 -6.34997547e-01
-9.74949718e-01 -4.00601834e-01 1.96022227e-01 -6.28517807e-01
-3.60762328e-01 4.87364292e-01 3.11333746e-01 -1.57475635e-01
6.75499320e-01 7.38273859e-02 1.37038291e-01 -2.87779421e-01
1.53206468e-01 9.69046593e-01 1.51070908e-01 -2.75217891e-01
7.10621953e-01 6.04065359e-01 -2.26713717e-01 -1.21109700e+00
-8.66842031e-01 -4.55028325e-01 -6.33314431e-01 -8.81171167e-01
1.15006268e+00 -8.23303819e-01 -5.93883812e-01 5.84044039e-01
-1.44008243e+00 -1.35126784e-02 4.48774695e-01 2.29820088e-01
-3.14451963e-01 5.75940669e-01 -7.55184770e-01 -1.41483784e+00
-1.22645237e-01 -9.26169336e-01 9.37254548e-01 1.18505374e-01
-4.34396267e-01 -1.08679962e+00 3.65758426e-02 7.05222070e-01
2.75449336e-01 4.33677107e-01 1.15864170e+00 -5.13284504e-01
-1.52970746e-01 -1.28989279e-01 -6.29753768e-01 2.89863765e-01
-3.90644282e-01 2.97510952e-01 -1.20835507e+00 -1.79831415e-01
1.69506043e-01 -7.19758928e-01 8.85230362e-01 -1.02328300e-01
5.64981341e-01 -4.05121773e-01 -3.97718370e-01 -7.58128315e-02
1.38479936e+00 2.09485859e-01 7.89955616e-01 4.10302937e-01
9.21948493e-01 1.26267958e+00 7.69899309e-01 2.49916241e-01
4.07707870e-01 5.70855498e-01 4.99790341e-01 -2.09885836e-01
-2.43432969e-01 -2.92427391e-01 7.58033872e-01 7.87515581e-01
7.22158626e-02 -3.61567825e-01 -1.34432125e+00 6.61182463e-01
-1.96332848e+00 -1.27850056e+00 -6.13335431e-01 1.69878924e+00
5.23301184e-01 -3.60758573e-01 1.67132318e-01 -1.49460731e-03
1.00918591e+00 4.19225335e-01 -9.06865373e-02 -6.26657546e-01
-4.62203622e-01 -7.40297437e-01 5.51813953e-02 2.14468107e-01
-1.01604974e+00 1.06835341e+00 6.32769108e+00 5.94438672e-01
-1.28343225e+00 3.59140188e-01 5.27641654e-01 6.04335815e-02
-2.75795877e-01 -2.59074092e-01 -4.07022804e-01 5.88979423e-01
8.20469379e-01 3.94386142e-01 4.68963265e-01 5.59171438e-01
3.03666770e-01 -4.99419093e-01 -1.05174613e+00 1.13909078e+00
8.46718848e-01 -9.61126566e-01 1.50357217e-01 8.46671239e-02
4.71378386e-01 -3.87130022e-01 3.33220005e-01 2.00487986e-01
-4.98559773e-01 -1.19056761e+00 1.20894718e+00 8.10564220e-01
5.04731655e-01 -4.36538041e-01 8.81455243e-01 3.45646828e-01
-6.37983978e-01 -1.59105033e-01 -3.16032171e-01 -1.51633069e-01
2.20642705e-02 3.75069559e-01 -8.36390853e-01 -2.69487947e-01
8.36619198e-01 5.53746283e-01 -1.09568882e+00 5.25195420e-01
-2.03437537e-01 6.66357994e-01 4.73754078e-01 -3.42698872e-01
1.89942703e-01 4.25112844e-02 6.28663242e-01 1.56673622e+00
8.48526135e-02 -3.67616087e-01 1.59670226e-02 1.24039018e+00
2.13982493e-01 2.22663805e-01 -8.90434861e-01 -9.67959285e-01
2.16293916e-01 1.26963544e+00 -7.71597147e-01 -9.17839259e-02
-4.99510050e-01 1.15187728e+00 3.66884559e-01 2.76173830e-01
-8.15554857e-01 -3.03713232e-01 3.26873571e-01 3.16391140e-01
-2.76572168e-01 -3.01895708e-01 -4.50228959e-01 -1.03712785e+00
-2.88510263e-01 -8.07787776e-01 1.55196100e-01 -1.42743838e+00
-1.61474800e+00 3.63884896e-01 -1.40434429e-01 -7.52266765e-01
1.87367797e-01 -7.25975752e-01 -3.88905078e-01 7.03074813e-01
-1.23455322e+00 -1.68879926e+00 -3.95097554e-01 4.63212162e-01
3.37782890e-01 -8.37301016e-02 5.53219497e-01 2.61174321e-01
-5.92791796e-01 1.58724040e-01 -3.33301485e-01 3.80992472e-01
1.14747024e+00 -9.04808164e-01 -4.85936612e-01 5.91645122e-01
-9.38026309e-02 7.75071621e-01 9.22930241e-01 -8.35836887e-01
-1.26624429e+00 -6.27498865e-01 1.16541147e+00 -9.00914490e-01
9.44436789e-01 -2.48343885e-01 -1.12102056e+00 5.15328109e-01
7.87448108e-01 -5.55062175e-01 8.62262785e-01 -1.61023811e-01
-6.42202675e-01 3.72549117e-01 -1.06924307e+00 6.22728229e-01
5.88721275e-01 -1.12316394e+00 -9.04153049e-01 2.17167079e-01
1.36656612e-01 1.27885610e-01 -6.32624090e-01 -8.16373751e-02
5.40545642e-01 -1.14616477e+00 7.40759790e-01 -5.55477083e-01
1.05888236e+00 -4.38241124e-01 -1.63882807e-01 -1.07530057e+00
-3.13523561e-01 5.61611019e-02 1.62175428e-02 1.58074951e+00
2.65771478e-01 -1.67797402e-01 1.34574801e-01 4.56417918e-01
7.79833868e-02 -2.06824109e-01 -6.26183689e-01 -3.46964240e-01
1.12819403e-01 -1.77327424e-01 -1.79419860e-01 1.51177204e+00
2.36811399e-01 5.51417470e-01 -9.05200005e-01 -2.10967604e-02
2.94745803e-01 -1.49281383e-01 6.35636508e-01 -1.07202411e+00
5.62471151e-01 -5.91653883e-01 -4.30718035e-01 -1.60417676e-01
4.02182698e-01 -8.68540764e-01 -1.27348155e-01 -1.57961631e+00
8.28746378e-01 2.58252144e-01 -7.45370693e-04 6.59963369e-01
-8.35209489e-02 6.80384934e-01 6.46227181e-01 5.35233855e-01
-7.58176923e-01 1.50368199e-01 1.30541074e+00 -4.32628155e-01
1.17080078e-01 -8.94987166e-01 -6.54059052e-01 8.03310454e-01
7.36377954e-01 -4.65256453e-01 -4.97359000e-02 -3.53190809e-01
7.01166451e-01 -2.24524617e-01 7.92689800e-01 -6.49878860e-01
7.72091299e-02 -3.46846998e-01 5.40935099e-01 -4.81093854e-01
4.46652889e-01 -7.33507216e-01 -1.25060007e-01 4.11956370e-01
-2.47870550e-01 4.38013226e-02 3.29116821e-01 2.26061374e-01
-4.48584706e-01 -3.91852170e-01 1.01791191e+00 -2.08456829e-01
-7.82686353e-01 -5.05607605e-01 -9.25417483e-01 8.61173421e-02
1.04021788e+00 -3.52116913e-01 -9.19649184e-01 -4.90893811e-01
-5.04110098e-01 -5.04567400e-02 8.17236722e-01 6.50681973e-01
6.83526814e-01 -1.33325803e+00 -4.73709285e-01 -2.07599968e-01
5.37271559e-01 -1.09046328e+00 2.87217855e-01 1.24486411e+00
-3.76059383e-01 3.94837707e-01 -5.35592258e-01 -6.16074383e-01
-1.45038760e+00 8.98049235e-01 1.21864624e-01 4.00379837e-01
-7.57593662e-02 5.36911964e-01 2.60674119e-01 1.89016312e-02
1.20768063e-01 4.75500643e-01 -3.96432847e-01 6.83551550e-01
6.53311372e-01 6.72045946e-01 -5.11196434e-01 -1.41609752e+00
-5.20635486e-01 6.84480071e-01 1.34508207e-01 -1.29457808e-03
8.42276812e-01 -4.94113564e-01 -6.64841354e-01 1.01173532e+00
1.28364539e+00 1.14806771e-01 -5.14496267e-01 2.33732313e-01
1.59601152e-01 -6.32966399e-01 8.32896531e-02 -1.04859209e+00
-7.05770433e-01 1.48559058e+00 6.32604480e-01 7.53943384e-01
7.86620975e-01 1.33420274e-01 5.10828078e-01 4.58599739e-02
1.82461828e-01 -1.50331199e+00 5.04832566e-01 4.64833170e-01
1.20063937e+00 -1.62188256e+00 -1.72260791e-01 -2.01197863e-01
-1.03646648e+00 1.31280172e+00 7.63254404e-01 3.02291304e-01
8.90216827e-02 -1.46179602e-01 2.65570015e-01 -5.46537161e-01
-4.84850913e-01 -3.14729273e-01 6.02887690e-01 6.28554702e-01
8.91958416e-01 4.86158691e-02 -4.75788534e-01 3.36244017e-01
2.35074997e-01 -3.10165018e-01 6.11140966e-01 7.91949987e-01
-6.62131131e-01 -4.62164253e-01 -9.93625402e-01 6.49021193e-02
-3.75666559e-01 7.40471855e-02 -1.27225482e+00 9.16775823e-01
3.17647815e-01 1.27314210e+00 6.80462569e-02 -8.58929813e-01
3.62590626e-02 3.92598987e-01 3.26812238e-01 -4.03615773e-01
-7.92510331e-01 6.78145364e-02 1.61948830e-01 -4.09579009e-01
-7.93391228e-01 -3.56336921e-01 -1.01696920e+00 -7.78470576e-01
-5.61741404e-02 -1.40703976e-01 7.62797594e-01 8.65563393e-01
1.74790308e-01 1.52533904e-01 3.78300935e-01 -9.09076214e-01
1.03232965e-01 -1.00455976e+00 -5.89955568e-01 8.52159262e-01
4.04388279e-01 -7.18578398e-01 -6.54747009e-01 1.08459830e-01] | [8.481924057006836, 10.689946174621582] |
3324b33d-8010-4b87-806a-df9e276523bd | semantically-aligned-task-decomposition-in | 2305.10865 | null | https://arxiv.org/abs/2305.10865v1 | https://arxiv.org/pdf/2305.10865v1.pdf | Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning | The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved. Automatic subgoal generation (ASG) has recently emerged as a viable MARL approach inspired by utilizing subgoals in intrinsically motivated reinforcement learning. However, end-to-end learning of complex task planning from sparse rewards without prior knowledge, undoubtedly requires massive training samples. Moreover, the diversity-promoting nature of existing ASG methods can lead to the "over-representation" of subgoals, generating numerous spurious subgoals of limited relevance to the actual task reward and thus decreasing the sample efficiency of the algorithm. To address this problem and inspired by the disentangled representation learning, we propose a novel "disentangled" decision-making method, Semantically Aligned task decomposition in MARL (SAMA), that prompts pretrained language models with chain-of-thought that can suggest potential goals, provide suitable goal decomposition and subgoal allocation as well as self-reflection-based replanning. Additionally, SAMA incorporates language-grounded RL to train each agent's subgoal-conditioned policy. SAMA demonstrates considerable advantages in sample efficiency compared to state-of-the-art ASG methods, as evidenced by its performance on two challenging sparse-reward tasks, Overcooked and MiniRTS. | ['Hongyuan Zha', 'Bo Jin', 'Xiangfeng Wang', 'Baoxiang Wang', 'Dan Qiao', 'Wenhao Li'] | 2023-05-18 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [ 2.66638756e-01 5.27368724e-01 -4.06116545e-01 -1.23678386e-01
-1.16792464e+00 -2.03206345e-01 6.05264068e-01 2.14800790e-01
-4.57988709e-01 1.01930761e+00 6.19437635e-01 -7.95426685e-03
-4.34950888e-01 -5.64695120e-01 -3.45106572e-01 -6.80091381e-01
-2.25394115e-01 7.39521861e-01 -2.85891056e-01 -7.29093134e-01
3.12531531e-01 1.36252254e-01 -1.45025158e+00 8.64643008e-02
1.25511265e+00 7.24944949e-01 7.43877113e-01 1.34145275e-01
4.68228459e-02 1.30740237e+00 -4.51964736e-01 -1.32707700e-01
2.07582489e-01 -5.71598232e-01 -8.97773087e-01 1.26524150e-01
-2.63930291e-01 -3.13320130e-01 -5.34581803e-02 8.73031199e-01
3.98953527e-01 6.09229207e-01 3.94031942e-01 -9.98767018e-01
-4.77130204e-01 1.04304218e+00 -4.81619567e-01 2.82113459e-02
4.89476055e-01 4.25254315e-01 1.27388883e+00 -7.67433286e-01
3.53679895e-01 1.46397471e+00 5.94919734e-02 8.38864267e-01
-1.39231670e+00 -4.23360378e-01 5.76980233e-01 8.20940360e-02
-8.73895586e-01 -3.96098703e-01 9.65947032e-01 -2.36879751e-01
1.06046140e+00 7.31047764e-02 7.48670101e-01 1.37619615e+00
1.76884994e-01 9.90122914e-01 1.43671370e+00 -3.31266612e-01
6.55853152e-01 -3.59377533e-01 -2.77251333e-01 8.77527952e-01
2.02340454e-01 6.96058393e-01 -9.10383046e-01 -2.55593687e-01
7.46011913e-01 -1.58791140e-01 6.36739656e-02 -5.10668755e-01
-1.54611468e+00 1.02670133e+00 4.27262813e-01 5.63225001e-02
-9.32987511e-01 3.65345269e-01 3.89086694e-01 3.40395749e-01
3.51854652e-01 1.05493784e+00 -2.47628704e-01 -2.60220379e-01
-6.98154509e-01 6.73666656e-01 3.29576194e-01 8.82490933e-01
7.87584841e-01 5.33001423e-01 -5.15603721e-01 6.61808252e-01
3.42150569e-01 2.53406227e-01 7.22518802e-01 -1.13851345e+00
7.08961248e-01 6.73336864e-01 2.03120321e-01 -6.92589223e-01
-6.99689388e-01 -7.93141901e-01 -5.68891823e-01 5.90271175e-01
3.28280658e-01 -6.01459779e-02 -5.86612582e-01 2.00014567e+00
2.80834973e-01 -2.23080218e-01 5.12641072e-01 1.18201244e+00
3.54725927e-01 2.05853626e-01 2.97305435e-01 -2.95379966e-01
1.23181462e+00 -1.08008170e+00 -6.95988119e-01 -9.73942459e-01
7.49417961e-01 -2.09939688e-01 1.32145727e+00 5.69731772e-01
-1.20786250e+00 -3.70837152e-01 -7.48245180e-01 2.47764900e-01
2.75127590e-01 -5.39638326e-02 1.06774235e+00 1.58160418e-01
-8.04264843e-01 5.30246079e-01 -6.87981486e-01 -8.92035961e-02
6.09240234e-01 2.46763125e-01 -1.29046842e-01 -2.51277119e-01
-1.20292664e+00 1.10599291e+00 6.16402745e-01 1.76781431e-01
-1.43976939e+00 -2.08698690e-01 -9.79573965e-01 1.09675668e-01
1.11243796e+00 -7.53506601e-01 1.39971888e+00 -7.49380946e-01
-1.80860841e+00 4.87460405e-01 2.22558379e-01 -4.42796350e-01
2.59551376e-01 -2.61610448e-01 6.69720396e-02 5.53826764e-02
3.83944005e-01 6.59981370e-01 1.06121027e+00 -1.21455038e+00
-4.55227137e-01 -4.14852172e-01 3.10787231e-01 8.80665839e-01
7.24171922e-02 -5.57248630e-02 3.55226785e-01 -7.39449263e-01
2.29157388e-01 -7.67064333e-01 -1.02274251e+00 -5.62324345e-01
-1.62728190e-01 -5.69429696e-01 -1.61888540e-01 -3.55226994e-01
9.46628988e-01 -1.99531984e+00 6.08180583e-01 -9.77665856e-02
3.87898117e-01 -2.41548643e-01 -5.69014370e-01 4.87751305e-01
1.27881810e-01 -2.71772355e-01 -1.09556764e-01 -3.33252341e-01
1.32884890e-01 3.31017137e-01 -3.98505837e-01 2.34751210e-01
3.87825519e-01 9.51410890e-01 -1.63709784e+00 -3.50955814e-01
-3.05121038e-02 -3.20468992e-01 -7.60701478e-01 5.76820135e-01
-7.28029728e-01 7.72346973e-01 -9.08132315e-01 6.78683937e-01
3.29562537e-02 -2.14065015e-01 3.58490258e-01 2.83599794e-01
-3.40590514e-02 6.59972072e-01 -8.85713518e-01 2.27304149e+00
-4.04803246e-01 -7.71748498e-02 -1.88711196e-01 -1.10192490e+00
1.17744017e+00 2.21390888e-01 4.51955467e-01 -9.07118857e-01
2.40027867e-02 2.52404690e-01 9.03674737e-02 -4.60958838e-01
7.28847682e-01 -2.84114927e-01 -3.76460195e-01 5.45093834e-01
-5.24820834e-02 -4.20958579e-01 1.65720373e-01 2.91249514e-01
1.02723312e+00 8.27307701e-01 5.21770895e-01 -2.62943208e-01
2.14117929e-01 3.51845384e-01 6.38665855e-01 9.79597330e-01
-2.14007437e-01 2.38657713e-01 5.26658654e-01 -3.77341658e-01
-6.02764010e-01 -8.38459313e-01 6.07173860e-01 1.37229419e+00
9.16187465e-02 -2.93465495e-01 -3.73048037e-01 -6.96709156e-01
-2.52120018e-01 1.00365162e+00 -5.60429692e-01 -4.77505654e-01
-7.39608347e-01 -7.00038791e-01 2.09950387e-01 3.30149114e-01
2.54430830e-01 -1.63183594e+00 -1.13180494e+00 6.64309680e-01
-3.92832845e-01 -1.01162875e+00 -4.73760575e-01 5.24275601e-01
-1.03718245e+00 -1.03214014e+00 -6.30629480e-01 -5.58838129e-01
7.83758819e-01 2.82198787e-01 1.27932298e+00 -1.05842061e-01
-4.64593992e-02 2.91630715e-01 -6.23720706e-01 -1.17922053e-01
-4.19003129e-01 -1.28918335e-01 2.46963605e-01 -2.79895008e-01
8.89214128e-02 -5.09842694e-01 -5.37666500e-01 1.36012927e-01
-4.18907791e-01 4.27509457e-01 8.59058619e-01 1.14438009e+00
4.78327066e-01 -2.36102611e-01 1.12574875e+00 -6.00442231e-01
1.13384044e+00 -6.01036131e-01 -6.33607030e-01 1.05119340e-01
-9.04320955e-01 4.40777719e-01 6.39204502e-01 -5.30672789e-01
-1.19920576e+00 -2.53702011e-02 3.37021142e-01 -2.23796666e-01
2.56259218e-02 9.54784513e-01 1.52828380e-01 2.86750257e-01
1.08283436e+00 4.89480346e-01 3.62798125e-01 -2.26104796e-01
6.24994814e-01 7.92692825e-02 2.40024239e-01 -1.11315572e+00
4.89538997e-01 3.37352268e-02 -3.10869678e-03 -2.30318904e-01
-1.10993254e+00 -1.04665175e-01 -6.70089051e-02 -2.53315419e-01
6.18419290e-01 -1.04907477e+00 -8.60326231e-01 9.07091051e-02
-1.05053377e+00 -6.41678631e-01 -7.66739309e-01 6.65602386e-01
-1.26823938e+00 2.72769690e-01 -2.54698873e-01 -1.01440811e+00
-1.33272111e-01 -1.34024870e+00 8.27151060e-01 7.56516904e-02
-4.24405843e-01 -5.90266883e-01 -3.12304813e-02 5.91049969e-01
3.64043355e-01 2.23826572e-01 9.62762654e-01 -5.40852547e-01
-7.51587570e-01 2.23786652e-01 2.87845749e-02 -1.19012751e-01
1.25128711e-02 -1.13752878e+00 -5.54178953e-01 -4.42016602e-01
1.72430903e-01 -1.21265888e+00 5.63497663e-01 2.82093167e-01
8.17698002e-01 -5.35759151e-01 6.20161183e-02 2.13305950e-01
1.03206444e+00 3.20815220e-02 2.59966850e-01 5.87785959e-01
4.00391191e-01 9.26568449e-01 1.28705919e+00 9.56104577e-01
6.13635480e-01 7.19797134e-01 9.82207477e-01 1.76170990e-01
6.94500795e-03 -4.73139793e-01 8.26147795e-01 4.80400980e-01
-2.51233011e-01 9.71250311e-02 -5.79591215e-01 2.81427085e-01
-2.24902654e+00 -8.99731934e-01 1.58297479e-01 2.05785561e+00
1.01739764e+00 9.07684788e-02 2.51612991e-01 -1.73094422e-01
2.20228985e-01 3.99506778e-01 -8.60958815e-01 -2.37581208e-01
6.68050861e-03 6.56346083e-02 1.51889190e-01 5.34211099e-01
-4.55334514e-01 1.38702631e+00 5.37900829e+00 8.29796076e-01
-5.15471995e-01 1.22675009e-01 4.31405157e-01 -1.75016329e-01
-4.81449544e-01 1.53116450e-01 -6.14875197e-01 2.68893708e-02
4.55086887e-01 -2.85170406e-01 1.04495955e+00 8.35878372e-01
4.94398683e-01 -2.77887225e-01 -1.18995178e+00 7.50562429e-01
-1.00675263e-01 -1.06660473e+00 -6.79445714e-02 6.18236847e-02
4.61511612e-01 -5.37556335e-02 -1.96007993e-02 8.16606820e-01
8.45141351e-01 -1.13560140e+00 1.03152597e+00 3.48852009e-01
6.31476939e-01 -6.45342588e-01 3.53026181e-01 7.65493333e-01
-8.41804147e-01 -4.02914107e-01 -4.48960066e-01 -4.21167523e-01
5.09583950e-02 2.48200297e-01 -9.09397840e-01 7.01457322e-01
1.77665159e-01 5.96122980e-01 -1.89621508e-01 4.42539662e-01
-6.13134205e-01 3.53588969e-01 6.16577603e-02 -3.19884509e-01
5.94730675e-01 -3.49013865e-01 6.51459992e-01 4.70757484e-01
1.77367017e-01 1.90851107e-01 7.73030937e-01 1.23563552e+00
3.82543772e-01 1.57823786e-02 -5.54515064e-01 -2.47433022e-01
4.01518464e-01 1.08170116e+00 -4.50738013e-01 -2.05650721e-02
-3.76709215e-02 6.97681427e-01 8.42316985e-01 4.76549476e-01
-5.99111319e-01 2.87137657e-01 4.82365280e-01 -1.15414418e-01
-1.63518131e-01 -3.85963291e-01 -9.91847515e-02 -1.14084387e+00
-2.22551614e-01 -1.27897942e+00 3.72956753e-01 -7.29138672e-01
-1.17503357e+00 7.83220828e-01 2.36322917e-02 -1.26054609e+00
-6.89050257e-01 -1.50416881e-01 -2.84062862e-01 1.01215637e+00
-1.79856241e+00 -1.07548976e+00 -6.37007356e-02 6.32347345e-01
8.87742341e-01 -5.67595899e-01 1.10147905e+00 -3.92012715e-01
-4.51271206e-01 2.43284121e-01 -3.79699200e-01 -4.97357666e-01
3.84726673e-01 -1.12806284e+00 9.54273567e-02 5.18903196e-01
-1.50482267e-01 3.27288002e-01 7.05847144e-01 -6.98808968e-01
-1.54886866e+00 -7.24568069e-01 6.58367634e-01 -1.78807676e-01
6.83212698e-01 -3.25933307e-01 -6.02319062e-01 3.72924745e-01
-4.01548818e-02 -1.66217193e-01 5.46122313e-01 3.56126040e-01
-1.10527761e-01 1.73124656e-01 -9.72846270e-01 8.84112775e-01
1.23690450e+00 -8.69911611e-02 -9.88282502e-01 5.45542479e-01
9.25726354e-01 -4.68030393e-01 -4.88506675e-01 7.45524615e-02
9.52463076e-02 -9.18872476e-01 9.25917327e-01 -7.84165740e-01
6.24088824e-01 -8.62339288e-02 -1.17308959e-01 -1.53131735e+00
-7.84717202e-01 -9.10166740e-01 -3.22666854e-01 7.13856995e-01
2.07799509e-01 -5.57910085e-01 9.33813512e-01 5.30995071e-01
-4.21023399e-01 -1.04224861e+00 -9.27843213e-01 -8.75890255e-01
-2.97855735e-01 -3.68084222e-01 5.71217120e-01 8.81070852e-01
4.18697447e-01 4.54494894e-01 -6.51175737e-01 -1.24933630e-01
8.78235519e-01 2.49334633e-01 6.18885040e-01 -1.21665335e+00
-5.39770842e-01 -4.19759274e-01 3.49149287e-01 -1.11810827e+00
3.49875301e-01 -1.13300288e+00 4.03407902e-01 -1.52975154e+00
-2.54312921e-02 -9.33258712e-01 -4.11987841e-01 8.10806036e-01
-2.74474055e-01 -5.19925416e-01 4.44393635e-01 3.64927262e-01
-9.12021518e-01 1.11107242e+00 1.90806639e+00 1.96592230e-02
-4.06915814e-01 -1.82729438e-02 -1.13208199e+00 6.01643801e-01
9.48645949e-01 -6.68660879e-01 -6.16027057e-01 -2.25170940e-01
5.39165497e-01 7.67033041e-01 2.12211296e-01 -6.92140281e-01
-3.80885936e-02 -8.43085647e-01 -1.59647644e-01 -2.76906163e-01
4.72844601e-01 -6.66578770e-01 -3.71598482e-01 7.30633616e-01
-8.07361901e-01 3.84077840e-02 -7.99645782e-02 7.96927989e-01
-1.24929789e-02 -5.02618134e-01 3.77352089e-01 -5.47905147e-01
-6.89766705e-01 2.05233544e-01 -6.73048258e-01 2.77247399e-01
8.89074981e-01 -2.04623058e-01 -1.49915099e-01 -3.16922158e-01
-6.97720826e-01 5.94570220e-01 2.48761848e-02 4.12833720e-01
9.48754549e-01 -1.21004534e+00 -8.73981476e-01 3.26219536e-02
2.27065489e-01 4.89780635e-01 1.77386925e-01 7.10456192e-01
1.88197762e-01 2.31753886e-01 -3.27534437e-01 -1.69917285e-01
-6.70092523e-01 5.45959055e-01 1.11212112e-01 -1.01408172e+00
-7.17581451e-01 7.01166332e-01 2.04311714e-01 -3.53452772e-01
3.22496951e-01 -2.76949584e-01 -4.93267804e-01 -1.00070573e-01
1.72946796e-01 2.10731596e-01 -3.22152913e-01 -4.65079993e-02
-1.75254926e-01 -8.57508406e-02 -8.09469372e-02 -2.08637819e-01
1.34602726e+00 -3.25908884e-02 8.03188309e-02 2.59195179e-01
1.87178448e-01 -2.58445680e-01 -1.66677976e+00 -4.20461357e-01
1.80107489e-01 -4.21141863e-01 7.23970458e-02 -1.10462499e+00
-5.35899460e-01 6.41859949e-01 1.27979010e-01 2.34203617e-04
9.63842452e-01 2.45023128e-02 4.69525427e-01 7.01778769e-01
1.00257242e+00 -1.10673022e+00 8.30450237e-01 6.78749561e-01
1.22066069e+00 -1.35998845e+00 -7.23847747e-03 -1.19546138e-01
-1.16951346e+00 9.96536374e-01 9.08195555e-01 -1.77722409e-01
-2.21425235e-01 -2.31806815e-01 -1.35026798e-01 -3.65833521e-01
-9.56045330e-01 -3.49878937e-01 3.56052406e-02 7.51520634e-01
2.13655010e-01 1.54337227e-01 -3.96181643e-01 8.31557035e-01
-1.72668561e-01 9.13901022e-04 5.24051666e-01 1.06422532e+00
-5.75100780e-01 -1.09292734e+00 -3.25094193e-01 3.83454353e-01
-1.72327217e-02 -2.48519361e-01 3.00774127e-02 3.44674319e-01
-2.61602402e-01 9.70542431e-01 -4.50372398e-01 -1.11131810e-01
2.20958397e-01 -1.79545626e-01 7.22400546e-01 -1.01920378e+00
-8.27273905e-01 1.94734901e-01 2.64079034e-01 -8.71010065e-01
-4.00801152e-01 -5.96406639e-01 -1.66077280e+00 2.64283240e-01
-1.91312030e-01 2.21272051e-01 3.51153582e-01 1.07004225e+00
4.39254969e-01 8.00587833e-01 6.92114532e-01 -9.94572341e-01
-1.38554978e+00 -8.51684034e-01 -3.96948546e-01 4.08780128e-01
2.71641463e-01 -9.86816168e-01 -8.33867490e-02 -4.97518718e-01] | [4.053691864013672, 1.756701946258545] |
7ff4bda1-9860-49c9-9892-b117df6bb0f5 | an-emotional-comfort-framework-for-improving | null | null | https://aclanthology.org/2021.naacl-industry.17 | https://aclanthology.org/2021.naacl-industry.17.pdf | An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots | E-commerce has grown substantially over the last several years, and chatbots for intelligent customer service are concurrently drawing attention. We presented AliMe Assist, a Chinese intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), AliMe Assist offers assistance service, customer service, and chatting service. According to the survey of user studies and the real online testing, emotional comfort of customers{'} negative emotions, which make up more than 5{\%} of whole number of customer visits on AliMe, is a key point for providing considerate service. In this paper, we propose a framework to obtain proper answer to customers{'} emotional questions. The framework takes emotion classification model as a core, and final answer selection is based on topic classification and text matching. Our experiments on real online systems show that the framework is very promising. | ['Huan Chen', 'Haiqing Chen', 'Chao Wang', 'Shuangyong Song'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['answer-selection'] | ['natural-language-processing'] | [-6.83680832e-01 2.15864498e-02 -3.57520103e-01 -7.68797576e-01
-4.14509624e-01 -4.13656861e-01 5.30816838e-02 1.54980376e-01
-3.81754607e-01 1.88719869e-01 -4.61639240e-02 -2.05440432e-01
-4.16009612e-02 -8.67947459e-01 2.02947646e-01 -5.13574839e-01
3.49641949e-01 4.32703793e-01 -2.07293645e-01 -7.07211375e-01
6.79522693e-01 9.87653807e-03 -1.38912702e+00 5.04275858e-01
1.19689846e+00 1.56322920e+00 1.73651889e-01 2.97462642e-01
-1.06073987e+00 7.19055116e-01 -6.25391603e-01 -1.01684093e+00
-4.66384441e-01 -5.89631140e-01 -1.19510460e+00 2.38818794e-01
-8.08142841e-01 -2.94035733e-01 1.01780221e-01 9.70602751e-01
5.17500579e-01 2.68905103e-01 3.69731396e-01 -1.70215046e+00
-8.83925974e-01 7.46560931e-01 4.00168002e-02 -2.88540184e-01
6.42568111e-01 -4.40884918e-01 1.05018079e+00 -8.02137554e-01
5.32372713e-01 1.08964026e+00 4.86299574e-01 4.63076323e-01
-4.25782412e-01 -4.50479567e-01 3.94426733e-01 4.51895833e-01
-9.25680876e-01 -9.02019814e-03 1.07484889e+00 -1.68769211e-02
8.80790830e-01 5.54984748e-01 7.50925422e-01 6.38948679e-01
6.40855879e-02 1.27315521e+00 5.80386162e-01 -3.61139923e-01
3.82440865e-01 1.14000678e+00 6.13284051e-01 2.07017407e-01
-5.23866355e-01 -8.38816583e-01 -5.14040552e-02 -4.25837368e-01
1.28429234e-01 3.49697202e-01 -6.16373532e-02 2.12435693e-01
-6.75102293e-01 1.21624112e+00 1.53767034e-01 4.54445690e-01
-4.75815266e-01 -5.94317317e-01 7.26070225e-01 5.73779881e-01
5.21212578e-01 2.75412709e-01 -6.30808115e-01 -7.15104401e-01
4.84356992e-02 -1.92177277e-02 1.49194336e+00 1.21247041e+00
3.43643695e-01 -3.33176732e-01 6.69527277e-02 1.23169732e+00
2.92892933e-01 3.36786181e-01 5.73721528e-01 -1.01418805e+00
4.82264124e-02 9.50666726e-01 3.89044553e-01 -1.09370494e+00
-1.25172436e-01 -2.04969868e-01 -7.72246420e-01 -5.12370348e-01
1.76427141e-01 -4.20744777e-01 7.33828768e-02 1.18908370e+00
3.98203254e-01 -6.34542704e-01 1.23223744e-01 8.34163308e-01
9.90106821e-01 8.22591126e-01 4.01469678e-01 -6.31001353e-01
1.60692394e+00 -9.57330167e-01 -1.47583508e+00 2.21404031e-01
7.57640004e-01 -9.44487333e-01 1.29827368e+00 8.19862068e-01
-9.29678679e-01 -2.61005998e-01 -4.79321867e-01 -3.32885794e-02
-5.77839553e-01 1.11956529e-01 1.23810804e+00 8.89983952e-01
-4.94919002e-01 1.82111099e-01 2.83892862e-02 -6.61110282e-01
-1.10068463e-01 2.32817054e-01 8.76964107e-02 1.64892599e-01
-1.53270745e+00 7.82858372e-01 1.50239104e-02 2.27498874e-01
4.21275496e-01 7.46052414e-02 -5.00892699e-01 1.94290996e-01
2.64059931e-01 -2.06230536e-01 1.48644423e+00 -1.25003481e+00
-1.70993412e+00 8.83551955e-01 -2.39473939e-01 9.55449119e-02
1.68467864e-01 -2.39068344e-01 -1.01928318e+00 1.52024269e-01
-1.95269510e-01 3.00890774e-01 2.89237082e-01 -9.65124846e-01
-7.54839599e-01 -4.10506457e-01 -7.21337879e-03 3.17785829e-01
-5.05812049e-01 7.94915855e-01 -4.96374488e-01 -2.64706790e-01
2.71162927e-01 -5.21457553e-01 -1.72748834e-01 -4.72997248e-01
9.22748968e-02 -7.90585399e-01 9.43076074e-01 -9.11911607e-01
1.41264081e+00 -2.00175810e+00 -3.38615209e-01 4.34310317e-01
-1.25570685e-01 1.12219803e-01 2.43137881e-01 5.98920286e-01
1.27063766e-01 1.55012950e-01 4.22944456e-01 3.07586432e-01
6.91889167e-01 8.58334918e-03 -2.40720525e-01 -2.40109578e-01
-4.67878915e-02 8.51833403e-01 -6.06688619e-01 -6.51350260e-01
4.88382541e-02 2.26422966e-01 -5.51634133e-01 5.01249075e-01
-5.15785441e-02 -2.86178868e-02 -8.29128563e-01 9.86367643e-01
7.19516933e-01 -1.53843937e-02 1.04279667e-01 1.64623514e-01
-2.05995087e-02 1.07502013e-01 -9.64658976e-01 1.19081318e+00
-5.43707609e-01 -1.37770185e-02 5.30191183e-01 -8.90489161e-01
1.49280751e+00 4.60833937e-01 5.66327214e-01 -1.01081586e+00
8.06261718e-01 1.88104928e-01 -2.62801558e-01 -1.16805112e+00
7.22462654e-01 1.57364514e-02 -4.34282482e-01 6.60654008e-01
-1.94220826e-01 -8.71710777e-02 -8.99805129e-02 4.02937263e-01
4.80805486e-01 -2.20019460e-01 2.11368501e-01 -1.06597751e-01
6.65800214e-01 -6.49293959e-02 4.97628540e-01 2.78119564e-01
-7.72280812e-01 -1.73426002e-01 6.70234621e-01 -2.03946307e-01
-6.53123677e-01 -5.20810902e-01 -8.95184800e-02 1.33198535e+00
4.08121675e-01 -1.79632902e-01 -1.09758461e+00 -5.28454125e-01
-4.19169039e-01 9.16598976e-01 -2.25703344e-01 -1.61286533e-01
-3.80862541e-02 -4.94075596e-01 7.10865706e-02 2.72122830e-01
8.24679494e-01 -1.61625934e+00 -3.67113948e-01 4.60427374e-01
-9.42628622e-01 -7.86399782e-01 -3.94761562e-01 2.24031955e-01
-7.90687263e-01 -7.06913710e-01 -4.78141814e-01 -1.22207630e+00
2.36900166e-01 2.51725882e-01 1.14223123e+00 3.65918696e-01
-1.13403790e-01 4.67871875e-01 -8.97281945e-01 -3.80665004e-01
-3.61262970e-02 4.50084135e-02 -1.08539082e-01 8.44211951e-02
1.20227861e+00 -3.94671530e-01 -7.07607388e-01 7.41438925e-01
-5.43806255e-01 -4.24105287e-01 2.22561032e-01 7.19630361e-01
-1.12862475e-01 5.10040447e-02 1.45396578e+00 -9.48082507e-01
1.14455080e+00 -8.16027224e-01 -4.37968373e-02 3.78782690e-01
-7.90560007e-01 -6.07565641e-01 5.63090086e-01 -3.15106153e-01
-1.49254358e+00 -5.03547490e-01 -7.75319755e-01 4.39294696e-01
-5.17263770e-01 3.74908537e-01 -6.79240584e-01 2.62484431e-01
1.22534037e-01 -7.09910765e-02 2.17012372e-02 -3.58626813e-01
1.78187743e-01 1.55095530e+00 3.36167842e-01 -4.43277329e-01
-1.77440658e-01 -8.29499587e-02 -9.03898835e-01 -5.06087899e-01
-3.80520761e-01 -9.04829025e-01 -1.46955848e-01 -6.37082398e-01
7.62388051e-01 -3.17331403e-01 -2.12782574e+00 3.80288452e-01
-1.15101218e+00 1.98016018e-01 1.34705842e-01 5.49439192e-01
-3.80186647e-01 2.54454553e-01 -1.14369380e+00 -1.45388210e+00
-6.65140748e-01 -7.21474409e-01 4.89549488e-01 5.14330029e-01
-4.81619090e-01 -8.38225663e-01 -5.05039752e-01 7.01466799e-01
5.05116940e-01 -3.25479060e-01 1.04319406e+00 -8.86002898e-01
1.17665846e-02 -5.08657932e-01 -1.67426184e-01 2.68758714e-01
-3.38934571e-01 -3.47765833e-02 -8.34270775e-01 4.10962552e-01
6.81801438e-01 -5.71570039e-01 2.64309883e-01 1.79808736e-01
1.25271118e+00 -3.36545467e-01 -6.13185950e-02 -1.68881118e-02
1.11492610e+00 1.09138429e+00 1.14093649e+00 4.20855492e-01
-2.28492156e-01 1.22338414e+00 1.43444896e+00 9.17454958e-01
6.96604490e-01 2.37281814e-01 3.67741972e-01 1.23193480e-01
9.60474253e-01 -1.55894130e-01 3.66638482e-01 1.20140243e+00
3.88187170e-02 -1.94875896e-01 -3.58861446e-01 3.50839555e-01
-1.92495608e+00 -9.91988242e-01 -4.16477710e-01 1.65296793e+00
5.71381450e-01 -8.88863876e-02 1.20970905e-01 1.69119835e-01
8.76725674e-01 -5.20481646e-01 -5.60222447e-01 -1.00442410e+00
1.11241795e-01 8.14504772e-02 -2.40616724e-01 2.64363617e-01
-7.55677760e-01 8.35991800e-01 5.75167274e+00 9.58273947e-01
-7.84893513e-01 1.15639649e-01 1.07640505e+00 3.89044255e-01
-3.69709790e-01 -2.15139046e-01 -3.99963558e-01 4.62993890e-01
7.00779080e-01 -9.50710028e-02 4.01085287e-01 1.45917284e+00
2.25027159e-01 -4.39148039e-01 -6.53495669e-01 1.22794449e+00
1.04156889e-01 -8.25771034e-01 -5.34278691e-01 -3.21187943e-01
2.97702163e-01 -8.70518863e-01 -3.01312488e-02 9.42135692e-01
8.82125050e-02 -5.70755064e-01 4.49995369e-01 3.99128318e-01
1.46536216e-01 -1.18880904e+00 1.29917598e+00 6.87607884e-01
-8.28645825e-01 -8.65874290e-02 -2.76578724e-01 -2.37244025e-01
3.31524044e-01 3.46960247e-01 -2.33636111e-01 3.70343477e-01
1.00862372e+00 1.59514427e-01 4.64801379e-02 7.20915437e-01
1.70518965e-01 4.37694848e-01 -1.30919612e-03 -8.82875800e-01
-1.57381725e-02 -7.98248291e-01 -1.89609647e-01 1.09287930e+00
4.64706749e-01 8.13746393e-01 5.97835407e-02 7.37482786e-01
-2.89708376e-02 8.49858880e-01 -1.79759994e-01 -2.47090142e-02
3.73656213e-01 1.63486004e+00 -8.33228111e-01 -4.08762574e-01
-3.25563788e-01 1.29172707e+00 2.30984148e-02 3.17763209e-01
-9.14420366e-01 -1.23831475e+00 3.26044887e-01 -5.25501668e-01
-3.71086448e-02 4.05382514e-01 -4.75382119e-01 -1.04457414e+00
9.25416127e-03 -7.63246298e-01 3.57341111e-01 -8.61323178e-01
-1.34967363e+00 5.11758268e-01 -7.19783843e-01 -9.85587776e-01
-9.42202881e-02 -4.23516333e-01 -9.30932879e-01 5.66767037e-01
-1.06348228e+00 -7.14209914e-01 -2.51475722e-01 6.30415499e-01
4.84323353e-01 1.85737774e-01 1.20940459e+00 6.07371271e-01
-5.68973660e-01 5.05131900e-01 -8.12986940e-02 -1.12887239e-02
6.79683268e-01 -8.73790979e-01 -3.56731236e-01 2.34941393e-02
-7.18879879e-01 7.19197452e-01 6.74657106e-01 -3.52451175e-01
-1.46402276e+00 -3.78206819e-01 1.62082672e+00 -2.31000945e-01
4.24413800e-01 -3.26667130e-01 -7.87782431e-01 4.04847443e-01
5.05456030e-01 -8.66882682e-01 1.19436228e+00 5.32522738e-01
4.51685954e-03 -8.58791247e-02 -1.59536612e+00 4.02122170e-01
6.27127171e-01 -3.94415259e-01 -5.49110591e-01 3.05841386e-01
6.77232981e-01 1.28523067e-01 -1.05872095e+00 -1.20308384e-01
5.93143165e-01 -1.12104487e+00 4.25631821e-01 -5.60061693e-01
3.47264677e-01 2.84592897e-01 9.30416118e-03 -1.05276668e+00
-2.11538635e-02 -5.80136895e-01 3.84920806e-01 1.74969614e+00
2.67263323e-01 -8.65545750e-01 7.44805992e-01 1.42862701e+00
-3.75699438e-02 -6.95539773e-01 -2.86757499e-01 -1.34142384e-01
-3.31587791e-01 -7.39357769e-01 6.76099420e-01 1.29545879e+00
1.11987722e+00 4.19391900e-01 -3.10233891e-01 -4.66334999e-01
-2.10873019e-02 3.63288552e-01 4.95555013e-01 -1.12702751e+00
-1.47913441e-01 -5.03618419e-01 7.75367096e-02 -1.15554607e+00
3.77338938e-02 -5.12587845e-01 -8.42368603e-02 -1.49199641e+00
2.13640973e-01 -4.19086814e-01 -1.42566070e-01 1.56362131e-01
2.52259728e-02 -1.71596020e-01 -1.70396958e-02 -8.49721283e-02
-9.20654655e-01 6.45732164e-01 1.09594774e+00 2.18764201e-01
-2.70624220e-01 3.50808471e-01 -1.02930236e+00 7.58022606e-01
9.81377602e-01 1.07274413e-01 -1.11683160e-01 2.21671760e-01
6.78865373e-01 5.32733977e-01 -2.82097042e-01 -7.80050308e-02
1.28531978e-01 -2.74479687e-01 -2.01579332e-02 -6.78790033e-01
3.02928895e-01 -1.25898218e+00 -2.32775331e-01 2.74573743e-01
-5.25650084e-01 -1.20496288e-01 -3.93018216e-01 1.49336860e-01
-5.41111648e-01 -4.94983971e-01 2.58732975e-01 -8.81461799e-02
-6.35661662e-01 8.88330862e-02 -7.69007623e-01 -2.34930485e-01
9.48941112e-01 -2.21672639e-01 -1.77084520e-01 -1.11068237e+00
-7.99262702e-01 6.46200359e-01 -2.01229364e-01 5.54156303e-01
5.21168053e-01 -1.38668394e+00 -1.26557484e-01 1.80811211e-02
1.62591994e-01 -6.73431039e-01 6.55151546e-01 6.77104414e-01
-3.15998673e-01 4.99058485e-01 -6.32361770e-02 2.22208668e-02
-1.20888996e+00 5.29562473e-01 -2.86604986e-02 -8.01544040e-02
8.09054822e-02 8.09358358e-01 -2.47183084e-01 -8.32790196e-01
6.20661318e-01 -2.17831686e-01 -6.59356594e-01 2.50946492e-01
4.58357185e-01 4.94968921e-01 -1.13617770e-01 -4.14683193e-01
-3.08457837e-02 9.30715129e-02 -1.09175090e-02 -1.31036460e-01
1.12588167e+00 -6.42397702e-01 -5.08253574e-01 4.85140204e-01
1.16573358e+00 -9.28049535e-02 -3.90244275e-01 -4.39420044e-02
2.40545526e-01 -4.68026906e-01 -3.01414490e-01 -1.02424324e+00
-9.38988209e-01 8.39062035e-01 3.29342008e-01 6.54405296e-01
1.28619933e+00 1.01942522e-02 1.40018249e+00 7.25119770e-01
4.61792737e-01 -1.91382432e+00 -9.32282023e-03 5.70286334e-01
6.82252407e-01 -1.40880919e+00 -7.39660859e-01 -6.36236966e-01
-1.16777098e+00 1.12969482e+00 7.94813573e-01 2.74059802e-01
9.50648487e-01 -1.65629461e-01 3.79040748e-01 -1.79803357e-01
-8.57357144e-01 -5.68452943e-03 -3.64893377e-01 3.35114807e-01
7.33634770e-01 2.44646221e-01 -9.45302844e-01 1.77239120e+00
-1.93619564e-01 6.63993731e-02 1.08118996e-01 9.71930981e-01
-8.89884889e-01 -1.08984256e+00 -2.83906549e-01 3.59523743e-01
-6.90247178e-01 1.38213307e-01 -5.14619768e-01 4.91321832e-01
5.16967997e-02 1.71729088e+00 -4.37715352e-02 -6.41701400e-01
3.79133075e-01 5.43203652e-01 -1.04670398e-01 1.74489785e-02
-1.10905766e+00 2.48149812e-01 4.04806912e-01 -3.78563464e-01
-3.23860198e-01 -3.71591538e-01 -1.71519530e+00 -5.90174794e-01
-6.60479784e-01 9.45096374e-01 1.02383924e+00 9.44958389e-01
2.65362054e-01 -2.93822270e-02 1.01122439e+00 -1.78414106e-01
-4.10524309e-01 -1.07780683e+00 -1.06970012e+00 6.78046584e-01
-5.97651720e-01 -2.21493959e-01 -3.07611614e-01 -9.85870585e-02] | [12.934101104736328, 6.179238319396973] |
1b344a7e-79bf-4c46-b0ee-3e5b34abb017 | mixed-reality-depth-contour-occlusion-using | 2203.02300 | null | https://arxiv.org/abs/2203.02300v1 | https://arxiv.org/pdf/2203.02300v1.pdf | Mixed Reality Depth Contour Occlusion Using Binocular Similarity Matching and Three-dimensional Contour Optimisation | Mixed reality applications often require virtual objects that are partly occluded by real objects. However, previous research and commercial products have limitations in terms of performance and efficiency. To address these challenges, we propose a novel depth contour occlusion (DCO) algorithm. The proposed method is based on the sensitivity of contour occlusion and a binocular stereoscopic vision device. In this method, a depth contour map is combined with a sparse depth map obtained from a two-stage adaptive filter area stereo matching algorithm and the depth contour information of the objects extracted by a digital image stabilisation optical flow method. We also propose a quadratic optimisation model with three constraints to generate an accurate dense map of the depth contour for high-quality real-virtual occlusion. The whole process is accelerated by GPU. To evaluate the effectiveness of the algorithm, we demonstrate a time con-sumption statistical analysis for each stage of the DCO algorithm execution. To verify the relia-bility of the real-virtual occlusion effect, we conduct an experimental analysis on single-sided, enclosed, and complex occlusions; subsequently, we compare it with the occlusion method without quadratic optimisation. With our GPU implementation for real-time DCO, the evaluation indicates that applying the presented DCO algorithm can enhance the real-time performance and the visual quality of real-virtual occlusion. | ['Dingguo Yu', 'Youbing Zhao', 'Haoxiang Zhang', 'Fan Zhang', 'Naye Ji'] | 2022-03-04 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 3.05865437e-01 -1.85582846e-01 3.32792848e-01 -1.07400060e-01
-1.81793824e-01 -3.25985968e-01 4.67756033e-01 -1.10910945e-01
-4.71901834e-01 7.32065797e-01 6.30571023e-02 -2.20089659e-01
6.99818358e-02 -8.51408660e-01 -4.20366317e-01 -5.81791818e-01
1.80483535e-01 2.31873289e-01 5.53477645e-01 -3.66582833e-02
4.33198959e-01 6.30612493e-01 -1.94325328e+00 1.21819355e-01
1.28079844e+00 1.11917448e+00 3.60108346e-01 7.08331168e-01
-1.45349428e-01 5.01680255e-01 -5.25740147e-01 -3.48802716e-01
6.19638979e-01 -2.31825501e-01 -1.49001986e-01 2.20906362e-01
6.18847370e-01 -6.99881673e-01 -3.64592016e-01 9.76967335e-01
7.75985003e-01 9.79846418e-02 4.08886135e-01 -1.14542937e+00
9.05174240e-02 -5.21711588e-01 -7.47159898e-01 1.39961019e-01
7.68038988e-01 3.55786890e-01 5.31272888e-01 -8.66756320e-01
9.80730236e-01 1.19096231e+00 3.28185350e-01 1.44317195e-01
-1.20202923e+00 -5.96117437e-01 3.24893780e-02 1.58769339e-01
-1.30933356e+00 -2.46831894e-01 9.09586430e-01 -6.11974001e-01
6.00213349e-01 4.54900444e-01 1.01711619e+00 2.44105816e-01
5.50845027e-01 5.45001447e-01 1.24886644e+00 -3.99114490e-01
1.99872717e-01 2.49752253e-01 4.73302901e-02 6.31579220e-01
3.68838489e-01 6.48555279e-01 -4.18538898e-01 -7.37064630e-02
9.94326711e-01 -3.66883397e-01 -6.85707986e-01 -6.70399189e-01
-8.31058323e-01 5.64835131e-01 3.40508729e-01 5.79242483e-02
-1.98467761e-01 -6.61659539e-02 2.77161688e-01 8.27457383e-02
5.80776393e-01 -5.62463105e-02 -5.62910214e-02 -7.13532269e-02
-8.35904002e-01 2.76759744e-01 7.67372727e-01 7.52028346e-01
8.49471867e-01 -1.39935396e-03 -1.27408028e-01 4.08474296e-01
5.42782009e-01 5.93155622e-01 3.40139568e-01 -9.58667517e-01
4.38179731e-01 5.88208258e-01 2.31066093e-01 -1.24429631e+00
-3.30532759e-01 -3.61808985e-01 -6.03493750e-01 1.02947521e+00
5.14631152e-01 6.80594370e-02 -7.63709247e-01 1.20084238e+00
8.56708229e-01 3.32952797e-01 -5.23824506e-02 1.28923547e+00
1.00674951e+00 6.48282588e-01 -2.57100463e-01 -6.23477519e-01
1.11954296e+00 -8.90418589e-01 -1.15264094e+00 -1.79421902e-02
4.28932041e-01 -1.09787297e+00 7.44668245e-01 5.48935294e-01
-1.09733617e+00 -7.53788650e-01 -1.23442698e+00 -3.53051335e-01
1.39377922e-01 -2.11967621e-02 6.49200916e-01 9.32447553e-01
-8.03373992e-01 4.58578020e-01 -6.39052868e-01 1.36626512e-01
1.98005691e-01 4.18915063e-01 -1.58818364e-01 -1.70344815e-01
-8.14111233e-01 7.42862582e-01 1.76206619e-01 3.02650541e-01
-3.16558331e-01 -7.28959680e-01 -8.59061062e-01 -2.72711390e-03
1.09826490e-01 -7.87570417e-01 7.11968124e-01 -1.06571400e+00
-1.66763091e+00 8.92643392e-01 -3.69679660e-01 -7.48136565e-02
8.75646114e-01 -2.25471199e-01 -6.72463849e-02 3.90398413e-01
-3.27791944e-02 4.46076930e-01 7.19259679e-01 -1.26562703e+00
-7.48963654e-01 -4.36151445e-01 1.68215465e-02 4.62422103e-01
1.38250589e-01 -1.41198292e-01 -5.79500914e-01 -4.76210862e-01
4.56778407e-01 -6.15397036e-01 -1.70841575e-01 7.08596230e-01
-3.30143375e-03 2.28718355e-01 8.39529514e-01 -7.21415401e-01
1.19818389e+00 -2.36318231e+00 -1.13217458e-01 2.06022605e-01
1.93273515e-01 3.47637683e-01 1.98355421e-01 -7.67686889e-02
2.20657117e-03 -5.61608493e-01 -1.18621543e-01 -5.91557086e-01
-3.21974635e-01 9.83301103e-02 -1.71119228e-01 8.02837133e-01
-9.33079720e-02 6.84700012e-01 -7.97941864e-01 -8.92734945e-01
8.32369268e-01 7.13297129e-01 -6.52772844e-01 4.27451074e-01
-2.17514392e-02 6.91449106e-01 -4.82790917e-02 5.48354924e-01
1.19897616e+00 2.13880137e-01 -9.60940421e-02 -3.26238573e-01
-4.86256808e-01 -1.21602036e-01 -1.72157407e+00 1.61899841e+00
-5.36799312e-01 8.79489303e-01 2.21636027e-01 -3.41719657e-01
1.06132269e+00 2.47570083e-01 5.74858069e-01 -1.24926221e+00
3.88696998e-01 5.27385652e-01 -1.77122489e-01 -5.19858241e-01
5.55752873e-01 -6.84349006e-03 6.54454827e-01 1.34340897e-01
-4.88072425e-01 -5.48442185e-01 2.37680506e-02 3.70203853e-02
6.18513823e-01 4.72369343e-01 1.86977476e-01 -2.80927777e-01
8.07220340e-01 -4.74203974e-02 6.66842461e-01 3.17938060e-01
-3.92819583e-01 9.17423725e-01 2.68736660e-01 -4.93966430e-01
-9.93412316e-01 -9.18434620e-01 -5.09171605e-01 1.45862550e-01
8.38381946e-01 -1.98959261e-01 -5.09208560e-01 -3.07249371e-02
-7.98774436e-02 3.51968229e-01 -2.69552410e-01 2.21951038e-01
-7.74435639e-01 -6.17874324e-01 -3.30345631e-01 1.04869798e-01
6.67544246e-01 -9.08451676e-01 -1.02131820e+00 3.10550064e-01
-2.02325478e-01 -1.22420299e+00 -4.19205070e-01 -4.12708014e-01
-9.79570270e-01 -1.06484842e+00 -6.07821941e-01 -7.40475893e-01
5.66721022e-01 5.45005500e-01 9.05142367e-01 1.81094140e-01
-6.51622415e-01 1.72392935e-01 -7.16850087e-02 1.95269641e-02
-6.62184432e-02 -5.81786990e-01 -3.64544153e-01 3.14293891e-01
-1.26552373e-01 -6.52729452e-01 -1.07049823e+00 5.75723052e-01
-8.66997302e-01 3.83315712e-01 2.48796895e-01 5.41408360e-01
5.36076963e-01 -3.21428478e-02 -1.08288616e-01 -3.97529483e-01
2.39242435e-01 6.14434853e-03 -1.19662941e+00 -1.61664963e-01
-4.17642474e-01 -1.87912270e-01 1.28077015e-01 -5.09709418e-01
-1.27018595e+00 4.11017150e-01 -1.08904645e-01 -5.06796181e-01
2.49368131e-01 1.32584646e-01 -2.70046622e-01 -3.98323268e-01
4.98200446e-01 -1.78846233e-02 1.85682684e-01 -2.41197571e-01
2.02771798e-01 7.00722933e-01 6.37782037e-01 -2.94679999e-01
6.88612580e-01 1.07325590e+00 1.98630914e-01 -8.86116445e-01
-4.53303933e-01 -6.38773978e-01 -4.55498964e-01 -6.67189658e-01
9.23804462e-01 -8.65985990e-01 -1.09644616e+00 5.98796725e-01
-1.43018556e+00 -2.61986136e-01 -2.32043847e-01 8.49031448e-01
-6.77418232e-01 8.07454884e-01 -4.01863992e-01 -1.01038980e+00
-2.86131650e-01 -1.38213027e+00 1.07887709e+00 2.69960135e-01
1.45771906e-01 -8.28539848e-01 1.01434886e-01 3.51282805e-01
2.72846073e-01 5.11430979e-01 2.75196373e-01 4.45412189e-01
-1.16123402e+00 -2.60562748e-02 -5.07937968e-01 1.56617075e-01
-1.89263791e-01 2.00970694e-01 -1.05350542e+00 -3.30687553e-01
3.81908059e-01 1.75191864e-01 4.55367357e-01 6.86738670e-01
7.04167843e-01 1.48553520e-01 -2.18614712e-01 1.00246429e+00
1.86221504e+00 3.05439055e-01 1.07089317e+00 3.55812341e-01
5.59215188e-01 7.56650507e-01 1.09167802e+00 4.95147020e-01
2.78835949e-02 1.18502247e+00 6.37773335e-01 -4.16880459e-01
-4.92754996e-01 1.29139990e-01 6.61915988e-02 7.04031050e-01
-1.55125469e-01 -1.24249689e-01 -5.54745674e-01 3.66296798e-01
-1.71604669e+00 -7.80100763e-01 -8.07806015e-01 2.51296449e+00
5.57822704e-01 3.27743977e-01 -3.45031857e-01 3.68637204e-01
7.42864788e-01 5.87870292e-02 -1.89513862e-01 -5.32140195e-01
-2.45213538e-01 2.93683887e-01 3.55339885e-01 1.04225731e+00
-8.91447127e-01 5.45413613e-01 5.87743425e+00 1.00585675e+00
-1.25437236e+00 1.18348718e-01 2.90890932e-01 -9.78732482e-02
-3.36883903e-01 1.63999975e-01 -6.69690907e-01 5.05157590e-01
3.11073691e-01 -2.15056419e-01 2.08432451e-01 5.78350008e-01
3.87167156e-01 -7.85875320e-01 -6.81810200e-01 1.34996712e+00
1.34963423e-01 -9.72494841e-01 -3.60619366e-01 3.04189086e-01
8.22423875e-01 -2.73432791e-01 -1.22609541e-01 -2.54155874e-01
-3.53609592e-01 -6.74411058e-01 6.57907307e-01 4.35971022e-01
7.62481153e-01 -4.87386286e-01 7.24776924e-01 1.52979076e-01
-1.30256236e+00 1.22948438e-01 -2.78252453e-01 -3.20221424e-01
6.31519675e-01 8.07752430e-01 -2.26803750e-01 6.49975181e-01
3.35655719e-01 4.38809544e-01 -4.09528822e-01 1.58003032e+00
-1.85886383e-01 -5.09852506e-02 -4.27981257e-01 1.66345716e-01
-1.38661072e-01 -6.17731571e-01 7.18065441e-01 8.62296999e-01
2.37763915e-02 3.21617812e-01 -5.01099415e-02 8.12373161e-01
6.39135420e-01 4.45223391e-01 -4.41861212e-01 6.73321366e-01
5.51197678e-03 1.04099667e+00 -7.76082695e-01 -4.28390443e-01
-5.68380892e-01 9.44271684e-01 -9.86964256e-02 3.42569828e-01
-1.01126671e+00 -2.79451847e-01 4.83953238e-01 2.97377288e-01
6.52814209e-02 -3.64308298e-01 -7.57030427e-01 -1.16291308e+00
3.28601032e-01 -3.96406084e-01 -1.00067223e-03 -1.08860075e+00
-6.14433289e-01 6.02177858e-01 -2.21179262e-01 -1.60986435e+00
5.25070354e-02 -4.05344963e-01 -3.85587603e-01 1.08749068e+00
-1.64662063e+00 -7.37955511e-01 -8.54019225e-01 5.40640771e-01
4.19490427e-01 2.94770300e-01 4.31371778e-01 6.68485522e-01
-2.31661946e-01 1.96875781e-01 1.23647861e-02 -3.07723135e-01
4.27222937e-01 -6.70080245e-01 1.50324449e-01 9.39493954e-01
-3.14791262e-01 1.52314350e-01 8.32502067e-01 -7.94173360e-01
-1.06770480e+00 -5.71078420e-01 7.94103861e-01 -1.38784379e-01
1.52421966e-01 -4.12584633e-01 -9.75822330e-01 2.03648239e-01
-6.09595068e-02 2.95776725e-01 8.70617852e-02 -5.84033370e-01
1.35507569e-01 -1.18756764e-01 -1.29178417e+00 5.52082241e-01
1.08146775e+00 -2.60928780e-01 -4.54918414e-01 9.55234542e-02
7.92205215e-01 -8.91162276e-01 -6.77495837e-01 6.12399578e-01
8.72775435e-01 -1.44804394e+00 1.04100931e+00 2.70388842e-01
3.64494056e-01 -5.63193679e-01 -2.24243682e-02 -6.52059615e-01
3.47271822e-02 -6.31685853e-01 -4.97376174e-02 8.73637438e-01
-1.44623205e-01 -8.62672985e-01 9.36218262e-01 7.21509814e-01
-1.68021381e-01 -6.47110283e-01 -1.25800252e+00 -6.72851801e-01
-5.94303131e-01 -4.66001064e-01 2.17974573e-01 6.45788074e-01
-2.53742397e-01 -5.73719554e-02 -1.18768781e-01 2.83595026e-01
7.45167375e-01 3.67955655e-01 1.09643018e+00 -1.16368449e+00
-2.58661568e-01 -1.89151362e-01 -9.06490028e-01 -1.33915305e+00
-2.46228844e-01 -2.95303285e-01 -1.85170665e-01 -1.48403943e+00
-5.81418052e-02 -5.41193247e-01 2.87559062e-01 -4.27702665e-01
-2.73751706e-01 4.82531607e-01 2.34145060e-01 2.87601382e-01
-1.14486068e-01 7.60744274e-01 1.82090628e+00 1.23429917e-01
-6.34041607e-01 5.57389185e-02 1.46136925e-01 5.34258425e-01
3.22048008e-01 -2.92492479e-01 -4.19782639e-01 -2.93896616e-01
1.07857443e-01 3.70056152e-01 4.13531244e-01 -1.12922144e+00
1.73954174e-01 5.94107658e-02 1.34644732e-01 -9.45591807e-01
7.06549466e-01 -1.02301025e+00 4.20165926e-01 8.35402012e-01
4.62357014e-01 -3.06408077e-01 2.68662930e-01 3.36451262e-01
-2.81383902e-01 -2.48137400e-01 8.87088180e-01 2.28657678e-01
-5.79776943e-01 1.29521370e-01 -3.76057513e-02 -2.22571179e-01
1.21910405e+00 -8.42119038e-01 -2.79701322e-01 -3.54726285e-01
-6.85618341e-01 2.21462250e-02 7.01291203e-01 1.81063920e-01
8.13888252e-01 -1.20413911e+00 -6.34245932e-01 6.23150051e-01
-1.08688094e-01 1.41707450e-01 3.66096228e-01 8.79449964e-01
-1.28675532e+00 3.70455384e-01 -2.91387141e-01 -9.91141677e-01
-1.38553333e+00 8.16055059e-01 4.51384813e-01 -3.55847366e-02
-7.97441542e-01 4.38830316e-01 5.90441346e-01 -8.30008239e-02
2.46677667e-01 -2.73693800e-01 -1.99608967e-01 -9.86540541e-02
5.05954504e-01 5.73102415e-01 1.06656305e-01 -6.02137089e-01
-4.31381352e-02 1.13698959e+00 4.51990813e-01 -4.80634838e-01
7.88363695e-01 -3.41547489e-01 -6.29958212e-02 3.98171470e-02
1.25485921e+00 3.03261787e-01 -1.43207073e+00 1.07867971e-01
-6.42858982e-01 -1.29869986e+00 2.20628455e-01 -3.46565425e-01
-1.24049675e+00 8.89647484e-01 9.97529566e-01 -1.58275634e-01
1.27562904e+00 -5.25116742e-01 6.14667177e-01 -5.62612414e-01
5.34369886e-01 -7.97989845e-01 -1.64687932e-02 4.14803252e-02
8.39361370e-01 -1.13537776e+00 3.31691325e-01 -1.26251268e+00
-2.26036474e-01 1.01596189e+00 6.54700816e-01 -1.13095686e-01
7.47857332e-01 2.05131352e-01 1.71523958e-01 -1.72917128e-01
-3.12461942e-01 -3.96771163e-01 2.65348613e-01 5.05935669e-01
5.38242571e-02 -2.32987911e-01 -9.39725161e-01 -2.83632696e-01
-1.07132867e-01 1.23758838e-01 5.95011711e-01 8.50110233e-01
-4.52120095e-01 -7.87142098e-01 -6.28787398e-01 -6.36166707e-02
-1.00238360e-01 1.25774726e-01 2.62216181e-01 6.49042547e-01
2.43568480e-01 1.02443516e+00 3.31720173e-01 2.20695697e-02
4.96434689e-01 -5.70616663e-01 5.42405009e-01 -2.42890745e-01
-4.68209833e-01 3.24678391e-01 -8.32981020e-02 -9.75362480e-01
-4.87592876e-01 -4.97757822e-01 -1.06832349e+00 -3.55002224e-01
-5.82975268e-01 -2.08267957e-01 1.05205476e+00 5.29462993e-01
1.59222543e-01 3.96900594e-01 6.73153222e-01 -1.01422942e+00
1.78857520e-01 -4.98324513e-01 -3.85333359e-01 5.67042232e-01
2.77993530e-01 -7.84937143e-01 -7.30695009e-01 -2.08475783e-01] | [9.330720901489258, -2.481557607650757] |
5f7cfb45-6237-4307-bccb-b7b462d4386d | distributed-multi-agent-deep-q-learning-for | 2304.01210 | null | https://arxiv.org/abs/2304.01210v1 | https://arxiv.org/pdf/2304.01210v1.pdf | Distributed Multi-Agent Deep Q-Learning for Fast Roaming in IEEE 802.11ax Wi-Fi Systems | The innovation of Wi-Fi 6, IEEE 802.11ax, was be approved as the next sixth-generation (6G) technology of wireless local area networks (WLANs) by improving the fundamental performance of latency, throughput, and so on. The main technical feature of orthogonal frequency division multiple access (OFDMA) supports multi-users to transmit respective data concurrently via the corresponding access points (APs). However, the conventional IEEE 802.11 protocol for Wi-Fi roaming selects the target AP only depending on received signal strength indication (RSSI) which is obtained by the received Response frame from the APs. In the long term, it may lead to congestion in a single channel under the scenarios of dense users further increasing the association delay and packet drop rate, even reducing the quality of service (QoS) of the overall system. In this paper, we propose a multi-agent deep Q-learning for fast roaming (MADAR) algorithm to effectively minimize the latency during the station roaming for Smart Warehouse in Wi-Fi 6 system. The MADAR algorithm considers not only RSSI but also channel state information (CSI), and through online neural network learning and weighting adjustments to maximize the reward of the action selected from Epsilon-Greedy. Compared to existing benchmark methods, the MADAR algorithm has been demonstrated for improved roaming latency by analyzing the simulation result and realistic dataset. | ['Kai-Ten Feng', 'Li-Hsiang Shen', 'Ting-Hui Wang'] | 2023-03-25 | null | null | null | null | ['q-learning'] | ['methodology'] | [ 5.67856506e-02 1.41913474e-01 -6.45705879e-01 -1.38885811e-01
-4.77047771e-01 -1.53837949e-01 -2.75034338e-01 -2.16433525e-01
-4.41053092e-01 1.14421916e+00 -1.55170202e-01 -5.72721899e-01
-1.00403237e+00 -1.19560754e+00 -3.73693079e-01 -1.09524119e+00
-8.78895879e-01 1.58650964e-01 -2.31340504e-03 -4.01554883e-01
1.23395458e-01 4.10682619e-01 -1.06222546e+00 -1.80238426e-01
7.45672643e-01 1.59912264e+00 5.47938049e-01 5.28551757e-01
-2.74355471e-01 5.45510113e-01 -7.64720201e-01 1.41357064e-01
1.62093908e-01 -2.04078689e-01 -5.64251602e-01 -4.86557811e-01
-5.80085218e-01 -5.72890759e-01 -3.74793291e-01 3.54432076e-01
9.14948940e-01 2.11000040e-01 7.18225241e-02 -1.51195252e+00
1.73822120e-01 1.02985811e+00 -7.58454680e-01 2.98580199e-01
2.39179581e-01 -3.29048127e-01 7.35824406e-01 -2.77162582e-01
1.82350874e-01 1.00899661e+00 4.00231868e-01 1.42099604e-01
-4.03293759e-01 -1.02296817e+00 1.18284032e-01 3.17805260e-01
-1.40960121e+00 -2.69099325e-01 3.82102996e-01 5.10837495e-01
4.53220010e-01 2.85162926e-01 5.10249138e-01 5.44604719e-01
6.68202043e-01 7.49861419e-01 1.68443635e-01 -4.72822100e-01
4.79906589e-01 -4.03372347e-01 -2.40212515e-01 4.10606951e-01
3.60116005e-01 8.89640003e-02 -6.79674223e-02 -1.99843258e-01
9.59806919e-01 1.14170007e-01 -8.23611021e-03 -4.10228968e-01
-1.38186479e+00 6.94305122e-01 3.71380925e-01 5.39286792e-01
-1.03162491e+00 3.34745318e-01 1.42519195e-02 6.72902703e-01
-3.82821649e-01 2.66434997e-01 -6.88801050e-01 -1.21323101e-01
-3.58964026e-01 6.43581077e-02 7.83318281e-01 1.21362817e+00
3.34607363e-01 2.72280812e-01 -2.13019967e-01 4.64066684e-01
6.57271445e-01 9.08576250e-01 3.88713062e-01 -1.33289361e+00
5.32654941e-01 -2.06710458e-01 5.92982769e-01 -6.92152500e-01
-9.02519345e-01 -1.26285768e+00 -8.18206072e-01 -6.06100261e-02
4.38042641e-01 -8.85887980e-01 -2.86063910e-01 1.60984278e+00
9.25357938e-02 4.16170090e-01 3.12118053e-01 4.65849817e-01
2.19775304e-01 8.62278581e-01 1.09856255e-01 -5.94169140e-01
9.42316830e-01 -5.96916616e-01 -1.10926056e+00 4.15453017e-02
8.11408997e-01 -6.87972128e-01 3.24242264e-01 5.20762980e-01
-1.11214101e+00 -6.02403104e-01 -1.18376267e+00 1.07695544e+00
-9.45100412e-02 -1.31258681e-01 7.64923930e-01 1.10173500e+00
-5.59421420e-01 1.95801720e-01 -4.56970006e-01 -2.52404690e-01
4.34263170e-01 9.25566792e-01 3.79733950e-01 -2.37605870e-01
-1.56942451e+00 2.36216143e-01 4.60693896e-01 1.00773461e-01
-3.96254748e-01 -4.94756699e-01 -2.88851798e-01 3.64141077e-01
7.05760539e-01 -3.63071203e-01 1.22214580e+00 -6.79948866e-01
-1.85411429e+00 -4.59863722e-01 1.54242769e-01 -4.83392775e-01
2.32904285e-01 -1.50036678e-01 -9.77279365e-01 -1.14607401e-01
-1.42081365e-01 3.08774859e-01 1.42581448e-01 -1.04715383e+00
-1.46025538e+00 -1.30678967e-01 5.18975973e-01 2.55157053e-01
5.00856340e-02 -4.91974533e-01 1.53053388e-01 -2.87118167e-01
1.53436795e-01 -6.66787088e-01 -6.23396873e-01 -3.56630296e-01
1.75134391e-01 -3.58278304e-01 7.04884589e-01 -2.22623274e-01
1.51426041e+00 -2.22149205e+00 -3.79955649e-01 7.17595696e-01
-4.06652331e-01 5.38448170e-02 -2.01808110e-01 2.57432044e-01
3.84973228e-01 -2.39755556e-01 3.34740102e-01 5.03868163e-01
-9.44026411e-02 2.71171242e-01 -2.41913535e-02 1.56553119e-01
-4.96795237e-01 4.13403690e-01 -1.13003838e+00 -1.33660033e-01
3.09782743e-01 5.89972921e-02 -3.29600513e-01 -1.52259227e-02
3.04245770e-01 4.80709344e-01 -1.06288707e+00 4.39330220e-01
1.12052917e+00 -5.16314320e-02 1.60835236e-01 -1.36899397e-01
-3.43636364e-01 -2.88329720e-01 -1.64657176e+00 1.64681840e+00
-9.10959423e-01 -1.74006864e-01 8.30161050e-02 -1.34300399e+00
8.97117376e-01 6.04741514e-01 1.27190542e+00 -1.32722318e+00
2.18409792e-01 4.87913191e-01 1.37620926e-01 -6.00994051e-01
-3.42774503e-02 4.41461504e-01 -2.08482146e-01 4.07501936e-01
-1.86814919e-01 8.17974150e-01 9.60566476e-02 -3.34267281e-02
1.04342651e+00 3.24167795e-02 3.11554372e-01 -4.82290499e-02
8.42217565e-01 -4.60390925e-01 6.23823106e-01 1.35379899e+00
-3.36716175e-01 -2.05605581e-01 8.23911801e-02 -1.86914936e-01
-4.00456935e-01 -1.27916741e+00 -1.45170346e-01 1.23502851e+00
6.54868722e-01 4.16819334e-01 -1.28621295e-01 -5.54349124e-01
1.03080370e-01 1.05607176e+00 -2.75526438e-02 -2.05970272e-01
-5.35015464e-01 -6.87504590e-01 3.60256553e-01 -2.93249972e-02
7.99596429e-01 -1.00291812e+00 -6.34195805e-01 9.29811954e-01
-2.49978513e-01 -1.05156589e+00 -3.23180691e-03 2.84700215e-01
-4.46829885e-01 -5.86546183e-01 -9.51735437e-01 -7.19862342e-01
2.55354885e-02 7.94067919e-01 3.61044258e-01 -1.49478868e-01
2.44352117e-01 4.71040547e-01 -4.38961148e-01 -4.71275181e-01
7.24022835e-02 3.64797086e-01 -4.31934968e-02 1.51127115e-01
2.04859301e-01 -3.24259192e-01 -8.68239284e-01 7.58162975e-01
-5.65649927e-01 -4.86484855e-01 9.43382442e-01 3.11984807e-01
3.92947763e-01 5.72030246e-01 1.58767343e+00 -4.11352903e-01
3.06553960e-01 -9.65030611e-01 -3.62219632e-01 2.56338269e-01
-3.47439587e-01 -1.36776134e-01 5.69130301e-01 4.32338528e-02
-1.30311656e+00 -1.39178202e-01 -3.16455364e-01 7.00220525e-01
5.54303965e-03 7.13993251e-01 -3.89547825e-01 -2.03180596e-01
1.58995137e-01 -5.74713796e-02 -9.61547494e-02 -2.09588725e-02
9.60659608e-02 9.84961092e-01 1.79009080e-01 2.35722084e-02
5.33737421e-01 6.05577409e-01 3.35687310e-01 -8.95328283e-01
-7.04033196e-01 -5.88473022e-01 -1.02010652e-01 -4.09437537e-01
5.27599573e-01 -9.52409267e-01 -1.07035995e+00 1.64468393e-01
-8.00974488e-01 -6.94218129e-02 2.11606711e-01 1.16116178e+00
-4.87752110e-01 2.47999564e-01 -1.11944206e-01 -8.64795387e-01
-1.77307129e-01 -7.93208361e-01 2.23595440e-01 5.74756503e-01
1.50816008e-01 -5.95545709e-01 -4.74403411e-01 1.52270481e-01
1.00917745e+00 1.52820334e-01 5.18202841e-01 -2.32685179e-01
-6.69836462e-01 -3.90057951e-01 -1.18841842e-01 -2.51155436e-01
2.20429718e-01 -5.91350555e-01 -4.33919013e-01 -3.35804313e-01
-2.70393699e-01 1.59103379e-01 1.91539779e-01 1.10689819e+00
1.30984616e+00 5.23199551e-02 -5.97857237e-01 4.30342436e-01
1.43731630e+00 9.90858614e-01 8.90212536e-01 5.77096224e-01
1.48947254e-01 2.52888829e-01 1.29951990e+00 1.02124476e+00
2.17037871e-01 5.57773888e-01 9.04971600e-01 -1.42506305e-02
5.38312376e-01 3.09886247e-01 3.48794878e-01 2.46698797e-01
-1.25487551e-01 -9.25146103e-01 -1.32007107e-01 -1.10783391e-01
-1.95067239e+00 -1.10522401e+00 -8.21999609e-02 2.46436787e+00
-4.58938330e-02 3.40339869e-01 4.06651422e-02 3.33088577e-01
6.40299141e-01 -1.50985286e-01 -5.54172575e-01 -1.44030869e-01
-8.36195573e-02 3.17718446e-01 1.40692306e+00 5.77541471e-01
-8.19564939e-01 3.05410415e-01 4.50749588e+00 1.07513976e+00
-9.08093214e-01 4.15210128e-02 3.18309784e-01 5.69128096e-02
-2.44504794e-01 -4.26511049e-01 -5.43514252e-01 4.39173877e-01
9.63501215e-01 -2.37525865e-01 5.96950650e-01 5.77060103e-01
8.01930547e-01 -4.47392374e-01 -3.56940240e-01 7.45677710e-01
-5.69355726e-01 -1.14291906e+00 -2.40679219e-01 1.54553980e-01
4.78449315e-01 -1.27708279e-02 -6.87123388e-02 5.25895059e-01
4.34543155e-02 -5.59097230e-01 3.27817142e-01 6.44635737e-01
3.39845300e-01 -1.45222604e+00 1.25806201e+00 7.53467306e-02
-1.41125774e+00 -8.49200666e-01 -1.81189030e-01 1.33365810e-01
6.01264715e-01 4.73480642e-01 -3.38611782e-01 1.04672730e+00
5.61991930e-01 1.67974740e-01 2.93707609e-01 1.43012440e+00
4.28454071e-01 5.17249167e-01 -5.55991411e-01 -2.41625950e-01
5.17293096e-01 8.55647922e-02 3.36615801e-01 6.25135005e-01
8.69834900e-01 1.68022782e-01 2.30814040e-01 -2.26506572e-02
2.90071517e-01 1.01575747e-01 -1.35724202e-01 5.05045831e-01
9.20927346e-01 9.79834378e-01 -7.60236859e-01 -2.35770140e-02
-6.45324349e-01 4.69308376e-01 -6.41271830e-01 7.18037009e-01
-9.15455282e-01 -1.22954714e+00 2.91807711e-01 1.16653508e-02
5.21372676e-01 -4.43295360e-01 -1.46629974e-01 -1.93674996e-01
-4.06264096e-01 -3.02313149e-01 5.47693789e-01 -3.19660038e-01
-7.31517971e-01 -1.66958809e-01 -3.04772496e-01 -1.46295512e+00
-1.08434163e-01 -2.48871446e-01 -3.25086415e-01 3.99605781e-01
-1.96352792e+00 -3.92732412e-01 -4.19193596e-01 5.73707700e-01
3.94392759e-01 -3.37425709e-01 9.37215984e-01 9.51394022e-01
-3.41794699e-01 7.96506643e-01 8.30184758e-01 -2.87305504e-01
4.83124107e-01 -7.39664018e-01 -3.84066582e-01 1.32825300e-01
-6.19938135e-01 2.97186822e-02 5.75582266e-01 -8.70705768e-02
-1.54772329e+00 -6.98520660e-01 4.55247253e-01 6.86428785e-01
2.64178991e-01 2.06228569e-01 -1.76647455e-01 2.78394341e-01
7.75464922e-02 -2.33711079e-01 6.97314501e-01 -2.75700718e-01
8.82932961e-01 -7.19549060e-01 -1.28841031e+00 4.14871275e-01
9.11128283e-01 5.54187357e-01 4.11188245e-01 6.77166879e-02
5.81175685e-01 -6.23942055e-02 -8.79763842e-01 1.23234361e-01
1.00814676e+00 -6.81783378e-01 1.15484345e+00 -1.66439518e-01
-4.85598028e-01 -3.55729759e-01 -3.89685303e-01 -1.11548364e+00
-7.19236255e-01 -7.45545089e-01 -2.14734405e-01 7.73845255e-01
4.22383904e-01 -6.48467243e-01 7.61337996e-01 -1.70438245e-01
9.07852203e-02 -4.77917671e-01 -1.24509060e+00 -8.24290395e-01
-5.61747015e-01 -3.52107346e-01 1.10606253e+00 5.43318748e-01
-9.28761140e-02 3.63887608e-01 -4.78845805e-01 5.12932897e-01
6.04125738e-01 -4.08209473e-01 6.40469372e-01 -1.17121708e+00
-1.89393952e-01 -2.27675363e-01 -6.09697960e-02 -1.47213960e+00
-3.38138819e-01 -4.54771340e-01 -1.70538053e-01 -1.61003220e+00
-8.58671546e-01 -1.07042599e+00 -1.05172455e+00 -1.41673135e-02
4.57432061e-01 -3.49093199e-01 -1.94694951e-01 -2.51486599e-01
-8.86966348e-01 3.97755802e-01 1.37867057e+00 8.38008076e-02
-5.51598310e-01 1.00237668e+00 -3.61290336e-01 3.54752004e-01
1.08858573e+00 -3.06864589e-01 -5.91294408e-01 -3.09735030e-01
3.33409607e-01 9.66118395e-01 -2.33187109e-01 -1.29731238e+00
1.03671297e-01 -5.58007121e-01 4.93313968e-01 -6.50524199e-01
-9.79235955e-03 -1.47959912e+00 -4.98832427e-02 9.93731380e-01
-3.09457898e-01 -3.36353630e-01 -1.70865446e-01 9.30212975e-01
3.55398983e-01 -2.07524210e-01 5.69597900e-01 1.77571341e-01
-9.46104884e-01 4.69860911e-01 -1.01718819e+00 -4.60925430e-01
1.23895073e+00 -2.98677921e-01 1.29880115e-01 -8.99640143e-01
-5.62515736e-01 6.67654753e-01 -7.55669594e-01 4.65320587e-01
4.71065998e-01 -1.29337335e+00 -5.21290898e-01 7.16891289e-02
-1.21236444e-01 -5.26671052e-01 5.85489392e-01 8.35010469e-01
-3.31040561e-01 5.95831156e-01 -5.29992104e-01 -2.35537231e-01
-6.95183635e-01 4.72670719e-02 4.79607522e-01 -2.99383700e-01
5.20297959e-02 4.02270854e-01 -3.14671129e-01 -2.96839513e-02
6.92445874e-01 -1.16586342e-01 -8.89087796e-01 -1.62718266e-01
5.37249327e-01 8.92406881e-01 -2.27858156e-01 -1.66429952e-01
-3.10170680e-01 2.88896143e-01 1.22845843e-01 -7.16163591e-02
1.07172370e+00 -9.69313145e-01 4.58570540e-01 -1.14621809e-02
8.43534708e-01 2.07274958e-01 -8.50402534e-01 -2.63622880e-01
5.58351353e-03 -3.68736088e-01 4.79194015e-01 -7.86735058e-01
-1.10899997e+00 9.10779834e-02 1.32220483e+00 5.09787500e-01
8.08106244e-01 -3.82929355e-01 1.30628026e+00 8.08431029e-01
9.89220083e-01 -1.17888069e+00 2.76667718e-02 3.49253744e-01
2.24817351e-01 -9.80559111e-01 -1.85814679e-01 -2.17282400e-01
-2.85232306e-01 1.49136889e+00 4.06644940e-01 6.20387197e-02
1.16478741e+00 2.41232608e-02 1.68630615e-01 1.50464326e-01
-1.67533517e-01 -2.69633442e-01 -6.65627122e-01 6.49446011e-01
3.52244645e-01 2.27380544e-01 -5.12428761e-01 5.14745474e-01
1.48195982e-01 3.25227499e-01 4.81756210e-01 1.09528208e+00
-1.07753301e+00 -1.12870896e+00 -1.94811568e-01 6.66273117e-01
-6.57100976e-01 6.26705408e-01 1.00511551e+00 5.46536326e-01
3.34638834e-01 1.42259657e+00 1.47041678e-01 -7.66893923e-02
3.96054476e-01 -7.20588386e-01 3.15752655e-01 1.77686632e-01
2.06506342e-01 3.79436836e-02 1.90911546e-01 -5.54002941e-01
-4.73560035e-01 -4.56432223e-01 -1.90259159e+00 -3.54550034e-01
-5.15973508e-01 5.18845737e-01 6.93021655e-01 1.29524267e+00
3.45714450e-01 9.32902753e-01 1.31859064e+00 -4.04220313e-01
-3.96516502e-01 -6.05155706e-01 -9.67754483e-01 -3.60680670e-01
2.89660275e-01 -6.73655927e-01 -1.17590621e-01 -7.58598447e-01] | [6.018355369567871, 1.5609800815582275] |
17db3e2b-6220-4d5e-b0fa-7df90b364e4a | knowrob-2-0-a-2nd-generation-knowledge | null | null | https://ieeexplore.ieee.org/abstract/document/8460964 | https://ieeexplore.ieee.org/abstract/document/8460964 | KnowRob 2.0 — A 2nd Generation Knowledge Processing Framework for Cognition-enabled Robotic Agents | Abstract— In this paper we present K NOW R OB 2, a second
generation knowledge representation and reasoning framework
for robotic agents. K NOW R OB 2 is an extension and partial
redesign of K NOW R OB , currently one of the most advanced
knowledge processing systems for robots that has enabled them
to successfully perform complex manipulation tasks such as
making pizza, conducting chemical experiments, and setting
tables. The knowledge base appears to be a conventional first-
order time interval logic knowledge base, but it exists to a large
part only virtually: many logical expressions are constructed on
demand from data structures of the control program, computed
through robotics algorithms including ones for motion planning
and solving inverse kinematics problems, and log data stored in
noSQL databases. Novel features and extensions of K NOW R OB 2
substantially increase the capabilities of robotic agents of
acquiring open-ended manipulation skills and competence,
reasoning about how to perform manipulation actions more
realistically, and acquiring commonsense knowledge. | ['Georg Bartels', 'Asil Kaan Bozcuo ̆glu', 'Mihai Pomarlan', 'Andrei Haidu', 'Daniel Beßler', 'Michael Beetz'] | 2018-05-21 | null | null | null | 2018-ieee-international-conference-on | ['motion-planning'] | ['robots'] | [-1.56903833e-01 5.57995677e-01 -5.04609227e-01 -7.22371489e-02
1.88322663e-01 -1.02463138e+00 3.93475384e-01 3.97664428e-01
-3.22847694e-01 1.18937230e+00 -3.38048965e-01 -5.19064546e-01
-8.79613400e-01 -1.16898811e+00 -9.19326782e-01 -9.07564014e-02
-3.16772938e-01 9.13917184e-01 5.04513979e-01 -7.74723530e-01
4.40936178e-01 1.14810979e+00 -1.75930929e+00 -1.97819714e-02
4.25717026e-01 9.43670392e-01 2.50351191e-01 4.34501618e-01
1.33763328e-01 1.49136209e+00 -2.88650751e-01 -2.78665386e-02
1.34651735e-01 1.38054490e-01 -1.26475537e+00 -5.64795315e-01
-4.33459371e-01 -4.57170814e-01 -3.82443845e-01 9.63468790e-01
-2.47461602e-01 4.84920084e-01 4.55372989e-01 -1.69584227e+00
-6.79628730e-01 9.85024095e-01 4.21214968e-01 -2.88060576e-01
7.79301405e-01 3.87452543e-01 4.16980803e-01 -1.52880296e-01
1.17027354e+00 1.45808184e+00 4.32643324e-01 4.26185250e-01
-7.67790139e-01 -3.63382906e-01 -1.63382560e-01 7.38977075e-01
-1.49762440e+00 -1.98452428e-01 1.90661281e-01 -4.33265567e-01
1.54299879e+00 1.13423094e-01 8.62241387e-01 4.72637862e-01
5.16468644e-01 3.22801024e-01 8.33400011e-01 -4.89723593e-01
5.30408382e-01 1.24502555e-01 -6.87368438e-02 9.09764171e-01
6.52959049e-01 2.47515649e-01 -5.56826830e-01 -1.10883325e-01
1.13840568e+00 -2.72717208e-01 2.04054099e-02 -6.47786021e-01
-1.38427126e+00 5.07346690e-01 3.57900172e-01 1.84044987e-01
-4.36429650e-01 6.28895104e-01 4.63228106e-01 3.78619820e-01
-7.35017061e-01 1.23468804e+00 -8.01712275e-01 -3.85291219e-01
2.58279562e-01 7.93101072e-01 1.19396722e+00 1.63785577e+00
7.42698848e-01 -7.32191429e-02 2.90280312e-01 1.40407100e-01
1.71793193e-01 2.98077136e-01 3.13530862e-01 -1.71897948e+00
-5.77032864e-02 8.52528811e-01 6.62320435e-01 -6.12099409e-01
-5.86023688e-01 4.61184710e-01 -1.62695795e-02 5.57718933e-01
3.25973421e-01 -3.12071759e-03 -6.63850188e-01 1.33444214e+00
3.58457237e-01 -4.23005432e-01 7.06540406e-01 5.56807458e-01
6.03339791e-01 5.73063493e-01 2.21405327e-01 -1.04006633e-01
1.31658041e+00 -4.57392186e-01 -7.21117139e-01 1.04771331e-01
6.76282048e-01 -1.95422754e-01 6.63212955e-01 9.89158452e-01
-1.24947953e+00 -1.46985576e-01 -1.39203334e+00 -5.30330539e-01
-1.15924764e+00 -2.40142286e-01 1.63284159e+00 2.12660864e-01
-7.66756296e-01 7.92511940e-01 -5.76986849e-01 -4.89747465e-01
3.22486252e-01 6.00445747e-01 -5.48191726e-01 -3.53518665e-01
-1.31940925e+00 1.78564703e+00 1.29107857e+00 -3.97800989e-02
-1.01120889e+00 -7.25580037e-01 -1.03279197e+00 -7.51802102e-02
1.03497112e+00 -5.30695915e-01 1.47152412e+00 1.50891230e-01
-1.87992191e+00 5.64281940e-01 5.04457057e-01 -5.59196293e-01
2.49519255e-02 -1.20479375e-01 -4.14135754e-01 1.69037268e-01
3.40186083e-03 8.06268811e-01 -1.06412666e-02 -8.97708058e-01
-5.25428712e-01 -5.32082736e-01 8.55714083e-01 6.82170466e-02
4.28039938e-01 8.53133737e-04 -7.47368112e-02 7.51645351e-03
1.46888137e-01 -9.53519464e-01 -3.97066116e-01 3.21851164e-01
-2.23191269e-02 -5.21481276e-01 4.68621939e-01 -2.05808133e-01
5.98805845e-01 -1.79242265e+00 1.96998879e-01 3.52120131e-01
-1.88865587e-01 3.61588523e-02 2.05964416e-01 7.25130498e-01
7.21115945e-03 -3.00317556e-02 2.31839851e-01 1.30229175e+00
4.75197762e-01 6.05186164e-01 -4.71390337e-01 -6.52417075e-03
2.20163092e-01 9.72880423e-01 -1.11547756e+00 -4.45153594e-01
3.38899076e-01 -2.88395733e-01 -4.30972427e-01 -1.80874690e-01
-1.11047029e+00 5.30875586e-02 -7.02880204e-01 6.67716444e-01
1.20329171e-01 6.09600097e-02 5.66387117e-01 1.62002340e-01
-4.09225225e-01 2.61063635e-01 -1.17473745e+00 2.04515433e+00
-2.65680134e-01 1.36710331e-01 -1.29357547e-01 -6.75722957e-01
8.79195631e-01 4.45567578e-01 4.07618344e-01 -3.60134900e-01
2.18507200e-01 1.69012904e-01 -8.71240795e-02 -8.64286244e-01
7.57613003e-01 -1.19877741e-01 -5.64731479e-01 1.86011434e-01
-3.29290666e-02 -1.28963530e+00 5.56618989e-01 -3.12413089e-02
1.07149351e+00 7.35981226e-01 9.02933598e-01 -1.97928801e-01
4.20400977e-01 1.12858140e+00 5.08961916e-01 6.44427478e-01
-5.72522953e-02 -6.05437875e-01 5.35556495e-01 -6.08384907e-01
-1.04148006e+00 -1.06875646e+00 7.25581646e-02 9.21879172e-01
6.18224144e-01 -3.14450175e-01 -3.94379705e-01 1.68700695e-01
2.31818795e-01 1.28666365e+00 -1.58588916e-01 -2.68508017e-01
-4.85408366e-01 1.50709584e-01 8.36000443e-01 6.06213391e-01
3.67260993e-01 -1.27213573e+00 -1.14065218e+00 3.82289588e-01
1.63191602e-01 -1.16108501e+00 5.55917323e-01 5.13332844e-01
-8.23786497e-01 -1.32208776e+00 5.44641972e-01 -6.21085644e-01
2.85649270e-01 -1.44419536e-01 7.39475965e-01 -1.39008444e-02
-7.32925951e-01 4.48438048e-01 -2.86371946e-01 -1.12992728e+00
-5.35813808e-01 -4.40828532e-01 1.59562021e-01 -1.35019612e+00
2.79270291e-01 -5.92294514e-01 1.44738093e-01 2.01037467e-01
-9.37421381e-01 -9.27925780e-02 5.39950132e-01 3.43908072e-01
5.25607109e-01 7.59315670e-01 6.30242825e-01 -4.72153515e-01
4.92680669e-01 -3.62944990e-01 -9.51264083e-01 5.48760414e-01
-5.80778539e-01 1.74729168e-01 4.98816758e-01 -2.98969179e-01
-1.04404533e+00 9.25764143e-02 6.21527493e-01 -1.47481903e-01
-2.76681393e-01 7.82580018e-01 -3.66423368e-01 1.31094441e-01
6.58851564e-01 -1.30367875e-01 2.18893886e-01 5.94586544e-02
8.13690126e-01 4.24030870e-01 8.31016958e-01 -1.22341955e+00
4.06156212e-01 2.75529236e-01 6.07905149e-01 -6.74713135e-01
-1.16710372e-01 -1.20422862e-01 -6.33763134e-01 -6.33305907e-02
8.50995779e-01 -5.43641269e-01 -1.66326523e+00 1.72937363e-01
-1.10901427e+00 -6.44857347e-01 -6.52585387e-01 5.25291681e-01
-1.20958269e+00 -2.51769647e-02 -4.30166394e-01 -9.02304590e-01
1.21215284e-01 -9.92161870e-01 5.92041433e-01 7.83424452e-02
-2.75780112e-01 -3.55075628e-01 -1.82153791e-01 1.35083809e-01
2.27406174e-01 5.68892062e-01 1.50581861e+00 -4.55192566e-01
-9.43838537e-01 -2.54938811e-01 2.77529079e-02 -5.24597578e-02
9.44761734e-04 -3.00634336e-02 -2.62005627e-01 3.56595516e-01
-4.01618212e-01 -8.33034813e-01 -2.10634217e-01 -2.87937105e-01
1.21048415e+00 -1.71792880e-01 -4.75666940e-01 -2.91428000e-01
1.21307552e+00 8.59436035e-01 1.04303980e+00 4.10786450e-01
-1.61498070e-01 5.55016279e-01 1.16606438e+00 3.21606368e-01
4.88135159e-01 5.26607156e-01 5.40233135e-01 1.14092481e+00
9.92928371e-02 -2.24444240e-01 2.17284113e-01 1.10387594e-01
-6.14045560e-01 2.38998607e-01 -8.42296839e-01 4.78739351e-01
-1.99964690e+00 -1.17355490e+00 1.05679594e-01 1.75679123e+00
1.12313998e+00 1.95266623e-02 -2.23197460e-01 1.70055494e-01
4.44811285e-01 -7.96663880e-01 -9.43537354e-01 -5.96973002e-01
2.47449845e-01 4.67000842e-01 6.42771602e-01 2.72028506e-01
-7.02970982e-01 1.33557451e+00 6.74642611e+00 6.65523291e-01
-6.56928957e-01 -4.45107579e-01 -4.56002206e-01 9.01773572e-02
1.40542448e-01 2.22444579e-01 -5.09233594e-01 -3.21695924e-01
8.06471109e-01 -4.24399734e-01 1.11622775e+00 1.45458460e+00
-1.12794444e-01 -5.12994349e-01 -1.52515590e+00 8.33842278e-01
-3.63854527e-01 -1.60138965e+00 1.51640564e-01 -2.12306812e-01
3.46384168e-01 -3.25438559e-01 -4.24354821e-01 3.63280863e-01
1.07615066e+00 -1.28770816e+00 1.14135361e+00 8.94970179e-01
5.00228882e-01 -7.88361251e-01 4.39164579e-01 4.01414335e-01
-7.74561882e-01 -4.55469579e-01 -4.43434983e-01 -4.77890640e-01
5.42286187e-02 9.48974937e-02 -1.26486266e+00 8.18506062e-01
6.34164453e-01 1.96189284e-01 -2.28748173e-02 7.42609501e-01
-3.45358431e-01 -4.22646493e-01 -4.90151256e-01 -5.76777518e-01
-5.21954335e-02 -1.58063993e-01 2.76158124e-01 5.73382497e-01
1.37740687e-01 6.48617625e-01 -2.34081168e-02 1.01109159e+00
3.28340918e-01 -5.82209766e-01 -9.24550653e-01 -4.73629445e-01
7.83337176e-01 8.09589326e-01 -6.79589868e-01 -5.58149874e-01
1.40267625e-01 2.71330595e-01 1.54676303e-01 1.62187591e-01
-8.14156711e-01 -8.63807976e-01 7.34505057e-01 -2.79379308e-01
2.37735406e-01 -7.80527413e-01 -1.72169507e-01 -5.04722774e-01
-2.57643044e-01 -8.31919312e-01 1.96066171e-01 -1.56920266e+00
-8.28632534e-01 4.13137442e-03 8.54824185e-01 -6.90161645e-01
-5.70640147e-01 -1.39075983e+00 5.20578772e-02 3.55381578e-01
-1.14700902e+00 -1.02624261e+00 -4.24617916e-01 6.58300579e-01
1.04779959e-01 -1.26553066e-02 1.22189248e+00 -3.78872216e-01
5.32740243e-02 -1.98962986e-01 -4.60149646e-01 -2.89925992e-01
2.95627803e-01 -9.27149773e-01 -3.49554658e-01 8.18177238e-02
-6.29078090e-01 1.04341614e+00 6.77256882e-01 -7.61678874e-01
-2.30637002e+00 -8.62502992e-01 2.12668940e-01 -4.78514612e-01
9.36558604e-01 6.97115362e-02 -4.42793757e-01 1.19215846e+00
-2.03379169e-01 -4.65077519e-01 3.30057383e-01 -2.64481723e-01
-3.98862898e-01 -1.13300055e-01 -1.57572484e+00 9.08925474e-01
1.32253742e+00 -4.37109113e-01 -1.13427329e+00 6.68382108e-01
1.00934255e+00 -6.67787671e-01 -1.57258928e+00 5.10783434e-01
7.46540308e-01 -3.46290380e-01 1.04254353e+00 -1.03609955e+00
3.27322543e-01 -9.50646520e-01 -3.93268317e-01 -7.28390992e-01
-3.80072221e-02 -8.07316184e-01 -3.25397402e-02 5.36382973e-01
3.11208189e-01 -8.07143688e-01 3.61965239e-01 1.01590872e+00
-3.65406215e-01 -4.57579404e-01 -8.22005510e-01 -1.10354960e+00
-1.47772599e-02 -6.72890484e-01 1.06890774e+00 9.74046290e-01
1.01259089e+00 4.38680649e-02 4.73343134e-01 2.90548861e-01
3.00885618e-01 1.94672436e-01 7.60370374e-01 -1.18875253e+00
-1.23084642e-01 -3.29140514e-01 -6.43781602e-01 -6.18291795e-01
1.02588676e-01 -8.94084215e-01 3.22537482e-01 -1.62337792e+00
-3.83116037e-01 -8.11400235e-01 2.30039865e-01 9.82295990e-01
9.43534195e-01 -3.69453460e-01 -4.98917773e-02 2.77961604e-03
-5.59101701e-01 1.51710123e-01 1.38007021e+00 -6.54848740e-02
-1.57564819e-01 -6.16915643e-01 -5.84750295e-01 9.27486241e-01
8.72884870e-01 -1.66234523e-02 -6.27491117e-01 -9.17848796e-02
9.28536832e-01 3.18349987e-01 5.76429665e-01 -9.38401163e-01
5.84923685e-01 -1.01500094e+00 1.49516493e-01 -4.35827017e-01
4.37518924e-01 -1.12379742e+00 6.84948802e-01 5.80121279e-01
-2.27287248e-01 -6.74245283e-02 4.22317863e-01 3.82384777e-01
2.35325202e-01 -5.95941544e-01 3.26680362e-01 -6.53568447e-01
-1.35579121e+00 -3.57704431e-01 -7.77045548e-01 -5.02949834e-01
1.58883071e+00 -9.64089707e-02 -7.19515681e-01 7.09986836e-02
-8.67914796e-01 4.13879722e-01 5.08620560e-01 3.37140858e-01
6.16087914e-01 -9.94927347e-01 2.13691577e-01 -2.89062619e-01
2.49747351e-01 4.32376325e-01 -1.51754484e-01 3.16410154e-01
-1.18612719e+00 8.22869599e-01 -8.17124426e-01 4.51392569e-02
-6.14745200e-01 1.09987080e+00 1.59590449e-02 -5.80433086e-02
-4.56087351e-01 3.59899640e-01 -3.29572797e-01 -6.21448755e-01
5.84428050e-02 -9.77678895e-01 -8.29588771e-02 -5.85726261e-01
4.97248948e-01 7.39766061e-01 -7.77025297e-02 8.08454230e-02
-5.13921618e-01 2.21339762e-01 3.44219059e-01 -8.06179270e-02
1.46838510e+00 2.93308884e-01 -8.32980633e-01 5.01701772e-01
6.41237497e-02 -4.96984124e-01 -5.35575688e-01 4.20096040e-01
1.09603480e-01 1.37137130e-01 -4.57646877e-01 -1.25937414e+00
-1.95810702e-02 1.92413166e-01 -1.23971708e-01 -3.35575780e-03
6.20226383e-01 2.36493334e-01 4.00587559e-01 1.63452494e+00
1.38959432e+00 -1.49009967e+00 -4.81330929e-03 7.66455352e-01
1.33113861e+00 -4.35873985e-01 5.98894060e-01 -6.75493479e-01
-4.09004182e-01 1.31918681e+00 6.93687379e-01 -7.82186456e-04
3.65319461e-01 4.50882673e-01 -5.15399456e-01 -5.04601419e-01
-6.55935228e-01 -1.26340270e-01 -4.68524903e-01 9.87837851e-01
-2.41956502e-01 1.55379266e-01 -8.71529132e-02 5.84403217e-01
-5.81343710e-01 7.34783888e-01 8.89366269e-01 1.69577873e+00
-8.17202270e-01 -1.02901173e+00 -5.32282650e-01 5.54393902e-02
4.05865200e-02 4.10762459e-01 -4.31239635e-01 1.23860157e+00
3.39719355e-01 9.64686871e-01 -2.38626212e-01 -1.12138078e-01
6.04493618e-01 2.72491604e-01 1.28049612e+00 -5.47448516e-01
-1.94423571e-01 -9.31937873e-01 6.52103662e-01 -8.52074444e-01
-3.48693699e-01 -4.11185205e-01 -2.34020162e+00 -3.65813732e-01
-2.43329227e-01 1.93895131e-01 1.12723005e+00 8.32019091e-01
3.95397872e-01 5.26288509e-01 -3.15753043e-01 -7.99793422e-01
-6.46937013e-01 -7.34612167e-01 -5.71266294e-01 -8.21245313e-02
-3.91930252e-01 -1.13608348e+00 3.29119384e-01 3.22242469e-01] | [4.51237678527832, 0.9926543235778809] |
d63cc79e-4c20-44ac-b8dd-ecd356f5c032 | frustum-pointpillars-a-multi-stage-approach | null | null | https://ieeexplore.ieee.org/document/9607424 | https://hal.archives-ouvertes.fr/hal-03354114/document | Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR | Accurate 3D object detection is a key part of the perception module for autonomous vehicles. A better understanding of the objects in 3D facilitates better decision-making and path planning. RGB Cameras and LiDAR are the most commonly used sensors in autonomous vehicles for environment perception. Many approaches have shown promising results for 2D detection with RGB Images, but efficiently localizing small objects like pedestrians in the 3D point cloud of large scenes has remained a challenging area of research. We propose a novel method, Frustum-PointPillars, for 3D object detection using LiDAR data. Instead of solely relying on point cloud features, we leverage the mature field of 2D object detection to reduce the search space in the 3D space. Then, we use the Pillar Feature Encoding network for object localization in the reduced point cloud. We also propose a novel approach for masking point clouds to further improve the localization of objects. We train our network on the KITTI dataset and perform experiments to show the effectiveness of our network. On the KITTI test set our method outperforms other multi-sensor SOTA approaches for 3D pedestrian localization (Bird’s Eye View) while achieving a significantly faster runtime of 14 Hz. | ['Christian Laugier', 'Özgür Erkent', 'David Sierra-Gonzalez', 'Anshul Paigwar'] | 2021-10-11 | null | null | null | 2021-ieee-cvf-international-conference-on | ['birds-eye-view-object-detection'] | ['computer-vision'] | [-1.78721204e-01 -6.73467040e-01 3.94005105e-02 -4.26619917e-01
-6.53746545e-01 -6.58949435e-01 4.09193575e-01 2.04376757e-01
-7.88664699e-01 1.38007373e-01 -5.08540750e-01 -5.12887836e-01
2.90105641e-01 -1.03523302e+00 -9.40950215e-01 -4.88434881e-01
-1.02495879e-01 6.26825869e-01 8.37772369e-01 -2.48212054e-01
2.08094180e-01 1.04753292e+00 -1.76247549e+00 -9.68014915e-03
5.28266728e-01 1.19008529e+00 3.61619532e-01 6.28109694e-01
-6.74217120e-02 -3.42544653e-02 -5.73603630e-01 -8.61370042e-02
5.59280097e-01 4.19595718e-01 3.70387100e-02 2.31877863e-01
7.50170588e-01 -4.60693836e-01 -1.72078699e-01 1.02986789e+00
6.39634490e-01 1.13743685e-01 4.01993722e-01 -1.47074366e+00
-1.30588502e-01 -4.97018062e-02 -7.72838771e-01 2.58380055e-01
5.60433328e-01 3.81161213e-01 6.06652319e-01 -1.17821014e+00
1.50506377e-01 1.69290268e+00 8.76977146e-01 2.36061573e-01
-9.47090626e-01 -8.16587746e-01 1.82987586e-01 6.86856568e-01
-1.86667073e+00 -2.30816141e-01 7.84543097e-01 -2.05385074e-01
1.10304570e+00 2.56995022e-01 7.23139167e-01 7.69049764e-01
-1.29301965e-01 7.01379716e-01 1.04420626e+00 -2.58246362e-01
2.20092818e-01 2.73059040e-01 1.29878223e-01 7.64529467e-01
7.76584864e-01 3.07974279e-01 -4.67264175e-01 6.18267991e-02
5.60489476e-01 2.90812045e-01 1.66704446e-01 -7.48167455e-01
-1.05511725e+00 8.12044501e-01 8.94963562e-01 -2.90345252e-01
-2.14194208e-01 2.60753483e-01 1.23932324e-01 3.54827456e-02
3.48768800e-01 -2.62075603e-01 -2.37684757e-01 7.39319921e-02
-4.95830566e-01 5.10089517e-01 4.01449263e-01 1.35501540e+00
8.27233613e-01 -3.53636384e-01 1.93033412e-01 3.97631139e-01
5.14043450e-01 1.24085236e+00 -2.06400141e-01 -8.24604154e-01
6.17560327e-01 9.71137941e-01 3.80603284e-01 -9.72293496e-01
-4.47192818e-01 -2.62626737e-01 -4.34068769e-01 7.33053148e-01
4.12489176e-01 2.43894890e-01 -9.09948289e-01 9.48874533e-01
7.99839735e-01 2.83257267e-03 -1.15782768e-01 1.20189226e+00
9.52299774e-01 5.71595967e-01 -2.90699691e-01 4.54320282e-01
1.57967806e+00 -5.18458843e-01 -1.90148875e-02 -4.95125055e-01
4.99141812e-01 -5.39296925e-01 6.73974335e-01 1.91246063e-01
-5.72046220e-01 -6.86908960e-01 -1.06413162e+00 -2.58685619e-01
-6.93354487e-01 2.76114851e-01 4.81254488e-01 9.69010115e-01
-8.51425588e-01 2.40932517e-02 -9.01340544e-01 -6.43284738e-01
7.10454881e-01 5.04162073e-01 -3.77753109e-01 -4.52115983e-01
-6.05257869e-01 1.12864780e+00 4.55450535e-01 7.43525475e-02
-6.95216715e-01 -4.78243291e-01 -9.59258080e-01 -3.02586630e-02
5.92294216e-01 -5.12402534e-01 8.40829730e-01 1.01438224e-01
-1.00664675e+00 9.03834701e-01 -4.81538266e-01 -6.72969103e-01
4.95375723e-01 -3.16135019e-01 -1.05991483e-01 9.92533565e-02
3.53295624e-01 9.27459240e-01 6.85757816e-01 -1.29006326e+00
-1.25236642e+00 -8.47904921e-01 -4.08506840e-02 2.28868678e-01
8.09897557e-02 -1.08038537e-01 -6.06471539e-01 1.30783424e-01
5.45924962e-01 -1.07762110e+00 -4.35287267e-01 4.68219817e-01
-3.42557937e-01 -5.61575830e-01 1.09096611e+00 -8.75833109e-02
2.28974581e-01 -2.07494903e+00 -5.14212847e-01 2.85143614e-01
1.57697737e-01 1.68039978e-01 1.03280336e-01 -1.06025231e-03
4.16910648e-01 -1.04328722e-01 9.10930410e-02 -5.25170147e-01
4.31293324e-02 3.03299695e-01 -1.72426552e-01 7.06191003e-01
2.97910213e-01 8.36541533e-01 -7.43350804e-01 -4.29881960e-01
7.54718184e-01 7.62832642e-01 -5.04664540e-01 -1.61615029e-01
1.45844258e-02 1.71028093e-01 -5.49977243e-01 1.05036902e+00
1.20991325e+00 8.47885609e-02 -5.95317185e-01 -1.49615705e-01
-6.48019195e-01 2.43036404e-01 -1.34798563e+00 1.50239468e+00
-9.37662795e-02 5.55355251e-01 -1.18111350e-01 -7.03159213e-01
1.00516641e+00 -1.61820322e-01 3.43662083e-01 -7.40477622e-01
2.45452970e-02 3.31217855e-01 -2.14311048e-01 -1.70341522e-01
4.85112011e-01 2.90054649e-01 -1.17101930e-01 1.20963575e-03
-4.09678459e-01 -3.51618499e-01 6.84992671e-02 -9.47505310e-02
1.03165305e+00 5.42331114e-02 1.95604399e-01 1.20205164e-01
4.33666289e-01 4.90288556e-01 2.70680100e-01 1.00936341e+00
-4.50187504e-01 3.76568913e-01 -1.55816928e-01 -6.04230225e-01
-9.82999980e-01 -1.24444628e+00 -3.17367166e-01 7.02698231e-01
5.69616497e-01 -2.74530351e-01 -2.73842514e-01 -5.82529783e-01
5.70582569e-01 4.48229134e-01 -2.51829803e-01 1.13145970e-01
-7.10986674e-01 -4.93363172e-01 2.78522193e-01 6.39831007e-01
7.11277843e-01 -4.17116463e-01 -1.42131460e+00 -1.73270162e-02
-5.71035109e-02 -1.58613193e+00 -7.04476843e-03 2.37702549e-01
-7.40424693e-01 -1.01911068e+00 -4.02626157e-01 -5.03715634e-01
5.81664860e-01 1.21757984e+00 7.89571762e-01 -4.44393083e-02
-4.49057341e-01 5.78456044e-01 -3.53912622e-01 -8.58690262e-01
5.79412021e-02 -2.36694083e-01 4.01488870e-01 -2.77476877e-01
1.00237584e+00 -2.08539888e-01 -7.42499471e-01 5.20087540e-01
-7.60162473e-02 -2.39551932e-01 6.76309526e-01 2.84547657e-01
7.97423899e-01 1.43791795e-01 -1.90017536e-01 -3.23119760e-02
-3.27381156e-02 -1.77913427e-01 -1.23864567e+00 -3.22336197e-01
-1.93196222e-01 -3.25344503e-01 -1.40056256e-02 -2.27397516e-01
-3.96371126e-01 6.75645590e-01 -1.53627813e-01 -5.73226750e-01
-5.40657580e-01 -1.66537285e-01 -3.31283182e-01 -6.27840757e-01
6.00523412e-01 2.21068770e-01 -8.86724964e-02 -5.36882102e-01
4.02923226e-01 6.85154915e-01 4.32466745e-01 -2.02178448e-01
1.15458882e+00 1.06943369e+00 4.55784321e-01 -1.04691350e+00
-5.66015363e-01 -9.87629473e-01 -1.00274992e+00 -3.45040709e-01
8.60816002e-01 -1.23801100e+00 -1.19696116e+00 8.34351256e-02
-1.46862245e+00 2.14939371e-01 -1.44294515e-01 5.19023299e-01
-3.34886074e-01 2.75476068e-01 1.76621646e-01 -1.14603317e+00
-1.39078021e-01 -1.19354379e+00 1.54433036e+00 7.94778541e-02
2.83918083e-01 -3.31327707e-01 -3.31108183e-01 2.27187142e-01
8.97181407e-02 1.87312841e-01 3.48511815e-01 -3.94362837e-01
-1.24438500e+00 -6.59002841e-01 -5.68601370e-01 -1.02258205e-01
-6.54637394e-03 -4.55860466e-01 -1.08649707e+00 -2.22650155e-01
-1.54591754e-01 1.73920095e-01 1.02384043e+00 3.89388800e-01
8.25881898e-01 2.05828324e-01 -8.54501724e-01 5.69229424e-01
1.25325286e+00 4.62106541e-02 1.47665009e-01 4.65598464e-01
7.36363649e-01 4.50176418e-01 9.01459396e-01 4.41205055e-01
8.21075797e-01 1.00691509e+00 9.41844940e-01 -1.57992542e-02
-2.36186609e-01 -9.98194665e-02 3.47829193e-01 1.50589168e-01
-1.19559944e-01 5.51452115e-02 -1.06913459e+00 4.41387951e-01
-1.72242594e+00 -8.64362419e-01 -4.45737064e-01 2.25479746e+00
1.34883299e-01 5.74910879e-01 4.00963545e-01 2.90099859e-01
6.53192043e-01 -3.07077467e-01 -5.38958669e-01 2.74732679e-01
-5.16746826e-02 -9.45778266e-02 1.03059363e+00 4.48147863e-01
-1.42896712e+00 1.00978267e+00 5.25785780e+00 4.32925344e-01
-8.74027848e-01 6.54861331e-02 -3.13195549e-02 -1.71521053e-01
3.91451865e-01 -1.36120826e-01 -1.65892446e+00 3.70110154e-01
5.71155965e-01 2.93461174e-01 1.56806037e-03 1.20932901e+00
3.67782652e-01 -3.27084452e-01 -1.06304383e+00 1.32763147e+00
8.58828649e-02 -1.13170922e+00 -1.86200142e-01 2.69015402e-01
1.42609075e-01 4.00636584e-01 5.14450036e-02 1.75498143e-01
2.13328168e-01 -6.84353054e-01 1.04563284e+00 8.67830683e-03
4.94900554e-01 -8.83139431e-01 6.23948216e-01 6.53824568e-01
-1.58737648e+00 -2.38627136e-01 -9.17426825e-01 -8.54887739e-02
1.60758018e-01 5.23611665e-01 -1.38540089e+00 1.66292951e-01
1.12893975e+00 6.09779954e-01 -8.99174392e-01 1.61282253e+00
-2.25231767e-01 1.86322778e-01 -9.70535398e-01 -3.81074786e-01
2.01832056e-01 -3.68213169e-02 8.81901741e-01 1.04587126e+00
4.85573947e-01 8.29316005e-02 5.09052217e-01 9.33614373e-01
3.32974851e-01 -2.64472395e-01 -9.08994734e-01 5.90335548e-01
6.99797928e-01 1.07540023e+00 -8.55433345e-01 -2.45875642e-01
-4.92138147e-01 6.85270250e-01 3.62202972e-02 1.22804165e-01
-7.57152140e-01 -3.85191560e-01 8.42516363e-01 3.49580854e-01
8.85969877e-01 -8.87518466e-01 -2.92546272e-01 -8.13899994e-01
2.60288537e-01 -4.25328940e-01 1.09971995e-02 -5.86107433e-01
-8.48576069e-01 3.02845359e-01 8.89356062e-02 -1.46001184e+00
9.24944207e-02 -9.79361534e-01 -2.71876246e-01 8.49256217e-01
-1.83805156e+00 -1.38441646e+00 -7.48044193e-01 6.96594596e-01
5.28231204e-01 9.79673415e-02 4.62269604e-01 3.22531819e-01
-1.63853377e-01 1.60425514e-01 -2.27793634e-01 6.36394545e-02
3.19375485e-01 -1.03111923e+00 8.04637849e-01 1.03618777e+00
1.66867316e-01 3.67992133e-01 6.29111886e-01 -7.94386506e-01
-1.83060479e+00 -1.22213936e+00 6.39720738e-01 -8.03133905e-01
2.22986951e-01 -8.61444771e-01 -5.35092056e-01 4.98368979e-01
-3.66678149e-01 1.27887338e-01 2.53031105e-01 -2.82160752e-02
-3.40012729e-01 -4.01124179e-01 -1.16794801e+00 4.79597002e-01
1.32738781e+00 -1.44850537e-01 -3.05679470e-01 4.83955532e-01
8.03300917e-01 -5.85342765e-01 -3.14634889e-01 4.28522974e-01
4.64960098e-01 -8.24746251e-01 1.62834716e+00 -1.25372618e-01
-4.40255821e-01 -8.40149939e-01 -5.01443803e-01 -9.91359055e-01
-2.03917548e-01 -1.38175681e-01 -1.62671566e-01 7.36305177e-01
-1.20324716e-01 -5.45721591e-01 1.06013715e+00 2.04558358e-01
-2.21915737e-01 -9.41696987e-02 -1.43343174e+00 -9.28961635e-01
-5.02540231e-01 -9.11257625e-01 6.92256749e-01 9.29656923e-02
-6.44789696e-01 3.43900681e-01 8.49661380e-02 8.38421106e-01
1.21454561e+00 3.36903602e-01 1.27782202e+00 -1.51183403e+00
3.60359043e-01 -4.45613414e-01 -1.05356205e+00 -1.43825924e+00
-1.97649553e-01 -7.50053525e-01 2.45860711e-01 -1.50678325e+00
-1.50351927e-01 -6.87223196e-01 1.35690033e-01 3.20642442e-01
-2.51725484e-02 5.56631267e-01 3.22249740e-01 6.16010427e-02
-7.10579932e-01 3.78369570e-01 7.41703391e-01 -2.49576956e-01
-5.17063625e-02 4.43516284e-01 -2.84565896e-01 7.04417765e-01
5.54235160e-01 -4.08459663e-01 7.77207538e-02 -5.34678578e-01
-1.07203104e-01 -4.73997891e-01 1.10073328e+00 -1.57475162e+00
4.13846582e-01 -2.23117881e-02 6.94404900e-01 -1.74436212e+00
9.84305441e-01 -1.28631938e+00 -3.37532192e-01 7.11840987e-01
3.65364045e-01 1.19125769e-01 3.40477973e-01 5.81850231e-01
3.34935755e-01 6.29803911e-02 7.05215514e-01 -2.09125817e-01
-1.16861045e+00 4.33943659e-01 -2.65360177e-01 -5.48256040e-01
1.26620340e+00 -6.19203925e-01 -1.86120331e-01 1.33736804e-02
-3.22398841e-01 4.32943642e-01 5.16819954e-01 4.84280735e-01
1.14940250e+00 -1.39602566e+00 -6.88521624e-01 5.64704001e-01
2.51888067e-01 3.72131467e-01 -1.81962103e-01 6.79059803e-01
-5.32858789e-01 8.75301301e-01 -9.42681059e-02 -1.46843135e+00
-1.55033600e+00 6.12781942e-01 1.75623640e-01 5.46400011e-01
-7.35860109e-01 9.22291517e-01 -3.27931019e-03 -4.68765110e-01
3.54899257e-01 -6.55035555e-01 -3.04185543e-02 -1.77476287e-01
5.71393430e-01 5.35257578e-01 1.46173894e-01 -9.33072388e-01
-8.25072825e-01 9.01651442e-01 7.37175047e-02 2.93656047e-02
1.32609034e+00 -2.70913452e-01 1.78816259e-01 2.87544906e-01
9.10378635e-01 -1.31471142e-01 -1.32661808e+00 -2.76303917e-01
7.70648271e-02 -8.44434917e-01 6.74573928e-02 -2.96623498e-01
-4.28212106e-01 1.13343143e+00 1.19801879e+00 1.01823628e-01
6.14118636e-01 2.71306008e-01 5.39060354e-01 7.53586531e-01
8.14966917e-01 -6.89655960e-01 6.37863879e-04 5.74879706e-01
5.60848892e-01 -1.75278938e+00 2.68587649e-01 -6.74187839e-01
-7.49283805e-02 1.06699765e+00 5.52122712e-01 -1.12357661e-01
3.87675464e-01 1.82443753e-01 -2.40712240e-02 -2.01264620e-01
-2.65013218e-01 -8.04728985e-01 3.01961333e-01 1.07573009e+00
-3.72212231e-01 1.22534722e-01 4.70966399e-01 1.60532951e-01
-2.29638502e-01 -3.76525939e-01 6.11787364e-02 1.06755805e+00
-1.05461156e+00 -8.45786989e-01 -9.44158912e-01 1.48216143e-01
2.56731123e-01 1.20188177e-01 -2.79004246e-01 8.25242043e-01
3.10982764e-01 1.00219297e+00 2.09252760e-01 -3.54318708e-01
6.60302043e-01 -2.21002832e-01 6.42126858e-01 -6.33006573e-01
-3.98631766e-02 1.35130510e-02 -2.36919299e-01 -6.67949319e-01
-2.66779870e-01 -8.61939669e-01 -1.06755519e+00 -2.47066513e-01
-3.75982076e-01 -3.13042015e-01 1.28618586e+00 8.14936519e-01
4.30714905e-01 2.84018368e-02 6.16855800e-01 -1.34103632e+00
-5.02532601e-01 -5.70017755e-01 -8.76071453e-02 4.44839522e-02
4.69201088e-01 -9.46848273e-01 1.45158738e-01 -3.27882677e-01] | [7.734566688537598, -2.5401973724365234] |
6e33be63-e3ca-4fac-b643-a87fb3e1814a | vit5-pretrained-text-to-text-transformer-for | 2205.06457 | null | https://arxiv.org/abs/2205.06457v2 | https://arxiv.org/pdf/2205.06457v2.pdf | ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation | We present ViT5, a pretrained Transformer-based encoder-decoder model for the Vietnamese language. With T5-style self-supervised pretraining, ViT5 is trained on a large corpus of high-quality and diverse Vietnamese texts. We benchmark ViT5 on two downstream text generation tasks, Abstractive Text Summarization and Named Entity Recognition. Although Abstractive Text Summarization has been widely studied for the English language thanks to its rich and large source of data, there has been minimal research into the same task in Vietnamese, a much lower resource language. In this work, we perform exhaustive experiments on both Vietnamese Abstractive Summarization and Named Entity Recognition, validating the performance of ViT5 against many other pretrained Transformer-based encoder-decoder models. Our experiments show that ViT5 significantly outperforms existing models and achieves state-of-the-art results on Vietnamese Text Summarization. On the task of Named Entity Recognition, ViT5 is competitive against previous best results from pretrained encoder-based Transformer models. Further analysis shows the importance of context length during the self-supervised pretraining on downstream performance across different settings. | ['Trieu H. Trinh', 'Hieu Nguyen', 'Hieu Tran', 'Long Phan'] | 2022-05-13 | null | https://aclanthology.org/2022.naacl-srw.18 | https://aclanthology.org/2022.naacl-srw.18.pdf | naacl-acl-2022-7 | ['named-entity-recognition-in-vietnamese'] | ['natural-language-processing'] | [ 4.54191923e-01 4.35272485e-01 -3.31100941e-01 -2.47615322e-01
-1.44276178e+00 -4.80335832e-01 7.38170564e-01 1.88767701e-01
-6.98368013e-01 1.08446586e+00 1.23842430e+00 -3.26734781e-01
6.32554531e-01 -5.59103966e-01 -6.81613266e-01 -2.15878367e-01
1.71044484e-01 8.08001757e-01 4.70827594e-02 -6.32119596e-01
3.19335997e-01 -5.98660521e-02 -9.46478009e-01 6.99210405e-01
1.42127299e+00 2.86060363e-01 5.60089350e-02 8.86456192e-01
-1.06804401e-01 9.34444010e-01 -1.27038145e+00 -8.53901386e-01
-2.39344165e-01 -8.88231099e-01 -1.08825696e+00 -9.59802195e-02
4.74819452e-01 -3.57601076e-01 -4.76749986e-01 6.40562654e-01
8.64974976e-01 1.88845620e-02 8.61744165e-01 -8.34543765e-01
-8.67192149e-01 1.33400011e+00 -1.57947704e-01 3.44677567e-01
4.51928765e-01 -3.57002690e-02 1.28891432e+00 -8.58705223e-01
1.06799042e+00 9.80140746e-01 5.53180635e-01 6.32901788e-01
-9.13127840e-01 -3.91380787e-01 -2.28842497e-01 1.39790311e-01
-1.11909473e+00 -7.63387978e-01 3.34505290e-01 1.71845511e-01
1.63140202e+00 1.17490180e-01 4.11042511e-01 1.48133290e+00
6.89272702e-01 1.32272446e+00 4.98496801e-01 -1.66143268e-01
-1.16403410e-02 -1.37584098e-02 -1.23006016e-01 4.92737740e-01
3.09733808e-01 -3.33139837e-01 -7.06413150e-01 7.92298988e-02
1.90856963e-01 -6.15397573e-01 -2.45922789e-01 3.83165807e-01
-1.60448122e+00 9.20325875e-01 2.24614322e-01 3.21219116e-01
-4.05924827e-01 -3.35869715e-02 1.12121272e+00 5.61355233e-01
8.16663742e-01 6.72777593e-01 -5.25368333e-01 -5.69604337e-01
-1.26045239e+00 1.68644488e-01 1.28645515e+00 1.47945929e+00
1.91533446e-01 5.89063942e-01 -6.68602169e-01 1.05489266e+00
-2.56413013e-01 8.07720661e-01 8.12360883e-01 -4.15329933e-01
1.25851810e+00 5.61518133e-01 -1.92861512e-01 -4.03126687e-01
4.05044295e-02 -4.14709032e-01 -1.15480137e+00 -5.62407732e-01
-8.06976035e-02 -6.72621906e-01 -9.33601320e-01 1.41387343e+00
-1.79741979e-01 -3.53102058e-01 8.49708974e-01 4.23307538e-01
1.36700833e+00 1.29041064e+00 -7.32428441e-03 -3.67614955e-01
1.23169875e+00 -1.18991888e+00 -8.39878321e-01 -3.39017391e-01
8.68056655e-01 -8.69662762e-01 5.89599490e-01 -5.23805209e-02
-1.25863421e+00 -4.04891908e-01 -9.20493364e-01 -6.35857105e-01
-2.08764762e-01 4.15465266e-01 1.99777782e-01 5.07761538e-01
-1.07218206e+00 5.39539754e-01 -7.34082282e-01 -7.21615851e-01
2.69250900e-01 1.14765316e-01 -4.62249994e-01 6.11134395e-02
-1.34013617e+00 1.12760198e+00 8.75361323e-01 -3.32372785e-02
-9.52858686e-01 -6.80548787e-01 -1.14572382e+00 2.90139914e-01
2.29544833e-01 -9.06328797e-01 1.67510843e+00 -6.20356262e-01
-1.64034986e+00 7.10977733e-01 -4.33926523e-01 -1.01301360e+00
5.33349335e-01 -3.66258770e-01 -5.18889725e-01 1.29483610e-01
4.65299964e-01 6.98058844e-01 4.99426961e-01 -8.07434857e-01
-7.16239870e-01 3.83172892e-02 -5.55434346e-01 5.08859456e-01
-1.97972372e-01 1.17695004e-01 -2.25718558e-01 -9.80053604e-01
-6.70891941e-01 -6.85218871e-01 -6.54393509e-02 -1.16644895e+00
-7.42295921e-01 -5.39080858e-01 7.72074640e-01 -1.03623939e+00
1.48092079e+00 -1.67126834e+00 1.89488724e-01 -5.29156685e-01
-9.79010686e-02 5.16725302e-01 -4.17763054e-01 1.23062670e+00
2.52081081e-02 3.03689301e-01 -4.61295396e-01 -5.29323518e-01
4.45968062e-02 1.93789512e-01 -6.76749051e-01 1.18957430e-01
5.70898235e-01 1.34952509e+00 -1.00999641e+00 -8.16452026e-01
-1.23786412e-01 2.56392241e-01 -4.67564672e-01 3.98941696e-01
-2.20775023e-01 1.89287797e-01 -4.68195409e-01 4.53404635e-01
1.78092703e-01 1.16147973e-01 1.98096082e-01 4.05359194e-02
-3.00111324e-01 9.65026855e-01 -4.25924838e-01 1.87752795e+00
-5.84345043e-01 9.63529527e-01 -4.47029501e-01 -9.09355283e-01
8.67249727e-01 7.64621198e-01 6.95496202e-02 -6.62407637e-01
1.93092585e-01 3.99792403e-01 -1.58952385e-01 -3.05225998e-01
1.44530380e+00 -1.34547129e-01 -6.85981572e-01 5.84206343e-01
5.48223794e-01 -5.05569339e-01 6.95022345e-01 6.85003102e-01
1.24790573e+00 7.76037797e-02 7.89472878e-01 -1.38167396e-01
3.77499402e-01 4.29909915e-01 4.62138176e-01 7.44484901e-01
1.31596893e-01 7.77575910e-01 5.30932605e-01 1.30418748e-01
-1.27056801e+00 -9.53244269e-01 2.05557402e-02 1.17901802e+00
-2.89053649e-01 -8.80675614e-01 -8.35929930e-01 -8.72180164e-01
-3.42143089e-01 1.32503831e+00 -6.03371143e-01 -2.89629608e-01
-1.05031574e+00 -6.22196734e-01 1.15307868e+00 6.37319505e-01
7.68775344e-01 -1.48283672e+00 -2.98165828e-01 5.92615008e-01
-6.67472422e-01 -1.18364966e+00 -8.41492474e-01 7.65730664e-02
-1.00452638e+00 -5.92543364e-01 -1.02803409e+00 -1.07193553e+00
1.26725376e-01 -3.19135152e-02 1.37839353e+00 -4.37409103e-01
2.03539044e-01 1.43116906e-01 -6.10832572e-01 -6.52384877e-01
-1.07397139e+00 9.62927639e-01 -3.49485040e-01 -4.26212788e-01
3.42602022e-02 -2.56542265e-01 -1.56510696e-01 -1.09398223e-01
-8.88165176e-01 1.73692554e-01 9.89230216e-01 1.00460732e+00
1.53333679e-01 -3.78868341e-01 9.23460245e-01 -1.18135917e+00
9.49218094e-01 -4.67938572e-01 -3.24899256e-02 4.79468614e-01
-3.30242991e-01 4.01515335e-01 9.02778804e-01 -1.55417651e-01
-1.52567589e+00 -5.27709484e-01 -4.97892886e-01 3.00028890e-01
-3.19628380e-02 7.43144453e-01 -1.63899302e-01 7.03687847e-01
5.30817032e-01 6.25107229e-01 -3.96908641e-01 -3.04927856e-01
5.20906270e-01 9.42064643e-01 7.46576726e-01 -1.88824713e-01
6.23205423e-01 -4.77408580e-02 -5.20612657e-01 -1.25277579e+00
-1.07520568e+00 -3.91672015e-01 -6.06243789e-01 3.27247679e-01
1.03021240e+00 -1.06336713e+00 -8.23557377e-02 3.01482767e-01
-1.44106388e+00 -3.85804892e-01 -6.13635182e-01 1.57885879e-01
-5.32918513e-01 4.29084122e-01 -8.97206962e-01 -3.35375309e-01
-1.20038402e+00 -8.67593169e-01 1.53852534e+00 5.31443208e-02
-4.55692708e-01 -1.20513594e+00 4.44326580e-01 4.09248352e-01
2.93456942e-01 1.30821377e-01 8.05591464e-01 -1.15065038e+00
-2.65420452e-02 -6.49552345e-02 3.66243273e-02 4.50693816e-01
3.02900933e-02 -1.40380338e-01 -5.24848700e-01 -2.33141392e-01
-1.78027570e-01 -5.11372566e-01 1.30991590e+00 1.57726482e-01
4.55178797e-01 -6.68991268e-01 -1.88369080e-01 4.20693070e-01
1.00234962e+00 1.11619951e-02 8.41955841e-01 2.88065165e-01
7.46175885e-01 3.61135811e-01 5.59087217e-01 3.67102116e-01
7.54363418e-01 2.23676950e-01 -1.24414511e-01 -2.61234671e-01
-2.63833076e-01 -6.46193504e-01 8.20587277e-01 1.53476930e+00
-5.49945189e-03 -9.14007127e-01 -6.79878950e-01 7.74750113e-01
-1.63559282e+00 -1.36547053e+00 -2.31962740e-01 1.88384306e+00
1.21784616e+00 5.83510380e-03 9.55153406e-02 -3.17653567e-01
6.41616285e-01 5.70340872e-01 -3.74724478e-01 -8.13542664e-01
-4.03747618e-01 4.92940158e-01 5.52049577e-01 1.32940501e-01
-1.15438950e+00 1.44912481e+00 6.37745953e+00 9.24268544e-01
-9.74962592e-01 1.10701784e-01 3.87471825e-01 7.23577738e-02
-2.50974536e-01 -4.50221449e-02 -9.96050775e-01 2.11946338e-01
1.42135429e+00 -8.82807255e-01 -2.89893299e-01 7.24515915e-01
1.72426209e-01 1.07405491e-01 -1.20924747e+00 5.26565135e-01
5.73812842e-01 -1.51434910e+00 5.49769402e-01 -4.43671048e-02
1.11463141e+00 4.19715285e-01 -3.28773797e-01 8.49290371e-01
4.84939069e-01 -9.90818262e-01 6.82153046e-01 3.72710958e-04
1.03995883e+00 -7.77587295e-01 1.01985693e+00 2.63055027e-01
-1.05661011e+00 3.92726332e-01 -4.70880657e-01 3.91099811e-01
5.82733095e-01 1.96971148e-01 -1.23647594e+00 9.43218470e-01
2.03400001e-01 1.13339090e+00 -6.72345340e-01 7.07247257e-01
-5.60663700e-01 1.08757639e+00 7.88303986e-02 -2.72251636e-01
4.78894323e-01 -3.72212715e-02 7.60189831e-01 1.88142300e+00
2.76840061e-01 1.03939332e-01 1.01448238e-01 4.26999301e-01
-5.47167301e-01 3.21478367e-01 -6.97241068e-01 -4.10762161e-01
1.54308170e-01 9.34696615e-01 -2.45871440e-01 -9.28874254e-01
-3.11132342e-01 1.47104907e+00 4.35934454e-01 2.12785617e-01
-6.82077825e-01 -7.77659059e-01 2.36424655e-01 -1.69971615e-01
6.49830163e-01 -3.30727130e-01 -1.77265286e-01 -1.70528853e+00
-3.32164526e-01 -1.15585768e+00 5.17920613e-01 -5.26191354e-01
-1.06936145e+00 8.67735624e-01 9.97351781e-02 -1.10957146e+00
-7.03533530e-01 -2.62734592e-01 -1.02433026e+00 5.20799160e-01
-1.36297631e+00 -1.19985676e+00 2.41921991e-01 4.32962567e-01
1.38443255e+00 -3.35142970e-01 8.41671228e-01 -1.76713899e-01
-8.23045552e-01 7.23044872e-01 4.12801594e-01 3.76648605e-01
1.06901848e+00 -1.47379231e+00 1.08427036e+00 1.09048033e+00
1.04246683e-01 3.67235929e-01 6.04877532e-01 -9.17617023e-01
-1.35314870e+00 -1.42584217e+00 1.60265636e+00 -4.57847327e-01
6.80306554e-01 -4.29730356e-01 -8.57480705e-01 9.94442940e-01
1.06255674e+00 -9.28596914e-01 7.92175710e-01 1.09336870e-02
-4.56791446e-02 2.35938415e-01 -6.00627840e-01 7.10538924e-01
1.10430121e+00 -2.38506287e-01 -1.35116529e+00 5.09775579e-01
9.04408813e-01 -4.67919171e-01 -1.05736721e+00 2.03779101e-01
2.01916575e-01 -7.12495744e-01 4.59424198e-01 -6.97486401e-01
8.97803009e-01 2.36705184e-01 2.92323887e-01 -1.79105043e+00
-8.22922215e-02 -7.94125736e-01 7.05805123e-02 1.67485416e+00
8.11447024e-01 -5.41231513e-01 4.60664570e-01 -1.46094650e-01
-6.78673148e-01 -3.56193900e-01 -8.51993740e-01 -6.78105116e-01
4.54619735e-01 -1.42014027e-02 3.70127738e-01 7.22355902e-01
2.52913207e-01 1.29939175e+00 -4.05160844e-01 -3.48846495e-01
3.19149971e-01 1.22858666e-01 8.60750139e-01 -8.01966548e-01
2.02145189e-01 -3.56582701e-01 -4.97824475e-02 -1.17541134e+00
6.30115807e-01 -1.25495887e+00 2.43772954e-01 -2.13602185e+00
3.59882355e-01 2.16866240e-01 4.71149862e-01 4.89273995e-01
-3.81460458e-01 -6.89312220e-02 7.79951066e-02 2.37178072e-01
-7.57485211e-01 1.12235773e+00 1.09625685e+00 -3.26570868e-01
-2.26289660e-01 -1.18547171e-01 -8.03375661e-01 1.22934803e-01
8.87960792e-01 -4.82935339e-01 -2.19312385e-01 -7.50465155e-01
-7.99432695e-02 3.28290701e-01 -3.44696432e-01 -8.52897286e-01
2.29241729e-01 7.85433576e-02 2.68567711e-01 -8.54622066e-01
-2.34524831e-02 1.19305156e-01 -4.01621282e-01 4.27239537e-01
-6.72014654e-01 4.36956853e-01 1.54196963e-01 2.72820741e-01
-5.21126568e-01 -1.96047843e-01 4.55062002e-01 -2.47286826e-01
-7.50584066e-01 7.68865868e-02 -9.99765813e-01 8.83793354e-01
5.40053368e-01 2.50509996e-02 -4.90096569e-01 -6.56571388e-01
-1.26433775e-01 3.96472335e-01 2.69714743e-01 5.17862082e-01
6.65016532e-01 -9.31437492e-01 -1.59654748e+00 -4.33958508e-02
2.34683096e-01 -1.57980904e-01 5.26227057e-02 5.41781902e-01
-5.81120908e-01 8.46285641e-01 -1.01734556e-01 -3.39812636e-01
-1.19490182e+00 2.64688790e-01 -1.27730027e-01 -8.38896930e-01
-7.59306848e-01 3.37706536e-01 -1.44653022e-02 -4.57242101e-01
-1.21438414e-01 -4.92962360e-01 -3.29216808e-01 5.71011156e-02
4.09131646e-01 5.75956464e-01 1.85222149e-01 -8.30932379e-01
-1.14605106e-01 -5.16044721e-03 -4.11826104e-01 -3.35308045e-01
1.25045860e+00 2.69949585e-02 -8.86735618e-02 3.20138156e-01
1.34707642e+00 9.20836180e-02 -5.43075919e-01 -8.23331550e-02
1.95311278e-01 3.26247036e-01 -2.26223901e-01 -6.93072259e-01
-5.79710960e-01 8.01481307e-01 -4.13454413e-01 -1.11888379e-01
9.04606700e-01 -7.63233528e-02 1.28048515e+00 8.68405879e-01
-3.94472852e-02 -1.22670817e+00 7.92619511e-02 1.28687799e+00
1.08913398e+00 -1.23173535e+00 2.17054598e-02 -1.42639875e-01
-1.34966385e+00 9.77654040e-01 4.61815774e-01 -1.07585333e-01
-8.93679634e-02 2.23036662e-01 2.85631847e-02 4.93918210e-02
-1.10992944e+00 -1.23374291e-01 4.38167781e-01 4.41870391e-01
8.42957139e-01 -3.92594859e-02 -5.55625439e-01 2.51816720e-01
-9.25320625e-01 -2.56582975e-01 9.25130844e-01 9.25174475e-01
-3.62724423e-01 -1.10500872e+00 2.08983086e-02 6.02690697e-01
-6.90440893e-01 -6.10904932e-01 -6.54481828e-01 9.67442870e-01
-6.23977005e-01 1.20776653e+00 7.58828372e-02 -5.04107848e-02
5.88799894e-01 1.08721741e-01 2.83206820e-01 -1.02988768e+00
-1.15603185e+00 -2.92229559e-03 7.94660091e-01 -1.58699676e-01
-2.20706612e-01 -7.12566197e-01 -1.08524334e+00 -3.76369566e-01
-2.37299517e-01 7.39324689e-01 4.32158709e-01 9.67362821e-01
4.76674765e-01 4.72974241e-01 4.86976892e-01 -7.16631889e-01
-7.15162456e-01 -1.42373669e+00 -3.44717205e-01 2.58067250e-01
1.48575798e-01 1.32275775e-01 -1.19698070e-01 1.69918656e-01] | [12.283703804016113, 9.499119758605957] |
76f87031-ec31-41b6-8790-47f4836166e0 | semi-supervised-local-cluster-extraction-by | 2211.11114 | null | https://arxiv.org/abs/2211.11114v1 | https://arxiv.org/pdf/2211.11114v1.pdf | Semi-supervised Local Cluster Extraction by Compressive Sensing | Local clustering problem aims at extracting a small local structure inside a graph without the necessity of knowing the entire graph structure. As the local structure is usually small in size compared to the entire graph, one can think of it as a compressive sensing problem where the indices of target cluster can be thought as a sparse solution to a linear system. In this paper, we propose a new semi-supervised local cluster extraction approach by applying the idea of compressive sensing based on two pioneering works under the same framework. Our approves improves the existing works by making the initial cut to be the entire graph and hence overcomes a major limitation of existing works, which is the low quality of initial cut. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our approach. | ['Sheng Li', 'Ming-Jun Lai', 'Zhaiming Shen'] | 2022-11-20 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 4.51415658e-01 2.75752127e-01 -2.75004476e-01 7.53917843e-02
-6.01721525e-01 -4.49982792e-01 1.70201689e-01 -1.27771452e-01
1.20420679e-01 4.81000453e-01 1.80455267e-01 -5.13451658e-02
-3.69075686e-01 -7.35461473e-01 -7.12071121e-01 -8.98294330e-01
1.04252966e-02 8.45967829e-02 1.85500264e-01 3.78434658e-02
2.55321383e-01 6.85944930e-02 -9.42782581e-01 1.32442728e-01
8.90493512e-01 7.50686824e-01 2.59008199e-01 2.37134784e-01
1.13561079e-01 8.78128469e-01 -3.30404758e-01 1.94574058e-01
5.05093217e-01 -8.25314701e-01 -6.63204789e-01 7.49603331e-01
6.02115393e-02 9.55446586e-02 -4.20553267e-01 1.48429179e+00
1.85363248e-01 -2.61450112e-01 1.87838912e-01 -1.10082221e+00
-4.85503137e-01 9.51814532e-01 -1.13502276e+00 6.12124950e-02
4.91959661e-01 -5.61264336e-01 9.07792449e-01 -8.85688782e-01
6.81834877e-01 7.10791528e-01 8.55492830e-01 -9.44884047e-02
-1.16657710e+00 -3.95445496e-01 2.55604655e-01 2.79512674e-01
-2.05863690e+00 -5.59062362e-01 1.26667058e+00 -1.74597308e-01
2.89431185e-01 2.25521788e-01 5.03552258e-01 4.32410002e-01
-2.24100560e-01 6.83056772e-01 1.32222414e+00 -6.78808868e-01
4.49934393e-01 -1.58939213e-01 3.87407951e-02 7.76893735e-01
4.92272943e-01 -2.40120232e-01 -3.86359513e-01 -3.70213181e-01
6.34031415e-01 3.22775900e-01 -7.58303165e-01 -8.80206048e-01
-1.32059956e+00 8.23548794e-01 4.83004808e-01 7.98063993e-01
-4.14720625e-01 -9.35107544e-02 8.06104206e-03 3.52948904e-01
1.76620409e-01 -1.19864002e-01 1.31652743e-01 4.29405659e-01
-1.41487777e+00 -3.43692780e-01 9.21966434e-01 1.07044709e+00
8.78617644e-01 5.77748828e-02 3.48513961e-01 3.44521284e-01
4.07726794e-01 3.57825547e-01 3.55717748e-01 -8.41176689e-01
4.92899299e-01 6.93464994e-01 -1.68390259e-01 -1.50738442e+00
-3.17545235e-02 -7.00194180e-01 -1.21283853e+00 -2.41038069e-01
2.35618442e-01 -3.67925972e-01 -5.54663837e-01 1.44389641e+00
3.77701700e-01 7.23335087e-01 -7.67792165e-02 7.79134870e-01
4.92593527e-01 6.50896251e-01 -6.43887758e-01 -6.70515358e-01
7.81721890e-01 -1.06400394e+00 -6.77886784e-01 -1.59762681e-01
3.09076875e-01 -6.48258150e-01 3.03289652e-01 5.58452547e-01
-8.37259412e-01 -4.10781652e-01 -1.27528298e+00 5.27667999e-01
1.13099962e-01 2.46627763e-01 5.26179969e-01 5.89427054e-01
-1.35136425e+00 4.07767504e-01 -6.85596347e-01 -3.92207086e-01
1.59056336e-01 3.03729564e-01 -4.17667031e-01 -6.00910902e-01
-5.89979172e-01 1.61809683e-01 5.77192783e-01 1.62239835e-01
-5.37664831e-01 -3.01591843e-01 -7.57631719e-01 2.18152568e-01
9.99292076e-01 -3.45626742e-01 3.97320688e-01 -8.81999254e-01
-1.08970547e+00 3.25324267e-01 -3.02346170e-01 -4.20572519e-01
1.67803735e-01 1.66643739e-01 -3.41651410e-01 5.86602807e-01
1.39088735e-01 -1.51987880e-01 1.20090914e+00 -1.53208983e+00
-1.36996925e-01 -4.32677031e-01 -2.88306832e-01 1.39723182e-01
-3.21077794e-01 -3.72774273e-01 -6.25981390e-01 -6.78695679e-01
8.11283827e-01 -1.02222896e+00 -6.07495666e-01 -4.55190569e-01
-5.47922492e-01 1.67254254e-01 1.19697475e+00 -6.10431552e-01
1.55320466e+00 -2.38273859e+00 3.27029437e-01 7.90920734e-01
7.54431844e-01 9.60778967e-02 -1.28511101e-01 8.58479977e-01
-3.95940721e-01 -4.26157154e-02 -4.92122293e-01 -8.30546841e-02
-2.48418689e-01 5.88493273e-02 -1.21880114e-01 8.75712752e-01
-4.09602255e-01 7.08023071e-01 -9.85420048e-01 -7.31131375e-01
2.10370332e-01 3.96646947e-01 -3.59096885e-01 -1.52164757e-01
3.52041453e-01 4.20316786e-01 -7.28789866e-01 6.18119597e-01
1.07536900e+00 -4.34995592e-01 6.01555347e-01 -1.87515289e-01
1.03105925e-01 -1.95816636e-01 -1.98015189e+00 2.05609822e+00
1.34831041e-01 4.29466926e-02 7.42438018e-01 -1.84292698e+00
8.26164842e-01 3.19076627e-01 1.00018287e+00 -1.31697968e-01
1.26894802e-01 7.71702453e-02 -1.37756318e-01 -6.50115758e-02
8.15545842e-02 -1.43942863e-01 -7.32761845e-02 5.64180255e-01
-1.59383014e-01 1.07430182e-01 2.37507313e-01 6.88613117e-01
1.34979022e+00 -2.37817004e-01 6.39771938e-01 -4.19120193e-01
6.23097360e-01 -1.41554147e-01 6.52988374e-01 7.44451821e-01
-6.47885278e-02 7.84151673e-01 2.89068758e-01 8.97287577e-02
-6.09741807e-01 -9.18439150e-01 9.80757251e-02 2.66645730e-01
3.38822752e-01 -9.48905528e-01 -8.78138900e-01 -7.44107485e-01
-2.87664354e-01 -1.01743579e-01 -5.04616082e-01 -1.38919611e-04
-4.32180554e-01 -7.02253520e-01 1.32186443e-01 3.35101873e-01
4.43200856e-01 -4.67218906e-01 -1.98706403e-01 1.19386576e-01
-5.48423588e-01 -1.31545866e+00 -5.60015202e-01 -1.19381048e-01
-9.46719646e-01 -1.19564497e+00 -5.59554994e-01 -1.03512514e+00
9.70166028e-01 1.15906870e+00 8.23064387e-01 5.02397835e-01
-1.15155093e-01 4.26859647e-01 -7.04085588e-01 9.87809375e-02
-1.27672732e-01 -1.94154233e-02 -4.52770367e-02 5.36797702e-01
7.45643973e-02 -9.41114724e-01 -4.15091753e-01 1.78330198e-01
-9.98616457e-01 -2.07666039e-01 7.53534317e-01 5.83705544e-01
9.21202421e-01 7.50556946e-01 5.24084091e-01 -1.12304592e+00
5.34766853e-01 -8.28632832e-01 -2.85996497e-01 2.67355859e-01
-6.62114322e-01 -3.15389037e-02 8.34349275e-01 -1.73299201e-02
-6.53797030e-01 5.32346725e-01 4.61746067e-01 -5.79266906e-01
5.21568172e-02 9.56255674e-01 -2.07725585e-01 -3.60528558e-01
1.53266862e-01 5.56890547e-01 1.25950780e-02 -5.06518602e-01
4.26971048e-01 5.50241292e-01 5.11098087e-01 -4.71812457e-01
1.39316630e+00 8.26632321e-01 1.96243539e-01 -9.68467891e-01
-6.18180394e-01 -9.69055772e-01 -9.93785441e-01 -2.54954062e-02
3.23386103e-01 -1.06874311e+00 -3.70702684e-01 4.09917869e-02
-5.31520426e-01 1.59000173e-01 -4.75872129e-01 4.90825951e-01
-1.95668861e-01 1.17144465e+00 -3.79782945e-01 -5.40866733e-01
3.86353247e-02 -7.70586073e-01 1.06303072e+00 -1.13849699e-01
1.30665481e-01 -1.01701355e+00 1.90478280e-01 3.52215856e-01
2.88492590e-01 6.31769359e-01 4.67176139e-01 -2.74661005e-01
-6.35464013e-01 -4.03871596e-01 -9.89735872e-02 2.00807035e-01
2.17569947e-01 -2.93136775e-01 -5.00600040e-01 -8.11754048e-01
5.72587788e-01 -1.51402593e-01 9.33564842e-01 3.24463248e-01
9.88021672e-01 -3.01548898e-01 -5.58609188e-01 5.53109348e-01
2.02226090e+00 -1.45170867e-01 5.63654900e-01 -2.48458505e-01
9.09589589e-01 1.77632585e-01 4.59874302e-01 6.20439947e-01
2.27421954e-01 4.37256098e-01 3.92210096e-01 -3.68784443e-02
-1.25229970e-01 -3.19348425e-01 2.62049913e-01 1.46864235e+00
-1.24863699e-01 -1.34853218e-02 -5.41920722e-01 8.10786426e-01
-1.97368205e+00 -1.08707583e+00 -3.27271521e-01 2.17927074e+00
4.26076800e-01 -2.02967793e-01 3.59949358e-02 6.31438673e-01
9.62000370e-01 4.28630620e-01 -3.12558591e-01 2.77635217e-01
-1.17665142e-01 2.09551081e-01 3.63504231e-01 5.64376354e-01
-9.06175494e-01 6.43095493e-01 6.02196312e+00 9.13315833e-01
-8.47340405e-01 9.34809074e-02 2.30314612e-01 1.02476336e-01
-6.21566884e-02 5.01549721e-01 -3.00451428e-01 3.40970665e-01
6.07180595e-01 -1.56750306e-01 7.22408235e-01 5.75809896e-01
4.75286283e-02 -1.71455815e-01 -7.42063701e-01 1.07659817e+00
4.35091525e-01 -1.16492617e+00 -1.61107585e-01 2.85158873e-01
1.14028645e+00 9.79251713e-02 -2.52260089e-01 -2.42935687e-01
3.65506560e-01 -8.43985021e-01 4.07617122e-01 3.36233735e-01
6.81785405e-01 -7.08079994e-01 6.14564598e-01 6.77798092e-01
-1.66247106e+00 2.50773318e-02 -4.05756056e-01 -1.98053584e-01
9.99139547e-02 1.07784140e+00 -4.69360918e-01 1.12063241e+00
5.33296704e-01 1.10994840e+00 -4.25485313e-01 8.81139576e-01
-1.23727709e-01 9.00620282e-01 -5.99301100e-01 4.64291483e-01
2.78368741e-01 -7.39798844e-01 7.18738317e-01 8.69655013e-01
4.00134951e-01 3.42014611e-01 7.51304746e-01 6.81967497e-01
-8.76756459e-02 2.13911206e-01 -8.46324325e-01 5.38847707e-02
5.70678234e-01 1.29523146e+00 -9.94742572e-01 -2.64496714e-01
-6.68815732e-01 9.26637352e-01 1.78320304e-01 4.20998931e-01
-5.08647025e-01 -3.53979051e-01 -3.65288287e-01 2.01317266e-01
9.28753078e-01 -3.58547807e-01 -3.13664198e-01 -1.41929531e+00
3.66758585e-01 -1.00788641e+00 4.77565467e-01 -3.48062098e-01
-8.67020845e-01 3.27287018e-01 -2.03692168e-01 -1.53035951e+00
1.39101418e-02 -1.18663330e-02 -6.72127068e-01 3.20490152e-01
-1.22981012e+00 -1.15016079e+00 -5.14669240e-01 1.02558601e+00
2.55213737e-01 8.67129490e-02 4.70175833e-01 6.55526966e-02
-5.25810719e-01 2.63079435e-01 4.78123218e-01 2.64962643e-01
4.84648049e-01 -1.03977430e+00 -4.37155485e-01 1.44883931e+00
3.75990272e-01 6.79797649e-01 3.44131559e-01 -7.66996562e-01
-1.64918077e+00 -1.04761946e+00 7.19372869e-01 6.19288236e-02
7.66093016e-01 -2.70772308e-01 -6.99453533e-01 8.43731225e-01
4.63843495e-01 2.05078721e-01 8.01334918e-01 -1.28917024e-01
-1.09271385e-01 -1.21671729e-01 -1.11922991e+00 7.74543136e-02
1.17284858e+00 -3.87831867e-01 -5.18401206e-01 4.36950326e-01
3.18124682e-01 -1.55545063e-02 -1.03235805e+00 3.11206520e-01
-4.21253443e-02 -1.00451779e+00 9.71997440e-01 -2.67917868e-02
2.38292187e-01 -6.94253623e-01 -3.40418011e-01 -1.29965436e+00
-4.81116951e-01 -9.47089553e-01 -3.36904764e-01 1.15197277e+00
-1.82451665e-01 -4.71342832e-01 9.11887109e-01 -1.84407040e-01
7.66822323e-02 -4.93512750e-01 -9.04082477e-01 -7.12197065e-01
-4.69783783e-01 1.01137245e-02 5.54477990e-01 1.51226115e+00
3.72878313e-01 4.86551583e-01 -5.01297712e-01 4.38204557e-01
1.13002515e+00 6.53131902e-01 5.76079369e-01 -1.30463254e+00
-5.65984428e-01 1.20796710e-02 -3.89705062e-01 -1.09190655e+00
-2.65525933e-02 -1.04161251e+00 -7.88572505e-02 -1.46988821e+00
4.42397565e-01 -4.26434904e-01 -3.95783424e-01 3.60614330e-01
2.03944799e-02 3.40021819e-01 2.69957870e-01 4.68418211e-01
-1.01243091e+00 4.62722480e-01 9.84718323e-01 -1.97222680e-01
-5.66335358e-02 -4.77129333e-02 -1.20195162e+00 7.04653502e-01
5.71647525e-01 -6.17285132e-01 -7.23006487e-01 -3.28123331e-01
-4.80805933e-02 3.97429526e-01 1.34706795e-01 -1.25764668e+00
5.72786391e-01 -5.58996089e-02 -2.61938609e-02 -5.37196815e-01
1.83624122e-02 -1.14796722e+00 3.01195800e-01 6.42161787e-01
1.94575623e-01 -1.83136165e-01 -3.30637932e-01 1.03726101e+00
-5.29436648e-01 -2.93574572e-01 5.86688697e-01 -2.61662066e-01
-6.49820268e-01 2.05294311e-01 6.05630316e-03 6.42468268e-03
1.22886062e+00 -3.88640493e-01 4.29962166e-02 -5.57707429e-01
-8.61787498e-01 6.77168146e-02 6.40330493e-01 1.04366522e-02
6.97606564e-01 -1.24797726e+00 -8.40408444e-01 2.26601794e-01
-1.77515652e-02 9.52234864e-02 1.73914686e-01 1.15290606e+00
-4.05399591e-01 5.16332209e-01 2.38790110e-01 -5.43439567e-01
-1.15608215e+00 9.95587885e-01 -1.40310839e-01 -3.58782262e-01
-7.37926304e-01 4.28136587e-01 1.02022119e-01 -1.78738549e-01
-5.63407429e-02 -9.45147723e-02 -1.71462283e-01 -3.09440076e-01
5.24162710e-01 4.79203165e-01 -2.02554706e-02 -8.94020498e-01
-3.50845665e-01 9.11079109e-01 1.40088722e-01 3.25987577e-01
1.58678663e+00 -5.46254337e-01 -5.86652339e-01 1.47058681e-01
1.24488473e+00 6.11334145e-01 -8.70950997e-01 -6.31967366e-01
-1.69934854e-01 -5.14562249e-01 2.65995920e-01 -1.18828319e-01
-1.67188227e+00 4.05248404e-01 2.18751714e-01 3.96755099e-01
1.33121955e+00 6.17818199e-02 7.44119167e-01 4.32926238e-01
7.12565184e-01 -9.42932904e-01 -1.14601389e-01 -3.37670147e-02
5.14756680e-01 -1.04554057e+00 5.10089219e-01 -1.02220535e+00
-2.91386962e-01 8.70315373e-01 3.46657708e-02 -5.64108014e-01
9.93994474e-01 3.61389250e-01 -3.36281061e-01 -4.42115277e-01
-1.85808748e-01 -1.33910358e-01 1.28705725e-01 5.43896914e-01
3.53103876e-02 1.22483984e-01 -5.01499116e-01 4.37783092e-01
5.91806620e-02 1.27933204e-01 8.04976583e-01 1.14916408e+00
-5.37176311e-01 -1.13509417e+00 -6.65950596e-01 4.06379163e-01
-2.88101643e-01 -1.55260796e-02 -6.26394272e-01 5.32398403e-01
3.32834460e-02 1.29804909e+00 -4.61591929e-01 -4.67014611e-01
-1.06036402e-01 -3.43685389e-01 3.39137942e-01 -7.98920453e-01
3.19780186e-02 4.42435652e-01 -3.59903753e-01 -6.61409318e-01
-8.79093468e-01 -8.34318876e-01 -1.15299261e+00 -9.82592478e-02
-5.07132053e-01 6.08156919e-01 2.28469491e-01 9.66941059e-01
4.32227582e-01 2.35379174e-01 9.00353670e-01 -5.43695629e-01
-4.86203998e-01 -7.36835003e-01 -1.05924892e+00 3.85526717e-01
2.20746994e-01 -3.89926255e-01 -4.43243742e-01 1.77494809e-01] | [7.7162933349609375, 4.748518943786621] |
afa53faa-2bf7-4e9a-9d17-552086228ff6 | action-units-that-constitute-trainable-micro | 2112.01730 | null | https://arxiv.org/abs/2112.01730v7 | https://arxiv.org/pdf/2112.01730v7.pdf | How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset? | This paper does not contain technical novelty but introduces our key discoveries in a data generation protocol, a database and insights. We aim to address the lack of large-scale datasets in micro-expression (MiE) recognition due to the prohibitive cost of data collection, which renders large-scale training less feasible. To this end, we develop a protocol to automatically synthesize large scale MiE training data that allow us to train improved recognition models for real-world test data. Specifically, we discover three types of Action Units (AUs) that can constitute trainable MiEs. These AUs come from real-world MiEs, early frames of macro-expression videos, and the relationship between AUs and expression categories defined by human expert knowledge. With these AUs, our protocol then employs large numbers of face images of various identities and an off-the-shelf face generator for MiE synthesis, yielding the MiE-X dataset. MiE recognition models are trained or pre-trained on MiE-X and evaluated on real-world test sets, where very competitive accuracy is obtained. Experimental results not only validate the effectiveness of the discovered AUs and MiE-X dataset but also reveal some interesting properties of MiEs: they generalize across faces, are close to early-stage macro-expressions, and can be manually defined. | ['Liang Zheng', 'Tom Gedeon', 'Zhongdao Wang', 'Yuchi Liu'] | 2021-12-03 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 3.95966291e-01 8.58682021e-02 -9.17622168e-03 -7.11272359e-01
-6.57264471e-01 -4.69537020e-01 5.87701797e-01 -9.76751566e-01
8.27751495e-03 7.92538583e-01 -5.15740514e-02 2.61767179e-01
4.17747349e-02 -6.60489321e-01 -8.55034947e-01 -6.49000823e-01
-2.60952920e-01 2.43407711e-01 -4.31660384e-01 -3.14476639e-01
-1.94317535e-01 6.02084458e-01 -2.02074075e+00 6.95950806e-01
5.11948287e-01 1.30897820e+00 -2.53967583e-01 5.37867606e-01
2.21388012e-01 8.51531684e-01 -9.76173460e-01 -9.41916764e-01
3.17462057e-01 -8.23258519e-01 -4.56372350e-01 3.29086959e-01
6.16811395e-01 -3.71886104e-01 -9.29086804e-02 8.60356629e-01
5.72640657e-01 -2.22004294e-01 5.79408407e-01 -1.68839276e+00
-6.82099998e-01 2.25769997e-01 -3.30883175e-01 -2.33451307e-01
5.17296016e-01 2.60819763e-01 7.36732721e-01 -1.07309461e+00
1.00772715e+00 1.21901226e+00 6.03978217e-01 1.26595712e+00
-1.04332316e+00 -9.53686893e-01 -2.72615552e-01 3.86992954e-02
-1.59167910e+00 -1.03898621e+00 7.04871774e-01 -2.94917554e-01
7.04191267e-01 3.63135427e-01 6.47462547e-01 1.68574357e+00
-1.60033822e-01 8.18563402e-01 1.33891833e+00 -3.83539915e-01
2.00260490e-01 2.83128053e-01 -3.32208306e-01 9.20205832e-01
-2.48245299e-01 2.38907278e-01 -7.68904686e-01 -3.34670730e-02
8.81247461e-01 -3.87971491e-01 -4.01593179e-01 4.68062460e-02
-9.19680953e-01 5.25869668e-01 -5.68824075e-02 2.98144609e-01
-4.02674496e-01 -1.88501075e-01 3.17058355e-01 6.54603422e-01
3.54568124e-01 5.22568166e-01 -4.28600580e-01 -4.07635927e-01
-6.18044019e-01 5.84124885e-02 8.63813579e-01 9.62281942e-01
8.23033273e-01 3.51470202e-01 -8.16227198e-02 1.16541445e+00
-1.67111173e-01 4.88465518e-01 4.99494046e-01 -1.01732683e+00
2.09685266e-01 6.10697210e-01 -5.83907776e-02 -8.98226500e-01
-1.07020503e-02 -7.54310517e-03 -8.91647279e-01 2.25904539e-01
3.07746857e-01 -3.64870638e-01 -6.95449054e-01 2.21507740e+00
1.95959941e-01 4.01647627e-01 2.71880090e-01 6.31319344e-01
8.21103275e-01 5.52757382e-01 -1.50417000e-01 -3.51610303e-01
1.18509364e+00 -5.51985562e-01 -6.61774755e-01 1.27188116e-01
5.56140184e-01 -3.71195674e-01 1.08066225e+00 4.24509078e-01
-8.74589503e-01 -7.70000160e-01 -8.49481344e-01 4.98769403e-01
-3.42623144e-01 6.12677276e-01 9.02882576e-01 8.45949590e-01
-1.19524860e+00 4.06795591e-01 -3.10901910e-01 -2.74702311e-01
8.57878745e-01 7.34216809e-01 -8.94695759e-01 2.05191094e-02
-1.05391085e+00 5.89109480e-01 2.02440657e-02 3.89945924e-01
-1.03885221e+00 -6.78481758e-01 -8.79657805e-01 -5.43492325e-02
3.91124636e-01 -3.52255821e-01 1.11340618e+00 -1.86169136e+00
-1.90752268e+00 1.23994505e+00 -1.79247066e-01 -5.69214597e-02
1.51858404e-01 -2.01651994e-02 -8.35541487e-01 2.85343766e-01
-1.60593837e-01 8.75035405e-01 9.42815244e-01 -1.35840261e+00
-3.19993347e-01 -2.29750007e-01 1.29370898e-01 -3.79778951e-01
-4.70099837e-01 3.34873766e-01 -3.93186450e-01 -6.07326627e-01
-7.09282696e-01 -9.48992908e-01 2.70455182e-01 -6.84078336e-02
-4.19934958e-01 -2.28074268e-01 8.01710129e-01 -4.37175959e-01
9.43699658e-01 -2.35235620e+00 -5.46911471e-02 2.98473239e-01
1.08941436e-01 5.13452768e-01 -6.44071043e-01 4.76510748e-02
-3.69298548e-01 2.03337133e-01 1.07323623e-03 -1.08928062e-01
3.55791263e-02 3.06290239e-01 -3.93495440e-01 2.89531481e-02
7.54393160e-01 1.06564629e+00 -6.78779483e-01 -3.26919973e-01
-9.16611925e-02 3.26267064e-01 -5.93030930e-01 7.00024903e-01
-2.06994429e-01 5.37926435e-01 -3.59033138e-01 1.01578236e+00
4.99511719e-01 -9.77136120e-02 1.09359033e-01 -3.43306154e-01
3.26079160e-01 -3.10012698e-01 -8.12803626e-01 1.26021957e+00
-5.28656662e-01 6.74504697e-01 1.27695128e-01 -1.07312024e+00
1.10801601e+00 3.79160166e-01 6.65781021e-01 -6.85662866e-01
3.14458877e-01 2.26382568e-01 2.00868584e-02 -5.54893196e-01
-2.26317719e-02 -2.22941309e-01 -5.16140983e-02 5.02619565e-01
6.23136818e-01 -2.11567134e-02 4.80073631e-01 -1.34650022e-01
1.10444498e+00 2.02310845e-01 1.52338833e-01 -5.33533609e-03
5.98119199e-01 -4.68926638e-01 7.70009995e-01 3.99196893e-01
-6.86228201e-02 5.07350147e-01 7.18402326e-01 -4.72218364e-01
-9.25747514e-01 -9.03327227e-01 -1.15732193e-01 1.15621042e+00
-3.31567705e-01 -6.23661399e-01 -1.13911664e+00 -8.22165787e-01
-3.24063689e-01 3.52535605e-01 -1.05258417e+00 -2.93003201e-01
-4.59916234e-01 -8.82007897e-01 9.13769841e-01 4.43488866e-01
7.02254474e-01 -1.35151649e+00 -2.55328089e-01 4.38597798e-02
-6.76409751e-02 -1.30801225e+00 -4.02432948e-01 -3.08830976e-01
-3.83365512e-01 -1.25147665e+00 -4.49050784e-01 -7.39759445e-01
9.32338655e-01 -2.45741874e-01 1.31799006e+00 1.45730972e-01
-4.76375878e-01 4.72038239e-01 -4.07245696e-01 -3.71522278e-01
-6.36609018e-01 -4.94802326e-01 1.97616309e-01 7.14466631e-01
6.41192973e-01 -7.41417229e-01 -2.31226787e-01 6.44638717e-01
-7.96661377e-01 -3.45692299e-02 7.59802103e-01 9.41375852e-01
5.57039380e-01 -1.37150154e-01 8.75863791e-01 -7.97933877e-01
4.73583698e-01 -2.65388131e-01 -5.55390894e-01 6.42557025e-01
-1.08734027e-01 -1.35286465e-01 6.79574847e-01 -7.65712738e-01
-1.27513611e+00 1.81695759e-01 -4.01259005e-01 -6.53638244e-01
-1.79519013e-01 1.90085515e-01 -6.11323655e-01 -1.42678291e-01
5.76774299e-01 2.94531584e-01 4.77204435e-02 -1.15676507e-01
3.29636097e-01 8.30119193e-01 7.08204389e-01 -9.12557364e-01
5.87752044e-01 2.34071776e-01 -1.26359195e-01 -9.56495881e-01
-7.17203796e-01 2.67956376e-01 -6.58583164e-01 -3.23702931e-01
7.08262324e-01 -8.99449110e-01 -9.32887673e-01 6.59481764e-01
-1.05908370e+00 -4.55710709e-01 -3.30740124e-01 2.42071524e-02
-6.77564323e-01 -1.95026398e-01 -6.54682875e-01 -6.82879090e-01
-2.91905582e-01 -1.01375043e+00 1.33778751e+00 3.32476050e-01
-3.76853496e-01 -5.06645799e-01 -5.82405999e-02 3.10590178e-01
1.51891291e-01 5.54400742e-01 6.90781236e-01 -3.95427078e-01
-4.67393547e-01 -1.96421266e-01 -1.26428932e-01 7.00433552e-01
1.88665971e-01 5.82698703e-01 -1.15447640e+00 -1.09168757e-02
-1.91829130e-01 -9.16083097e-01 3.38634491e-01 -6.82875793e-03
1.50522625e+00 -4.79817331e-01 -1.13952495e-01 8.86922002e-01
9.64218199e-01 3.59925121e-01 8.74283671e-01 -2.89181978e-01
3.50696832e-01 8.09637368e-01 5.05787492e-01 5.33348083e-01
8.38933140e-02 8.95362496e-01 -8.54917150e-03 -1.76258758e-01
-8.76187757e-02 -3.32335949e-01 7.58715153e-01 7.67425060e-01
-4.94715899e-01 -1.32072598e-01 -4.88789111e-01 2.86646366e-01
-1.36554670e+00 -1.30060971e+00 4.36128259e-01 1.87701273e+00
1.18907797e+00 -2.92302012e-01 2.16993600e-01 -1.23670630e-01
5.44732571e-01 -2.20436737e-01 -4.24342155e-01 -3.63161892e-01
-4.17222619e-01 8.04591119e-01 -2.63853133e-01 6.19660653e-02
-9.50748861e-01 1.01756632e+00 6.14281845e+00 7.88581252e-01
-1.42316985e+00 1.00320749e-01 9.96225238e-01 -2.15437293e-01
1.21393017e-02 -5.00556886e-01 -8.22459400e-01 3.50252599e-01
1.07630730e+00 -1.90802053e-01 5.20551682e-01 1.11682594e+00
-5.84040470e-02 3.51885468e-01 -1.42580569e+00 1.31366384e+00
2.89985180e-01 -1.35571587e+00 1.01756506e-01 1.34190068e-01
8.53608727e-01 -3.95609498e-01 -3.80348191e-02 6.66714251e-01
3.83643776e-01 -1.32049179e+00 5.61987519e-01 4.82840836e-01
1.40135217e+00 -6.41715407e-01 6.69328332e-01 5.32540865e-02
-9.94222641e-01 -2.17471167e-01 -3.70891303e-01 1.92322299e-01
-1.03276506e-01 4.45831895e-01 -5.98184705e-01 3.08389932e-01
5.47232211e-01 7.10953355e-01 -7.47859597e-01 3.39304179e-01
-3.87921542e-01 6.70308709e-01 -3.17151994e-01 -7.00794440e-03
-2.23757207e-01 -3.44119251e-01 8.17859173e-02 1.08816493e+00
5.22034764e-01 2.10774422e-01 -2.64311910e-01 1.03108561e+00
-5.48827052e-01 -2.59428788e-02 -8.15645278e-01 -4.65227008e-01
3.11827421e-01 1.54787111e+00 -1.45186692e-01 -3.91685873e-01
-4.47877705e-01 1.12983620e+00 3.46615195e-01 4.38016534e-01
-8.38362753e-01 -1.26447916e-01 1.05487835e+00 -9.44937393e-02
2.19961137e-01 2.75652856e-01 4.23261255e-01 -1.54678583e+00
7.36892521e-02 -1.54087675e+00 1.34153575e-01 -7.93420911e-01
-1.40972626e+00 1.12534201e+00 -1.34359106e-01 -1.15920925e+00
-7.51555443e-01 -8.61342132e-01 -6.43573940e-01 3.97871822e-01
-8.54887128e-01 -1.40338874e+00 -4.45826381e-01 7.86370277e-01
3.47512752e-01 -5.59728682e-01 1.19699156e+00 4.91005480e-01
-8.49578381e-01 1.09469771e+00 -2.82452941e-01 6.12639129e-01
5.28736830e-01 -7.50071049e-01 3.74568328e-02 5.81558406e-01
6.10966802e-01 7.18564689e-01 1.52789623e-01 -1.97528079e-01
-1.48953485e+00 -1.11655283e+00 5.93468070e-01 -6.86610162e-01
3.72625977e-01 -1.02246559e+00 -6.99146986e-01 6.86197519e-01
-1.38723552e-01 3.32698792e-01 1.04475272e+00 6.56668767e-02
-3.79146546e-01 -4.51688528e-01 -1.14087546e+00 6.12679660e-01
1.48419392e+00 -7.16266453e-01 -2.75230139e-01 1.66371122e-01
2.04841122e-01 -1.90172538e-01 -1.08450627e+00 6.56285584e-01
8.33509803e-01 -9.73381996e-01 8.47888470e-01 -7.63562441e-01
7.45782197e-01 3.54168527e-02 -1.23166859e-01 -1.25729954e+00
2.20509171e-01 -8.87667120e-01 -3.77007984e-02 1.73738110e+00
4.70322520e-01 -4.47210491e-01 8.54184687e-01 6.56231999e-01
1.95119575e-01 -9.38360393e-01 -1.01444650e+00 -7.62559831e-01
-3.54414910e-01 -3.70412856e-01 9.21264529e-01 1.08248293e+00
-1.53879732e-01 4.87493604e-01 -6.14413798e-01 -2.84286618e-01
3.45455408e-01 2.74306059e-01 1.27883995e+00 -9.81499672e-01
-3.47024351e-01 -4.01898265e-01 -5.70420682e-01 -8.51951122e-01
7.41044998e-01 -6.99832797e-01 -3.40856537e-02 -5.54702461e-01
1.04317717e-01 -3.71572822e-01 -7.94473365e-02 7.92789638e-01
-7.54711926e-02 6.61503732e-01 -4.91006346e-03 -2.92797796e-02
-4.28139687e-01 9.14205670e-01 1.21310782e+00 -1.00599574e-02
5.72296977e-03 -3.92660797e-02 -7.01024711e-01 5.86301088e-01
5.38491726e-01 -1.88411430e-01 -5.65540016e-01 -3.71889994e-02
-5.11916541e-03 2.75361501e-02 3.44849199e-01 -9.53211248e-01
-4.79332358e-01 -1.09872319e-01 5.83407581e-01 1.00687474e-01
4.86598969e-01 -6.16700470e-01 3.93322080e-01 -8.31535310e-02
-2.07788572e-01 -2.20713109e-01 1.56843364e-01 2.94236280e-02
-5.18717706e-01 -1.09759264e-01 7.62794733e-01 -4.84193396e-03
-9.63151813e-01 5.41351914e-01 -1.37264922e-01 -4.88553904e-02
1.19089580e+00 -3.06619018e-01 -2.03039706e-01 -6.54545009e-01
-6.27069890e-01 -2.44202241e-01 4.06900138e-01 5.93543410e-01
5.34613967e-01 -1.56189215e+00 -7.67458141e-01 6.23270154e-01
3.51272017e-01 -3.58692855e-01 1.89047195e-02 5.49017191e-01
-6.99798763e-02 1.02083623e-01 -6.06286824e-01 -4.66428250e-01
-1.44683218e+00 4.79431599e-01 3.89823735e-01 -1.01127580e-01
-1.23263821e-01 9.76239145e-01 4.31229919e-01 -5.95726848e-01
-1.35607615e-01 3.92230116e-02 -1.29518762e-01 -4.19597663e-02
7.62331605e-01 -2.66900688e-01 -3.13246608e-01 -9.40299928e-01
-2.19730809e-01 5.13358176e-01 2.79880345e-01 2.23850496e-02
1.48496342e+00 2.64122277e-01 -1.62133321e-01 1.20613314e-02
1.31654954e+00 7.21152872e-02 -1.27204216e+00 1.00996032e-01
-6.34261370e-02 -6.62943721e-01 -5.69058299e-01 -7.55641878e-01
-1.36723030e+00 7.61790395e-01 5.27931631e-01 -3.08887810e-01
1.56488705e+00 1.37900800e-01 7.05215633e-01 5.28517365e-01
5.30239582e-01 -1.06234765e+00 4.93312150e-01 1.60268247e-01
1.19961548e+00 -1.07705331e+00 -4.51340467e-01 -4.75637287e-01
-7.88067043e-01 1.09169328e+00 1.07717848e+00 2.20754176e-01
4.04977381e-01 4.20527101e-01 2.53256589e-01 -2.49373347e-01
-1.00196433e+00 -1.53067678e-01 4.86098826e-02 9.40698564e-01
3.56816381e-01 -1.83509827e-01 2.66462117e-02 8.72310519e-01
-4.67721015e-01 5.59697688e-01 2.22539186e-01 6.30632102e-01
1.29201993e-01 -1.35342121e+00 -5.30556552e-02 4.31639493e-01
-4.63272452e-01 1.76281363e-01 -7.58640230e-01 8.67196560e-01
4.46773440e-01 7.26793289e-01 6.58453777e-02 -7.99590826e-01
4.30799782e-01 3.33270848e-01 8.18045795e-01 -4.89421219e-01
-4.15126473e-01 -2.72264183e-01 5.48032582e-01 -6.93580151e-01
-6.54773533e-01 -4.86692488e-01 -8.41554344e-01 -2.61687875e-01
-1.20263569e-01 1.30086631e-01 3.62914830e-01 9.14514840e-01
6.92525446e-01 1.05681643e-01 9.46895182e-01 -7.86370158e-01
-3.08846742e-01 -1.04453540e+00 -4.80883688e-01 9.24635112e-01
-1.09290786e-01 -7.25397468e-01 -1.78400934e-01 5.85491717e-01] | [13.391661643981934, 1.3999828100204468] |
448b1faf-ac08-40ca-839e-97850224aa69 | a-hybrid-cnn-rnn-approach-for-survival | 2303.10789 | null | https://arxiv.org/abs/2303.10789v1 | https://arxiv.org/pdf/2303.10789v1.pdf | A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study | In this study, we present a hybrid CNN-RNN approach to investigate long-term survival of subjects in a lung cancer screening study. Subjects who died of cardiovascular and respiratory causes were identified whereby the CNN model was used to capture imaging features in the CT scans and the RNN model was used to investigate time series and thus global information. The models were trained on subjects who underwent cardiovascular and respiratory deaths and a control cohort matched to participant age, gender, and smoking history. The combined model can achieve an AUC of 0.76 which outperforms humans at cardiovascular mortality prediction. The corresponding F1 and Matthews Correlation Coefficient are 0.63 and 0.42 respectively. The generalisability of the model is further validated on an 'external' cohort. The same models were applied to survival analysis with the Cox Proportional Hazard model. It was demonstrated that incorporating the follow-up history can lead to improvement in survival prediction. The Cox neural network can achieve an IPCW C-index of 0.75 on the internal dataset and 0.69 on an external dataset. Delineating imaging features associated with long-term survival can help focus preventative interventions appropriately, particularly for under-recognised pathologies thereby potentially reducing patient morbidity. | ['Joseph Jacob', 'Daniel C. Alexander', 'Mark Emberton', 'David Barber', 'Ahmed Shahin', 'An Zhao', 'Shahab Aslani', 'Yaozhi Lu'] | 2023-03-19 | null | null | null | null | ['mortality-prediction', 'survival-analysis'] | ['medical', 'miscellaneous'] | [ 3.49125341e-02 1.03163876e-01 -5.27332187e-01 -2.54877150e-01
-8.34840357e-01 -2.28469774e-01 4.37513828e-01 3.85276794e-01
-8.05301845e-01 5.52107990e-01 4.92792547e-01 -7.16735482e-01
-5.36492109e-01 -8.17408979e-01 -1.60874519e-02 -7.29163349e-01
-4.81793106e-01 4.54487592e-01 7.45129436e-02 1.94140077e-01
-3.68977875e-01 6.59553766e-01 -1.03561592e+00 1.61025584e-01
1.50012374e-01 8.54435027e-01 -9.96778533e-02 1.14108491e+00
3.52153987e-01 9.91086602e-01 -3.78171295e-01 -1.82310045e-01
3.60039286e-02 -3.60988766e-01 -8.02319765e-01 -6.14959598e-01
2.59478986e-01 -3.63543540e-01 -6.34253025e-01 1.65612087e-01
9.33745444e-01 7.46569410e-02 6.28276527e-01 -8.04863453e-01
-2.88140833e-01 3.65537554e-01 -5.51201142e-02 5.40087461e-01
-1.82560936e-01 1.61897540e-01 5.16729474e-01 -3.41394573e-01
4.58097190e-01 8.01573455e-01 1.42209148e+00 8.61482203e-01
-1.05311799e+00 -4.63089108e-01 -5.83969712e-01 2.92150886e-03
-1.09490716e+00 -1.51292905e-01 4.01327945e-02 -4.60831374e-01
8.96460712e-01 5.07876933e-01 8.02436233e-01 1.19278729e+00
5.75028300e-01 6.94735646e-02 7.65302062e-01 -2.50333697e-01
-1.08501807e-01 -9.08889323e-02 1.68875650e-01 7.53539920e-01
2.33484164e-01 8.85233283e-01 -4.27996293e-02 -3.51490766e-01
8.16843867e-01 3.13328087e-01 -1.54682277e-02 2.01961145e-01
-1.38344812e+00 9.32658434e-01 6.41331017e-01 5.06787777e-01
-5.96973717e-01 3.94060493e-01 8.77100289e-01 4.85509150e-02
3.41937840e-01 2.18295306e-01 -6.45943701e-01 2.00757921e-01
-1.10352767e+00 -1.62347242e-01 8.11800957e-01 1.66517973e-01
-3.50327104e-01 -5.70680201e-02 -5.48083544e-01 7.23151267e-01
1.57350823e-01 3.22794884e-01 9.35374677e-01 -9.51935709e-01
-1.04659326e-01 4.31964695e-01 -3.17652404e-01 -7.25808084e-01
-1.15355492e+00 -9.65255141e-01 -1.41483426e+00 -1.99108526e-01
5.61872184e-01 -1.74659580e-01 -1.07157707e+00 1.69543934e+00
1.23830199e-01 -1.99615285e-02 2.02485427e-01 4.35617417e-01
1.07740736e+00 1.73043296e-01 7.27031231e-01 -2.61946499e-01
1.58003235e+00 -5.10676086e-01 -4.11303401e-01 2.79338419e-01
8.80902052e-01 -3.68561670e-02 4.05160815e-01 -1.76446185e-01
-9.13245380e-01 -4.48109537e-01 -5.73979497e-01 8.32724571e-02
-3.31250221e-01 3.21633190e-01 3.80931377e-01 8.06403339e-01
-1.25481522e+00 9.30194795e-01 -1.12307680e+00 -7.47846186e-01
5.35578847e-01 6.62954986e-01 -2.31170088e-01 1.43570840e-01
-1.47389126e+00 1.13711250e+00 3.93018544e-01 2.04821806e-02
-1.28196490e+00 -1.04420650e+00 -5.04085302e-01 -3.10493801e-02
-2.10389152e-01 -1.42286754e+00 1.16744351e+00 -5.36674738e-01
-8.59832585e-01 9.22039568e-01 -6.30739033e-02 -8.06797326e-01
8.30070674e-01 1.71152964e-01 -3.55401337e-01 2.41202533e-01
2.87919790e-01 4.92608935e-01 5.77613972e-02 -5.77076316e-01
-5.98116517e-01 -4.59996462e-01 -6.08095884e-01 1.01498589e-01
-3.53343666e-01 1.59800410e-01 -2.83572286e-01 -6.10084116e-01
-2.43673325e-01 -9.94672060e-01 -7.49484479e-01 3.11007593e-02
-2.51820803e-01 -7.97601119e-02 3.69897485e-01 -1.11304414e+00
1.19148958e+00 -1.71947145e+00 -2.46040672e-01 2.64832109e-01
6.68726802e-01 1.62849545e-01 1.18000314e-01 1.75433561e-01
-4.67081219e-01 5.41406572e-01 -5.75409867e-02 -1.15778735e-02
-7.20616698e-01 -4.36630175e-02 6.00247264e-01 6.13311648e-01
8.69084820e-02 1.32083094e+00 -6.94367707e-01 -5.00453949e-01
1.10593244e-01 8.42293203e-01 -9.01323259e-02 1.19867824e-01
5.21176696e-01 4.91485327e-01 -2.74125159e-01 5.08799970e-01
-1.38785290e-02 -4.69376057e-01 2.03233838e-01 1.03830077e-01
-1.48001164e-02 -5.62802814e-02 -1.13514647e-01 1.22127163e+00
-3.94235015e-01 6.54788256e-01 -2.67824054e-01 -7.37343788e-01
7.95544863e-01 8.55573058e-01 7.28760660e-01 -6.10320449e-01
3.31704408e-01 8.70468691e-02 3.31213385e-01 -5.48800349e-01
-1.69826552e-01 -7.58002341e-01 1.19934574e-01 2.10526466e-01
-3.01785886e-01 5.51602423e-01 -1.90536439e-01 6.22712914e-03
1.47168255e+00 -4.04275864e-01 6.37924433e-01 -3.30222219e-01
7.00565696e-01 2.31204763e-01 3.45945716e-01 9.70673561e-01
-4.92209733e-01 6.19026303e-01 5.80708027e-01 -8.46312046e-01
-9.73881185e-01 -9.46296692e-01 -4.23896670e-01 8.27543795e-01
-7.51493633e-01 -3.37015055e-02 -3.48892033e-01 -8.28268290e-01
-1.47688881e-01 5.20350993e-01 -1.06779051e+00 -3.62084538e-01
-5.92730641e-01 -1.15530694e+00 1.05290031e+00 9.03565347e-01
2.13453904e-01 -1.00456691e+00 -8.64991605e-01 3.11931491e-01
-1.53744832e-01 -4.76063907e-01 1.51138501e-02 3.91004205e-01
-1.35259831e+00 -1.35085821e+00 -1.23333204e+00 -6.99705422e-01
3.20383608e-01 -3.11638772e-01 1.31597042e+00 4.87826854e-01
-4.30165827e-01 3.84548128e-01 3.78110409e-02 -4.46677327e-01
-6.88722849e-01 2.94917822e-01 -8.80604982e-02 -6.99899137e-01
1.40645236e-01 -3.45147640e-01 -1.00168073e+00 9.49442312e-02
-5.53182781e-01 -1.87153205e-01 6.72114432e-01 1.11183262e+00
5.12245178e-01 -4.96932631e-03 8.15995753e-01 -9.14288104e-01
3.17613542e-01 -8.57959926e-01 5.53391576e-02 1.55732557e-01
-9.10752594e-01 -4.01765794e-01 4.83813792e-01 -5.59711635e-01
-6.58151329e-01 3.81153859e-02 3.16894837e-02 -2.62024313e-01
-1.84196502e-01 7.11247265e-01 5.32149136e-01 1.75996095e-01
7.21962214e-01 -3.95187251e-02 3.13972414e-01 -1.06376104e-01
-2.77384341e-01 1.84102714e-01 6.80923104e-01 -1.54641986e-01
4.19322073e-01 4.88576472e-01 7.93824077e-01 -6.82718396e-01
-6.51320279e-01 -4.88304943e-01 -8.09091508e-01 -1.60045609e-01
1.05170298e+00 -1.04670775e+00 -7.35153913e-01 1.90952361e-01
-8.12521875e-01 -3.81262720e-01 -3.87583137e-01 8.50593388e-01
-3.55416209e-01 3.32613699e-02 -6.34500325e-01 -6.85929239e-01
-8.85187268e-01 -7.21988976e-01 5.44293702e-01 1.53556257e-01
-5.09383261e-01 -1.61537731e+00 3.79417479e-01 1.01593032e-01
7.72956491e-01 7.97069848e-01 1.02481544e+00 -1.08766174e+00
1.38484731e-01 -7.54651487e-01 -3.95251930e-01 1.13704264e-01
-6.75991997e-02 -2.30627641e-01 -7.87805796e-01 -2.40346491e-01
-1.34836420e-01 8.54217783e-02 1.00701451e+00 9.09207106e-01
1.19595408e+00 -5.56456856e-02 -5.28329492e-01 5.36287785e-01
1.34183764e+00 4.04078603e-01 6.22907400e-01 4.02042866e-01
6.69868767e-01 7.16281712e-01 -2.07049787e-01 7.74358660e-02
3.75892639e-01 2.21477732e-01 3.95332724e-01 -4.06451792e-01
-2.51647025e-01 1.70277730e-02 -3.18004824e-02 4.80351895e-01
-3.82395953e-01 -2.30637178e-01 -1.46381164e+00 7.47696877e-01
-1.35785353e+00 -9.94747221e-01 -7.94765890e-01 2.05689335e+00
4.86083150e-01 -4.24656231e-04 2.47106031e-01 1.45679535e-02
6.91279054e-01 -1.03344791e-01 -3.72493118e-01 -2.77384460e-01
4.08887975e-02 3.62233281e-01 8.30630302e-01 1.65394079e-02
-1.16783512e+00 1.87694788e-01 7.26035929e+00 3.40694129e-01
-9.13224757e-01 3.12546849e-01 1.12951767e+00 -1.46403044e-01
2.42778197e-01 -1.78645805e-01 -3.75173867e-01 1.31789237e-01
1.96239460e+00 -1.05803013e-01 -1.60484895e-01 4.62885618e-01
5.96634388e-01 2.44497210e-02 -8.62619340e-01 3.37034822e-01
-2.11477995e-01 -1.23509991e+00 -3.72653574e-01 2.11763635e-01
2.95410752e-01 3.07557553e-01 -1.94070973e-02 4.15446818e-01
9.88092571e-02 -1.41952515e+00 6.51516691e-02 1.05332994e+00
1.37643957e+00 -6.79981768e-01 1.33452809e+00 3.51438731e-01
-1.17350829e+00 -1.64768592e-01 5.00439741e-02 4.73040603e-02
7.67033249e-02 4.08302069e-01 -1.17693591e+00 4.88890499e-01
7.62901902e-01 6.51878834e-01 -8.73551607e-01 9.68210816e-01
3.39500129e-01 9.71587539e-01 -1.13738626e-01 -3.80640700e-02
1.06802173e-01 5.32912612e-01 2.11946860e-01 1.33788133e+00
4.92380679e-01 2.06823200e-01 -2.52296239e-01 5.07286787e-01
6.72581866e-02 1.32869810e-01 -5.05417049e-01 9.64959711e-02
3.53820249e-02 1.27831924e+00 -8.89414608e-01 -3.49322975e-01
-4.31329340e-01 5.02364516e-01 -1.79927513e-01 7.83114582e-02
-6.36487722e-01 -9.96906534e-02 -6.03761785e-02 3.83898199e-01
-1.01322502e-01 3.85634005e-01 -4.49032098e-01 -6.30101383e-01
-6.67446911e-01 -4.39561069e-01 9.15819645e-01 -5.08931398e-01
-1.36042547e+00 5.37846684e-01 4.12261412e-02 -8.98041010e-01
-1.68042630e-01 -5.70600808e-01 -9.98916268e-01 1.25807393e+00
-1.10285771e+00 -1.08799005e+00 -3.58943969e-01 2.60073245e-01
1.58839121e-01 -3.05577189e-01 1.11326802e+00 1.14783898e-01
-8.19409013e-01 5.06259084e-01 1.85174584e-01 3.68113875e-01
5.04053950e-01 -1.25407076e+00 1.11664385e-01 2.69091249e-01
-6.67580962e-01 7.46765912e-01 1.98313475e-01 -9.59605753e-01
-6.79221988e-01 -1.58890176e+00 1.22158802e+00 -7.08422899e-01
3.98516804e-01 5.80351233e-01 -8.85906935e-01 3.95385295e-01
-1.48977250e-01 1.12710059e-01 1.14613092e+00 9.14128218e-03
-1.35964537e-02 2.19021812e-01 -1.22459781e+00 1.39563039e-01
5.37473142e-01 -4.61012393e-01 -3.19212645e-01 2.11566865e-01
4.61694151e-01 -2.47746870e-01 -1.43062782e+00 6.71236277e-01
1.03963137e+00 -8.49710882e-01 1.33041501e+00 -1.03899407e+00
5.85570097e-01 2.50457972e-01 8.97084847e-02 -6.84865713e-01
-7.11716294e-01 4.53688018e-02 -5.22988811e-02 8.49078655e-01
5.22544920e-01 -4.11296755e-01 9.46895242e-01 6.89534426e-01
1.27432823e-01 -1.16001618e+00 -1.05370629e+00 -5.88731885e-01
5.45364022e-01 -4.83338952e-01 2.50903845e-01 7.36151338e-01
-6.13299191e-01 8.11079666e-02 -1.34705648e-01 1.23746693e-01
5.09283781e-01 -6.70257807e-01 1.37016490e-01 -1.58691478e+00
-8.50934461e-02 -4.11659867e-01 -3.91114354e-01 1.31999344e-01
-2.41308910e-04 -1.12104952e+00 -4.16155905e-01 -1.68751371e+00
8.77404630e-01 -2.52085328e-01 -7.64419138e-01 3.81638378e-01
-1.78364217e-01 2.75671750e-01 4.28867638e-02 4.83794361e-01
-3.24600041e-02 8.41585081e-03 9.51324582e-01 9.40182731e-02
-2.41635472e-01 3.76471698e-01 -6.95287168e-01 5.53774536e-01
1.18040669e+00 -8.14511657e-01 -8.78544450e-02 1.89716756e-01
-4.38454188e-02 6.69807673e-01 1.01525128e+00 -1.09169674e+00
6.86328784e-02 3.29731381e-03 1.16079831e+00 -6.48562789e-01
-1.22121058e-01 -7.17923641e-01 6.09630406e-01 1.27466846e+00
-6.72606528e-01 2.71379709e-01 2.65181631e-01 8.67339313e-01
2.42918432e-01 -9.57379863e-02 8.54343772e-01 -4.30060267e-01
-3.80971394e-02 4.07122493e-01 -6.91723943e-01 -1.65223256e-01
9.54268038e-01 -2.04889685e-01 -1.56484157e-01 -4.42256540e-01
-1.14240754e+00 1.90141112e-01 2.76834983e-02 -4.89725843e-02
4.34922010e-01 -1.34357893e+00 -7.82558739e-01 -6.14401065e-02
-1.67316630e-01 -1.24806434e-01 3.39975506e-01 1.47208977e+00
-7.12495863e-01 7.33871937e-01 -4.96040657e-02 -4.12264943e-01
-1.47304702e+00 3.76627207e-01 1.01015580e+00 -8.19458723e-01
-5.23692012e-01 6.34301782e-01 1.64351225e-01 -2.74027765e-01
1.48259342e-01 -1.08921677e-01 -4.97107536e-01 1.77872583e-01
3.13916415e-01 8.60646129e-01 -9.08508375e-02 -8.15339386e-01
-4.05533254e-01 3.16781908e-01 9.54413414e-03 2.56179785e-03
1.42655110e+00 6.47153240e-03 -9.44447964e-02 4.62765723e-01
1.49519396e+00 -5.09310961e-01 -6.60062492e-01 -1.77737791e-02
1.38328940e-01 3.31835061e-01 3.30714583e-01 -1.18564141e+00
-1.11313450e+00 8.01469386e-01 1.19782841e+00 2.12172404e-01
1.15683186e+00 1.05114989e-01 5.78746200e-01 3.54536697e-02
-3.48136902e-01 -3.40045959e-01 -2.75460988e-01 4.64746356e-01
6.23575449e-01 -1.20447171e+00 1.32493526e-01 1.68366805e-01
-5.33896506e-01 1.45573139e+00 2.54949152e-01 5.80975674e-02
6.84601724e-01 -2.09323511e-01 1.21894509e-01 -5.14161110e-01
-8.16174626e-01 7.97279552e-02 5.63191175e-01 6.51965797e-01
7.96052158e-01 2.00213447e-01 -3.19207489e-01 8.25670779e-01
-1.63231462e-01 2.40086153e-01 3.33801329e-01 6.21280551e-01
6.29217888e-04 -6.75296247e-01 -3.42520744e-01 1.13083220e+00
-1.04936206e+00 3.25215049e-03 -3.85418653e-01 1.16151118e+00
1.16732270e-01 6.28525674e-01 4.85779196e-02 -2.56318867e-01
2.98242092e-01 4.55962837e-01 -2.12997943e-01 -4.33752477e-01
-1.07474518e+00 -6.91125467e-02 2.46197551e-01 -4.11736846e-01
-6.21811807e-01 -8.43721330e-01 -1.14978051e+00 -2.00409845e-01
-2.87192732e-01 -1.33984670e-01 4.99589831e-01 5.65633833e-01
7.25336522e-02 1.12463498e+00 3.63070786e-01 -4.07919616e-01
-4.24478054e-01 -9.94299650e-01 -3.17092210e-01 -4.03230451e-02
6.14991009e-01 -2.21128955e-01 -5.37993371e-01 1.80695939e-03] | [15.2874116897583, -2.2323901653289795] |
fb89ed00-cadd-42b6-b623-c4a7b04c2a7c | gender-lost-in-translation-how-bridging-the | 2305.16935 | null | https://arxiv.org/abs/2305.16935v1 | https://arxiv.org/pdf/2305.16935v1.pdf | Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation | Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In this work, we explore how bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT, specifically for zero-shot directions. We evaluate translation between grammatical gender languages which requires preserving the inherent gender information from the source in the target language. We study the effect of encouraging language-agnostic hidden representations on models' ability to preserve gender and compare pivot-based and zero-shot translation regarding the influence of the bridge language (participating in all language pairs during training) on gender preservation. We find that language-agnostic representations mitigate zero-shot models' masculine bias, and with increased levels of gender inflection in the bridge language, pivoting surpasses zero-shot translation regarding fairer gender preservation for speaker-related gender agreement. | ['Jan Niehues', 'Lena Cabrera'] | 2023-05-26 | null | null | null | null | ['nmt'] | ['computer-code'] | [-3.60204548e-01 5.40473819e-01 -6.47363842e-01 -6.73111558e-01
-7.57873714e-01 -5.34319162e-01 1.08600473e+00 5.30482754e-02
-7.33178556e-01 7.72146702e-01 6.30051613e-01 -6.38378680e-01
2.08115995e-01 -8.00127983e-01 -7.52662361e-01 -5.83626270e-01
4.28734004e-01 1.00404334e+00 -8.43437970e-01 -7.13982224e-01
1.18533939e-01 -5.14259078e-02 -9.97396827e-01 1.05731674e-01
1.05550563e+00 -2.97816992e-02 -3.45039278e-01 1.03478417e-01
-1.47932231e-01 3.36812466e-01 -6.19953573e-01 -1.04811037e+00
4.52910721e-01 -4.88734186e-01 -6.75609708e-01 -5.16861320e-01
1.06026673e+00 -1.16885558e-01 -2.35134855e-01 9.99550045e-01
7.70823479e-01 -1.00053623e-01 7.90178120e-01 -1.35695827e+00
-1.01794171e+00 1.34697735e+00 -8.68625343e-01 2.08194628e-01
-1.75454319e-01 2.99872190e-01 9.88584578e-01 -9.72998440e-01
1.06899345e+00 2.00253868e+00 8.05513561e-01 1.07994449e+00
-1.66682065e+00 -1.17181349e+00 -1.00205429e-01 -2.63491064e-01
-1.32798803e+00 -8.97873104e-01 5.43465674e-01 -4.94550765e-01
7.20432699e-01 2.14356214e-01 3.55825961e-01 1.67900205e+00
6.42681777e-01 7.00859204e-02 1.34610438e+00 -3.04072678e-01
-2.41283432e-01 2.83385366e-01 2.11732611e-01 3.58837783e-01
6.96081519e-01 3.65538329e-01 -9.07187283e-01 -2.28577912e-01
4.00318176e-01 -5.20795643e-01 3.07697952e-01 -1.74340487e-01
-1.19979060e+00 1.02375722e+00 1.93137392e-01 5.78692377e-01
6.87996522e-02 5.19884229e-02 5.76020300e-01 5.59936047e-01
7.64787257e-01 5.96434891e-01 -4.24816608e-01 -1.14970999e-02
-1.20794332e+00 4.36090976e-01 6.09417677e-01 7.86599636e-01
7.82861114e-01 5.47335505e-01 -5.59146464e-01 9.70578730e-01
2.12771781e-02 7.91242778e-01 5.99764943e-01 -8.51051688e-01
7.44286716e-01 3.78891289e-01 -1.79196298e-01 -9.27423358e-01
-4.09866720e-01 -6.09559357e-01 -8.42043757e-01 -1.46503985e-01
8.46258938e-01 -1.40747562e-01 -7.68773317e-01 2.60197830e+00
3.54651101e-02 -8.33745718e-01 7.33346865e-02 8.35021973e-01
6.22627854e-01 4.66448843e-01 6.86256051e-01 -2.25148484e-01
1.47606885e+00 -3.46816778e-01 -5.60012162e-01 -7.73120344e-01
8.92059982e-01 -8.71268868e-01 1.18810654e+00 -4.67867494e-01
-1.22772098e+00 -4.04988676e-01 -8.76425803e-01 -5.57834625e-01
-3.76124471e-01 8.67131054e-02 4.91195023e-01 1.10094035e+00
-1.07785809e+00 7.74416924e-01 -5.84587693e-01 -6.03890240e-01
4.80200917e-01 5.76498389e-01 -5.46032965e-01 2.23858003e-02
-1.49493039e+00 1.46956515e+00 -2.67657161e-01 -9.61144269e-02
-5.69531858e-01 -9.71283615e-01 -9.54027295e-01 -1.33089840e-01
-2.27905318e-01 -8.25316548e-01 1.04750431e+00 -1.34193134e+00
-8.77512932e-01 1.48120952e+00 -4.04207379e-01 -3.05856705e-01
6.18236601e-01 6.79097399e-02 -2.93950886e-01 -5.85430026e-01
7.76076496e-01 1.10975111e+00 6.04698122e-01 -1.18944526e+00
-8.75825509e-02 -1.02839780e+00 -2.73906946e-01 3.47622663e-01
-4.48333949e-01 2.99272805e-01 3.60691011e-01 -6.76090956e-01
9.30467844e-02 -1.07775450e+00 -4.26183492e-02 -4.02328581e-01
-5.16277313e-01 -1.82678893e-01 3.41874242e-01 -1.17462194e+00
8.50738883e-01 -2.07546353e+00 2.01271892e-01 6.09786026e-02
3.22149754e-01 -3.91832203e-01 -1.90889850e-01 4.01923746e-01
-6.57592639e-02 4.50812966e-01 -1.82043929e-02 -7.00598240e-01
3.38186204e-01 2.64342666e-01 -3.34782928e-01 5.98280132e-01
5.69700375e-02 1.07590818e+00 -5.57308853e-01 -6.00093246e-01
-4.52710181e-01 5.54033279e-01 -7.52278984e-01 -4.51270103e-01
2.87353128e-01 6.55148268e-01 4.83534098e-01 6.01959825e-01
8.46159816e-01 6.19792640e-01 5.75898528e-01 5.77258170e-02
-3.28226477e-01 4.82477337e-01 -4.67893124e-01 1.34174776e+00
-5.57132006e-01 6.94689512e-01 2.25047976e-01 -4.09702241e-01
1.11807442e+00 -1.01000570e-01 -8.13123956e-02 -1.03898549e+00
3.96222293e-01 7.10471570e-01 9.60611403e-01 1.47894770e-01
1.02882588e+00 -8.54739726e-01 -4.51338887e-01 6.28684223e-01
2.51618266e-01 -6.63506016e-02 2.18068644e-01 1.94167301e-01
1.12530706e-03 -8.88087377e-02 -1.56243473e-01 -1.23169327e+00
-7.08523393e-02 -5.46606146e-02 1.04806817e+00 6.28468692e-01
-1.38354912e-01 4.18593079e-01 8.94043982e-01 -3.25160265e-01
-1.41511571e+00 -1.11550450e+00 -1.10094063e-01 1.70836902e+00
-3.28027010e-01 -2.44537145e-01 -6.84775352e-01 -3.49337488e-01
1.58534184e-01 1.42697489e+00 -1.08780456e+00 -8.85151625e-01
-8.62787068e-01 -1.01601565e+00 1.11994922e+00 4.40084726e-01
1.36249855e-01 -2.66269326e-01 -2.61430681e-01 -3.76160204e-01
-5.02119362e-01 -4.52494562e-01 -6.58568263e-01 -1.42880334e-02
-1.10682523e+00 -3.80374610e-01 -9.77238238e-01 -6.69788778e-01
5.99488318e-01 -3.54132801e-01 1.42542303e+00 -1.01129301e-01
1.51345432e-01 -1.66319191e-01 4.18356389e-01 -6.21002972e-01
-7.70748436e-01 6.06810927e-01 4.31756109e-01 -3.08444709e-01
5.93074024e-01 -5.07707596e-01 -2.47623533e-01 2.19625965e-01
-2.63008922e-01 -1.34239532e-03 1.30329609e-01 8.22707534e-01
-2.10595757e-01 -8.54442656e-01 5.18974245e-01 -1.00372970e+00
6.53085828e-01 -4.74981040e-01 -5.23684509e-02 -2.93388739e-02
-7.45770872e-01 1.58996776e-01 2.15613291e-01 -4.98281598e-01
-1.03919721e+00 -9.20846879e-01 1.88555777e-01 -1.21595748e-01
6.89258695e-01 1.22139402e-01 -1.57360956e-01 2.45785609e-01
9.90673304e-01 -1.50567263e-01 2.55641013e-01 -3.19589078e-01
1.82755768e-01 4.44889516e-01 2.57081449e-01 -1.21151769e+00
6.97593153e-01 1.74885750e-01 -1.22300223e-01 -5.13359845e-01
-2.64885664e-01 4.34357703e-01 -6.04880929e-01 9.88983288e-02
7.88353324e-01 -1.10258126e+00 -3.22689980e-01 2.96916366e-01
-9.16493714e-01 -3.27330321e-01 -4.06729013e-01 5.98519921e-01
-4.43085730e-01 -2.82599658e-01 -7.51852572e-01 -5.88632286e-01
-5.95708609e-01 -1.05237901e+00 1.01976585e+00 2.60596592e-02
-9.04439807e-01 -8.30005825e-01 1.35690480e-01 3.33964616e-01
4.45495784e-01 -1.29408419e-01 1.65751207e+00 -8.95489633e-01
4.30066884e-02 1.62272379e-01 -1.49239436e-01 -2.07481891e-01
-5.76038361e-02 8.71648043e-02 -8.71735454e-01 -4.53595221e-01
-1.25867784e-01 -3.16777289e-01 8.97936404e-01 4.73680139e-01
1.96559325e-01 -4.61603165e-01 -1.48933083e-01 5.86870432e-01
1.06407237e+00 -3.37735146e-01 4.66464520e-01 1.70250371e-01
7.86791027e-01 1.08511651e+00 2.43041873e-01 5.37536405e-02
7.45482564e-01 4.90921527e-01 -2.76448220e-01 -2.09723324e-01
-1.38311431e-01 -5.94391346e-01 4.94264185e-01 6.94300175e-01
-2.97407508e-01 2.94367969e-01 -1.16581607e+00 8.02448690e-01
-1.25913858e+00 -8.76082957e-01 -1.30090669e-01 2.42155075e+00
1.08682179e+00 -6.17208518e-02 1.50991797e-01 -2.59206921e-01
7.28608429e-01 2.31695354e-01 -2.16394603e-01 -1.07558048e+00
-4.81302053e-01 1.98914826e-01 6.45537734e-01 7.77656913e-01
-4.77125645e-01 1.21312165e+00 6.26299429e+00 6.29449308e-01
-1.15737927e+00 4.32212442e-01 1.10210228e+00 -3.02511424e-01
-6.66661441e-01 -1.87935643e-02 -7.72629559e-01 2.29029626e-01
1.18282723e+00 -6.30983710e-01 1.99787974e-01 4.35261965e-01
2.00086191e-01 6.74892813e-02 -1.39544261e+00 6.42937899e-01
8.73383284e-02 -9.96729434e-01 1.85474962e-01 4.62565273e-01
7.74211109e-01 -1.59695536e-01 6.34158134e-01 7.47141659e-01
4.53264236e-01 -1.14442003e+00 1.11554289e+00 2.91310586e-02
1.16518807e+00 -1.04259980e+00 6.48564339e-01 2.13530332e-01
-3.09808761e-01 -1.17509350e-01 -4.91187215e-01 -3.33828926e-01
-1.63089216e-01 3.46578330e-01 -7.63400972e-01 2.36663520e-01
4.71728057e-01 9.21632722e-02 -7.32996523e-01 -9.59021226e-02
-6.95412932e-03 4.84057695e-01 -8.51752330e-03 3.77986521e-01
6.28608316e-02 -1.82772011e-01 5.48787951e-01 1.02185631e+00
4.24330831e-01 -4.38290536e-01 -2.30966032e-01 1.02221835e+00
-3.44050586e-01 4.32626039e-01 -8.73621941e-01 -2.93293089e-01
4.91290569e-01 8.94806981e-01 -6.75172508e-01 -3.48659396e-01
-1.24149114e-01 7.32600808e-01 1.79525033e-01 2.60649472e-01
-5.53321958e-01 9.16306004e-02 1.14052892e+00 4.51605469e-01
-5.52405834e-01 -3.24991912e-01 -1.04367518e+00 -1.10434258e+00
-4.86696035e-01 -1.14254713e+00 2.33326703e-01 -4.44400519e-01
-1.04596186e+00 1.14939131e-01 -1.21942898e-02 -6.82505608e-01
-5.52219093e-01 -2.83266902e-01 -4.85432237e-01 1.40544724e+00
-5.05090117e-01 -1.87177265e+00 5.94108760e-01 2.96725720e-01
3.14720601e-01 -2.73924768e-01 8.68574619e-01 3.90991330e-01
-6.25591516e-01 1.33732891e+00 -1.29105911e-01 2.84909368e-01
1.29452360e+00 -8.68370175e-01 6.50097907e-01 6.60457194e-01
-7.25410879e-02 1.53717411e+00 1.01984596e+00 -1.02237821e+00
-1.05208278e+00 -7.98400521e-01 1.97737229e+00 -7.18305230e-01
3.39262217e-01 -8.56655836e-01 -4.87288982e-01 1.21220458e+00
4.01356250e-01 -6.65851772e-01 7.93313444e-01 9.06197190e-01
-9.56265807e-01 8.05512443e-02 -1.24155235e+00 1.19224620e+00
1.15707278e+00 -8.18987727e-01 -5.81975877e-01 1.05455890e-02
7.04724848e-01 -9.89200994e-02 -6.60473287e-01 3.31931978e-01
7.58741856e-01 -6.60953104e-01 7.52026141e-01 -9.38663900e-01
9.52585518e-01 3.27927351e-01 -3.19027036e-01 -1.51622367e+00
-3.78862977e-01 -3.42900217e-01 6.37223661e-01 1.50602043e+00
7.19751477e-01 -7.59319365e-01 5.80680490e-01 6.56266272e-01
1.00362692e-02 -3.96384984e-01 -1.38887918e+00 -6.78619325e-01
1.33809412e+00 2.37057611e-01 7.59457529e-01 1.37310874e+00
-9.97190922e-02 7.02454865e-01 -3.72937381e-01 -2.76450038e-01
6.69253051e-01 2.79907677e-02 7.08913088e-01 -1.22896421e+00
-5.41390181e-02 -5.92135727e-01 -1.41400903e-01 2.83938915e-01
5.35204768e-01 -1.42258537e+00 -3.17978501e-01 -1.14401877e+00
5.45478463e-01 -1.12702049e-01 1.50116056e-01 4.93230641e-01
-5.29226623e-02 4.34792012e-01 5.16193748e-01 1.84561804e-01
3.48961145e-01 4.75810289e-01 1.06292629e+00 -3.70934159e-01
-2.41176668e-03 -3.07573885e-01 -1.35314786e+00 5.31334817e-01
7.48454928e-01 -7.64729142e-01 -1.73436835e-01 -7.91688621e-01
3.69678587e-01 -2.99423933e-01 2.65797049e-01 -4.11713719e-01
-2.03999966e-01 -2.48753473e-01 7.39533305e-01 7.68813342e-02
3.45528603e-01 -1.62515774e-01 4.10942510e-02 6.75674081e-01
-4.76497740e-01 3.71455133e-01 3.69768083e-01 -3.46204877e-01
2.00511083e-01 1.70617625e-01 9.09668803e-01 -2.84986109e-01
1.29969746e-01 6.19075745e-02 -3.59184474e-01 3.43682051e-01
3.46178949e-01 -1.68857291e-01 -4.94977266e-01 -2.07929865e-01
-4.61456597e-01 -2.64797471e-02 9.59753931e-01 6.81100786e-01
-6.72884136e-02 -1.43051398e+00 -1.17299891e+00 3.60903352e-01
1.08434416e-01 -1.02616227e+00 2.38954887e-01 1.05796707e+00
2.13003717e-02 2.00270876e-01 -7.00419962e-01 -1.75352708e-01
-1.28189087e+00 1.30034655e-01 4.10649776e-01 -6.38273656e-02
8.98729786e-02 1.13551271e+00 4.68925595e-01 -1.27043605e+00
-3.73953730e-01 1.76104516e-01 1.92211375e-01 6.03930235e-01
-1.08659323e-02 6.38428330e-01 -2.12485157e-02 -1.10980284e+00
-4.73934829e-01 3.26265872e-01 -2.69437969e-01 -8.05836558e-01
1.00384903e+00 -4.95934039e-02 -4.55075175e-01 8.87738526e-01
8.79115760e-01 3.59584838e-01 -6.33831143e-01 2.85848558e-01
-1.95896044e-01 -5.77828228e-01 -3.46213728e-01 -8.40156257e-01
-8.41091812e-01 1.09171879e+00 5.01106739e-01 -7.48095095e-01
2.85608828e-01 -1.44308180e-01 5.10062814e-01 -1.00677237e-01
4.81759757e-01 -1.50448608e+00 -6.38799727e-01 7.20869482e-01
8.52587104e-01 -9.98745441e-01 -2.60886680e-02 -2.84090452e-02
-6.41066611e-01 4.46067721e-01 9.48333263e-01 1.50712699e-01
9.48661640e-02 -4.60816585e-02 2.48198718e-01 8.52062106e-02
-6.95875406e-01 2.71350592e-01 -5.71761206e-02 6.24532878e-01
9.88344133e-01 5.25385439e-01 -1.01434481e+00 9.08164859e-01
-1.08261514e+00 -6.29234195e-01 3.98187429e-01 4.15680259e-01
4.93538231e-02 -1.16445220e+00 -6.59601152e-01 3.89231712e-01
-5.71147382e-01 -4.94495004e-01 -1.00873756e+00 1.08444214e+00
5.57513893e-01 6.49680793e-01 6.61718011e-01 -3.22752535e-01
4.21423614e-02 6.77229404e-01 7.99398482e-01 -5.10769665e-01
-1.08784258e+00 -6.58515096e-02 4.94883627e-01 -2.40022436e-01
2.79333770e-01 -9.96758699e-01 -9.67175066e-01 -9.91351545e-01
1.42466500e-01 -6.99903667e-02 6.47291064e-01 6.60745561e-01
3.99676621e-01 1.04483552e-02 1.23225667e-01 -7.17458487e-01
-7.41065681e-01 -1.30768037e+00 -6.71745777e-01 3.64116192e-01
-3.02074887e-02 -6.08254790e-01 -2.50097275e-01 -3.48147064e-01] | [9.61294174194336, 10.253093719482422] |
b33e325d-9271-4336-84ec-144ddbf27dac | snore-gans-improving-automatic-snore-sound | 1903.12422 | null | http://arxiv.org/abs/1903.12422v1 | http://arxiv.org/pdf/1903.12422v1.pdf | Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data | One of the frontier issues that severely hamper the development of automatic
snore sound classification (ASSC) associates to the lack of sufficient
supervised training data. To cope with this problem, we propose a novel data
augmentation approach based on semi-supervised conditional Generative
Adversarial Networks (scGANs), which aims to automatically learn a mapping
strategy from a random noise space to original data distribution. The proposed
approach has the capability of well synthesizing 'realistic' high-dimensional
data, while requiring no additional annotation process. To handle the mode
collapse problem of GANs, we further introduce an ensemble strategy to enhance
the diversity of the generated data. The systematic experiments conducted on a
widely used Munich-Passau snore sound corpus demonstrate that the scGANs-based
systems can remarkably outperform other classic data augmentation systems, and
are also competitive to other recently reported systems for ASSC. | ['Christoph Janott', 'Zixing Zhang', 'Yanan Guo', 'Kun Qian', 'Jing Han', 'Bjoern Schuller'] | 2019-03-29 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 4.97967571e-01 2.14364886e-01 3.50898385e-01 -1.59919962e-01
-1.06407714e+00 -4.76859391e-01 7.45484233e-01 -3.14536363e-01
-3.82678330e-01 8.33922803e-01 1.17800526e-01 -2.46716112e-01
4.73060943e-02 -6.13722920e-01 -3.99545819e-01 -9.51438963e-01
3.83590281e-01 7.48103619e-01 1.12828054e-01 -4.22031254e-01
-2.96103302e-02 2.72929907e-01 -1.65383482e+00 -9.71574932e-02
9.04365122e-01 7.96184838e-01 5.96275963e-02 7.09707677e-01
-1.28839433e-01 6.83881700e-01 -9.59815919e-01 -4.31368858e-01
1.28858298e-01 -9.06429589e-01 -6.34446204e-01 -2.34482870e-01
1.36385500e-01 2.46660352e-01 -1.41818479e-01 1.23234105e+00
7.34150350e-01 1.70936868e-01 6.21650636e-01 -1.23133945e+00
-3.32642406e-01 8.73199403e-01 -7.47575685e-02 1.03791423e-01
6.94619864e-02 1.21303260e-01 9.24936235e-01 -5.21192372e-01
4.53263193e-01 9.12624717e-01 7.15610027e-01 1.09902465e+00
-1.35081613e+00 -7.32526720e-01 -3.88728380e-01 -1.61281988e-01
-1.20304668e+00 -3.96104723e-01 1.20440853e+00 -3.59362513e-01
2.89342552e-01 4.72714782e-01 6.25858665e-01 1.45638227e+00
-4.68008786e-01 4.08893555e-01 1.19677508e+00 -6.87338591e-01
4.79119420e-01 1.13043271e-01 -3.18087876e-01 3.99130613e-01
5.49075985e-03 1.02848656e-01 -5.23247659e-01 -1.09802179e-01
5.24107397e-01 -5.38186908e-01 -2.77159512e-01 -1.64292261e-01
-8.97942901e-01 9.00036991e-01 9.08470824e-02 7.83621073e-01
-1.87096134e-01 1.19066745e-01 5.03615797e-01 2.22757891e-01
5.95464587e-01 7.43502319e-01 -1.50407225e-01 -3.69427949e-01
-1.11410165e+00 2.53281027e-01 6.33359492e-01 7.71281183e-01
2.96907961e-01 7.35359311e-01 7.10049346e-02 9.22624826e-01
1.44249558e-01 5.66596150e-01 8.22827518e-01 -6.43705964e-01
6.30304992e-01 4.90763396e-01 -2.78214037e-01 -6.99341118e-01
-3.23516697e-01 -7.17806339e-01 -1.17974937e+00 1.66574404e-01
2.98835307e-01 -2.61977077e-01 -8.72844636e-01 2.00042439e+00
2.48420864e-01 1.90849558e-01 3.65828186e-01 5.51726878e-01
8.04358721e-01 5.04626513e-01 3.75132225e-02 -2.79018253e-01
7.83243299e-01 -5.93025088e-01 -9.24253285e-01 -9.89790484e-02
3.60276908e-01 -6.20895445e-01 1.08849990e+00 3.69851232e-01
-8.89975488e-01 -7.72375584e-01 -1.05559325e+00 3.69862348e-01
-3.59754324e-01 3.04699987e-01 5.50325751e-01 9.37942266e-01
-8.87394547e-01 5.53476512e-01 -7.94037819e-01 3.46580781e-02
4.12159562e-01 3.92982870e-01 -5.26469111e-01 3.60986561e-01
-1.25145519e+00 4.92674112e-01 7.16009021e-01 1.57615885e-01
-7.63425589e-01 -6.76855564e-01 -5.95335305e-01 -1.00483738e-01
1.34887144e-01 -4.63650644e-01 1.16721153e+00 -9.85901892e-01
-1.85875583e+00 6.79971814e-01 5.03945827e-01 -6.90463543e-01
5.29652715e-01 -8.98121968e-02 -6.78530276e-01 -6.03103004e-02
-1.39779195e-01 4.83643055e-01 1.08712006e+00 -1.27928376e+00
-3.48797739e-01 -1.60434350e-01 -4.37608182e-01 -7.55258426e-02
-5.90428472e-01 -4.59953398e-02 -1.00472607e-01 -1.14498615e+00
1.00460827e-01 -9.02691007e-01 -4.02995646e-01 -7.56036639e-01
-6.59228921e-01 -1.33804247e-01 7.83559501e-01 -4.77086782e-01
1.29596424e+00 -2.31910086e+00 4.66934979e-01 2.94892162e-01
-2.55605672e-02 6.96379304e-01 -1.35385722e-01 3.48839760e-01
-2.37546802e-01 1.32801309e-01 -6.33808970e-01 -6.86244965e-01
2.26890631e-02 3.50807369e-01 -3.91152591e-01 6.79135695e-02
1.66106656e-01 5.45373321e-01 -7.44747341e-01 -3.49475235e-01
7.72138461e-02 4.12819475e-01 -7.21667886e-01 5.08348882e-01
-5.00396371e-01 9.27157879e-01 -2.10471421e-01 2.12582082e-01
5.42434871e-01 2.66088963e-01 9.58737954e-02 9.75401998e-02
1.10401802e-01 2.80805826e-01 -1.16926897e+00 1.75950265e+00
-4.26716357e-01 3.61650795e-01 -6.36115000e-02 -1.15079892e+00
1.20031857e+00 4.89917219e-01 4.81871784e-01 -3.09823126e-01
2.84625381e-01 4.28553164e-01 1.96803048e-01 -2.14139134e-01
3.12568933e-01 -5.96540570e-01 -3.45100224e-01 4.65024233e-01
4.77890879e-01 -4.92502749e-01 6.56727934e-03 -1.28749982e-02
9.81591344e-01 5.07951565e-02 1.89150255e-02 -1.07918799e-01
7.28860259e-01 -2.00356200e-01 7.52716601e-01 6.57644391e-01
8.98412764e-02 9.37951028e-01 4.90044594e-01 -1.31672636e-01
-1.03233075e+00 -6.82058990e-01 5.86767048e-02 7.20670879e-01
-4.81022924e-01 -4.06755865e-01 -1.24565828e+00 -9.10822988e-01
-3.51161689e-01 7.24795580e-01 -6.02454484e-01 -2.05375522e-01
-6.04438663e-01 -9.55281079e-01 1.05287528e+00 5.51029503e-01
6.25823021e-01 -1.25236523e+00 -9.39466208e-02 4.34611648e-01
-2.41053432e-01 -1.24383307e+00 -1.47299722e-01 4.46945846e-01
-6.58681810e-01 -7.30030835e-01 -5.55586934e-01 -5.77802300e-01
4.47888523e-01 -6.08012676e-01 8.75826240e-01 -5.26912808e-02
-1.18487172e-01 1.02852464e-01 -7.23755062e-01 -6.37874603e-01
-1.32610989e+00 4.68345761e-01 3.23323131e-01 2.08337903e-01
8.80082399e-02 -9.17114794e-01 -2.01877609e-01 1.54399529e-01
-1.08445847e+00 4.80220616e-02 4.46057677e-01 1.11121023e+00
4.59141612e-01 4.36677188e-01 1.01181972e+00 -1.14293981e+00
5.36141336e-01 -2.90192187e-01 -6.04698896e-01 -1.10068738e-01
-5.88055789e-01 1.77335814e-01 9.59031761e-01 -4.66715366e-01
-1.09759009e+00 3.21817249e-01 -8.72201025e-01 -3.97416800e-01
-4.65878427e-01 1.24246590e-01 -5.04146159e-01 -3.01672425e-02
7.12196767e-01 3.78732532e-01 1.06064379e-01 -8.25051010e-01
2.45015398e-01 8.22496116e-01 9.55292106e-01 -4.32367355e-01
1.24227798e+00 1.68895125e-01 1.17080234e-01 -7.29190707e-01
-7.44041324e-01 -1.12937257e-01 -8.11785936e-01 6.42999858e-02
9.43408012e-01 -6.68423474e-01 -1.11622989e-01 7.43495703e-01
-8.73443425e-01 -3.54923606e-01 -7.45243847e-01 4.18054610e-01
-7.76080966e-01 2.28307784e-01 -4.09377575e-01 -8.94946277e-01
-5.08417428e-01 -8.74216735e-01 8.04925442e-01 5.17699048e-02
-1.11190148e-01 -9.82208610e-01 5.47632217e-01 5.34418583e-01
4.60693449e-01 2.97865152e-01 9.98453081e-01 -1.14822555e+00
-1.94805473e-01 -4.30923194e-01 4.85054106e-01 1.06058669e+00
1.30845919e-01 -2.05251142e-01 -1.21880496e+00 -1.97232500e-01
1.28000826e-01 -2.47329235e-01 6.57381535e-01 -1.25047252e-01
1.18948209e+00 -1.64750725e-01 1.20789304e-01 6.61915839e-01
1.19462693e+00 1.53208300e-01 7.63603091e-01 1.16071679e-01
7.45772898e-01 2.36816257e-01 5.31423330e-01 3.47085953e-01
-1.13390647e-01 7.03032732e-01 4.98968959e-01 -1.71568051e-01
-3.43092740e-01 -5.92845261e-01 2.23498374e-01 1.25393879e+00
-3.87797773e-01 -4.08056259e-01 -6.73264802e-01 3.06690753e-01
-1.38913321e+00 -8.85854542e-01 3.32545419e-03 2.03187704e+00
8.44112337e-01 2.66231656e-01 2.84346998e-01 9.25258398e-01
6.16250098e-01 1.04353100e-01 -2.28906661e-01 -8.25347975e-02
-1.92099139e-01 7.42263556e-01 6.40870631e-02 1.82160020e-01
-1.24489737e+00 1.04590702e+00 6.32371759e+00 1.08550727e+00
-9.42346454e-01 2.37245157e-01 1.91319227e-01 1.82612970e-01
-3.10862303e-01 -2.19975919e-01 -6.75332606e-01 6.78566992e-01
1.08596730e+00 3.07556689e-01 4.02864993e-01 7.16655433e-01
-1.56969428e-01 4.25872535e-01 -8.17408323e-01 9.32724714e-01
2.22739056e-01 -1.09099996e+00 5.84757626e-02 1.23688513e-02
8.45766544e-01 -2.43399754e-01 1.91915348e-01 2.93261677e-01
2.60163516e-01 -9.36375141e-01 5.78647614e-01 4.45715517e-01
1.09154570e+00 -8.53568137e-01 1.00271451e+00 4.86159861e-01
-7.75250971e-01 -4.47634347e-02 -1.31964624e-01 2.16335058e-01
1.95628405e-02 4.08112854e-01 -8.68296385e-01 6.07916594e-01
3.63229841e-01 1.98747024e-01 -6.47550106e-01 6.68184459e-01
-2.76423424e-01 1.04029071e+00 -3.49886447e-01 -2.85768807e-02
-1.64558273e-02 4.67877574e-02 7.53940821e-01 1.01811385e+00
4.61855859e-01 -7.37089664e-02 -3.54709208e-01 9.04675126e-01
-1.62130371e-01 1.97980121e-01 -5.89503944e-01 -4.14368272e-01
3.93450886e-01 1.08838367e+00 -5.20450771e-01 -9.28502902e-02
-1.22383676e-01 9.64652359e-01 1.38740897e-01 -2.03241594e-02
-6.80246532e-01 -3.34707439e-01 2.92109549e-01 1.82940755e-02
2.87372828e-01 1.29157118e-02 -2.11220384e-01 -1.07180619e+00
-1.02884322e-01 -1.16301048e+00 3.66392672e-01 -4.17782843e-01
-1.36115324e+00 1.11401629e+00 -3.52167159e-01 -1.47529125e+00
-4.98458862e-01 -3.32927942e-01 -7.52978683e-01 5.67335069e-01
-1.11842871e+00 -1.46496391e+00 -1.32768035e-01 5.92927396e-01
3.81115854e-01 -9.82753754e-01 1.19691980e+00 4.04682904e-01
-6.50977254e-01 8.71763945e-01 9.57777128e-02 4.53225002e-02
4.19923484e-01 -1.55335331e+00 5.03489256e-01 9.06374156e-01
4.99430418e-01 1.77286670e-01 7.98262358e-01 -4.20204282e-01
-9.00681138e-01 -1.10335171e+00 5.54619253e-01 -3.94471258e-01
5.40820420e-01 -5.52288890e-01 -9.06808138e-01 4.29682583e-01
-4.12779674e-02 7.39490762e-02 8.42921793e-01 -5.63403256e-02
-2.36222774e-01 -1.86320618e-01 -1.24927747e+00 2.02930823e-01
9.20515358e-01 -5.43334901e-01 -6.28222585e-01 4.35547717e-02
5.33111632e-01 -3.72604370e-01 -8.27931523e-01 5.83904982e-01
2.12422647e-02 -8.90739679e-01 8.54462385e-01 -6.43331528e-01
3.21958303e-01 -1.98110312e-01 -1.87901899e-01 -1.54028165e+00
-8.49081203e-02 -9.08094585e-01 -7.45229200e-02 1.86952567e+00
3.11602563e-01 -5.13552248e-01 8.02399218e-01 2.37381414e-01
-2.57766306e-01 -2.88795203e-01 -1.20772886e+00 -6.59371257e-01
2.82622457e-01 -5.85172415e-01 9.43308830e-01 8.26391518e-01
-2.09513366e-01 3.80518824e-01 -5.86021185e-01 4.12638374e-02
5.71267068e-01 -1.40577689e-01 8.80885124e-01 -1.49610722e+00
-5.88661075e-01 -2.99680471e-01 -6.62981629e-01 -4.05126363e-01
3.92817080e-01 -9.67447042e-01 2.52647787e-01 -8.84177148e-01
-1.06913015e-01 -7.77005792e-01 -3.77623320e-01 4.32221502e-01
-1.12657323e-01 7.21432149e-01 9.03177485e-02 -3.47992890e-02
-3.01522285e-01 6.83381259e-01 1.11392331e+00 2.12546900e-01
-2.12735921e-01 4.10499096e-01 -6.34461522e-01 8.24269414e-01
9.10294354e-01 -5.69362640e-01 -3.95250916e-01 4.14676070e-02
6.83898479e-02 -1.73324381e-03 3.78145814e-01 -1.46251547e+00
-2.17462387e-02 2.46223971e-01 -3.35739627e-02 -4.95133996e-01
3.14476967e-01 -7.50873446e-01 3.00019741e-01 2.66164631e-01
-3.11991245e-01 -2.55684048e-01 1.51415572e-01 5.61307907e-01
-4.88814056e-01 -4.72179919e-01 1.13035166e+00 6.44844100e-02
-3.19380671e-01 7.66426325e-02 -3.43485981e-01 2.69851953e-01
8.22184563e-01 4.27955329e-01 -1.49250235e-02 -3.36652160e-01
-8.33292842e-01 -3.49797279e-01 1.19436964e-01 3.09449762e-01
3.11078638e-01 -1.44514835e+00 -6.48603559e-01 7.96243072e-01
-8.60548317e-02 8.45580548e-02 3.76780272e-01 4.37046885e-01
-4.93078530e-01 2.90320754e-01 -1.58879876e-01 -3.23603094e-01
-1.08449161e+00 4.22856122e-01 3.23065907e-01 -4.05868411e-01
-6.96543932e-01 8.32034349e-01 -3.83039922e-01 -6.83467627e-01
3.21631014e-01 -9.41329896e-02 -3.45316738e-01 -1.11368403e-01
2.01457068e-01 2.08421305e-01 1.92286417e-01 -7.16787279e-01
-3.04704718e-02 2.25795403e-01 2.15299055e-01 -2.95193851e-01
1.47625279e+00 3.07323724e-01 1.11641511e-01 3.53179246e-01
9.19564784e-01 1.45028889e-01 -8.65518868e-01 -1.67101234e-01
-1.41870439e-01 -1.87106177e-01 6.17312938e-02 -6.68718815e-01
-1.15766048e+00 9.40744340e-01 7.16334105e-01 4.38411117e-01
1.29103649e+00 -8.02182406e-02 8.50742340e-01 1.58289209e-01
1.58722445e-01 -1.14519811e+00 1.39343649e-01 2.91282207e-01
9.04375672e-01 -9.81252551e-01 -5.00845075e-01 -2.82388777e-01
-6.79706573e-01 9.41597104e-01 5.89104295e-01 5.46050034e-02
5.08051217e-01 5.03977120e-01 1.63365319e-01 2.00657602e-02
-4.96195644e-01 -3.69483054e-01 1.82961762e-01 7.52227247e-01
8.73695612e-02 -4.33036983e-02 -2.04284132e-01 1.02382350e+00
-7.04447567e-01 -2.11683720e-01 3.61694455e-01 4.98972446e-01
6.60422444e-03 -1.55021572e+00 -3.14343482e-01 2.98414797e-01
-6.29648387e-01 4.89563495e-02 -3.60414445e-01 7.73487151e-01
3.97699386e-01 6.82601988e-01 -3.16686332e-02 -7.19949424e-01
2.67022073e-01 4.31547523e-01 2.92515993e-01 -6.29884541e-01
-6.30950272e-01 2.59847164e-01 6.73892498e-02 6.05566166e-02
-5.59114814e-01 -7.65091181e-01 -9.85602260e-01 1.65960014e-01
-4.69247758e-01 5.17289221e-01 6.90865219e-01 9.25028801e-01
-4.01023701e-02 7.01079607e-01 8.84734154e-01 -5.56949139e-01
-7.41253495e-01 -1.26830792e+00 -7.57626295e-01 6.81604445e-01
3.40517670e-01 -5.34625828e-01 -6.56649768e-01 5.41638620e-02] | [15.537519454956055, 5.868506908416748] |
f5ab3495-ec62-4a6f-b9ee-1f8be151cb92 | logltn-differentiable-fuzzy-logic-in-the | 2306.14546 | null | https://arxiv.org/abs/2306.14546v1 | https://arxiv.org/pdf/2306.14546v1.pdf | logLTN: Differentiable Fuzzy Logic in the Logarithm Space | The AI community is increasingly focused on merging logic with deep learning to create Neuro-Symbolic (NeSy) paradigms and assist neural approaches with symbolic knowledge. A significant trend in the literature involves integrating axioms and facts in loss functions by grounding logical symbols with neural networks and operators with fuzzy semantics. Logic Tensor Networks (LTN) is one of the main representatives in this category, known for its simplicity, efficiency, and versatility. However, it has been previously shown that not all fuzzy operators perform equally when applied in a differentiable setting. Researchers have proposed several configurations of operators, trading off between effectiveness, numerical stability, and generalization to different formulas. This paper presents a configuration of fuzzy operators for grounding formulas end-to-end in the logarithm space. Our goal is to develop a configuration that is more effective than previous proposals, able to handle any formula, and numerically stable. To achieve this, we propose semantics that are best suited for the logarithm space and introduce novel simplifications and improvements that are crucial for optimization via gradient-descent. We use LTN as the framework for our experiments, but the conclusions of our work apply to any similar NeSy framework. Our findings, both formal and empirical, show that the proposed configuration outperforms the state-of-the-art and that each of our modifications is essential in achieving these results. | ['Michael Spranger', 'Luciano Serafini', 'Samy Badreddine'] | 2023-06-26 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-1.79532602e-01 6.59223571e-02 -1.70513406e-01 -5.69972396e-01
-2.39130226e-03 -4.58101422e-01 3.62759620e-01 1.87081963e-01
-5.63314259e-01 7.71809995e-01 -3.24932903e-01 -3.55388999e-01
-7.80593574e-01 -1.13053977e+00 -7.38268614e-01 -2.44728655e-01
-4.32545185e-01 6.91236258e-01 3.55220437e-01 -6.97151303e-01
1.67945474e-01 8.17147732e-01 -1.74201953e+00 2.22316995e-01
1.17286098e+00 1.26654077e+00 -2.95846015e-01 1.40368342e-01
-3.48961711e-01 8.12374651e-01 -2.19668403e-01 -9.53934491e-01
3.37736905e-01 -3.71519662e-02 -9.76852894e-01 -5.51539242e-01
4.85972762e-01 4.23998833e-02 -5.29073514e-02 1.28791344e+00
1.35315374e-01 1.80112153e-01 5.57383537e-01 -1.37408674e+00
-6.14329040e-01 1.03299129e+00 1.73354909e-01 -6.15908252e-03
1.37430072e-01 -1.48742422e-01 1.23561215e+00 -6.01495802e-01
5.63881218e-01 1.37785113e+00 9.47671711e-01 3.52801353e-01
-1.23037672e+00 -3.66999388e-01 7.89339095e-02 5.50779760e-01
-1.62072933e+00 -1.40823543e-01 7.14639008e-01 -2.76653081e-01
1.05262184e+00 3.08745444e-01 5.55517912e-01 5.98034263e-01
5.05694598e-02 6.41620338e-01 9.58518922e-01 -7.03239441e-01
3.77730340e-01 4.81221169e-01 3.14174116e-01 9.91318643e-01
4.46780682e-01 -9.37261805e-02 -2.15449929e-01 9.94230062e-02
5.41314423e-01 -2.11729586e-01 -1.79354519e-01 -5.55956781e-01
-1.02316594e+00 9.60151494e-01 5.88599861e-01 7.62186229e-01
-4.04658467e-01 3.42625737e-01 5.50679982e-01 5.80057800e-01
1.24103546e-01 8.25016141e-01 -3.12183350e-01 -9.61236060e-02
-1.19773173e+00 5.03322899e-01 1.18824184e+00 6.73147380e-01
5.79865873e-01 7.90567100e-02 -1.49745839e-02 5.35909832e-01
1.43322408e-01 2.03633189e-01 2.97008216e-01 -1.12573767e+00
2.08659723e-01 8.36597800e-01 -1.28707126e-01 -1.23843718e+00
-5.03775597e-01 -5.38395345e-01 -6.45677984e-01 3.69494528e-01
4.66213793e-01 7.06620663e-02 -3.92065585e-01 1.81330764e+00
-9.27625299e-02 2.45878369e-01 1.23079203e-01 7.60953426e-01
3.26628566e-01 2.85792410e-01 -1.49794400e-01 -7.19268024e-02
1.14627266e+00 -5.09221196e-01 -6.61901653e-01 3.97414237e-01
6.67524636e-01 -3.12870085e-01 1.13902545e+00 7.80652463e-01
-1.22255254e+00 -3.70965749e-01 -1.33435404e+00 -6.97187036e-02
-9.62561607e-01 6.27911985e-02 9.74078119e-01 8.70004833e-01
-1.33221400e+00 1.01193559e+00 -7.29008377e-01 -2.61154562e-01
4.02982473e-01 7.19183564e-01 -1.55528337e-01 3.03850651e-01
-1.66792440e+00 1.45485187e+00 9.00575459e-01 1.72742337e-01
-1.95445985e-01 -5.57719111e-01 -7.04151034e-01 3.95190895e-01
7.12304175e-01 -6.95515931e-01 1.10532439e+00 -9.23174620e-01
-1.63163066e+00 5.56468427e-01 2.04896107e-01 -1.16192508e+00
6.35083854e-01 -2.12289505e-02 -4.42105442e-01 2.54450709e-01
-3.67739975e-01 6.09681785e-01 3.87543201e-01 -9.43434536e-01
-4.82101202e-01 -1.23484038e-01 6.60421431e-01 -1.58551142e-01
-6.50157452e-01 -1.94258884e-01 -8.01353827e-02 -4.93447602e-01
7.36482292e-02 -6.63231611e-01 1.52323663e-01 -5.94419986e-02
-6.71406612e-02 -4.09537911e-01 3.58975887e-01 -2.93157190e-01
1.41777837e+00 -1.89961779e+00 3.45932007e-01 6.91029847e-01
7.75503740e-02 4.74144101e-01 3.37057561e-01 2.55385041e-01
1.08781774e-02 2.36585408e-01 -3.02377373e-01 -1.53127879e-01
5.64201534e-01 3.88247669e-01 -4.29396063e-01 2.01168716e-01
3.96488130e-01 9.67564225e-01 -8.01969528e-01 -6.80614710e-01
2.21676603e-01 4.48028833e-01 -8.31768990e-01 -5.02849102e-01
-5.85278451e-01 -2.58769184e-01 -1.75259128e-01 5.08767545e-01
4.68165249e-01 2.03840639e-02 1.72259599e-01 -4.02758241e-01
-1.28052741e-01 1.97935462e-01 -1.55510068e+00 1.44161129e+00
-3.92383128e-01 4.01520610e-01 -1.03804551e-01 -1.34356892e+00
9.47770715e-01 2.05205142e-01 3.62251699e-01 -5.02365649e-01
4.54881012e-01 6.34747148e-01 1.59909561e-01 -5.05088210e-01
5.50584495e-01 -5.49536228e-01 1.64210856e-01 1.55221164e-01
2.57259101e-01 -2.47009054e-01 6.80427492e-01 -2.15872098e-02
6.49754465e-01 2.88987190e-01 8.68577063e-02 -3.75389278e-01
9.83428717e-01 -1.02308884e-01 1.83289424e-01 5.44254422e-01
-2.74954010e-02 9.47802737e-02 7.19574511e-01 -3.68472964e-01
-7.73633599e-01 -9.53438520e-01 -2.80926824e-01 8.75391304e-01
-9.52065922e-03 -4.13249105e-01 -7.24529982e-01 -3.79400194e-01
1.04064740e-01 1.05499136e+00 -5.42741001e-01 -1.90861866e-01
-5.91344297e-01 -4.04254317e-01 9.72067952e-01 4.73567575e-01
5.42165577e-01 -9.47467804e-01 -7.38545716e-01 1.98349029e-01
8.91207978e-02 -1.00359547e+00 2.14272305e-01 3.67010832e-01
-9.27927434e-01 -9.92427588e-01 -2.73183703e-01 -6.14351511e-01
2.40778267e-01 -5.11800706e-01 1.01146066e+00 1.69123024e-01
3.54400799e-02 1.74148396e-01 -2.57723659e-01 -4.73526686e-01
-5.62868655e-01 1.33347750e-01 2.41951749e-01 9.28776935e-02
4.10938263e-01 -7.38042831e-01 6.69308975e-02 -9.73842070e-02
-1.23501134e+00 -1.47548959e-01 4.15636063e-01 6.06067777e-01
3.29779387e-01 4.75415647e-01 3.71047407e-01 -7.79990435e-01
7.17787504e-01 -1.97713822e-01 -7.92052269e-01 5.46374381e-01
-8.19761515e-01 6.02185071e-01 9.47661459e-01 -1.95209950e-01
-7.43554235e-01 -2.82572687e-01 -7.83976391e-02 -5.51901162e-01
4.60739434e-02 8.54112327e-01 -3.88947241e-02 -3.35929066e-01
7.92766690e-01 -7.99625441e-02 1.42952561e-01 -2.77825028e-01
4.92516875e-01 3.41686517e-01 5.40847242e-01 -9.45329070e-01
5.29862165e-01 4.17783439e-01 6.07616603e-01 -5.19585967e-01
-6.13906384e-01 1.63822677e-02 -6.62601054e-01 3.96988764e-02
4.89882290e-01 -2.28720173e-01 -1.18580806e+00 9.77868363e-02
-1.05422699e+00 4.74237762e-02 -4.33760792e-01 4.78822261e-01
-6.88407719e-01 2.88114130e-01 -3.86553735e-01 -8.49181235e-01
-1.54661253e-01 -1.28197634e+00 4.63950872e-01 7.79625252e-02
-1.56559974e-01 -1.30891669e+00 -2.93365836e-01 -1.23557985e-01
6.18012607e-01 3.07811648e-01 1.18482077e+00 -7.73969889e-01
-4.09016341e-01 -1.93700269e-01 -1.44525573e-01 8.29085112e-01
-2.58366525e-01 1.33806258e-01 -8.30078781e-01 6.45745546e-02
3.98550369e-02 -2.42700934e-01 9.17799413e-01 1.75058767e-01
9.75691080e-01 -3.00561994e-01 9.20774937e-02 6.09863937e-01
1.62150741e+00 4.54126000e-02 5.55264711e-01 5.04087567e-01
2.72871554e-01 6.43177867e-01 4.23394740e-01 3.31861712e-02
5.08716643e-01 8.23115826e-01 6.14085555e-01 3.21094215e-01
1.65188536e-01 6.25196248e-02 3.05334181e-01 4.20959920e-01
-3.30415726e-01 1.44224912e-01 -1.02375603e+00 2.41317213e-01
-1.94393766e+00 -1.07909656e+00 1.67673379e-01 2.04076195e+00
1.00609803e+00 6.21560693e-01 2.58599222e-01 6.77691996e-01
4.30770069e-01 -3.84461164e-01 -1.17225207e-01 -8.22866440e-01
-2.46269822e-01 6.24235809e-01 4.00444359e-01 7.09044755e-01
-1.16225517e+00 8.39602351e-01 5.63495636e+00 7.54014313e-01
-1.40882409e+00 -1.35590866e-01 1.01917550e-01 -3.40945125e-02
-2.61458486e-01 -1.05453983e-01 -6.62001431e-01 2.18508661e-01
1.01489103e+00 -1.39035434e-01 7.64641702e-01 7.89421618e-01
-6.98514283e-02 1.28973842e-01 -1.42404127e+00 8.92340779e-01
1.60539076e-01 -1.44107962e+00 1.36358351e-01 -3.68862510e-01
4.92101699e-01 -2.47152761e-01 -2.26376075e-02 4.89015371e-01
-3.23995538e-02 -1.24067664e+00 1.02842069e+00 9.08506215e-01
3.85927916e-01 -8.69913161e-01 1.06498122e+00 9.74759310e-02
-8.54294717e-01 -2.62878299e-01 -1.89720154e-01 -3.23502839e-01
-7.12624844e-03 4.58532751e-01 -7.58522809e-01 9.79983628e-01
4.44715500e-01 4.76186454e-01 -6.91643834e-01 1.01445150e+00
-2.88106631e-02 2.66983449e-01 -6.02489531e-01 -4.08153623e-01
5.16388297e-01 -2.73078471e-01 3.84318262e-01 1.26812637e+00
2.55112976e-01 -3.31029654e-01 6.55339807e-02 1.35487807e+00
4.77877259e-02 1.80975676e-01 -4.97823179e-01 -6.61833286e-02
3.83189738e-01 8.86384845e-01 -8.59934807e-01 -4.13808465e-01
-1.52574956e-01 4.73367482e-01 2.80819982e-01 2.10045248e-01
-1.03305137e+00 -8.75694811e-01 3.36937189e-01 -1.78087375e-03
4.46101964e-01 -2.17666417e-01 -6.26968861e-01 -1.08767378e+00
3.20363879e-01 -8.32315624e-01 3.59977394e-01 -5.10419011e-01
-1.18465436e+00 5.77420771e-01 5.19838870e-01 -8.06837380e-01
-3.34989488e-01 -1.11639774e+00 -8.24148357e-02 7.77809858e-01
-1.59381139e+00 -1.16885746e+00 -4.79557104e-02 4.84252661e-01
-1.55965701e-01 -1.40399083e-01 7.76463687e-01 4.49630469e-01
-1.13371573e-01 7.38363981e-01 -2.11711794e-01 -8.57887268e-02
1.79097921e-01 -1.37940085e+00 -2.08459884e-01 6.91581011e-01
2.04996243e-01 1.03770173e+00 8.32442760e-01 -3.00364017e-01
-1.33369160e+00 -7.38813162e-01 1.02852213e+00 -3.05909961e-01
7.95619845e-01 -1.18274897e-01 -8.46437037e-01 5.72699904e-01
-1.96964011e-01 -2.17225775e-01 1.85435712e-01 1.74170524e-01
-3.89884949e-01 -3.59823704e-01 -1.30519319e+00 8.16633224e-01
8.21038067e-01 -4.36685145e-01 -9.17278588e-01 -4.22157943e-02
5.46646118e-01 -3.92328054e-01 -9.89718378e-01 6.26184940e-01
6.45800352e-01 -1.30743885e+00 1.01864254e+00 -4.93176788e-01
1.42120078e-01 -4.98862982e-01 -3.14622879e-01 -9.12611485e-01
-3.39397378e-02 -5.99535823e-01 -2.38558769e-01 9.58704531e-01
3.85668695e-01 -9.21847105e-01 4.54943031e-01 4.86515701e-01
-2.82533944e-01 -9.74907339e-01 -9.73901272e-01 -1.08929372e+00
3.33678961e-01 -7.86333323e-01 8.58353496e-01 8.32982481e-01
2.83228219e-01 1.39540017e-01 6.94642067e-02 -1.18413851e-01
1.16539679e-01 6.69490546e-02 3.05657655e-01 -1.53040361e+00
-2.54997194e-01 -1.15033376e+00 -8.52815509e-01 -4.59120810e-01
3.98887843e-01 -1.32856333e+00 -3.80891651e-01 -1.33538055e+00
-3.46118122e-01 -4.67240095e-01 -4.85362649e-01 8.32938850e-01
4.26429927e-01 2.26094529e-01 4.21927661e-01 -1.97478026e-01
-4.87571210e-01 3.47099006e-01 1.04179168e+00 -1.61545590e-01
-2.59348631e-01 -1.59892723e-01 -6.41642988e-01 9.12973464e-01
7.14827240e-01 -1.59830507e-02 -4.78688329e-01 -1.64308101e-01
6.86463475e-01 -5.13300121e-01 5.73636770e-01 -1.28767788e+00
3.37811351e-01 -9.97059606e-03 -8.93978402e-02 -3.01764429e-01
2.87175566e-01 -1.06757927e+00 -2.97091622e-02 5.55954456e-01
-4.00863379e-01 -2.87966523e-02 2.82322407e-01 -1.06482401e-01
-3.18050683e-01 -6.98644996e-01 7.38577843e-01 -5.96846193e-02
-7.07844973e-01 -1.97862849e-01 2.21878052e-01 2.93682702e-02
9.15671110e-01 -3.17780226e-01 -4.85587865e-03 -1.93797335e-01
-7.04987466e-01 1.42357066e-01 4.17863250e-01 2.19693512e-01
3.88062358e-01 -1.21338820e+00 -4.21160072e-01 5.44375107e-02
-2.17994004e-01 -9.47906598e-02 -2.63393879e-01 1.22805989e+00
-8.97089422e-01 9.14964497e-01 -2.63889045e-01 -3.70145828e-01
-9.34532702e-01 5.52251756e-01 5.85365057e-01 -2.73493737e-01
-3.67168695e-01 7.11141944e-01 -6.68148518e-01 -3.78175348e-01
7.69253194e-01 -1.07199907e+00 -2.63930202e-01 1.02888569e-01
2.42706776e-01 5.55531979e-01 3.66285861e-01 -3.08357060e-01
-4.02502298e-01 4.52015132e-01 2.90083289e-01 -3.40120606e-02
1.41007876e+00 4.14853126e-01 -5.01789093e-01 6.85575366e-01
9.00206327e-01 -1.58788905e-01 -6.06841028e-01 -9.96420979e-02
3.15496832e-01 5.55165969e-02 -1.72841921e-02 -9.32286024e-01
-7.56586790e-01 9.36342239e-01 4.21302110e-01 6.10739827e-01
1.14970601e+00 -2.67526865e-01 5.27412415e-01 8.98796916e-01
5.39395154e-01 -1.04952717e+00 -1.95515871e-01 8.96596730e-01
7.99342394e-01 -7.66758263e-01 4.15732563e-02 -2.68877000e-01
-3.67413163e-01 1.51626515e+00 4.00062591e-01 -2.65352547e-01
6.07095480e-01 2.15567455e-01 -4.03251767e-01 -2.64429897e-01
-3.72849882e-01 -2.81732678e-01 3.41865033e-01 3.44805628e-01
4.69434947e-01 -7.43675902e-02 -5.24132907e-01 6.90340996e-01
-6.20247960e-01 5.36658525e-01 2.81018406e-01 8.96281600e-01
-3.49629819e-01 -1.24297166e+00 -3.47853601e-01 3.15937549e-01
-4.52282995e-01 -1.64109915e-01 -2.05406472e-01 1.07514691e+00
5.57894468e-01 6.52010441e-01 -2.08655015e-01 -3.29204977e-01
4.82127994e-01 4.98404711e-01 8.56971800e-01 -3.64877701e-01
-7.22741008e-01 -5.14750063e-01 1.06536679e-01 -4.35896367e-01
-5.53735673e-01 -5.19883215e-01 -1.58667541e+00 -4.36638117e-01
-2.89802879e-01 2.28263289e-01 6.50666416e-01 1.11096716e+00
1.16729409e-01 6.18372142e-01 9.72108915e-02 -5.70601225e-01
-9.57033634e-01 -6.83961451e-01 -5.68521202e-01 3.32010865e-01
2.06708848e-01 -1.08337927e+00 -1.80870354e-01 -1.05746500e-01] | [8.641151428222656, 6.466348171234131] |
6089bc1d-ecd6-44cf-8e25-364356f0db9c | an-ensemble-approach-for-automated-theorem | 2305.08676 | null | https://arxiv.org/abs/2305.08676v1 | https://arxiv.org/pdf/2305.08676v1.pdf | An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations | Using reinforcement learning for automated theorem proving has recently received much attention. Current approaches use representations of logical statements that often rely on the names used in these statements and, as a result, the models are generally not transferable from one domain to another. The size of these representations and whether to include the whole theory or part of it are other important decisions that affect the performance of these approaches as well as their runtime efficiency. In this paper, we present NIAGRA; an ensemble Name InvAriant Graph RepresentAtion. NIAGRA addresses this problem by using 1) improved Graph Neural Networks for learning name-invariant formula representations that is tailored for their unique characteristics and 2) an efficient ensemble approach for automated theorem proving. Our experimental evaluation shows state-of-the-art performance on multiple datasets from different domains with improvements up to 10% compared to the best learning-based approaches. Furthermore, transfer learning experiments show that our approach significantly outperforms other learning-based approaches by up to 28%. | ['Radu Marinescu', 'Ndivhuwo Makondo', 'Guilherme Lima', 'Akihiro Kishimoto', 'Shajith Ikbal', 'Maxwell Crouse', 'Ibrahim Abdelaziz', 'Achille Fokoue'] | 2023-05-15 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 8.0087423e-02 2.1622506e-01 -5.0389349e-01 -2.6766181e-01
-7.2069335e-01 -6.2342548e-01 4.8406529e-01 3.8555011e-01
1.3883583e-01 8.3771634e-01 -1.8327719e-01 -9.8390657e-01
-4.4463229e-01 -1.1704704e+00 -1.0570214e+00 -6.3104667e-02
-2.4765311e-01 8.3836097e-01 5.2166361e-01 -4.0572190e-01
1.9206588e-01 3.4730557e-01 -1.5254453e+00 5.3332591e-01
8.7720221e-01 7.6947951e-01 -4.6268618e-01 6.6329134e-01
-4.6790469e-01 1.5937048e+00 -3.8239673e-01 -2.9421145e-01
5.4920357e-02 -4.6725816e-01 -1.2854277e+00 -5.4127502e-01
7.8306681e-01 -2.7049452e-01 -3.5103455e-01 1.2859231e+00
-1.6394129e-01 -5.2102838e-02 6.3954288e-01 -1.5799580e+00
-4.7805738e-01 1.2205276e+00 -4.2997351e-01 8.0572039e-02
4.9423054e-01 -2.8303349e-01 1.4764078e+00 -2.0634478e-01
7.3470974e-01 1.3939666e+00 7.6749402e-01 4.5449519e-01
-1.4025427e+00 -9.5347214e-01 1.2379071e-01 7.6877451e-01
-1.2506818e+00 -3.0491924e-01 9.5207322e-01 -5.3929865e-01
1.3205974e+00 9.7224534e-02 2.1539167e-01 8.8309830e-01
3.0061114e-01 5.7760197e-01 1.0094543e+00 -7.0780629e-01
2.7511185e-01 -2.1179037e-03 5.4232669e-01 1.1674528e+00
5.3425950e-01 -8.2097836e-02 -1.3411431e-01 -5.1488429e-01
4.1925037e-01 -3.2480767e-01 6.1961453e-02 -8.6421150e-01
-9.7249287e-01 1.2317483e+00 3.0869734e-01 4.2565927e-01
-2.7260672e-02 5.9737766e-01 8.1801921e-01 7.5917566e-01
1.7710502e-01 7.2661066e-01 -5.9898436e-01 4.8390934e-03
-8.7374794e-01 4.9121809e-01 9.7011262e-01 9.9251974e-01
8.4707642e-01 4.5983672e-02 -1.3070289e-02 4.3890354e-01
1.6827002e-01 2.5906575e-01 1.7922780e-01 -8.2470334e-01
6.5903813e-01 9.7692370e-01 -1.1118673e-01 -9.8072702e-01
-4.3786210e-01 -2.2721602e-01 -3.7360936e-01 3.3346179e-01
5.8136797e-01 -1.0135844e-01 -6.4401823e-01 1.8921489e+00
1.8700626e-01 3.2328841e-01 1.7833443e-01 1.9043604e-01
7.9752344e-01 5.0449598e-01 1.4053580e-02 9.3166158e-02
1.0159701e+00 -8.5722023e-01 -6.1851090e-01 -5.5143372e-03
1.1359444e+00 -2.1650282e-01 5.0565630e-01 4.1668791e-01
-7.6106799e-01 -2.4902371e-01 -1.2279980e+00 1.2242994e-01
-6.5251756e-01 -1.5293883e-01 9.0543050e-01 6.4302146e-01
-8.9912051e-01 8.3437538e-01 -6.1080343e-01 -1.7312022e-01
3.7126857e-01 6.5848011e-01 -3.7037092e-01 -2.6141933e-01
-1.4616276e+00 8.1463253e-01 8.0739176e-01 -6.5128440e-01
-6.7859566e-01 -8.2155412e-01 -1.2730428e+00 3.0967486e-01
7.4203277e-01 -6.6718429e-01 1.3470362e+00 -8.6548084e-01
-1.2947963e+00 4.9128327e-01 2.4211888e-01 -8.3900011e-01
4.5296153e-01 3.8238842e-02 -4.9616694e-01 7.8816086e-02
1.8774343e-01 9.8206572e-02 7.1351361e-01 -9.4764704e-01
-5.8277267e-01 -3.3063054e-01 5.9677374e-01 -3.7054497e-01
-1.0516235e-01 9.4665371e-02 1.6116674e-01 -2.9074353e-01
-2.2303133e-01 -8.5164189e-01 2.1271139e-01 -2.1477002e-01
-2.6801869e-01 -6.5061557e-01 1.0439850e+00 -4.5665467e-01
1.2247279e+00 -1.8371247e+00 1.4914359e-01 2.6412278e-01
4.0505958e-01 2.0515776e-01 -1.0020754e-01 4.7528917e-01
-4.5770532e-01 1.8625584e-01 3.2812234e-02 3.2713860e-01
2.8554979e-01 1.4130998e-01 -3.5768273e-01 2.6974329e-01
2.7651230e-01 7.4965501e-01 -1.1071261e+00 -6.8037796e-01
1.5259224e-01 -3.0207688e-02 -6.1417437e-01 -1.0870239e-02
-6.3564986e-01 -1.7693149e-01 -6.6006809e-01 4.1414762e-01
5.1786798e-01 -4.4748971e-01 6.6531682e-01 -1.7324117e-01
3.3154753e-01 5.2350521e-01 -1.2842941e+00 1.4447393e+00
-3.5981038e-01 3.8632116e-01 -3.2523802e-01 -1.4534690e+00
8.8258135e-01 2.8151074e-01 3.1797361e-01 -5.6099999e-01
1.2951387e-01 2.4277641e-01 2.7802658e-01 -2.4928178e-01
2.2980228e-01 -2.0320795e-01 6.9148652e-04 6.0325575e-01
2.4882364e-01 -1.0940448e-01 5.4153699e-01 3.3965480e-01
1.4713860e+00 3.4488860e-01 6.1621070e-01 -3.0777350e-01
7.3111308e-01 1.2740996e-01 6.7717636e-01 7.7634257e-01
-2.0347698e-02 6.7037508e-02 9.6407104e-01 -5.7137060e-01
-7.5748956e-01 -6.8668926e-01 1.2146494e-01 1.1446159e+00
-1.8709286e-01 -6.9564146e-01 -6.3132489e-01 -1.3357959e+00
4.1766059e-01 1.1551530e+00 -7.6553243e-01 -4.1307130e-01
-4.3568459e-01 -3.3753350e-02 7.5205821e-01 7.6812685e-01
3.5029873e-01 -9.1719729e-01 -2.6954249e-01 2.1521623e-01
1.5211901e-01 -1.2873818e+00 2.0337856e-01 1.9915530e-01
-8.7406385e-01 -1.6746594e+00 -6.0647577e-02 -6.3686800e-01
4.9873778e-01 -5.4382119e-02 1.2819893e+00 2.7042645e-01
2.9594734e-02 3.7491348e-01 -4.5403132e-01 -4.7104633e-01
-9.4867504e-01 1.7849964e-01 -2.8828669e-02 -2.9400924e-01
2.9921007e-01 -5.6611794e-01 3.4452835e-01 -5.2907184e-02
-7.8750330e-01 -5.4222580e-02 3.5780281e-01 8.7198412e-01
1.2143989e-02 2.6989004e-01 6.1100054e-01 -1.5105622e+00
6.2385392e-01 -3.8948244e-01 -1.0225857e+00 6.8876082e-01
-8.7557107e-01 5.6342101e-01 9.8547512e-01 -1.7384475e-01
-7.0043421e-01 -3.2183951e-01 4.5007595e-01 -5.1953036e-01
-3.2647031e-03 7.2034955e-01 7.9634286e-02 -1.6761339e-01
8.5625184e-01 -9.3532212e-02 9.6588405e-03 -1.6083958e-02
2.3561697e-01 4.0146339e-01 9.7857587e-02 -1.0294349e+00
9.6945268e-01 -1.3136935e-01 2.9385135e-01 -3.4874469e-01
-8.2856053e-01 -1.8683955e-01 -4.7646213e-01 6.2692605e-02
4.1002944e-01 -5.9628952e-01 -6.2586236e-01 1.2457165e-01
-1.1298012e+00 -5.3701812e-01 -6.9507860e-02 4.6496004e-01
-7.1700197e-01 4.3433815e-01 -5.6842840e-01 -6.8298733e-01
-3.3149424e-01 -1.2473186e+00 5.8945495e-01 -7.4270710e-02
-3.0798551e-01 -1.2609565e+00 2.4287271e-01 3.8146260e-01
3.9038980e-01 4.5658773e-01 1.6311134e+00 -1.1060197e+00
-2.9447359e-01 -1.8557310e-01 -4.4930127e-01 2.6789835e-01
2.5438255e-01 2.3924469e-03 -7.7760738e-01 -3.7055659e-01
-6.2502021e-01 -5.5762392e-01 7.5119722e-01 1.4127555e-01
1.0009304e+00 -3.2846740e-01 -3.8863182e-01 9.1825068e-02
1.4001445e+00 -5.3697962e-02 4.8317036e-01 5.5207181e-01
5.5695736e-01 2.2188091e-01 4.7568849e-01 2.1506779e-01
2.5682509e-01 5.2599037e-01 5.1524353e-01 2.5175801e-01
-1.8957327e-01 -2.7347097e-01 5.1764774e-01 3.3078128e-01
-1.1646236e-01 1.0873653e-01 -1.2457962e+00 5.1569140e-01
-2.0455897e+00 -1.1580225e+00 -5.4758938e-04 2.0015748e+00
8.2750100e-01 3.1910986e-01 1.2190395e-01 3.0107522e-01
7.2156554e-01 1.2311103e-02 -4.4292006e-01 -7.1367013e-01
2.8264862e-01 6.3291401e-01 3.8141930e-01 3.8943994e-01
-1.1361432e+00 9.8991168e-01 6.5103307e+00 7.3553628e-01
-1.0624653e+00 -1.3824134e-01 -8.2245708e-02 5.3049397e-01
-2.2279152e-01 2.0154889e-01 -4.6768034e-01 -3.6288526e-02
1.1453817e+00 -5.4844242e-01 6.2503678e-01 1.1580137e+00
-4.8039532e-01 2.7278307e-01 -1.5618809e+00 6.3320947e-01
6.1348498e-02 -1.4976456e+00 2.7570632e-01 -1.5170394e-01
7.3820597e-01 5.4099946e-03 -3.8456401e-01 8.4526509e-01
7.4287313e-01 -1.1081172e+00 5.2488661e-01 3.2952943e-01
6.7589033e-01 -1.0233430e+00 1.0220741e+00 1.1037193e-01
-1.0222121e+00 -7.6573603e-02 -2.6022491e-01 -2.8409380e-01
-7.4968159e-01 3.1559318e-01 -1.1437049e+00 1.1138967e+00
4.3439001e-01 8.3979744e-01 -7.2899210e-01 6.3897204e-01
-4.3371350e-01 8.0920690e-01 -1.3224371e-01 -2.9125249e-01
2.6926565e-01 1.5809339e-01 3.0749223e-01 1.2312419e+00
3.1258583e-02 -2.2012217e-01 3.6355194e-01 1.2445107e+00
-2.0630859e-01 -4.8113775e-02 -9.8876160e-01 -3.2567027e-01
2.7629563e-01 1.0998039e+00 -4.1612047e-01 -4.6243936e-01
-6.1366367e-01 2.9735923e-01 6.4469492e-01 2.7769920e-01
-1.0210088e+00 -7.0538425e-01 4.2979440e-01 1.6829002e-01
6.7258006e-01 -5.7711191e-02 2.8087530e-01 -1.0416148e+00
-8.8040054e-02 -1.2373351e+00 8.1576747e-01 -4.7354075e-01
-1.1917748e+00 3.4190008e-01 3.7184289e-01 -1.0262245e+00
-5.9082466e-01 -9.2810637e-01 -4.5167464e-01 5.3650081e-01
-1.6588181e+00 -1.3809227e+00 1.6189147e-02 5.6921130e-01
-6.1083797e-02 -4.8418242e-01 1.2634470e+00 6.2753700e-02
-3.7885323e-01 8.4745610e-01 -1.5986467e-03 6.2637353e-01
6.3154882e-01 -1.4430236e+00 1.5681133e-01 6.3428980e-01
3.5165483e-01 6.7155927e-01 7.0450830e-01 -4.3031555e-01
-1.7099590e+00 -1.1412241e+00 5.8738995e-01 -2.7811006e-01
1.1801717e+00 -4.1440427e-02 -1.0152217e+00 1.2126316e+00
2.1765184e-01 2.5386664e-01 6.2414205e-01 7.4671745e-01
-1.0872452e+00 -2.8259942e-01 -1.1882759e+00 3.6674872e-01
8.0377412e-01 -6.3572836e-01 -9.9795073e-01 3.7059134e-01
5.3894103e-01 -4.1474342e-01 -8.7936938e-01 4.1094521e-01
2.6140347e-01 -1.0047741e+00 4.8073387e-01 -1.2842944e+00
5.8002657e-01 -3.9286634e-01 -1.0699097e-01 -1.0388435e+00
-5.1261121e-01 -4.8950225e-01 -5.7054174e-01 1.0703307e+00
5.1473528e-01 -8.1877965e-01 5.8141202e-01 4.0165851e-01
2.2558923e-01 -5.0296742e-01 -8.9873165e-01 -1.0151041e+00
3.8439259e-01 -5.4512084e-01 6.5784883e-01 1.3691834e+00
4.3135613e-01 7.3294240e-01 1.1880832e-01 1.0274328e-01
5.4716915e-01 5.3016633e-01 8.5822964e-01 -1.5040959e+00
-3.6492708e-01 -5.7371092e-01 -9.8219168e-01 -2.4720329e-01
1.0505815e+00 -1.4815601e+00 -5.0083542e-01 -1.5225524e+00
3.4983501e-01 -2.9517919e-01 -6.3747853e-01 1.0739192e+00
1.2685689e-01 -3.6061460e-01 5.4632314e-02 -3.7042922e-01
-8.4798110e-01 2.6022112e-01 8.0267990e-01 -6.7684245e-01
1.5153573e-01 -2.4005614e-01 -6.1077046e-01 6.3139659e-01
8.0546397e-01 -4.1622391e-01 -4.0787858e-01 -1.4026946e-01
3.0685142e-01 3.6543176e-02 2.9481050e-01 -1.2096645e+00
1.5036483e-01 -2.6227903e-01 -4.8593912e-02 -1.5481932e-01
-1.8146771e-01 -6.9355738e-01 -1.5793934e-01 5.1780277e-01
-4.2197755e-01 4.9066123e-02 5.9483081e-01 5.8682162e-01
-1.8525332e-01 -4.6110123e-01 5.6200689e-01 -2.7733553e-02
-8.0954981e-01 -3.0744119e-02 -1.1788781e-01 2.6744205e-01
1.0383333e+00 1.7902523e-01 -3.4164944e-01 -3.9278355e-01
-3.1172961e-01 1.7655636e-01 3.2479900e-01 4.4651547e-01
3.2394737e-01 -1.3120350e+00 -8.5212523e-01 -1.2539221e-01
3.6338776e-01 -2.0656675e-01 -1.7566657e-01 6.6716057e-01
-4.8459241e-01 5.5484021e-01 -2.2531500e-01 -3.2238460e-01
-1.5126284e+00 7.6320058e-01 4.7528183e-01 -7.9740989e-01
-7.0310682e-01 2.6500300e-01 -1.3048159e-01 -6.4142072e-01
-1.7971231e-02 -6.0232669e-01 -2.2650044e-01 -3.5111365e-01
4.7447863e-01 2.9454595e-01 2.8416002e-01 -1.7426579e-01
-5.1464164e-01 4.6762407e-01 -4.8059371e-01 4.2257315e-01
1.5667678e+00 8.1384271e-01 -3.6709711e-01 4.8057160e-01
9.8546582e-01 -7.4626327e-02 -3.7656456e-01 -4.1262445e-01
2.8432477e-01 -5.1279485e-02 1.1583842e-01 -7.1514589e-01
-9.1436380e-01 6.0470927e-01 1.6313359e-01 4.3331301e-01
7.5534338e-01 -2.0544397e-02 3.5647371e-01 8.6497521e-01
6.5740842e-01 -1.0012375e+00 -1.6528286e-01 8.8511097e-01
7.5606549e-01 -1.0836575e+00 4.1805911e-01 -3.7198797e-01
-9.0377286e-02 1.6580305e+00 4.1339722e-01 -3.1232592e-01
2.3785883e-01 2.1737799e-01 -1.6006388e-01 -2.1602798e-01
-8.0958301e-01 7.7309519e-02 2.8538212e-01 6.1437881e-01
6.5241098e-01 1.2091028e-01 -6.7313984e-02 4.9527976e-01
-3.8421813e-02 1.7602935e-01 4.8644644e-01 1.0525739e+00
-2.4457255e-01 -1.4113342e+00 -4.5003742e-01 5.0183809e-01
-2.9530349e-01 9.7272448e-02 -5.4051524e-01 1.3027409e+00
-2.2435461e-01 8.9764947e-01 -3.6336437e-01 -4.4189724e-01
2.5139976e-01 4.7282979e-01 9.5152283e-01 -7.5198823e-01
-4.4963872e-01 -6.3920540e-01 4.4759631e-01 -4.7115999e-01
-4.1114888e-01 -5.6718916e-01 -1.5388153e+00 -4.3415746e-01
-5.3382301e-01 3.1127879e-01 1.5728733e-01 1.0662438e+00
2.4320742e-01 9.2151701e-01 3.3697107e-01 -1.7771861e-01
-1.0241069e+00 -8.6839765e-01 -5.9536386e-01 3.3665583e-01
1.6511336e-01 -9.5212239e-01 -1.9894743e-01 -2.8670180e-01] | [8.941193580627441, 7.181590557098389] |
362c925e-c58b-4885-998c-578bb7726b3b | speaker-diarization-and-identification-from | 2207.00660 | null | https://arxiv.org/abs/2207.00660v1 | https://arxiv.org/pdf/2207.00660v1.pdf | Speaker Diarization and Identification from Single-Channel Classroom Audio Recording Using Virtual Microphones | Speaker identification in noisy audio recordings, specifically those from collaborative learning environments, can be extremely challenging. There is a need to identify individual students talking in small groups from other students talking at the same time. To solve the problem, we assume the use of a single microphone per student group without any access to previous large datasets for training. This dissertation proposes a method of speaker identification using cross-correlation patterns associated to an array of virtual microphones, centered around the physical microphone. The virtual microphones are simulated by using approximate speaker geometry observed from a video recording. The patterns are constructed based on estimates of the room impulse responses for each virtual microphone. The correlation patterns are then used to identify the speakers. The proposed method is validated with classroom audios and shown to substantially outperform diarization services provided by Google Cloud and Amazon AWS. | ['Antonio Gomez'] | 2022-07-01 | null | null | null | null | ['speaker-identification'] | ['speech'] | [-1.23691469e-01 -3.28180730e-01 6.82788491e-01 -3.29368263e-01
-1.19404078e+00 -9.46891963e-01 1.14577010e-01 1.59346849e-01
8.82907212e-02 2.02140287e-01 1.75428659e-01 -3.89002711e-01
-3.36453766e-01 -3.31352293e-01 -5.80039024e-01 -9.10481751e-01
-8.84923562e-02 7.04031661e-02 1.07646808e-01 6.56448007e-02
4.05502230e-01 6.52454078e-01 -2.22806835e+00 3.40945452e-01
6.33160114e-01 8.15529048e-01 3.67244303e-01 1.29116750e+00
-3.77702892e-01 4.93582487e-01 -1.33835804e+00 1.37722462e-01
4.94140200e-02 -1.20127909e-02 -2.79442787e-01 1.05507419e-01
9.24840450e-01 -1.60824046e-01 1.01549082e-01 9.96365130e-01
9.65456665e-01 5.71130097e-01 3.86808038e-01 -1.15636384e+00
3.11399959e-02 8.60631764e-01 -4.84575003e-01 4.35492426e-01
8.98338318e-01 -4.17427063e-01 5.22171319e-01 -8.15570295e-01
-4.26738374e-02 1.24301660e+00 7.29043305e-01 4.75378036e-01
-1.25687754e+00 -1.40056467e+00 1.40381604e-01 8.51423740e-02
-1.89471436e+00 -6.59017622e-01 7.50893533e-01 -5.59878647e-01
4.60935235e-01 5.23344696e-01 3.96540105e-01 1.07406676e+00
-3.20834696e-01 3.24022889e-01 9.87343013e-01 -6.66570306e-01
3.59862328e-01 7.47127831e-01 3.79702657e-01 2.04344064e-01
-3.28425407e-01 -4.54997212e-01 -7.51102030e-01 -4.42154467e-01
4.43271369e-01 -2.27185324e-01 -3.33501101e-01 4.77006659e-02
-8.59770119e-01 5.13447285e-01 -4.16745514e-01 5.06335735e-01
-1.84250355e-01 -2.88227409e-01 6.76505938e-02 4.28439558e-01
2.02713221e-01 1.20700747e-01 -1.36521384e-01 -4.27000135e-01
-8.69002044e-01 1.01310583e-02 1.11085260e+00 1.13494027e+00
3.28373760e-01 1.75045148e-01 4.89848912e-01 9.81473923e-01
4.41317737e-01 5.38954616e-01 6.79733574e-01 -9.50021744e-01
4.51352984e-01 2.50485241e-01 -1.53329298e-01 -8.92863691e-01
8.44231322e-02 -1.82641819e-01 -3.52627128e-01 8.22791234e-02
6.49744928e-01 -3.43799353e-01 -2.68058509e-01 1.25468373e+00
8.92466068e-01 1.13735938e+00 1.28422648e-01 2.17715174e-01
9.20212507e-01 6.78820312e-01 -5.56001008e-01 -2.06313431e-01
1.08152294e+00 -6.06292307e-01 -9.30018842e-01 4.10600036e-01
3.48420233e-01 -1.28211808e+00 8.33864093e-01 9.14537251e-01
-9.23317850e-01 -6.44877851e-01 -9.10277843e-01 6.33541763e-01
-3.26710016e-01 -1.31636769e-01 1.28430277e-01 1.61516702e+00
-1.17177427e+00 7.29558319e-02 -7.00840175e-01 6.09185584e-02
-2.92001903e-01 7.46409893e-01 -3.08752984e-01 2.87170082e-01
-4.59298819e-01 -4.09384593e-02 -5.63538432e-01 2.63740700e-02
-8.88161957e-01 -9.21616793e-01 -4.99270201e-01 1.40289858e-01
-2.93691397e-01 1.61080003e-01 1.40043843e+00 -8.56119633e-01
-2.05373192e+00 3.71835172e-01 -3.58822227e-01 -9.38180927e-03
3.35942686e-01 -1.66173682e-01 -6.94442272e-01 1.65724680e-01
-3.26115973e-02 -2.30035827e-01 7.85034895e-01 -1.41857588e+00
-6.41295016e-01 -4.19149488e-01 -4.66422677e-01 1.94391936e-01
-7.72998512e-01 5.11029363e-01 -1.24301314e-02 -9.85735953e-02
5.12054980e-01 -6.82892203e-01 4.11671586e-02 -7.87131011e-01
-4.61557925e-01 -1.31123707e-01 1.12937713e+00 -6.07573807e-01
1.17643404e+00 -2.31456733e+00 -5.41267037e-01 8.06292117e-01
-3.68637927e-02 6.20500855e-02 9.39491838e-02 3.87074888e-01
-4.05635327e-01 -4.11393568e-02 6.71669126e-01 -6.25009060e-01
-1.88924387e-01 -2.77180940e-01 -3.84131163e-01 5.21311879e-01
-8.27106595e-01 -5.40813029e-01 -5.67822039e-01 -4.73171502e-01
3.37684393e-01 7.07238615e-01 -4.19146210e-01 6.58716857e-01
7.49920368e-01 5.93615234e-01 -3.41922522e-01 3.44223797e-01
7.99940765e-01 3.83405775e-01 1.43436402e-01 5.90928376e-01
-5.36842346e-01 5.16641498e-01 -2.25988197e+00 1.23300242e+00
-8.21673453e-01 8.33331347e-01 1.00135136e+00 -1.02544510e+00
1.11337030e+00 1.09899390e+00 4.88260090e-01 7.47427940e-02
8.67871195e-03 1.69945329e-01 -1.34537444e-02 -6.18010402e-01
8.13190714e-02 3.62459809e-01 2.80118525e-01 7.15876102e-01
2.59341091e-01 -1.45173550e-01 -2.34621003e-01 -1.85039453e-02
9.54779685e-01 -6.77552700e-01 -2.00305164e-01 -2.32561156e-01
9.05229867e-01 -6.80875421e-01 3.16917956e-01 9.10379529e-01
-5.82412779e-02 5.41599274e-01 -7.30153844e-02 1.44759417e-01
-2.77232677e-01 -1.33943021e+00 -3.10918063e-01 1.27094543e+00
-3.49079370e-01 -4.26271975e-01 -1.11566675e+00 -2.41708115e-01
-2.80990839e-01 4.61958647e-01 -2.02739805e-01 3.89998764e-01
-5.27748227e-01 -9.50954333e-02 7.09710538e-01 2.08104357e-01
1.06972083e-01 -3.39377463e-01 -9.94820148e-02 1.92551062e-01
-1.02700144e-01 -1.13717961e+00 -6.37481809e-01 1.51059762e-01
-7.13647246e-01 -7.04298496e-01 -3.37165624e-01 -1.15433860e+00
4.44665909e-01 7.62410462e-01 7.47240126e-01 -1.65165707e-01
-1.76632270e-01 1.07632613e+00 -1.18991649e-02 -9.45951939e-01
-4.66316581e-01 -2.96324670e-01 7.02904105e-01 2.74088562e-01
5.90770543e-01 -1.22355056e+00 -3.90268147e-01 6.12209499e-01
-3.26587230e-01 -7.58289456e-01 -3.70776325e-01 3.90831321e-01
2.16360405e-01 5.93860507e-01 5.71995616e-01 -4.72597539e-01
6.48882091e-01 -4.86469805e-01 -8.63596320e-01 9.56718326e-02
-4.31239307e-02 -6.41659021e-01 6.96619511e-01 -9.70485151e-01
-1.03654659e+00 2.27434382e-01 -1.65331319e-01 -5.23431301e-01
-9.59444344e-01 -1.31584078e-01 -4.68757719e-01 -3.28660727e-01
4.83438402e-01 1.74792886e-01 -1.95717260e-01 -6.79852903e-01
-1.78560346e-01 1.41851616e+00 3.80333394e-01 -6.59446061e-01
5.74779332e-01 6.88591152e-02 -4.08888668e-01 -1.55243206e+00
-3.42403501e-01 -1.04181159e+00 -6.01343632e-01 -5.07011235e-01
6.05511725e-01 -1.25005472e+00 -1.44163024e+00 4.79178607e-01
-9.53574598e-01 4.67392989e-02 9.59685519e-02 1.02286661e+00
-1.11715861e-01 1.36424959e-01 -4.76264864e-01 -1.53653061e+00
2.54639499e-02 -1.24802184e+00 7.95882642e-01 4.84840810e-01
-1.82068527e-01 -1.17570591e+00 2.35549405e-01 9.18948591e-01
1.42712519e-01 -4.83692974e-01 3.22867662e-01 -1.19466341e+00
-4.08670247e-01 -5.25017262e-01 7.65812993e-01 4.80574697e-01
3.23546678e-01 5.76773942e-01 -1.79091895e+00 -4.34980281e-02
5.05272090e-01 1.87040299e-01 -2.17008501e-01 5.47884822e-01
1.24152923e+00 -7.96770900e-02 -1.26689836e-01 3.76217157e-01
1.05836582e+00 6.08864129e-01 -4.61829603e-02 -2.25354582e-02
6.78062081e-01 9.22126949e-01 -1.86923239e-02 4.14431632e-01
2.33706430e-01 4.35377210e-01 5.16732410e-02 5.42530477e-01
2.90641248e-01 -1.31200001e-01 6.39512241e-01 1.59056652e+00
1.61577299e-01 -4.64866571e-02 -1.17006731e+00 5.87764204e-01
-1.14042759e+00 -1.09128606e+00 -3.62351507e-01 2.55671287e+00
5.34434438e-01 -3.58337909e-01 2.54584134e-01 5.54492831e-01
1.06757534e+00 -3.77810448e-01 1.20910563e-01 -5.00303984e-01
2.93225050e-01 4.94591117e-01 1.78687736e-01 7.69503534e-01
-7.76907325e-01 1.84586018e-01 6.81789303e+00 6.02376282e-01
-1.14945912e+00 3.36921774e-02 3.33714396e-01 -2.10307300e-01
8.98081362e-02 -4.07043129e-01 -1.19843400e+00 4.77508098e-01
1.50355303e+00 -2.10612431e-01 5.18130898e-01 8.61451387e-01
4.62305963e-01 -1.37915790e-01 -1.39081812e+00 1.19562197e+00
8.63068700e-02 -8.66828799e-01 -4.41508055e-01 2.42373064e-01
8.67821217e-01 -3.48624885e-01 4.03212816e-01 1.73320528e-02
5.96139729e-01 -1.03236783e+00 3.71487588e-01 1.26326412e-01
-7.64995664e-02 -8.43403399e-01 4.25795883e-01 6.96003079e-01
-1.42863369e+00 -2.76512951e-01 -1.77427918e-01 -9.32182297e-02
-5.11884391e-01 4.26991820e-01 -1.28272116e+00 8.04207921e-02
1.00537431e+00 5.30148111e-02 -2.24738240e-01 1.25585330e+00
2.86507189e-01 1.12253928e+00 -6.05908453e-01 -6.12811595e-02
-1.70936763e-01 -4.81636405e-01 5.57763219e-01 1.18252552e+00
9.84349728e-01 -1.92020871e-02 1.13459744e-01 4.50518161e-01
1.13524802e-01 4.58191156e-01 -8.78092349e-01 4.64697212e-01
1.11552334e+00 1.27257228e+00 -5.21173656e-01 -1.39909148e-01
-4.32545424e-01 4.25441325e-01 -2.60442764e-01 4.72393095e-01
-3.94229561e-01 -3.68757695e-01 9.61342037e-01 8.05504173e-02
9.42677408e-02 -1.64585993e-01 -1.89615160e-01 -6.18220448e-01
-1.32420540e-01 -1.18335938e+00 1.00674614e-01 -5.51073194e-01
-1.09904611e+00 1.67379722e-01 -1.06878005e-01 -1.27180457e+00
-2.86845624e-01 -3.18956912e-01 -1.31709325e+00 1.28616548e+00
-5.42269051e-01 -2.83304453e-01 -2.59957731e-01 9.91326272e-01
5.10627508e-01 -3.78328204e-01 1.21490908e+00 5.53114831e-01
-7.83324361e-01 7.10283756e-01 7.03894317e-01 2.05751240e-01
7.90152073e-01 -1.43435347e+00 -1.18169919e-01 4.94331092e-01
3.41747403e-01 9.87823308e-01 1.01275182e+00 -6.90345559e-03
-1.39114726e+00 -4.48345780e-01 7.29832530e-01 -7.00404644e-01
7.45293736e-01 -7.37019002e-01 -9.80261028e-01 5.41175961e-01
3.51397693e-01 -5.37392087e-02 1.55130208e+00 4.78746593e-01
-1.73118159e-01 -3.20547700e-01 -1.15398002e+00 1.94481015e-01
3.97357881e-01 -8.76541138e-01 -3.66711169e-01 4.05592144e-01
2.26814091e-01 -3.43593299e-01 -9.37601447e-01 -5.95187902e-01
5.85494757e-01 -1.09122217e+00 1.17113209e+00 -1.49274588e-01
-2.36096069e-01 -7.20318109e-02 -4.25968975e-01 -1.32631683e+00
3.70886534e-01 -8.87018323e-01 2.89523542e-01 2.01126814e+00
2.24488288e-01 -6.58898771e-01 1.05702353e+00 7.60441065e-01
6.07332513e-02 5.28401416e-03 -1.16860330e+00 -8.39986086e-01
1.45469740e-01 -6.51395917e-01 7.66212344e-01 1.08051479e+00
2.95628607e-01 2.39436880e-01 1.16795406e-01 9.46482718e-01
8.35933506e-01 -9.22144055e-02 1.07260692e+00 -1.44779527e+00
-3.04723740e-01 -2.27643192e-01 -5.33289433e-01 -1.01799452e+00
3.38190019e-01 -2.26142436e-01 2.06565455e-01 -8.81260216e-01
-3.15533787e-01 -5.61846018e-01 -1.78393319e-01 -3.87232870e-01
-2.65758578e-02 -2.30062038e-01 -2.00444490e-01 -3.48735660e-01
-3.12780708e-01 2.75558624e-02 7.10998714e-01 2.77163815e-02
-5.19947231e-01 7.18381882e-01 -3.08652073e-01 1.05086195e+00
6.82579696e-01 -4.97546047e-01 -7.05722511e-01 -1.26823932e-01
-3.54933888e-01 3.77566099e-01 -2.13494569e-01 -1.41032076e+00
7.55343318e-01 6.99872226e-02 2.18924478e-01 -5.09149849e-01
5.30486584e-01 -1.16489124e+00 1.69088930e-01 -1.44048244e-01
-4.90706116e-01 2.39671394e-01 8.49872679e-02 3.93178523e-01
-4.30161804e-01 -5.38799822e-01 4.07950044e-01 1.78602487e-01
3.70548815e-02 -2.83778518e-01 -1.02273583e+00 -3.60169947e-01
1.02758062e+00 -4.34368759e-01 1.93540454e-01 -8.37745130e-01
-8.35258305e-01 5.00013493e-02 -1.54278308e-01 1.84498265e-01
5.02526999e-01 -1.03642237e+00 -2.69919485e-01 6.35151684e-01
-3.24532658e-01 3.71022150e-02 5.27834058e-01 5.21299362e-01
-1.98003978e-01 2.63767719e-01 1.22238435e-01 -8.37974012e-01
-2.47579408e+00 2.12596864e-01 3.63051474e-01 2.97961324e-01
-2.07837373e-01 1.32258677e+00 1.79615363e-01 -6.72752023e-01
1.01147592e+00 -2.48963311e-01 -5.11913598e-01 1.48674160e-01
1.02733123e+00 6.10503137e-01 2.84046352e-01 -6.63572013e-01
-1.44485459e-01 5.56777120e-01 1.54580206e-01 -3.35129648e-01
1.15684128e+00 -3.99262100e-01 1.40147388e-01 9.12746549e-01
1.21933401e+00 1.01212835e+00 -7.15088785e-01 -1.76924258e-01
-9.12380889e-02 -6.67994857e-01 -3.27012241e-02 -8.41925144e-02
-8.81612480e-01 1.12782574e+00 1.11981726e+00 4.92423683e-01
9.22495186e-01 -3.61462444e-01 1.29128367e-01 5.26955485e-01
2.42972776e-01 -1.12136483e+00 1.45968497e-01 4.00135636e-01
1.82595849e-01 -1.07422328e+00 -4.03055280e-01 -6.68118834e-01
-1.09519325e-01 1.16708851e+00 6.02737248e-01 6.61477894e-02
1.13731742e+00 6.77363098e-01 5.95715106e-01 1.85507029e-01
-7.11858690e-01 4.33209330e-01 2.59923134e-02 9.05717194e-01
8.87658715e-01 1.62294596e-01 7.39793360e-01 6.41764343e-01
-6.91452742e-01 -6.75444901e-01 8.66411090e-01 7.69389927e-01
-5.63894153e-01 -1.09534812e+00 -1.43727994e+00 1.83561057e-01
-9.46571231e-01 1.10985013e-02 -3.67495596e-01 2.25781575e-01
4.40165550e-02 1.67713964e+00 1.85171068e-01 -5.66317260e-01
3.56789678e-01 4.83626515e-01 1.86177135e-01 -7.15634108e-01
-1.19931686e+00 2.26573542e-01 -1.13876492e-01 -1.15226403e-01
-2.16198802e-01 -9.86782432e-01 -9.37311649e-01 -5.24516761e-01
-5.56715667e-01 8.12945068e-01 1.21441627e+00 4.10545886e-01
8.65941495e-02 5.73977947e-01 1.05701137e+00 -7.61634529e-01
-6.49816394e-01 -9.13090765e-01 -1.02485836e+00 3.92748117e-02
5.75125039e-01 -2.83331960e-01 -8.36810708e-01 9.61926356e-02] | [14.832048416137695, 5.863192081451416] |
1e59363f-56be-4d17-9def-ad02dd608740 | the-sound-of-silence-in-eeg-cognitive-voice | 2010.05497 | null | https://arxiv.org/abs/2010.05497v1 | https://arxiv.org/pdf/2010.05497v1.pdf | The "Sound of Silence" in EEG -- Cognitive voice activity detection | Speech cognition bears potential application as a brain computer interface that can improve the quality of life for the otherwise communication impaired people. While speech and resting state EEG are popularly studied, here we attempt to explore a "non-speech"(NS) state of brain activity corresponding to the silence regions of speech audio. Firstly, speech perception is studied to inspect the existence of such a state, followed by its identification in speech imagination. Analogous to how voice activity detection is employed to enhance the performance of speech recognition, the EEG state activity detection protocol implemented here is applied to boost the confidence of imagined speech EEG decoding. Classification of speech and NS state is done using two datasets collected from laboratory-based and commercial-based devices. The state sequential information thus obtained is further utilized to reduce the search space of imagined EEG unit recognition. Temporal signal structures and topographic maps of NS states are visualized across subjects and sessions. The recognition performance and the visual distinction observed demonstrates the existence of silence signatures in EEG. | ['Hema A Murthy', 'Rini A Sharon'] | 2020-10-12 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 4.04750288e-01 -5.90151437e-02 3.75513524e-01 -3.14581573e-01
-2.83511460e-01 -3.70946527e-01 7.01452434e-01 -9.69453454e-02
-4.42487508e-01 6.88069165e-01 3.91021132e-01 -4.86892134e-01
-2.90565014e-01 -3.11363906e-01 -2.12403283e-01 -8.45053136e-01
-2.77568758e-01 -1.11229114e-01 -3.28181945e-02 -1.78844213e-01
5.84628582e-01 6.41383410e-01 -1.76224244e+00 5.67927361e-01
8.77749085e-01 7.96397090e-01 6.63745463e-01 4.87191349e-01
-1.99591979e-01 1.98216662e-01 -1.35342908e+00 -6.85877120e-03
-3.73578131e-01 -6.41091287e-01 -6.27827585e-01 2.22687781e-01
-4.64213878e-01 2.76695956e-02 -3.25385451e-01 1.25720847e+00
8.41162026e-01 1.81812778e-01 4.03973192e-01 -9.09252882e-01
-2.22852498e-01 2.39912763e-01 3.00911348e-02 1.02173042e+00
8.62302601e-01 3.75504009e-02 9.64533165e-02 -7.66375542e-01
3.92202407e-01 7.87462831e-01 -9.15961415e-02 3.30316156e-01
-9.30399239e-01 -7.14343667e-01 -3.17964256e-02 6.85658097e-01
-1.52276421e+00 -7.33282864e-01 9.42269027e-01 -4.70149279e-01
1.17530942e+00 3.51603568e-01 1.19076943e+00 1.40388227e+00
4.22611803e-01 2.56809711e-01 1.28301144e+00 -3.56187731e-01
4.68420565e-01 4.22950417e-01 4.16783243e-01 2.21299902e-01
-1.37115106e-01 5.91304712e-02 -1.09955657e+00 -5.11237197e-02
4.78741944e-01 -2.99440563e-01 -6.91287637e-01 3.45829248e-01
-1.26065278e+00 2.10287094e-01 -4.97416034e-02 8.70903909e-01
-7.74998307e-01 -5.82146287e-01 3.85660470e-01 3.88272762e-01
4.21846181e-01 4.23388958e-01 2.79525239e-02 -5.93229413e-01
-1.13171315e+00 -7.41519153e-01 5.95143616e-01 3.97212535e-01
2.61350095e-01 2.19915330e-01 -6.55996203e-02 8.15696895e-01
-4.41394262e-02 2.90501505e-01 9.74832654e-01 -4.21633571e-01
4.99052834e-03 6.33135736e-01 -2.39044532e-01 -6.08594358e-01
-4.49165076e-01 -4.89972562e-01 -3.95983934e-01 1.76209003e-01
-9.02860314e-02 6.75605163e-02 -5.84725022e-01 1.45813322e+00
-7.45004043e-02 2.35770047e-01 2.23329216e-01 7.66033351e-01
5.42697310e-01 6.14776671e-01 3.08238473e-02 -7.08712578e-01
1.70429158e+00 -1.76129743e-01 -1.14879286e+00 -2.30540738e-01
2.67529219e-01 -2.10386485e-01 8.78455877e-01 5.55751681e-01
-7.90714979e-01 -4.20504659e-01 -1.21234488e+00 6.21891201e-01
-4.98860359e-01 9.99023914e-02 4.64776754e-01 1.03053272e+00
-1.12915301e+00 2.48868540e-01 -9.85287070e-01 -5.61631620e-01
2.62798697e-01 5.85346341e-01 -5.81563294e-01 6.24838591e-01
-1.26288331e+00 1.10261214e+00 3.18577141e-01 4.00083601e-01
-7.88586020e-01 -3.16831350e-01 -3.94641966e-01 1.35508850e-01
-1.58434883e-01 -2.77872175e-01 6.31753743e-01 -9.32048380e-01
-1.49592972e+00 1.11323869e+00 -5.72658598e-01 -2.06210881e-01
8.09203908e-02 4.40979302e-01 -1.03854871e+00 6.03059649e-01
-3.88194993e-02 2.76324540e-01 8.07395577e-01 -6.84764683e-01
-1.65549502e-01 -7.48169005e-01 -4.40663397e-01 2.28409514e-01
-2.59008318e-01 4.57614154e-01 1.49421647e-01 -3.96470338e-01
4.25216407e-01 -5.74635148e-01 6.27927780e-01 -4.02341813e-01
-1.06532387e-01 -3.95239800e-01 6.15097642e-01 -8.47527385e-01
1.03456211e+00 -2.39285827e+00 -3.32466006e-01 5.22318602e-01
1.36114031e-01 8.62695873e-02 -2.66316757e-02 3.41806501e-01
-6.17183268e-01 -1.09515198e-01 -9.79210958e-02 2.06977606e-01
-1.32688925e-01 3.18618119e-02 -1.27273396e-01 8.75430465e-01
-8.65591094e-02 5.93010306e-01 -3.08873951e-01 -2.21179761e-02
2.27967858e-01 5.57022870e-01 -1.18138574e-01 2.99763262e-01
5.73246121e-01 6.39651895e-01 4.73778099e-02 5.70243239e-01
4.19022143e-01 2.04355255e-01 1.94172055e-01 -3.02919924e-01
-5.13297200e-01 7.65094519e-01 -6.80034876e-01 1.60948980e+00
-1.59689277e-01 1.01659787e+00 1.24950089e-01 -1.16690981e+00
9.46854830e-01 9.24341083e-01 1.89900711e-01 -1.09364367e+00
4.40137058e-01 -8.93449690e-03 6.06981575e-01 -9.60330486e-01
-2.07533687e-01 -5.01606613e-02 4.88162756e-01 3.94448906e-01
2.04363763e-01 4.15361151e-02 -3.57461870e-02 -5.00260293e-02
9.97526944e-01 -4.91723478e-01 3.37443143e-01 -6.21949017e-01
4.78688002e-01 -5.91907859e-01 -9.96026695e-02 4.83628482e-01
-3.76619041e-01 1.24028832e-01 5.46659589e-01 4.72726338e-02
-2.31470168e-01 -1.10390627e+00 -3.63067061e-01 9.36885953e-01
1.98814899e-01 -6.26698509e-02 -8.70767415e-01 -1.20619416e-01
-6.16584599e-01 1.01503122e+00 -5.62254548e-01 -5.49162328e-01
-8.98581296e-02 -7.40761042e-01 2.38981038e-01 2.02311397e-01
4.67386186e-01 -1.50733268e+00 -1.02445519e+00 2.27269024e-01
-3.10476929e-01 -7.75929987e-01 1.25973523e-01 6.20902240e-01
-5.94375134e-01 -7.12500453e-01 -5.51294446e-01 -7.82954991e-01
4.29526567e-01 -2.03131922e-02 4.58651066e-01 -2.44008541e-01
-4.11317468e-01 7.31093228e-01 -1.74069107e-01 -4.76028323e-01
-3.61831069e-01 -3.81202102e-01 3.78308147e-01 1.86256111e-01
4.65440422e-01 -1.07153654e+00 -6.63663685e-01 2.24366814e-01
-5.39226592e-01 -1.58437595e-01 6.40670180e-01 4.07826126e-01
5.63781187e-02 7.51032829e-02 9.62539077e-01 7.35668838e-02
1.19310760e+00 -4.08922642e-01 -3.75160575e-02 3.59011948e-01
-5.39299667e-01 -1.56337127e-01 8.05381238e-02 -7.00624824e-01
-1.25766575e+00 -3.48763078e-01 -2.87222117e-01 8.24478269e-02
-7.96025693e-01 2.93225765e-01 -3.64423573e-01 -5.12448475e-02
6.10582709e-01 8.55395496e-01 2.01892808e-01 -5.36708087e-02
-5.10876536e-01 1.14936090e+00 8.76405299e-01 -1.55443102e-01
-2.36666098e-01 2.48857170e-01 -5.69979727e-01 -1.53262544e+00
-5.68218790e-02 -4.07382488e-01 -4.34293956e-01 -5.81422806e-01
1.01286471e+00 -5.97956479e-01 -7.96002090e-01 3.51527870e-01
-1.38614905e+00 9.14469138e-02 1.08437091e-01 8.21938515e-01
-4.83627886e-01 3.08214158e-01 -2.41313368e-01 -1.41737771e+00
-4.77204263e-01 -1.12366760e+00 6.53234661e-01 7.56998882e-02
-4.68283623e-01 -4.80767429e-01 -5.44384792e-02 1.80801526e-01
2.40388796e-01 -3.57081354e-01 8.96061540e-01 -1.02585888e+00
5.89313097e-02 -2.31230214e-01 2.30408996e-01 2.07025096e-01
2.30819941e-01 -6.14125907e-01 -1.37284923e+00 -1.15965791e-02
9.73622262e-01 2.21566871e-01 5.08101918e-02 3.69349271e-01
7.86430299e-01 -9.08974111e-02 -3.59426826e-01 2.14381367e-01
6.16925240e-01 1.33172607e+00 9.83644664e-01 9.99781396e-03
-1.67707995e-01 9.34168518e-01 -1.68123081e-01 8.50062966e-02
-2.66394109e-01 4.37825054e-01 1.08035944e-01 7.48666152e-02
-4.61790934e-02 2.11085469e-01 6.13291323e-01 8.67323339e-01
-1.19486891e-01 -1.83582529e-01 -9.51823294e-01 3.38263631e-01
-1.13340652e+00 -9.70167279e-01 1.96245667e-02 2.11319208e+00
3.98404181e-01 2.16045722e-01 -2.46150732e-01 5.09324849e-01
9.43192482e-01 -2.29147643e-01 -4.79919851e-01 -3.82313341e-01
-2.23386437e-01 5.37250936e-01 -2.84028322e-01 2.83687618e-02
-3.83673877e-01 4.00125861e-01 6.73626423e+00 5.46488702e-01
-1.47077954e+00 3.84510934e-01 2.66617984e-01 -2.60034770e-01
1.24180861e-01 -4.73009288e-01 -9.54645053e-02 7.42823243e-01
1.46271479e+00 -2.53247201e-01 5.85032165e-01 2.86394984e-01
5.67223668e-01 -6.07527077e-01 -1.07384300e+00 1.46365356e+00
2.71179676e-01 -9.91315305e-01 -9.38034281e-02 1.13154858e-01
-2.96789527e-01 -6.20552376e-02 -4.82269712e-02 1.16241448e-04
-9.84371066e-01 -9.82881367e-01 9.13905501e-01 5.25066614e-01
1.06687534e+00 -6.79727554e-01 5.57213604e-01 4.70435351e-01
-9.11720276e-01 -6.26793504e-02 6.47786409e-02 -2.25285411e-01
1.87244609e-01 1.74211934e-01 -8.16831768e-01 -3.16204168e-02
4.86146539e-01 9.46156457e-02 -5.69952786e-01 9.34961677e-01
-2.13461086e-01 7.97036827e-01 -2.52378166e-01 -3.88653666e-01
-1.52026340e-01 -1.02119647e-01 9.08152580e-01 9.74952936e-01
6.88846171e-01 3.23545694e-01 -6.25982702e-01 1.14523268e+00
5.29540658e-01 -5.79150915e-02 -7.51522839e-01 -2.70217478e-01
4.62549329e-01 9.00147438e-01 -1.10856807e+00 -8.45934972e-02
-1.33763433e-01 1.36527896e+00 -2.05181465e-01 3.65614682e-01
-4.86444801e-01 -4.52988118e-01 2.66624421e-01 3.87413688e-02
-3.91271979e-01 -1.89686343e-01 -2.16141537e-01 -9.87972140e-01
1.51543036e-01 -6.35274351e-01 -1.25580847e-01 -1.09827614e+00
-4.98410821e-01 9.84449089e-01 1.12020992e-01 -9.57183063e-01
-1.16900042e-01 -4.80548382e-01 -9.01001453e-01 1.03900123e+00
-7.42422044e-01 -2.67245471e-01 -9.31114256e-02 8.60473633e-01
7.96350658e-01 -2.41675347e-01 1.05784833e+00 1.65181488e-01
-6.69590414e-01 2.62361199e-01 -3.52674007e-01 -2.30255470e-01
3.06672305e-01 -6.83251202e-01 -4.33392860e-02 8.10946465e-01
1.86938375e-01 9.36385930e-01 7.39925444e-01 -6.32169127e-01
-1.00936496e+00 -8.44221190e-02 8.79710197e-01 7.93838128e-02
5.82699895e-01 -5.93045890e-01 -9.64115024e-01 -8.57240111e-02
5.34425378e-01 -6.34830713e-01 6.73744559e-01 -2.63633251e-01
1.45940065e-01 -1.35166094e-01 -1.09735143e+00 6.38251126e-01
1.02607203e+00 -1.17286050e+00 -1.12898862e+00 3.26202661e-01
9.85086560e-02 3.31823379e-01 -5.91706634e-01 -5.66153750e-02
5.67651868e-01 -1.11228275e+00 6.05086207e-01 -2.28851274e-01
-4.45365071e-01 6.28496483e-02 1.18013635e-01 -1.46747470e+00
-5.77210113e-02 -7.03388035e-01 4.14941698e-01 9.68929350e-01
4.25548732e-01 -1.15526509e+00 4.12842333e-01 5.51601052e-01
-3.87807935e-01 -1.79690987e-01 -1.33915389e+00 -7.48430014e-01
-5.87920010e-01 -5.23729384e-01 2.71299005e-01 6.09214664e-01
8.96855295e-01 3.30374926e-01 3.60789299e-01 2.66296446e-01
1.12643041e-01 -3.23110819e-01 -1.98375434e-01 -1.10324192e+00
1.43590122e-01 -4.90970492e-01 -7.08517075e-01 -4.97576416e-01
4.05760676e-01 -1.05868709e+00 6.97395951e-02 -1.47136927e+00
8.51885416e-03 3.10005158e-01 -3.84752154e-01 2.35808253e-01
3.66856486e-01 -7.59139284e-02 -4.36769992e-01 4.46179993e-02
3.47201787e-02 4.21404481e-01 6.79678559e-01 8.00313312e-04
-3.86024624e-01 -6.03319779e-02 -4.58664030e-01 5.07021606e-01
7.93390930e-01 -4.08166111e-01 -7.93319225e-01 1.82160705e-01
-8.75324681e-02 5.87571800e-01 3.88270169e-01 -1.36306834e+00
5.23129582e-01 5.04873216e-01 3.94841254e-01 -6.47866428e-01
5.67915916e-01 -6.70070529e-01 3.21503788e-01 5.33162892e-01
-2.25241721e-01 6.18995959e-03 1.59682781e-01 4.60294127e-01
-1.89847812e-01 -3.10435891e-01 7.45173812e-01 -1.31993473e-01
-5.70084512e-01 -3.13961565e-01 -1.30439985e+00 -4.99402255e-01
1.06084120e+00 -8.25618505e-01 -4.02547330e-01 -4.22309190e-01
-1.09874773e+00 -5.24234295e-01 -3.15228611e-01 2.64700055e-01
6.99345410e-01 -8.53356838e-01 -1.32930309e-01 8.37857664e-01
6.20376766e-02 -9.30486083e-01 5.04216194e-01 1.46711779e+00
-8.11506286e-02 5.91432750e-01 -6.26124084e-01 -5.29702604e-01
-1.27693343e+00 4.07759100e-01 4.86389250e-01 5.75661898e-01
-8.11531603e-01 8.07553113e-01 1.83416694e-01 5.39792359e-01
3.16078782e-01 -1.39869913e-01 -5.44276893e-01 4.66529697e-01
7.62426257e-01 5.05166590e-01 6.82450116e-01 -4.86651808e-01
-6.61565602e-01 -2.90020883e-01 1.87888756e-01 -5.96802533e-01
1.23235762e+00 -3.33324552e-01 -3.43968600e-01 6.67345703e-01
1.08714449e+00 -1.31625161e-01 -7.57268608e-01 5.20489156e-01
1.30008832e-01 1.48749113e-01 1.85263202e-01 -1.10700202e+00
-6.71096027e-01 1.06238830e+00 1.24025512e+00 4.77863133e-01
1.23511410e+00 4.05147336e-02 1.51222259e-01 4.57310319e-01
5.87888420e-01 -1.11487567e+00 -1.81496888e-01 3.17046531e-02
1.13987947e+00 -6.37284994e-01 -5.61147749e-01 -6.72446862e-02
-5.44234931e-01 1.05190146e+00 2.07900941e-01 1.57042593e-01
7.97030032e-01 5.51167727e-01 -1.94114909e-01 -4.37220931e-01
-5.88401914e-01 -1.63450941e-01 2.80729413e-01 7.38334656e-01
3.33896607e-01 1.97338294e-02 -6.08579516e-01 1.05183434e+00
-4.99365538e-01 -2.02677175e-01 3.63129765e-01 7.99796820e-01
-4.96919662e-01 -4.28054214e-01 -5.06662071e-01 6.77543700e-01
-3.50543290e-01 -8.05323347e-02 -4.73244250e-01 4.18152243e-01
1.38809541e-02 1.31882453e+00 2.90619940e-01 -3.67417991e-01
4.29089338e-01 8.90385330e-01 4.80139107e-01 -5.00049531e-01
-5.45878410e-01 2.62286425e-01 1.33058473e-01 -5.11318982e-01
-3.79120618e-01 -6.02429509e-01 -1.25140584e+00 2.58174419e-01
-2.09823847e-01 3.59987080e-01 1.16016412e+00 1.20439351e+00
3.04474056e-01 6.74208105e-01 1.77035108e-01 -6.99934006e-01
7.06545785e-02 -1.18196487e+00 -9.05978799e-01 -1.28836691e-01
3.51653755e-01 -7.91805446e-01 -6.90923274e-01 8.79527815e-03] | [13.240442276000977, 3.3468427658081055] |
f12d9468-34e5-4aa6-b36d-0db0b24c3c5b | automatic-true-false-question-generation-for | null | null | https://aclanthology.org/2022.bea-1.10 | https://aclanthology.org/2022.bea-1.10.pdf | Automatic True/False Question Generation for Educational Purpose | In field of teaching, true/false questioning is an important educational method for assessing students’ general understanding of learning materials. Manually creating such questions requires extensive human effort and expert knowledge. Question Generation (QG) technique offers the possibility to automatically generate a large number of questions. However, there is limited work on automatic true/false question generation due to the lack of training data and difficulty finding question-worthy content. In this paper, we propose an unsupervised True/False Question Generation approach (TF-QG) that automatically generates true/false questions from a given passage for reading comprehension test. TF-QG consists of a template-based framework that aims to test the specific knowledge in the passage by leveraging various NLP techniques, and a generative framework to generate more flexible and complicated questions by using a novel masking-and-infilling strategy. Human evaluation shows that our approach can generate high-quality and valuable true/false questions. In addition, simulated testing on the generated questions challenges the state-of-the-art inference models from NLI, QA, and fact verification tasks. | ['Ai Ti Aw', 'Liangming Pan', 'Pengfei Li', 'Bowei Zou'] | null | null | null | null | naacl-bea-2022-7 | ['question-generation'] | ['natural-language-processing'] | [ 2.15667859e-01 5.53835511e-01 6.43299162e-01 -3.72517228e-01
-9.91214752e-01 -9.63348269e-01 7.82380819e-01 3.64204198e-01
1.00059763e-01 1.24310291e+00 2.48659048e-02 -8.72942686e-01
-4.56132561e-01 -1.28704858e+00 -7.65780687e-01 5.32255694e-02
4.33658063e-01 5.34787536e-01 6.78542614e-01 -7.00603426e-01
6.17283762e-01 1.69227168e-01 -1.90392148e+00 4.65631932e-01
1.55534935e+00 4.40445662e-01 3.07126582e-01 9.87616003e-01
-7.46159852e-01 1.20834386e+00 -1.06270278e+00 -6.50326490e-01
-1.23127699e-01 -1.12038517e+00 -1.40300024e+00 -8.59010071e-02
7.00344026e-01 -3.11479092e-01 2.60547549e-01 9.21110392e-01
3.28893512e-01 3.37393165e-01 5.66638172e-01 -1.09901202e+00
-8.99893165e-01 5.87714612e-01 7.22262859e-02 3.34290087e-01
1.20023298e+00 1.68494135e-01 6.92802131e-01 -6.09156430e-01
4.25735474e-01 1.09819949e+00 3.73680621e-01 6.39795661e-01
-6.78083181e-01 -4.22338396e-01 -3.29817533e-01 5.83120704e-01
-1.00591862e+00 3.00270151e-02 6.46683753e-01 -4.43827122e-01
7.57490098e-01 6.11747980e-01 8.45348120e-01 6.97249711e-01
2.10121304e-01 6.70235515e-01 1.60567343e+00 -9.44909632e-01
2.39293456e-01 6.62349403e-01 3.00786644e-01 8.21037173e-01
2.15745091e-01 -9.42641422e-02 -5.28276801e-01 8.55674744e-02
5.26688516e-01 -5.19410610e-01 -2.32606351e-01 2.29583517e-01
-8.09789419e-01 8.95778298e-01 -3.31876338e-01 3.29779863e-01
-2.18328431e-01 -2.76241452e-01 -1.41055668e-02 6.85359538e-01
-1.81727335e-01 8.56740475e-01 -5.70055485e-01 -3.80402267e-01
-1.21838355e+00 7.72984385e-01 1.39209855e+00 1.13445830e+00
4.66210812e-01 -9.76793095e-02 -8.24525237e-01 4.38260615e-01
5.60014009e-01 3.33320916e-01 6.46023333e-01 -8.37601185e-01
3.42506081e-01 1.02425289e+00 1.07694395e-01 -7.39986241e-01
3.32178712e-01 -4.55844134e-01 -2.52884962e-02 3.13523173e-01
4.59868282e-01 -1.48587927e-01 -8.44795287e-01 1.51357234e+00
6.99933469e-01 7.60257021e-02 1.47518158e-01 3.87861788e-01
1.42202759e+00 5.88846922e-01 -1.08786531e-01 4.22015153e-02
1.76734304e+00 -9.14969683e-01 -9.80139434e-01 2.37751603e-01
3.90000284e-01 -1.06745589e+00 1.28864157e+00 7.10206032e-01
-1.48058248e+00 -5.78462601e-01 -7.79618204e-01 -2.04654068e-01
-5.00118673e-01 1.05100796e-01 1.18854299e-01 1.13978827e+00
-9.40248907e-01 1.44782335e-01 -3.59669924e-02 -1.88933238e-01
3.38853925e-01 4.74371873e-02 -6.53062239e-02 -2.37544701e-01
-1.20884001e+00 8.53532612e-01 4.00479227e-01 -2.03581005e-01
-1.15163422e+00 -8.61688673e-01 -8.19095910e-01 2.57373363e-01
5.78063428e-01 -6.66840434e-01 1.55664968e+00 -5.58631778e-01
-1.95361137e+00 6.85166240e-01 -5.24093732e-02 -3.51649523e-01
5.15790224e-01 -3.30021858e-01 -1.52235508e-01 3.79475832e-01
1.66972518e-01 6.42622709e-01 4.94030356e-01 -1.04389322e+00
-3.37005466e-01 -4.09140736e-02 4.42414552e-01 1.74784988e-01
1.57289863e-01 -1.57110825e-01 2.78086573e-01 -4.03357476e-01
1.20414510e-01 -3.37024719e-01 2.17014357e-01 -3.02796096e-01
-3.16620797e-01 -7.38350272e-01 7.94704437e-01 -8.98443103e-01
1.22151339e+00 -1.43341279e+00 -6.91808701e-01 1.40137896e-01
-2.10826122e-03 6.70473695e-01 6.84092045e-02 7.77697206e-01
4.23520729e-02 2.47333094e-01 -3.52937281e-02 3.57393801e-01
3.29213232e-01 2.48089075e-01 -3.93763661e-01 -5.27931035e-01
3.16054612e-01 9.54667687e-01 -1.16196251e+00 -9.31286573e-01
2.61084765e-01 -5.68280593e-02 -7.26536036e-01 8.15117657e-01
-7.02335835e-01 2.27914259e-01 -4.60764766e-01 4.82802153e-01
5.96645832e-01 -6.02529310e-02 -2.18320593e-01 2.21616521e-01
1.16003275e-01 8.64590466e-01 -1.38587058e+00 1.47440386e+00
-4.44600403e-01 4.45167452e-01 -5.39371908e-01 -9.18366849e-01
1.16006958e+00 6.03287220e-01 -5.15621543e-01 -4.64461833e-01
1.26928851e-01 2.30414033e-01 1.33516133e-01 -1.22584701e+00
5.93810618e-01 -3.34803045e-01 2.52303779e-01 6.35984123e-01
5.26567996e-01 -6.58565640e-01 6.27057791e-01 5.27636170e-01
9.76425946e-01 6.86749160e-01 3.83172244e-01 -8.83059725e-02
1.08784914e+00 8.72251391e-02 1.96975227e-02 1.04144156e+00
-3.06284372e-02 4.50393617e-01 4.73492056e-01 1.41790099e-02
-6.38144255e-01 -1.01777589e+00 1.96394503e-01 8.23600173e-01
-5.12804508e-01 -2.52264261e-01 -1.07304037e+00 -8.06585431e-01
-3.78081799e-01 1.39971113e+00 -5.06661355e-01 -3.10525345e-03
-3.21168631e-01 -2.60046646e-02 6.44827664e-01 4.92107235e-02
7.11364090e-01 -1.39044738e+00 -7.36550212e-01 4.51245129e-01
-3.55537802e-01 -8.16636324e-01 -4.77255434e-02 -3.38657081e-01
-7.88425744e-01 -1.10588849e+00 -2.41398215e-01 -9.11905885e-01
8.21773529e-01 -1.83449611e-02 1.39182973e+00 4.18413311e-01
-2.41922691e-01 5.92755139e-01 -7.87684262e-01 -6.49657190e-01
-9.03125107e-01 -1.95945650e-01 -8.47070277e-01 -4.71624583e-01
4.52646792e-01 -4.58076954e-01 -5.40057421e-01 -2.89647076e-02
-1.27377260e+00 8.67992193e-02 5.51925719e-01 7.64484227e-01
2.19006583e-01 8.79369825e-02 9.79087174e-01 -1.16582453e+00
1.22681153e+00 -4.63187397e-01 -6.04174197e-01 7.08248317e-01
-5.10428548e-01 1.95572406e-01 6.78589702e-01 -2.59312063e-01
-1.31090760e+00 -6.61107481e-01 -4.85054880e-01 2.41523013e-01
-4.78695661e-01 5.09269536e-01 -5.86753711e-02 -1.95601597e-01
8.36915612e-01 6.24919415e-01 -5.27358875e-02 -2.33457863e-01
3.87731105e-01 4.68970180e-01 3.20467889e-01 -9.55042362e-01
8.84392560e-01 -3.99707526e-01 4.66919020e-02 -4.22448874e-01
-1.09022450e+00 -1.77484721e-01 -2.45680019e-01 -5.39679468e-01
6.43815637e-01 -4.70208913e-01 -9.75662708e-01 8.81737918e-02
-1.04636323e+00 -1.55792668e-01 -5.81518114e-01 2.84549654e-01
-3.46827835e-01 4.34682012e-01 -3.67576361e-01 -1.05247009e+00
-5.17513216e-01 -1.05852342e+00 6.18235648e-01 7.28905797e-01
-2.71435320e-01 -9.11991000e-01 1.67190120e-01 1.12130249e+00
4.17196244e-01 1.76820651e-01 1.10106409e+00 -1.03786898e+00
-8.18380713e-01 -1.52410120e-01 1.13179803e-01 6.18214786e-01
-1.97284874e-02 3.60992625e-02 -7.70949721e-01 2.95470536e-01
3.12042713e-01 -7.39849389e-01 9.83941481e-02 -1.83680311e-01
9.87864137e-01 -8.39519322e-01 3.87745917e-01 -3.30752462e-01
1.48017406e+00 8.23627636e-02 7.92421937e-01 8.49756673e-02
2.22525269e-01 6.46972775e-01 6.14298522e-01 1.12517610e-01
7.17444837e-01 1.25134647e-01 7.42058977e-02 6.54481649e-01
-3.89017969e-01 -4.94389772e-01 1.67997047e-01 8.99182379e-01
2.86083817e-01 -1.69205189e-01 -8.56965184e-01 9.57765937e-01
-1.19397438e+00 -1.18176079e+00 -6.25845134e-01 2.00919104e+00
1.51381886e+00 1.71483427e-01 -1.81092694e-01 5.52039444e-01
1.64796054e-01 -5.37273586e-01 2.28134871e-01 -7.45403767e-01
2.97984332e-01 1.07834959e+00 -3.69976431e-01 7.81393230e-01
-2.01438561e-01 6.88427448e-01 5.45957518e+00 8.16500127e-01
-4.82850641e-01 1.06870249e-01 3.22050899e-01 3.91712993e-01
-8.65369499e-01 2.20841855e-01 -8.79145741e-01 2.18557999e-01
1.07300460e+00 -4.06326532e-01 8.07475522e-02 6.25764608e-01
1.20751075e-01 -4.67625558e-01 -8.03088546e-01 3.06317270e-01
4.67041999e-01 -1.27669489e+00 5.64932883e-01 -2.79203027e-01
8.91911030e-01 -9.18501973e-01 -3.82734835e-01 5.83048165e-01
4.89314258e-01 -1.00392687e+00 6.26823545e-01 7.29690909e-01
2.34244704e-01 -6.71302259e-01 8.97596955e-01 5.51568151e-01
-5.94569445e-01 1.46819264e-01 -2.33883813e-01 -1.76448479e-01
-2.03291759e-01 4.30197477e-01 -1.51548874e+00 7.08990276e-01
3.93296242e-01 -2.56944388e-01 -1.04755938e+00 1.24620008e+00
-8.29465151e-01 1.12742460e+00 -1.23346515e-01 -7.02995479e-01
1.25024334e-01 -1.14422724e-01 2.75077701e-01 9.83483255e-01
5.12495995e-01 5.84564686e-01 -5.19721657e-02 1.27331436e+00
4.51264307e-02 3.19565445e-01 -3.32529306e-01 7.48237595e-02
5.52885592e-01 1.29598296e+00 -4.46736455e-01 -4.70700681e-01
-8.13067332e-02 6.36949062e-01 4.67648618e-02 7.78013840e-02
-5.48026860e-01 -6.41618490e-01 -3.83898258e-01 3.34240258e-01
1.44668117e-01 4.56091054e-02 -2.37226680e-01 -8.57333183e-01
1.12722509e-01 -1.44047618e+00 2.71657676e-01 -1.22263920e+00
-9.58692968e-01 4.49175924e-01 1.82965592e-01 -1.02167535e+00
-5.16470611e-01 -4.15700704e-01 -1.04097974e+00 1.12547731e+00
-1.52748632e+00 -9.32844877e-01 -6.45763695e-01 6.04414940e-01
6.85420752e-01 -9.97034833e-02 7.31217742e-01 3.24315988e-02
-2.50456221e-02 6.60760283e-01 -6.60207272e-01 5.05970209e-04
3.76594037e-01 -1.68298674e+00 4.36472632e-02 9.82586205e-01
6.98338524e-02 8.02075744e-01 9.30334449e-01 -6.18494570e-01
-1.31124413e+00 -7.77676225e-01 1.25052631e+00 -4.86745864e-01
3.24669719e-01 -1.85290109e-02 -1.28738940e+00 4.26357597e-01
7.29208827e-01 -5.13573229e-01 1.15083206e+00 -4.72522914e-01
-1.31038856e-03 2.48404834e-02 -1.54043925e+00 5.13474822e-01
3.60713750e-01 -3.58158052e-01 -1.56689394e+00 5.52598357e-01
5.96129000e-01 -4.16069537e-01 -8.02745640e-01 1.35651408e-02
2.60473937e-01 -1.00491393e+00 6.08497083e-01 -5.13721585e-01
7.24349618e-01 -5.37486076e-01 1.85480073e-01 -8.31004918e-01
2.07084239e-01 -7.39999533e-01 1.40489172e-02 1.67107689e+00
4.82564241e-01 -4.21754569e-01 7.17449903e-01 6.43458605e-01
-2.04451382e-01 -8.01633537e-01 -6.35322452e-01 -4.93347794e-01
5.88707775e-02 -2.28525266e-01 7.57240653e-01 9.21436071e-01
2.85057686e-02 3.75420928e-01 2.00160205e-01 4.41821292e-03
4.03775066e-01 1.43652156e-01 8.74468982e-01 -1.04530299e+00
-4.78556812e-01 -8.26845244e-02 -1.99083999e-01 -8.88839781e-01
-2.25991845e-01 -8.05928230e-01 1.90243930e-01 -1.88773632e+00
-9.85313058e-02 -5.54225892e-02 3.81765604e-01 2.66796470e-01
-6.39233947e-01 -1.85900390e-01 2.69926544e-02 -5.09043932e-01
-4.12480205e-01 4.37092811e-01 1.81287849e+00 3.88580292e-01
1.61128044e-01 3.97049310e-03 -7.86422133e-01 4.41711813e-01
8.64059806e-01 -4.94685709e-01 -9.05179679e-01 -6.34794161e-02
7.19432890e-01 2.90963143e-01 5.39304972e-01 -1.11374915e+00
2.56690979e-01 -3.28571409e-01 2.77815193e-01 -8.50731611e-01
-1.47613108e-01 -4.79200840e-01 -2.21732304e-01 3.64420474e-01
-4.55597073e-01 3.02091658e-01 2.84438312e-01 9.46311802e-02
-4.18331534e-01 -1.18429756e+00 4.13453251e-01 -6.75792456e-01
-2.13115916e-01 -4.71089125e-01 -4.27820563e-01 5.27550101e-01
9.33142543e-01 -3.61920059e-01 -4.21055853e-01 -4.75259632e-01
-4.22964066e-01 2.36568779e-01 -2.71248519e-01 1.21896856e-01
8.36661577e-01 -1.02051890e+00 -9.84443963e-01 1.90453500e-01
-1.68553993e-01 1.01534538e-01 3.12071383e-01 2.64391184e-01
-9.41586733e-01 5.17314255e-01 -2.55919397e-01 -1.86315224e-01
-1.26229382e+00 1.66785821e-01 2.77834535e-01 -6.35952294e-01
-1.54069468e-01 1.07424569e+00 -4.66561586e-01 -8.61314714e-01
-1.02592260e-01 -5.95007181e-01 -7.44546413e-01 -1.41128913e-01
6.57606065e-01 3.27699840e-01 1.97420374e-01 6.98647127e-02
1.75133824e-01 8.66218880e-02 2.25197729e-02 -4.52218741e-01
8.65342379e-01 4.45161127e-02 -6.55315369e-02 2.49841541e-01
5.61506331e-01 2.19652787e-01 -5.10352850e-01 1.20957822e-01
-1.49117112e-02 -4.25714821e-01 -3.60127211e-01 -1.43082118e+00
-2.63756186e-01 8.58961463e-01 3.73054713e-01 5.95547497e-01
1.04221201e+00 -2.71968991e-01 7.43882120e-01 4.42934573e-01
1.82683080e-01 -8.43904912e-01 3.80057693e-01 5.26581287e-01
1.05488229e+00 -1.01148653e+00 -3.14068906e-02 -5.83776891e-01
-1.75982401e-01 1.17681170e+00 9.61279929e-01 7.85776079e-02
2.56742954e-01 -2.35315695e-01 -5.57927713e-02 -3.43590766e-01
-1.03750622e+00 5.26112653e-02 4.97928917e-01 4.06185210e-01
7.93394148e-01 -1.53682515e-01 -8.23253930e-01 6.14520431e-01
-9.46316361e-01 5.40834188e-01 1.05437469e+00 1.35176206e+00
-7.87775755e-01 -1.44620991e+00 -6.45720899e-01 6.28189027e-01
-5.41570902e-01 -4.90925103e-01 -4.30582970e-01 6.05715990e-01
1.87356681e-01 1.42207360e+00 -5.86892188e-01 3.00910957e-02
3.99853021e-01 6.87233865e-01 9.18001294e-01 -1.01841557e+00
-1.26698041e+00 -6.69740438e-01 1.72560304e-01 1.10782035e-01
-2.22174764e-01 -4.75951135e-01 -1.03201187e+00 -1.59237996e-01
-6.14274383e-01 6.85349941e-01 5.39345682e-01 1.30465794e+00
1.45929113e-01 7.57434309e-01 2.15013340e-01 4.19786125e-01
-9.03165519e-01 -1.28577399e+00 3.24234203e-03 4.74475503e-01
5.35211749e-02 -3.32263619e-01 -2.44783610e-01 2.13093266e-01] | [11.339670181274414, 7.99311637878418] |
5ba84b9e-7b45-4053-bb51-406100f4fddc | sudowoodo-contrastive-self-supervised | 2207.04122 | null | https://arxiv.org/abs/2207.04122v2 | https://arxiv.org/pdf/2207.04122v2.pdf | Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation | Machine learning (ML) is playing an increasingly important role in data management tasks, particularly in Data Integration and Preparation (DI&P). The success of ML-based approaches, however, heavily relies on the availability of large-scale, high-quality labeled datasets for different tasks. Moreover, the wide variety of DI&P tasks and pipelines oftentimes requires customizing ML solutions which can incur a significant cost for model engineering and experimentation. These factors inevitably hold back the adoption of ML-based approaches to new domains and tasks. In this paper, we propose Sudowoodo, a multi-purpose DI&P framework based on contrastive representation learning. Sudowoodo features a unified, matching-based problem definition capturing a wide range of DI&P tasks including Entity Matching (EM) in data integration, error correction in data cleaning, semantic type detection in data discovery, and more. Contrastive learning enables Sudowoodo to learn similarity-aware data representations from a large corpus of data items (e.g., entity entries, table columns) without using any labels. The learned representations can later be either directly used or facilitate fine-tuning with only a few labels to support different DI&P tasks. Our experiment results show that Sudowoodo achieves multiple state-of-the-art results on different levels of supervision and outperforms previous best specialized blocking or matching solutions for EM. Sudowoodo also achieves promising results in data cleaning and semantic type detection tasks showing its versatility in DI&P applications. | [] | 2022-07-08 | sudowoodo-contrastive-self-supervised-1 | https://ui.adsabs.harvard.edu/abs/2022arXiv220704122W/abstract | https://arxiv.org/pdf/2207.04122.pdf | arxiv-2022-10 | ['data-integration'] | ['knowledge-base'] | [-4.12939377e-02 -1.05955169e-01 -4.86114204e-01 -5.11647582e-01
-9.39758122e-01 -4.64863569e-01 5.12478769e-01 8.81878912e-01
-4.82753843e-01 5.79743385e-01 -3.11152432e-02 -2.15803340e-01
-4.07801509e-01 -8.30313802e-01 -8.69744778e-01 -3.66358608e-01
-7.15549961e-02 7.38820672e-01 9.57163200e-02 -1.52281210e-01
2.78573424e-01 4.89356935e-01 -2.04541016e+00 5.52699268e-01
9.67407048e-01 1.03887725e+00 3.88025016e-01 2.29252562e-01
-6.61588013e-01 5.54442823e-01 -5.24459004e-01 -4.86231178e-01
4.20182407e-01 3.19929540e-01 -8.53075624e-01 -4.15621728e-01
5.70071638e-01 3.67394984e-01 1.05562389e-01 9.70397353e-01
5.71853042e-01 1.17382750e-01 4.76576835e-01 -1.52774513e+00
-5.64311981e-01 7.36101925e-01 -4.60569471e-01 1.09402589e-01
2.82131284e-01 -8.28138664e-02 1.13944447e+00 -1.12226987e+00
8.51603925e-01 1.24516749e+00 9.95262802e-01 3.85437995e-01
-1.13346744e+00 -8.62183034e-01 1.84062064e-01 3.38818550e-01
-1.27917385e+00 -4.90642190e-01 2.25143924e-01 -3.90257031e-01
1.26856565e+00 3.98583114e-01 -1.15192914e-02 5.87613821e-01
-1.65900979e-02 1.05312574e+00 9.13589835e-01 -5.03644288e-01
3.09646755e-01 3.76876086e-01 5.32471001e-01 5.36549091e-01
6.82404816e-01 -3.33239257e-01 -5.81903219e-01 -8.36634189e-02
1.23475239e-01 7.55423158e-02 1.10020287e-01 -5.33859134e-01
-1.03881454e+00 5.84304452e-01 3.56058598e-01 2.77695328e-01
-3.85218590e-01 -2.45049596e-01 7.12976754e-01 5.74942172e-01
2.61871397e-01 8.83690655e-01 -1.11955690e+00 -3.77766341e-02
-7.32150495e-01 4.31986958e-01 7.70543694e-01 1.26242387e+00
9.35983717e-01 -4.20266092e-01 -1.92784160e-01 1.13795590e+00
1.43651679e-01 3.52419168e-01 3.93247128e-01 -5.61682880e-01
9.18344975e-01 1.25225198e+00 8.63404498e-02 -6.91297233e-01
-6.33726656e-01 -1.78992465e-01 -8.36834967e-01 1.44737661e-01
1.43402755e-01 1.46172166e-01 -9.31449890e-01 1.57323265e+00
5.48922300e-01 -6.80695102e-02 8.18872750e-02 3.21164131e-01
1.10038960e+00 4.07993883e-01 3.46475989e-01 -5.18810786e-02
1.46087587e+00 -7.92192221e-01 -7.34016657e-01 -3.69787306e-01
9.80599046e-01 -7.80852497e-01 8.75290930e-01 5.52963972e-01
-9.60660577e-01 -6.95294917e-01 -8.92166018e-01 -3.74473691e-01
-9.46512222e-01 3.68239395e-02 7.88129508e-01 3.20872426e-01
-5.07846236e-01 6.64588928e-01 -6.01784766e-01 -5.34760594e-01
6.29248619e-01 5.39554656e-01 -6.69944108e-01 -3.35780203e-01
-1.08096576e+00 1.00838971e+00 5.60216665e-01 -1.25061870e-01
-4.15657610e-01 -1.00719428e+00 -1.03145826e+00 1.17730722e-02
6.36944532e-01 -6.66333139e-01 1.05182922e+00 -2.94927955e-01
-6.91132605e-01 1.04847026e+00 -1.94531679e-01 -5.69599628e-01
1.55574828e-01 -6.73183560e-01 -7.43993700e-01 -4.44100916e-01
4.03091937e-01 5.10681152e-01 5.49948096e-01 -1.18731773e+00
-1.02055430e+00 -3.53110373e-01 -1.37425616e-01 1.37889227e-02
-1.23083770e-01 2.23904312e-01 -4.37776774e-01 -5.51731229e-01
-4.73758876e-02 -7.02102542e-01 -1.49247110e-01 -2.52568871e-01
-4.76468652e-01 -6.14399850e-01 5.87447643e-01 -5.52229524e-01
1.60139906e+00 -2.06655860e+00 1.02425516e-01 5.69753312e-02
1.30695060e-01 7.22961664e-01 -1.91233248e-01 6.10627294e-01
-1.99903905e-01 1.57759219e-01 -4.27821070e-01 -5.00747263e-01
1.53065994e-01 3.53447735e-01 -1.50117323e-01 7.22945109e-02
4.04106081e-01 8.65751803e-01 -9.47372496e-01 -5.27827203e-01
2.31780395e-01 4.09021303e-02 -6.02969706e-01 4.94369954e-01
-2.97325969e-01 2.02876642e-01 -2.60143280e-01 9.16862667e-01
7.86312163e-01 -1.58235520e-01 2.26385921e-01 -3.58907640e-01
-2.92700022e-01 5.22607863e-01 -1.74519336e+00 1.86038101e+00
-7.48031497e-01 2.87723154e-01 1.53154582e-01 -1.17699409e+00
9.40166414e-01 -1.25144962e-02 4.94090080e-01 -7.26220310e-01
-3.03450283e-02 4.51512337e-01 -2.03106791e-01 -7.26146221e-01
5.62948644e-01 2.80697197e-01 -3.48329365e-01 3.01980406e-01
2.59032309e-01 1.78350553e-01 5.60550094e-01 1.86029464e-01
1.25303757e+00 1.81657240e-01 7.65813112e-01 -2.68494368e-01
5.08204997e-01 2.74644822e-01 9.99454796e-01 7.86799014e-01
1.78673089e-01 4.40760165e-01 1.49525613e-01 -4.89996552e-01
-7.63410389e-01 -7.41436601e-01 -4.87227470e-01 1.45673394e+00
5.99395409e-02 -7.95573592e-01 -4.46811587e-01 -9.23421204e-01
6.42915368e-01 7.28050351e-01 -5.68349242e-01 -1.26176238e-01
-8.14033985e-01 -1.06941712e+00 3.89154613e-01 5.57035506e-01
3.39015782e-01 -1.41056871e+00 -2.12664634e-01 3.58771235e-01
-1.33965807e-02 -9.57279325e-01 -5.31158932e-02 6.86244607e-01
-7.56804109e-01 -1.45102787e+00 1.50169834e-01 -7.22776771e-01
6.53114438e-01 3.65235120e-01 1.45747972e+00 5.68496063e-04
-6.04452074e-01 4.66476567e-02 -4.60721046e-01 -7.21406043e-01
-4.80162591e-01 3.71516824e-01 1.58680633e-01 -2.13343084e-01
8.96532357e-01 -3.02392274e-01 -2.77662992e-01 3.04412842e-01
-9.89843249e-01 -3.35798264e-01 6.34655476e-01 8.45048785e-01
7.03427732e-01 -3.31803411e-02 8.20256293e-01 -1.49126565e+00
3.53146285e-01 -9.61700857e-01 -5.52045107e-01 5.55007935e-01
-1.12862694e+00 3.56355906e-01 6.87921703e-01 -2.32166275e-01
-9.33416128e-01 -2.45380715e-01 -2.13955358e-01 -1.85679927e-01
-2.36138940e-01 5.59795022e-01 -6.87003791e-01 4.25318003e-01
7.52554119e-01 -3.33619624e-01 -2.57326186e-01 -1.05777514e+00
4.09186900e-01 9.34251785e-01 6.26290917e-01 -5.94481707e-01
7.60018229e-01 1.58461690e-01 -2.50722498e-01 -3.57045472e-01
-1.05321383e+00 -7.81009436e-01 -7.51831353e-01 6.25512302e-01
5.77601075e-01 -1.01570690e+00 -4.72142398e-01 3.50748509e-01
-6.74332082e-01 -2.04238761e-02 -4.31281269e-01 7.46268332e-02
-7.57580698e-02 5.15409261e-02 -2.19329566e-01 -5.15861690e-01
-5.78862011e-01 -9.64891136e-01 1.14038181e+00 2.03363717e-01
-3.78712326e-01 -7.05518246e-01 5.23089580e-02 5.90738297e-01
2.40805581e-01 9.34392493e-03 1.15289724e+00 -1.03516173e+00
-5.65840006e-01 -3.55503261e-01 -2.45688826e-01 3.32365960e-01
2.02517942e-01 -9.55628231e-02 -9.65794027e-01 -2.06244469e-01
-4.37520862e-01 -3.57785970e-01 8.86421323e-01 7.07688108e-02
1.39585602e+00 -1.48291811e-01 -5.32841742e-01 8.53263736e-01
1.33440554e+00 1.87634155e-02 4.70629930e-01 7.14920759e-01
7.64265656e-01 6.11498177e-01 1.18574476e+00 4.54862148e-01
6.09575391e-01 8.19246948e-01 3.59731644e-01 -3.44446376e-02
-1.19937040e-01 -2.26474583e-01 6.11109212e-02 6.31294250e-01
3.23850453e-01 -9.39283371e-02 -9.35499787e-01 6.64697766e-01
-2.10782123e+00 -8.46955597e-01 -2.98163831e-01 2.43107772e+00
1.02279139e+00 -5.09602018e-02 -1.08945057e-01 2.42443055e-01
5.94970345e-01 -1.50706649e-01 -6.57263637e-01 -4.82055962e-01
-6.45727813e-02 5.11023402e-01 5.68005323e-01 -3.39429192e-02
-1.34351909e+00 8.15381825e-01 5.41153383e+00 8.25744033e-01
-6.92890346e-01 1.49986446e-01 1.26197115e-01 -5.00387102e-02
-1.46966621e-01 -7.95846730e-02 -1.40109420e+00 4.27267283e-01
8.49586904e-01 -1.21652931e-01 2.66733497e-01 9.59810197e-01
-2.98702419e-01 -1.67792395e-01 -1.45059288e+00 1.19830930e+00
-1.74247503e-01 -1.54238474e+00 -1.56757802e-01 -1.45679727e-01
6.27145708e-01 3.34151298e-01 -2.62179017e-01 8.13929915e-01
6.36049509e-01 -1.00149238e+00 3.82374495e-01 3.54255199e-01
7.50911415e-01 -6.53092384e-01 9.06834960e-01 3.72158051e-01
-1.20929289e+00 -2.98359364e-01 -4.24607635e-01 2.83036828e-01
4.32946496e-02 7.48431087e-01 -7.67591596e-01 9.51305449e-01
1.05440152e+00 8.58065963e-01 -7.75447845e-01 1.05687058e+00
-5.90890786e-03 3.02092969e-01 -2.82027990e-01 3.37757230e-01
-1.51074752e-01 -2.69533545e-02 1.52524233e-01 1.48721254e+00
1.00867905e-01 -3.59821171e-01 2.91442871e-01 4.81561244e-01
-4.89752918e-01 2.66705126e-01 -4.95831311e-01 1.39403433e-01
1.03709638e+00 1.53093398e+00 -2.11480245e-01 -4.82340515e-01
-4.54812050e-01 4.59345281e-01 6.61889374e-01 -1.84089750e-01
-5.15968263e-01 -3.88703257e-01 9.93134618e-01 1.42463312e-01
3.01893860e-01 2.03762367e-01 -3.81483406e-01 -1.23732805e+00
2.32374325e-01 -1.24222326e+00 7.97789335e-01 -1.69026613e-01
-1.52972138e+00 3.44076335e-01 1.83594733e-01 -1.17673135e+00
-7.99424052e-02 -4.85836893e-01 -4.29211080e-01 7.01207340e-01
-1.75029397e+00 -1.04966569e+00 -4.31652218e-01 3.65287572e-01
4.96130198e-01 -3.62318218e-01 9.60601509e-01 8.02755892e-01
-9.92703557e-01 6.76818669e-01 2.25050464e-01 1.35696337e-01
1.17933929e+00 -1.49704385e+00 6.27773166e-01 6.84952438e-01
1.92517832e-01 1.01232350e+00 4.29307640e-01 -6.21202350e-01
-1.56774759e+00 -1.47804141e+00 1.09102917e+00 -7.17644095e-01
4.05831695e-01 -5.09185016e-01 -1.20511663e+00 7.64899254e-01
-1.31187484e-01 3.60169321e-01 8.99971545e-01 6.26106739e-01
-6.41946435e-01 -5.31997383e-01 -1.37011468e+00 8.56449381e-02
1.31027055e+00 -3.03686827e-01 -8.75253558e-01 3.55131298e-01
3.88011575e-01 -2.29370415e-01 -1.02841198e+00 7.82192707e-01
3.45835358e-01 -6.62398040e-01 1.06193459e+00 -9.90853250e-01
1.21754490e-01 -4.00914520e-01 -2.28255585e-01 -1.16402030e+00
-2.63555557e-01 -4.54219133e-01 -3.71572822e-01 1.65121543e+00
5.32725811e-01 -5.29936969e-01 3.71284842e-01 6.72166467e-01
-3.59541565e-01 -6.92210078e-01 -6.80708289e-01 -7.64676571e-01
-1.10434234e-01 -4.76198167e-01 9.63346303e-01 1.23613858e+00
-1.61829546e-01 3.03514779e-01 -2.17917532e-01 3.17395657e-01
5.51693916e-01 4.10196155e-01 1.11740184e+00 -1.71040845e+00
-1.10012241e-01 -2.41067126e-01 -2.40671992e-01 -6.55426264e-01
4.19648848e-02 -1.41251624e+00 -1.59758985e-01 -1.62124324e+00
4.34648618e-02 -1.17889762e+00 -7.22920656e-01 7.03632712e-01
-5.36940813e-01 -8.15186724e-02 8.34055617e-02 3.12066019e-01
-7.87685454e-01 1.65253833e-01 4.91221339e-01 -1.26654193e-01
-2.89376497e-01 -1.22679189e-01 -8.03841174e-01 6.80118203e-01
6.90763831e-01 -7.70361185e-01 -6.88669160e-02 -5.21282375e-01
4.34531152e-01 -4.83453304e-01 -1.37263164e-01 -8.79327834e-01
1.72974199e-01 -1.15515128e-01 3.04749936e-01 -6.70598865e-01
-2.79353838e-02 -6.87036037e-01 2.39061832e-01 2.33379081e-01
-3.13766122e-01 3.20040554e-01 1.00464776e-01 3.79309654e-01
-3.87673795e-01 -4.12166476e-01 7.33513713e-01 -1.85316369e-01
-1.11630511e+00 2.98406512e-01 2.14524940e-01 3.36419433e-01
9.90378141e-01 5.41796423e-02 -3.64837706e-01 6.24665856e-01
-5.67502499e-01 5.87173641e-01 3.85073662e-01 7.99315810e-01
2.39190087e-01 -1.11362708e+00 -6.91344500e-01 4.81869131e-01
6.69928730e-01 4.79119807e-01 1.87591370e-02 6.12366259e-01
-1.99155405e-01 2.46141925e-01 -2.05299541e-01 -4.45692360e-01
-1.44135702e+00 8.09369981e-01 -3.86423729e-02 -7.03056335e-01
-5.78145087e-01 8.75230014e-01 4.58381278e-03 -9.44895267e-01
3.06189597e-01 -3.15417141e-01 -2.85807967e-01 5.04714251e-01
6.47443295e-01 4.97577965e-01 6.11247540e-01 -1.61837086e-01
-5.83548129e-01 1.88931510e-01 -4.23263788e-01 6.76540315e-01
1.66127002e+00 9.78918895e-02 -3.07059765e-01 3.76851618e-01
9.04069722e-01 8.06176141e-02 -7.35745192e-01 -4.98283952e-01
7.34250546e-01 -4.10933733e-01 -2.16323957e-01 -1.01861107e+00
-9.71180022e-01 6.40599430e-01 5.14605641e-01 9.32529289e-03
1.00509453e+00 9.70050991e-02 8.16979945e-01 6.51917279e-01
6.63980246e-01 -1.34995162e+00 -4.90769625e-01 2.73005396e-01
7.45504916e-01 -1.63365066e+00 2.31468737e-01 -4.12299246e-01
-5.34581423e-01 7.74531424e-01 7.41578817e-01 -1.77286193e-02
5.92885733e-01 3.95176768e-01 1.06144629e-01 -1.88937306e-01
-9.42941368e-01 -4.13480401e-01 3.72238785e-01 5.81234634e-01
4.96349573e-01 6.40490055e-02 -2.31217504e-01 6.42446935e-01
8.40566978e-02 -2.25294251e-02 6.04575165e-02 1.17397201e+00
-3.76064301e-01 -1.79322386e+00 -3.37920249e-01 8.05760324e-01
-4.21045691e-01 -1.31310776e-01 -9.30738896e-02 9.16904628e-01
6.56867743e-01 8.33961606e-01 -1.58668205e-01 -1.50857359e-01
6.10947907e-01 3.55287224e-01 2.14668125e-01 -1.00217485e+00
-1.16014719e+00 -3.94015968e-01 3.93063277e-01 -7.93931007e-01
-2.72370011e-01 -8.22251379e-01 -1.38131547e+00 -1.30835906e-01
-2.43375078e-01 1.91152707e-01 8.20314884e-01 8.57713103e-01
8.77509594e-01 4.21504289e-01 4.81777430e-01 -2.57039011e-01
-5.71336091e-01 -1.12219810e+00 -2.99781740e-01 9.75339055e-01
1.41186088e-01 -9.26087677e-01 -3.50382626e-02 -1.83442459e-01] | [9.494815826416016, 8.479342460632324] |
43cf82d1-938e-461c-8aeb-78f4e860c337 | insights-into-the-robustness-of-control-point | 1803.03025 | null | http://arxiv.org/abs/1803.03025v2 | http://arxiv.org/pdf/1803.03025v2.pdf | Insights into the robustness of control point configurations for homography and planar pose estimation | In this paper, we investigate the influence of the spatial configuration of a
number of $n \geq 4$ control points on the accuracy and robustness of space
resection methods, e.g. used by a fiducial marker for pose estimation. We find
robust configurations of control points by minimizing the first order perturbed
solution of the DLT algorithm which is equivalent to minimizing the condition
number of the data matrix. An empirical statistical evaluation is presented
verifying that these optimized control point configurations not only increase
the performance of the DLT homography estimation but also improve the
performance of planar pose estimation methods like IPPE and EPnP, including the
iterative minimization of the reprojection error which is the most accurate
algorithm. We provide the characteristics of stable control point
configurations for real-world noisy camera data that are practically
independent on the camera pose and form certain symmetric patterns dependent on
the number of points. Finally, we present a comparison of optimized
configuration versus the number of control points. | ['Volker Willert', 'Raul Acuna'] | 2018-03-08 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.34168893e-01 1.48078844e-01 -3.83946374e-02 1.74952537e-01
-5.99771142e-01 -6.73813045e-01 5.07293880e-01 2.14273408e-01
-5.83398938e-01 6.70804679e-01 -1.71440318e-01 6.22486556e-03
-5.88512480e-01 -3.59649748e-01 -8.38345945e-01 -8.12199533e-01
7.20349550e-02 5.61455727e-01 3.68558615e-02 -1.63347170e-01
5.51265240e-01 7.16823280e-01 -1.09206748e+00 -9.22927678e-01
8.41549516e-01 8.99384022e-01 1.33162856e-01 4.39896971e-01
5.76173186e-01 -1.74366422e-02 -5.41565478e-01 -8.96983594e-02
5.28358877e-01 -1.09006852e-01 -4.75783288e-01 2.94643790e-01
2.70297527e-01 2.98118889e-02 6.86369464e-02 1.23170352e+00
5.91318667e-01 3.09837013e-01 7.46109664e-01 -8.61245871e-01
1.68470308e-01 1.04755044e-01 -4.60892051e-01 -2.04342484e-01
5.27703822e-01 -1.59838080e-01 3.88831973e-01 -8.41695249e-01
1.14798868e+00 6.73479855e-01 8.84639144e-01 1.34664804e-01
-1.09202194e+00 -3.66394669e-01 -5.72838545e-01 1.80839200e-03
-1.83681703e+00 -4.51354980e-01 7.55315959e-01 -5.37049830e-01
4.11702514e-01 5.01219153e-01 5.95541835e-01 5.08134842e-01
5.52301288e-01 -9.95475203e-02 7.90164828e-01 -7.70489097e-01
1.88079044e-01 1.98987663e-01 -4.25704708e-03 9.23004329e-01
7.12916255e-01 2.09785387e-01 -7.97304139e-02 -3.10617924e-01
1.21226215e+00 -2.51202494e-01 -7.06649125e-01 -1.00869882e+00
-1.48302650e+00 7.82307863e-01 3.15846264e-01 5.75655043e-01
-2.22876757e-01 1.54007956e-01 -9.53673664e-03 -1.60950497e-01
-2.35765073e-02 9.51945961e-01 -1.07057177e-01 -8.74509439e-02
-6.20499849e-01 -1.92587404e-03 6.73436761e-01 1.18268013e+00
6.36150777e-01 -1.03198998e-01 2.72981793e-01 5.87918401e-01
1.91485181e-01 7.28780031e-01 4.80502009e-01 -1.12123048e+00
5.71199775e-01 5.35769224e-01 2.57753193e-01 -1.31966317e+00
-6.74606800e-01 -5.08555055e-01 -6.99275672e-01 -1.42788859e-02
5.19793153e-01 -4.33145553e-01 -4.84525144e-01 1.32624269e+00
3.91082287e-01 -2.42146581e-01 1.33138364e-02 9.80807424e-01
2.07120419e-01 3.48351538e-01 -6.54432654e-01 -5.91937423e-01
9.56195474e-01 -4.74503309e-01 -7.57544279e-01 1.29855564e-02
7.34091520e-01 -1.05878460e+00 6.27796471e-01 3.79644245e-01
-9.96501982e-01 -1.85873210e-01 -1.12014878e+00 2.93653041e-01
1.07769342e-02 7.30699539e-01 1.21699482e-01 6.00163817e-01
-7.43403792e-01 8.47159028e-01 -8.29500198e-01 -4.17098880e-01
-4.63845909e-01 6.41935885e-01 -7.77992010e-01 3.84143323e-01
-6.75069094e-01 1.37084675e+00 4.42632854e-01 3.87350887e-01
-2.59004027e-01 -4.62808281e-01 -7.85031199e-01 -1.08016022e-01
3.16701978e-01 -4.86225486e-01 7.44242549e-01 -4.62621093e-01
-1.57028985e+00 7.96779215e-01 6.03268817e-02 -3.61289978e-01
7.92687476e-01 -8.09329376e-02 -4.23008353e-02 2.82813579e-01
5.50893024e-02 2.40805849e-01 7.45843530e-01 -1.25344515e+00
2.80619431e-02 -4.98153836e-01 -3.96350592e-01 4.01423901e-01
1.11975364e-01 -3.53371918e-01 -5.41725993e-01 -4.29450452e-01
6.72911525e-01 -1.50874567e+00 -6.18343830e-01 -1.40937820e-01
-6.61972582e-01 4.17885214e-01 5.38821280e-01 -6.85601711e-01
1.10204434e+00 -2.36595535e+00 4.96558398e-01 7.66408801e-01
-1.38181686e-01 -1.38268754e-01 4.08348083e-01 4.98207480e-01
-1.90918878e-01 -2.61225384e-02 -9.89706367e-02 8.91696811e-02
-3.02984893e-01 8.28043073e-02 1.33284554e-01 1.25169277e+00
-5.26766539e-01 2.70759612e-01 -4.23151553e-01 -5.31555235e-01
3.13003540e-01 2.56806165e-01 -4.41781282e-01 -7.06562549e-02
3.70444238e-01 5.56463957e-01 -5.00499129e-01 1.31508008e-01
5.61339915e-01 1.39984523e-03 1.39021263e-01 -4.46287960e-01
-4.40265387e-01 -1.99709624e-01 -1.65369403e+00 1.54206979e+00
-3.23419780e-01 4.52206165e-01 1.71702281e-01 -5.53021610e-01
1.09423625e+00 3.02916676e-01 6.93524837e-01 -6.64323941e-02
5.43127477e-01 4.60567683e-01 -5.24794683e-03 -1.01227634e-01
5.14467001e-01 -1.44446522e-01 -1.10590972e-01 -1.82496950e-01
-6.69168234e-02 -5.48418045e-01 1.02460392e-01 -5.86048141e-02
5.10888696e-01 -2.40663290e-01 8.70140612e-01 -7.90538371e-01
6.69645667e-01 2.24951077e-02 5.81118405e-01 2.95038998e-01
1.01427160e-01 8.41044605e-01 6.38998747e-01 -1.19300544e-01
-1.17660284e+00 -6.35165691e-01 -5.98932981e-01 -3.43454570e-01
6.10558271e-01 -3.21028471e-01 -8.07544887e-01 -1.95018694e-01
1.80642630e-04 6.39698923e-01 -4.52629179e-01 -1.99955419e-01
-6.74262106e-01 -6.72220588e-01 8.22182894e-02 7.82340020e-02
3.58940125e-01 -3.03094417e-01 -6.92642868e-01 -1.28063038e-01
-1.75273582e-01 -1.26783037e+00 -4.53909963e-01 1.63237616e-01
-1.15025473e+00 -1.38147914e+00 -5.74081540e-01 -3.75445336e-01
1.03482425e+00 -9.62448940e-02 5.94954073e-01 3.00452393e-02
6.82918727e-02 6.43719018e-01 -2.41361931e-01 -2.28902921e-01
-3.60104591e-01 1.29465714e-01 4.13348407e-01 -1.33780941e-01
-4.56469119e-01 -2.34837011e-01 -3.98859918e-01 8.12444627e-01
-4.91365880e-01 -1.80616051e-01 3.52180392e-01 6.49043262e-01
8.79112661e-01 -2.49893893e-03 -1.11768365e-01 -3.93735856e-01
4.59507763e-01 5.07111438e-02 -1.03289986e+00 1.45912766e-01
-6.01949632e-01 3.49588782e-01 4.77784067e-01 -2.88472623e-01
-6.21381879e-01 4.93735313e-01 1.95922285e-01 -8.11276913e-01
2.48693943e-01 3.41274112e-01 -8.30571167e-03 -7.53545702e-01
7.85529077e-01 6.95402175e-02 3.12981009e-01 -3.91610384e-01
5.65303350e-03 1.74413070e-01 4.58061069e-01 -4.40144569e-01
8.81542325e-01 4.33088332e-01 6.10439181e-01 -1.19375229e+00
-7.12527707e-03 -6.46177232e-01 -7.84756124e-01 -3.02024662e-01
8.89832556e-01 -4.04900581e-01 -7.08099067e-01 2.14582264e-01
-1.11776006e+00 1.57615647e-01 -2.42772520e-01 8.78292441e-01
-9.17469621e-01 7.58756816e-01 -1.35033771e-01 -5.82724631e-01
-1.61277503e-01 -1.56227219e+00 1.02429211e+00 4.31570224e-02
-1.92451745e-01 -1.01709354e+00 1.19362012e-01 -1.11732349e-01
-1.56037927e-01 6.41922116e-01 5.50836205e-01 -1.45217836e-01
-6.77851737e-01 -6.46618962e-01 4.15078938e-01 -9.57041085e-02
-2.48515218e-01 1.93686962e-01 -3.07894707e-01 -5.53901136e-01
4.60189581e-01 5.65541387e-01 1.73487529e-01 6.37065530e-01
6.98493302e-01 -3.43737185e-01 -5.85282803e-01 9.55153644e-01
1.83267260e+00 2.35347077e-01 7.24434435e-01 4.38844413e-01
5.66989601e-01 4.17990148e-01 8.52278113e-01 3.82340938e-01
-2.78621048e-01 1.10197735e+00 5.66232741e-01 2.53888011e-01
2.84243405e-01 -1.50521234e-01 1.19215630e-01 8.30611646e-01
-3.73490691e-01 1.60573736e-01 -9.58647490e-01 3.22667152e-01
-1.52354765e+00 -4.08441603e-01 -3.17123085e-01 2.81206226e+00
3.09918791e-01 -2.50082761e-01 -2.34464154e-01 2.90754884e-01
7.82571852e-01 -5.22380136e-02 -7.66959041e-02 -2.78874576e-01
1.19164623e-01 -1.96125358e-01 1.26180375e+00 8.61290872e-01
-9.10677433e-01 4.23112601e-01 6.50457048e+00 6.84045076e-01
-1.20486021e+00 -1.64156616e-01 3.56841013e-02 1.45340189e-01
4.45474498e-02 2.03337185e-02 -9.23760653e-01 5.14913738e-01
6.01311922e-01 -1.91924691e-01 2.27028370e-01 8.96173537e-01
2.37437580e-02 -4.85933602e-01 -7.67594814e-01 1.26364779e+00
2.27353498e-01 -1.21475768e+00 -3.35673451e-01 4.02151376e-01
6.65772021e-01 -2.27831498e-01 -1.27229959e-01 -4.68948394e-01
-4.67028320e-01 -5.57827353e-01 6.68320000e-01 5.41072249e-01
7.92776465e-01 -6.65072501e-01 6.99950278e-01 4.43788677e-01
-8.46108258e-01 -2.12518647e-02 -4.10152584e-01 3.89722377e-01
2.73267835e-01 5.65451324e-01 -9.64909732e-01 7.66186893e-01
2.56303191e-01 3.41115147e-01 -4.50606167e-01 1.41040039e+00
-1.54140651e-01 -1.16668880e-01 -8.35090160e-01 -9.49730054e-02
3.29857878e-02 -7.18198299e-01 1.12796140e+00 5.60038269e-01
7.73970902e-01 2.18711376e-01 -2.67925113e-01 5.14630556e-01
4.30991769e-01 4.12850142e-01 -7.21362889e-01 3.66551429e-01
2.94622600e-01 1.02877390e+00 -7.39270806e-01 2.81791240e-01
1.56072885e-01 7.17580080e-01 -1.29016548e-01 2.51033217e-01
-6.05182588e-01 -2.57513821e-01 3.92892659e-01 3.93862545e-01
1.26250118e-01 -7.25448132e-01 -4.24964577e-01 -1.21779871e+00
2.13291824e-01 -5.66796601e-01 1.42480955e-01 -6.57456994e-01
-4.50911611e-01 5.12913823e-01 2.41707340e-01 -1.72233593e+00
-5.56110978e-01 -8.39848936e-01 -4.56868082e-01 5.42292416e-01
-6.53774142e-01 -5.08965313e-01 -6.96566254e-02 4.77199763e-01
-1.54673681e-01 -5.08267507e-02 6.80041432e-01 -6.78513795e-02
-5.93536675e-01 4.10857141e-01 4.17501181e-01 -5.16265035e-02
5.97672462e-01 -1.02176487e+00 -3.25926006e-01 1.04014325e+00
-1.16650887e-01 8.62986922e-01 1.14386964e+00 -6.17733836e-01
-1.55350733e+00 -3.88513535e-01 6.15418136e-01 -4.87291515e-01
3.30245584e-01 -1.35536075e-01 -3.51713777e-01 7.01332271e-01
-2.48611078e-01 -1.68709785e-01 1.33052632e-01 -6.36304617e-02
3.30428928e-01 -2.08904639e-01 -1.30862987e+00 5.75777471e-01
4.80573982e-01 -2.42511511e-01 -3.92154545e-01 3.11350912e-01
3.21224838e-01 -9.37079608e-01 -1.13465202e+00 6.19248688e-01
3.48724097e-01 -9.08160567e-01 1.09890723e+00 1.72857389e-01
-8.91550183e-02 -3.85788441e-01 1.53999943e-02 -1.36864090e+00
-1.23019859e-01 -7.88921416e-01 4.18553859e-01 6.09616399e-01
3.60788912e-01 -7.59903431e-01 8.48876953e-01 6.32831037e-01
-2.33927369e-02 -7.08615959e-01 -1.34926271e+00 -8.51160824e-01
-1.74828559e-01 7.88495690e-02 2.54685562e-02 8.49566758e-01
2.62189269e-01 1.02747791e-01 -2.04665080e-01 5.08957624e-01
7.28572965e-01 -5.36557734e-02 7.62004554e-01 -1.08254409e+00
-1.89422533e-01 -1.50430620e-01 -8.21953535e-01 -7.10846722e-01
-1.76556259e-01 -4.56860691e-01 -1.26109898e-01 -1.09027052e+00
-4.10720229e-01 -5.55918694e-01 4.99750197e-01 -2.63317883e-01
2.56616116e-01 -1.69351801e-01 2.98074812e-01 3.60839754e-01
-7.06468662e-03 3.43957424e-01 1.29488754e+00 4.48199391e-01
-3.66979390e-01 1.67409018e-01 -7.16838986e-02 8.80153060e-01
4.69455123e-01 -3.50423515e-01 -2.75461853e-01 -2.29959235e-01
3.34533781e-01 5.45387805e-01 2.24615276e-01 -1.34203994e+00
3.32346171e-01 -1.09479129e-02 3.37947965e-01 -4.69201863e-01
4.45641547e-01 -1.32431459e+00 5.78649402e-01 6.19628370e-01
2.17877537e-01 3.65802437e-01 5.40869310e-02 4.25447762e-01
-1.77253008e-01 -8.05902481e-01 1.04229772e+00 2.02041231e-02
-3.13697100e-01 -3.46958674e-02 -9.31283981e-02 -4.38528448e-01
1.29887569e+00 -5.14625072e-01 -1.26812398e-01 -5.51838994e-01
-7.61449814e-01 -2.11630747e-01 8.32271218e-01 -8.87084454e-02
2.99118370e-01 -1.27209234e+00 -2.13035107e-01 3.81491035e-01
-1.49810553e-01 8.70081112e-02 2.62077455e-03 1.26186419e+00
-1.08588564e+00 6.11557484e-01 -1.85486823e-01 -7.73835659e-01
-1.29009104e+00 6.42470360e-01 8.34359765e-01 -1.05955191e-01
-2.28796974e-01 4.02042359e-01 -1.58175454e-01 -4.72409427e-01
5.67049049e-02 -5.52759290e-01 -2.22396344e-01 -1.45829856e-01
-6.38020337e-02 6.60123289e-01 1.38843477e-01 -7.71003306e-01
-3.23171794e-01 1.28561974e+00 4.63598043e-01 -9.77315605e-02
9.61973071e-01 -1.89852521e-01 -2.27570668e-01 1.16992675e-01
1.23446858e+00 3.37082684e-01 -9.88750160e-01 3.56055975e-01
-2.27542132e-01 -6.56297088e-01 1.61769986e-02 -4.45974022e-01
-9.58152592e-01 3.73800814e-01 6.62585258e-01 -1.76544502e-01
9.31923151e-01 -3.48362595e-01 8.08318406e-02 3.38533074e-01
6.54695272e-01 -9.90403175e-01 -3.82838100e-01 4.14954513e-01
1.24425292e+00 -8.14962804e-01 3.86683077e-01 -9.39995110e-01
-4.28452551e-01 1.18998706e+00 3.96619350e-01 -4.42854255e-01
6.66557550e-01 1.67047426e-01 -2.16385704e-02 1.56269837e-02
1.32334843e-01 1.88810885e-01 4.19336051e-01 2.32538164e-01
1.24973923e-01 1.37735670e-02 -1.02387953e+00 1.72477737e-01
-4.06331241e-01 -1.87048286e-01 7.18695700e-01 6.25380158e-01
-2.63369441e-01 -1.02839899e+00 -8.91195297e-01 -5.54578863e-02
-2.62390614e-01 2.97810644e-01 -2.61662722e-01 1.22988391e+00
-1.46756217e-01 4.73817885e-01 -2.42434628e-02 -1.67932466e-01
6.10497236e-01 -1.90570995e-01 7.19736338e-01 -9.61600170e-02
-4.79428113e-01 2.21326813e-01 1.12046331e-01 -5.66821098e-01
-1.76048934e-01 -8.63774121e-01 -1.11938679e+00 -1.84088781e-01
-6.97548926e-01 5.36220372e-01 9.14052248e-01 7.59146154e-01
1.94133684e-01 -1.72783613e-01 4.71414268e-01 -9.07600641e-01
-7.07819939e-01 -7.50021875e-01 -7.73758173e-01 1.72096938e-01
2.07524806e-01 -8.27778637e-01 -5.69411457e-01 -2.48977274e-01] | [7.9368414878845215, -2.2082462310791016] |
2cd0e7fb-3731-418c-b75a-29bcb5fc7432 | idt5-indonesian-version-of-multilingual-t5 | 2302.00856 | null | https://arxiv.org/abs/2302.00856v1 | https://arxiv.org/pdf/2302.00856v1.pdf | idT5: Indonesian Version of Multilingual T5 Transformer | Indonesian language is spoken by almost 200 million people and is the 10th most spoken language in the world, but it is under-represented in NLP (Natural Language Processing) research. A sparsity of language resources has hampered previous work on Indonesian. The Transformer is a new architecture rapidly becoming dominant for NLP, surpassing alternatives like convolutional and recurrent neural networks. T5 (Text-to-Text Transfer Transformer) is a Transformer model that converts all text-based language problems to text-to-text format for English. The multilingual variant is mT5 (multilingual T5) which has shown promising results on many NLP tasks across languages. However, the size of this multilingual model is a drawback for its application in real production applications, which sometimes require only one language. In this study, the mT5 model was adapted for only one language, Indonesian, resulting in a pre-trained T5 model that was specific only for Indonesian with a smaller size. For performance comparison, we fine-tuned this model and the mT5 model to the Sentiment Analysis (SA), Question Generation (QG), and Question Answering (QA) tasks with the exact mechanism and dataset. Fine-tuned model based on our model achieved 77.18% accuracy on SA, 8% higher than the mT5-based model, and obtained nearly the same score as the mT5-based model on QG and QA. The results confirm that it is possible to produce a smaller pre-trained model that maintains comparable yields while reducing the model size by up to 58%. In addition, the resulting model requires less memory, loads faster, and inference times faster. | ['Surya Sumpeno', 'Adhi Dharma Wibawa', 'Mukhlish Fuadi'] | 2023-02-02 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-9.01545808e-02 2.20166564e-01 2.39151910e-01 -3.59349608e-01
-1.05621195e+00 -6.37889862e-01 5.51652133e-01 9.53854546e-02
-6.16004288e-01 8.03106666e-01 4.12335753e-01 -8.32983911e-01
2.98754990e-01 -9.58638847e-01 -5.70645511e-01 -3.53327662e-01
3.11972022e-01 6.58448756e-01 -1.53255425e-02 -7.13584483e-01
1.06976993e-01 1.97449043e-01 -1.19904613e+00 6.53526008e-01
1.09549248e+00 8.09627533e-01 3.93083811e-01 6.30902946e-01
-6.57514989e-01 9.45096195e-01 -8.69031787e-01 -6.06372893e-01
-3.05270869e-03 -4.52889413e-01 -1.11838841e+00 -3.94790381e-01
4.97373968e-01 -9.36680585e-02 7.66284987e-02 7.12402463e-01
6.00940883e-01 -1.73888449e-02 3.85246307e-01 -9.54100072e-01
-8.40505302e-01 7.98545420e-01 -8.93369839e-02 -9.88970324e-02
5.18000543e-01 -2.59107113e-01 1.14048576e+00 -1.08066416e+00
4.73554641e-01 1.38582015e+00 6.96888864e-01 5.83754718e-01
-7.92072654e-01 -7.04065979e-01 -1.11656100e-01 3.26910242e-02
-1.03852439e+00 -1.92771599e-01 3.38758647e-01 2.79825889e-02
1.72471857e+00 1.88864946e-01 4.52811718e-01 9.56472278e-01
5.57033598e-01 8.38097632e-01 1.29596400e+00 -6.49143577e-01
-1.72406919e-02 3.64024013e-01 -1.07084185e-01 6.01725936e-01
-1.93916559e-02 -4.48704153e-01 -4.80466217e-01 4.60676104e-02
4.50513393e-01 -3.40125710e-01 -5.27995601e-02 4.09130335e-01
-1.22363043e+00 1.20288920e+00 2.34430864e-01 6.78046167e-01
-3.22869956e-01 -2.17027009e-01 6.22383237e-01 6.10421836e-01
6.26173258e-01 5.43865800e-01 -9.79835272e-01 -3.04589242e-01
-8.53514314e-01 2.78186053e-01 1.22351003e+00 7.97726810e-01
6.68430507e-01 4.87415016e-01 -2.08079249e-01 1.08622622e+00
1.38821453e-01 8.81218076e-01 9.30363715e-01 -5.77397287e-01
8.97772372e-01 8.79225373e-01 -1.30705073e-01 -8.44747901e-01
-4.62434769e-01 -2.30360374e-01 -9.00441289e-01 -1.92140907e-01
5.96814275e-01 -5.19658804e-01 -1.11990201e+00 1.59024298e+00
-2.57509854e-02 -6.88061535e-01 5.51296175e-01 5.47696829e-01
1.03448951e+00 1.40261507e+00 -2.91079748e-02 1.17710918e-01
1.51916623e+00 -1.22257853e+00 -6.84123397e-01 -4.30673748e-01
8.12450230e-01 -1.12032914e+00 1.39645135e+00 4.16637272e-01
-1.00486815e+00 -4.47800219e-01 -7.27145612e-01 -4.07995045e-01
-9.20295954e-01 2.22399265e-01 4.87988293e-01 8.88895929e-01
-1.39187777e+00 2.02040672e-01 -4.32042181e-01 -8.02838087e-01
-1.58884615e-01 2.77672708e-01 -4.45519328e-01 -1.17845021e-01
-1.63177025e+00 1.39188743e+00 3.45195502e-01 2.21425816e-01
-5.35315514e-01 -4.39125597e-01 -1.02266872e+00 -1.44432858e-02
1.01945497e-01 -3.43390852e-01 1.44615340e+00 -1.00886059e+00
-2.06646752e+00 5.77271342e-01 -5.06822526e-01 -5.23131073e-01
4.40088585e-02 -2.19962656e-01 -6.28450692e-01 -4.79503050e-02
1.07568331e-01 7.09558249e-01 5.14322996e-01 -7.31500208e-01
-7.09163308e-01 -1.34806648e-01 6.90096840e-02 3.31519574e-01
-2.60619193e-01 2.64954060e-01 -2.77169436e-01 -5.59572101e-01
-2.33890995e-01 -8.13332200e-01 -9.80732366e-02 -6.94787562e-01
1.33246079e-01 -5.36703289e-01 7.51385331e-01 -1.14083660e+00
1.11695385e+00 -1.67518127e+00 -2.81702369e-01 -4.05872203e-02
-4.54496235e-01 5.98509073e-01 -5.63789189e-01 1.01773942e+00
2.97000119e-03 2.98840612e-01 -3.16215307e-01 -2.45446369e-01
5.07803559e-02 4.69254613e-01 -5.41615784e-01 -1.29232407e-01
4.68120903e-01 1.12792337e+00 -8.25167477e-01 -3.27788293e-01
1.22929186e-01 5.24805427e-01 -4.87661928e-01 9.86059532e-02
-2.64336079e-01 1.16733603e-01 -2.35833198e-01 6.82410777e-01
4.70615864e-01 6.33685365e-02 1.26122132e-01 -2.00623553e-02
-2.64302522e-01 7.90112853e-01 -7.59694040e-01 1.56457663e+00
-1.03126538e+00 6.25995278e-01 4.41139527e-02 -8.56461704e-01
1.21521723e+00 6.70410395e-01 8.26292932e-02 -1.05582452e+00
9.00209323e-02 6.03828549e-01 -1.03987353e-02 -4.72160518e-01
9.18330848e-01 -2.05786034e-01 -3.76875550e-01 4.68255103e-01
3.41536373e-01 -4.99242812e-01 3.66175026e-01 9.08250958e-02
7.06012189e-01 4.71351705e-02 3.53217095e-01 -3.86508346e-01
6.94506049e-01 1.42177373e-01 2.63322026e-01 6.32211864e-01
1.69450283e-01 5.97193956e-01 3.57138485e-01 -4.59010601e-01
-9.89471376e-01 -7.95790315e-01 1.33747607e-01 1.13170159e+00
-5.43542922e-01 -4.20565635e-01 -8.89577746e-01 -6.53490365e-01
-3.51753831e-01 1.01059473e+00 -3.45310897e-01 2.47177392e-01
-7.58098423e-01 -7.78548300e-01 8.46270084e-01 2.00389817e-01
7.91860461e-01 -1.54212248e+00 -2.50609338e-01 5.28580070e-01
-6.73846424e-01 -1.07191133e+00 -4.36835140e-01 8.77085254e-02
-8.07894051e-01 -7.07984090e-01 -8.07656825e-01 -1.09216821e+00
3.17596704e-01 -2.48232260e-01 1.19959342e+00 -3.99076015e-01
2.17090070e-01 8.61813053e-02 -6.38493240e-01 -6.14162982e-01
-6.53409362e-01 4.93015528e-01 -1.93994507e-01 -5.40471561e-02
5.77333212e-01 -8.04231316e-02 -1.79040432e-01 5.25929146e-02
-1.03576839e+00 -6.84579536e-02 6.94384277e-01 8.79117846e-01
1.72915488e-01 -7.30551630e-02 9.73449111e-01 -8.68886650e-01
9.98212516e-01 -2.92960197e-01 -3.43848348e-01 3.48040134e-01
-6.79860413e-01 8.14628899e-02 9.69273269e-01 -2.03819469e-01
-1.33480763e+00 -2.49027833e-01 -7.46816337e-01 2.41330713e-01
1.47504797e-02 8.61562073e-01 -9.04243290e-02 2.17679158e-01
4.17829782e-01 3.48942190e-01 8.83585587e-02 -4.47206110e-01
2.57956892e-01 8.71692240e-01 1.84644416e-01 -3.65683138e-01
4.84611303e-01 -5.08730300e-02 -4.97474581e-01 -1.01384366e+00
-8.88245046e-01 -2.78804809e-01 -2.15897724e-01 6.61093816e-02
8.19975138e-01 -8.68399262e-01 -4.93972421e-01 7.45893061e-01
-1.38624692e+00 -3.35487515e-01 -2.30607912e-01 3.68324250e-01
-5.08523062e-02 1.51238188e-01 -8.39546323e-01 -6.64654911e-01
-9.44708943e-01 -9.82904255e-01 1.00991678e+00 1.24719322e-01
-2.36717895e-01 -1.23400116e+00 9.91296470e-02 6.39238060e-01
8.42423201e-01 -1.83904320e-01 1.12995410e+00 -9.03913975e-01
4.72852699e-02 -1.38157293e-01 -1.94518551e-01 7.65729308e-01
2.79349178e-01 -2.17003137e-01 -1.00945020e+00 -2.33892784e-01
1.67780191e-01 -5.11797965e-01 6.90113962e-01 1.34677172e-01
6.66757524e-01 -5.15835166e-01 3.33706021e-01 -1.36694238e-02
1.29096556e+00 1.87469199e-01 7.67168999e-01 3.92641932e-01
5.21097541e-01 7.76428401e-01 4.64121282e-01 -8.82631242e-02
9.07141149e-01 3.53329927e-01 6.38738722e-02 -3.00009966e-01
-7.90976509e-02 -4.22361732e-01 1.04075086e+00 1.54102314e+00
1.89384609e-01 -4.10170496e-01 -1.07348514e+00 7.21144676e-01
-1.60879803e+00 -6.68724716e-01 -1.41338289e-01 2.02949595e+00
1.13947439e+00 -1.08019568e-01 -2.44287729e-01 -9.75927413e-02
3.62261683e-01 4.12252583e-02 -1.07610680e-01 -1.17132652e+00
-2.63939023e-01 6.79245591e-01 2.20291913e-01 7.28869319e-01
-8.36534441e-01 1.37227118e+00 6.50404549e+00 9.15576220e-01
-1.36031377e+00 1.58587426e-01 5.17575383e-01 2.93582588e-01
-3.34330946e-01 -9.53090936e-02 -1.08306491e+00 1.77095518e-01
1.47685981e+00 -2.94777215e-01 3.20724517e-01 5.95017672e-01
2.71582454e-01 -2.37111151e-02 -8.45507979e-01 6.87967241e-01
3.91349047e-01 -1.15397632e+00 5.98264396e-01 -2.48002991e-01
7.45523632e-01 3.32207650e-01 -1.16300792e-01 8.41286480e-01
3.43096495e-01 -1.15460682e+00 5.54399192e-01 1.48325503e-01
6.19240940e-01 -8.55901778e-01 1.32781875e+00 2.85862714e-01
-1.11562431e+00 6.86776489e-02 -3.81269902e-01 -3.27634335e-01
2.33912975e-01 4.75470304e-01 -1.22007585e+00 6.98931277e-01
8.85740578e-01 3.85203123e-01 -5.84796011e-01 3.57505411e-01
-2.15771705e-01 9.44668949e-01 -3.76967639e-01 -4.01894897e-01
6.53492332e-01 -3.17452312e-01 1.97810188e-01 1.52046478e+00
6.28801703e-01 -3.43414992e-01 1.42720304e-02 4.49873984e-01
-1.32025063e-01 6.40684605e-01 -5.66910446e-01 -1.61272109e-01
1.26935303e-01 1.36227787e+00 -4.16874498e-01 -3.34948629e-01
-6.34079754e-01 8.76997471e-01 3.26018900e-01 3.70765746e-01
-5.20814240e-01 -6.89799368e-01 1.17442481e-01 -1.30302384e-01
1.44505531e-01 -1.16053775e-01 2.96687186e-02 -1.18372977e+00
2.09682737e-03 -1.40334189e+00 3.64915341e-01 -8.25560212e-01
-1.44543505e+00 1.11728966e+00 -1.73544154e-01 -8.36663663e-01
-6.39383078e-01 -8.66307199e-01 -2.98021078e-01 1.21336377e+00
-1.77845037e+00 -1.38877642e+00 8.54275823e-02 5.91816783e-01
8.34477007e-01 -2.07854062e-01 1.25850153e+00 3.70698571e-01
-3.95746738e-01 5.80174804e-01 7.14279860e-02 3.13058913e-01
8.15125763e-01 -1.27022243e+00 5.48026085e-01 8.12529981e-01
1.92260053e-02 5.86920917e-01 2.11450458e-01 -4.32003319e-01
-1.30138242e+00 -1.25956583e+00 1.94964480e+00 -3.52760434e-01
8.95116687e-01 -5.13325155e-01 -9.26107109e-01 7.10313201e-01
7.54098117e-01 -6.68909192e-01 5.80411434e-01 8.53309222e-03
-2.12519199e-01 -3.11861187e-01 -1.10466599e+00 5.81479251e-01
2.35348478e-01 -6.47806764e-01 -8.21609497e-01 1.51169285e-01
8.71985555e-01 -3.08915079e-01 -8.75795841e-01 1.92898601e-01
4.97729063e-01 -6.17788553e-01 4.33791012e-01 -1.93739235e-01
4.26787525e-01 -1.40432239e-01 -1.98067963e-01 -1.44118941e+00
4.77432907e-02 -4.67878014e-01 3.57641757e-01 1.44639599e+00
1.04671276e+00 -1.17339826e+00 4.09235626e-01 4.34281021e-01
-1.38938591e-01 -7.52725005e-01 -9.73080456e-01 -7.34035134e-01
6.27003074e-01 -3.53952855e-01 6.75014138e-01 8.18911374e-01
-5.37171401e-02 8.19843829e-01 -1.95374176e-01 -2.06503823e-01
-2.42552198e-02 1.11548387e-01 4.73106265e-01 -8.97260010e-01
4.17988598e-02 -3.80134612e-01 2.67830789e-01 -9.94866192e-01
1.27499163e-01 -1.06545222e+00 3.94967273e-02 -1.80025589e+00
-1.26155436e-01 -3.18622231e-01 -4.54512984e-02 7.94712007e-01
6.42499328e-02 2.16192812e-01 2.02941671e-01 -1.74680695e-01
-1.26600573e-02 6.44793689e-01 1.17632723e+00 -1.78237543e-01
-1.64238647e-01 -2.14403853e-01 -7.34479070e-01 5.59986174e-01
1.11221778e+00 -3.10080349e-01 -2.57656127e-01 -7.09555387e-01
4.92388904e-01 -1.21479504e-01 -1.88471347e-01 -5.80763280e-01
1.47212639e-01 1.28395483e-01 1.94528461e-01 -7.22284436e-01
2.19007924e-01 -6.15279675e-01 -2.79103130e-01 3.83259743e-01
-1.43010527e-01 3.87222409e-01 5.37389100e-01 -2.02686906e-01
-6.39913797e-01 -3.51668596e-01 5.52210689e-01 -2.42614523e-01
-6.56594276e-01 2.23498475e-02 -8.21312904e-01 1.03921980e-01
4.63718385e-01 -7.11472780e-02 -3.82050663e-01 -6.96290314e-01
-1.82696387e-01 2.93957740e-01 -1.85472757e-01 7.19226837e-01
5.60107827e-01 -1.00168085e+00 -1.07884991e+00 2.27761969e-01
-1.01619117e-01 -9.62930992e-02 1.08435005e-03 4.99690354e-01
-8.44865322e-01 1.04821551e+00 -5.21253049e-02 -2.07294881e-01
-8.77159774e-01 2.79983915e-02 4.31560129e-01 -8.57796013e-01
-2.49177322e-01 4.78208035e-01 -1.13450281e-01 -1.31142008e+00
-1.00935191e-01 -5.76888800e-01 -6.08505905e-01 1.71302289e-01
4.06423926e-01 3.13885927e-01 4.27210569e-01 -6.45757556e-01
-2.96090454e-01 4.83675331e-01 -1.90920085e-01 -3.41991007e-01
1.23009038e+00 1.40003907e-02 -5.48476934e-01 5.07288337e-01
1.04827738e+00 2.48221084e-01 -3.52362216e-01 -1.01107046e-01
1.11998748e-02 2.33463556e-01 -2.53567994e-02 -1.17026079e+00
-8.72722805e-01 9.95741904e-01 2.09143639e-01 1.69807345e-01
1.04514742e+00 -1.67503059e-01 1.15730762e+00 7.63791084e-01
3.94513905e-01 -1.12718141e+00 -1.71231791e-01 1.29133952e+00
1.20357656e+00 -1.15280378e+00 -4.46172446e-01 -4.35508676e-02
-7.32634306e-01 1.14629936e+00 6.71232104e-01 1.31244510e-01
5.55710614e-01 1.64616868e-01 4.63261873e-01 1.33868773e-02
-8.07183504e-01 4.27585468e-02 2.38539666e-01 4.24683332e-01
7.36839950e-01 1.33697525e-01 -3.30545098e-01 5.69188893e-01
-7.79283285e-01 -8.01499118e-04 5.25720537e-01 7.25614727e-01
-2.64768362e-01 -1.42610574e+00 -2.94732481e-01 4.41436857e-01
-6.76061690e-01 -6.62353277e-01 -3.39930326e-01 9.09174979e-01
1.27380602e-02 1.39203000e+00 6.22694008e-02 -1.83096990e-01
4.36791360e-01 3.68849993e-01 2.05253914e-01 -7.50058472e-01
-1.17849958e+00 -1.85933292e-01 4.50070918e-01 -4.37569946e-01
-3.64108831e-01 -4.05515075e-01 -1.19166708e+00 -3.40795189e-01
-2.50451684e-01 5.21720111e-01 8.42569530e-01 9.40984011e-01
3.62311721e-01 1.54604435e-01 4.08141553e-01 -4.70763355e-01
-3.43131810e-01 -1.45162964e+00 -4.20517057e-01 5.80681041e-02
1.82979167e-01 7.53456056e-02 -3.56871396e-01 6.28146604e-02] | [10.968360900878906, 9.756656646728516] |
0ba17b48-c0e3-48e0-b34b-ae8563e99de9 | uukg-unified-urban-knowledge-graph-dataset | 2306.11443 | null | https://arxiv.org/abs/2306.11443v1 | https://arxiv.org/pdf/2306.11443v1.pdf | UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction | Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to the development and operation of the smart city. As an emerging building block, multi-sourced urban data are usually integrated as urban knowledge graphs (UrbanKGs) to provide critical knowledge for urban spatiotemporal prediction models. However, existing UrbanKGs are often tailored for specific downstream prediction tasks and are not publicly available, which limits the potential advancement. This paper presents UUKG, the unified urban knowledge graph dataset for knowledge-enhanced urban spatiotemporal predictions. Specifically, we first construct UrbanKGs consisting of millions of triplets for two metropolises by connecting heterogeneous urban entities such as administrative boroughs, POIs, and road segments. Moreover, we conduct qualitative and quantitative analysis on constructed UrbanKGs and uncover diverse high-order structural patterns, such as hierarchies and cycles, that can be leveraged to benefit downstream USTP tasks. To validate and facilitate the use of UrbanKGs, we implement and evaluate 15 KG embedding methods on the KG completion task and integrate the learned KG embeddings into 9 spatiotemporal models for five different USTP tasks. The extensive experimental results not only provide benchmarks of knowledge-enhanced USTP models under different task settings but also highlight the potential of state-of-the-art high-order structure-aware UrbanKG embedding methods. We hope the proposed UUKG fosters research on urban knowledge graphs and broad smart city applications. The dataset and source code are available at https://github.com/usail-hkust/UUKG/. | ['Hui Xiong', 'Zhenyu Zeng', 'Hao Wang', 'Hao liu', 'Yansong Ning'] | 2023-06-20 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-6.45465255e-01 2.91489244e-01 -6.65891230e-01 -1.90655321e-01
-6.65120184e-01 -1.84146985e-01 7.16235399e-01 3.17192435e-01
1.41274944e-01 8.25693965e-01 8.67610335e-01 -5.46258271e-01
-3.15144867e-01 -1.46196008e+00 -7.16457188e-01 -5.87372780e-01
-1.56257153e-01 4.52555150e-01 5.11540353e-01 -3.56350124e-01
-2.34286621e-01 3.41824651e-01 -1.15564847e+00 1.06324978e-01
1.10654056e+00 7.56629527e-01 6.96334019e-02 2.69579649e-01
-3.73381041e-02 8.06675732e-01 1.78543851e-01 -5.86140871e-01
2.19479293e-01 3.67686093e-01 -7.22863019e-01 -2.80509770e-01
1.63478747e-01 -2.33176738e-01 -1.12840784e+00 5.92533171e-01
3.66976678e-01 2.65585244e-01 6.59388006e-01 -1.61413872e+00
-1.06322229e+00 7.28488624e-01 -9.73303616e-02 2.94298470e-01
-6.37296066e-02 4.37804013e-01 1.41712284e+00 -9.64644194e-01
6.76929474e-01 9.19420183e-01 7.79489219e-01 -2.94669509e-01
-9.47699249e-01 -7.60315359e-01 3.43314439e-01 7.22234309e-01
-1.81034935e+00 -3.53978068e-01 6.54029548e-01 -5.86521566e-01
1.22301316e+00 6.82635680e-02 1.21390080e+00 8.79896224e-01
-3.27369012e-02 1.12187469e+00 5.10448039e-01 2.58484900e-01
-1.92837983e-01 -4.77117337e-02 1.79147273e-01 8.80324364e-01
4.88836616e-01 2.01643720e-01 -4.00699079e-01 1.03681445e-01
6.47032738e-01 1.91132650e-01 -2.24467739e-01 -3.42000842e-01
-1.30687892e+00 9.28429842e-01 1.14071834e+00 2.75668859e-01
-4.59872216e-01 6.00986302e-01 3.44410390e-01 -1.10905118e-01
8.73829782e-01 -1.80114470e-02 -4.43414778e-01 -3.44417483e-01
-7.14728653e-01 2.39802286e-01 3.97124559e-01 1.11895096e+00
1.13465631e+00 2.21847579e-01 -6.34140149e-02 6.83023870e-01
4.19825137e-01 6.91163719e-01 -7.97608793e-02 -5.72478831e-01
1.07180262e+00 1.06785929e+00 -4.32875240e-03 -1.39011681e+00
-4.29113537e-01 -3.24915022e-01 -7.28650093e-01 -6.63031340e-01
1.59878358e-01 5.79339191e-02 -6.37027860e-01 1.42712104e+00
5.83750725e-01 8.67551744e-01 -5.80822825e-02 6.75274312e-01
1.19431365e+00 8.02140296e-01 3.65088731e-01 4.49231356e-01
9.92783010e-01 -1.20488226e+00 -4.57659245e-01 -8.10521469e-02
1.26321542e+00 -2.14538842e-01 1.13811946e+00 -5.16663194e-01
-4.55701917e-01 -2.21515954e-01 -5.86112797e-01 -1.16361596e-01
-1.13988769e+00 5.49760349e-02 8.25657785e-01 2.82529563e-01
-9.78339374e-01 2.70250797e-01 -8.53328705e-01 -5.91434002e-01
7.70686448e-01 -1.50438296e-04 -2.56901324e-01 -3.48508984e-01
-1.68235469e+00 7.81104267e-01 6.41764164e-01 -3.40142362e-02
-4.01043624e-01 -1.27612078e+00 -1.06240785e+00 1.97255433e-01
9.82856154e-02 -7.60890126e-01 5.90187490e-01 1.25055149e-01
-6.99891150e-01 5.82890987e-01 -6.43842667e-02 -3.94942492e-01
1.41692966e-01 4.73696366e-02 -8.23389709e-01 -2.19081089e-01
4.61207569e-01 4.78521883e-01 3.07152212e-01 -1.10329425e+00
-9.14256752e-01 -2.09077612e-01 4.53975722e-02 -2.34620832e-02
-4.87738281e-01 -5.31816065e-01 -6.01836622e-01 -6.46285653e-01
-8.97970945e-02 -8.22170436e-01 -2.42627725e-01 -3.26103508e-01
-5.63912690e-01 -4.22636360e-01 9.46475565e-01 -8.61203492e-01
1.88798833e+00 -2.11164975e+00 -1.95222884e-01 3.76072705e-01
5.65453231e-01 2.25227460e-01 -1.84088707e-01 8.39500248e-01
1.37146160e-01 3.35117102e-01 -1.37966469e-01 -2.39607155e-01
3.69860172e-01 3.78934771e-01 -4.61741090e-01 2.35641971e-01
-1.02181017e-01 1.81960595e+00 -1.32907140e+00 -6.45209372e-01
5.77760816e-01 4.97297049e-01 -3.20400357e-01 -3.28978598e-01
-1.31354779e-01 2.69726932e-01 -9.99477506e-01 7.39231408e-01
4.41957533e-01 -4.85085607e-01 -6.06108680e-02 -3.52849752e-01
-1.01308949e-01 5.29226184e-01 -7.91924715e-01 1.08437371e+00
-5.70910215e-01 9.43733811e-01 -6.48046553e-01 -9.63698387e-01
6.71900332e-01 8.03238079e-02 6.47699416e-01 -7.71843135e-01
-2.67264068e-01 9.27156061e-02 -5.45765281e-01 -4.00363177e-01
7.03242004e-01 3.02360386e-01 -3.34004879e-01 3.59148264e-01
-3.26545149e-01 -4.17521223e-02 1.01435833e-01 5.20646453e-01
1.22389746e+00 -1.69694409e-01 3.33642274e-01 -1.12902068e-01
9.95757431e-02 3.12382132e-01 4.90960747e-01 3.17989796e-01
-4.82178330e-01 1.73204497e-01 2.07093313e-01 -8.53437543e-01
-9.58758414e-01 -1.43315804e+00 -1.34401262e-01 8.57629538e-01
5.16244322e-02 -7.31641412e-01 -2.09469311e-02 -7.50989437e-01
5.62737525e-01 8.36480558e-01 -4.86667156e-01 3.53974802e-03
-5.86910129e-01 -7.82541633e-01 8.22645724e-01 6.44864678e-01
6.48662686e-01 -6.92432523e-01 -8.08327273e-02 3.14230859e-01
-6.10223651e-01 -1.43175185e+00 -2.90974647e-01 -4.11584318e-01
-5.90482473e-01 -1.38675129e+00 -1.09337941e-01 -5.60420990e-01
3.14252615e-01 5.95284581e-01 1.28111947e+00 -2.55274829e-02
1.54658645e-01 6.93706036e-01 -4.92397755e-01 -6.51726723e-02
1.97864816e-01 5.00630021e-01 4.68335785e-02 -7.40074068e-02
5.30732155e-01 -1.01431680e+00 -7.19004273e-01 2.56280452e-01
-4.37507778e-01 4.33232129e-01 3.20377201e-01 4.13795859e-01
5.93214214e-01 2.52373815e-01 7.44732022e-01 -7.43507087e-01
3.13558578e-01 -1.13840187e+00 -3.72318804e-01 2.03807071e-01
-7.06291795e-01 -1.35690659e-01 5.23425102e-01 -3.38033512e-02
-8.60649586e-01 -2.51209468e-01 -1.24729145e-02 -4.11404639e-01
5.26772533e-03 9.55668747e-01 -1.22770503e-01 4.84630316e-02
4.28273082e-01 4.35320646e-01 -7.96767116e-01 -2.11493656e-01
8.56910050e-01 6.20611608e-01 9.15624425e-02 -5.31916916e-01
1.16694355e+00 4.97319788e-01 -1.28760293e-01 -1.05016291e+00
-6.39437854e-01 -7.28599429e-01 -5.77782750e-01 -4.16812807e-01
7.30362713e-01 -1.30039299e+00 -3.95678192e-01 6.72828704e-02
-8.68816257e-01 -8.03514838e-01 -3.63593668e-01 3.25137615e-01
-4.60151374e-01 2.70864278e-01 -4.50806946e-01 -4.28585202e-01
-2.44557604e-01 -7.41226733e-01 1.10533834e+00 -9.69273970e-02
3.42341214e-02 -1.41263402e+00 1.58389926e-01 6.33865833e-01
4.63468790e-01 4.46446210e-01 1.20515382e+00 -2.84952104e-01
-1.33476079e+00 -1.98066626e-02 -6.80021763e-01 -3.14999402e-01
2.95474619e-01 -5.60748652e-02 -6.32205188e-01 2.02958316e-01
-1.21829557e+00 -3.94612504e-03 1.02902436e+00 3.32814395e-01
9.27440464e-01 -5.62073648e-01 -8.41714025e-01 6.77365720e-01
1.43203259e+00 -2.94401228e-01 7.10766196e-01 5.02641261e-01
1.35166156e+00 4.93680328e-01 6.66335404e-01 4.65221107e-01
1.38180506e+00 8.34966242e-01 5.10443270e-01 1.26658246e-01
-3.77430499e-01 -7.87841976e-01 1.58205912e-01 1.19321167e+00
-2.29244769e-01 -2.83724606e-01 -1.50445890e+00 1.46710181e+00
-2.40441227e+00 -1.12174702e+00 -6.30150616e-01 1.72366464e+00
4.86185402e-01 -1.72214270e-01 -1.43186725e-03 -1.04815021e-01
3.58392686e-01 7.47258663e-01 -5.21241307e-01 1.05980687e-01
-2.02679098e-01 7.11276233e-02 8.39166760e-01 4.97170329e-01
-1.16666448e+00 1.73470092e+00 4.70663929e+00 1.24573243e+00
-7.86132932e-01 4.43661958e-01 3.23046595e-01 -5.38346078e-03
-6.02155805e-01 4.45014983e-02 -8.14712107e-01 5.75585485e-01
1.04263985e+00 -3.94822627e-01 2.93903202e-01 9.94809270e-01
5.39334774e-01 3.35838675e-01 -5.41489303e-01 8.88455391e-01
-7.09309995e-01 -1.95626366e+00 1.58752382e-01 2.79909134e-01
1.11636925e+00 7.86351502e-01 1.39123678e-01 6.84225798e-01
9.12725031e-01 -9.29705501e-01 3.15034121e-01 4.72010553e-01
7.60064363e-01 -6.22491777e-01 4.34226364e-01 1.87344253e-01
-2.23933578e+00 -3.06266155e-02 -2.84304589e-01 3.43770571e-02
5.07664084e-01 7.26908326e-01 -1.01950085e+00 9.25895095e-01
8.26236427e-01 1.57785487e+00 -5.55301070e-01 8.81369770e-01
-5.85417449e-01 9.76171613e-01 -4.67964381e-01 1.20092429e-01
5.87255895e-01 -3.56402963e-01 4.39550102e-01 1.19566870e+00
4.64519113e-01 2.89534897e-01 1.08332977e-01 6.93228185e-01
-1.71719477e-01 2.95866489e-01 -1.24991357e+00 -1.26855612e-01
8.42292964e-01 9.89172995e-01 -2.73847193e-01 -4.00564134e-01
-5.57076991e-01 4.75259691e-01 7.19413757e-01 6.95186496e-01
-1.12085044e+00 2.03193501e-02 1.33321917e+00 5.24678171e-01
4.50550705e-01 -4.79444116e-01 -4.10622537e-01 -1.28695047e+00
-2.29630470e-01 -1.01801276e-01 3.28889370e-01 -8.63394737e-01
-1.22051048e+00 1.28493264e-01 1.23471566e-01 -1.19282389e+00
3.62165459e-02 -1.63025633e-01 -8.05500090e-01 3.69390637e-01
-1.87814176e+00 -2.03246689e+00 -3.94530326e-01 7.17270553e-01
2.09766239e-01 8.12367424e-02 4.95292962e-01 5.65330505e-01
-7.12601602e-01 4.54047024e-01 3.82229984e-01 2.23284215e-01
2.21386760e-01 -9.37126338e-01 7.42617309e-01 8.01081419e-01
2.84194857e-01 2.67817587e-01 1.37181669e-01 -8.85856330e-01
-1.02276874e+00 -2.15120745e+00 1.43971908e+00 -6.87538803e-01
1.31400299e+00 -8.99469778e-02 -7.22164273e-01 1.20275176e+00
-2.70081371e-01 1.06586166e-01 6.72513545e-01 3.91741574e-01
-4.19790119e-01 -2.84382880e-01 -7.07423568e-01 9.48139787e-01
1.61864865e+00 -6.41483665e-01 -2.77260512e-01 6.26082957e-01
1.12807786e+00 7.91331157e-02 -1.33912468e+00 3.74424815e-01
3.63135397e-01 -6.84325993e-01 1.30028033e+00 -5.91864467e-01
3.52469057e-01 -3.48415434e-01 -3.99923652e-01 -1.44180846e+00
-6.25049710e-01 2.03782246e-02 -5.44779122e-01 1.05872858e+00
5.80891848e-01 -1.06395936e+00 9.90184128e-01 6.18275464e-01
-4.60464954e-01 -1.01194108e+00 -1.16120768e+00 -6.88269675e-01
1.05113372e-01 -9.31612253e-01 1.18184483e+00 1.25103641e+00
6.13408498e-02 9.50366259e-02 -4.45777148e-01 5.02762616e-01
5.12247205e-01 2.53848404e-01 1.02455163e+00 -1.14167237e+00
3.22466403e-01 -5.06756604e-01 -7.56235659e-01 -1.01623940e+00
3.12323898e-01 -1.17245495e+00 -7.34468400e-01 -2.06027722e+00
-1.43553868e-01 -8.74742925e-01 -2.50489950e-01 8.78817558e-01
-7.57575184e-02 1.87420443e-01 8.95846412e-02 2.56579340e-01
-7.04963148e-01 1.18792415e+00 1.04437625e+00 -5.21279752e-01
-2.61601359e-01 -3.28476392e-02 -5.05908668e-01 3.79575938e-01
1.08399189e+00 -2.82147467e-01 -5.91308832e-01 -4.95240420e-01
5.77498436e-01 -3.47979128e-01 5.62589765e-01 -9.47672725e-01
2.70626098e-01 -3.16008717e-01 -1.56646430e-01 -8.11632574e-01
3.84963810e-01 -9.02518928e-01 5.20463824e-01 1.77874684e-01
4.19069350e-01 -3.16907540e-02 1.50193021e-01 8.72398853e-01
-3.10508341e-01 7.95238614e-01 1.54595360e-01 1.74006283e-01
-1.31266451e+00 9.92010534e-01 -2.22913846e-01 1.10028021e-01
1.20880234e+00 -4.44807202e-01 -5.92202485e-01 -4.36200947e-01
-6.80520773e-01 8.01680684e-01 4.17068779e-01 4.19846654e-01
7.08944380e-01 -1.87488091e+00 -5.93878210e-01 -2.56631430e-02
7.02654958e-01 -3.65246960e-04 4.24006045e-01 9.91413593e-01
-3.99852544e-01 8.04522872e-01 2.75597841e-01 -2.20502138e-01
-8.30462337e-01 3.33539039e-01 5.63338399e-02 -6.54497862e-01
-9.34488952e-01 4.41241294e-01 1.70486763e-01 -9.19206083e-01
-3.38557065e-01 -7.30865777e-01 -1.82354927e-01 1.28074497e-01
-9.11317617e-02 6.20671391e-01 -1.52319014e-01 -1.08947241e+00
-3.11039031e-01 5.66821396e-01 5.99384725e-01 3.20573658e-01
1.57225549e+00 -3.58889997e-01 2.60299683e-01 2.94405133e-01
1.13204443e+00 -2.18087167e-01 -9.28003550e-01 -5.28541446e-01
3.93857956e-02 -6.11581385e-01 3.79701704e-02 -3.92224640e-01
-1.33577454e+00 7.29764640e-01 6.31420538e-02 -3.64728156e-03
8.26971889e-01 3.08245927e-01 1.39431322e+00 4.75865722e-01
6.79347038e-01 -9.71582174e-01 -2.08178058e-01 5.66816747e-01
6.33259594e-01 -1.21724522e+00 -5.16208149e-02 -6.31911635e-01
-6.46688819e-01 5.23381054e-01 5.01160979e-01 2.15827618e-02
1.32580864e+00 -3.47012043e-01 -3.29074264e-01 -6.19251847e-01
-6.59794748e-01 -8.95420074e-01 3.39618236e-01 7.93199420e-01
-1.48942977e-01 7.59002209e-01 3.88497859e-02 5.79092383e-01
-3.73330235e-01 -6.76681548e-02 6.79383427e-02 5.12053192e-01
-4.52217430e-01 -8.42858434e-01 1.24442369e-01 8.43928993e-01
3.77828449e-01 -5.19048810e-01 5.56428218e-03 1.12667549e+00
1.59084529e-01 8.02944779e-01 -2.95312941e-01 -7.74783552e-01
2.97004998e-01 -4.88709658e-02 -1.06334493e-01 -4.71192092e-01
-2.30551600e-01 -7.68599033e-01 4.40244615e-01 -7.75927722e-01
-1.25977278e-01 -5.28344512e-01 -1.12574029e+00 -9.43950176e-01
7.62663707e-02 1.10037429e-02 1.67110205e-01 8.20827186e-01
8.68183613e-01 2.44174987e-01 3.05240840e-01 -7.18813360e-01
4.83441621e-01 -8.32860529e-01 -6.45102203e-01 3.22032094e-01
-4.00923239e-03 -9.87018108e-01 -7.66360238e-02 -1.57017753e-01] | [6.494648456573486, 2.055243492126465] |
ed20babf-8b58-4fdd-8374-a76f444b23fe | fishing-for-clickbaits-in-social-images-and | 1710.06390 | null | http://arxiv.org/abs/1710.06390v1 | http://arxiv.org/pdf/1710.06390v1.pdf | Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models | This paper presents the results and conclusions of our participation in the
Clickbait Challenge 2017 on automatic clickbait detection in social media. We
first describe linguistically-infused neural network models and identify
informative representations to predict the level of clickbaiting present in
Twitter posts. Our models allow to answer the question not only whether a post
is a clickbait or not, but to what extent it is a clickbait post e.g., not at
all, slightly, considerably, or heavily clickbaity using a score ranging from 0
to 1. We evaluate the predictive power of models trained on varied text and
image representations extracted from tweets. Our best performing model that
relies on the tweet text and linguistic markers of biased language extracted
from the tweet and the corresponding page yields mean squared error (MSE) of
0.04, mean absolute error (MAE) of 0.16 and R2 of 0.43 on the held-out test
data. For the binary classification setup (clickbait vs. non-clickbait), our
model achieved F1 score of 0.69. We have not found that image representations
combined with text yield significant performance improvement yet. Nevertheless,
this work is the first to present preliminary analysis of objects extracted
using Google Tensorflow object detection API from images in clickbait vs.
non-clickbait Twitter posts. Finally, we outline several steps to improve model
performance as a part of the future work. | ['Ellyn Ayton', 'Maria Glenski', 'Dustin Arendt', 'Svitlana Volkova'] | 2017-10-17 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [-7.47837797e-02 -2.38039687e-01 -3.30928206e-01 -4.82966393e-01
-1.00629389e+00 -6.15509987e-01 9.58075345e-01 3.16006154e-01
-6.21445239e-01 5.04320204e-01 2.03485176e-01 -4.69735116e-01
-1.12358462e-02 -6.54651344e-01 -7.43318737e-01 -1.81057081e-01
-7.53373355e-02 2.88397819e-01 1.49996668e-01 3.94947417e-02
4.28942144e-01 2.38852993e-01 -1.60035908e+00 6.43843055e-01
3.17775875e-01 1.66130948e+00 -1.21905562e-02 7.75059342e-01
-1.73994541e-01 9.02363777e-01 -6.77222908e-01 -3.51787001e-01
2.60606855e-01 -1.42124444e-02 -5.46515644e-01 -1.15549795e-01
1.16543627e+00 -4.28672165e-01 -4.94307995e-01 8.97441566e-01
3.14666659e-01 -3.19283694e-01 6.11824274e-01 -9.47240591e-01
-6.56930566e-01 5.40696442e-01 -4.18024927e-01 8.29452038e-01
1.08520433e-01 1.90780878e-01 1.45061517e+00 -9.36075389e-01
6.51176751e-01 1.01871538e+00 5.54863513e-01 -1.40554681e-01
-1.35186064e+00 -7.93476701e-01 -6.22111969e-02 9.38154459e-02
-1.37778819e+00 -2.16431901e-01 4.28690523e-01 -9.26841855e-01
6.45029187e-01 7.08631277e-01 5.54676175e-01 1.23112130e+00
-3.95405330e-02 1.14481997e+00 1.56389499e+00 -1.81417048e-01
3.11940107e-02 4.84425247e-01 5.24885416e-01 6.55673265e-01
2.00658664e-01 -1.87801883e-01 -4.89070117e-01 -2.35491931e-01
2.05113664e-01 -1.86633915e-01 2.18287334e-01 2.35972404e-01
-1.25648522e+00 1.31752360e+00 7.95427799e-01 6.04630470e-01
-6.72021270e-01 4.02252495e-01 2.86024958e-01 1.40303150e-01
9.26427603e-01 4.11490619e-01 -2.96898752e-01 -9.53110829e-02
-1.49593639e+00 4.64339375e-01 7.73324907e-01 5.45579553e-01
7.13572979e-01 -5.71356602e-02 -3.87486964e-01 9.16399181e-01
8.73355493e-02 5.39135873e-01 5.83479345e-01 -5.78231692e-01
5.71932852e-01 4.52659965e-01 1.15148775e-01 -1.40001643e+00
-2.97067493e-01 -6.48605883e-01 -1.85011551e-01 -2.01211780e-01
4.79437500e-01 -1.35567307e-01 -9.72989261e-01 1.15647840e+00
-1.28210500e-01 -3.70300949e-01 -8.38360369e-01 8.45207036e-01
9.16728675e-01 5.07040739e-01 2.98068017e-01 2.62429267e-01
1.74456418e+00 -6.65858150e-01 -7.30476797e-01 -4.23150629e-01
5.39135396e-01 -9.97287154e-01 1.04987979e+00 1.55862600e-01
-9.58443701e-01 -3.53641719e-01 -9.60373521e-01 5.00406846e-02
-9.05488312e-01 5.10423481e-01 5.96180022e-01 7.78803289e-01
-7.36895502e-01 4.00826633e-01 -4.28297609e-01 -3.77552569e-01
5.83587468e-01 1.45492166e-01 -1.13843940e-02 3.20333958e-01
-1.14313900e+00 7.91252315e-01 1.89101234e-01 -7.88901597e-02
-6.61578119e-01 -7.04220533e-01 -5.07526062e-02 -4.43033390e-02
2.23596528e-01 -2.23809425e-02 1.17498755e+00 -1.22995830e+00
-7.36947060e-01 1.06129265e+00 2.25287839e-03 -9.30398762e-01
8.82111609e-01 -2.60756075e-01 -4.33269978e-01 1.58571571e-01
2.90079981e-01 6.56584144e-01 8.54606450e-01 -7.39189088e-01
-8.05970907e-01 -2.82520384e-01 -6.94219489e-03 -3.86239439e-01
-4.83924955e-01 5.18227577e-01 -3.59971136e-01 -6.59839809e-01
-2.37951670e-02 -1.13112974e+00 2.59787023e-01 -1.49339987e-02
-6.82437062e-01 -3.95222485e-01 5.90127051e-01 -1.09797108e+00
1.40537333e+00 -2.09263110e+00 -6.96946859e-01 3.06012034e-01
4.63179499e-01 2.21843079e-01 -1.15186516e-02 3.66344720e-01
-7.37293512e-02 7.63576090e-01 4.74863321e-01 1.33465916e-01
2.86672890e-01 -3.37140262e-01 -3.99788946e-01 4.25020576e-01
9.46634188e-02 1.04535174e+00 -5.23276448e-01 -5.27957499e-01
-7.02572986e-02 3.57564449e-01 -4.61403966e-01 -1.44859657e-01
-1.89507127e-01 -1.79188430e-01 -7.11262584e-01 6.02417231e-01
6.03856683e-01 -4.88222778e-01 -3.23737800e-01 -5.43628037e-01
-5.31043828e-01 7.14563608e-01 -7.21766829e-01 6.64350331e-01
-5.44494629e-01 1.43412888e+00 -2.37180460e-02 -5.32018721e-01
8.05288315e-01 5.27176354e-03 3.75018597e-01 -1.00884807e+00
2.48188153e-01 2.83413738e-01 -4.09668591e-03 -6.54887676e-01
6.26263618e-01 1.52969956e-01 5.37320971e-02 6.46865666e-01
-3.56729418e-01 3.95773649e-01 4.65991884e-01 3.45723331e-01
1.02379692e+00 -4.25924033e-01 -4.48729917e-02 -1.59002319e-01
2.54250973e-01 5.51995598e-02 -3.44982982e-01 1.06255150e+00
-3.94193441e-01 7.38055408e-01 5.88227391e-01 -3.60633373e-01
-1.21050763e+00 -8.24431419e-01 -3.82264555e-01 1.60112023e+00
-4.30414349e-01 -4.24846143e-01 -5.78032434e-01 -7.33118594e-01
1.62146732e-01 8.74467611e-01 -8.42381239e-01 3.92803669e-01
-4.96673286e-01 -7.87462294e-01 5.54863989e-01 2.43421942e-01
6.66562438e-01 -6.76966012e-01 -1.72198325e-01 -2.29841731e-02
-3.96282047e-01 -1.06210756e+00 -3.64565015e-01 -1.51477717e-02
-4.30159360e-01 -8.43181431e-01 -8.13271880e-01 -5.54176867e-01
3.26058656e-01 8.58958066e-02 9.72775817e-01 5.06628379e-02
-4.01926428e-01 4.59337443e-01 -1.73088878e-01 -3.20026547e-01
-1.18746050e-01 1.79330125e-01 -4.72865850e-01 3.85565013e-01
5.90218425e-01 1.56054199e-01 -9.19489861e-01 4.80621129e-01
-9.10837650e-01 -2.78523713e-01 3.80655378e-01 5.18672407e-01
1.34779096e-01 -1.76556751e-01 1.58349484e-01 -5.08946419e-01
1.04004204e+00 -7.84635782e-01 -4.58207667e-01 4.26318720e-02
-3.91389251e-01 -2.60276884e-01 -9.43006277e-02 -6.80090547e-01
-5.27999878e-01 -9.24061537e-02 -3.02432775e-02 7.32608885e-02
-3.46866325e-02 7.44262338e-01 8.80831718e-01 5.02778739e-02
9.45154130e-01 1.13949597e-01 -1.18954062e-01 -6.31751120e-01
1.20627031e-01 1.08896136e+00 -1.85308412e-01 -5.24108447e-02
6.18480682e-01 5.58838487e-01 -4.22627360e-01 -1.12394524e+00
-1.09879065e+00 -5.73273301e-01 -2.67198950e-01 -3.17065001e-01
9.73884046e-01 -8.86211216e-01 -8.78870606e-01 1.17397852e-01
-8.67840350e-01 2.84695663e-02 2.60017902e-01 5.78221798e-01
2.53597684e-02 3.99940610e-02 -5.47061205e-01 -7.98883498e-01
-2.18425885e-01 -9.44223583e-01 7.82160163e-01 -2.33038574e-01
-5.45314908e-01 -8.25296223e-01 -2.53926158e-01 8.92757714e-01
9.33949530e-01 1.74510896e-01 5.60976803e-01 -1.31235766e+00
-8.02551806e-01 -1.04767048e+00 -8.10856402e-01 4.48599011e-01
-1.44301459e-01 9.46688130e-02 -9.64370310e-01 2.43757069e-02
-3.32203805e-01 -6.13905013e-01 1.07370782e+00 2.74438530e-01
1.32560635e+00 -7.73295045e-01 -1.45672902e-01 -1.48178279e-01
1.14637041e+00 -3.16061318e-01 5.35774112e-01 7.21983731e-01
3.64515364e-01 6.41444623e-01 4.36740816e-01 3.46656322e-01
1.58248663e-01 6.73353255e-01 4.28758889e-01 2.86188215e-01
-2.47958511e-01 -2.86433369e-01 2.15379030e-01 4.09438163e-01
1.88828677e-01 -7.29201213e-02 -1.00937772e+00 5.28510213e-01
-1.22964716e+00 -9.57656443e-01 -5.51680088e-01 2.05177236e+00
6.40274882e-01 2.82793313e-01 3.83142650e-01 -2.89620701e-02
9.12352502e-01 4.23603326e-01 -3.67768742e-02 -2.55625814e-01
4.84659784e-02 1.21643119e-01 8.55669975e-01 3.97865385e-01
-1.38302898e+00 7.18291283e-01 5.96754599e+00 1.00495088e+00
-1.47114718e+00 1.19069934e-01 7.68942893e-01 -3.14631015e-01
-8.97607282e-02 -3.01713794e-01 -8.78423512e-01 8.49699438e-01
1.12663376e+00 5.43969125e-02 5.27490854e-01 1.02168083e+00
3.40370864e-01 -1.63702443e-01 -8.12344730e-01 8.32731366e-01
1.63380831e-01 -1.44334471e+00 -1.34968862e-01 3.40538502e-01
3.96772742e-01 7.51379669e-01 5.08146048e-01 4.35808748e-01
-4.13299203e-02 -9.53661740e-01 1.08614588e+00 2.93377966e-01
4.42469269e-01 3.50434445e-02 5.48392296e-01 1.50622517e-01
-6.17433548e-01 -1.09768301e-01 3.77385272e-03 1.41165242e-01
-1.33686945e-01 6.32588744e-01 -1.28373778e+00 -2.94427156e-01
6.50071442e-01 5.54686904e-01 -1.29278159e+00 1.20636451e+00
4.74303663e-02 1.11034143e+00 -5.73791504e-01 -8.82929921e-01
5.81490040e-01 3.39526653e-01 4.84596819e-01 1.48809338e+00
2.24656105e-01 -5.98801970e-01 -1.86675917e-02 9.48026180e-01
-1.83076218e-01 2.63212562e-01 -1.94662899e-01 -7.23624229e-01
1.97441459e-01 1.28595400e+00 -7.34088659e-01 -5.63326538e-01
-3.53839993e-01 5.42951286e-01 1.80416882e-01 4.48393941e-01
-9.64020908e-01 -5.54582417e-01 1.35933831e-01 7.49802172e-01
5.97937405e-01 -2.48125702e-01 -4.02720422e-01 -1.05787909e+00
6.00258075e-02 -6.26575649e-01 2.40351215e-01 -1.02940392e+00
-1.32650590e+00 5.10477006e-01 -1.16160445e-01 -8.94193053e-01
1.02417082e-01 -7.05601156e-01 -2.13499695e-01 7.98160374e-01
-1.40467179e+00 -1.03232229e+00 -4.05532300e-01 1.43518686e-01
3.66240323e-01 -9.26714465e-02 2.57663071e-01 7.06466198e-01
-4.08299267e-01 4.13753957e-01 1.97549742e-02 5.27799606e-01
4.44733381e-01 -9.67196882e-01 1.77437708e-01 1.22920774e-01
1.66965410e-01 8.92819047e-01 8.10626984e-01 -6.23729765e-01
-9.54836845e-01 -1.05963302e+00 1.36750758e+00 -4.55304116e-01
1.51885402e+00 -4.03394938e-01 -5.85045338e-01 7.77484536e-01
1.73820928e-01 -9.78321135e-02 6.14485025e-01 3.33300203e-01
-5.97232521e-01 -1.16404094e-01 -9.75786150e-01 6.09253347e-01
2.70041466e-01 -7.43712723e-01 -5.44211388e-01 8.81760597e-01
1.73633710e-01 1.55874893e-01 -7.33096123e-01 -3.31867635e-02
8.51262450e-01 -9.94032979e-01 1.10730958e+00 -7.46271789e-01
5.34707367e-01 9.09295306e-02 -3.56974214e-01 -7.63911068e-01
-2.96850353e-01 -7.57041872e-02 3.74046415e-01 1.17711163e+00
7.38910854e-01 -5.96485674e-01 9.52387035e-01 5.98760903e-01
2.91550815e-01 -4.87189829e-01 -1.03655052e+00 -5.96035421e-01
4.93573025e-02 -7.73748934e-01 5.86431362e-02 6.77190602e-01
-5.19724250e-01 2.81690001e-01 -2.64904678e-01 -2.92613983e-01
4.17720646e-01 -4.90350975e-03 7.61551619e-01 -1.10371470e+00
-4.73809987e-03 -6.50592923e-01 -4.40131307e-01 -1.06104958e+00
-1.66353378e-02 -1.12802351e+00 -2.79680967e-01 -1.33439255e+00
3.37226778e-01 -3.99103642e-01 -2.35345811e-01 3.06626797e-01
1.73100904e-01 7.36296713e-01 3.81047964e-01 4.68017936e-01
-6.62072182e-01 -1.12833800e-02 7.94974685e-01 -5.94641328e-01
1.84527710e-01 1.87929213e-01 -5.25417030e-01 6.29402995e-01
9.93530810e-01 -5.38034260e-01 4.27181393e-01 -1.75919384e-01
6.91267967e-01 -4.82003242e-01 6.89519107e-01 -7.55483210e-01
-1.16226904e-01 8.14767107e-02 4.35118765e-01 -8.38977933e-01
5.41634202e-01 -5.88665903e-01 -4.11642700e-01 5.25602996e-01
-9.89573479e-01 -2.82438338e-01 -1.25327766e-01 5.54978967e-01
-2.24745050e-01 -3.99293631e-01 6.17754877e-01 -2.30780169e-01
-2.92185843e-01 -7.59602785e-02 -8.49399090e-01 1.17541261e-01
6.27545297e-01 -1.80097464e-02 -4.77485180e-01 -5.87108731e-01
-8.65130365e-01 -1.30474687e-01 -2.56544739e-01 5.50837517e-01
2.91491330e-01 -1.20610797e+00 -7.32061625e-01 -3.73003702e-03
2.55757660e-01 -1.10919535e+00 -1.22851603e-01 9.76305246e-01
-6.70575261e-01 8.95186841e-01 2.36368284e-01 -6.39735878e-01
-1.40084696e+00 2.33457312e-01 8.56363699e-02 -1.42966077e-01
-3.40776821e-03 6.78652585e-01 -6.92564249e-02 -2.05545872e-01
2.80459285e-01 -4.43733692e-01 -1.01323925e-01 6.55570865e-01
5.94400167e-01 6.39108419e-01 2.57826507e-01 -7.60075331e-01
-2.19283178e-01 2.85985291e-01 -2.60217011e-01 -2.02955574e-01
1.13499081e+00 -2.16991585e-02 -5.34903370e-02 5.62707067e-01
1.79324901e+00 1.22486398e-01 -6.03838861e-01 -9.36818868e-02
2.40639657e-01 -6.37337863e-01 5.32705069e-01 -1.02177179e+00
-9.48924899e-01 8.07758689e-01 7.85004258e-01 9.34302270e-01
2.97127485e-01 3.11561018e-01 6.87866509e-01 4.91488993e-01
-1.84021518e-01 -1.32951856e+00 4.40323502e-01 4.34704661e-01
1.29603541e+00 -1.42204309e+00 3.88287216e-01 -3.53215262e-02
-5.97382545e-01 9.81892347e-01 9.42279026e-02 -2.26283178e-01
8.56517613e-01 -4.38340932e-01 -1.62198111e-01 -4.19003725e-01
-4.79437858e-01 -3.17558348e-01 6.80849791e-01 7.60275219e-03
6.61100805e-01 1.31622389e-01 -4.86420393e-01 7.86210671e-02
-4.48473334e-01 -6.76527023e-02 1.86893404e-01 7.07902431e-01
-7.15763807e-01 -3.50122094e-01 -4.75232214e-01 9.72227752e-01
-1.02951336e+00 -3.17888886e-01 -7.32391536e-01 9.49414730e-01
-1.25888646e-01 9.71276045e-01 3.17226142e-01 -4.35931593e-01
4.68550064e-02 2.15598539e-01 5.85216517e-03 -2.04480380e-01
-8.59152913e-01 1.94276959e-01 5.95200717e-01 -4.75564182e-01
-2.90025979e-01 -7.03334689e-01 -5.44779778e-01 -3.01272452e-01
-4.75905448e-01 3.57584059e-02 1.56344104e+00 5.20748973e-01
3.53315473e-01 2.26350829e-01 5.62900662e-01 -5.58845103e-01
-4.77294296e-01 -1.28578830e+00 -4.48019177e-01 5.14167607e-01
6.24158859e-01 -4.46295202e-01 -8.51691663e-01 -1.66755572e-01] | [7.793302059173584, 9.814762115478516] |
8450804e-436b-4791-ba31-b11db81fd22b | putting-humans-in-the-image-captioning-loop | 2306.03476 | null | https://arxiv.org/abs/2306.03476v1 | https://arxiv.org/pdf/2306.03476v1.pdf | Putting Humans in the Image Captioning Loop | Image Captioning (IC) models can highly benefit from human feedback in the training process, especially in cases where data is limited. We present work-in-progress on adapting an IC system to integrate human feedback, with the goal to make it easily adaptable to user-specific data. Our approach builds on a base IC model pre-trained on the MS COCO dataset, which generates captions for unseen images. The user will then be able to offer feedback on the image and the generated/predicted caption, which will be augmented to create additional training instances for the adaptation of the model. The additional instances are integrated into the model using step-wise updates, and a sparse memory replay component is used to avoid catastrophic forgetting. We hope that this approach, while leading to improved results, will also result in customizable IC models. | ['Daniel Sonntag', 'Mareike Hartmann', 'Aliki Anagnostopoulou'] | 2023-06-06 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 5.82126021e-01 4.29606736e-01 2.81990208e-02 -4.91145164e-01
-6.19201660e-01 -5.73825419e-01 6.37469172e-01 1.03446968e-01
-4.54976231e-01 7.50414431e-01 3.84623528e-01 -1.53769240e-01
5.27291417e-01 -3.92756134e-01 -9.55328822e-01 -2.35129431e-01
2.01895684e-01 8.15249205e-01 3.66371185e-01 -4.59120534e-02
2.36943305e-01 3.56494844e-01 -1.67779529e+00 7.85231650e-01
7.46279299e-01 7.79099762e-01 8.05148423e-01 8.80220950e-01
-4.83517870e-02 9.48436797e-01 -4.45647806e-01 -4.02330786e-01
-2.32790802e-02 -5.00634968e-01 -8.07527721e-01 2.94676900e-01
3.79784346e-01 -3.87979388e-01 -1.52525276e-01 4.24769878e-01
4.01475042e-01 1.02496147e-01 4.26476151e-01 -9.51868355e-01
-7.32817292e-01 3.70523572e-01 -8.46551880e-02 1.00976406e-02
4.18749630e-01 3.54740232e-01 4.64906454e-01 -9.82417047e-01
9.51998293e-01 1.09464610e+00 4.09685552e-01 1.13866425e+00
-1.23488796e+00 -5.08315504e-01 3.18762064e-01 3.07865739e-01
-9.02975678e-01 -6.99836969e-01 5.96396625e-01 -1.69877455e-01
1.03111720e+00 2.47702748e-01 5.12301207e-01 1.26676905e+00
-8.38949382e-02 1.03147149e+00 8.81502151e-01 -7.92920887e-01
3.50424588e-01 7.98454344e-01 -1.71296611e-01 2.73000449e-01
6.15503155e-02 -1.41094334e-03 -4.81096298e-01 1.74354598e-01
6.14560843e-01 -2.15533093e-01 -3.14756364e-01 -5.16023457e-01
-9.52419877e-01 5.67196786e-01 7.00153053e-01 3.28325510e-01
-5.71164072e-01 1.53847888e-01 2.68322527e-01 2.02308804e-01
2.89944112e-01 7.17969477e-01 -4.57288712e-01 -8.64567161e-02
-1.19364917e+00 1.37660041e-01 7.03247964e-01 8.74211967e-01
8.58387649e-01 -1.60023168e-01 -4.03728604e-01 8.00277531e-01
3.10956240e-01 3.27277362e-01 7.44931459e-01 -9.67886984e-01
4.13047701e-01 5.65534830e-01 3.09296906e-01 -6.60597980e-01
-1.18626200e-01 -5.35894990e-01 -1.44049317e-01 1.75940603e-01
-1.85296685e-01 4.17189822e-02 -1.55273700e+00 1.80378115e+00
3.30524053e-03 3.08055311e-01 9.50050354e-02 7.68344998e-01
6.39435410e-01 6.50386572e-01 3.62898171e-01 2.56492291e-02
9.61018622e-01 -1.10842884e+00 -5.90999484e-01 -7.02578187e-01
6.33978546e-01 -7.52487719e-01 1.19724059e+00 1.55830234e-01
-1.11580718e+00 -6.81505442e-01 -1.16935647e+00 2.78995503e-02
-2.58187443e-01 1.22862346e-01 3.58191401e-01 5.25978029e-01
-1.30492198e+00 3.72959763e-01 -7.68158853e-01 -5.59417248e-01
3.57065350e-01 4.78424788e-01 -2.90540665e-01 -4.39467520e-01
-9.93495822e-01 1.15059650e+00 6.18782699e-01 7.01494440e-02
-1.20602965e+00 -5.01281023e-01 -9.02351916e-01 -5.04235141e-02
3.59616637e-01 -8.36181223e-01 1.50868773e+00 -1.59839785e+00
-1.41217542e+00 5.79585373e-01 -2.98822522e-01 -5.18320560e-01
4.83048707e-01 -1.17374517e-01 -4.18931633e-01 1.64336786e-01
4.03266922e-02 1.41442227e+00 1.03609920e+00 -1.81084692e+00
-3.66032362e-01 -1.14249744e-01 1.09117068e-01 4.74569291e-01
-4.80166852e-01 -3.97523791e-02 -8.64536881e-01 -4.60741758e-01
-2.96400905e-01 -1.30403364e+00 -2.34841242e-01 -2.66063720e-01
-8.65909010e-02 4.96042788e-01 1.00085187e+00 -7.58370876e-01
1.18953955e+00 -1.99487782e+00 1.22314498e-01 -1.28419306e-02
-3.02037150e-01 6.32326782e-01 -5.92868507e-01 4.58260208e-01
-8.56551677e-02 -7.85903409e-02 -1.84487179e-01 -7.94361889e-01
-3.89604360e-01 2.73119271e-01 -2.41468161e-01 -1.88437164e-01
5.69902897e-01 9.48278427e-01 -9.15048182e-01 -3.63793910e-01
2.95560896e-01 4.76217896e-01 -6.60464823e-01 5.22721171e-01
-7.77868152e-01 6.23639405e-01 -1.00368917e-01 1.72783285e-01
4.77318347e-01 -4.03129786e-01 -3.29494849e-02 -5.32064587e-02
1.24190755e-01 1.60556167e-01 -9.75883842e-01 1.57541203e+00
-8.75514567e-01 6.69934332e-01 -9.12690759e-02 -5.12845218e-01
7.55937219e-01 4.90870148e-01 -2.07162499e-02 -7.97856987e-01
-3.66876684e-02 9.13326293e-02 -4.87907417e-02 -4.78534490e-01
6.49672210e-01 2.58548856e-02 2.36319274e-01 5.39251268e-01
1.64576083e-01 -4.15351316e-02 3.05613607e-01 4.76802528e-01
8.84784222e-01 3.47878367e-01 -1.85820311e-01 3.45679998e-01
6.20566189e-01 2.16060832e-01 1.52773201e-01 7.70798802e-01
2.43482925e-02 1.17101228e+00 -2.04472262e-02 -3.51998001e-01
-1.37674832e+00 -7.59558141e-01 1.55886263e-01 1.05034971e+00
-1.03751667e-01 -2.43745610e-01 -8.79997194e-01 -7.81028152e-01
-4.00036126e-01 1.13492298e+00 -7.16765761e-01 -3.67483348e-01
-6.30176425e-01 -3.91770035e-01 -3.48108560e-02 6.33342683e-01
3.00313085e-01 -1.65037799e+00 -6.66729033e-01 4.94652569e-01
-1.71722397e-01 -1.01612389e+00 -5.04060328e-01 2.49586850e-01
-9.19412255e-01 -6.41535699e-01 -8.92144740e-01 -8.01930070e-01
1.05550838e+00 1.71159014e-01 1.17684221e+00 2.71028966e-01
-1.56854257e-01 6.07207716e-01 -4.55304682e-01 -4.08979475e-01
-8.80991697e-01 2.02513129e-01 -2.49406487e-01 7.30170459e-02
1.45769134e-01 -2.33154491e-01 -6.88291907e-01 2.35331461e-01
-1.34222770e+00 4.61529881e-01 7.20578253e-01 7.49731839e-01
3.50010484e-01 -5.93451858e-01 6.99427783e-01 -1.07899964e+00
5.11370003e-01 -4.08758700e-01 -3.37381333e-01 3.41058284e-01
-6.89122140e-01 3.32908392e-01 6.34371519e-01 -7.36124873e-01
-1.36140943e+00 4.50479090e-01 -1.12841770e-01 -4.34499353e-01
-9.14644673e-02 4.72977042e-01 -1.41382486e-01 -6.83630705e-02
8.51672292e-01 1.84730917e-01 -1.59830943e-01 -5.01621425e-01
5.73834240e-01 7.97012746e-01 6.63158178e-01 -2.32459009e-01
4.47470576e-01 1.28829122e-01 -5.87572575e-01 -4.56154615e-01
-7.76996315e-01 -2.44855404e-01 -5.26681840e-01 -3.04418087e-01
6.62131369e-01 -9.59251642e-01 7.55048543e-02 1.37932017e-01
-1.20671713e+00 -5.86949289e-01 -2.57486135e-01 2.59555250e-01
-6.27977192e-01 -6.73639774e-02 -5.49535632e-01 -6.96305275e-01
-2.33418256e-01 -1.11983740e+00 9.08891678e-01 5.00313401e-01
-3.95838767e-01 -9.20719087e-01 2.18356252e-01 5.78513563e-01
6.26009345e-01 -1.95327297e-01 7.86456943e-01 -7.91063845e-01
-7.20966756e-01 -4.31898177e-01 -1.53909579e-01 5.69412291e-01
-2.39668489e-01 -2.41692692e-01 -1.21388948e+00 -3.51504833e-01
-1.65931612e-01 -5.69633126e-01 7.66716421e-01 1.10814460e-01
9.70924199e-01 -2.43518040e-01 -4.90522802e-01 1.77607238e-01
1.32937741e+00 1.84166849e-01 9.02095020e-01 4.95340854e-01
4.82623041e-01 4.32733685e-01 5.35657823e-01 1.06033608e-01
3.35521132e-01 7.52878129e-01 5.84752560e-01 -7.45068416e-02
-5.38800180e-01 -5.59694648e-01 3.84588033e-01 5.88840723e-01
4.45947707e-01 -4.85637784e-01 -8.18041563e-01 8.02753329e-01
-2.09367681e+00 -8.11992586e-01 1.32678792e-01 2.31948042e+00
7.74630606e-01 2.19390646e-01 -1.15535542e-01 -2.64461577e-01
7.02092111e-01 -6.91579953e-02 -7.22402215e-01 -5.27860522e-01
1.24480113e-01 2.54018813e-01 3.94892633e-01 6.16944909e-01
-7.34547198e-01 1.12419510e+00 6.86682749e+00 3.04203331e-01
-1.30602288e+00 2.25035459e-01 7.19515324e-01 -1.19276345e-01
-4.90722209e-01 2.86435395e-01 -7.42488623e-01 5.47337055e-01
1.38482332e+00 -2.36184355e-02 4.49562788e-01 6.61336184e-01
2.07346648e-01 -2.98933506e-01 -1.07364774e+00 5.86829722e-01
4.35841620e-01 -1.24234235e+00 2.20209956e-01 -2.05394387e-01
8.18511248e-01 1.13349082e-02 1.99978232e-01 2.87792504e-01
2.30267122e-01 -8.27723205e-01 6.37593150e-01 4.69806641e-01
7.45714307e-01 -5.67072749e-01 7.71630108e-01 4.71916378e-01
-5.54887652e-01 -1.35366857e-01 -2.39066973e-01 2.76572537e-03
3.32136661e-01 3.02978545e-01 -1.41760409e+00 1.54110193e-01
4.68935609e-01 3.51427883e-01 -1.01558340e+00 1.26476979e+00
-2.86047548e-01 6.47267938e-01 -2.25330725e-01 9.65413544e-03
7.16494396e-02 2.95007259e-01 2.73027450e-01 1.19961596e+00
2.71499008e-01 -1.53053790e-01 -7.62468725e-02 6.82990134e-01
-5.43417111e-02 1.09608352e-01 -5.20691514e-01 2.65015438e-02
3.89592260e-01 1.22814178e+00 -6.73113346e-01 -5.67450225e-01
-3.09037626e-01 1.53474653e+00 5.09741843e-01 3.94846410e-01
-6.79615557e-01 -2.64667347e-02 3.81749421e-02 3.89324456e-01
5.23317277e-01 3.90193947e-02 -1.45957798e-01 -1.03740013e+00
2.68879700e-02 -9.23808396e-01 2.62093335e-01 -1.40345895e+00
-7.42429912e-01 9.89610016e-01 -1.02145515e-01 -9.39481020e-01
-7.32908309e-01 -3.42216909e-01 -5.51210940e-01 8.18683505e-01
-1.28514290e+00 -1.31747580e+00 -3.86886060e-01 2.67856956e-01
6.24821484e-01 5.87694906e-02 8.85788739e-01 3.46111745e-01
-3.41628164e-01 6.21598005e-01 -2.16531798e-01 -3.57124448e-01
9.16214049e-01 -1.07598221e+00 4.31206942e-01 7.60113657e-01
2.80737281e-01 5.23251414e-01 1.24382758e+00 -8.24959695e-01
-8.42876554e-01 -1.27520084e+00 9.92530227e-01 -7.29565263e-01
2.70361394e-01 -5.47902584e-01 -1.09455431e+00 8.16708863e-01
3.62922102e-01 -4.67438586e-02 6.20242596e-01 -7.49779642e-02
-2.34056756e-01 1.26286969e-01 -1.05043304e+00 6.62904739e-01
7.23929644e-01 -4.46299404e-01 -6.65478408e-01 3.05229545e-01
8.01351786e-01 -3.05560529e-01 -2.96699375e-01 2.19218746e-01
3.95830095e-01 -6.70536399e-01 7.11762071e-01 -5.42989552e-01
4.00758237e-01 -4.61198479e-01 1.85650885e-01 -1.42683315e+00
-4.02317166e-01 -4.34081465e-01 -1.49489433e-01 1.20560288e+00
9.43225801e-01 -1.61009356e-01 9.67946529e-01 9.34474349e-01
-2.08629712e-01 -2.85268873e-01 -7.02192664e-01 -6.38365090e-01
-2.69404113e-01 -3.57854754e-01 4.44080561e-01 4.02475029e-01
8.35039001e-03 6.07098639e-01 -7.37572789e-01 2.50559184e-03
1.02935769e-01 -2.44127512e-01 9.00070190e-01 -8.64287436e-01
-4.63094413e-01 1.70508981e-01 -1.77469894e-01 -8.33162606e-01
-7.66890496e-03 -7.64017284e-01 3.37060988e-01 -1.58236277e+00
4.51262593e-01 -3.89712989e-01 -2.43770286e-01 6.19027376e-01
-5.04213989e-01 6.44811392e-01 5.58915436e-01 4.33439672e-01
-8.94973516e-01 5.88462591e-01 1.10836840e+00 7.73765221e-02
-5.23783028e-01 -9.63697508e-02 -7.24746287e-01 2.88350910e-01
6.44941986e-01 -5.89548230e-01 -6.03806317e-01 -4.65939671e-01
-9.07420218e-02 -1.63670272e-01 1.88286930e-01 -1.41978955e+00
2.89858907e-01 2.17673942e-01 6.83669508e-01 -3.97017568e-01
4.37740326e-01 -9.73222256e-01 4.53283727e-01 3.42292219e-01
-6.09819889e-01 2.27241516e-01 5.33944130e-01 4.40975487e-01
-2.06611678e-01 -6.32933795e-01 7.39421189e-01 -2.93135852e-01
-7.61737704e-01 1.08410493e-01 -2.79056996e-01 -3.84633332e-01
1.02603006e+00 -2.68355042e-01 -2.57347733e-01 -8.55201185e-01
-9.73395169e-01 2.76975960e-01 9.58325446e-01 6.85903609e-01
4.44563776e-01 -1.25477290e+00 -6.31520391e-01 2.33425528e-01
4.42309618e-01 -2.92193383e-01 2.65738875e-01 2.64915854e-01
-3.55456948e-01 5.21507621e-01 -1.94537371e-01 -4.11768585e-01
-1.12879431e+00 1.00542629e+00 1.66691810e-01 -3.02895039e-01
-3.60089213e-01 5.57281554e-01 1.32717669e-01 -2.13597491e-01
2.58416176e-01 1.77087978e-01 -6.22731745e-02 -1.50225282e-01
8.11442554e-01 -2.86702394e-01 2.86911279e-01 -4.56655920e-01
-1.85832784e-01 7.01820850e-02 -6.74225092e-01 -6.87161088e-01
1.33542001e+00 -2.62073815e-01 3.66022497e-01 3.00155848e-01
1.08966875e+00 -2.25202411e-01 -1.63182998e+00 -1.12030052e-01
3.10669281e-02 -2.71981776e-01 -1.12158373e-01 -1.42239392e+00
-7.04758108e-01 7.55463779e-01 8.07378650e-01 -2.53907293e-01
1.18344235e+00 1.36448786e-01 7.22292781e-01 1.39206618e-01
2.45211586e-01 -1.15249920e+00 4.20314580e-01 3.94180954e-01
9.39008892e-01 -1.24774516e+00 -1.96570128e-01 -1.02107681e-01
-9.06383097e-01 9.22596037e-01 7.88027287e-01 -3.21342610e-02
3.77345905e-02 8.76782387e-02 1.78867236e-01 1.39621407e-01
-1.18332803e+00 -1.88717276e-01 2.06345588e-01 8.70633185e-01
4.73512232e-01 -2.64753848e-01 -1.33341387e-01 3.00168306e-01
9.86824483e-02 5.10960460e-01 6.95756197e-01 1.02513158e+00
-5.72412670e-01 -1.56154871e+00 -3.27968061e-01 3.71701121e-01
-1.17201611e-01 -3.03144902e-01 -6.16123617e-01 5.22797883e-01
6.98214471e-02 8.61448109e-01 1.00270502e-01 -2.13780746e-01
3.48293453e-01 2.58902907e-01 4.36781436e-01 -1.00215328e+00
-6.15135789e-01 -9.13735777e-02 1.83183342e-01 -4.57521826e-01
-4.88266349e-02 -6.23041868e-01 -1.02818179e+00 2.85660058e-01
-2.80941039e-01 1.79943144e-01 8.24865580e-01 7.25531578e-01
7.58276999e-01 4.56771731e-01 2.81730354e-01 -1.12397790e+00
-1.03708006e-01 -1.11444211e+00 2.25491092e-01 6.48631275e-01
2.88740486e-01 -3.25523674e-01 -2.43376896e-01 4.13828015e-01] | [10.97092056274414, 0.9697921276092529] |
83b9fedf-138e-4a87-8daa-52abeb92fab6 | soda-story-oriented-dense-video-captioning | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6406_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510511.pdf | SODA: Story Oriented Dense Video Captioning Evaluation Framework | Dense Video Captioning (DVC) is a challenging task that localizes all events in a short video and describes them with natural language sentences. The main goal of DVC is video story description, that is, to generate a concise video story that supports human video comprehension without watching it. In recent years, DVC has attracted increasing attention in the research community of Vision and Language, and has been employed as a task of the workshop, ActivityNet Challenge. In the current research community, the official scorer provided by AcivityNet Challenge is the de-facto standard evaluation framework for DVC systems. It computes averaged METEOR scores for matched pairs between generated and reference captions whose Interval of Union (IoU) exceeds a specific threshold value. However, the current framework does not take into account the story of the video, the ordering of captions. It also tends to give high scores to systems that generate redundant, several hundred captions, that humans cannot read. This paper proposes a new evaluation framework, Story Oriented Dense video cAptioning evaluation framework (SODA), for measuring the performance of video story description systems. SODA first tries to find temporally optimal matching between generated and reference captions to capture a story for a video. Then, it computes METEOR scores for the matching and derives F-measure scores from the METEOR scores to penalize redundant captions. To demonstrate that SODA gives low scores for inadequate captions in terms of video story description, we evaluate two state-of-the-art systems with it, varying the number of captions. The results show that SODA can give low scores against too many or too few captions and high scores against captions whose number equals to that of a reference, while the current framework gives good scores for all the cases. Furthermore, we show that SODA tends to give lower scores than the current evaluation framework in evaluating captions with incorrect order. | ['Tsutomu Hirao', 'Masaaki Nagata', 'Hidetaka Kamigaito', 'Soichiro Fujita', 'Manabu Okumura'] | null | null | null | null | eccv-2020-8 | ['dense-video-captioning'] | ['computer-vision'] | [ 2.21957371e-01 -9.74597111e-02 -9.42025483e-02 -1.48768082e-01
-9.93582785e-01 -6.01893306e-01 8.34752738e-01 3.82897295e-02
-2.63336033e-01 7.94094861e-01 5.90171337e-01 1.49940878e-01
2.70929605e-01 -2.82396317e-01 -8.79905164e-01 -4.25320119e-01
4.63947691e-02 4.12836999e-01 6.10150576e-01 -8.75959992e-02
7.05528930e-02 1.88657586e-02 -1.72423470e+00 7.68855453e-01
3.78977358e-01 7.93843329e-01 5.11545300e-01 9.76851583e-01
-9.73525569e-02 1.22028148e+00 -8.43522251e-01 -4.62703913e-01
-1.18657082e-01 -9.03230071e-01 -7.99171090e-01 1.37661651e-01
5.72054923e-01 -2.12288395e-01 -5.47678709e-01 9.28085506e-01
3.35023552e-01 -5.79958186e-02 6.48219526e-01 -1.57014179e+00
-5.12467623e-01 5.86296201e-01 -3.02580565e-01 5.44674516e-01
9.49745953e-01 2.05437511e-01 8.82463634e-01 -8.27598453e-01
9.67919171e-01 9.59958434e-01 3.85707825e-01 6.83281958e-01
-7.62733757e-01 -3.52106005e-01 1.16536364e-01 5.38073301e-01
-1.36089063e+00 -2.99158096e-01 4.56755519e-01 -5.46726048e-01
8.63691807e-01 5.28524995e-01 5.87209880e-01 1.42765892e+00
-9.65285152e-02 7.92958140e-01 4.28495914e-01 -3.39596778e-01
3.12486470e-01 8.42285156e-02 1.80446953e-01 3.04375917e-01
3.61657329e-02 -2.67655522e-01 -6.56736314e-01 -5.47966845e-02
4.24780220e-01 -3.68046463e-01 -5.92220128e-01 -4.20407206e-02
-1.60086179e+00 6.34391010e-01 4.21317853e-02 5.14107108e-01
-6.23888671e-01 3.34765047e-01 6.37192190e-01 1.01255268e-01
1.74001515e-01 4.42562610e-01 1.08842246e-01 -5.45587122e-01
-1.12478912e+00 5.43160856e-01 7.24910319e-01 1.27520919e+00
1.84721485e-01 -7.54876584e-02 -8.54508519e-01 6.79825187e-01
-2.34118234e-02 5.82245171e-01 4.82248276e-01 -1.16355050e+00
6.79322958e-01 2.78114796e-01 4.43060011e-01 -1.06390548e+00
2.60818768e-02 -7.75780976e-02 -5.24892867e-01 -2.20544294e-01
2.36911848e-01 -4.19385172e-02 -8.52739871e-01 1.82190979e+00
-2.08207622e-01 4.21165168e-01 3.04180831e-01 1.27009761e+00
1.18746591e+00 1.28717375e+00 9.93125364e-02 -6.22156858e-01
1.36959255e+00 -1.08926141e+00 -9.15919363e-01 -3.12439680e-01
3.51425529e-01 -9.11435485e-01 9.14469838e-01 1.62534088e-01
-1.16321766e+00 -5.91810048e-01 -1.03409183e+00 2.12619051e-01
-4.95793447e-02 -6.22389205e-02 5.44786341e-02 1.61573395e-01
-1.16608763e+00 3.00948173e-01 -4.77258086e-01 -6.28613293e-01
-5.63912541e-02 -1.27117977e-01 -3.73713017e-01 -3.52888882e-01
-1.36143887e+00 9.91416633e-01 6.44331813e-01 -3.11336607e-01
-1.22574258e+00 -4.23673809e-01 -9.07843292e-01 1.25451818e-01
3.92883927e-01 -5.13028026e-01 1.47535765e+00 -1.15381467e+00
-8.82431090e-01 7.29178667e-01 -1.77057698e-01 -8.16340446e-01
5.16963959e-01 -1.78969353e-01 -6.69991374e-01 6.26386404e-01
3.42429727e-01 1.05393291e+00 6.46713078e-01 -1.19666362e+00
-6.66689992e-01 3.22966784e-01 2.60227770e-01 3.78558517e-01
1.39074549e-02 4.35104609e-01 -1.01896429e+00 -5.59041142e-01
-3.65662783e-01 -8.33184481e-01 1.27192959e-01 -1.78158998e-01
-4.44933288e-02 -2.79031157e-01 9.53041673e-01 -5.52435100e-01
1.40391588e+00 -2.18536949e+00 1.23860687e-01 -2.81017512e-01
-2.01788358e-03 3.70603949e-01 -3.12183440e-01 3.98985237e-01
-1.11827664e-01 9.38450843e-02 -2.32692555e-01 -2.54766405e-01
-1.57738462e-01 2.69287080e-01 -2.66802222e-01 1.97475210e-01
2.76617557e-01 6.47394001e-01 -1.14715445e+00 -8.48944128e-01
1.32341966e-01 4.40679252e-01 -2.30218410e-01 4.16581571e-01
-5.36209881e-01 -4.41894643e-02 -1.79244071e-01 4.18203950e-01
3.79529148e-01 -4.10296023e-01 -3.87483612e-02 -1.67731479e-01
-2.02287108e-01 -1.26802146e-01 -9.27861631e-01 1.53359628e+00
-7.79397637e-02 1.18709838e+00 -3.80286753e-01 -7.53624737e-01
7.04674363e-01 8.95795047e-01 5.37075460e-01 -5.94563365e-01
2.47682855e-02 1.12247124e-01 -4.05415237e-01 -7.72039056e-01
6.54321551e-01 1.69533849e-01 -3.10581028e-01 2.82566637e-01
2.40311716e-02 -8.60233884e-03 7.88597226e-01 6.25852942e-01
1.34001851e+00 1.90965682e-01 2.55283654e-01 2.08945855e-01
6.30738139e-01 4.52000856e-01 3.28674853e-01 7.99691856e-01
-3.73583496e-01 1.42540431e+00 6.03086948e-01 -3.58815491e-01
-1.34199941e+00 -1.04115713e+00 3.94539416e-01 8.57581913e-01
5.16419709e-01 -7.16477096e-01 -1.03618395e+00 -5.71229458e-01
-4.87586260e-01 8.06025386e-01 -4.46960062e-01 -1.92278344e-02
-5.80578983e-01 -3.47258389e-01 5.62987208e-01 3.48730952e-01
5.01106560e-01 -1.45793056e+00 -7.90315926e-01 1.69491693e-01
-9.48962390e-01 -1.54287612e+00 -8.25491488e-01 -4.09233123e-01
-2.13334173e-01 -1.19675601e+00 -1.29589951e+00 -9.41445887e-01
3.75737369e-01 5.13150871e-01 1.34443283e+00 -7.82698765e-02
1.11332595e-01 5.94517827e-01 -9.07036781e-01 -3.44916284e-01
-6.64310575e-01 -2.24385068e-01 -4.83349152e-02 3.00440262e-03
4.58988130e-01 -7.35985581e-03 -2.50640333e-01 5.74666023e-01
-1.04084337e+00 4.53253895e-01 3.45808715e-01 4.32117760e-01
5.99693656e-01 -2.96356946e-01 4.49550718e-01 -4.14011925e-01
5.48956573e-01 -6.30151927e-01 -3.98947448e-01 4.95912880e-01
-2.20988512e-01 5.25039062e-02 5.82412302e-01 -5.57988882e-01
-6.88947678e-01 2.18842357e-01 6.92881122e-02 -8.40507746e-01
-1.23280600e-01 3.87835592e-01 6.17600940e-02 4.21412289e-01
7.07337081e-01 4.78375971e-01 -2.32648164e-01 -5.37410825e-02
6.54834732e-02 7.29355514e-01 1.04492748e+00 -1.71485782e-01
6.26007497e-01 8.12928677e-02 -4.69216704e-01 -7.60370255e-01
-9.21704471e-01 -7.96321273e-01 -1.00632779e-01 -8.96805346e-01
1.13037300e+00 -1.09667635e+00 -2.30197981e-01 2.02037200e-01
-1.79682136e+00 1.75911948e-01 -3.78408730e-02 6.69977427e-01
-8.55015993e-01 4.06496644e-01 -3.25087845e-01 -6.79192603e-01
-3.32017064e-01 -1.23571920e+00 1.02605212e+00 2.43757486e-01
-4.48751539e-01 -4.79682654e-01 9.62877646e-02 4.21116859e-01
2.17808783e-01 5.69890320e-01 4.16459113e-01 -5.08987844e-01
-4.71518546e-01 -2.51298010e-01 -2.42729872e-01 3.65373850e-01
-2.08716422e-01 1.00565955e-01 -8.04707110e-01 6.29457366e-03
-2.61100709e-01 -1.50128052e-01 6.50353074e-01 3.79114062e-01
7.54671633e-01 -3.09853911e-01 -1.02244183e-01 1.27257228e-01
1.44714546e+00 4.14149255e-01 1.01560283e+00 4.05611813e-01
4.57711071e-01 4.16364133e-01 9.12236929e-01 3.28828037e-01
1.88347027e-01 9.25842702e-01 5.81003129e-01 1.45674139e-01
-2.31990620e-01 -2.57581413e-01 7.52600729e-01 6.58238888e-01
-2.13364258e-01 -7.95031250e-01 -9.10256922e-01 7.27034748e-01
-2.18186665e+00 -1.47930694e+00 -3.60929638e-01 2.03803563e+00
5.00412822e-01 2.81626672e-01 2.78443873e-01 1.26491964e-01
1.36500311e+00 2.65255541e-01 -1.89838022e-01 -4.43099618e-01
-2.06497192e-01 -3.88998985e-01 2.90926635e-01 2.28760466e-01
-1.03900063e+00 7.42438495e-01 6.44418144e+00 6.95111692e-01
-8.69078040e-01 2.19016299e-01 3.67332250e-01 -2.60441273e-01
-3.88009958e-02 -1.73872307e-01 -5.33666313e-01 8.69938850e-01
1.36175680e+00 -6.33626521e-01 1.65995568e-01 8.95974338e-01
5.06703854e-01 -1.97698474e-01 -1.24259853e+00 1.39752829e+00
6.57314539e-01 -1.51674294e+00 3.18114966e-01 -3.47731262e-01
7.49784589e-01 2.17430070e-02 -3.21505874e-01 3.31648082e-01
-1.26804143e-01 -8.43356490e-01 1.21813524e+00 4.46215481e-01
6.90913081e-01 -5.25864184e-01 1.19704497e+00 2.60771841e-01
-1.20695353e+00 2.47322842e-01 -3.47091168e-01 5.47338929e-03
6.92566216e-01 2.02219069e-01 -8.47007453e-01 3.88419896e-01
6.52982414e-01 5.57019293e-01 -4.80462402e-01 1.41368473e+00
-4.41125520e-02 5.62644482e-01 -1.12290047e-01 -2.20465004e-01
5.55493176e-01 2.28569165e-01 7.75984526e-01 1.52763116e+00
4.80654597e-01 1.83065027e-01 1.15062214e-01 4.40126628e-01
-1.50621071e-01 2.00974077e-01 -7.79657125e-01 -4.13947105e-02
2.98236102e-01 7.55665660e-01 -6.57789528e-01 -6.65454686e-01
-4.11874771e-01 1.12082267e+00 -1.57270119e-01 2.86842942e-01
-1.46394527e+00 -1.63687706e-01 2.55637735e-01 2.56292850e-01
2.94333607e-01 1.21785648e-01 2.38138407e-01 -9.84945953e-01
3.44817013e-01 -7.27250278e-01 4.77366239e-01 -1.56352878e+00
-8.07537079e-01 1.07518184e+00 3.80478680e-01 -1.66179776e+00
-5.63970625e-01 -1.29033372e-01 -6.34572744e-01 5.50591290e-01
-1.23899174e+00 -6.61836863e-01 -7.69985795e-01 5.20823181e-01
1.12900138e+00 -1.67984024e-01 5.68083048e-01 3.61964762e-01
-4.07813430e-01 2.44788423e-01 -1.49888009e-01 5.90180606e-02
7.61498749e-01 -9.51472104e-01 3.26176167e-01 1.16063905e+00
2.34562263e-01 -1.70669049e-01 1.29740345e+00 -5.84456027e-01
-8.13270569e-01 -1.27179325e+00 1.24902880e+00 -3.89625132e-01
3.69619697e-01 -4.72016074e-02 -8.87050927e-01 3.17755580e-01
3.73727977e-01 -1.04624420e-01 3.15470040e-01 -6.30148232e-01
-2.72660583e-01 1.92981251e-02 -9.16757762e-01 3.85187745e-01
8.61037076e-01 -3.79715115e-01 -8.69675219e-01 6.58529818e-01
1.01056457e+00 -2.68408567e-01 -5.22348106e-01 8.47173482e-02
4.22975510e-01 -9.48314607e-01 6.84754729e-01 -3.58439982e-01
8.69097650e-01 -6.12927675e-01 -1.93461642e-01 -1.01503313e+00
-2.74697691e-01 -5.22965372e-01 -1.18883424e-01 1.31917083e+00
5.66223383e-01 3.42066675e-01 6.68517113e-01 4.65235233e-01
-3.13734055e-01 -1.57350495e-01 -7.84852505e-01 -9.96565223e-01
-5.91692626e-01 -5.20016015e-01 2.73533881e-01 6.48358464e-01
-9.46098566e-03 4.38194126e-01 -6.98228300e-01 1.64418191e-01
4.29217041e-01 -1.73833996e-01 5.20506740e-01 -9.21562791e-01
-7.73208737e-02 -2.55906314e-01 -7.55379319e-01 -6.31802201e-01
-2.28579696e-02 -4.92251515e-01 4.16500300e-01 -1.92458451e+00
3.75507504e-01 3.96857619e-01 -4.95741293e-02 4.18560982e-01
-5.64618595e-02 5.83690405e-01 5.64495623e-01 4.57739264e-01
-1.25175500e+00 2.54222512e-01 9.17605579e-01 -4.06333238e-01
-1.73396915e-01 -2.31205583e-01 -3.54391783e-01 5.08959472e-01
6.08330190e-01 -5.76781332e-01 -4.15477931e-01 -4.62738425e-01
6.40237704e-02 3.45708549e-01 3.41584355e-01 -1.49436247e+00
2.82099783e-01 -2.16664568e-01 2.82714218e-02 -7.19067574e-01
3.19073468e-01 -6.75590992e-01 5.87248325e-01 2.71563888e-01
-4.05549079e-01 3.32852960e-01 8.14020336e-02 4.17295218e-01
-6.75309598e-01 -4.60844487e-01 7.26028562e-01 -2.24111974e-01
-1.09898067e+00 1.72197893e-02 -5.20118773e-01 1.11423612e-01
1.39607084e+00 -4.41932887e-01 -3.24168444e-01 -7.58026063e-01
-6.76354706e-01 4.61270422e-01 4.49924171e-01 7.10292816e-01
8.46702099e-01 -1.48129189e+00 -1.21306634e+00 -4.86848414e-01
6.15026236e-01 -2.50854343e-01 1.68991134e-01 4.47022259e-01
-9.80240166e-01 5.77213347e-01 -2.37147704e-01 -6.49435818e-01
-1.63552666e+00 6.85539007e-01 1.62239242e-02 -1.07090302e-01
-6.31045759e-01 4.16527063e-01 1.18385687e-01 6.63637459e-01
5.02231717e-01 -1.78699017e-01 -5.43101788e-01 4.40413654e-02
1.04871702e+00 1.27659604e-01 -1.63253829e-01 -1.01618910e+00
-3.70940328e-01 4.02851850e-01 -8.24226215e-02 -1.32692784e-01
1.13615596e+00 -9.91645083e-02 4.15087700e-01 2.59219557e-01
1.06740034e+00 -5.57822049e-01 -1.09659076e+00 1.72122508e-01
-4.94363569e-02 -2.73367941e-01 -1.26675025e-01 -7.69751370e-01
-7.12869287e-01 4.61404979e-01 5.40224612e-01 3.27114493e-01
1.16732550e+00 2.82480299e-01 7.56384611e-01 1.72966808e-01
2.62849033e-01 -1.03150749e+00 1.64647937e-01 4.90390927e-01
1.15313458e+00 -1.16168952e+00 -2.91502655e-01 -3.92782718e-01
-1.14386630e+00 9.57319379e-01 6.59858108e-01 8.83501545e-02
-1.12954713e-01 -3.77448201e-02 -8.77316818e-02 -1.25683412e-01
-9.96746302e-01 -2.60566384e-01 2.31353521e-01 5.90976655e-01
1.60766870e-01 -8.73973444e-02 -5.08715808e-01 4.64487672e-01
2.62777880e-03 2.01990202e-01 1.01024115e+00 6.99254215e-01
-6.07390344e-01 -5.78693330e-01 -6.07203603e-01 1.70784622e-01
-4.24488127e-01 -6.56768605e-02 -4.05919671e-01 6.83515429e-01
6.22085631e-02 1.06751466e+00 2.56440252e-01 -3.95692408e-01
4.75733906e-01 -2.06970796e-02 1.26765981e-01 -5.10368407e-01
-4.84503746e-01 -9.32680145e-02 2.23573774e-01 -4.66393739e-01
-7.90562987e-01 -6.09795511e-01 -1.07446980e+00 -1.39618680e-01
8.35512392e-03 7.33262181e-01 5.69715977e-01 8.74235034e-01
1.32676959e-01 3.40383351e-01 5.18475115e-01 -7.50644565e-01
7.07010971e-04 -1.01322532e+00 -1.80800527e-01 7.89917290e-01
2.50157624e-01 -5.11647820e-01 -6.15921438e-01 6.51868284e-01] | [10.527751922607422, 0.6423630714416504] |
444cff91-c415-42c7-9aa7-700d789f1211 | time-expressions-in-mental-health-records-for | null | null | https://aclanthology.org/W18-5621 | https://aclanthology.org/W18-5621.pdf | Time Expressions in Mental Health Records for Symptom Onset Extraction | For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes. Data included in electronic health records (EHRs) can be useful for retrospective clinical studies, but much of this is stored as unstructured text which cannot be directly used in computation. Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these. We are developing an EHR corpus annotated with time expressions, clinical entities and their relations, to be used for NLP development. In this study, we focus on the first step, identifying time expressions in EHRs for patients with schizophrenia. We developed a gold standard corpus, compared this corpus to other related corpora in terms of content and time expression prevalence, and adapted two NLP systems for extracting time expressions. To the best of our knowledge, this is the first resource annotated for temporal entities in the mental health domain. | ["Andr{\\'e} Bittar", 'Rina Dutta', 'Natalia Viani', 'Lucia Yin', 'Rashmi Patel', 'Robert Stewart', 'Sumithra Velupillai', 'Joyce Kam', 'Ayunni Alawi'] | 2018-10-01 | null | null | null | ws-2018-10 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 4.06758673e-02 3.04826409e-01 -4.07152176e-01 -5.61452210e-01
-5.21148741e-01 -5.76453209e-01 3.58512282e-01 1.08800209e+00
-6.27901971e-01 9.45647478e-01 6.46308541e-01 -3.23193163e-01
-3.58174622e-01 -5.00514150e-01 3.56094658e-01 -4.15687025e-01
-5.14266968e-01 9.62632060e-01 -1.55176312e-01 1.77553833e-01
2.21452210e-02 7.00866222e-01 -9.59026158e-01 6.09235168e-01
5.93939185e-01 4.07657743e-01 -8.01054835e-02 1.90450802e-01
-1.99946195e-01 7.50944972e-01 -3.68062437e-01 -2.27962866e-01
-4.04551148e-01 -3.79104018e-01 -1.18883550e+00 -2.46280849e-01
-7.88116753e-01 -1.70921132e-01 -1.44864023e-01 5.87823689e-01
6.44516289e-01 -2.59320945e-01 5.36397219e-01 -1.11209488e+00
-2.12276816e-01 7.65002966e-01 -1.54023871e-01 2.31013864e-01
9.34054613e-01 1.96691658e-02 6.85002208e-01 -5.01548827e-01
1.46837211e+00 9.43009436e-01 9.21615005e-01 6.19587183e-01
-9.92601991e-01 -4.70494717e-01 -2.91761190e-01 1.55538037e-01
-1.31863463e+00 -6.62670732e-01 1.29463881e-01 -8.65388930e-01
1.53438020e+00 1.40515447e-01 1.16226673e+00 1.18867278e+00
1.52517632e-01 3.42220157e-01 9.41648364e-01 -5.71453393e-01
3.28895539e-01 -7.53600001e-02 3.03407967e-01 4.21498984e-01
-1.78094760e-01 -1.92702740e-01 -3.39992136e-01 -6.50834680e-01
2.72056192e-01 1.50540695e-01 -2.16706738e-01 5.29983282e-01
-1.25733447e+00 6.03935957e-01 -1.30863175e-01 7.86706746e-01
-8.08206141e-01 -1.32753462e-01 9.84713256e-01 1.84455931e-01
9.15941834e-01 2.64835775e-01 -9.27237689e-01 -5.20743489e-01
-9.32200372e-01 2.09559932e-01 8.96062136e-01 7.20399797e-01
1.40127614e-01 -5.72712004e-01 -3.22617233e-01 7.87492871e-01
3.51944238e-01 1.72594622e-01 7.41374671e-01 -5.34973145e-01
3.27252597e-01 8.85070026e-01 1.46069854e-01 -5.88825405e-01
-1.11370587e+00 4.16163772e-01 -5.68296909e-01 -6.82922542e-01
4.24525678e-01 -4.53505993e-01 -8.53224277e-01 1.72667551e+00
3.08868140e-01 6.05878942e-02 4.48030829e-01 2.84757674e-01
7.77659416e-01 4.50432122e-01 5.22543967e-01 -1.00893402e+00
1.89684212e+00 4.10485938e-02 -1.30158019e+00 4.17618938e-02
1.36496484e+00 -5.72193623e-01 1.22067571e-01 1.37921482e-01
-9.65412557e-01 6.25777364e-01 -1.72549352e-01 3.05439699e-02
-7.29931593e-01 -6.47831336e-03 9.03618038e-01 4.92313415e-01
-1.08717263e+00 7.71285057e-01 -1.27105761e+00 -1.05132794e+00
5.91185689e-01 4.41905618e-01 -5.62394679e-01 1.57777473e-01
-1.39334357e+00 1.05398655e+00 6.34672940e-01 2.49274522e-02
-2.73967415e-01 -9.07732904e-01 -8.70677888e-01 -2.82111704e-01
-1.28025301e-02 -7.53357768e-01 1.50918496e+00 -3.43898565e-01
-8.49635601e-01 1.27635694e+00 -4.29452747e-01 -5.17974198e-01
9.67998207e-02 2.66677231e-01 -8.43210638e-01 3.32616538e-01
3.70919913e-01 3.32839489e-01 7.05537573e-02 -3.37074578e-01
-3.17693055e-01 -9.90336061e-01 -5.34389734e-01 -8.77259150e-02
-2.88583457e-01 9.24575031e-01 -2.58115858e-01 -4.18060899e-01
1.52427005e-02 -9.49372113e-01 -5.62870800e-01 -3.06413800e-01
-1.95004061e-01 -4.08241808e-01 4.69625890e-01 -1.08218801e+00
1.72226679e+00 -1.91465044e+00 -1.80445299e-01 -2.26045344e-02
2.38407031e-01 8.86869431e-03 3.93841416e-01 9.62818742e-01
-4.77434009e-01 3.58410776e-01 -2.14840949e-01 -2.47261897e-01
-2.75806576e-01 3.50085050e-01 -1.95365325e-01 7.09775984e-01
2.67879963e-01 1.00696003e+00 -1.21972227e+00 -6.97597682e-01
-3.55796009e-01 4.51372892e-01 -5.17872155e-01 -6.68489709e-02
-1.04703046e-01 5.99003434e-01 -6.48528576e-01 6.50127590e-01
3.47101092e-02 -1.59792304e-01 6.08849645e-01 8.36494565e-02
-4.25733179e-01 6.63296223e-01 -5.06280482e-01 1.47438169e+00
-1.20045289e-01 6.19569063e-01 -2.72241861e-01 -7.53160894e-01
6.23682201e-01 1.28389359e+00 1.35577095e+00 -4.41509098e-01
1.55341730e-01 2.82945424e-01 2.65483949e-02 -9.43345547e-01
1.47997558e-01 -1.64445356e-01 -1.32985741e-01 4.85462397e-01
-2.14904234e-01 -2.88954321e-02 3.57613385e-01 -3.61119732e-02
1.51275015e+00 -6.03126101e-02 9.43125665e-01 2.73340438e-02
6.30249828e-02 2.76852280e-01 8.38797569e-01 -4.17017192e-02
-2.40863219e-01 1.85091257e-01 8.25351477e-01 -5.92029512e-01
-1.15647173e+00 -3.89413625e-01 -5.89062810e-01 2.36471817e-01
-8.71645808e-01 -9.25955653e-01 -4.13954973e-01 -1.53670341e-01
-4.41129565e-01 5.70395589e-01 -4.14863437e-01 2.43868709e-01
-3.10098022e-01 -9.28831041e-01 8.44170392e-01 3.77710372e-01
-1.91508889e-01 -1.34500372e+00 -7.97704577e-01 7.37851501e-01
-3.18232358e-01 -1.21184683e+00 -1.29574955e-01 1.97251260e-01
-1.05877674e+00 -1.09334624e+00 -5.24857998e-01 -4.17564392e-01
4.20090705e-01 -6.54588640e-01 8.42803240e-01 -2.65437722e-01
-3.68979484e-01 1.96517959e-01 -4.54042196e-01 -8.53968561e-01
-3.24083239e-01 -1.77078947e-01 2.03022584e-01 -4.48328108e-01
8.50529671e-01 -6.56330824e-01 -4.96156275e-01 -2.75005490e-01
-9.62183297e-01 -1.21259026e-01 5.48138656e-02 5.42998374e-01
2.39240468e-01 -8.38650689e-02 5.98422706e-01 -1.07495916e+00
7.71563768e-01 -9.14611220e-01 -3.16726387e-01 6.96082190e-02
-7.59757459e-01 -1.31634757e-01 9.48499590e-02 -2.75258482e-01
-9.51144457e-01 1.70829028e-01 -3.04753214e-01 8.95542204e-02
-4.05075938e-01 1.19606340e+00 3.03120822e-01 7.72922039e-01
5.58315992e-01 -3.33347082e-01 -2.31053844e-01 -4.16653305e-01
4.27764393e-02 9.82264996e-01 1.01606660e-01 -2.83111572e-01
-1.65470764e-01 5.45202971e-01 -1.18072197e-01 -9.02066767e-01
-6.81863368e-01 -9.80947018e-01 -7.66869664e-01 3.80216539e-02
9.97525573e-01 -8.34880412e-01 -7.78896034e-01 3.72511953e-01
-1.42792308e+00 -1.60115883e-01 -2.81051219e-01 8.19651544e-01
-5.27981639e-01 9.66707915e-02 -7.03044474e-01 -1.14893496e+00
-5.72943628e-01 -6.62063122e-01 9.54396486e-01 -6.05376698e-02
-8.67665410e-01 -1.07624543e+00 7.62868583e-01 -1.47062600e-01
-1.19139165e-01 5.08136153e-01 8.63509715e-01 -1.01937044e+00
4.24933761e-01 -2.40873963e-01 -7.30898231e-02 -6.97813988e-01
2.84242541e-01 2.60591805e-01 -7.84770429e-01 7.68670663e-02
1.97387233e-01 -3.60031519e-03 3.82244319e-01 5.35032272e-01
5.74313521e-01 -7.37760246e-01 -8.28642964e-01 2.09069461e-01
1.25819552e+00 8.36381197e-01 8.27216148e-01 3.57222885e-01
3.14549088e-01 9.90861714e-01 5.50265014e-01 1.08236253e+00
4.16262090e-01 7.20563233e-01 -1.16148055e-01 2.31785193e-01
6.74101055e-01 2.66412407e-01 2.59513944e-01 7.61515796e-01
-3.57265979e-01 -1.47933960e-01 -1.59027016e+00 1.13419664e+00
-1.97850192e+00 -1.01580203e+00 -4.91764158e-01 1.79157174e+00
1.36867881e+00 -2.75900036e-01 1.10166796e-01 6.54167160e-02
4.87836540e-01 -4.06112641e-01 -8.35430771e-02 -4.47019279e-01
2.01550797e-01 5.34722447e-01 3.61091971e-01 -3.63899991e-02
-8.06853473e-01 7.00520575e-01 6.43441010e+00 1.85772344e-01
-8.40351880e-01 4.28068280e-01 3.86107475e-01 -7.31319487e-02
-6.22500479e-02 3.81594330e-01 -4.84182060e-01 3.70438874e-01
1.73802805e+00 -2.52127767e-01 2.46824190e-01 4.40807462e-01
9.80165362e-01 4.51599918e-02 -1.26532590e+00 9.28812444e-01
-6.03093266e-01 -1.17026603e+00 -7.44085670e-01 2.95407146e-01
5.32897413e-01 1.86465204e-01 -5.94409585e-01 -1.17250279e-01
2.96389282e-01 -9.71957326e-01 3.12061399e-01 9.58108544e-01
9.68071938e-01 -4.89458442e-01 1.05506194e+00 -7.66739622e-02
-1.10838675e+00 1.66106671e-02 1.57708809e-01 4.07528132e-02
6.69438899e-01 8.25613320e-01 -1.56145060e+00 7.02453434e-01
5.38320899e-01 1.07213545e+00 -1.02437966e-01 1.02047932e+00
-1.64552376e-01 5.31512141e-01 -3.09423655e-01 2.01376975e-01
3.38184267e-01 -2.04120636e-01 2.91020036e-01 1.24866784e+00
6.53141618e-01 7.43071735e-01 -4.43144981e-03 5.47083557e-01
1.82780340e-01 4.59893435e-01 -9.90647793e-01 -1.05659974e+00
5.17167926e-01 1.07094491e+00 -8.63684535e-01 -3.87140781e-01
-5.07834315e-01 7.48509288e-01 2.15986803e-01 2.89578319e-01
-5.02856135e-01 -1.34640738e-01 6.41523480e-01 2.44076669e-01
-3.29438031e-01 -1.16928257e-01 5.76521689e-03 -1.06115925e+00
-3.34180862e-01 -7.83074379e-01 8.74469995e-01 -9.31611598e-01
-1.10991001e+00 6.67276025e-01 3.43372636e-02 -1.13074350e+00
-6.74402654e-01 -2.99010783e-01 -2.22129866e-01 9.50123549e-01
-7.66113877e-01 -1.00396323e+00 2.85447478e-01 6.09064400e-01
-5.16001508e-03 9.70687866e-02 1.32500720e+00 4.15587753e-01
-7.39598334e-01 -1.96031019e-01 -4.98708375e-02 1.36062980e-01
9.52814817e-01 -1.01890957e+00 6.94570132e-03 2.19687060e-01
-3.59730393e-01 9.11834538e-01 6.33903265e-01 -1.14926505e+00
-8.88081312e-01 -1.06732225e+00 1.81438458e+00 -3.93055826e-01
1.08340573e+00 1.24493591e-01 -8.80041957e-01 9.75483477e-01
1.58000395e-01 -5.16089797e-01 1.08778262e+00 1.52836636e-01
-5.05650230e-02 5.59332907e-01 -1.38987947e+00 5.43514609e-01
8.19846809e-01 -8.25786531e-01 -7.67703593e-01 7.73290992e-01
6.37059271e-01 -3.12891781e-01 -1.66230965e+00 2.58758187e-01
5.19307733e-01 -2.37937495e-01 6.98542535e-01 -1.01865590e+00
4.04518217e-01 2.09181458e-01 4.02776986e-01 -1.12951040e+00
7.28057250e-02 -8.32726955e-01 5.11563085e-02 1.24781537e+00
4.89661515e-01 -7.23820746e-01 3.47080976e-01 9.87611353e-01
-1.19669825e-01 -5.61075270e-01 -7.79160976e-01 -3.34024012e-01
-4.28774863e-01 -5.77419221e-01 5.40815771e-01 1.53251994e+00
9.62497532e-01 2.30123371e-01 -5.57300895e-02 1.34135202e-01
-1.24417581e-01 -1.61756277e-01 -6.02672361e-02 -1.42056310e+00
2.79639542e-01 -2.25443155e-01 -4.97954667e-01 2.65454710e-01
1.41147792e-01 -8.67881477e-01 -2.11704612e-01 -1.85646141e+00
3.09130192e-01 -4.59493756e-01 2.17676356e-01 1.18344092e+00
2.56994128e-01 -1.65267184e-01 -4.72363681e-01 3.18917811e-01
5.46133481e-02 1.73206598e-01 5.76311707e-01 6.73139021e-02
-8.18775713e-01 -2.18665957e-01 -2.86979318e-01 8.43997419e-01
9.82257247e-01 -9.54367578e-01 -3.39476228e-01 9.39414576e-02
5.66832542e-01 5.36505163e-01 -7.50312284e-02 -2.76798278e-01
1.72337070e-01 -4.91821319e-01 -6.79979324e-02 -8.35417151e-01
1.44993708e-01 -7.08624542e-01 7.97588050e-01 7.27107167e-01
-1.31386712e-01 2.66915768e-01 4.61207032e-01 1.69886187e-01
-1.44141555e-01 -6.36840284e-01 3.31239074e-01 -3.28633457e-01
-3.48519087e-01 3.67858738e-01 -8.38542461e-01 -4.67081405e-02
1.00800848e+00 1.16494492e-01 -2.44933575e-01 -1.66445434e-01
-1.33021140e+00 1.23902693e-01 3.45278442e-01 -1.19204774e-01
4.27819729e-01 -1.27841425e+00 -6.62074983e-01 -6.64060771e-01
3.39194268e-01 -2.94278443e-01 2.44286969e-01 1.54998970e+00
-5.95462143e-01 7.83173442e-01 -2.23324209e-01 -2.32415348e-01
-1.33731425e+00 9.70432222e-01 -1.69820994e-01 -8.03685069e-01
-8.57559383e-01 1.70897692e-01 -3.46185565e-01 -2.83940017e-01
-1.45613968e-01 -5.63536763e-01 -7.85023928e-01 8.56872380e-01
6.89582527e-01 -4.90280353e-02 1.71869799e-01 -6.40980899e-01
-7.66287148e-01 2.60414891e-02 3.40401637e-03 -5.62231600e-01
2.01977873e+00 -9.00531113e-02 -8.50371301e-01 5.49129128e-01
1.17158163e+00 4.00419049e-02 -6.95064664e-02 -1.08672336e-01
6.83158696e-01 8.41110200e-02 -1.74761504e-01 -3.61963630e-01
-6.73652828e-01 2.06973135e-01 3.78979504e-01 4.83734548e-01
1.23234797e+00 1.79276794e-01 5.16331673e-01 2.28650585e-01
1.77966997e-01 -8.26831579e-01 -5.76929569e-01 5.82117200e-01
7.03905046e-01 -6.53024971e-01 -6.53384775e-02 -3.38897705e-01
-5.08050978e-01 1.18492031e+00 -1.71936359e-02 5.97717404e-01
6.87107086e-01 3.39902282e-01 -1.16837174e-01 -9.04549241e-01
-1.09664690e+00 -3.41580540e-01 2.14398410e-02 5.88349879e-01
8.27978969e-01 1.10126868e-01 -1.08767533e+00 7.96199024e-01
1.03302365e-02 5.17004728e-01 5.18495142e-01 1.18415856e+00
1.40233546e-01 -1.45906055e+00 -3.03835809e-01 6.53588414e-01
-8.78513157e-01 -5.73604822e-01 -6.24076426e-01 4.80375886e-01
1.67092532e-01 7.63628423e-01 8.98887366e-02 1.78976774e-01
2.75834531e-01 6.50350869e-01 3.08238178e-01 -9.07985926e-01
-7.36447930e-01 2.39376828e-01 8.67687941e-01 -4.83398139e-01
-9.57179785e-01 -1.13198364e+00 -1.61271727e+00 1.94240492e-02
-2.24277247e-02 2.77072519e-01 6.88971281e-01 1.14861214e+00
2.74865806e-01 7.04194367e-01 7.36062974e-02 -2.26065889e-01
1.82160556e-01 -9.36252177e-01 -4.45056200e-01 2.86693573e-01
-2.33917944e-02 -3.20227176e-01 9.13192704e-02 4.23644096e-01] | [8.56897258758545, 8.97439956665039] |
3273ba70-1e47-4f51-8c92-b64732c41a0c | decentralized-equalization-for-massive-mimo | 2305.12805 | null | https://arxiv.org/abs/2305.12805v1 | https://arxiv.org/pdf/2305.12805v1.pdf | Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples | Recently, the decentralized baseband processing (DBP) paradigm and relevant detection methods have been proposed to enable extremely large-scale massive multiple-input multiple-output technology. Under the DBP architecture, base station antennas are divided into several independent clusters, each connected to a local computing fabric. However, current detection methods tailored to DBP only consider ideal white Gaussian noise scenarios, while in practice, the noise is often colored due to interference from neighboring cells. Moreover, in the DBP architecture, linear minimum mean-square error (LMMSE) detection methods rely on the estimation of the noise covariance matrix through averaging distributedly stored noise samples. This presents a significant challenge for decentralized LMMSE-based equalizer design. To address this issue, this paper proposes decentralized LMMSE equalization methods under colored noise scenarios for both star and daisy chain DBP architectures. Specifically, we first propose two decentralized equalizers for the star DBP architecture based on dimensionality reduction techniques. Then, we derive an optimal decentralized equalizer using the block coordinate descent (BCD) method for the daisy chain DBP architecture with a bandwidth reduction enhancement scheme based on decentralized low-rank decomposition. Finally, simulation results demonstrate that our proposed methods can achieve excellent detection performance while requiring much less communication bandwidth. | ['Qingjiang Shi', 'Tsung-Hui Chang', 'Enbin Song', 'Bo wang', 'Mian Li', 'Xiaotong Zhao'] | 2023-05-22 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-1.73807129e-01 -3.07562083e-01 1.88253298e-01 8.74612108e-02
-6.34298921e-01 -5.50483823e-01 -2.22377460e-02 -5.68942353e-02
3.89862806e-02 7.67932951e-01 1.96886942e-01 -3.79757166e-01
-2.60224789e-01 -6.98274910e-01 -3.88467610e-01 -1.20453298e+00
-1.05775870e-01 -5.69316223e-02 -3.31215501e-01 -2.27997035e-01
-2.59693623e-01 3.66163641e-01 -7.84636855e-01 -1.21226631e-01
8.95700753e-01 1.13672566e+00 1.90692782e-01 6.38876677e-01
4.56064790e-01 4.47570980e-01 -8.00024509e-01 -3.80352318e-01
5.95156133e-01 -4.85319585e-01 4.69585568e-01 2.20052317e-01
3.36403072e-01 -3.91453385e-01 -8.73087943e-01 1.14604723e+00
1.24634862e+00 -1.63552716e-01 5.94058454e-01 -1.01278150e+00
-5.44533014e-01 5.98845243e-01 -7.69043505e-01 4.58649844e-02
-1.83433354e-01 -2.89417386e-01 7.51552820e-01 -1.24284899e+00
1.94354281e-01 1.21724141e+00 7.55102992e-01 -5.85917442e-04
-1.27230632e+00 -7.44932115e-01 -6.23877160e-02 1.04683466e-01
-2.01825285e+00 -8.62850785e-01 5.38874626e-01 -1.21894307e-01
4.69973743e-01 3.27751875e-01 6.41526818e-01 2.82195270e-01
-2.43936747e-01 6.93511844e-01 8.17936242e-01 -5.62141240e-01
3.13268304e-01 3.03218458e-02 -1.74627513e-01 5.35911798e-01
1.17423165e+00 -3.72908086e-01 -3.52251440e-01 -3.81013840e-01
7.03511000e-01 -1.37365922e-01 -4.88601953e-01 -4.35535908e-01
-1.19090807e+00 3.25593263e-01 3.02484155e-01 2.26771548e-01
-5.59085667e-01 2.06466794e-01 5.84798418e-02 5.49153745e-01
2.03243062e-01 -3.02662421e-02 1.46509171e-01 5.43779910e-01
-1.21500337e+00 -3.57789174e-02 1.02921820e+00 1.42587447e+00
6.37745678e-01 3.64407867e-01 -5.58697164e-01 7.58256555e-01
7.25780785e-01 1.17507935e+00 -2.72574842e-01 -8.14610720e-01
1.03468347e+00 6.08130023e-02 2.63130188e-01 -1.49431014e+00
-3.43987674e-01 -1.38962424e+00 -1.65638065e+00 -3.41040581e-01
2.58721650e-01 -7.93538809e-01 -1.52061909e-01 1.33342469e+00
3.25638324e-01 2.71901220e-01 2.93905109e-01 1.15470028e+00
2.46610940e-01 5.60608268e-01 -6.58214092e-01 -7.57743537e-01
1.19976783e+00 -4.59051818e-01 -9.85989630e-01 -1.06886469e-01
4.24778402e-01 -7.34712422e-01 -1.38439476e-01 7.76551127e-01
-1.03065133e+00 -6.35831207e-02 -1.59761846e+00 3.54320168e-01
4.35261935e-01 9.58827674e-01 7.07440600e-02 1.17401099e+00
-1.04975748e+00 -2.50915438e-01 -5.00545382e-01 -1.10396497e-01
2.24790454e-01 3.23813230e-01 1.12276606e-01 -3.03220421e-01
-9.87541258e-01 2.45473638e-01 -8.97966921e-02 6.48201168e-01
-5.48513889e-01 -4.86168474e-01 -3.65340650e-01 4.17803347e-01
1.32029608e-01 -9.85446513e-01 8.15940440e-01 -2.53407300e-01
-1.34690988e+00 3.24981809e-01 -3.87993366e-01 -7.71440208e-01
3.67800385e-01 -2.85112169e-02 -5.98789513e-01 1.77344516e-01
-1.50190189e-01 -5.54118335e-01 8.44998360e-01 -1.18958926e+00
-7.80164421e-01 -4.58658040e-01 -3.06763023e-01 7.66328871e-02
-6.66791618e-01 -2.27428943e-01 -5.92126250e-01 -8.46746147e-01
6.70153916e-01 -8.22139919e-01 -7.03690410e-01 -1.61681160e-01
-6.10199392e-01 4.79645401e-01 5.71567953e-01 -5.50548375e-01
1.86456418e+00 -2.49979949e+00 -4.75558219e-03 7.34813154e-01
5.92139304e-01 2.97617108e-01 1.43029869e-01 7.34328270e-01
3.61739397e-01 -3.16674680e-01 2.77501971e-01 -3.19311351e-01
1.92495417e-02 -2.64896810e-01 -3.17144364e-01 9.95794535e-01
-3.44167084e-01 1.21713482e-01 -7.30617881e-01 -1.21249430e-01
-1.36561766e-01 2.02915981e-01 -8.22769761e-01 -1.58841178e-01
5.55244446e-01 2.98107207e-01 -6.47544324e-01 7.12980628e-01
1.26190674e+00 -2.29663089e-01 7.41485715e-01 -7.89622247e-01
-4.13038701e-01 -2.44791329e-01 -1.79705870e+00 1.31015134e+00
-3.46367121e-01 7.74667978e-01 1.11386883e+00 -1.30537248e+00
8.91979992e-01 5.80515444e-01 2.88381308e-01 -6.29136562e-01
9.50756371e-02 5.40664732e-01 -9.62917656e-02 -1.35286916e-02
3.55904281e-01 -5.90955764e-02 -1.68381289e-01 1.81965515e-01
-2.06263423e-01 3.55070591e-01 2.88101554e-01 4.43366170e-01
1.19390273e+00 -9.42019522e-01 4.37361389e-01 -3.73819590e-01
7.35980332e-01 -4.78190601e-01 1.14665031e+00 1.00576580e+00
-1.05284013e-01 5.34852445e-01 2.20011503e-01 2.42871985e-01
-9.44174767e-01 -1.02194798e+00 -1.50101379e-01 4.85500813e-01
5.07144749e-01 -5.21386743e-01 -3.65506947e-01 8.32526237e-02
2.46548116e-01 1.52566940e-01 3.74387652e-01 1.14358842e-01
-5.29990077e-01 -1.16889465e+00 6.94341004e-01 5.28668761e-02
8.62665355e-01 7.00200438e-01 1.61568224e-01 6.57046974e-01
-1.40548676e-01 -1.18757927e+00 -2.21815199e-01 -7.37507418e-02
-5.28358877e-01 -7.42299438e-01 -1.00645852e+00 -7.88367748e-01
8.42793524e-01 9.87005889e-01 6.69835269e-01 -2.46830955e-01
-2.48894855e-01 5.20450592e-01 -2.38939166e-01 -3.48403752e-01
2.63186134e-02 -3.15675944e-01 6.79792225e-01 6.32204115e-01
1.25218704e-01 -8.32493484e-01 -1.14555562e+00 6.26334071e-01
-3.11180949e-01 -2.29797363e-01 8.53376508e-01 6.64001822e-01
4.44316685e-01 7.61150345e-02 7.60935485e-01 -1.92344457e-01
8.27133954e-01 -6.79921031e-01 -9.10988986e-01 4.11808312e-01
-2.48882204e-01 -1.47545651e-01 7.04821646e-01 6.10044561e-02
-8.77911389e-01 7.66449869e-02 2.34098226e-01 -3.24281305e-01
6.06326282e-01 4.91838664e-01 -4.91083711e-01 -3.20067674e-01
8.64631295e-01 3.54719669e-01 -1.73235938e-01 -4.39834416e-01
4.98799801e-01 1.30878425e+00 2.39862934e-01 -5.47847927e-01
1.18271399e+00 8.54769528e-01 3.10397685e-01 -1.25826836e+00
-3.24109942e-01 -6.23330474e-01 -1.16844058e-01 -2.55229771e-01
6.84507787e-02 -1.89179289e+00 -7.83472598e-01 3.24464142e-01
-1.08844256e+00 2.28455812e-01 2.54942298e-01 8.65596056e-01
-1.39958978e-01 6.15460038e-01 -4.71905828e-01 -1.08967197e+00
-4.52988893e-01 -6.51795983e-01 6.81213617e-01 -4.29105423e-02
2.97008455e-01 -4.98371661e-01 -2.48006001e-01 4.12012152e-02
6.30438805e-01 1.24025770e-01 5.43739557e-01 -1.11497514e-01
-9.39628005e-01 -4.02498364e-01 -3.81694019e-01 3.47424954e-01
1.08336307e-01 -6.86369419e-01 -6.60094202e-01 -8.00243258e-01
7.97343254e-03 1.73491761e-01 5.38947761e-01 4.64201123e-01
5.36309123e-01 -4.19292718e-01 -6.18435800e-01 6.34264588e-01
1.57479620e+00 -1.81819856e-01 5.92476308e-01 -1.10682651e-01
3.83420110e-01 -2.19685316e-01 4.89971548e-01 1.26003301e+00
2.90403157e-01 6.90303743e-01 1.05674706e-01 2.93427538e-02
-1.65417045e-01 1.62416041e-01 2.31713429e-01 1.23198998e+00
2.80364364e-01 -5.41981399e-01 -6.52255476e-01 4.72269177e-01
-1.95875275e+00 -9.04682100e-01 -6.98764682e-01 2.39998865e+00
4.92873967e-01 -3.55188370e-01 -1.87623233e-01 2.86154926e-01
1.00084722e+00 -1.05689369e-01 -4.59029615e-01 2.27733716e-01
-6.29632354e-01 -4.51130956e-01 1.09400833e+00 9.76419896e-02
-1.01830399e+00 1.96672350e-01 5.44655848e+00 9.88088846e-01
-7.99766660e-01 1.97029173e-01 3.13691735e-01 -3.17993850e-01
5.00532612e-02 -1.50255650e-01 -1.06159985e+00 5.09408355e-01
6.68791652e-01 -5.60639501e-01 9.05400515e-02 6.07623518e-01
7.97531843e-01 5.08317053e-02 -7.06554949e-01 1.64883840e+00
-7.66324997e-02 -1.38013005e+00 -1.01403750e-01 2.95122445e-01
1.06251502e+00 -1.06080875e-01 -1.84495207e-02 6.64243847e-02
1.48970485e-01 -1.86123893e-01 5.86077154e-01 7.53545463e-01
7.22536743e-01 -7.13465393e-01 3.14186841e-01 4.84474897e-01
-1.36466062e+00 -7.59228110e-01 -5.59606493e-01 -1.38305902e-01
2.44490683e-01 1.71715868e+00 -5.45610487e-01 7.67961204e-01
3.75588924e-01 4.77191985e-01 1.15606293e-01 1.79244101e+00
-1.98104143e-01 7.39788830e-01 -7.01362491e-01 -3.64902951e-02
-7.80414864e-02 -4.35716957e-01 1.03362453e+00 1.24831522e+00
1.05705309e+00 3.87895554e-01 1.88066095e-01 2.52373785e-01
-2.61409253e-01 1.34030744e-01 -4.80604261e-01 4.37992573e-01
1.04238605e+00 1.11295664e+00 -1.03059575e-01 -2.67763913e-01
-6.38670444e-01 7.19682217e-01 -1.44536853e-01 8.18247855e-01
-4.10297573e-01 -8.54911506e-01 9.36921239e-01 1.76851153e-01
5.46803057e-01 -7.94112563e-01 -4.40679491e-01 -1.38325191e+00
2.80310094e-01 -9.96432543e-01 5.22156283e-02 -1.31607547e-01
-1.24923170e+00 -1.97966740e-01 -7.55629182e-01 -1.81942558e+00
2.23165870e-01 -2.10182190e-01 -4.04713362e-01 8.28245103e-01
-1.36633563e+00 -6.00841165e-01 -6.58995211e-02 5.37790596e-01
-1.67191640e-01 -4.05954123e-01 4.26919490e-01 1.12984478e+00
-8.02466214e-01 8.34975004e-01 1.34653962e+00 3.56969386e-01
7.30942905e-01 -7.35553503e-01 -1.28125876e-01 1.08139980e+00
-2.41739139e-01 8.22113752e-01 6.97750270e-01 -2.81372905e-01
-1.99964666e+00 -1.12446141e+00 5.72973490e-01 4.70686764e-01
5.56859672e-01 -6.12571478e-01 -2.41280481e-01 9.50483903e-02
1.35424554e-01 2.17929050e-01 1.05833852e+00 -5.61026111e-02
-2.76662018e-02 -7.56973147e-01 -1.00239885e+00 6.99287474e-01
9.43860233e-01 -2.98181444e-01 1.01569384e-01 5.03167868e-01
1.50823276e-02 -2.47414276e-01 -7.09781229e-01 2.37997666e-01
5.20259678e-01 -7.52757132e-01 1.01247120e+00 -4.69797179e-02
-3.60079497e-01 -9.67177629e-01 -6.09977722e-01 -1.25301397e+00
-6.87467515e-01 -1.18026853e+00 -6.27967834e-01 1.25833666e+00
2.91791230e-01 -4.69011664e-01 8.12713742e-01 1.90924972e-01
-7.02512171e-03 -2.29947120e-01 -1.13896298e+00 -9.94314790e-01
-5.32794774e-01 -2.00389966e-01 2.31825784e-01 6.54353797e-01
1.42012253e-01 4.39428419e-01 -7.44113326e-01 9.98245716e-01
1.12940085e+00 -1.32358866e-02 1.10807490e+00 -1.04795671e+00
-5.90598404e-01 -3.93732376e-02 -7.27533638e-01 -2.02374554e+00
-4.27100062e-01 -7.14474738e-01 -7.17608109e-02 -1.57038558e+00
-1.54991224e-01 -5.57805002e-01 -1.98328748e-01 -2.43898496e-01
-1.14228518e-03 4.65096295e-01 2.08398759e-01 2.90481508e-01
-8.98606062e-01 6.02591038e-01 7.87284017e-01 -2.11119816e-01
-1.18915766e-01 4.48716432e-01 -8.24439585e-01 2.82626748e-01
5.86024106e-01 -1.63608491e-01 -2.59868145e-01 -7.68779218e-01
6.02595329e-01 2.76670426e-01 1.47301555e-01 -1.65899372e+00
7.86796868e-01 4.57202643e-01 3.89636308e-01 -5.50499976e-01
3.19555610e-01 -1.08499539e+00 -3.76700200e-02 4.65485275e-01
4.10149187e-01 -6.16546214e-01 -8.64419192e-02 1.19581556e+00
-3.31039548e-01 3.97327989e-01 6.53885365e-01 4.05308902e-01
-3.03298712e-01 1.44169748e-01 -9.35081542e-01 -2.23351926e-01
1.16326046e+00 -2.17607230e-01 -2.58785069e-01 -6.58493876e-01
-8.07730734e-01 7.56037056e-01 -1.97274804e-01 -2.80106157e-01
4.81380403e-01 -1.37034822e+00 -1.09056878e+00 1.80407703e-01
-1.69957384e-01 -3.86212319e-01 3.96895468e-01 1.26633179e+00
-3.11568171e-01 7.79804289e-01 5.00887156e-01 -6.23059392e-01
-1.23379910e+00 -1.42210247e-02 2.93975741e-01 6.59224764e-02
-2.09621549e-01 8.21605980e-01 -1.95822213e-02 1.23965487e-01
1.95129767e-01 9.90310237e-02 3.52912366e-01 2.36299321e-01
6.04064226e-01 6.99930608e-01 2.83702374e-01 -3.72870922e-01
-9.70548019e-02 4.48363215e-01 1.27816692e-01 -1.27085196e-02
9.54109192e-01 -8.35271299e-01 -2.05906034e-01 -2.98310220e-01
1.11046827e+00 6.55195951e-01 -7.89419055e-01 -6.79598153e-01
-2.45375007e-01 -7.38867164e-01 1.45595402e-01 -1.21034168e-01
-1.03911626e+00 6.10750973e-01 6.48482561e-01 1.63985029e-01
1.34654343e+00 -4.57919002e-01 5.99131525e-01 9.80015576e-01
9.25784528e-01 -1.15794778e+00 -2.67045885e-01 5.14224470e-01
5.57686448e-01 -6.61782742e-01 3.72679114e-01 -4.32118803e-01
-4.05056914e-03 1.04668903e+00 1.53135687e-01 7.48255327e-02
9.59761500e-01 2.49658331e-01 -1.10869505e-01 4.37942520e-02
-5.14995396e-01 -2.46195689e-01 -1.98291421e-01 4.66938823e-01
1.94370940e-01 5.06746359e-02 -7.12638140e-01 7.81939566e-01
1.69525102e-01 -2.23361567e-01 4.26167190e-01 7.56383657e-01
-8.54962170e-01 -1.08280754e+00 -9.02386725e-01 5.97435713e-01
-3.09560627e-01 -2.13615358e-01 9.56419576e-03 1.66197732e-01
4.34081489e-03 1.31557167e+00 -1.09477580e-01 -2.74804860e-01
5.82795143e-01 -6.07136011e-01 2.30004475e-01 -4.68346119e-01
-3.71225253e-02 4.36112672e-01 2.41872087e-01 -6.57687038e-02
5.28338589e-02 -5.67512512e-01 -8.91277432e-01 -2.60621995e-01
-4.00611281e-01 3.72642338e-01 5.65853179e-01 6.08451724e-01
9.33454812e-01 2.69585490e-01 1.23963070e+00 -5.86111844e-01
-7.32119381e-01 -3.92562926e-01 -1.19229412e+00 -3.33426356e-01
4.64851946e-01 -1.74438134e-01 -2.86872119e-01 -2.51453966e-01] | [6.210707664489746, 1.3965989351272583] |
61f7e02c-6c99-4baf-b023-45487a96af16 | domain-smoothing-network-for-zero-shot-sketch | 2106.11841 | null | https://arxiv.org/abs/2106.11841v1 | https://arxiv.org/pdf/2106.11841v1.pdf | Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval | Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a novel cross-modal retrieval task, where abstract sketches are used as queries to retrieve natural images under zero-shot scenario. Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches. Toward this end, we propose a novel Domain-Smoothing Network (DSN) for ZS-SBIR. Specifically, a cross-modal contrastive method is proposed to learn generalized representations to smooth the domain gap by mining relations with additional augmented samples. Furthermore, a category-specific memory bank with sketch features is explored to reduce intra-class diversity in the sketch domain. Extensive experiments demonstrate that our approach notably outperforms the state-of-the-art methods in both Sketchy and TU-Berlin datasets. Our source code is publicly available at https://github.com/haowang1992/DSN. | ['Cheng Deng', 'Aming Wu', 'Jiexi Yan', 'Hao Wang', 'Zhipeng Wang'] | 2021-06-22 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 9.24848020e-02 -5.56947649e-01 -6.43953919e-01 -2.80696034e-01
-1.16707146e+00 -3.69546324e-01 8.87226999e-01 -2.00808987e-01
-1.72694817e-01 5.47261178e-01 2.37054780e-01 2.60351330e-01
-5.51707566e-01 -7.75124371e-01 -3.81592751e-01 -4.96101588e-01
1.57040581e-01 3.50566715e-01 1.72568724e-01 -4.21542466e-01
5.25097549e-01 4.00407463e-01 -1.58977056e+00 4.04439211e-01
6.27020180e-01 1.24867475e+00 2.23210648e-01 2.51466393e-01
-4.27890927e-01 3.97371978e-01 -3.10710132e-01 -4.79384422e-01
4.08351392e-01 -1.46554410e-01 -5.89800477e-01 -7.16887489e-02
7.01783657e-01 -6.51414514e-01 -7.81732559e-01 1.05409384e+00
7.43706882e-01 4.34783667e-01 9.15617108e-01 -1.42843425e+00
-1.23012936e+00 1.06346607e-01 -5.72368085e-01 -7.49898925e-02
3.48828077e-01 -6.86234981e-02 1.23066390e+00 -1.49461663e+00
9.69324350e-01 1.53518939e+00 2.37852871e-01 6.66728079e-01
-1.13710189e+00 -1.03193045e+00 1.67289764e-01 3.32137793e-01
-1.81734335e+00 -4.69063580e-01 1.26793754e+00 -2.34771863e-01
4.95792806e-01 2.41914243e-01 3.15466017e-01 1.25013936e+00
-3.73133838e-01 1.13797033e+00 6.77002847e-01 -3.56241167e-01
1.56099379e-01 2.75626063e-01 1.04029968e-01 6.11359715e-01
-1.19319685e-01 4.23554480e-02 -8.59162033e-01 -3.02163929e-01
1.09643841e+00 6.17534161e-01 -1.52312234e-01 -7.33014584e-01
-1.14930975e+00 8.17476034e-01 4.89538878e-01 3.42923075e-01
-1.30294636e-01 6.45375997e-03 6.55274630e-01 5.19475460e-01
6.13766849e-01 1.28464997e-01 -6.57865331e-02 3.30081612e-01
-1.06387913e+00 4.05531168e-01 4.82592225e-01 1.32056737e+00
7.16242194e-01 -1.85130760e-01 -5.12164474e-01 1.53092813e+00
8.85364041e-02 4.54094261e-01 4.75336879e-01 -7.29504526e-01
5.34548163e-01 5.69322467e-01 -1.71051230e-02 -1.21624148e+00
4.99479026e-01 2.31566317e-02 -1.05215645e+00 -7.28067160e-02
-2.11045556e-02 5.09750545e-01 -7.89276779e-01 1.59467542e+00
-3.90754780e-03 2.10321099e-01 -8.19853544e-02 1.14751625e+00
1.32893384e+00 6.30038440e-01 5.97232059e-02 8.96206349e-02
1.28224516e+00 -9.53360319e-01 -6.18201315e-01 2.95916144e-02
1.44418576e-04 -7.57183731e-01 1.39334822e+00 1.23606749e-01
-1.14663708e+00 -5.76313794e-01 -1.11150336e+00 -4.02881563e-01
-7.75156200e-01 3.02886695e-01 3.94031435e-01 1.79380104e-01
-7.43182242e-01 6.22867167e-01 -1.01329409e-01 -3.94805133e-01
7.08769917e-01 -3.52451717e-03 -4.83970731e-01 -4.65864182e-01
-1.37288427e+00 4.40670669e-01 2.98401773e-01 -1.98141873e-01
-7.76155889e-01 -8.89971972e-01 -8.04287195e-01 2.20683977e-01
6.26972079e-01 -4.64283824e-01 7.28479266e-01 -7.34059572e-01
-1.24130368e+00 1.01364899e+00 -1.50274783e-01 9.61087942e-02
5.02178967e-01 -8.22748244e-02 -3.70330721e-01 2.95727313e-01
1.00554049e-01 8.36169064e-01 1.10485530e+00 -1.40768969e+00
-1.98791951e-01 -4.68167841e-01 2.33987756e-02 2.52447844e-01
-6.36197329e-01 -2.46226072e-01 -8.52915108e-01 -1.12412381e+00
1.64645568e-01 -6.78624034e-01 1.64790481e-01 6.95707202e-01
-2.16761574e-01 -6.01905882e-01 8.70952785e-01 -4.81198490e-01
1.27559018e+00 -2.22159815e+00 1.60701662e-01 1.54770896e-01
8.44279602e-02 3.63098890e-01 -6.13545537e-01 7.67343640e-01
5.64558469e-02 3.66188511e-02 -1.53111115e-01 -3.71164739e-01
2.01167285e-01 4.73090932e-02 -7.43295908e-01 1.90374911e-01
3.30096483e-01 1.01844656e+00 -9.25379992e-01 -8.04257393e-01
2.58550763e-01 5.71483910e-01 -1.38932049e-01 2.01995417e-01
-1.51624769e-01 4.95243967e-02 -5.03173470e-01 1.05278277e+00
8.34369600e-01 -2.51150638e-01 -4.80192192e-02 -1.97166175e-01
1.57629296e-01 -1.26684830e-01 -1.10914040e+00 2.35319328e+00
-3.60061556e-01 4.41531003e-01 -1.13005012e-01 -9.27436650e-01
1.17245400e+00 1.14420101e-01 3.69097233e-01 -1.02054501e+00
-1.65511668e-01 2.27307767e-01 -8.40377390e-01 -3.18419278e-01
7.02777207e-01 -2.03692004e-01 -1.86911240e-01 3.88465226e-01
2.51949877e-01 -9.49515849e-02 7.57698193e-02 4.71895695e-01
6.73253655e-01 3.74348909e-02 1.41852587e-01 -2.30288163e-01
6.73169076e-01 -3.23300958e-01 3.43831688e-01 7.60052204e-01
-1.03858449e-01 7.49503374e-01 2.51428127e-01 -5.24823666e-01
-1.13015568e+00 -1.34475803e+00 -2.32867617e-02 1.22898281e+00
7.67925680e-01 -3.61856580e-01 -9.75109115e-02 -5.24869204e-01
2.70612478e-01 3.71273369e-01 -4.88290966e-01 -2.81437427e-01
-1.31577104e-01 5.43169584e-03 4.15544361e-01 3.16581994e-01
5.89258194e-01 -1.11472213e+00 -4.52188961e-02 -2.03707963e-01
-2.28822574e-01 -6.79811120e-01 -8.54738057e-01 -4.46651757e-01
-7.18967378e-01 -8.42575967e-01 -1.39365244e+00 -1.03616619e+00
4.65648711e-01 7.50469148e-01 1.12720752e+00 1.57885253e-01
-7.02944934e-01 5.63874602e-01 -3.53274167e-01 -3.83188948e-02
3.06170553e-01 -1.28587242e-02 -1.66080818e-01 -8.91124383e-02
5.63802958e-01 -6.17277801e-01 -9.19736028e-01 3.74269247e-01
-1.04162216e+00 -1.95783913e-01 5.36967099e-01 1.27619243e+00
6.90247536e-01 -3.61996710e-01 8.42819154e-01 -5.10419905e-01
1.00913656e+00 -5.71781576e-01 -3.85633230e-01 7.48099267e-01
-5.31946957e-01 1.99558698e-02 3.80761325e-01 -7.72506535e-01
-9.89109099e-01 -2.46179447e-01 3.82631779e-01 -1.15798092e+00
5.58548309e-02 1.89131320e-01 -1.18217200e-01 -2.25284956e-02
3.05715173e-01 5.68769813e-01 9.46864337e-02 -5.03107131e-01
5.24545968e-01 7.45191216e-01 3.00060093e-01 -7.30512798e-01
7.62558520e-01 5.57284236e-01 -9.70658809e-02 -8.94766927e-01
-6.86316550e-01 -7.30289876e-01 -4.02308077e-01 -1.49878740e-01
3.98236066e-01 -1.12724686e+00 -6.00850046e-01 1.66638911e-01
-1.12469649e+00 6.73430339e-02 -1.87234133e-01 5.62628247e-02
-5.14075220e-01 4.95390743e-01 -4.96507376e-01 -9.69170749e-01
-7.22365737e-01 -8.26802492e-01 1.43089700e+00 1.71768084e-01
1.58313602e-01 -6.81026578e-01 1.25808846e-02 1.75912827e-01
3.87169421e-01 -1.38758644e-01 9.18998420e-01 -5.50131440e-01
-8.12873065e-01 -2.48219162e-01 -7.80927420e-01 2.74370402e-01
1.06841497e-01 -3.26991469e-01 -7.99648702e-01 -3.96029294e-01
-5.64208388e-01 -7.23734915e-01 1.19853318e+00 4.72066402e-02
1.50299287e+00 -2.57971615e-01 -2.38470972e-01 4.33561534e-01
1.60630143e+00 1.26024578e-02 7.28684545e-01 -4.74216864e-02
4.47103858e-01 6.41759634e-01 9.93821740e-01 6.92702830e-01
2.23090708e-01 6.21840537e-01 5.02838530e-02 1.38461471e-01
-2.25127488e-01 -5.78350067e-01 -1.13582790e-01 6.09128654e-01
2.16429114e-01 -7.65522942e-02 -5.61939776e-01 8.29965234e-01
-1.93273556e+00 -1.09765637e+00 6.03762090e-01 2.30572581e+00
7.78718233e-01 -3.26640338e-01 5.17587401e-02 -1.67874873e-01
8.80354106e-01 5.99934578e-01 -6.85133338e-01 8.44688490e-02
-1.14897810e-01 1.99123710e-01 9.94775891e-02 2.23277375e-01
-1.12614942e+00 1.11344790e+00 4.97494984e+00 1.62498069e+00
-9.55361843e-01 8.91374366e-04 5.06405413e-01 -3.39628816e-01
-3.95541281e-01 -1.95792809e-01 -5.19313395e-01 4.70853269e-01
1.05816118e-01 -3.76704156e-01 6.27288818e-01 9.04827178e-01
-3.54276031e-01 1.55334249e-01 -1.02664721e+00 1.56298888e+00
3.69064301e-01 -1.45805144e+00 4.46550012e-01 -1.96397111e-01
5.27856410e-01 -3.25561583e-01 2.78655231e-01 4.25135344e-01
-1.16315506e-01 -7.96747863e-01 4.23693269e-01 8.73474538e-01
1.37538624e+00 -8.09036672e-01 2.32371405e-01 5.77053316e-02
-1.51024711e+00 -2.07880810e-02 -7.49769568e-01 3.27043027e-01
-1.83329731e-01 1.85675055e-01 -2.79845625e-01 6.66864455e-01
5.63881159e-01 9.74191964e-01 -4.27905172e-01 7.46749461e-01
2.77926594e-01 -1.38665333e-01 -1.16383741e-02 -5.43834865e-02
1.56665385e-01 -3.75538200e-01 6.71429694e-01 1.06598568e+00
3.25409025e-01 1.63728282e-01 5.03997616e-02 1.09830809e+00
-3.26784760e-01 1.24326319e-01 -9.91052449e-01 -2.70320028e-01
7.90783405e-01 1.15268195e+00 -3.17333400e-01 -6.12568140e-01
-2.51887023e-01 1.25204587e+00 3.10883552e-01 5.25286853e-01
-4.71253812e-01 -7.39383996e-01 8.38527262e-01 -1.08208694e-01
4.23110634e-01 6.46916553e-02 -5.92734143e-02 -1.43132615e+00
2.39614859e-01 -6.04275286e-01 4.59104091e-01 -8.27309012e-01
-1.98436046e+00 2.63966680e-01 -4.74307314e-02 -1.67769957e+00
4.86453660e-02 -2.86331683e-01 -4.25291270e-01 8.70599568e-01
-1.71595883e+00 -1.35979235e+00 -3.82079065e-01 8.62132549e-01
9.19148266e-01 -5.17708659e-01 7.83357918e-01 6.46685123e-01
-2.48317793e-01 9.46769178e-01 2.71361858e-01 2.23824054e-01
1.10319614e+00 -6.99349344e-01 1.51811555e-01 2.28010625e-01
1.04834162e-01 8.95175278e-01 2.14880243e-01 -7.41453230e-01
-1.60410297e+00 -1.01368749e+00 6.18825614e-01 -3.94916767e-03
5.85162640e-01 -4.90966529e-01 -1.06296992e+00 1.95203394e-01
-2.01277621e-02 3.20860684e-01 5.35608113e-01 -1.46729844e-02
-9.71380711e-01 -3.68059576e-01 -1.12433302e+00 7.35913873e-01
1.30768287e+00 -9.98612225e-01 -5.02396226e-01 2.25715876e-01
7.77710557e-01 1.88292582e-02 -9.54151392e-01 3.47165853e-01
1.15762973e+00 -7.48150587e-01 1.40711021e+00 -6.23247623e-01
7.04020143e-01 4.05343547e-02 -3.70909572e-01 -8.56224775e-01
-1.64497972e-01 -1.62956446e-01 -4.93168682e-02 1.30487275e+00
-1.42392457e-01 -3.45350623e-01 7.93933809e-01 4.55459118e-01
4.54442620e-01 -7.55797029e-01 -8.22525382e-01 -1.03598142e+00
8.57858360e-02 3.39304656e-02 6.14734352e-01 9.96325433e-01
-6.93628937e-02 3.45333159e-01 -7.02415049e-01 -2.63364196e-01
8.71201813e-01 4.88708109e-01 7.04122365e-01 -1.32917714e+00
1.20432965e-01 -4.79965150e-01 -3.29443902e-01 -1.14209878e+00
3.95511568e-01 -9.23019469e-01 -4.90280017e-02 -1.27281106e+00
5.32817960e-01 -5.23393869e-01 -5.70989966e-01 2.33303562e-01
-3.11133545e-02 2.74160147e-01 4.93139327e-01 5.03809214e-01
-9.60432947e-01 9.82321143e-01 1.24501431e+00 -4.85502839e-01
-1.86044257e-02 -3.06598276e-01 -4.31492954e-01 1.65460572e-01
6.34582579e-01 -2.80199766e-01 -6.36586845e-01 -2.64453501e-01
-1.66414127e-01 3.28288525e-01 6.41188085e-01 -6.46805286e-01
3.97993624e-01 -2.07705379e-01 3.12678903e-01 -1.04193485e+00
7.84447074e-01 -7.84067035e-01 -5.70186898e-02 1.22820787e-01
-7.44356930e-01 -3.01850051e-01 -2.85593998e-02 9.19635057e-01
-4.39380199e-01 -1.14118993e-01 6.14826262e-01 -2.85341859e-01
-8.47622633e-01 7.10177302e-01 1.79883391e-01 -7.96889607e-03
7.92605400e-01 -1.57753825e-01 -3.79392743e-01 -3.95680994e-01
-3.75484437e-01 2.95607567e-01 3.99015307e-01 7.24564970e-01
1.15209985e+00 -1.80605710e+00 -4.75517869e-01 2.19881266e-01
7.07402647e-01 -2.32947841e-01 6.94789052e-01 2.76454955e-01
-2.53218897e-02 5.44828653e-01 -3.76050323e-01 -3.60029221e-01
-1.38561630e+00 7.63854384e-01 -8.52300301e-02 -1.88893586e-01
-5.45423567e-01 9.32028890e-01 3.80055845e-01 -4.68596309e-01
5.36387682e-01 4.30074573e-01 7.03619942e-02 2.31259078e-01
6.97617590e-01 3.53957176e-01 -3.13583761e-01 -3.27812254e-01
-3.51958930e-01 5.98572135e-01 -2.58681238e-01 -1.67862833e-01
1.31857908e+00 -2.12392718e-01 -1.88877150e-01 6.12411201e-01
1.49636269e+00 -5.88677287e-01 -1.05483162e+00 -7.66350031e-01
-4.33956385e-02 -9.14776266e-01 -7.91382194e-02 -6.65485620e-01
-1.05464280e+00 9.93149817e-01 6.87282145e-01 -2.25836754e-01
1.13132393e+00 1.43983811e-01 9.57497776e-01 5.74407756e-01
3.88486534e-01 -1.20953918e+00 5.68996608e-01 3.21665347e-01
1.36391926e+00 -1.52533519e+00 8.79697576e-02 -1.70677692e-01
-6.43851519e-01 9.79818046e-01 5.30493617e-01 -4.97379124e-01
7.21600175e-01 -4.13008213e-01 -3.78277183e-01 -1.20639779e-01
-8.21000457e-01 -1.92118943e-01 5.02795517e-01 3.75333905e-01
1.36480153e-01 -4.24772091e-02 -3.67885411e-01 5.88317335e-01
5.07256150e-01 2.02458978e-01 -1.61132231e-01 9.91543949e-01
-2.54250228e-01 -1.10893834e+00 -1.30746216e-01 5.10941386e-01
1.99164562e-02 -3.70761096e-01 -6.42603219e-01 5.96748292e-01
-1.99911073e-01 6.06833994e-01 3.61499153e-02 -3.89397174e-01
2.25355595e-01 1.92401633e-01 4.75572139e-01 -3.62683415e-01
-1.97736248e-01 -1.41337337e-02 -3.34486902e-01 -6.03829563e-01
-2.88138479e-01 -2.87738889e-01 -5.84174693e-01 -2.65902609e-01
-2.40686208e-01 -1.54658973e-01 5.42157769e-01 4.53611434e-01
6.20600700e-01 8.59194770e-02 6.18956447e-01 -9.68108058e-01
-6.08606696e-01 -8.24233413e-01 -8.68406177e-01 5.46784699e-01
3.41248989e-01 -9.61693168e-01 -2.94541717e-01 -1.77294269e-01] | [11.5636625289917, 0.722950279712677] |
7fd18580-8dca-43ec-b924-2897fa334b13 | ultra-fast-image-categorization-in-vivo-and | 2205.03635 | null | https://arxiv.org/abs/2205.03635v4 | https://arxiv.org/pdf/2205.03635v4.pdf | Ultrafast Image Categorization in Biology and Neural Models | Humans are able to categorize images very efficiently, in particular to detect the presence of an animal very quickly. Recently, deep learning algorithms based on convolutional neural networks (CNNs) have achieved higher than human accuracy for a wide range of visual categorization tasks. However, the tasks on which these artificial networks are typically trained and evaluated tend to be highly specialized and do not generalize well, e.g., accuracy drops after image rotation. In this respect, biological visual systems are more flexible and efficient than artificial systems for more general tasks, such as recognizing an animal. To further the comparison between biological and artificial neural networks, we re-trained the standard VGG 16 CNN on two independent tasks that are ecologically relevant to humans: detecting the presence of an animal or an artifact. We show that re-training the network achieves a human-like level of performance, comparable to that reported in psychophysical tasks. In addition, we show that the categorization is better when the outputs of the models are combined. Indeed, animals (e.g., lions) tend to be less present in photographs that contain artifacts (e.g., buildings). Furthermore, these re-trained models were able to reproduce some unexpected behavioral observations from human psychophysics, such as robustness to rotation (e.g., an upside-down or tilted image) or to a grayscale transformation. Finally, we quantified the number of CNN layers required to achieve such performance and showed that good accuracy for ultrafast image categorization can be achieved with only a few layers, challenging the belief that image recognition requires deep sequential analysis of visual objects. | ['Laurent U Perrinet', 'Jean-Nicolas Jérémie'] | 2022-05-07 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 2.94433326e-01 -1.97070196e-01 5.01744092e-01 -3.02607864e-01
2.82620609e-01 -7.74202406e-01 6.16669834e-01 3.69782776e-01
-8.36533427e-01 4.17266935e-01 -4.48982298e-01 -2.75834978e-01
9.31172147e-02 -7.51886070e-01 -9.92525339e-01 -7.49908745e-01
7.82597288e-02 -1.96077731e-02 3.60545844e-01 -2.26039916e-01
3.62581372e-01 8.50341141e-01 -2.02110958e+00 4.18481737e-01
2.55327851e-01 1.11543310e+00 3.50663304e-01 7.04358399e-01
4.03919667e-01 5.45056522e-01 -7.49321222e-01 -1.12734891e-01
1.50573194e-01 -3.33291560e-01 -7.66152143e-01 6.99528009e-02
7.48473644e-01 -2.31217682e-01 -1.18715852e-01 1.05616724e+00
2.61744410e-01 1.73100516e-01 8.18353355e-01 -1.02226317e+00
-9.80348170e-01 8.57231766e-02 -1.92488253e-01 3.59595627e-01
1.99554771e-01 4.71615225e-01 6.92950547e-01 -6.50672138e-01
4.86750901e-01 1.33180344e+00 6.92707241e-01 5.81213295e-01
-1.64209497e+00 -5.02073050e-01 1.35820419e-01 2.55590022e-01
-1.13528049e+00 -2.30791539e-01 3.41404617e-01 -7.39156365e-01
1.06030953e+00 1.72185078e-01 7.65863478e-01 1.14668953e+00
3.85380924e-01 2.38361642e-01 1.33706403e+00 -2.45409504e-01
4.46715325e-01 8.28103200e-02 1.52445108e-01 4.32032883e-01
7.08851695e-01 1.77489847e-01 -6.84623495e-02 2.08068043e-01
8.25132728e-01 2.19268247e-01 -1.01881042e-01 -2.42452905e-01
-1.40700138e+00 5.07441103e-01 1.02241576e+00 6.58620954e-01
-3.38511527e-01 3.68371248e-01 3.52611601e-01 2.82749325e-01
-7.75923282e-02 9.68095601e-01 -4.57821190e-01 4.39185172e-01
-6.61977291e-01 1.67654008e-02 4.30795163e-01 5.41813433e-01
6.52377665e-01 9.43754166e-02 8.47948492e-02 7.32235551e-01
2.31802510e-03 3.31348896e-01 6.57321513e-01 -9.91202772e-01
-2.25767106e-01 6.10397458e-01 1.34112880e-01 -1.04401886e+00
-8.01896095e-01 -4.88668859e-01 -1.12152004e+00 7.88630009e-01
8.77087593e-01 3.55074018e-01 -1.13225102e+00 1.87854135e+00
-8.50734264e-02 -3.54073852e-01 -2.99372314e-03 1.01680624e+00
7.57832766e-01 3.31943691e-01 4.04569179e-01 1.36835575e-01
1.73170114e+00 -4.54099447e-01 -3.23957592e-01 -6.34290755e-01
4.03646380e-01 -4.06219751e-01 1.23523831e+00 4.53420341e-01
-7.86037028e-01 -1.00199163e+00 -1.27078784e+00 -6.74464107e-02
-1.07096159e+00 2.67706607e-02 6.56977177e-01 5.68434358e-01
-1.07458937e+00 9.89903092e-01 -5.73254585e-01 -9.21659887e-01
5.98095834e-01 4.28078294e-01 -7.15782881e-01 1.14372440e-01
-9.80470300e-01 1.10349917e+00 5.35288692e-01 1.69528767e-01
-1.09923553e+00 -4.77530926e-01 -7.03382313e-01 3.14061701e-01
-1.64445430e-01 -6.01969421e-01 1.05036616e+00 -1.41284347e+00
-1.12874603e+00 1.52465999e+00 6.27992898e-02 -5.87342978e-01
3.63575637e-01 1.27112746e-01 -3.03677469e-01 1.90437898e-01
-8.47984329e-02 9.33470488e-01 6.80657208e-01 -1.34422290e+00
-3.61745238e-01 -5.49032152e-01 2.73253709e-01 -2.50925303e-01
-2.45543510e-01 8.26285258e-02 3.38236034e-01 -4.88540828e-01
1.57076433e-01 -8.94644439e-01 -1.06451370e-01 4.61970061e-01
-8.43620971e-02 -8.40039104e-02 6.69740081e-01 -3.99913520e-01
4.58159834e-01 -2.25959063e+00 5.92762325e-03 -2.10234791e-01
3.74556839e-01 4.85930145e-01 -2.60423303e-01 2.54682034e-01
-4.25562203e-01 4.14735496e-01 -2.51669228e-01 7.68404603e-02
-1.04282312e-01 1.47393644e-01 -3.54632363e-02 7.78989673e-01
1.84487760e-01 9.38525856e-01 -8.20490241e-01 -1.26193717e-01
3.38623077e-01 3.32213938e-01 -2.95630902e-01 2.74130553e-02
-1.73178012e-03 4.44566578e-01 2.19905883e-01 4.97340441e-01
5.99000216e-01 -3.62491548e-01 7.61667043e-02 -2.23816454e-01
-1.73234165e-01 -5.46488091e-02 -6.05647326e-01 1.25544429e+00
-5.07015288e-01 1.08791459e+00 -1.30957335e-01 -1.26551116e+00
6.38722301e-01 1.43565491e-01 -2.22169608e-02 -9.22678471e-01
3.72088492e-01 8.84759650e-02 5.76599061e-01 -3.41702759e-01
1.29390210e-01 -2.09432468e-01 1.33727059e-01 2.78351337e-01
2.65305758e-01 -3.61405760e-01 1.38745740e-01 -2.38344729e-01
8.37538660e-01 -1.39991611e-01 5.74017882e-01 -4.30613250e-01
3.37436408e-01 3.38321016e-03 1.49357438e-01 1.03150797e+00
-3.16128343e-01 7.03016877e-01 4.96753216e-01 -8.96010518e-01
-1.21606171e+00 -1.01860750e+00 -3.20670098e-01 1.20000803e+00
2.70814151e-01 1.59190506e-01 -8.60752940e-01 -2.00336799e-01
-8.03020690e-03 6.46343827e-01 -9.49360311e-01 -5.49905300e-01
-3.58242363e-01 -7.99767256e-01 6.71182096e-01 6.03231192e-01
5.34717798e-01 -1.42564094e+00 -1.19682419e+00 1.97928667e-01
1.93707094e-01 -1.27483916e+00 3.54842305e-01 4.88096058e-01
-6.89409673e-01 -1.10113561e+00 -6.50051355e-01 -8.04895759e-01
7.73968995e-01 3.36403072e-01 1.05156636e+00 4.89747614e-01
-5.61720610e-01 2.48270214e-01 -2.28509545e-01 -4.97079283e-01
-3.44064415e-01 -2.52200991e-01 1.99048921e-01 -1.05265923e-01
2.77378380e-01 -5.52148700e-01 -8.41084421e-01 6.51770711e-01
-1.12568235e+00 -1.15698531e-01 6.78484619e-01 9.13554788e-01
2.73660034e-01 -1.35626540e-01 3.56484443e-01 -4.02270138e-01
4.40244824e-01 -2.50632465e-01 -4.03905034e-01 2.26580068e-01
-1.99638486e-01 -9.29627270e-02 1.05835044e+00 -9.24807727e-01
-5.47786295e-01 1.08208373e-01 -4.15270850e-02 2.88068708e-02
-7.73037553e-01 9.67690870e-02 1.57513991e-01 -4.29105341e-01
1.05863094e+00 2.89876848e-01 -2.33434394e-01 -1.27248988e-01
9.58451256e-02 5.81838727e-01 4.84568059e-01 -2.69040614e-01
6.85825050e-01 7.51301229e-01 1.71268597e-01 -1.09616554e+00
-5.61401725e-01 -2.48278230e-01 -8.61647546e-01 -3.53686213e-01
1.06686008e+00 -5.29153883e-01 -1.21445870e+00 7.95335174e-01
-1.26305759e+00 -6.41340077e-01 -7.64812380e-02 5.93370020e-01
-6.85924351e-01 2.07562491e-01 -4.52263892e-01 -6.62476957e-01
5.71585000e-02 -9.62145448e-01 9.02467370e-01 3.51334095e-01
-3.42954546e-01 -8.11654866e-01 -3.41646731e-01 1.30126923e-02
5.36898017e-01 4.54897702e-01 1.18420839e+00 -7.00295150e-01
-2.57245898e-01 -2.70504534e-01 -5.67684293e-01 4.75341558e-01
8.18230957e-02 1.66207060e-01 -1.31769919e+00 -3.30076844e-01
3.68801467e-02 -4.98628885e-01 9.53832746e-01 2.62415349e-01
1.42077327e+00 -6.75139800e-02 -2.75320619e-01 5.53978682e-01
1.32347262e+00 4.26230133e-01 8.38842869e-01 4.31131452e-01
3.74174505e-01 7.79087484e-01 1.15064308e-01 -8.85360017e-02
-1.33586213e-01 6.24562562e-01 6.30077779e-01 -5.12581944e-01
-1.87709421e-01 1.14759691e-01 3.64445187e-02 6.02543280e-02
-3.90124142e-01 -2.78014779e-01 -9.05249119e-01 4.53754574e-01
-1.49127698e+00 -9.62307453e-01 -3.31658870e-02 2.32458806e+00
3.23509693e-01 2.62384832e-01 -1.84474643e-02 3.31881046e-01
7.28623867e-01 -1.79660499e-01 -7.17105150e-01 -5.62475383e-01
-3.39744627e-01 1.60554647e-01 5.52714884e-01 -7.62748271e-02
-1.08157206e+00 9.37116981e-01 7.10859966e+00 3.42287511e-01
-1.47030032e+00 -3.70733961e-02 6.48371816e-01 2.20678613e-01
3.02026123e-01 -2.52273053e-01 -2.50774235e-01 4.10737097e-01
8.10284257e-01 9.83718187e-02 5.43179989e-01 8.62675309e-01
3.96456644e-02 -2.81233668e-01 -1.32683289e+00 1.00789881e+00
-1.70963164e-02 -9.72425401e-01 8.32117870e-02 -1.83019345e-03
3.12827945e-01 -1.13282315e-01 2.37860382e-01 1.79550320e-01
1.78245470e-01 -1.49739718e+00 8.61933768e-01 3.25030684e-01
7.21950114e-01 -1.47053733e-01 6.62421227e-01 3.08470339e-01
-8.11691403e-01 -2.81512350e-01 -8.32104862e-01 -4.25898433e-01
-3.22805911e-01 1.83988169e-01 -5.07750809e-01 -1.12828553e-01
1.11333859e+00 3.89147222e-01 -1.02482545e+00 1.13418233e+00
-2.06402078e-01 2.63405353e-01 -2.73119986e-01 -2.30382532e-01
2.87046582e-01 1.17687248e-01 1.80154070e-01 1.19354546e+00
2.25069702e-01 1.29773065e-01 -4.05225068e-01 1.01554811e+00
1.24136833e-02 -1.20662794e-01 -7.37354100e-01 -1.18393548e-01
-3.44308615e-02 1.38251042e+00 -1.07869530e+00 -3.97424608e-01
-3.08372647e-01 1.01344419e+00 4.61520284e-01 5.24304271e-01
-6.71293437e-01 -4.96989399e-01 6.68238223e-01 2.48151049e-01
2.30303869e-01 -3.38916749e-01 -2.87744641e-01 -1.02466524e+00
-1.99881494e-01 -7.16919899e-01 9.68398377e-02 -1.25264466e+00
-1.06785369e+00 7.90077150e-01 -1.53324157e-01 -1.01957047e+00
1.12937808e-01 -1.31180012e+00 -5.12896955e-01 5.77017605e-01
-1.09151280e+00 -9.32835460e-01 -6.62262976e-01 3.72150779e-01
2.27238744e-01 2.07216665e-01 7.87359238e-01 1.47852721e-02
-2.83214629e-01 2.48885468e-01 -1.26496658e-01 2.89626569e-01
6.28545225e-01 -1.18260312e+00 5.22851646e-01 7.81090915e-01
1.57663375e-01 8.28294218e-01 7.08566487e-01 -1.09616622e-01
-9.68686402e-01 -7.84075022e-01 4.38187659e-01 -4.58236337e-01
5.09451270e-01 -7.42301822e-01 -1.03078878e+00 5.74567914e-01
1.24941036e-01 2.76462406e-01 4.40320551e-01 -9.53314081e-02
-7.42963135e-01 -5.76413572e-02 -1.32046604e+00 5.94940484e-01
1.12739098e+00 -5.40450096e-01 -7.79661596e-01 4.18846071e-01
4.16660666e-01 -1.93564817e-02 -5.86150110e-01 4.45663095e-01
9.80644643e-01 -1.26338613e+00 1.19300377e+00 -9.74533558e-01
4.58039522e-01 -4.21783805e-01 -1.12110518e-01 -1.34869075e+00
-7.28287935e-01 1.85573578e-01 4.21266317e-01 6.29542947e-01
2.51424432e-01 -9.26588416e-01 2.26498187e-01 3.10708284e-01
1.15698338e-01 -3.74794245e-01 -7.81039774e-01 -1.11501312e+00
1.28024623e-01 -6.72889128e-02 2.51839012e-01 7.99846590e-01
-2.86032826e-01 5.30308411e-02 -1.99359842e-02 1.74715966e-01
4.51900214e-01 -6.08218797e-02 5.39965153e-01 -1.50676024e+00
4.27029245e-02 -7.22644389e-01 -1.06121182e+00 -6.04419529e-01
1.65118068e-01 -6.48883760e-01 9.72662643e-02 -1.36483824e+00
7.09265620e-02 -8.07398558e-02 -4.69837636e-01 5.68712175e-01
-2.32933871e-02 6.72916353e-01 2.42590547e-01 1.05247177e-01
-2.83888161e-01 9.42978170e-03 1.11694622e+00 -2.61879146e-01
1.75336689e-01 -2.09804565e-01 -7.72153616e-01 8.58693063e-01
9.53033447e-01 -4.08115238e-01 1.09223820e-01 -3.38832289e-01
2.32894093e-01 -4.25163507e-01 9.25063372e-01 -1.50980711e+00
9.33686122e-02 -5.97823188e-02 9.41953838e-01 -3.33466083e-02
3.13525170e-01 -9.12113070e-01 1.55396715e-01 9.25090253e-01
-3.49939525e-01 -1.15117058e-01 4.55240041e-01 3.98936689e-01
-1.27174303e-01 -2.45252758e-01 1.36611450e+00 -4.20150399e-01
-8.61029983e-01 -6.90951273e-02 -8.54933858e-01 -3.41847837e-01
9.68678117e-01 -4.92882639e-01 -6.39755905e-01 -2.35254467e-01
-7.11809933e-01 -2.84449905e-01 7.06698060e-01 3.69663030e-01
4.37438369e-01 -1.01608312e+00 -3.22714567e-01 2.71190181e-02
3.51404190e-01 -4.10997421e-01 2.64720589e-01 6.84774935e-01
-7.94242561e-01 4.41904515e-01 -8.85858178e-01 -7.20059395e-01
-1.18459308e+00 1.08310330e+00 6.82543457e-01 2.39366725e-01
-2.59527624e-01 5.57029307e-01 8.55331898e-01 -2.48921290e-01
-2.08260827e-02 -7.01946437e-01 -2.69012958e-01 3.69710438e-02
5.68088233e-01 9.37113166e-02 1.18970804e-01 -5.51154137e-01
-6.18635952e-01 8.23362768e-01 1.29789948e-01 2.95421213e-01
1.22068119e+00 1.60221785e-01 -1.37741730e-01 4.91545975e-01
1.07755852e+00 -5.02259612e-01 -1.09687579e+00 1.45831048e-01
-2.46065661e-01 -2.48839378e-01 -3.03002894e-01 -8.21461260e-01
-8.40602279e-01 1.26837718e+00 8.05649638e-01 5.56285203e-01
1.09443998e+00 3.93528491e-02 1.49152562e-01 7.81444132e-01
4.09338087e-01 -1.00567627e+00 2.70350426e-01 3.24929595e-01
1.09720969e+00 -1.34392297e+00 2.17028968e-02 -1.56856939e-01
-2.95904011e-01 1.27476394e+00 7.24724531e-01 -2.73104340e-01
2.48035505e-01 -1.04364045e-01 2.00110413e-02 -2.85159498e-01
-4.71593231e-01 -5.06023586e-01 2.37854674e-01 8.26390862e-01
2.97400773e-01 8.17806274e-02 -4.73464802e-02 3.44098002e-01
-1.66926488e-01 -1.94921419e-01 6.08207583e-01 6.17845237e-01
-5.68687618e-01 -2.64809102e-01 -4.26599681e-01 2.98071414e-01
-3.67117971e-01 2.37026578e-03 -6.74525499e-01 8.68180513e-01
3.19879174e-01 8.19398999e-01 2.42583439e-01 -4.26468641e-01
3.32958609e-01 9.59402137e-03 6.66521192e-01 -6.11872494e-01
-6.78819716e-01 -5.31045139e-01 -4.00729179e-01 -4.81685758e-01
-5.61120450e-01 -2.49415427e-01 -8.55871856e-01 -1.29384607e-01
-1.35158569e-01 -3.85669500e-01 7.14731395e-01 8.56745958e-01
5.35427332e-02 6.08461201e-01 1.81949630e-01 -1.08736539e+00
-4.45682816e-02 -9.70957279e-01 -6.36174262e-01 7.93266356e-01
2.97403008e-01 -7.86722362e-01 -8.29712868e-01 1.96758226e-01] | [9.854888916015625, 2.326892614364624] |
08982b06-fc06-4116-b68f-06bd55129132 | rmes-real-time-micro-expression-spotting | 2305.05523 | null | https://arxiv.org/abs/2305.05523v1 | https://arxiv.org/pdf/2305.05523v1.pdf | RMES: Real-Time Micro-Expression Spotting Using Phase From Riesz Pyramid | Micro-expressions (MEs) are involuntary and subtle facial expressions that are thought to reveal feelings people are trying to hide. ME spotting detects the temporal intervals containing MEs in videos. Detecting such quick and subtle motions from long videos is difficult. Recent works leverage detailed facial motion representations, such as the optical flow, and deep learning models, leading to high computational complexity. To reduce computational complexity and achieve real-time operation, we propose RMES, a real-time ME spotting framework. We represent motion using phase computed by Riesz Pyramid, and feed this motion representation into a three-stream shallow CNN, which predicts the likelihood of each frame belonging to an ME. In comparison to optical flow, phase provides more localized motion estimates, which are essential for ME spotting, resulting in higher performance. Using phase also reduces the required computation of the ME spotting pipeline by 77.8%. Despite its relative simplicity and low computational complexity, our framework achieves state-of-the-art performance on two public datasets: CAS(ME)2 and SAMM Long Videos. | ['Bertram Shi', 'Frederic Jumelle', 'Liang Wu', 'Didan Deng', 'Yini Fang'] | 2023-05-09 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 1.17608495e-02 -3.04329544e-01 -3.25809419e-01 -3.18425179e-01
-4.61495608e-01 -4.26143765e-01 5.07971942e-01 -4.19734180e-01
-5.15510738e-01 1.09372534e-01 3.26636314e-01 1.84979409e-01
3.22865874e-01 -5.45844674e-01 -4.12978083e-01 -7.10478783e-01
-4.24626797e-01 -2.24071309e-01 6.65631369e-02 5.56571372e-02
2.35310078e-01 6.85621262e-01 -1.57287240e+00 5.92084885e-01
8.20599198e-02 1.25336123e+00 -2.14817911e-01 9.02803063e-01
1.06090091e-01 1.26774669e+00 -4.23742294e-01 -3.75093073e-01
1.18753441e-01 -3.80055815e-01 -6.69580460e-01 6.04205430e-02
7.19915390e-01 -8.04022372e-01 -6.13931179e-01 9.08887327e-01
3.70790243e-01 2.27100044e-01 2.72480160e-01 -1.41406333e+00
-2.27750704e-01 2.70385239e-02 -9.51635480e-01 3.95740747e-01
5.95996022e-01 3.66194725e-01 8.56973112e-01 -1.08665872e+00
8.17252755e-01 1.50700402e+00 6.06312037e-01 7.24351168e-01
-1.09842086e+00 -8.99049044e-01 -6.83537796e-02 4.94662374e-01
-1.46822643e+00 -1.06581342e+00 6.94831491e-01 -3.54536921e-01
9.62038636e-01 2.98936456e-01 7.64653325e-01 1.03603911e+00
1.32051319e-01 9.80135560e-01 6.22854412e-01 1.59293175e-01
-4.62243408e-02 -4.99407947e-01 -4.48366761e-01 1.05005240e+00
-2.51807302e-01 -1.36522293e-01 -9.01008785e-01 1.58756599e-02
9.78532493e-01 1.66374028e-01 -5.24818897e-01 7.49249533e-02
-1.19982386e+00 5.38643062e-01 3.25145394e-01 1.01388015e-01
-5.01742661e-01 5.21362960e-01 3.67568552e-01 2.29692951e-01
4.69198197e-01 7.74948746e-02 -1.77015394e-01 -7.93229461e-01
-1.42585242e+00 2.85724163e-01 6.25255525e-01 4.53099817e-01
9.32369173e-01 -1.09771155e-01 -2.17169151e-01 3.88804615e-01
1.96145281e-01 4.71724570e-01 3.21199805e-01 -1.49132490e+00
2.69690931e-01 5.21506548e-01 2.70993292e-01 -1.66948795e+00
-5.05760670e-01 -3.57395411e-02 -8.04513872e-01 1.24658346e-01
4.42418814e-01 -9.04227570e-02 -6.22457147e-01 1.85102212e+00
3.07243228e-01 5.89371800e-01 -2.82550991e-01 1.21498966e+00
7.14622676e-01 8.61899734e-01 -1.98632613e-01 -3.30650955e-01
1.27146125e+00 -9.39389050e-01 -8.23017478e-01 -1.19916819e-01
7.75670767e-01 -8.02115381e-01 6.71943784e-01 5.05869150e-01
-1.11496449e+00 -3.54178101e-01 -6.02073491e-01 -3.30841869e-01
2.21049130e-01 2.22797528e-01 6.54810965e-01 3.40617418e-01
-1.12198138e+00 8.24375808e-01 -1.18128645e+00 -3.40991467e-02
7.47336030e-01 3.75233263e-01 -6.09042108e-01 4.90530357e-02
-7.83468962e-01 3.75171274e-01 -1.74329787e-01 3.66518974e-01
-7.68762589e-01 -7.48920739e-01 -1.21683478e+00 2.20185131e-01
1.66619495e-01 -3.13550621e-01 1.28585303e+00 -1.28390288e+00
-1.67547524e+00 9.16791677e-01 -9.83489931e-01 -2.33594343e-01
6.33060098e-01 -5.05982697e-01 -3.87998044e-01 8.74534070e-01
-7.81190097e-02 1.05558324e+00 1.23200381e+00 -5.62951684e-01
-3.79737616e-01 -5.37031814e-02 -4.87940609e-02 -8.35975781e-02
-3.14581424e-01 3.93346071e-01 -6.56628013e-01 -5.67123055e-01
1.94753319e-01 -9.97762024e-01 3.05034742e-02 6.36143744e-01
-1.59789860e-01 -1.76517993e-01 1.21208465e+00 -7.14697421e-01
1.37993467e+00 -2.33661437e+00 2.41300892e-02 -4.90230583e-02
5.10289550e-01 3.90411019e-01 -1.52774975e-01 2.74701323e-02
-3.42554487e-02 -3.21648642e-02 1.64085001e-01 -6.43631339e-01
-2.39184111e-01 7.48182684e-02 -1.90161794e-01 8.23457360e-01
6.12812698e-01 1.15266740e+00 -9.78068292e-01 -4.07278985e-01
2.06760034e-01 7.13180602e-01 -7.55935311e-01 2.67299086e-01
4.28599082e-02 4.65328693e-01 -8.24514478e-02 7.59312749e-01
7.73749948e-01 -4.41387147e-01 -1.04874238e-01 -3.81578684e-01
4.60116658e-03 2.93213576e-01 -1.09679532e+00 1.70121801e+00
-2.69209504e-01 1.29294622e+00 3.41861695e-01 -6.10433638e-01
6.29682302e-01 3.14555228e-01 7.05160260e-01 -6.25454187e-01
2.40019903e-01 -2.13240366e-02 -2.68153071e-01 -8.58273625e-01
4.12342787e-01 1.71831280e-01 2.52694488e-01 4.11254197e-01
-7.93658197e-02 3.23557526e-01 1.86235964e-01 4.28898111e-02
1.13242376e+00 3.41003865e-01 1.13375492e-01 9.51784402e-02
5.35826921e-01 -4.86081988e-01 8.91990185e-01 1.76909253e-01
-5.45241892e-01 6.60739124e-01 8.82819891e-01 -7.99321711e-01
-5.89713454e-01 -7.85238326e-01 3.62452865e-01 9.12953436e-01
1.44339055e-01 -9.00472403e-01 -6.50235713e-01 -3.22838724e-01
-2.25603595e-01 -1.12239458e-02 -7.03488410e-01 8.59931484e-03
-9.07362223e-01 -3.45764935e-01 4.90795583e-01 3.27183336e-01
6.23932719e-01 -1.01959336e+00 -1.12553036e+00 1.74976572e-01
-6.40275240e-01 -1.40932488e+00 -8.40919554e-01 -6.01501584e-01
-6.87411487e-01 -1.15651464e+00 -7.67823935e-01 -4.67315882e-01
4.80581373e-01 5.52626193e-01 9.80757833e-01 8.41565356e-02
-4.96321589e-01 7.07855225e-02 -1.78514600e-01 1.81484461e-01
2.49146502e-02 -3.82477820e-01 2.75810957e-02 5.33791721e-01
6.01963758e-01 -6.33780837e-01 -1.14310908e+00 3.14684063e-01
-8.21074605e-01 1.64963081e-01 3.99088204e-01 6.76169336e-01
2.82960206e-01 -4.05088663e-01 -1.26957878e-01 -3.16590786e-01
7.56478012e-02 -3.43445241e-01 -3.86541843e-01 -1.90450758e-01
1.49779301e-02 -1.56651765e-01 2.96016365e-01 -6.51274383e-01
-8.53213668e-01 7.33268932e-02 -5.31320162e-02 -9.36420798e-01
-5.53670637e-02 1.60982773e-01 1.79618612e-01 -2.45600834e-01
4.04766828e-01 2.04561636e-01 3.28913569e-01 -2.06852227e-01
2.14651972e-01 4.01528448e-01 6.24338150e-01 -8.99362862e-02
4.75755811e-01 9.75596786e-01 3.03402841e-01 -1.08168018e+00
-8.50227296e-01 -6.22220814e-01 -5.42396903e-01 -3.16464454e-01
8.82210910e-01 -1.09027708e+00 -1.32434511e+00 5.89636981e-01
-1.36792421e+00 -2.64628440e-01 1.87897980e-01 3.94391686e-01
-5.03146887e-01 5.66394448e-01 -1.02129853e+00 -7.44135201e-01
-5.27726173e-01 -1.15209794e+00 1.39819181e+00 1.95166618e-01
-6.84531927e-01 -6.34747624e-01 -1.25766680e-01 2.10816339e-01
3.97089928e-01 4.77640450e-01 6.82909787e-02 2.43378326e-01
-7.13176906e-01 -1.34144753e-01 -3.26828122e-01 7.12899789e-02
1.23963848e-01 3.45102727e-01 -1.27421713e+00 -3.11030686e-01
-7.98772424e-02 -2.23563522e-01 9.75419044e-01 4.24115747e-01
1.27153313e+00 -5.47110498e-01 -1.71107143e-01 1.16893125e+00
9.98829484e-01 -7.13281566e-03 8.00744295e-01 2.17650294e-01
6.74879432e-01 7.07548499e-01 6.83789372e-01 7.80610263e-01
1.94522426e-01 7.45297432e-01 5.24184823e-01 -7.66437501e-02
-7.31476247e-02 -6.39359877e-02 8.81754398e-01 5.22727370e-01
-8.35680664e-02 1.03587188e-01 -6.23512745e-01 4.96774346e-01
-1.84727776e+00 -1.48902249e+00 1.11846820e-01 1.74785411e+00
6.85794473e-01 -1.15596145e-01 1.23600796e-01 2.40391381e-02
4.34785604e-01 6.57600224e-01 -3.15321177e-01 -2.44572327e-01
-9.99529064e-02 2.31236145e-01 1.63399354e-01 4.63718653e-01
-1.26316190e+00 9.79776025e-01 5.62431097e+00 6.54494703e-01
-1.76160991e+00 1.31608881e-02 7.74077117e-01 -6.70749068e-01
9.61324647e-02 -1.95178866e-01 -6.22341394e-01 5.45443058e-01
1.00128889e+00 1.85871810e-01 2.86202490e-01 6.51453078e-01
7.17887342e-01 -1.92403063e-01 -1.10575259e+00 1.73056924e+00
1.27771676e-01 -1.67581666e+00 -1.01752520e-01 2.21564006e-02
5.09202361e-01 -7.33171478e-02 7.39208460e-02 -1.55383974e-01
-4.29527938e-01 -1.20004869e+00 6.86119378e-01 4.09083396e-01
9.25298154e-01 -7.98701167e-01 5.04851460e-01 1.04733422e-01
-1.51484907e+00 -4.21016589e-02 -2.73351967e-01 -3.46089780e-01
2.78000981e-01 6.15663409e-01 -5.43451190e-01 -6.17637560e-02
8.86231661e-01 1.27890646e+00 -2.73352206e-01 7.21912742e-01
-3.06019068e-01 5.64178228e-01 -3.83345813e-01 1.34742856e-01
3.57663006e-01 -1.04466185e-01 5.85003376e-01 1.45504653e+00
3.83743435e-01 9.82070118e-02 -1.93060702e-03 9.43355739e-01
-7.10605755e-02 -3.33061427e-01 -4.71400082e-01 -2.44310871e-01
2.49020889e-01 1.60139728e+00 -5.21767199e-01 -2.03775376e-01
-3.69421601e-01 1.52594304e+00 6.18603453e-02 2.73051083e-01
-8.66534770e-01 -3.63214999e-01 1.45223033e+00 1.36914641e-01
3.00951123e-01 -4.86040920e-01 2.76220709e-01 -1.49941444e+00
1.75717294e-01 -5.95463634e-01 1.33274406e-01 -7.07421303e-01
-6.65350437e-01 4.42148507e-01 -3.93333465e-01 -1.35589945e+00
-5.17513335e-01 -6.43447757e-01 -6.60762966e-01 5.84075093e-01
-1.63006687e+00 -8.73149037e-01 -7.06776261e-01 6.75846100e-01
5.34333229e-01 2.73094922e-01 6.84825540e-01 4.48059291e-01
-6.42636657e-01 6.48088157e-01 -5.01142681e-01 5.06190598e-01
9.01376247e-01 -6.88152015e-01 5.28786063e-01 8.65092635e-01
1.93274930e-01 6.58652067e-01 4.55195814e-01 -1.86966285e-01
-1.51854539e+00 -1.10594761e+00 1.04578245e+00 -3.77194405e-01
5.84244132e-01 -3.89654845e-01 -7.65997410e-01 5.54566622e-01
-1.83009148e-01 5.05042076e-01 5.93387246e-01 -4.70063716e-01
-5.10325611e-01 -1.07600242e-01 -9.23608303e-01 7.31626451e-01
1.02971530e+00 -7.67182648e-01 -2.10571110e-01 -5.15537784e-02
3.77660573e-01 -4.30557877e-01 -7.17695951e-01 5.74560314e-02
8.27158689e-01 -1.33566999e+00 1.00534129e+00 -3.81546915e-01
6.66986763e-01 -1.99661672e-01 2.12088853e-01 -8.46990705e-01
-2.57796347e-01 -1.38510144e+00 -6.32078528e-01 9.36772883e-01
-1.11252636e-01 -2.29343191e-01 1.07907283e+00 4.92599934e-01
3.81738633e-01 -7.82038271e-01 -9.67340291e-01 -4.41510171e-01
-6.12416446e-01 -5.75902522e-01 3.82501334e-01 1.10259175e+00
-5.96313737e-02 1.86039973e-02 -5.88859081e-01 -2.00777650e-02
4.77928162e-01 2.02397853e-01 8.89154077e-01 -9.69752789e-01
-9.87530053e-02 -5.86841762e-01 -8.57637167e-01 -1.39531910e+00
2.97817409e-01 -4.56548333e-01 -5.99480979e-02 -8.73810947e-01
9.73692611e-02 1.70754805e-01 -1.21823899e-01 6.43210888e-01
-7.47791454e-02 6.37348771e-01 3.46950501e-01 3.68319720e-01
-7.22279012e-01 5.80107987e-01 1.27850735e+00 3.09503954e-02
-1.61493033e-01 -3.07756811e-01 -1.98941305e-01 1.10208249e+00
5.32282710e-01 -2.94177651e-01 -1.17202193e-01 -4.30772692e-01
2.69395500e-01 2.46303856e-01 6.03781044e-01 -9.59503949e-01
2.52978981e-01 -1.70695886e-01 5.86832106e-01 -5.25226176e-01
8.23361933e-01 -4.22359735e-01 -7.60836825e-02 6.00557923e-01
-8.31071883e-02 2.60811418e-01 1.97107583e-01 3.84676158e-01
-4.75634336e-01 2.08580986e-01 9.54167485e-01 -3.13133956e-03
-1.03093565e+00 7.34046638e-01 -3.22958529e-01 -1.03038937e-01
9.76242304e-01 -2.74398565e-01 -1.07747450e-01 -7.78185129e-01
-6.21861398e-01 -8.56358111e-02 4.35598850e-01 4.18479234e-01
8.65948558e-01 -1.24692118e+00 -5.56459486e-01 3.85838658e-01
-2.20748976e-01 -6.62389249e-02 3.15783948e-01 1.24428272e+00
-9.52636480e-01 2.55907536e-01 -2.62086332e-01 -9.50005054e-01
-1.49745500e+00 1.72331259e-01 3.98905367e-01 1.98938802e-01
-8.05133164e-01 1.18241441e+00 2.91624516e-01 4.06252146e-01
1.07091889e-01 -2.85807192e-01 -2.02844143e-01 1.82699934e-01
1.14810050e+00 4.60469991e-01 -2.86989242e-01 -8.79487634e-01
-5.53850293e-01 6.52994096e-01 6.08394742e-02 -5.32713197e-02
1.25383997e+00 -2.91576684e-01 -3.88658822e-01 5.84197491e-02
1.76586390e+00 3.54200788e-02 -1.83676779e+00 -1.26122132e-01
-2.68529654e-02 -8.85847330e-01 4.68221121e-02 5.21774068e-02
-1.35458779e+00 1.25642908e+00 4.84961897e-01 -3.82767379e-01
1.29731226e+00 -2.18182743e-01 1.18301320e+00 2.92987078e-01
1.85244977e-01 -6.74314499e-01 3.97807062e-01 4.43578601e-01
6.03300333e-01 -1.30592823e+00 -1.94837108e-01 -4.41547483e-01
-4.65755939e-01 1.51247990e+00 5.11473715e-01 -1.51807189e-01
5.66052854e-01 2.54171401e-01 7.29268566e-02 -1.85307473e-01
-8.93886209e-01 1.35565534e-01 3.55183154e-01 3.21179211e-01
4.23737824e-01 -2.41509140e-01 1.92181870e-01 2.21657410e-01
-1.19931392e-01 2.96115130e-01 5.55967748e-01 7.68681645e-01
-2.96893537e-01 -5.38916230e-01 -8.23210478e-02 6.50607236e-03
-8.33187044e-01 7.41438419e-02 -4.24344689e-02 5.46042919e-01
-6.87333271e-02 9.90338147e-01 4.45597827e-01 -3.28497827e-01
-1.42661735e-01 -1.15962788e-01 3.94385636e-01 -2.44042620e-01
-2.81871319e-01 1.25112474e-01 1.54601121e-02 -1.64038730e+00
-6.49834454e-01 -4.30425972e-01 -1.24259472e+00 -7.66718686e-01
1.99180812e-01 -1.62829235e-01 2.50795871e-01 7.64933407e-01
7.12643683e-01 4.67586741e-02 6.14914417e-01 -1.34241962e+00
-2.31820762e-01 -7.32361257e-01 -2.93340147e-01 7.24178493e-01
8.90433490e-01 -6.17804766e-01 -3.73148650e-01 2.23069280e-01] | [13.58696460723877, 1.761581540107727] |
ca59ae54-44a3-4d23-b5d1-8ec884cad3da | adaptive-student-s-t-distribution-with-method | 2304.03069 | null | https://arxiv.org/abs/2304.03069v2 | https://arxiv.org/pdf/2304.03069v2.pdf | Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series | The real life time series are usually nonstationary, bringing a difficult question of model adaptation. Classical approaches like ARMA-ARCH assume arbitrary type of dependence. To avoid such bias, we will focus on recently proposed agnostic philosophy of moving estimator: in time $t$ finding parameters optimizing e.g. $F_t=\sum_{\tau<t} (1-\eta)^{t-\tau} \ln(\rho_\theta (x_\tau))$ moving log-likelihood, evolving in time. It allows for example to estimate parameters using inexpensive exponential moving averages (EMA), like absolute central moments $E[|x-\mu|^p]$ evolving for one or multiple powers $p\in\mathbb{R}^+$ using $m_{p,t+1} = m_{p,t} + \eta (|x_t-\mu_t|^p-m_{p,t})$. Application of such general adaptive methods of moments will be presented on Student's t-distribution, popular especially in economical applications, here applied to log-returns of DJIA companies. While standard ARMA-ARCH approaches provide evolution of $\mu$ and $\sigma$, here we also get evolution of $\nu$ describing $\rho(x)\sim |x|^{-\nu-1}$ tail shape, probability of extreme events - which might turn out catastrophic, destabilizing the market. | ['Jarek Duda'] | 2023-04-06 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-3.99459213e-01 -2.41275191e-01 -3.55439857e-02 -2.27160335e-01
-9.35600936e-01 -6.19686067e-01 2.43258566e-01 -1.61683813e-01
-5.88081956e-01 1.16891491e+00 -6.07583821e-01 -9.08299685e-01
-7.72116542e-01 -9.94005322e-01 -6.57430530e-01 -9.24019754e-01
-7.99030960e-01 4.63863045e-01 -1.36974752e-01 -1.57555953e-01
3.51732641e-01 2.67152637e-01 -1.30936503e+00 -4.38526124e-01
7.84032583e-01 1.44478858e+00 -2.17067137e-01 7.17922509e-01
-1.13167800e-01 2.33591199e-01 -7.49243200e-01 -5.84603727e-01
4.09079909e-01 -5.58569193e-01 -2.06911027e-01 -5.25239289e-01
-5.30327260e-01 1.28761426e-01 1.10521927e-01 1.04810369e+00
1.13172323e-01 3.61091137e-01 1.26003492e+00 -9.36170459e-01
-5.59368312e-01 6.33281767e-01 -1.17438912e+00 6.64877713e-01
-1.02600686e-01 9.89827588e-02 4.71203119e-01 -4.49520558e-01
-4.73373681e-02 8.26198399e-01 8.04899335e-01 1.25977531e-01
-1.19472241e+00 -1.04330182e+00 2.17278153e-02 -2.65585691e-01
-1.37462330e+00 1.41951740e-01 5.52767217e-01 -4.21997964e-01
8.97392452e-01 4.76319492e-01 5.35430133e-01 5.70530117e-01
8.04435253e-01 2.07001165e-01 1.22097659e+00 -4.89425957e-01
2.41328806e-01 7.76199773e-02 1.90929234e-01 1.37903437e-01
2.67841339e-01 1.01381570e-01 7.53228888e-02 -2.73438334e-01
1.07505059e+00 1.15926430e-01 2.26393327e-01 5.91145813e-01
-6.85401857e-01 9.28947687e-01 -3.74068856e-01 5.32445192e-01
-6.94703877e-01 4.82544839e-01 1.54890209e-01 5.44273794e-01
6.76755190e-01 -5.61584979e-02 -1.14409029e+00 -5.60788333e-01
-8.58312666e-01 2.08656281e-01 5.83232760e-01 9.65695143e-01
5.01131117e-01 5.00933945e-01 -4.89755981e-02 4.59454805e-01
3.52673084e-01 8.47365141e-01 4.08999979e-01 -1.03645825e+00
3.43655020e-01 1.22275099e-01 3.81675661e-01 -3.31491798e-01
-4.20006186e-01 -5.21812260e-01 -9.17703629e-01 2.11950392e-01
8.43490660e-01 -6.81632221e-01 -4.38809514e-01 1.98075497e+00
-6.53954819e-02 -3.18916775e-02 -3.41633499e-01 1.23637840e-02
-5.42498589e-01 9.85642254e-01 3.62871140e-01 -1.32926822e+00
1.29593015e+00 -8.95857587e-02 -4.91432577e-01 1.65103614e-01
2.14665040e-01 -8.76332760e-01 9.88353252e-01 6.00243866e-01
-1.67338634e+00 -3.81923318e-01 -2.13724419e-01 9.55717027e-01
-1.86287418e-01 -2.17033952e-01 5.51787794e-01 9.36634541e-01
-8.04957509e-01 7.78313994e-01 -8.91089857e-01 1.77762464e-01
2.25912213e-01 4.50523257e-01 1.50338903e-01 5.05661726e-01
-1.01899707e+00 5.92568099e-01 3.28453779e-02 1.76993944e-02
-5.88339984e-01 -8.60069633e-01 -2.75627822e-01 1.08238600e-01
1.30248293e-01 -4.76618439e-01 1.03713286e+00 -8.34719837e-01
-1.33490741e+00 4.77394223e-01 -5.83453923e-02 -6.25848949e-01
4.05712485e-01 2.51577664e-02 -9.03712571e-01 -3.72518063e-01
1.96532726e-01 -9.06052366e-02 9.88765478e-01 -7.97178030e-01
-7.55624652e-01 -8.08625281e-01 -5.49554944e-01 -4.91644740e-01
-3.76535282e-02 3.25451136e-01 3.20821404e-01 -8.60818863e-01
2.76301969e-02 -8.01335216e-01 -3.31163913e-01 -8.04238439e-01
2.55429614e-02 -2.48353377e-01 5.39729357e-01 -7.66456842e-01
1.82256269e+00 -1.94933856e+00 -4.54054415e-01 6.60320282e-01
-4.23449814e-01 -8.72552767e-02 6.55586898e-01 5.19182324e-01
-6.39271736e-01 2.46117800e-01 -4.50005770e-01 2.66180784e-01
4.49776417e-03 1.01016015e-02 -3.48995000e-01 6.11943662e-01
-8.75818133e-02 4.31764156e-01 -4.54477549e-01 2.70964485e-03
-1.93008631e-02 3.13282251e-01 -1.01746045e-01 -4.45575148e-01
-3.59938711e-01 5.37771344e-01 -6.17087066e-01 4.34563369e-01
7.14153528e-01 -1.83232293e-01 -1.99069992e-01 5.50769508e-01
-7.77225912e-01 -2.62452155e-01 -1.32008410e+00 9.21614230e-01
-2.80776441e-01 2.20214635e-01 1.65825039e-01 -1.55827439e+00
9.97135878e-01 3.79002184e-01 8.39891076e-01 -6.25182152e-01
5.38159609e-01 3.85490119e-01 1.43532291e-01 -1.10530749e-01
1.52901411e-01 -7.53487825e-01 -2.20003635e-01 6.51101589e-01
-1.68310732e-01 -8.24837610e-02 1.88499317e-01 -5.30271530e-01
9.98705149e-01 4.77331057e-02 1.77484795e-01 -5.93421638e-01
4.11873937e-01 -5.11148535e-02 4.89021838e-01 6.66633248e-01
-1.75620943e-01 -1.79557651e-01 1.12653160e+00 -1.50576755e-01
-1.11265767e+00 -1.24648678e+00 -7.01869488e-01 1.14646864e+00
-3.60072166e-01 3.41366023e-01 -6.31760538e-01 -8.86829272e-02
-8.14469904e-02 1.51848793e+00 -6.76393330e-01 -1.86269358e-02
-6.38759017e-01 -1.57192671e+00 2.54653454e-01 3.88331026e-01
3.60329449e-01 -1.17182958e+00 -8.39357376e-01 5.03980458e-01
3.77771467e-01 5.51964119e-02 -3.48629683e-01 6.00161493e-01
-1.17894435e+00 -4.95875508e-01 -9.28729832e-01 2.16127113e-02
4.41512018e-01 -4.63167220e-01 1.14115560e+00 -5.62970817e-01
-5.11038899e-01 5.08276761e-01 -3.18247750e-02 -1.23650467e+00
-1.76494539e-01 -4.62851793e-01 1.72302634e-01 -1.72310567e-03
4.00391370e-01 -9.92976248e-01 -9.89354074e-01 3.52141857e-01
-9.44257140e-01 -1.10484684e+00 2.32986480e-01 6.44231260e-01
6.69621170e-01 3.91935766e-01 1.00160563e+00 -4.53388989e-01
5.76604128e-01 -6.17622852e-01 -1.11700189e+00 1.72404259e-01
-8.96229744e-01 -1.76213026e-01 4.88837361e-01 -7.58660197e-01
-1.00552022e+00 -5.13055384e-01 1.25121337e-03 -4.72542673e-01
1.50890827e-01 6.92652643e-01 3.60560268e-01 6.05527341e-01
6.04260027e-01 2.73717105e-01 -5.65143451e-02 -4.65277791e-01
7.96715692e-02 3.14401448e-01 6.91879690e-01 -7.78509021e-01
3.72436136e-01 4.71271992e-01 3.00383657e-01 -4.07916009e-01
-1.98328644e-01 -6.87669739e-02 -1.05712451e-01 3.37558128e-02
7.85623312e-01 -7.16415763e-01 -1.11938250e+00 3.14182848e-01
-6.20251954e-01 -2.93892831e-01 -6.91696048e-01 8.62508595e-01
-9.07350898e-01 -4.73625436e-02 -4.47020262e-01 -1.63935220e+00
-3.87611359e-01 -5.51374972e-01 3.71189833e-01 5.12345970e-01
-2.23299026e-01 -1.20958018e+00 1.81150243e-01 -1.11231379e-01
5.07071853e-01 2.43231669e-01 1.22100258e+00 -7.31595755e-01
-9.16781947e-02 -5.93543530e-01 9.92820933e-02 5.20934999e-01
-4.70628738e-02 3.15376252e-01 -3.88349116e-01 -2.25525215e-01
5.59321344e-01 6.67895079e-01 2.85673261e-01 1.28579366e+00
1.09671438e+00 -4.94502962e-01 -2.20343426e-01 1.84990898e-01
1.47699320e+00 1.00135803e+00 8.94352198e-01 2.85780430e-01
-4.66912240e-01 2.15278909e-01 7.06034601e-01 1.21826208e+00
-5.12815528e-02 2.97776550e-01 3.27566624e-01 4.30083752e-01
9.43388462e-01 3.18230361e-01 6.76517367e-01 4.05463487e-01
-5.72229266e-01 -7.50982612e-02 -8.34231913e-01 7.50373602e-01
-1.33768761e+00 -1.12122977e+00 -4.53206927e-01 2.88187075e+00
7.81895339e-01 4.97408748e-01 4.32647198e-01 -2.35874638e-01
8.00846815e-01 -3.19562316e-01 -3.65299433e-01 -5.57265639e-01
-5.60818240e-02 1.15337062e+00 8.21239531e-01 2.77033150e-01
-5.80730736e-01 3.75381410e-01 5.54162169e+00 1.27657485e+00
-9.58678424e-01 2.20829308e-01 9.84360754e-01 -2.42828175e-01
-2.70420611e-01 4.09222484e-01 -7.89568186e-01 1.11896598e+00
1.47441244e+00 -6.69057190e-01 4.46750037e-02 9.92797256e-01
5.24323285e-01 -6.72733068e-01 -7.91377127e-01 7.55142093e-01
-4.86681491e-01 -7.34001160e-01 -3.97139311e-01 4.86460358e-01
7.47470319e-01 -4.60850060e-01 6.97206676e-01 6.72033668e-01
5.67322612e-01 -9.93716419e-01 5.62728584e-01 7.57273197e-01
7.43422568e-01 -1.19518018e+00 5.46698749e-01 6.69987559e-01
-1.13757348e+00 -3.40465516e-01 -3.42985928e-01 -1.80096656e-01
6.85166419e-01 9.76108730e-01 -1.40920654e-01 7.06725478e-01
9.71033931e-01 -2.23021284e-01 1.24388963e-01 6.63305521e-01
4.20965761e-01 6.29435658e-01 -6.92289829e-01 2.43650779e-01
2.26806492e-01 -9.64727521e-01 6.00800514e-01 7.99281836e-01
1.17361403e+00 5.63715100e-01 -3.58131617e-01 9.26129043e-01
5.10411382e-01 9.36282799e-02 -3.93417865e-01 7.10793119e-03
3.91659141e-01 8.23942304e-01 -9.19650733e-01 -3.32653731e-01
-4.09326136e-01 2.96809822e-01 -5.48108101e-01 3.16240042e-01
-1.28116727e+00 -6.24005675e-01 5.37132263e-01 4.69664216e-01
3.56208533e-01 -4.18281376e-01 -4.37922627e-01 -5.92971683e-01
-5.34260385e-02 -4.70570564e-01 7.49630392e-01 -5.16448915e-01
-1.27248847e+00 1.00872025e-01 4.80003297e-01 -1.19119561e+00
-6.37736619e-01 -6.20188117e-01 -8.01340401e-01 1.20344722e+00
-7.90920973e-01 -5.59476972e-01 7.47165561e-01 8.21093559e-01
3.49931866e-01 -4.40269738e-01 5.51330805e-01 2.67062068e-01
-4.27981824e-01 4.90726858e-01 6.37953281e-01 -2.81269521e-01
3.73890638e-01 -8.77270103e-01 3.54643725e-02 4.45754111e-01
-3.03695351e-01 6.51845634e-01 9.65278566e-01 -9.01202023e-01
-8.80142808e-01 -5.36818564e-01 7.04973936e-01 -5.29501438e-01
1.11809134e+00 4.36982661e-01 -8.71214867e-01 1.04121459e+00
1.23293474e-01 -2.43321940e-01 7.04268396e-01 -2.26716593e-01
1.33365601e-01 -1.05914302e-01 -1.34609520e+00 5.87120295e-01
3.49282652e-01 5.82972262e-03 -2.70800233e-01 9.66414362e-02
2.65320241e-01 1.67927265e-01 -1.34370399e+00 5.51158547e-01
5.58238864e-01 -1.32630122e+00 8.40613008e-01 -5.31015515e-01
-2.02043280e-01 2.47094423e-01 -2.03041673e-01 -9.30998325e-01
3.56526896e-02 -1.03892982e+00 2.26400301e-01 1.29547513e+00
6.12926483e-01 -1.12167513e+00 4.54746544e-01 8.46178770e-01
-1.30562708e-01 -4.12684530e-01 -1.76184893e+00 -9.29621816e-01
8.20967376e-01 -5.92395306e-01 3.84123027e-01 8.81390929e-01
-1.79912299e-01 -3.99509251e-01 -2.01063007e-01 -6.48093224e-02
7.71489203e-01 -3.78434479e-01 3.31610888e-01 -1.31436145e+00
-5.57536721e-01 -7.14174390e-01 1.08155161e-01 -6.00367844e-01
-1.80525631e-01 -2.77934194e-01 -4.87533420e-01 -5.83461344e-01
1.11874051e-01 -4.68558073e-01 -6.32378638e-01 1.29487574e-01
1.87754363e-01 -4.05032933e-01 -2.81193644e-01 -3.70275485e-03
7.07335100e-02 2.36930594e-01 8.68803918e-01 3.06730032e-01
-4.28642899e-01 5.67340672e-01 -3.35228980e-01 8.46536100e-01
6.38796210e-01 -6.79096639e-01 -3.56377125e-01 6.93724453e-02
5.04994988e-01 1.01134145e+00 2.24622726e-01 -8.06041777e-01
-1.15268648e-01 -5.74868858e-01 3.84798586e-01 -7.87366390e-01
1.65455446e-01 -7.34121144e-01 9.45829690e-01 4.35735047e-01
2.65244693e-01 6.82314813e-01 3.20474207e-01 5.75862229e-01
4.14667763e-02 -3.92799586e-01 8.20942700e-01 -5.08661211e-01
2.04015419e-01 1.48060784e-01 -4.02972043e-01 -2.25126386e-01
1.33409548e+00 -9.45150182e-02 -1.47679806e-01 -6.59783542e-01
-9.38832164e-01 1.42985880e-01 9.59639400e-02 -1.51209220e-01
-1.34432003e-01 -1.01745903e+00 -6.73677027e-01 8.86934716e-03
-5.51829934e-01 3.23727690e-02 7.79864728e-01 1.24509597e+00
-2.87248939e-01 1.57340065e-01 -1.68150485e-01 -5.13320148e-01
-5.51182568e-01 6.83943987e-01 3.24566036e-01 -5.10907412e-01
-9.84166339e-02 8.63668263e-01 1.90457911e-03 3.11902106e-01
-1.79216206e-01 -2.31946200e-01 2.05031127e-01 3.30359817e-01
3.03915173e-01 8.28723252e-01 -2.25692794e-01 -2.29332224e-01
-2.10027292e-01 5.90317786e-01 1.98487211e-02 -7.03818321e-01
1.42673731e+00 -2.81006664e-01 -4.66982096e-01 9.12503660e-01
8.62336040e-01 -1.25719115e-01 -1.34137309e+00 2.85372615e-01
2.59605795e-01 -2.29705781e-01 -6.53654516e-01 -4.72311974e-01
-7.58287013e-01 6.56334817e-01 8.88390303e-01 7.97214687e-01
9.60432410e-01 9.46502551e-04 3.43436331e-01 -2.00662270e-01
5.25626838e-01 -1.39181209e+00 -1.80483505e-01 4.47751790e-01
5.91346502e-01 -7.85340726e-01 -8.27650055e-02 3.32167774e-01
-4.42606747e-01 9.12154734e-01 -4.75785956e-02 -2.71815121e-01
1.18639565e+00 2.03392282e-01 -1.67806163e-01 -1.05491616e-01
-5.25453687e-01 2.57425964e-01 -1.57085523e-01 8.69890898e-02
6.65943682e-01 1.52834460e-01 -9.69177365e-01 1.16754234e+00
-3.91678751e-01 -5.27007990e-02 2.11717829e-01 1.08698940e+00
-5.10029614e-01 -1.22283733e+00 -7.53881335e-01 5.98392904e-01
-1.08784735e+00 2.90361345e-02 6.01533890e-01 1.28291309e+00
4.81438078e-02 6.49147630e-01 4.80686754e-01 3.42424721e-01
3.45274508e-01 7.16421843e-01 4.11839575e-01 4.14074585e-02
-4.66336429e-01 8.15094829e-01 -3.76906544e-01 -4.67336131e-03
-9.77842137e-02 -1.27294147e+00 -1.30169857e+00 -4.83263910e-01
-1.69476151e-01 3.99701178e-01 8.12696695e-01 7.03410804e-01
-2.06259862e-01 1.22101471e-01 7.76414514e-01 -6.06742620e-01
-9.99825418e-01 -1.03133082e+00 -1.40564537e+00 4.34724241e-02
-1.78666227e-02 -6.25092506e-01 -7.46498525e-01 9.44396779e-02] | [6.245291709899902, 4.163983345031738] |
b2bdac5c-5fd9-4964-9202-be853ea2f13c | purifier-defending-data-inference-attacks-via | 2212.00612 | null | https://arxiv.org/abs/2212.00612v1 | https://arxiv.org/pdf/2212.00612v1.pdf | Purifier: Defending Data Inference Attacks via Transforming Confidence Scores | Neural networks are susceptible to data inference attacks such as the membership inference attack, the adversarial model inversion attack and the attribute inference attack, where the attacker could infer useful information such as the membership, the reconstruction or the sensitive attributes of a data sample from the confidence scores predicted by the target classifier. In this paper, we propose a method, namely PURIFIER, to defend against membership inference attacks. It transforms the confidence score vectors predicted by the target classifier and makes purified confidence scores indistinguishable in individual shape, statistical distribution and prediction label between members and non-members. The experimental results show that PURIFIER helps defend membership inference attacks with high effectiveness and efficiency, outperforming previous defense methods, and also incurs negligible utility loss. Besides, our further experiments show that PURIFIER is also effective in defending adversarial model inversion attacks and attribute inference attacks. For example, the inversion error is raised about 4+ times on the Facescrub530 classifier, and the attribute inference accuracy drops significantly when PURIFIER is deployed in our experiment. | ['Kui Ren', 'Fan Zhang', 'Ee-Chien Chang', 'Ziming Zhao', 'Jie Wan', 'Da Yang', 'Lijin Wang', 'Ziqi Yang'] | 2022-12-01 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 3.03654343e-01 3.58126044e-01 6.00658134e-02 -5.04000664e-01
-6.46073937e-01 -1.04615045e+00 3.75369191e-01 3.23696807e-02
-3.57396543e-01 9.68173862e-01 -4.37055826e-01 -3.39417636e-01
3.38544697e-02 -1.12236631e+00 -1.02158642e+00 -9.35310185e-01
-2.52430469e-01 4.60635394e-01 2.80662179e-01 1.41818166e-01
1.59599647e-01 5.81302583e-01 -1.55327868e+00 2.53975242e-01
8.94405603e-01 1.43759346e+00 -7.53920972e-01 4.14760649e-01
2.51607716e-01 7.00638354e-01 -1.05423915e+00 -9.90226328e-01
3.50398421e-01 8.91437605e-02 -5.93625426e-01 -8.76976371e-01
4.85375971e-01 -6.53903663e-01 -3.00200254e-01 1.66062200e+00
2.69789934e-01 -1.79290101e-01 6.41666889e-01 -1.85555601e+00
-4.31211472e-01 9.26582754e-01 -4.03227955e-01 -2.58448154e-01
1.78132996e-01 -1.42260671e-01 6.63491845e-01 -3.97798747e-01
1.69216186e-01 1.49349988e+00 5.96386731e-01 8.06093693e-01
-1.15302300e+00 -1.65650582e+00 1.07934676e-01 2.79604018e-01
-1.61850309e+00 -4.24471319e-01 5.55319667e-01 -1.14743084e-01
2.13134125e-01 6.67097449e-01 -3.21892858e-03 1.51457953e+00
1.25763521e-01 6.86835647e-01 1.16893864e+00 9.07284245e-02
5.42247355e-01 3.77378464e-01 2.51434147e-01 5.57619810e-01
5.07172108e-01 5.60192049e-01 -1.38039365e-01 -9.73707736e-01
3.26564968e-01 -9.10111004e-04 -3.17219645e-01 -7.71927088e-02
-6.46401465e-01 9.96971488e-01 3.92151743e-01 -5.67444384e-01
3.17479074e-02 9.97052789e-02 5.58270574e-01 4.52625245e-01
2.45819479e-01 1.08166598e-01 -6.79730415e-01 3.99642020e-01
-3.76314670e-01 2.59079158e-01 1.04229081e+00 8.82852554e-01
5.63718021e-01 7.63615295e-02 2.54225340e-02 2.61186302e-01
3.90952229e-01 9.47387993e-01 1.71103030e-01 -7.73837030e-01
2.30637014e-01 2.63586104e-01 -9.67387706e-02 -1.02363384e+00
8.34354982e-02 -1.55536085e-01 -9.31431115e-01 5.71671605e-01
4.06839788e-01 -6.49123847e-01 -7.49511123e-01 2.17152500e+00
5.11727870e-01 6.37064934e-01 3.88962507e-01 4.21298414e-01
6.94397092e-01 4.40084755e-01 2.42353588e-01 -6.79471120e-02
1.43501639e+00 -1.76516727e-01 -5.83232522e-01 3.09447460e-02
2.47804344e-01 -4.01233226e-01 6.44735515e-01 5.48741698e-01
-7.20016062e-01 -3.35723102e-01 -1.46239150e+00 5.59635162e-01
-5.53332448e-01 -3.40708464e-01 6.56995773e-01 1.34642398e+00
-4.30702984e-01 5.36140084e-01 -6.93187594e-01 3.97172630e-01
9.58020568e-01 7.53792822e-01 -6.06938839e-01 3.85726020e-02
-1.68343687e+00 6.17883623e-01 7.07213759e-01 -3.91974568e-01
-1.12234628e+00 -9.38545167e-01 -7.50357568e-01 1.58296734e-01
3.25858682e-01 -2.97249913e-01 7.20742524e-01 -6.95837736e-01
-1.20407033e+00 6.00989282e-01 3.97576243e-01 -8.09689820e-01
6.79841697e-01 -1.18126445e-01 -6.52377069e-01 -1.26108127e-02
-3.59047413e-01 3.63861114e-01 1.13882756e+00 -1.05911279e+00
-6.88699663e-01 -6.83873236e-01 2.08196983e-01 -2.56595969e-01
-4.13060606e-01 -9.39683244e-02 1.55524492e-01 -5.43321848e-01
-2.08185881e-01 -8.19220781e-01 8.27733874e-02 1.83645681e-01
-8.91553819e-01 1.70130248e-03 1.23747671e+00 -3.45032334e-01
8.09548676e-01 -2.47504020e+00 -3.41126293e-01 7.89578974e-01
1.17025174e-01 6.16897881e-01 1.81363299e-01 -2.27678597e-01
-1.48575902e-01 2.96679050e-01 -3.04597110e-01 1.92028031e-01
1.20526485e-01 4.12513047e-01 -8.87778461e-01 6.24428034e-01
8.88196677e-02 4.07023281e-01 -3.91734153e-01 -1.56926557e-01
-1.65322080e-01 5.71451843e-01 -5.80590665e-01 3.53849739e-01
-6.77445978e-02 9.31444578e-03 -4.57995653e-01 5.76172650e-01
1.30884898e+00 2.24871397e-01 3.21619213e-01 -2.30075210e-01
7.67629802e-01 -4.35470929e-03 -1.35458267e+00 5.92673779e-01
1.28014768e-02 1.78384408e-01 2.68675894e-01 -9.63320255e-01
1.19944322e+00 2.41908535e-01 -2.88864166e-01 -4.79204692e-02
5.28615296e-01 5.27748745e-03 9.36107337e-02 2.27354523e-02
-1.07307516e-01 -6.43473193e-02 -3.11411768e-01 4.96192783e-01
-1.09527208e-01 3.78125191e-01 -4.94242340e-01 1.96032032e-01
8.53317618e-01 -3.55005026e-01 9.34565589e-02 -3.43605548e-01
8.74861121e-01 -5.91105461e-01 7.88339555e-01 8.73977602e-01
-3.50295812e-01 1.48861915e-01 8.01838994e-01 -3.02089870e-01
-5.03823698e-01 -1.59108067e+00 -4.83788729e-01 1.00313258e+00
3.49960923e-01 -5.08672446e-02 -1.05449510e+00 -1.36131907e+00
5.38375497e-01 7.53910899e-01 -7.85435379e-01 -8.31097007e-01
-2.22935840e-01 -8.59445989e-01 1.32773280e+00 6.19251013e-01
7.90849805e-01 -7.23872364e-01 -3.28745171e-02 -2.40536451e-01
-4.55583483e-02 -1.02200878e+00 -2.04668984e-01 1.94880903e-01
-4.70324367e-01 -1.38925159e+00 2.27862880e-01 -4.88164723e-01
7.61117041e-01 -4.39574599e-01 6.71058238e-01 2.42223158e-01
-6.05280884e-02 -2.57667601e-01 2.54755467e-02 -8.26665938e-01
-6.00048184e-01 -3.14214855e-01 4.79449630e-01 1.87720284e-01
5.40268183e-01 -7.66042292e-01 -2.77827084e-01 5.16614556e-01
-8.71353090e-01 -6.64557338e-01 3.74390095e-01 7.04531729e-01
4.32089418e-01 3.45907211e-01 9.96912181e-01 -1.39535451e+00
5.58505476e-01 -5.76791406e-01 -7.19474256e-01 3.69424105e-01
-7.21139431e-01 2.16203555e-02 9.58416522e-01 -7.96206951e-01
-8.68417561e-01 -5.20594381e-02 -5.04485488e-01 -4.21209961e-01
-2.73649842e-01 -5.14951535e-02 -9.01825547e-01 -2.26399764e-01
8.24888110e-01 1.19293034e-01 3.06554914e-01 -3.39428335e-01
9.32928920e-02 6.79895282e-01 8.41843843e-01 -8.56032073e-01
1.30190790e+00 5.36822140e-01 1.75753653e-01 -3.61304909e-01
-7.86694229e-01 4.49809641e-01 -2.12655976e-01 2.52212197e-01
4.29613113e-01 -7.99942791e-01 -1.35630691e+00 8.22624564e-01
-7.91108787e-01 2.08855882e-01 -3.99982333e-02 1.65672064e-01
-1.57613546e-01 4.47517067e-01 -6.05052829e-01 -8.36267948e-01
-6.73114419e-01 -1.19475722e+00 3.36947799e-01 3.57518405e-01
9.23058577e-03 -8.03751230e-01 -6.37526929e-01 2.83885360e-01
2.78830320e-01 7.15867758e-01 1.08869684e+00 -1.55840278e+00
-2.48813823e-01 -7.65295923e-01 -2.08488435e-01 5.65282524e-01
-1.13085009e-01 1.40313193e-01 -1.34171855e+00 -4.18739378e-01
7.45643377e-02 -6.73093796e-01 6.48444712e-01 -8.09858844e-04
1.58783364e+00 -7.98366725e-01 -2.15701193e-01 7.81865656e-01
1.06709456e+00 3.12374502e-01 9.06718135e-01 3.39407995e-02
4.07867372e-01 4.84539807e-01 6.09922230e-01 3.69449228e-01
-1.38737217e-01 2.46086746e-01 8.61623108e-01 2.36893028e-01
6.19741201e-01 -2.66699463e-01 4.09034103e-01 -2.86547154e-01
3.60253066e-01 5.59426961e-04 -3.78554434e-01 -1.42554894e-01
-1.53993452e+00 -1.05907798e+00 1.96573883e-01 2.46673322e+00
1.10887229e+00 4.79970396e-01 -7.13165035e-04 3.65272373e-01
9.00018513e-01 -1.44295454e-01 -8.85020852e-01 -5.14475405e-01
-2.96657458e-02 2.99552530e-01 5.31832874e-01 5.19546509e-01
-1.33486724e+00 7.28974402e-01 6.38962507e+00 1.10934925e+00
-7.04024911e-01 -9.32898894e-02 9.65899110e-01 -3.40080671e-02
-5.26667647e-02 -2.40193456e-01 -9.85490739e-01 6.28872156e-01
8.77410591e-01 -2.43194476e-01 3.02811623e-01 1.21983135e+00
-8.06753039e-01 3.24957669e-01 -1.24470663e+00 5.71311474e-01
5.58742173e-02 -1.15980804e+00 4.88113500e-02 2.29155228e-01
1.44572183e-01 -3.13852012e-01 5.22043169e-01 5.70222139e-01
7.79833555e-01 -1.20908976e+00 4.54686433e-01 1.70479387e-01
9.54103470e-01 -1.63480961e+00 1.02380824e+00 4.76664633e-01
-7.13920057e-01 -2.16589168e-01 -5.99609613e-01 1.81314602e-01
-5.13581753e-01 2.61928618e-01 -6.96815908e-01 3.49110156e-01
6.12543344e-01 -7.21096760e-03 -3.47307205e-01 2.94862658e-01
-4.52608615e-01 7.01252639e-01 -4.46612060e-01 3.00628096e-02
-1.40293181e-01 -2.52943486e-02 4.40674841e-01 8.03656399e-01
-1.65906847e-01 8.99780076e-03 1.99029401e-01 8.56516838e-01
-3.63161713e-01 -3.91809821e-01 -4.33350861e-01 3.23227137e-01
1.08654189e+00 1.15968394e+00 -1.29866838e-01 -3.64449233e-01
2.37000540e-01 6.54050827e-01 2.50894159e-01 1.19985417e-01
-1.15840125e+00 -7.28468180e-01 1.27031863e+00 -2.20368236e-01
2.56647795e-01 6.11389101e-01 -1.32835031e-01 -8.41745019e-01
-2.42979929e-01 -1.16444254e+00 7.68039763e-01 -2.33851418e-01
-1.59461021e+00 5.19160688e-01 -1.14834483e-03 -8.19018662e-01
-6.93717301e-02 -7.48545349e-01 -8.41409028e-01 9.77785230e-01
-1.10932362e+00 -8.66441965e-01 1.06894337e-01 1.00057900e+00
-2.46242762e-01 -6.16288781e-01 1.18746519e+00 6.06025159e-02
-8.37604523e-01 1.58581889e+00 6.54713959e-02 7.56931722e-01
5.74707389e-01 -1.04453349e+00 3.12745214e-01 7.31195629e-01
-1.45177096e-01 8.13710511e-01 7.40032852e-01 -4.81312394e-01
-1.20048535e+00 -1.21497118e+00 6.65220320e-02 -4.91760314e-01
5.38653374e-01 -5.46262503e-01 -1.04153919e+00 6.32319272e-01
-3.58444363e-01 2.45186180e-01 1.20663977e+00 1.39096826e-01
-1.14365494e+00 -3.84266913e-01 -2.18529701e+00 5.95215082e-01
5.05583346e-01 -5.89326203e-01 -4.56294805e-01 1.01838663e-01
8.06798339e-01 -3.02455723e-01 -1.05143070e+00 5.40899754e-01
8.04730892e-01 -7.54295766e-01 1.44860363e+00 -1.03373420e+00
1.50573477e-01 -1.18345216e-01 -5.08381009e-01 -9.43988562e-01
-1.70950025e-01 -3.73021066e-01 -4.82035369e-01 1.53364813e+00
5.10284126e-01 -1.15995026e+00 9.47130024e-01 7.88053393e-01
5.71994603e-01 -4.93882090e-01 -1.16098130e+00 -7.43780673e-01
3.62566948e-01 -2.36717224e-01 1.16103339e+00 1.13987446e+00
-1.57948166e-01 1.99521072e-02 -4.98472661e-01 7.43563414e-01
1.16379976e+00 -7.34820291e-02 6.99596405e-01 -1.53344083e+00
-4.14916843e-01 -1.18955530e-01 -6.62047386e-01 -3.77300531e-01
5.40899456e-01 -7.67912209e-01 -2.73668617e-01 -3.61789912e-01
-3.20880413e-02 -5.41455626e-01 -6.82374775e-01 7.72639513e-01
-3.36057842e-01 4.43217725e-01 -3.71637084e-02 -9.70455706e-02
-2.38995582e-01 2.54405469e-01 7.31162190e-01 -2.43121356e-01
2.84585804e-01 3.62472415e-01 -1.11096311e+00 8.65992785e-01
9.80913401e-01 -7.43357778e-01 -5.46821952e-01 3.17991585e-01
-1.16176493e-01 -3.27804759e-02 3.62343341e-01 -1.02099025e+00
1.01136253e-03 -1.34516060e-01 7.53280282e-01 -4.88817632e-01
2.88690954e-01 -1.11098552e+00 1.33654103e-01 8.31983447e-01
-4.51824367e-01 -3.73123229e-01 2.03540370e-01 8.20866287e-01
2.37043604e-01 -1.84698731e-01 1.10835671e+00 3.08036119e-01
-2.22427338e-01 5.08910358e-01 -1.06787793e-01 2.20252514e-01
1.29815364e+00 -5.63655607e-02 -8.85674417e-01 -8.98357481e-02
-6.56076968e-01 2.71326065e-01 2.42555484e-01 2.12666929e-01
7.02104807e-01 -1.43668437e+00 -8.64715874e-01 7.95242369e-01
-3.56771313e-02 -1.79333195e-01 2.86674686e-02 2.78706312e-01
-2.67961264e-01 -2.79093295e-01 -5.19526720e-01 -2.22664580e-01
-1.63117063e+00 7.19912469e-01 3.11291099e-01 5.87211587e-02
-4.25241560e-01 1.12804687e+00 4.15814817e-01 -6.07840896e-01
5.78383505e-01 4.32777613e-01 -1.30583525e-01 -1.37472421e-01
1.05378163e+00 4.24427599e-01 -2.35633016e-01 -4.27631140e-01
-4.84955758e-01 -2.13240441e-02 -6.26896977e-01 2.77925372e-01
8.66453171e-01 4.47692156e-01 -6.39947802e-02 -2.28863075e-01
1.25620210e+00 6.40431941e-02 -1.20104337e+00 -2.20537469e-01
-3.35221261e-01 -6.01596296e-01 -2.30220050e-01 -1.03567326e+00
-1.20039642e+00 7.13061154e-01 6.15762234e-01 4.61775661e-01
1.07885695e+00 -2.57788032e-01 6.29183948e-01 4.55670893e-01
3.24428529e-01 -7.10335851e-01 -3.38716775e-01 2.48141423e-01
5.70158541e-01 -1.04304707e+00 -6.84858263e-02 -6.23009086e-01
-6.21467710e-01 7.22635269e-01 9.01038229e-01 -2.90377945e-01
8.83896112e-01 7.41615355e-01 -3.46727343e-03 2.13302553e-01
-7.22533941e-01 6.64066613e-01 9.64502916e-02 1.10480857e+00
-3.01916718e-01 2.91704714e-01 3.08054924e-01 1.05558598e+00
-3.76942098e-01 -5.76845884e-01 3.62238109e-01 5.66198707e-01
-4.80461597e-01 -1.04603899e+00 -6.30758703e-01 5.27399302e-01
-9.05601621e-01 2.75598392e-02 -5.64849734e-01 6.15831673e-01
1.05509177e-01 9.14077878e-01 9.06288624e-02 -9.02757883e-01
1.17357828e-01 2.48437688e-01 3.99445929e-02 -1.35616556e-01
-6.27144277e-01 -5.31652808e-01 1.48897111e-01 -4.02028054e-01
3.59094948e-01 -3.84040684e-01 -1.23331201e+00 -8.85273039e-01
-4.72068906e-01 3.30449224e-01 3.63458157e-01 7.98572421e-01
2.08314791e-01 3.08910072e-01 1.06196904e+00 -1.20763265e-01
-1.39073384e+00 -7.49713421e-01 -7.68113852e-01 4.24181521e-01
2.04252198e-01 -6.71179295e-01 -7.99506783e-01 -3.20374787e-01] | [5.8664751052856445, 7.290331840515137] |
d0ff97e1-2fc7-42e5-b12e-3e80c66ea4d4 | on-the-role-of-event-boundaries-in-egocentric | 1809.00402 | null | http://arxiv.org/abs/1809.00402v2 | http://arxiv.org/pdf/1809.00402v2.pdf | On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams | Event boundaries play a crucial role as a pre-processing step for detection,
localization, and recognition tasks of human activities in videos. Typically,
although their intrinsic subjectiveness, temporal bounds are provided manually
as input for training action recognition algorithms. However, their role for
activity recognition in the domain of egocentric photostreams has been so far
neglected. In this paper, we provide insights of how automatically computed
boundaries can impact activity recognition results in the emerging domain of
egocentric photostreams. Furthermore, we collected a new annotated dataset
acquired by 15 people by a wearable photo-camera and we used it to show the
generalization capabilities of several deep learning based architectures to
unseen users. | ['Estefania Talavera', 'Petia Radeva', 'Alejandro Cartas', 'Mariella Dimiccoli'] | 2018-09-02 | null | null | null | null | ['egocentric-activity-recognition'] | ['computer-vision'] | [ 5.04704177e-01 -1.56287849e-01 -1.98842272e-01 -3.87437373e-01
-2.42268533e-01 -5.59786856e-01 7.22761631e-01 1.16410799e-01
-7.85522580e-01 6.48217499e-01 5.39673209e-01 2.58541733e-01
-7.37229511e-02 -3.42476189e-01 -6.05108202e-01 -6.26485467e-01
-4.65268433e-01 -9.49916765e-02 1.19219624e-01 2.01544330e-01
2.51240283e-02 5.24150252e-01 -1.56065285e+00 3.62697035e-01
2.91323870e-01 1.09618211e+00 -2.82055974e-01 6.67076528e-01
7.34887362e-01 8.94783854e-01 -5.56244969e-01 -1.08889922e-01
2.48277456e-01 -3.48308057e-01 -4.99134481e-01 6.24256968e-01
4.93291378e-01 -5.26829302e-01 -7.63717771e-01 7.93952763e-01
3.27239722e-01 5.31127930e-01 5.67437470e-01 -1.22377932e+00
-2.62959421e-01 4.05202806e-01 -2.17866227e-01 7.64340937e-01
7.76579797e-01 2.95842916e-01 8.36808681e-01 -7.25259542e-01
7.02700853e-01 6.48449540e-01 3.66185814e-01 3.27471942e-01
-1.03911400e+00 -3.31332475e-01 1.59257725e-01 7.01444983e-01
-1.43678975e+00 -6.15935862e-01 7.52027750e-01 -6.27660275e-01
9.53385055e-01 1.00890212e-01 9.89096224e-01 1.80866158e+00
-1.54498788e-02 9.43728387e-01 6.56395018e-01 -9.83033925e-02
6.31081879e-01 -2.30853230e-01 -8.34575668e-02 2.38165498e-01
1.15592092e-01 -1.80837646e-01 -7.85665572e-01 7.59386346e-02
7.97618389e-01 2.07173437e-01 -5.19609690e-01 -5.19276023e-01
-1.43986392e+00 4.28586900e-01 2.82461792e-01 4.49062854e-01
-5.18523395e-01 3.05936992e-01 5.00977874e-01 1.58864409e-01
5.04431725e-01 3.34474981e-01 -8.95064846e-02 -7.03167081e-01
-7.45368958e-01 -9.93523672e-02 5.34279466e-01 6.85883224e-01
2.88530618e-01 6.23972937e-02 -4.25283968e-01 3.98311555e-01
-3.18134755e-01 2.11788610e-01 5.85861921e-01 -8.48744929e-01
3.03467005e-01 6.25665009e-01 4.97340739e-01 -9.60322797e-01
-5.59530914e-01 -2.73456603e-01 -5.54283977e-01 -2.05284193e-01
7.18610466e-01 -1.11340918e-01 -5.01101315e-01 1.64141595e+00
2.93395072e-01 3.21300209e-01 -1.21415839e-01 1.29437590e+00
1.57970458e-01 2.05860838e-01 2.97679335e-01 -2.26445258e-01
1.28849971e+00 -6.54497564e-01 -7.13436007e-01 -2.32638016e-01
7.38368988e-01 -2.25859299e-01 1.03883076e+00 7.37264991e-01
-9.54908609e-01 -6.12127900e-01 -9.24349308e-01 1.15459427e-01
-3.58782977e-01 4.19978738e-01 5.46487153e-01 5.82696378e-01
-6.74966872e-01 6.19359195e-01 -1.01810455e+00 -6.41480207e-01
6.40851855e-01 1.68336973e-01 -5.82767665e-01 9.32117105e-02
-1.13408387e+00 7.76259422e-01 3.67764652e-01 -9.42914840e-03
-1.11297274e+00 -1.95612416e-01 -9.44410980e-01 1.57134578e-01
6.55653536e-01 -2.97788233e-01 1.15725243e+00 -1.17821777e+00
-1.28144848e+00 8.41386139e-01 1.57732107e-02 -8.00517023e-01
8.11571240e-01 -5.36809742e-01 -5.01889765e-01 4.61248517e-01
-2.07593322e-01 3.50627154e-01 9.32188272e-01 -5.04020870e-01
-4.81335938e-01 -3.99387002e-01 4.50567126e-01 3.11977804e-01
-5.84319830e-01 -8.76600295e-02 -1.45112708e-01 -5.90858877e-01
-1.01220697e-01 -8.28091860e-01 3.80131043e-02 5.10260463e-03
1.67246126e-02 -3.05555820e-01 7.42915690e-01 -6.27574682e-01
1.13593256e+00 -2.38530827e+00 1.06146388e-01 -2.20325440e-02
1.20299179e-02 1.41608387e-01 6.56715035e-02 5.87754071e-01
-2.53459394e-01 -2.68677473e-01 -4.94435057e-02 -1.81327015e-01
1.31226871e-02 3.18350233e-02 -2.80120671e-01 8.27193677e-01
1.76753968e-01 8.15099657e-01 -1.14196694e+00 -3.09855968e-01
4.89610553e-01 5.32182872e-01 -5.07416606e-01 2.55300164e-01
-7.77825192e-02 7.07044184e-01 -2.50109464e-01 5.94943583e-01
9.43637826e-03 -8.14438984e-02 1.75455242e-01 -2.67974496e-01
6.64989352e-02 1.49822295e-01 -9.53035355e-01 1.85486937e+00
-2.46753082e-01 8.99376392e-01 -4.02375579e-01 -1.13524914e+00
3.96635413e-01 5.15373826e-01 8.61946106e-01 -6.85885966e-01
3.40529859e-01 -4.50135678e-01 8.86251181e-02 -8.32428634e-01
6.16737962e-01 2.90130436e-01 -9.79177803e-02 5.36563635e-01
1.13905184e-01 5.46135128e-01 5.77568233e-01 7.81528056e-02
1.40695333e+00 3.31337929e-01 6.24689817e-01 1.86282918e-01
4.26938415e-01 -1.45933693e-02 3.64864558e-01 7.78789461e-01
-6.37995005e-01 7.27202952e-01 4.44995940e-01 -5.85321486e-01
-9.93659139e-01 -1.00876749e+00 -4.36607599e-02 1.25743651e+00
1.27265289e-01 -4.81599241e-01 -9.31849778e-01 -9.18820798e-01
-3.03271115e-01 5.60209155e-01 -7.52452254e-01 -4.32238758e-01
-5.59234202e-01 -4.61842567e-01 5.27458787e-01 8.85039866e-01
5.85705459e-01 -1.17003369e+00 -1.14750552e+00 1.77027613e-01
-3.59322131e-01 -1.62298882e+00 -5.14210582e-01 -9.98194516e-03
-6.71399534e-01 -1.50207782e+00 -6.79058909e-01 -8.40098932e-02
6.52176619e-01 2.33886823e-01 7.96449065e-01 -3.17430079e-01
-4.08877581e-01 1.10516310e+00 -4.57571596e-01 -1.24712586e-01
4.79200929e-02 -1.36748061e-01 4.43842351e-01 6.63785517e-01
5.64670086e-01 -6.66829944e-01 -9.57785726e-01 5.42011321e-01
-8.30632448e-01 -1.71042964e-01 3.87030303e-01 3.63455653e-01
9.91381928e-02 -1.50817737e-01 2.33159035e-01 -5.00814378e-01
4.55517888e-01 -3.98509175e-01 -3.66508514e-01 5.59554435e-03
-4.72178906e-02 -4.09446895e-01 5.49522102e-01 -7.49218166e-01
-9.66136873e-01 3.09637785e-01 3.13021153e-01 -5.14779091e-01
-5.58793783e-01 3.26180130e-01 -2.00746223e-01 2.31802255e-01
8.43884110e-01 2.51810610e-01 -3.50297481e-01 -3.09002995e-01
1.27422214e-01 6.17524445e-01 5.57611942e-01 -2.09897369e-01
5.02927899e-01 8.58594894e-01 -1.15833253e-01 -1.10894513e+00
-8.10017407e-01 -6.93661094e-01 -9.73631024e-01 -6.14759326e-01
1.00350392e+00 -1.14054525e+00 -8.47218752e-01 4.39443707e-01
-8.92454147e-01 -4.02074516e-01 -3.74687016e-01 6.76269114e-01
-8.38381886e-01 5.79240680e-01 -2.66852438e-01 -8.96702111e-01
1.32122338e-01 -6.55825436e-01 8.74909878e-01 1.08545996e-01
-6.21182501e-01 -9.05590892e-01 -6.10284731e-02 5.76881886e-01
1.20626628e-01 4.24930871e-01 1.65051237e-01 -7.08138704e-01
-5.71437240e-01 -5.73932230e-01 -1.61176901e-02 3.31608534e-01
2.96936989e-01 -2.98090458e-01 -1.17985749e+00 -2.32629344e-01
7.31700435e-02 -2.86795259e-01 6.82009459e-01 1.79806426e-01
1.30518675e+00 -3.78553867e-01 -1.70716330e-01 3.30852211e-01
8.46182287e-01 1.48502722e-01 7.14590013e-01 1.43220887e-01
5.17614722e-01 4.60854083e-01 7.07421720e-01 8.31571400e-01
2.89542656e-02 6.76710904e-01 4.47649479e-01 3.70027035e-01
1.40630394e-01 -1.39963746e-01 5.33523738e-01 2.11057559e-01
-4.54994380e-01 -3.77328873e-01 -7.68199146e-01 6.25323057e-01
-1.96460092e+00 -1.29889226e+00 3.86626482e-01 2.23647666e+00
4.15450603e-01 1.81742206e-01 2.91726172e-01 2.79068500e-01
6.15461171e-01 3.81990969e-01 -6.11897945e-01 9.65209752e-02
9.17318538e-02 -2.21161768e-01 3.70045155e-01 -1.10207677e-01
-1.48585713e+00 6.48967206e-01 6.11223888e+00 3.16930115e-01
-1.01777291e+00 1.40147194e-01 2.55030960e-01 -4.19424415e-01
5.94940007e-01 -2.08733410e-01 -2.51225501e-01 5.34175456e-01
8.02873850e-01 1.63697042e-02 4.00849164e-01 1.10791373e+00
7.12671816e-01 -5.80658495e-01 -1.71473265e+00 1.41384912e+00
3.16972882e-01 -8.09158325e-01 -2.00634107e-01 3.81029360e-02
4.88596797e-01 -7.25295097e-02 -6.74199164e-02 2.83325106e-01
-2.47717619e-01 -7.66961634e-01 5.77868283e-01 5.48254311e-01
4.84460533e-01 -4.30610985e-01 3.28170836e-01 3.01381290e-01
-8.83735061e-01 -3.51496220e-01 -2.61301070e-01 -4.65193778e-01
3.07939619e-01 3.57761919e-01 -1.05826116e+00 8.66724849e-02
6.38222635e-01 1.05562997e+00 -5.69022357e-01 1.05907238e+00
-2.79582232e-01 6.17761075e-01 -2.34973773e-01 2.23494425e-01
1.60121590e-01 -6.85189590e-02 5.81316531e-01 1.17269897e+00
2.69037247e-01 3.08153987e-01 8.78556967e-02 5.79813600e-01
-7.20329136e-02 -3.38619441e-01 -7.86402464e-01 -4.80469018e-01
-1.29147947e-01 1.14945900e+00 -8.12613785e-01 -3.07615638e-01
-3.88123184e-01 1.14129233e+00 1.66650400e-01 5.98592818e-01
-1.06431460e+00 -1.44385055e-01 9.02763307e-01 4.20677572e-01
2.56729454e-01 -5.99764109e-01 9.25148576e-02 -1.56108725e+00
1.00676835e-01 -5.64421713e-01 5.46067536e-01 -9.54495609e-01
-7.46849537e-01 1.45535246e-01 2.01707006e-01 -1.53551638e+00
-4.84843284e-01 -5.80182493e-01 -5.54760516e-01 1.69186383e-01
-9.14156020e-01 -7.49852777e-01 -8.05784047e-01 7.36065388e-01
7.25924611e-01 3.23764011e-02 5.58977664e-01 3.48148823e-01
-5.55643678e-01 2.52344459e-01 -2.07322925e-01 5.52474678e-01
7.23634481e-01 -1.03210628e+00 1.86757147e-01 9.74441886e-01
5.30750275e-01 3.97079647e-01 7.88101435e-01 -4.68675941e-01
-1.35448182e+00 -9.47239995e-01 4.79368657e-01 -7.54990339e-01
5.48391402e-01 -4.08834696e-01 -5.59892297e-01 9.43266928e-01
8.61570090e-02 1.87465236e-01 6.87537432e-01 -5.31226583e-02
-5.88010438e-02 1.17806969e-02 -8.43090057e-01 7.14875877e-01
1.43783939e+00 -7.58765697e-01 -7.53354251e-01 3.18414181e-01
1.01634532e-01 -3.21251303e-01 -8.02069783e-01 2.54705936e-01
6.93890870e-01 -1.08102727e+00 9.20857310e-01 -7.33576894e-01
2.02446193e-01 -1.32037222e-01 -6.60654483e-03 -1.26298380e+00
-1.52090862e-01 -4.56856877e-01 -4.03698593e-01 7.38504589e-01
-2.17204481e-01 -2.85929590e-01 8.95386100e-01 5.97207725e-01
3.71299051e-02 -2.05974549e-01 -9.63316619e-01 -6.99993849e-01
-7.93420374e-01 -6.92432165e-01 1.53858319e-01 9.12151933e-01
3.30924273e-01 8.99580866e-02 -5.25978565e-01 -5.39984629e-02
4.50423658e-01 -2.11441174e-01 8.70561361e-01 -9.97456908e-01
-9.86079425e-02 -1.20596446e-01 -8.15164447e-01 -9.63569045e-01
-8.55715275e-02 -4.02868450e-01 -2.61743963e-01 -1.12906730e+00
8.50824192e-02 3.07739258e-01 -5.25801063e-01 2.77525365e-01
2.88308680e-01 5.74952841e-01 3.24129984e-02 6.40176088e-02
-1.05086267e+00 5.12850463e-01 8.65090489e-01 -5.82122020e-02
-1.36925653e-01 1.03911206e-01 -1.28960490e-01 9.40003574e-01
7.73561180e-01 -3.75587523e-01 -5.63990295e-01 -2.85267800e-01
3.81987244e-01 -1.53560638e-01 7.01727331e-01 -1.52139306e+00
1.56209156e-01 -2.32203290e-01 6.36656106e-01 -4.73006934e-01
6.51589215e-01 -9.58974600e-01 -1.04684429e-02 2.51076281e-01
-3.72837931e-01 -2.69785225e-01 -6.50356635e-02 8.77154291e-01
-1.52054265e-01 -3.34591679e-02 3.81335795e-01 -3.00735921e-01
-8.93115580e-01 2.56846040e-01 -6.96705341e-01 2.86986351e-01
1.31792128e+00 -6.56004965e-01 3.06356978e-02 -6.15805745e-01
-1.14119887e+00 -1.39179230e-01 4.58082199e-01 5.14289021e-01
5.17626286e-01 -1.10661769e+00 -2.03895196e-01 1.86986014e-01
4.18116689e-01 -4.01950955e-01 4.17509288e-01 1.16982889e+00
-2.22439170e-01 4.33552384e-01 -4.75203365e-01 -7.58065283e-01
-1.10357261e+00 6.30453825e-01 1.88702822e-01 1.67765632e-01
-6.98600709e-01 4.12760437e-01 2.83756167e-01 4.17259306e-01
5.07433593e-01 -5.32798290e-01 -2.89691716e-01 4.09060478e-01
6.57070398e-01 4.82776225e-01 -5.26443943e-02 -5.99046707e-01
-4.00733739e-01 1.06939331e-01 1.99549153e-01 -7.90278167e-02
1.15143692e+00 -1.96677163e-01 5.68759918e-01 6.85827911e-01
9.52612221e-01 -3.91350359e-01 -1.68789291e+00 -1.62049875e-01
1.66374952e-01 -5.69433570e-01 -1.77274629e-01 -5.07996261e-01
-7.73281455e-01 9.64039147e-01 6.56101525e-01 1.07198603e-01
1.02693748e+00 -1.27058133e-01 6.11345291e-01 6.71235442e-01
4.72535253e-01 -1.39441693e+00 5.38025260e-01 2.91755110e-01
7.66488612e-01 -1.38154387e+00 -1.25236943e-01 5.42265438e-02
-8.28695834e-01 9.26163495e-01 5.25286734e-01 -8.12861845e-02
3.74045402e-01 -1.66214377e-01 -3.33198994e-01 7.77646080e-02
-5.73753417e-01 -4.27198946e-01 2.85032779e-01 6.93945169e-01
1.80638418e-01 -1.04384162e-01 -1.60817787e-01 4.98738527e-01
1.57805532e-01 3.30912799e-01 7.64499366e-01 9.98818040e-01
-1.59477785e-01 -4.28291678e-01 -2.25259602e-01 8.90476778e-02
-5.96593857e-01 4.55451846e-01 -4.57474768e-01 8.05397928e-01
-6.92111207e-03 7.36040533e-01 1.85856923e-01 -2.95301408e-01
2.79638350e-01 3.80450726e-01 6.51929557e-01 -5.90512037e-01
-4.54018384e-01 -3.26162368e-01 1.73088551e-01 -9.26075399e-01
-6.08305633e-01 -8.93082321e-01 -8.64605665e-01 1.78641722e-01
1.90081716e-01 -1.06606670e-01 4.94768351e-01 1.09538150e+00
3.63541961e-01 4.21797395e-01 4.54049230e-01 -1.03629434e+00
-4.59312350e-01 -1.16599667e+00 -5.31488121e-01 8.85155976e-01
2.95012832e-01 -7.54872918e-01 -5.10215700e-01 4.39596176e-01] | [8.188790321350098, 0.5390657782554626] |
05599e05-2b8f-4aa6-8db0-380f73cf0260 | learning-non-linguistic-skills-without | 2305.08246 | null | https://arxiv.org/abs/2305.08246v1 | https://arxiv.org/pdf/2305.08246v1.pdf | Learning Non-linguistic Skills without Sacrificing Linguistic Proficiency | The field of Math-NLP has witnessed significant growth in recent years, motivated by the desire to expand LLM performance to the learning of non-linguistic notions (numerals, and subsequently, arithmetic reasoning). However, non-linguistic skill injection typically comes at a cost for LLMs: it leads to catastrophic forgetting of core linguistic skills, a consequence that often remains unaddressed in the literature. As Math-NLP has been able to create LLMs that can closely approximate the mathematical skills of a grade-schooler or the arithmetic reasoning skills of a calculator, the practicality of these models fail if they concomitantly shed their linguistic capabilities. In this work, we take a closer look into the phenomena of catastrophic forgetting as it pertains to LLMs and subsequently offer a novel framework for non-linguistic skill injection for LLMs based on information theoretic interventions and skill-specific losses that enable the learning of strict arithmetic reasoning. Our model outperforms the state-of-the-art both on injected non-linguistic skills and on linguistic knowledge retention, and does so with a fraction of the non-linguistic training data (1/4) and zero additional synthetic linguistic training data. | ['Naren Ramakrishnan', 'Nikhil Muralidhar', 'Mandar Sharma'] | 2023-05-14 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 3.62347692e-01 5.69819868e-01 2.50444096e-02 -1.49580464e-02
-6.15684927e-01 -5.77443182e-01 6.09145641e-01 6.80425227e-01
-5.87730348e-01 7.94300973e-01 1.83670714e-01 -5.55696845e-01
-5.76514721e-01 -1.19595611e+00 -1.12579048e+00 -3.95268708e-01
-9.05601755e-02 5.85447073e-01 2.87038326e-01 -4.04917538e-01
2.65995562e-01 3.40606004e-01 -1.72171915e+00 6.04537129e-02
1.47810578e+00 6.91715956e-01 2.65015811e-01 4.22340363e-01
-3.39678198e-01 1.22272205e+00 -2.76697457e-01 -9.68655586e-01
9.86704230e-02 -4.71798033e-01 -9.04774189e-01 -4.93020236e-01
6.92928672e-01 -3.97983789e-02 -4.53642666e-01 1.22484469e+00
1.37475356e-01 3.60090524e-01 6.15727305e-01 -9.06748295e-01
-1.02713430e+00 1.11452961e+00 -2.02699363e-01 1.59532428e-01
5.13849318e-01 1.80770993e-01 8.69825184e-01 -5.45706451e-01
2.75779217e-01 1.34277797e+00 8.05154741e-01 5.05282998e-01
-1.25584865e+00 -4.46515828e-01 -6.42938763e-02 6.37303367e-02
-1.18321061e+00 -2.00221315e-01 4.29043233e-01 -4.45061058e-01
6.44551039e-01 -8.93277377e-02 8.74336302e-01 7.87834048e-01
2.33508959e-01 8.90527546e-01 1.51121426e+00 -7.82895327e-01
1.06071882e-01 3.43355268e-01 2.69277483e-01 1.03724110e+00
3.80835652e-01 5.34774184e-01 -9.04974818e-01 4.73657519e-01
6.00449681e-01 -3.43140662e-01 -8.90649185e-02 -2.84693241e-01
-9.19263721e-01 6.63148880e-01 1.71639308e-01 3.76897752e-01
-9.48501900e-02 9.08522531e-02 2.96827734e-01 8.71820807e-01
2.67625391e-01 7.64769733e-01 -4.81149346e-01 -3.85554373e-01
-1.24682593e+00 3.31101865e-01 7.72411764e-01 8.93316031e-01
5.76179206e-01 6.24258891e-02 -2.96154946e-01 3.41460168e-01
6.47688434e-02 5.02692699e-01 4.92843449e-01 -1.01089096e+00
4.60821390e-01 9.12152886e-01 -9.80867296e-02 -9.52122688e-01
-3.97647351e-01 -5.39359033e-01 -5.69820821e-01 2.22592339e-01
9.12772179e-01 1.74282819e-01 -7.32310712e-01 2.04844069e+00
-1.44973218e-01 1.27025500e-01 7.54651576e-02 4.52239245e-01
4.63459969e-01 4.20398235e-01 3.25667441e-01 -3.19180459e-01
9.87275481e-01 -5.96172094e-01 -5.68854451e-01 -3.82811606e-01
6.17524326e-01 -3.25859636e-01 1.58088195e+00 5.85954785e-01
-1.73400998e+00 -5.96034408e-01 -9.28063989e-01 -2.34417185e-01
-6.37526989e-01 -4.46192920e-01 7.51039684e-01 9.42843974e-01
-1.23186040e+00 1.05096424e+00 -6.47766232e-01 -2.05015428e-02
5.09163082e-01 2.64310777e-01 -1.09066926e-01 -3.41482311e-01
-1.60758233e+00 1.37663186e+00 5.89303136e-01 -3.71577501e-01
-7.19265282e-01 -1.50171554e+00 -9.60458815e-01 4.29708689e-01
5.85800648e-01 -7.87611067e-01 9.52619612e-01 -8.53453875e-01
-1.58325052e+00 1.05307055e+00 3.30123782e-01 -7.52405107e-01
8.41867983e-01 -4.37482685e-01 -1.53742984e-01 5.50222881e-02
-1.56537116e-01 5.29229343e-01 6.04836643e-01 -9.10807669e-01
-3.27195048e-01 -2.65658855e-01 3.05463225e-01 2.98979670e-01
-3.39379311e-01 -2.66924649e-01 3.55976373e-02 -7.83250988e-01
-9.84548628e-02 -5.81912100e-01 3.32210399e-02 -1.94876924e-01
2.73552556e-02 -2.90465951e-01 -1.76661253e-01 -5.82127571e-01
1.36795259e+00 -2.09354520e+00 4.71823335e-01 2.23040834e-01
1.17228121e-01 3.35405827e-01 -9.48349759e-02 2.85061300e-01
-3.13163362e-02 4.38074395e-02 -2.23878756e-01 -3.44576001e-01
3.34133446e-01 9.41108689e-02 -4.29596573e-01 2.54783839e-01
1.28314905e-02 1.11661661e+00 -1.37118578e+00 -5.09829283e-01
2.44612947e-01 2.28104517e-01 -8.97231400e-01 -1.34345591e-01
-3.92473966e-01 3.07153434e-01 -5.49571067e-02 4.12124634e-01
2.38553479e-01 -5.17612882e-02 -5.59825525e-02 2.74342686e-01
-1.07149743e-02 3.78251076e-01 -8.81962180e-01 1.82356572e+00
-6.61373675e-01 2.56428510e-01 -2.45880783e-01 -1.06596863e+00
5.11242986e-01 -4.76625711e-02 7.48363286e-02 -8.63563478e-01
1.86485760e-02 3.30432743e-01 3.21751118e-01 -2.37992659e-01
4.11053479e-01 -6.54033840e-01 -1.30934864e-01 4.55762208e-01
2.76200384e-01 -5.77951193e-01 3.78202379e-01 1.81654349e-01
1.03967226e+00 3.15426230e-01 6.14415631e-02 -6.18295729e-01
7.83967137e-01 -1.17259838e-01 1.24635123e-01 1.07891440e+00
-1.27478659e-01 2.42639501e-02 3.06774586e-01 -9.90361646e-02
-8.90670300e-01 -1.43196738e+00 -1.36720508e-01 1.55097759e+00
-2.71900028e-01 -2.74364710e-01 -7.60431349e-01 -4.23767596e-01
1.83788270e-01 1.22449684e+00 -5.10108709e-01 -8.49608243e-01
-4.73448068e-01 -2.38785550e-01 6.62167490e-01 3.34876895e-01
5.63958883e-01 -1.42196405e+00 -6.44119561e-01 1.31372645e-01
2.49254882e-01 -8.65225077e-01 -1.45988002e-01 1.97185144e-01
-9.08185601e-01 -8.50441456e-01 -6.73298180e-01 -6.91988349e-01
6.59203351e-01 -4.87738341e-01 1.45980883e+00 3.04010004e-01
-1.39533997e-01 7.18540668e-01 -3.68959643e-02 -3.83729160e-01
-7.70592034e-01 1.61346748e-01 4.68975544e-01 -4.87134665e-01
4.52464283e-01 -8.70148182e-01 -3.14606577e-01 -4.23988789e-01
-9.26533461e-01 5.72870821e-02 6.55678689e-01 6.40439034e-01
2.01986343e-01 4.58797634e-01 6.06030881e-01 -9.91384685e-01
7.08000660e-01 -2.79586256e-01 -7.14322984e-01 4.47064400e-01
-8.17170978e-01 4.15766209e-01 8.45431745e-01 -4.25645560e-01
-1.19710875e+00 -1.93894520e-01 1.05069757e-01 -3.96447808e-01
6.49778172e-02 8.81295919e-01 2.68045664e-01 -2.27855042e-01
6.79118752e-01 6.95255518e-01 -6.33872449e-02 -4.12984282e-01
6.20352328e-01 -6.81339297e-03 7.38416433e-01 -1.36188829e+00
9.43711400e-01 -1.56418473e-01 2.35047087e-01 -8.33117425e-01
-1.56604755e+00 9.03791711e-02 -5.20611465e-01 -1.16295926e-01
4.15433615e-01 -7.79308617e-01 -1.08057868e+00 6.14202678e-01
-7.16751218e-01 -6.14316881e-01 -8.07559192e-01 2.98854679e-01
-9.40743744e-01 3.38494539e-01 -7.65065849e-01 -9.33962107e-01
6.58370331e-02 -1.00457036e+00 4.27484095e-01 2.84268618e-01
-3.56305242e-01 -1.24305117e+00 -1.60011888e-01 3.54735196e-01
5.26339591e-01 -2.02944830e-01 1.48070276e+00 -5.91789842e-01
-7.09246099e-01 3.70744057e-02 2.18450231e-03 5.13431907e-01
-2.82139719e-01 -5.38835287e-01 -7.45536447e-01 -2.55587041e-01
6.97221383e-02 -9.34943736e-01 1.02844179e+00 2.68537164e-01
1.04356611e+00 -3.49162370e-01 1.67530239e-01 4.03835744e-01
1.34024334e+00 -2.08165184e-01 4.94915664e-01 1.91466942e-01
3.02376091e-01 7.54508376e-01 2.42870614e-01 8.43986720e-02
4.45718020e-01 2.77234554e-01 -7.20585138e-02 5.37765503e-01
-2.95099229e-01 -7.77416646e-01 3.73541236e-01 1.10764396e+00
2.28847433e-02 2.82054543e-01 -1.18636847e+00 6.82189465e-01
-1.41934240e+00 -8.02459240e-01 1.79257914e-01 2.52551770e+00
1.70486724e+00 3.96487713e-01 -2.80241407e-02 3.04447532e-01
8.94797742e-02 -1.37431055e-01 -4.66721535e-01 -5.03726780e-01
3.85712013e-02 7.91044772e-01 3.47826779e-01 9.23318207e-01
-6.85617864e-01 1.21511686e+00 6.29115582e+00 9.56974268e-01
-5.39701700e-01 1.31917065e-02 4.36095119e-01 5.20408377e-02
-3.95493835e-01 -2.68424392e-01 -8.55187535e-01 5.07714868e-01
1.37728250e+00 -5.06115317e-01 9.29069698e-01 5.09501040e-01
-3.26877952e-01 -4.27615106e-01 -1.40930247e+00 6.24413669e-01
-8.78428593e-02 -1.15298188e+00 2.51503080e-01 -2.68531054e-01
9.44923103e-01 -4.83653992e-01 5.04156768e-01 9.68088567e-01
6.13384545e-01 -1.41249919e+00 8.00835788e-01 9.15882230e-01
8.24976742e-01 -1.02615654e+00 4.97948855e-01 8.03203642e-01
-5.79733253e-01 -2.36801177e-01 -3.09257209e-01 -5.20634532e-01
-2.10615471e-01 5.33472836e-01 -3.13490033e-01 2.94011503e-01
4.42247748e-01 1.72631249e-01 -7.08145618e-01 8.40359926e-01
-5.10636866e-01 5.82657754e-01 -4.05174345e-01 1.63900852e-02
1.57204211e-01 -1.51229233e-01 1.66324779e-01 9.11556721e-01
2.67640412e-01 3.39670479e-01 1.43665150e-01 1.18793678e+00
-1.42030433e-01 3.28121819e-02 -6.43160462e-01 -3.09573412e-01
5.31986296e-01 4.73978519e-01 -4.45124477e-01 -2.27676332e-01
-3.76578778e-01 8.07051659e-01 7.36729145e-01 1.28307492e-01
-4.23657447e-01 -2.34322071e-01 3.20890188e-01 2.09153354e-01
-8.42867047e-02 -2.47880831e-01 -5.67592382e-01 -9.82670605e-01
-2.45818049e-01 -8.83659363e-01 2.56693929e-01 -4.80791062e-01
-1.29506743e+00 -2.30817556e-01 5.23388311e-02 -5.08868456e-01
-1.97177827e-01 -7.06404090e-01 -1.91813990e-01 9.96272743e-01
-1.49490619e+00 -7.06796944e-01 1.49209455e-01 4.68375266e-01
1.65796131e-01 -1.97991610e-01 8.62448573e-01 1.61464795e-01
-1.34791821e-01 9.51836467e-01 -1.85872000e-02 -1.62197482e-02
3.31773430e-01 -1.41793597e+00 1.47381768e-01 8.06955516e-01
1.12202659e-01 9.45160687e-01 9.26533997e-01 -6.17418885e-01
-1.38796389e+00 -7.36815214e-01 1.39735222e+00 -7.72575676e-01
1.00119376e+00 -2.51718432e-01 -9.34293747e-01 8.19960773e-01
-9.15682316e-02 -2.57179439e-01 4.37241673e-01 2.26689771e-01
-1.57570064e-01 1.09542690e-01 -1.13291442e+00 8.71984363e-01
1.24881697e+00 -7.20421433e-01 -1.21077859e+00 3.19620252e-01
9.37590122e-01 -5.90654254e-01 -9.98718083e-01 2.06740946e-01
1.89690426e-01 -8.84923875e-01 1.29116392e+00 -6.52900755e-01
8.74687135e-01 4.90348451e-02 1.40046448e-01 -1.46123636e+00
-1.53026626e-01 -6.00662470e-01 -4.74779099e-01 1.12274325e+00
1.95741832e-01 -3.11695695e-01 7.46041298e-01 6.40846729e-01
-1.12060972e-01 -6.62168384e-01 -9.98943985e-01 -9.87300396e-01
8.16279709e-01 -6.42669976e-01 2.28848591e-01 1.05315471e+00
3.38008583e-01 2.10699499e-01 -1.10978089e-01 -8.48322883e-02
7.24334478e-01 4.21131104e-02 2.19740644e-01 -1.43346882e+00
-3.81791145e-01 -8.65984142e-01 -2.56679595e-01 -9.45985019e-01
4.41134483e-01 -1.23105264e+00 -6.65055634e-03 -9.85669732e-01
-6.68182671e-02 -5.02780855e-01 -3.21346462e-01 5.46452284e-01
-1.90188691e-01 -2.79893558e-02 4.31883484e-01 -4.48293328e-01
-6.45523846e-01 5.56766868e-01 1.40386701e+00 6.77872263e-03
-1.54789448e-01 4.34668362e-02 -7.57723987e-01 7.66177118e-01
5.20500302e-01 -2.79341519e-01 -6.13221705e-01 4.51050736e-02
9.33672965e-01 2.31600657e-01 3.07290882e-01 -1.27494872e+00
3.69017422e-01 -2.80542448e-02 2.38715529e-01 -2.02040464e-01
1.47130219e-02 -8.73034716e-01 -3.71234417e-01 6.86869860e-01
-6.70792997e-01 -2.27247790e-01 5.06824255e-01 4.14373994e-01
6.15787804e-02 -5.06574392e-01 8.92448068e-01 -4.29862142e-01
-5.39252281e-01 -7.35271201e-02 -1.67641103e-01 7.08615005e-01
7.18873084e-01 6.14553094e-02 -3.11171591e-01 -1.72171131e-01
-1.01472235e+00 2.95208365e-01 3.55806082e-01 2.64486164e-01
4.28400278e-01 -1.12205064e+00 -6.03695333e-01 3.09935004e-01
-2.06830144e-01 7.86357895e-02 2.33269870e-01 1.02636528e+00
-4.87104774e-01 6.87427759e-01 -1.84081793e-01 -6.22992516e-02
-6.09157443e-01 9.68059301e-01 3.80539954e-01 -6.08108282e-01
-6.66868687e-01 1.14133465e+00 4.66977693e-02 -5.50561309e-01
5.83293617e-01 -5.98590612e-01 6.34325296e-02 9.04309601e-02
6.34796798e-01 3.93234462e-01 -6.87423944e-02 -3.62201408e-02
-7.14998543e-02 4.42634821e-01 -9.50093865e-02 7.01821968e-02
1.16068363e+00 2.14352570e-02 3.52092944e-02 8.36552620e-01
5.49818575e-01 6.84060082e-02 -1.20896995e+00 -4.05468106e-01
2.54103690e-01 -1.99197948e-01 -2.89004073e-02 -1.35130978e+00
-6.80514753e-01 9.63409185e-01 2.24214256e-01 7.20492527e-02
9.68885064e-01 -9.06531513e-03 5.18430710e-01 5.15327454e-01
7.36768782e-01 -1.07738853e+00 1.00266583e-01 9.98458624e-01
6.49687946e-01 -9.76125240e-01 -3.56605533e-03 -2.44356722e-01
-2.64706582e-01 8.57627034e-01 5.05347371e-01 -1.98220700e-01
5.32814443e-01 6.66497722e-02 -4.78829056e-01 -1.43086135e-01
-5.35754859e-01 -2.25791872e-01 3.96202594e-01 5.24812758e-01
5.60150623e-01 -9.16300248e-03 -2.04607368e-01 8.91187668e-01
-7.77924955e-01 3.97234797e-01 3.96568120e-01 8.74091506e-01
-5.45516908e-01 -8.51778626e-01 -1.03066392e-01 4.46523994e-01
-4.72795337e-01 -7.05281258e-01 -2.53452770e-02 8.79230380e-01
1.57168776e-01 4.16748285e-01 -1.06204830e-01 1.53708598e-02
3.62142712e-01 7.80529082e-01 1.16134799e+00 -7.69602299e-01
-7.67161548e-01 -7.96912849e-01 -2.16130033e-01 -4.86392677e-01
-8.04942176e-02 -6.93639457e-01 -1.30673742e+00 -7.51922369e-01
2.85833389e-01 -5.86934015e-02 -4.57414873e-02 1.22722816e+00
-1.84073016e-01 7.10752010e-01 -1.28949150e-01 -4.90262926e-01
-9.85644877e-01 -7.71892965e-01 -7.32266366e-01 4.69021618e-01
3.54012072e-01 -7.61596978e-01 -5.10763764e-01 -9.83954221e-02] | [9.594071388244629, 7.317295551300049] |
8692e2a3-2b98-4ca6-9608-c7951949e698 | restyle-a-residual-based-stylegan-encoder-via | 2104.02699 | null | https://arxiv.org/abs/2104.02699v2 | https://arxiv.org/pdf/2104.02699v2.pdf | ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement | Recently, the power of unconditional image synthesis has significantly advanced through the use of Generative Adversarial Networks (GANs). The task of inverting an image into its corresponding latent code of the trained GAN is of utmost importance as it allows for the manipulation of real images, leveraging the rich semantics learned by the network. Recognizing the limitations of current inversion approaches, in this work we present a novel inversion scheme that extends current encoder-based inversion methods by introducing an iterative refinement mechanism. Instead of directly predicting the latent code of a given real image using a single pass, the encoder is tasked with predicting a residual with respect to the current estimate of the inverted latent code in a self-correcting manner. Our residual-based encoder, named ReStyle, attains improved accuracy compared to current state-of-the-art encoder-based methods with a negligible increase in inference time. We analyze the behavior of ReStyle to gain valuable insights into its iterative nature. We then evaluate the performance of our residual encoder and analyze its robustness compared to optimization-based inversion and state-of-the-art encoders. | ['Daniel Cohen-Or', 'Or Patashnik', 'Yuval Alaluf'] | 2021-04-06 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Alaluf_ReStyle_A_Residual-Based_StyleGAN_Encoder_via_Iterative_Refinement_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Alaluf_ReStyle_A_Residual-Based_StyleGAN_Encoder_via_Iterative_Refinement_ICCV_2021_paper.pdf | iccv-2021-1 | ['real-to-cartoon-translation'] | ['computer-vision'] | [ 7.92749763e-01 5.29164493e-01 -1.70219958e-01 -1.05651215e-01
-8.49878967e-01 -6.47255898e-01 7.21942425e-01 -5.16300142e-01
-1.66244134e-01 7.90616870e-01 1.61269188e-01 -3.90304267e-01
2.92930007e-01 -7.82396495e-01 -1.22197437e+00 -6.17890894e-01
2.22747341e-01 5.23307145e-01 -3.68757546e-02 -1.63420901e-01
1.73535407e-01 2.22042203e-01 -1.33520043e+00 1.58475608e-01
8.95725548e-01 9.28695321e-01 1.61401510e-01 8.30203950e-01
2.15128049e-01 1.07778907e+00 -4.84389275e-01 -5.34942985e-01
2.10824147e-01 -9.49585497e-01 -6.98870718e-01 8.34663957e-02
2.65593529e-01 -5.28643847e-01 -3.64747107e-01 9.86753762e-01
7.66499937e-02 -2.20528036e-01 5.81225455e-01 -1.12002981e+00
-7.61344135e-01 3.55350673e-01 -3.06140929e-01 -2.58892208e-01
3.25700551e-01 1.23408381e-02 1.06734204e+00 -7.48569489e-01
8.95124078e-01 7.93018758e-01 6.52271748e-01 6.68872356e-01
-1.69826496e+00 -7.48150945e-01 -1.79863051e-01 3.03256903e-02
-1.46897304e+00 -6.59260690e-01 8.74566734e-01 -3.55530530e-01
6.67683780e-01 7.42572844e-02 6.53406382e-01 1.05128014e+00
1.84858128e-01 6.18599832e-01 1.10656726e+00 -6.47870660e-01
1.92427561e-01 1.94021910e-02 -6.92948997e-01 1.04441535e+00
-9.81982574e-02 2.90232599e-01 -6.02310717e-01 2.70358950e-01
8.63165438e-01 -1.61314666e-01 -3.24690998e-01 -5.86496234e-01
-1.20914376e+00 8.89254570e-01 6.86800718e-01 1.47591248e-01
-3.80943537e-01 6.21655285e-01 1.43622398e-01 1.96336031e-01
4.66321141e-01 4.83317971e-01 -1.85640633e-01 -3.05018038e-01
-1.34662521e+00 2.12645844e-01 8.28053534e-01 7.63679087e-01
9.56505477e-01 2.80621231e-01 -1.94158796e-02 4.78373021e-01
2.42985591e-01 2.41934896e-01 4.50172871e-01 -1.23437417e+00
2.36614555e-01 4.42148000e-01 -1.28683299e-01 -8.13683152e-01
2.97806442e-01 -5.52612007e-01 -9.00359690e-01 4.50500309e-01
2.04665050e-01 8.60230699e-02 -1.10218978e+00 1.86794376e+00
4.22612466e-02 3.80802691e-01 1.37564996e-02 3.20020705e-01
1.95477128e-01 7.17835963e-01 -4.45163518e-01 -7.79722780e-02
9.59489465e-01 -1.14070344e+00 -5.98013043e-01 -2.19183490e-01
3.30254167e-01 -6.45191193e-01 8.16152155e-01 3.09269994e-01
-1.21886432e+00 -5.05501926e-01 -1.32862592e+00 -1.87402949e-01
-1.40124097e-01 7.63321444e-02 5.15698493e-01 5.99933386e-01
-1.16215634e+00 6.45153821e-01 -9.87198532e-01 1.85875166e-02
5.32424927e-01 4.00724411e-01 -4.62319404e-01 -9.02496725e-02
-8.31085086e-01 8.18265796e-01 2.44467422e-01 -4.57973182e-02
-1.22459137e+00 -7.63386905e-01 -1.06536365e+00 -1.57458519e-04
3.18131387e-01 -8.54970217e-01 1.18121648e+00 -1.28636670e+00
-1.92642617e+00 8.90855730e-01 -4.68764573e-01 -6.21922314e-01
6.71149611e-01 -1.94979414e-01 -1.19449176e-01 2.18485951e-01
1.42814472e-01 9.08886015e-01 1.24369657e+00 -1.41054201e+00
-1.21227778e-01 8.98865461e-02 2.07772046e-01 -2.22724646e-01
-6.53409436e-02 -2.99467355e-01 -5.21863043e-01 -8.39237154e-01
7.10228309e-02 -1.17846572e+00 -1.60868431e-03 2.46670663e-01
-4.99509990e-01 3.65124047e-01 8.25397789e-01 -7.05492258e-01
9.64767396e-01 -2.13903666e+00 5.33820689e-01 9.95184183e-02
2.55356520e-01 2.77764142e-01 -4.46931785e-03 4.97141331e-01
-2.44662896e-01 3.65316197e-02 -7.04117179e-01 -6.68509662e-01
-1.76108554e-02 5.00320852e-01 -6.17629051e-01 3.28530937e-01
3.93145472e-01 1.23575056e+00 -8.60423267e-01 -4.26940203e-01
1.88590869e-01 6.63555026e-01 -1.07448590e+00 5.71751177e-01
-3.38488013e-01 7.97696590e-01 -1.34962142e-01 3.79862279e-01
4.14522380e-01 -4.16363478e-01 2.70192951e-01 -3.46870035e-01
-6.59939647e-02 4.08341944e-01 -6.58221543e-01 1.97958827e+00
-6.98706388e-01 7.17986703e-01 -8.29790533e-02 -1.17171025e+00
6.89408481e-01 3.24615210e-01 1.76909044e-01 -4.99182135e-01
-1.63629115e-01 2.47373343e-01 -2.25049704e-01 -9.34723094e-02
4.66200054e-01 -3.22482735e-01 1.16752177e-01 5.29336393e-01
2.21634969e-01 -4.13164496e-01 7.49000385e-02 3.26185375e-01
1.14788735e+00 7.75826335e-01 3.41350377e-01 7.57633299e-02
4.51777458e-01 -2.20363632e-01 2.37478808e-01 6.99841738e-01
3.39675754e-01 8.89566243e-01 5.17880142e-01 -2.44118392e-01
-1.33128715e+00 -1.21795273e+00 -6.61037117e-03 5.99653065e-01
-2.65680701e-01 -5.47502875e-01 -8.88569474e-01 -6.67644203e-01
-2.59725720e-01 7.79013395e-01 -9.80113029e-01 -2.74603993e-01
-5.66357076e-01 -4.62158799e-01 7.58661687e-01 4.47880328e-01
8.11710417e-01 -8.81493151e-01 -6.01817489e-01 1.21958107e-01
-3.20914477e-01 -1.18920207e+00 -3.20910931e-01 1.55181289e-01
-8.40568602e-01 -8.53773713e-01 -5.87287068e-01 -5.21637022e-01
9.06291366e-01 -3.12414557e-01 1.29574561e+00 1.86560333e-01
-6.20897301e-02 1.52350038e-01 -1.98104933e-01 -5.91774918e-02
-9.38419759e-01 2.92094558e-01 -3.92364234e-01 1.75937206e-01
-3.14878374e-01 -8.99930418e-01 -5.03618121e-01 7.57003948e-02
-1.32941186e+00 5.77915609e-01 6.32826209e-01 1.16061270e+00
4.57740486e-01 -9.48942602e-02 3.39086711e-01 -1.14624238e+00
1.63129821e-01 -2.71774977e-01 -6.53266847e-01 1.27567381e-01
-8.13057065e-01 5.31353474e-01 6.27148569e-01 -1.23970263e-01
-9.98788059e-01 1.74614668e-01 -4.11355704e-01 -3.71595919e-01
1.78362027e-01 4.75530952e-01 -5.33610359e-02 -2.17467040e-01
4.09097403e-01 5.38405955e-01 2.84930766e-01 -2.82666445e-01
5.76898754e-01 3.61738473e-01 9.70678151e-01 -5.58001757e-01
1.10271442e+00 5.81522346e-01 2.27087289e-01 -4.71613675e-01
-1.05028701e+00 1.50837421e-01 -6.81212366e-01 -1.36316285e-01
9.24460292e-01 -9.40653622e-01 -5.56214094e-01 5.82355142e-01
-1.22942090e+00 -4.93673295e-01 -5.35873532e-01 2.44358573e-02
-9.31520879e-01 1.63370326e-01 -5.83878577e-01 -3.88466150e-01
-5.29049989e-03 -1.42751789e+00 1.22862911e+00 -2.66954958e-01
-2.51776725e-01 -9.49614525e-01 2.26961702e-01 4.68294680e-01
4.26850289e-01 4.19922948e-01 8.69975865e-01 -1.02794595e-01
-9.79877412e-01 -2.32102573e-01 -1.86257765e-01 6.01194680e-01
1.51129901e-01 -1.27288580e-01 -9.80522096e-01 -2.23590568e-01
5.89507408e-02 -4.63598281e-01 8.58296216e-01 6.15530554e-03
1.18912613e+00 -4.54880983e-01 -6.16879798e-02 1.04861975e+00
1.56506860e+00 -6.36172518e-02 1.04280329e+00 3.87303829e-01
6.46325707e-01 -3.54721136e-02 3.21127251e-02 2.06947848e-01
3.01164418e-01 7.86804080e-01 7.80538619e-01 -3.45448852e-02
-2.15171129e-01 -7.39402950e-01 3.70221615e-01 8.74824941e-01
-1.75483629e-01 -1.20904058e-01 -5.71788490e-01 3.86311859e-01
-1.62541914e+00 -8.37051392e-01 4.64741468e-01 2.12443280e+00
1.09018290e+00 8.87315944e-02 -3.43489647e-01 3.42153937e-01
4.69792485e-01 4.18237686e-01 -5.27998090e-01 -3.00881803e-01
2.08031580e-01 5.87683797e-01 5.12512267e-01 6.36411428e-01
-8.06303442e-01 8.37840140e-01 7.13493156e+00 6.26303434e-01
-1.08222437e+00 1.01577044e-01 4.60464209e-01 1.69255853e-01
-4.92030382e-01 3.33828747e-01 -3.52820367e-01 4.47800428e-01
9.58099663e-01 1.26682743e-01 9.73724067e-01 6.10128105e-01
-2.05357090e-01 -3.60512882e-02 -1.20596969e+00 6.35531843e-01
3.59700710e-01 -1.65949976e+00 1.61382079e-01 2.75349021e-01
9.30981159e-01 -1.41978160e-01 1.65188342e-01 1.76915854e-01
3.93219233e-01 -1.11753619e+00 9.67314303e-01 6.24256134e-01
1.15725577e+00 -6.27616227e-01 4.37221974e-01 2.80721188e-01
-8.33790421e-01 1.78315967e-01 -1.54893268e-02 -1.50875956e-01
2.74032176e-01 6.30405068e-01 -6.95834756e-01 5.38427472e-01
2.73766428e-01 9.47526634e-01 -4.66241062e-01 2.81269282e-01
-7.05127776e-01 8.44860017e-01 -1.91930786e-01 6.65868342e-01
1.67972073e-01 -2.84077585e-01 4.25164193e-01 8.48557472e-01
3.62087727e-01 -2.06677809e-01 -1.13632657e-01 1.18236673e+00
-5.39851785e-01 -4.81061041e-01 -7.48577178e-01 -2.98995912e-01
1.48925141e-01 8.96012485e-01 -4.11101013e-01 -4.01867628e-01
-2.14694977e-01 1.54316652e+00 5.09050786e-01 3.68671000e-01
-9.82206762e-01 -1.96560204e-01 3.65919352e-01 7.22584873e-02
7.60114610e-01 -3.47116083e-01 -2.14919955e-01 -1.27377260e+00
8.23585615e-02 -9.99904215e-01 -1.81253254e-01 -1.05149972e+00
-5.43980300e-01 5.18340766e-01 -2.27007046e-01 -1.08829105e+00
-7.39025652e-01 -3.72874945e-01 -3.90231967e-01 6.99094117e-01
-1.54339767e+00 -1.39789724e+00 -2.60137767e-01 4.05491680e-01
3.27877998e-01 -1.30845621e-01 1.10971868e+00 1.26888856e-01
-3.27065051e-01 6.32925987e-01 3.14864546e-01 1.83609769e-01
4.56362903e-01 -1.16485870e+00 5.22368848e-01 1.06152952e+00
2.41265088e-01 5.94123542e-01 8.49448264e-01 -3.97564560e-01
-1.48001611e+00 -1.00504851e+00 7.90612102e-01 -3.08986872e-01
7.05026567e-01 -4.02046889e-01 -6.17589593e-01 1.12276936e+00
3.27927172e-01 1.85990527e-01 3.41829628e-01 -2.03459695e-01
-5.70831120e-01 -2.05971584e-01 -9.53182220e-01 6.39970005e-01
9.38129663e-01 -8.93624663e-01 -3.51793021e-01 8.71271174e-03
7.27457941e-01 -5.34858882e-01 -6.34952962e-01 3.79216790e-01
7.94936121e-01 -1.31515145e+00 1.08237791e+00 -2.35307515e-01
1.14448726e+00 -3.33442539e-01 -1.20518297e-01 -1.35689688e+00
-9.96244848e-02 -6.69981956e-01 -3.55432808e-01 1.00904012e+00
1.78348199e-01 -6.86846137e-01 8.73770773e-01 3.02406609e-01
-9.84479412e-02 -6.25010908e-01 -8.44997227e-01 -6.29263759e-01
-1.66283667e-01 -3.74004066e-01 4.98591721e-01 6.30042195e-01
-3.61455172e-01 2.62029856e-01 -7.69466817e-01 1.30938347e-02
6.06257319e-01 2.28262797e-01 9.57688808e-01 -6.60144389e-01
-7.07488060e-01 -4.99629937e-02 -5.59155166e-01 -1.34611738e+00
4.30355161e-01 -1.00091565e+00 2.28980333e-01 -1.13605762e+00
1.17065072e-01 -1.53633073e-01 -1.41591102e-01 4.31758165e-01
7.19495118e-02 9.17460501e-01 3.34308952e-01 2.58272648e-01
-3.02525103e-01 7.28088379e-01 1.17074192e+00 -1.54777899e-01
3.02009761e-01 -1.76653177e-01 -6.59490407e-01 5.68381190e-01
4.89335865e-01 -6.28571212e-01 -4.03288066e-01 -5.02419114e-01
4.79873925e-01 9.58838686e-02 6.92700326e-01 -1.14514732e+00
1.68884575e-01 2.42842674e-01 1.72112569e-01 -1.75994307e-01
4.54047084e-01 -7.87076712e-01 5.44137836e-01 5.57123423e-01
-4.99772131e-01 9.55730379e-02 -2.59345155e-02 4.83314723e-01
-3.97788674e-01 -3.45429152e-01 7.93773055e-01 -2.27266237e-01
-2.32365295e-01 4.07627299e-02 -3.87903214e-01 6.30376264e-02
7.40755618e-01 -1.91844523e-01 1.10451765e-01 -6.76038146e-01
-5.48326015e-01 -4.41794425e-01 8.41438949e-01 9.56035331e-02
5.53794682e-01 -1.22417068e+00 -5.09764254e-01 6.15389526e-01
3.79751474e-02 1.14895053e-01 -2.42045730e-01 6.19421244e-01
-6.32859588e-01 2.19097063e-01 -2.26440966e-01 -5.15312970e-01
-8.83061647e-01 4.64730889e-01 3.04564148e-01 -6.00729704e-01
-5.82885683e-01 5.59382975e-01 3.56935799e-01 -3.24569494e-01
-2.09024116e-01 -2.03639865e-02 4.51717436e-01 -4.22711313e-01
2.61881471e-01 5.58454320e-02 1.91651937e-02 -5.52972436e-01
-6.64385110e-02 4.66127664e-01 6.48987368e-02 -4.28033859e-01
1.36284578e+00 1.00824453e-01 -4.46645588e-01 3.72198552e-01
1.33278537e+00 1.36123732e-01 -1.46902287e+00 -2.62091439e-02
-4.22961086e-01 -5.20795107e-01 1.16854064e-01 -6.96078897e-01
-1.21097624e+00 8.20694387e-01 2.37315863e-01 -1.01390973e-01
1.25982606e+00 -1.78497076e-01 7.84675956e-01 1.21525094e-01
3.49819511e-01 -6.06035173e-01 1.77871093e-01 3.76071006e-01
8.04979265e-01 -9.75056410e-01 1.27327830e-01 -3.22619826e-01
-3.07625651e-01 9.10848618e-01 2.18581483e-02 -3.81948918e-01
6.04003310e-01 3.90002489e-01 -1.39160976e-01 -3.52783240e-02
-6.44111454e-01 3.86254936e-02 2.36383498e-01 4.35311198e-01
3.06910098e-01 -1.62657410e-01 1.63586006e-01 -2.07225814e-01
-2.59240866e-01 3.89820367e-01 4.90203291e-01 9.18538690e-01
-2.09690426e-02 -1.38367617e+00 -1.19362757e-01 1.95094734e-01
-5.40073812e-01 -4.29422498e-01 6.22738735e-04 5.99134207e-01
1.57408059e-01 4.63321209e-01 2.90051792e-02 -2.73101240e-01
-1.48233920e-01 2.55301625e-01 6.74841166e-01 -4.45944428e-01
-3.42557311e-01 -2.88476825e-01 -9.16347727e-02 -6.86303139e-01
-6.57767832e-01 -6.43428028e-01 -9.19299304e-01 -2.06062481e-01
-1.97278053e-01 2.73788460e-02 7.99595654e-01 1.17382383e+00
3.58800292e-01 6.70624197e-01 6.01123989e-01 -1.07895589e+00
-4.57393348e-01 -6.72975421e-01 -2.33155578e-01 4.66210693e-01
5.29451907e-01 -4.79146183e-01 -2.58587658e-01 5.79688191e-01] | [11.611285209655762, -0.4108065068721771] |
4f7fcb0b-99a1-4a21-95b1-1438edc6efbe | when-age-invariant-face-recognition-meets | 2103.01520 | null | https://arxiv.org/abs/2103.01520v2 | https://arxiv.org/pdf/2103.01520v2.pdf | When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework | To minimize the effects of age variation in face recognition, previous work either extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features, called age-invariant face recognition (AIFR), or removes age variation by transforming the faces of different age groups into the same age group, called face age synthesis (FAS); however, the former lacks visual results for model interpretation while the latter suffers from artifacts compromising downstream recognition. Therefore, this paper proposes a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn age-invariant identity-related representation while achieving pleasing face synthesis. Specifically, we first decompose the mixed face feature into two uncorrelated components -- identity- and age-related feature -- through an attention mechanism, and then decorrelate these two components using multi-task training and continuous domain adaption. In contrast to the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS, with a weight-sharing strategy to improve the age smoothness of synthesized faces. In addition, we collect and release a large cross-age face dataset with age and gender annotations to advance the development of the AIFR and FAS. Extensive experiments on five benchmark cross-age datasets demonstrate the superior performance of our proposed MTLFace over existing state-of-the-art methods for AIFR and FAS. We further validate MTLFace on two popular general face recognition datasets, showing competitive performance for face recognition in the wild. The source code and dataset are available at~\url{https://github.com/Hzzone/MTLFace}. | ['Hongming Shan', 'Junping Zhang', 'Zhizhong Huang'] | 2021-03-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Huang_When_Age-Invariant_Face_Recognition_Meets_Face_Age_Synthesis_A_Multi-Task_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Huang_When_Age-Invariant_Face_Recognition_Meets_Face_Age_Synthesis_A_Multi-Task_CVPR_2021_paper.pdf | cvpr-2021-1 | ['age-invariant-face-recognition'] | ['computer-vision'] | [ 2.14179114e-01 -1.36268631e-01 -2.89567690e-02 -8.78749132e-01
-6.68393791e-01 -2.46898368e-01 5.56218803e-01 -4.39155728e-01
-1.69894829e-01 5.54627895e-01 2.29780644e-01 2.41232261e-01
-5.76379970e-02 -5.12422442e-01 -6.00578308e-01 -9.19494450e-01
1.55142829e-01 1.11781821e-01 -4.70301986e-01 6.55148551e-02
6.67924387e-03 4.86739755e-01 -1.82279742e+00 1.77439123e-01
1.02222180e+00 1.34397459e+00 -2.18605727e-01 1.84305474e-01
1.68795004e-01 3.43186229e-01 -4.14820224e-01 -7.59925127e-01
5.14143944e-01 -4.55327839e-01 -4.65083718e-01 7.60944113e-02
9.15814281e-01 -4.41851199e-01 -4.36174750e-01 9.66131866e-01
8.03609908e-01 -6.11442551e-02 6.97523117e-01 -1.61415207e+00
-1.10163534e+00 3.57434839e-01 -8.85845304e-01 -2.63780802e-01
2.27259502e-01 -2.14661025e-02 6.14842355e-01 -1.32410920e+00
3.19579452e-01 1.60756552e+00 7.87721395e-01 1.09014666e+00
-1.19407463e+00 -1.38814890e+00 3.04733306e-01 1.94487005e-01
-1.71012354e+00 -1.08725691e+00 8.31230998e-01 -4.59974825e-01
4.80112195e-01 1.43358827e-01 8.44384059e-02 1.49819016e+00
-1.28378958e-01 6.22586787e-01 1.26636434e+00 -2.09117264e-01
-1.42033607e-01 -1.94304198e-01 -3.98927182e-02 8.12205017e-01
9.54437703e-02 2.14732379e-01 -8.31946671e-01 -1.00679703e-01
7.71092355e-01 5.84357977e-02 -2.38538697e-01 -2.24926874e-01
-9.54762578e-01 5.09635508e-01 2.00839937e-01 4.25235480e-02
-3.16901356e-01 -6.74796775e-02 2.40536854e-01 3.87665510e-01
8.44069839e-01 -8.45804587e-02 -5.11307180e-01 2.60975450e-01
-9.17258680e-01 2.63056427e-01 4.69642699e-01 8.75546992e-01
7.82047868e-01 2.22263739e-01 -4.77983385e-01 1.23778558e+00
2.96221405e-01 5.61071634e-01 3.97728115e-01 -8.94642353e-01
1.62060753e-01 4.48018521e-01 -2.52083927e-01 -7.48599112e-01
-1.96790040e-01 -2.66069353e-01 -9.15179551e-01 3.27996880e-01
4.81680512e-01 -1.41648442e-01 -1.15651739e+00 2.37477326e+00
4.08597320e-01 5.62184989e-01 -7.74720609e-02 6.82575703e-01
9.80858743e-01 2.54325271e-01 3.26865196e-01 -4.14756656e-01
1.48073924e+00 -8.59371483e-01 -6.50575519e-01 -2.55957365e-01
7.49562159e-02 -7.10569322e-01 7.31781960e-01 1.97740808e-01
-1.01654363e+00 -7.89802611e-01 -8.72217774e-01 -9.01059583e-02
-1.85735911e-01 4.90238011e-01 6.85966790e-01 7.98997283e-01
-1.01821589e+00 4.92409050e-01 -6.10482216e-01 -1.72535568e-01
8.39277804e-01 5.86514235e-01 -7.18033135e-01 -2.64568210e-01
-9.96156752e-01 6.25186801e-01 -3.54886167e-02 1.04478121e-01
-9.44451749e-01 -1.16753590e+00 -9.69460547e-01 -1.10048987e-01
2.30776161e-01 -8.82426143e-01 8.56852412e-01 -1.18234563e+00
-1.50170577e+00 1.12395513e+00 -5.02133429e-01 1.24983536e-02
4.21963692e-01 -4.12690043e-01 -5.66870809e-01 -1.06839530e-01
8.31264853e-02 8.25570285e-01 1.53430605e+00 -1.24637747e+00
-4.13445920e-01 -7.54141808e-01 -2.55130678e-01 -3.47198136e-02
-7.16966510e-01 3.90255868e-01 -5.64460039e-01 -9.92136836e-01
-1.97524890e-01 -6.97328448e-01 2.95185566e-01 3.54346633e-01
4.51488830e-02 -4.65088904e-01 7.91361928e-01 -1.01228762e+00
1.24863493e+00 -2.37951088e+00 3.79381090e-01 -1.18675895e-01
2.76812196e-01 2.43209884e-01 -5.27032316e-01 -3.43513601e-02
-4.66786444e-01 -1.60725892e-01 -3.64792258e-01 -8.26837957e-01
-3.50275449e-02 4.01008390e-02 -1.53159797e-01 5.04804075e-01
6.56435907e-01 6.90941632e-01 -6.30523622e-01 -4.18926388e-01
-1.46242231e-01 7.73211956e-01 -6.11479163e-01 3.22512954e-01
1.81553364e-01 6.22851014e-01 -3.31274360e-01 9.80206490e-01
1.10910165e+00 1.75926745e-01 -3.36350799e-02 -4.49295253e-01
8.80587175e-02 -2.39500135e-01 -9.78360534e-01 1.68479109e+00
-4.87622082e-01 -3.74953523e-02 3.26669216e-01 -8.79057884e-01
1.21748078e+00 3.91478390e-01 6.79382622e-01 -5.98678410e-01
1.87517092e-01 2.53196031e-01 -1.49375677e-01 -8.70836899e-02
7.99527615e-02 -1.62391156e-01 2.60982011e-02 2.55505949e-01
4.54063445e-01 4.36398894e-01 3.66761275e-02 -1.21124223e-01
7.66185105e-01 3.62618059e-01 1.02300517e-01 -2.45197058e-01
8.44250679e-01 -9.11936700e-01 1.18366766e+00 2.37100810e-01
-4.05101299e-01 8.43557417e-01 2.57563502e-01 -2.80108631e-01
-7.54343033e-01 -1.27536607e+00 -2.48335376e-01 1.23483539e+00
-9.43438336e-02 -4.07657623e-01 -8.07952642e-01 -9.90893006e-01
3.32635939e-01 2.94928253e-01 -8.98257732e-01 -3.96562696e-01
-6.05260313e-01 -7.88197279e-01 5.27931392e-01 5.82100093e-01
5.78592300e-01 -8.72612536e-01 3.42137247e-01 -1.11372001e-01
-1.49268275e-02 -9.92045105e-01 -9.20530081e-01 -3.59191567e-01
-4.08718616e-01 -9.51460600e-01 -9.34080958e-01 -7.42291868e-01
9.07647073e-01 -2.00265422e-02 9.07933950e-01 -2.67732926e-02
-2.71389812e-01 3.44353616e-01 -1.80897295e-01 -3.90546620e-01
1.58969760e-02 -4.99312580e-02 5.04964232e-01 7.29129851e-01
4.29393172e-01 -8.22144270e-01 -7.26141810e-01 3.38095009e-01
-5.14584064e-01 -1.02870628e-01 6.49392724e-01 9.56377745e-01
3.51723760e-01 -2.83417255e-01 1.16446841e+00 -6.83784902e-01
3.12998354e-01 -4.67898339e-01 -2.37157494e-01 2.93323606e-01
-7.64985085e-01 -3.11021619e-02 5.28451741e-01 -5.91747940e-01
-1.26555729e+00 2.32645899e-01 -2.06758231e-01 -7.69250751e-01
-1.49775445e-01 -9.62523452e-04 -7.34018564e-01 -2.28796914e-01
3.44887853e-01 1.68599650e-01 2.52360910e-01 -6.53690577e-01
2.20608056e-01 6.42053783e-01 7.70231307e-01 -8.02511454e-01
1.10055935e+00 3.61263365e-01 -1.06500506e-01 -5.22763133e-01
-9.01706219e-01 -1.16116308e-01 -5.32049537e-01 -1.41338333e-01
6.91544354e-01 -1.31735146e+00 -6.04229927e-01 1.07827222e+00
-8.93593192e-01 -4.60685939e-02 -1.03781298e-02 3.05049181e-01
-4.41864103e-01 2.55409390e-01 -5.45105636e-01 -7.28447139e-01
-5.71989119e-01 -9.31675911e-01 1.27231610e+00 4.35124397e-01
-9.01629627e-02 -7.00261474e-01 -1.62504762e-01 4.71434832e-01
2.83368349e-01 3.50546062e-01 6.54373109e-01 -4.59818602e-01
-1.25409499e-01 9.43643451e-02 -4.76911992e-01 5.75777173e-01
5.22383213e-01 4.47450951e-02 -1.26735687e+00 -5.84614336e-01
-2.16686368e-01 -4.50621903e-01 9.86012399e-01 2.49671429e-01
1.40883386e+00 -2.50840783e-01 -2.06656232e-01 1.01727617e+00
1.11093891e+00 3.05867549e-02 5.95097899e-01 -1.68986440e-01
8.88254344e-01 7.10621536e-01 5.33924878e-01 6.40380263e-01
4.99803931e-01 7.75985777e-01 7.10606501e-02 -1.13995604e-01
-4.40367103e-01 -2.16425538e-01 5.45326948e-01 7.12563455e-01
-3.05282593e-01 2.19500750e-01 -5.40232897e-01 5.54175675e-01
-1.63015044e+00 -8.73294175e-01 3.02484304e-01 2.08712411e+00
1.06678867e+00 -4.28199291e-01 2.99255401e-01 -8.02080110e-02
7.92821467e-01 2.17131928e-01 -6.63129807e-01 -1.85241371e-01
-1.62283719e-01 6.35182917e-01 2.63640493e-01 2.26498187e-01
-1.18056571e+00 8.40503573e-01 5.21312761e+00 9.73955870e-01
-1.21764386e+00 2.52115935e-01 7.33164668e-01 -1.96676478e-01
-5.86100742e-02 -3.70171636e-01 -9.74629700e-01 5.18824697e-01
6.93474174e-01 -4.80818599e-01 6.73104703e-01 9.40240324e-01
1.74464285e-03 5.49258888e-01 -1.24513590e+00 1.21519864e+00
3.49593550e-01 -7.71537721e-01 4.50120196e-02 -1.03903465e-01
6.69039667e-01 -5.65346241e-01 4.83966142e-01 4.31158662e-01
7.96981007e-02 -1.11962211e+00 8.04542780e-01 5.99253476e-01
1.34601867e+00 -7.65454650e-01 3.72843415e-01 -2.43876517e-01
-1.54136693e+00 -2.54822671e-01 -1.23405069e-01 9.82091948e-02
-1.60312667e-01 4.52278435e-01 -1.13211505e-01 9.41569090e-01
8.08857977e-01 9.36206341e-01 -8.37014019e-01 6.03142798e-01
-3.32251401e-03 2.85794020e-01 1.48281539e-02 8.43655884e-01
-3.87728870e-01 -1.71167597e-01 2.53082275e-01 8.15250397e-01
6.68902874e-01 1.15258584e-03 8.29778314e-02 7.79115736e-01
-3.88768703e-01 -5.09757847e-02 -2.91679174e-01 -2.73094699e-02
6.24747694e-01 1.39142680e+00 -1.18626252e-01 -9.51536000e-02
-6.54732406e-01 1.23694372e+00 4.31210995e-01 2.77056754e-01
-8.63725901e-01 -3.42286289e-01 1.31124961e+00 1.04419822e-02
3.30179751e-01 -4.84344549e-02 -2.43205398e-01 -1.28589487e+00
1.69530496e-01 -1.15221810e+00 4.51383829e-01 -3.15521985e-01
-1.75339448e+00 6.49949074e-01 -1.08279875e-02 -9.61937428e-01
-6.32171631e-02 -5.34229159e-01 -6.84251845e-01 1.21155560e+00
-1.43751562e+00 -1.78987491e+00 -4.55347300e-01 8.12200665e-01
5.41763604e-01 -4.02781844e-01 8.10120046e-01 7.58542001e-01
-1.02188814e+00 1.28903615e+00 -2.07729578e-01 2.10162997e-02
1.38389552e+00 -9.37137127e-01 4.13581997e-01 8.83588731e-01
-2.80010641e-01 7.74644732e-01 2.76440442e-01 -5.77754438e-01
-1.35700262e+00 -1.42501831e+00 9.28926408e-01 -4.15994585e-01
2.98004061e-01 -6.37657166e-01 -1.10151136e+00 6.02188468e-01
-5.25278300e-02 3.53393495e-01 6.65132642e-01 1.43141389e-01
-8.84676993e-01 -5.80771029e-01 -1.26179266e+00 5.55585265e-01
1.55667746e+00 -5.34577549e-01 -4.05456185e-01 -3.94222327e-02
5.46465993e-01 -3.34641039e-02 -1.12027740e+00 8.39524448e-01
8.78650963e-01 -8.27233970e-01 1.17561173e+00 -3.98387879e-01
3.78002942e-01 -2.36179233e-01 -3.28922868e-02 -1.27054882e+00
-5.54445505e-01 -6.45622075e-01 -3.34863842e-01 1.97318482e+00
1.54537363e-02 -7.49279678e-01 6.92047954e-01 4.70236301e-01
-1.57589152e-01 -7.65509367e-01 -8.57450545e-01 -9.37799752e-01
2.81947970e-01 6.30047470e-02 9.34478521e-01 1.02816093e+00
-6.53484404e-01 1.78311333e-01 -6.53168976e-01 2.72206903e-01
8.48838568e-01 1.69890653e-02 6.29841506e-01 -1.30733061e+00
-3.77724618e-02 -4.46428120e-01 -1.70855537e-01 -5.07819772e-01
7.53847003e-01 -8.43933821e-01 -8.24945644e-02 -1.03373861e+00
2.39419594e-01 -4.32959706e-01 -4.75771338e-01 7.70460427e-01
-5.26563942e-01 5.40828228e-01 1.21178746e-01 -8.76446515e-02
-2.20015064e-01 9.21242893e-01 1.13282287e+00 -7.17418864e-02
1.14461318e-01 -1.31753922e-01 -9.49594319e-01 4.96891677e-01
6.76129758e-01 -3.39726686e-01 -4.59247440e-01 -3.17250133e-01
-4.97329742e-01 -2.72639722e-01 2.19166502e-01 -1.00111794e+00
-5.55465370e-02 -2.82624185e-01 7.34359086e-01 -1.26079768e-01
4.59648609e-01 -5.24692237e-01 2.97691315e-01 2.95876622e-01
-7.31195137e-02 -4.82849777e-02 1.84561342e-01 2.62283146e-01
-1.84012085e-01 2.11965501e-01 1.02535069e+00 2.16695771e-01
-7.08387911e-01 9.97756362e-01 2.10766599e-01 -9.58807468e-02
1.04986250e+00 -1.13822870e-01 -4.06331122e-01 -6.93636164e-02
-5.46788216e-01 1.97287127e-01 5.83277166e-01 8.86274278e-01
6.03513956e-01 -1.72288442e+00 -1.14435446e+00 6.72009289e-01
2.96124965e-01 -4.21096832e-01 5.30519783e-01 7.35959589e-01
1.26429483e-01 -3.89574952e-02 -5.89902103e-01 -2.22364604e-01
-1.56261945e+00 6.57312214e-01 2.16128856e-01 2.94050928e-02
-1.48265049e-01 1.09990835e+00 7.36473441e-01 -4.80150044e-01
1.74449727e-01 4.23999906e-01 -1.26319394e-01 2.11412624e-01
7.37059653e-01 1.59487322e-01 -2.98930947e-02 -8.98578346e-01
-5.98656356e-01 8.16953063e-01 -2.74132639e-01 2.04195112e-01
1.36910522e+00 -1.37212113e-01 -2.47967735e-01 5.99685460e-02
1.24272203e+00 -9.23848990e-03 -1.57052267e+00 -2.80607373e-01
-1.58643335e-01 -7.71920025e-01 -1.84832364e-01 -6.62706256e-01
-1.40552843e+00 6.68238163e-01 7.00601876e-01 -5.60317516e-01
1.58991861e+00 -1.18293785e-01 7.25274026e-01 -3.32965374e-01
2.38708317e-01 -7.83901930e-01 1.54740527e-01 3.30537111e-01
1.22826648e+00 -1.21516502e+00 -5.67866266e-02 -6.15283191e-01
-4.47885275e-01 8.13637733e-01 1.03296268e+00 6.01446442e-02
6.23873591e-01 1.30124509e-01 4.66966033e-02 2.56543308e-01
-7.85475373e-01 -1.60468131e-01 5.39135695e-01 8.68682444e-01
5.30815959e-01 -1.30519435e-01 -3.98305714e-01 1.10365438e+00
-1.46892935e-01 9.39153731e-02 -2.15567872e-01 6.37386441e-01
1.18095666e-01 -1.52839780e+00 -2.99879372e-01 4.81134653e-01
-5.37718177e-01 -3.84258330e-02 -4.56254900e-01 4.32198256e-01
6.04553461e-01 7.26049960e-01 1.26086459e-01 -5.89621305e-01
1.82132304e-01 3.44443500e-01 8.11933339e-01 -5.47619879e-01
-6.11482441e-01 -1.94864288e-01 5.88191375e-02 -6.19986236e-01
-2.68915802e-01 -9.17369366e-01 -7.56253004e-01 -5.25524437e-01
6.53176457e-02 -2.47362569e-01 4.22060132e-01 5.85047901e-01
5.96358657e-01 4.00516987e-01 1.05287766e+00 -9.18855727e-01
-5.07030547e-01 -9.56767499e-01 -5.31306088e-01 6.69076025e-01
2.45261058e-01 -1.00289273e+00 -3.20334017e-01 2.85259187e-01] | [13.296815872192383, 0.6241703629493713] |
9f1975ea-901d-4af8-b913-3147040dcf6c | real-time-controllable-denoising-for-image | 2303.16425 | null | https://arxiv.org/abs/2303.16425v1 | https://arxiv.org/pdf/2303.16425v1.pdf | Real-time Controllable Denoising for Image and Video | Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness. In traditional filter-based denoising methods, this can be easily achieved by adjusting the filtering strength. However, for NN (Neural Network)-based models, adjusting the final denoising strength requires performing network inference each time, making it almost impossible for real-time user interaction. In this paper, we introduce Real-time Controllable Denoising (RCD), the first deep image and video denoising pipeline that provides a fully controllable user interface to edit arbitrary denoising levels in real-time with only one-time network inference. Unlike existing controllable denoising methods that require multiple denoisers and training stages, RCD replaces the last output layer (which usually outputs a single noise map) of an existing CNN-based model with a lightweight module that outputs multiple noise maps. We propose a novel Noise Decorrelation process to enforce the orthogonality of the noise feature maps, allowing arbitrary noise level control through noise map interpolation. This process is network-free and does not require network inference. Our experiments show that RCD can enable real-time editable image and video denoising for various existing heavy-weight models without sacrificing their original performance. | ['Jinwei Gu', 'Kaimo Lin', 'Ping Luo', 'Xiaogang Wang', 'Wenqi Shao', 'Yitong Jiang', 'Zhaoyang Zhang'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Real-Time_Controllable_Denoising_for_Image_and_Video_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Real-Time_Controllable_Denoising_for_Image_and_Video_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-denoising'] | ['computer-vision'] | [ 3.96963000e-01 4.80450206e-02 5.01521587e-01 -3.95063758e-01
-4.28888977e-01 -5.33066571e-01 4.20454383e-01 -1.36743084e-01
-4.65064436e-01 3.76204640e-01 -7.86745083e-03 -3.51551056e-01
-1.99399679e-03 -9.81012046e-01 -8.08995128e-01 -7.78183818e-01
3.45977396e-01 -1.83103696e-01 2.27862999e-01 -4.00492311e-01
-5.52398432e-03 4.23190892e-01 -1.46729052e+00 1.71424150e-01
9.88268375e-01 1.07704782e+00 3.90851855e-01 1.06004357e+00
1.11243464e-01 6.83347046e-01 -6.30344391e-01 -3.44833583e-01
5.64798415e-01 -3.31526697e-01 8.28315839e-02 -1.90976001e-02
7.43144691e-01 -6.11267090e-01 -5.10838866e-01 1.32687223e+00
8.00008297e-01 1.95589721e-01 6.46888763e-02 -9.71305609e-01
-9.59270597e-01 5.62668979e-01 -2.96154022e-01 -2.41053477e-01
5.27548194e-02 5.66505313e-01 4.65311825e-01 -5.52275658e-01
4.49653238e-01 1.29146492e+00 9.44180846e-01 6.33013964e-01
-1.62628019e+00 -6.28950059e-01 4.91756462e-02 -1.13689542e-01
-1.17880523e+00 -5.93626559e-01 8.08646679e-01 -2.18989477e-01
6.74245536e-01 4.70882028e-01 8.27563822e-01 1.09194374e+00
2.30981752e-01 1.71929687e-01 8.78837466e-01 -2.67554194e-01
4.29774761e-01 -3.22645545e-01 -3.31534803e-01 6.40510559e-01
5.05046844e-02 4.86872271e-02 -4.52696830e-01 2.47087464e-01
1.57079422e+00 5.86995408e-02 -6.19441926e-01 -2.27336287e-01
-1.29387665e+00 5.83933711e-01 4.82231110e-01 -1.08924277e-01
-3.68206412e-01 6.82821512e-01 3.58559221e-01 7.30285466e-01
4.79176044e-01 3.49794477e-01 -3.69858772e-01 -1.47318512e-01
-1.13602734e+00 1.08175963e-01 7.90918946e-01 9.84760523e-01
6.27655804e-01 4.49647218e-01 -4.51810867e-01 6.94850028e-01
9.88302380e-02 4.12466079e-01 1.36216193e-01 -1.52224505e+00
1.50894344e-01 1.89200044e-01 2.37089440e-01 -1.13538444e+00
-2.04814211e-01 -5.81760406e-01 -1.35343409e+00 1.00795078e+00
3.91345352e-01 -2.68580586e-01 -1.10711849e+00 1.61096847e+00
2.63755918e-01 3.54659826e-01 -4.05480117e-01 9.60126758e-01
6.93954706e-01 5.20617127e-01 -1.79493040e-01 -1.67006366e-02
1.14596808e+00 -9.77747202e-01 -8.74729216e-01 -2.29232591e-02
-3.11926603e-01 -9.11432207e-01 1.36917043e+00 7.63995409e-01
-1.20770121e+00 -6.44790649e-01 -1.17135596e+00 -5.61256111e-01
-1.92243591e-01 -6.98634610e-02 4.24356610e-01 4.37945515e-01
-1.29752302e+00 9.90387857e-01 -1.15686095e+00 1.14558570e-01
2.42320076e-01 4.32404995e-01 -4.15349782e-01 6.75322637e-02
-8.87482584e-01 7.44653523e-01 -9.76559818e-02 7.19759524e-01
-9.18494165e-01 -8.72687578e-01 -1.00236237e+00 1.83205068e-01
2.98603117e-01 -9.81647193e-01 1.05025280e+00 -1.10566258e+00
-2.09995818e+00 4.42832649e-01 -9.69783440e-02 -2.66617179e-01
1.02074897e+00 -4.39072162e-01 -2.71710873e-01 5.61107807e-02
-3.23953450e-01 7.79820919e-01 1.47056782e+00 -1.41285586e+00
-4.99661677e-02 1.03756435e-01 3.19969893e-01 -1.37313455e-01
-4.69350070e-01 -2.24810511e-01 -7.93039441e-01 -1.03306139e+00
1.94880754e-01 -4.55087215e-01 -5.79430103e-01 8.66938829e-01
-3.84864897e-01 3.47533882e-01 9.65448618e-01 -8.02011728e-01
1.13994229e+00 -2.24298954e+00 2.86594957e-01 3.83776695e-01
5.59808791e-01 2.70414203e-01 -4.06144232e-01 -1.14988061e-02
-1.50345974e-02 1.14474438e-01 -2.64059931e-01 -7.59492338e-01
7.83384815e-02 3.84296536e-01 7.51283541e-02 3.39325517e-01
2.41058201e-01 7.52966166e-01 -9.94198740e-01 -2.11649746e-01
5.86068213e-01 1.14463353e+00 -7.47930288e-01 1.12729557e-01
-1.37881413e-01 6.31026030e-01 1.20190427e-01 3.72958869e-01
9.16961193e-01 2.15157464e-01 4.77564335e-02 -7.62894928e-01
-2.52458155e-01 -8.25464167e-03 -1.51355660e+00 1.75512969e+00
-9.20424700e-01 8.10345590e-01 8.93262327e-01 -7.15867102e-01
9.09780324e-01 3.76614094e-01 -2.37754937e-02 -5.51444590e-01
6.34231195e-02 5.88179193e-02 -2.84584671e-01 -4.18484360e-01
4.16746020e-01 9.81153995e-02 2.36390337e-01 2.07298938e-02
-5.72714284e-02 -4.80174661e-01 1.30503241e-03 -9.80086550e-02
1.27123547e+00 2.41289914e-01 -2.07914501e-01 -2.89562970e-01
2.21602723e-01 -5.17238319e-01 7.24233389e-01 9.39948022e-01
3.60284857e-02 1.21989095e+00 4.77834612e-01 -4.34921771e-01
-1.23956406e+00 -1.20773113e+00 6.85839355e-02 8.56801391e-01
1.51452869e-01 -3.64976346e-01 -9.60876942e-01 -8.74998197e-02
-1.17198214e-01 5.70611775e-01 -6.78685665e-01 -1.81419738e-02
-6.73716426e-01 -2.38906860e-01 3.70685518e-01 4.43120778e-01
7.19440401e-01 -8.67554307e-01 -5.03737867e-01 3.03492397e-01
1.55820046e-02 -6.96957886e-01 -9.20622170e-01 3.69740337e-01
-9.07342732e-01 -5.84977984e-01 -5.22857964e-01 -8.64951313e-01
1.00400531e+00 1.69129193e-01 1.07481432e+00 3.99712414e-01
-2.51853526e-01 1.94730967e-01 2.99505331e-02 -7.28168860e-02
-3.22885096e-01 -1.81381851e-01 -1.29081443e-01 -6.43478855e-02
-2.59469897e-01 -1.15101278e+00 -1.06738603e+00 3.02639991e-01
-1.30468678e+00 3.76829356e-01 2.19058409e-01 9.18951094e-01
7.78597951e-01 2.94308633e-01 3.22807193e-01 -6.53813124e-01
9.15660679e-01 5.04140221e-02 -8.45653415e-01 3.26284617e-02
-2.53218263e-01 4.45149280e-02 8.39090168e-01 -8.45358670e-01
-1.03124666e+00 1.79605141e-01 -2.74595201e-01 -6.07703388e-01
5.61800450e-02 4.75243419e-01 -3.53046656e-01 -1.39277592e-01
7.23678231e-01 -4.44214158e-02 2.75229275e-01 -6.09189034e-01
6.54821754e-01 5.50576866e-01 1.00609934e+00 -3.34830493e-01
1.13011956e+00 4.83118147e-01 -1.77343905e-01 -6.55605376e-01
-5.37703454e-01 -4.41418495e-03 -6.59176409e-01 -3.53233695e-01
8.42385828e-01 -1.01956904e+00 -1.01030087e+00 7.67047822e-01
-1.24423337e+00 -8.46622050e-01 -3.31275523e-01 1.17180951e-01
-3.69155020e-01 1.32332131e-01 -8.06068659e-01 -3.26700062e-01
-5.62475502e-01 -1.28621936e+00 9.98329520e-01 1.02228507e-01
-2.93559521e-01 -9.77476656e-01 -5.72967529e-01 -3.57464515e-02
1.02447820e+00 5.08083761e-01 4.82522875e-01 4.29784775e-01
-7.04578221e-01 -3.05968046e-01 -9.92912799e-02 7.89995909e-01
2.57429004e-01 1.50558010e-01 -8.18603575e-01 -3.14513654e-01
1.93134710e-01 2.73395255e-02 9.39209342e-01 4.95518178e-01
1.48854780e+00 -3.67843390e-01 2.45655149e-01 9.30756509e-01
1.43548632e+00 -1.92172721e-01 1.05757606e+00 4.18424159e-01
8.83679211e-01 1.01841703e-01 -9.83942375e-02 3.01743746e-01
-3.52019160e-05 5.51421523e-01 6.06415689e-01 -5.35228550e-01
-4.71331060e-01 -2.03107640e-01 4.66887206e-01 6.78958178e-01
-2.59835839e-01 -7.14732334e-02 -3.17548126e-01 3.09680730e-01
-1.73619664e+00 -9.58825290e-01 -1.41235620e-01 2.26901650e+00
1.28532755e+00 3.72291088e-01 -4.42249060e-01 7.75925145e-02
6.86818957e-01 1.24447934e-01 -6.39462352e-01 -4.40011442e-01
1.13058817e-02 5.62071383e-01 6.30835652e-01 9.36365843e-01
-9.96484935e-01 5.72577417e-01 5.93487692e+00 8.27242136e-01
-1.21937871e+00 6.37296513e-02 4.12595719e-01 -4.22823578e-01
-6.85658216e-01 -1.71453863e-01 -2.07832336e-01 4.27079499e-01
4.47294384e-01 1.64236605e-01 8.64660323e-01 6.18930578e-01
9.67102230e-01 5.03772274e-02 -1.11558008e+00 8.78192067e-01
-4.20847416e-01 -1.33751285e+00 9.76145566e-02 -3.30024719e-01
5.54656386e-01 -3.36549282e-01 2.50270702e-02 -7.05284700e-02
3.42028797e-01 -1.08400273e+00 9.94272649e-01 9.03414726e-01
9.38309193e-01 -7.80084312e-01 3.83683652e-01 1.83637336e-01
-1.04404914e+00 -2.48244368e-02 -4.61211920e-01 -1.21680945e-01
4.71487403e-01 1.24618721e+00 -1.17354123e-02 3.44022475e-02
9.89949584e-01 5.22510111e-01 -2.22836807e-01 9.50896204e-01
-5.59953034e-01 6.11300707e-01 -4.08901662e-01 3.69697958e-01
-2.19589904e-01 -4.43749666e-01 6.11755729e-01 1.11674845e+00
4.61134374e-01 -7.42280409e-02 -5.75344041e-02 9.54193592e-01
-2.01403379e-01 -5.67650974e-01 -2.12607488e-01 4.82884198e-01
4.62727159e-01 1.35968840e+00 -6.62206471e-01 -2.67657697e-01
-2.30032116e-01 1.45026493e+00 7.33470246e-02 5.01676142e-01
-9.49796557e-01 -8.65658283e-01 1.04243541e+00 2.72216201e-01
5.26720822e-01 -5.94490826e-01 -6.74150825e-01 -1.21763396e+00
1.76804811e-01 -1.02132845e+00 -4.20335680e-01 -9.00801241e-01
-1.30973411e+00 5.41813433e-01 -5.27655423e-01 -1.17689884e+00
3.06523561e-01 -4.41316247e-01 -7.27981150e-01 9.99347746e-01
-1.32236171e+00 -1.12208736e+00 -7.20950246e-01 6.62021101e-01
4.21839654e-01 3.92149538e-01 8.07049990e-01 5.61167359e-01
-5.62403917e-01 7.29275465e-01 8.62861872e-02 1.27689913e-01
8.64470184e-01 -1.33794367e+00 6.79196537e-01 9.34440374e-01
-4.31154907e-01 9.49331164e-01 1.08036971e+00 -5.87955534e-01
-1.44938707e+00 -1.13863146e+00 4.30804372e-01 -9.70519185e-02
5.48948646e-01 -6.83252454e-01 -8.49388719e-01 5.09397089e-01
5.56023121e-01 2.82017767e-01 1.11105554e-01 -3.53093207e-01
-3.67144614e-01 -5.33485055e-01 -1.29064298e+00 1.00292444e+00
1.27219534e+00 -4.38935161e-01 -4.67872620e-03 7.32069686e-02
1.00500131e+00 -6.95105731e-01 -1.06531036e+00 1.88142553e-01
6.03554249e-01 -9.97933924e-01 1.19486606e+00 -6.30658492e-02
5.50580502e-01 -8.56616437e-01 1.13200232e-01 -1.47333562e+00
-4.57321912e-01 -1.06082380e+00 -9.61600468e-02 1.19426584e+00
2.62753040e-01 -5.96320748e-01 4.73538190e-01 8.81601274e-01
-3.15298170e-01 -4.88819867e-01 -8.65387678e-01 -5.59792757e-01
-2.84227163e-01 -6.21518731e-01 5.83403409e-01 7.03336120e-01
-4.61250156e-01 -1.55496821e-01 -4.89055485e-01 4.14428055e-01
7.74369121e-01 -3.25328261e-01 7.71292627e-01 -9.16702330e-01
-5.01684964e-01 -4.00787383e-01 -2.86998361e-01 -1.34881115e+00
-3.29459012e-01 -3.39426309e-01 3.60792905e-01 -1.69045174e+00
-3.55142117e-01 -2.28482917e-01 -2.61015724e-02 6.55433655e-01
-8.98370594e-02 5.84321737e-01 7.22227246e-02 -1.49673834e-01
-1.94993690e-01 4.85530794e-01 1.50152326e+00 -1.69135466e-01
-3.10299546e-01 -1.15903720e-01 -7.03842938e-01 7.52899766e-01
7.40996420e-01 -3.16287816e-01 -5.32993495e-01 -1.09556878e+00
3.45680356e-01 -1.99726418e-01 7.62594521e-01 -1.13901663e+00
4.01162356e-01 -4.63629998e-02 6.14370286e-01 -3.14089805e-02
2.26754799e-01 -9.64033663e-01 4.62729663e-01 2.98158377e-01
-2.12494448e-01 2.25475691e-02 2.42521733e-01 5.72678387e-01
-1.38280943e-01 -2.96311863e-02 8.57654691e-01 -1.74075961e-01
-3.55390280e-01 1.97908357e-01 -5.19006014e-01 -4.38734859e-01
6.99089408e-01 -3.61880392e-01 -1.72307029e-01 -6.87727273e-01
-1.00168216e+00 9.81260985e-02 6.03277147e-01 2.60423899e-01
7.74527609e-01 -1.25883389e+00 -5.36141932e-01 3.27614903e-01
-5.00472188e-01 4.76230145e-01 3.34823668e-01 5.36716521e-01
-8.78089309e-01 -7.40857363e-01 -3.58144455e-02 -4.84904826e-01
-1.11689830e+00 3.66160899e-01 6.53926849e-01 -1.44814830e-02
-8.43851447e-01 8.94409716e-01 -3.69664729e-01 -5.46159446e-01
4.58930850e-01 -4.63602334e-01 2.29747549e-01 -1.29440874e-01
6.18551433e-01 3.55942696e-01 2.21199036e-01 6.99062645e-02
1.82297975e-01 5.15732765e-01 3.16507280e-01 -1.74157009e-01
1.62768221e+00 -2.39001051e-01 -5.04046082e-01 9.79305655e-02
1.03162467e+00 7.71604553e-02 -1.94071114e+00 1.38238028e-01
-7.04692662e-01 -4.48844403e-01 4.39788073e-01 -6.72903836e-01
-1.61350739e+00 6.90734744e-01 6.62209034e-01 1.34953633e-01
1.45947003e+00 -8.33223641e-01 9.87567663e-01 1.91089034e-01
1.94610313e-01 -1.11661673e+00 -6.32123696e-03 3.20869327e-01
1.18236434e+00 -9.24093127e-01 -6.75439462e-02 -5.23769140e-01
-9.11301300e-02 1.30646443e+00 6.04603350e-01 -1.87985912e-01
1.00774658e+00 7.88020194e-01 4.07487184e-01 4.88782935e-02
-4.50118214e-01 2.57352918e-01 7.12803304e-02 6.36618674e-01
3.31224054e-01 -2.00775877e-01 -1.69901952e-01 3.09462667e-01
-2.68037379e-01 2.59481728e-01 4.46467251e-01 8.42208266e-01
-4.31705356e-01 -1.20590687e+00 -3.74495417e-01 3.63895863e-01
-1.59885287e-01 -4.16237891e-01 3.07388812e-01 3.04249108e-01
3.60581458e-01 1.11721683e+00 1.80028200e-01 -2.54053503e-01
6.18700922e-01 -3.81987900e-01 3.13765734e-01 -2.87395537e-01
-6.50629401e-01 3.71653587e-02 -1.30517736e-01 -7.17880011e-01
-1.91237658e-01 -1.71841711e-01 -1.02520037e+00 -6.17915094e-01
-2.34911472e-01 -3.39510977e-01 5.72850347e-01 5.54555535e-01
4.51591641e-01 7.87433088e-01 3.91004533e-01 -1.29423296e+00
-4.12717819e-01 -7.99967885e-01 -3.45068008e-01 4.88456517e-01
5.47402918e-01 -3.83876920e-01 -4.44647968e-01 4.91937131e-01] | [11.51828670501709, -2.130319833755493] |
c70bb256-5112-4978-9bc6-6aa317c39940 | deep-bilateral-learning-for-real-time-image | 1707.02880 | null | http://arxiv.org/abs/1707.02880v2 | http://arxiv.org/pdf/1707.02880v2.pdf | Deep Bilateral Learning for Real-Time Image Enhancement | Performance is a critical challenge in mobile image processing. Given a
reference imaging pipeline, or even human-adjusted pairs of images, we seek to
reproduce the enhancements and enable real-time evaluation. For this, we
introduce a new neural network architecture inspired by bilateral grid
processing and local affine color transforms. Using pairs of input/output
images, we train a convolutional neural network to predict the coefficients of
a locally-affine model in bilateral space. Our architecture learns to make
local, global, and content-dependent decisions to approximate the desired image
transformation. At runtime, the neural network consumes a low-resolution
version of the input image, produces a set of affine transformations in
bilateral space, upsamples those transformations in an edge-preserving fashion
using a new slicing node, and then applies those upsampled transformations to
the full-resolution image. Our algorithm processes high-resolution images on a
smartphone in milliseconds, provides a real-time viewfinder at 1080p
resolution, and matches the quality of state-of-the-art approximation
techniques on a large class of image operators. Unlike previous work, our model
is trained off-line from data and therefore does not require access to the
original operator at runtime. This allows our model to learn complex,
scene-dependent transformations for which no reference implementation is
available, such as the photographic edits of a human retoucher. | ['Frédo Durand', 'Samuel W. Hasinoff', 'Jonathan T. Barron', 'Michaël Gharbi', 'Jiawen Chen'] | 2017-07-10 | null | null | null | null | ['image-retouching'] | ['computer-vision'] | [ 5.27673662e-01 -2.85515726e-01 6.53056204e-02 -4.45990890e-01
-8.35385561e-01 -6.03148222e-01 4.06072497e-01 -2.49881893e-01
-6.44647717e-01 2.07104936e-01 -3.50556709e-03 -5.94581068e-01
2.71244764e-01 -9.77701366e-01 -1.16849506e+00 -2.56763190e-01
9.57740322e-02 2.66401261e-01 4.76444811e-01 -2.08796263e-01
2.78071433e-01 8.93180847e-01 -1.49768209e+00 6.10444844e-01
7.02028692e-01 1.27774739e+00 1.82075724e-01 1.39471257e+00
1.85547650e-01 7.22404659e-01 -2.34375224e-01 -4.33202326e-01
8.69626820e-01 -2.04040170e-01 -8.30749929e-01 1.03700355e-01
1.16052794e+00 -1.08701360e+00 -5.39605021e-01 9.80846226e-01
3.70059341e-01 8.52825493e-02 2.50023842e-01 -8.68628502e-01
-7.10866749e-01 1.95347276e-02 -7.10782588e-01 1.79470494e-01
2.27553472e-01 5.32164335e-01 6.34319246e-01 -1.03344274e+00
8.42616796e-01 1.16273499e+00 9.00283575e-01 1.97102502e-01
-1.38224363e+00 -4.43016827e-01 -8.57337639e-02 1.02635212e-01
-1.27036333e+00 -5.10528207e-01 3.96035314e-01 -7.60564879e-02
1.22918606e+00 2.86925822e-01 8.28842342e-01 6.99262917e-01
3.13361377e-01 2.95684427e-01 9.22606528e-01 -3.42205197e-01
8.54655504e-02 -3.09179515e-01 -4.57405686e-01 9.68623579e-01
-1.87366143e-01 1.22816078e-01 -6.74767196e-01 -2.86219199e-03
1.42153394e+00 1.13821104e-01 -5.06416798e-01 -4.85966712e-01
-1.27658284e+00 2.87503719e-01 7.60393381e-01 -9.03517902e-02
-6.01090074e-01 4.45506096e-01 1.57283559e-01 5.08472979e-01
4.52266216e-01 2.59196818e-01 -4.89940673e-01 -2.35494748e-01
-1.27785289e+00 1.31014898e-01 6.58978045e-01 8.21151137e-01
1.06877112e+00 -9.63757336e-02 5.41216470e-02 4.63502079e-01
-2.00993419e-01 4.93081957e-01 1.80130929e-01 -1.68728149e+00
4.75517303e-01 2.29887217e-01 8.92089605e-02 -1.01306283e+00
-1.89094067e-01 -1.74674913e-01 -8.52198184e-01 9.43015456e-01
4.58518893e-01 1.02661528e-01 -8.63516212e-01 1.21941125e+00
3.24888974e-01 1.30134344e-01 -2.09601939e-01 1.01299393e+00
1.17295571e-01 7.62880743e-01 -4.25647646e-01 1.19761206e-01
1.19390309e+00 -1.18542910e+00 -2.34669507e-01 -3.42473745e-01
3.09159994e-01 -1.01212800e+00 1.45947754e+00 5.80108106e-01
-1.69687927e+00 -6.11174703e-01 -1.17878640e+00 -9.93391812e-01
-2.09892690e-01 -4.05948944e-02 4.02016193e-01 2.31841981e-01
-1.57657552e+00 7.93551207e-01 -1.08655012e+00 -3.28430772e-01
4.07339066e-01 4.10993069e-01 -4.20249760e-01 -2.46917456e-01
-6.45090938e-01 6.55098796e-01 -8.70697722e-02 3.48644610e-03
-5.72956264e-01 -1.09409690e+00 -6.75818861e-01 1.27128989e-01
2.04220936e-01 -1.05622268e+00 1.48377538e+00 -1.40095723e+00
-1.66426730e+00 8.79956484e-01 -2.90777296e-01 -5.80074489e-01
7.99394608e-01 -2.34374940e-01 -8.68848190e-02 4.62286681e-01
7.89165124e-03 9.33320940e-01 1.13695920e+00 -9.89093721e-01
-7.77412415e-01 -2.62525827e-01 3.05289596e-01 4.52122122e-01
-2.61417449e-01 -4.58298102e-02 -1.00829315e+00 -5.54959178e-01
6.65875971e-02 -6.98208392e-01 -2.23573521e-01 9.25497711e-01
7.21876696e-03 4.86567110e-01 1.01335287e+00 -9.25492942e-01
7.57645369e-01 -2.38692451e+00 -6.53548390e-02 3.35959554e-01
2.57920057e-01 3.51057053e-02 -2.02064916e-01 -2.83277649e-02
-4.10395861e-02 -1.62415892e-01 -3.41413110e-01 -4.83604938e-01
-2.91064709e-01 1.32461861e-01 -3.82975906e-01 4.35113907e-01
1.35389999e-01 8.79397929e-01 -8.47169578e-01 -3.05073172e-01
3.47515285e-01 8.20433795e-01 -1.00809896e+00 3.57975841e-01
-1.17762059e-01 1.76806644e-01 1.79190591e-01 4.87225890e-01
8.42563212e-01 -2.59468108e-01 6.35193884e-02 -7.19320655e-01
-2.45203540e-01 2.11357564e-01 -1.11057115e+00 2.03447294e+00
-8.80926669e-01 9.99906123e-01 3.10744911e-01 -5.01401305e-01
5.78682721e-01 -1.52167752e-01 3.37747395e-01 -9.57014740e-01
-1.02291048e-01 3.30798298e-01 -3.02401841e-01 -2.37776175e-01
7.40400076e-01 3.76728386e-01 4.27603215e-01 7.07161546e-01
-8.96128342e-02 -4.38377619e-01 -1.16072400e-02 1.94315791e-01
1.20669413e+00 3.63199800e-01 1.48834124e-01 6.68581352e-02
2.10365266e-01 -1.20717630e-01 1.13005914e-01 6.77456737e-01
9.86982435e-02 1.08760870e+00 4.23196942e-01 -9.78276074e-01
-1.58717048e+00 -1.31687021e+00 1.80349693e-01 1.28670001e+00
9.41172913e-02 -2.58753985e-01 -8.65048945e-01 -3.27033401e-01
-2.84006983e-01 4.03314471e-01 -4.36587214e-01 1.94801867e-01
-9.71151054e-01 -2.51253825e-02 3.17575127e-01 7.27672040e-01
9.80285645e-01 -7.19318867e-01 -9.37224984e-01 -2.89363234e-04
-5.65865189e-02 -1.05912757e+00 -1.11979640e+00 1.11741386e-01
-9.37443972e-01 -1.08637917e+00 -5.82296610e-01 -7.81051695e-01
8.61597955e-01 5.22386432e-01 1.20682490e+00 1.79421857e-01
-2.31708542e-01 2.71622658e-01 2.26592928e-01 7.33704939e-02
-2.97277838e-01 5.85022122e-02 -2.36642897e-01 7.17410296e-02
-1.36720082e-02 -8.76415730e-01 -9.35787320e-01 1.10822469e-01
-1.05799055e+00 4.17885244e-01 6.05400801e-01 8.29681396e-01
9.89255726e-01 -2.12328229e-02 -5.01863480e-01 -7.04982936e-01
5.66285610e-01 2.19690740e-01 -1.01628697e+00 1.45968586e-01
-4.38145667e-01 2.20825002e-01 8.12166035e-01 -4.00238216e-01
-1.12910175e+00 3.33533764e-01 -1.09749585e-01 -7.86152303e-01
5.41806370e-02 1.42594635e-01 1.57497093e-01 -3.25122803e-01
9.82721031e-01 1.45683065e-01 7.06667453e-02 -1.87232867e-01
7.99710810e-01 4.29020703e-01 1.26197278e+00 -3.93570453e-01
9.52801645e-01 8.26386273e-01 -8.39683264e-02 -5.23386955e-01
-5.07825851e-01 -1.53181240e-01 -7.99086034e-01 -9.93767232e-02
8.84016097e-01 -9.06309128e-01 -7.20579088e-01 4.59024906e-01
-1.24844134e+00 -8.77894878e-01 -5.24986982e-01 1.40197203e-01
-7.06571519e-01 6.37356713e-02 -7.35541999e-01 -1.94232225e-01
-4.59015876e-01 -1.16989958e+00 1.29808307e+00 7.71684349e-02
-7.69406632e-02 -6.23949170e-01 -3.11440408e-01 -6.57995988e-04
7.84353495e-01 -3.50141712e-02 7.51970708e-01 2.02495262e-01
-1.17937791e+00 -2.34030280e-02 -6.09219074e-01 3.93032491e-01
-1.62644703e-02 1.31896511e-01 -9.04698491e-01 -2.62544364e-01
-1.10792192e-02 -2.74220139e-01 6.18916154e-01 3.41278523e-01
1.51927698e+00 -5.48028767e-01 -3.16522382e-02 1.44584358e+00
1.33941007e+00 -1.10558994e-01 7.73961306e-01 5.03416002e-01
7.88321078e-01 1.84539139e-01 2.46818572e-01 9.63609070e-02
4.89196837e-01 7.09225237e-01 3.44295561e-01 -4.81894046e-01
-2.83878863e-01 -2.37746075e-01 3.09653878e-01 4.52561557e-01
-2.82442659e-01 3.08276445e-01 -7.83725083e-01 1.36856169e-01
-1.59144330e+00 -9.57284927e-01 1.90475687e-01 2.37027287e+00
9.34068263e-01 1.04017727e-01 1.50805032e-02 -2.10172802e-01
2.32946008e-01 3.20703626e-01 -7.60943234e-01 -6.46214008e-01
8.99439603e-02 5.16621768e-01 1.02566409e+00 8.99543285e-01
-9.14540648e-01 8.91080379e-01 6.54464293e+00 4.62621272e-01
-1.57422793e+00 3.74377519e-02 1.08291304e+00 -4.32650000e-01
-3.38438630e-01 -8.07109103e-02 -1.25053480e-01 4.41094376e-02
8.93834352e-01 -3.40073481e-02 1.22914195e+00 7.78261602e-01
2.50606179e-01 1.63499434e-02 -1.18821621e+00 1.17868328e+00
1.76908240e-01 -1.62893951e+00 -2.18463242e-02 9.48813111e-02
8.14422250e-01 3.28414232e-01 2.19573334e-01 -1.13289535e-01
2.77269423e-01 -1.01013994e+00 7.25901306e-01 6.09516084e-01
1.20502520e+00 -7.73023248e-01 2.69050509e-01 7.37885088e-02
-1.13131332e+00 -3.66944671e-02 -3.57665956e-01 -8.34630057e-02
2.18565732e-01 2.93362796e-01 -5.96657634e-01 1.35400653e-01
1.09616303e+00 4.69365269e-01 -6.30446017e-01 6.03339136e-01
-5.50008342e-02 1.76181376e-01 -5.44940054e-01 5.72040737e-01
7.33750463e-02 -3.29905093e-01 4.29283790e-02 9.87375557e-01
5.84327817e-01 1.64639041e-01 -1.79417998e-01 8.61034274e-01
-4.18117225e-01 -1.51382551e-01 -5.39321065e-01 3.65617692e-01
4.22317833e-01 1.38269091e+00 -6.51035905e-01 -4.59906131e-01
-5.96770346e-01 1.69293010e+00 5.20318270e-01 6.12812877e-01
-7.45159328e-01 -3.73543292e-01 6.92103386e-01 4.65231359e-01
3.85458738e-01 -4.85566348e-01 -4.60496038e-01 -1.16658747e+00
2.46398136e-01 -1.15581572e+00 8.09204429e-02 -1.35894537e+00
-7.72177100e-01 6.79829657e-01 -2.36884773e-01 -1.24994087e+00
-5.21704257e-01 -7.43288338e-01 -6.84394777e-01 1.02900827e+00
-1.42883468e+00 -1.09293175e+00 -7.02422917e-01 7.44103312e-01
5.10366380e-01 6.33643270e-02 6.00186706e-01 3.85200560e-01
6.75873533e-02 5.50699055e-01 -5.22619933e-02 3.98037918e-02
8.88567626e-01 -1.19722760e+00 9.84306514e-01 1.16196907e+00
1.66965276e-01 5.32598555e-01 4.39742208e-01 -2.74537951e-01
-1.49149251e+00 -9.78917480e-01 5.54175794e-01 -3.59802902e-01
4.95198786e-01 -2.72661656e-01 -8.39066684e-01 8.78981471e-01
3.94795746e-01 5.66729665e-01 5.15794707e-03 -3.67688417e-01
-5.58934212e-01 -5.73898375e-01 -9.38634098e-01 9.53142941e-01
1.27602315e+00 -9.65499163e-01 1.20619861e-02 2.47330308e-01
7.71985412e-01 -1.12269711e+00 -6.66517675e-01 -1.17560521e-01
8.60276222e-01 -1.39092827e+00 1.33347571e+00 -1.83347136e-01
6.42394245e-01 -3.99697900e-01 -1.00018874e-01 -1.16357052e+00
-2.55712807e-01 -5.97899854e-01 -4.96703647e-02 5.13626695e-01
2.63694942e-01 -5.52742898e-01 7.99290121e-01 8.74449611e-01
-1.11171447e-01 -8.89409602e-01 -8.33360016e-01 -2.71513939e-01
-1.97449788e-01 -4.13314760e-01 6.81340039e-01 5.94295800e-01
-6.20728374e-01 -1.82059780e-02 -2.58865803e-01 4.06026095e-01
5.48955262e-01 1.56483069e-01 1.13496494e+00 -4.72816944e-01
-5.50949931e-01 -4.99728173e-01 -2.66714871e-01 -1.47693217e+00
-3.07053447e-01 -5.50065279e-01 -9.27931890e-02 -1.32035196e+00
-2.14191586e-01 -2.01845169e-01 1.74002051e-01 4.18246120e-01
3.09038479e-02 6.81291401e-01 2.61154115e-01 2.62665987e-01
-2.51222849e-01 1.21310599e-01 1.18467581e+00 -4.33695801e-02
-2.85863012e-01 -1.91665903e-01 -5.97068012e-01 8.88745368e-01
5.74299812e-01 -2.85660978e-02 -4.80197370e-01 -1.02276742e+00
3.36627632e-01 -1.41695037e-01 6.11584246e-01 -1.18537903e+00
5.37755907e-01 -1.17100216e-01 8.06416810e-01 -4.12014216e-01
4.42094594e-01 -8.88730228e-01 1.93067297e-01 2.44629726e-01
-3.13385576e-01 5.65653384e-01 8.48532002e-03 1.26456201e-01
-7.36290067e-02 2.20094398e-01 8.15969944e-01 -1.56479895e-01
-6.47282422e-01 5.62309980e-01 1.16090933e-02 -2.03514948e-01
6.24058545e-01 -5.17489314e-01 -2.44339913e-01 -6.42764390e-01
-3.54070932e-01 -2.01050326e-01 1.04443145e+00 1.89122975e-01
6.85584188e-01 -1.27093434e+00 -4.22347724e-01 5.88936567e-01
-2.09214613e-01 4.37912315e-01 2.36848667e-01 6.45208836e-01
-1.31482244e+00 -1.96754426e-01 -3.75464618e-01 -6.78564787e-01
-1.10359561e+00 3.80036592e-01 7.46760905e-01 -1.74057588e-01
-7.11642861e-01 7.08417714e-01 2.14838609e-01 -2.92192221e-01
6.00189567e-02 -5.85207582e-01 4.90260184e-01 -4.79682177e-01
7.21513808e-01 2.45919287e-01 2.40660369e-01 -4.14122611e-01
-2.63779387e-02 6.33586645e-01 3.65474373e-02 -5.41689932e-01
1.20542419e+00 -1.68603569e-01 -1.98835045e-01 1.58083960e-01
1.45825124e+00 1.46221936e-01 -1.86993337e+00 -1.93526760e-01
-7.13398933e-01 -8.25561643e-01 2.69914091e-01 -5.61699033e-01
-1.17359602e+00 9.17695105e-01 7.91637599e-01 -1.23707734e-01
1.56390011e+00 -4.38946456e-01 9.11802232e-01 5.78835845e-01
2.40763888e-01 -1.08412087e+00 1.47061065e-01 4.37650293e-01
9.55912292e-01 -1.08350182e+00 8.24651346e-02 -3.06324571e-01
-2.49353275e-01 1.35115731e+00 7.33246505e-01 -2.28548542e-01
4.75404143e-01 6.30903602e-01 2.80114383e-01 1.17541872e-01
-6.45537913e-01 3.03450078e-01 2.78216153e-01 6.77049875e-01
3.33810627e-01 -2.39723504e-01 2.69282639e-01 -3.18057328e-01
-5.38052320e-01 1.43028572e-01 4.18389112e-01 7.42676437e-01
-1.51473656e-01 -9.78915989e-01 -4.02863562e-01 3.91498148e-01
-2.00024117e-02 -3.61418098e-01 5.72377406e-02 5.66604376e-01
2.66189384e-03 2.45850533e-01 5.72386444e-01 -1.84928134e-01
4.59158957e-01 -3.02041233e-01 4.92154896e-01 -1.50863394e-01
-4.28621858e-01 -6.75270110e-02 -1.91991314e-01 -1.29108644e+00
-2.64404416e-01 -3.89073193e-01 -1.06848383e+00 -7.67750919e-01
3.71195644e-01 -5.60882568e-01 5.79522908e-01 6.35051489e-01
7.21905410e-01 3.53100955e-01 5.34313977e-01 -1.53142655e+00
-2.58003742e-01 -4.85323340e-01 -1.31422821e-02 5.26939392e-01
4.83069479e-01 1.32333100e-01 -2.96752099e-02 4.84586984e-01] | [9.983405113220215, -2.318472146987915] |
20eb4700-3924-4ddf-b720-031d91a42a90 | multitask-detection-of-speaker-changes | 2210.14755 | null | https://arxiv.org/abs/2210.14755v2 | https://arxiv.org/pdf/2210.14755v2.pdf | Multitask Detection of Speaker Changes, Overlapping Speech and Voice Activity Using wav2vec 2.0 | Self-supervised learning approaches have lately achieved great success on a broad spectrum of machine learning problems. In the field of speech processing, one of the most successful recent self-supervised models is wav2vec 2.0. In this paper, we explore the effectiveness of this model on three basic speech classification tasks: speaker change detection, overlapped speech detection, and voice activity detection. First, we concentrate on only one task -- speaker change detection -- where our proposed system surpasses the previously reported results on four different corpora, and achieves comparable performance even when trained on out-of-domain data from an artificially designed dataset. Then we expand our approach to tackle all three tasks in a single multitask system with state-of-the-art performance on the AMI corpus. The implementation of the algorithms in this paper is publicly available at https://github.com/mkunes/w2v2_audioFrameClassification. | ['Zbyněk Zajíc', 'Marie Kunešová'] | 2022-10-26 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 1.54160962e-01 -1.35767430e-01 -9.05847270e-03 -3.98309350e-01
-1.12944639e+00 -4.72497702e-01 6.82980359e-01 2.51630753e-01
-4.75516409e-01 5.43369055e-01 4.37464952e-01 -3.03117841e-01
2.73661494e-01 -2.40302518e-01 -3.06712717e-01 -5.59894085e-01
-1.31000370e-01 3.17458808e-01 4.55707580e-01 -2.13552728e-01
-2.11174935e-02 -5.34792207e-02 -1.86140883e+00 2.52152264e-01
5.21672726e-01 9.35575068e-01 8.28312635e-02 7.93112159e-01
-6.54652566e-02 5.77349424e-01 -4.77117181e-01 -1.53134376e-01
-1.35773733e-01 -3.39897156e-01 -9.61675823e-01 1.60416484e-01
3.34356666e-01 1.84730336e-01 -3.70655537e-01 8.87117505e-01
1.04316211e+00 2.33046129e-01 3.40952516e-01 -1.11231267e+00
-2.07420707e-01 8.83261323e-01 -9.62826833e-02 7.81103790e-01
4.50425595e-01 3.60923596e-02 1.11025107e+00 -1.09591413e+00
3.39341223e-01 1.05546260e+00 5.26764095e-01 4.56119925e-01
-1.20556450e+00 -7.16417134e-01 6.51904494e-02 6.08637154e-01
-1.42565656e+00 -1.28382528e+00 9.77991998e-01 -4.07625228e-01
1.23914587e+00 3.82886738e-01 3.17248702e-01 1.29444563e+00
-2.96385467e-01 1.16702974e+00 1.10095692e+00 -6.44519866e-01
4.08615589e-01 1.66730136e-01 3.38452160e-01 4.01817113e-01
-3.71494502e-01 -1.43678412e-01 -7.57244408e-01 -7.30660111e-02
2.56089956e-01 -3.71441633e-01 -4.75668162e-01 -9.29246470e-02
-1.35170984e+00 7.57880151e-01 -2.00433403e-01 8.49728405e-01
-1.88771233e-01 -2.31437087e-01 7.39237547e-01 5.79486310e-01
1.05655408e+00 -5.15196333e-03 -7.10393488e-01 -7.29194045e-01
-9.99636531e-01 -3.63007486e-02 8.04527760e-01 6.51247203e-01
3.99905741e-01 3.56739014e-01 2.22048955e-03 1.40094697e+00
1.12447314e-01 2.65551150e-01 1.05625498e+00 -4.33520734e-01
4.25382823e-01 1.32808283e-01 -2.18287483e-01 -3.91574711e-01
-4.76114750e-01 -5.34370601e-01 -5.52203894e-01 -1.40463874e-01
1.39335021e-01 -2.88716793e-01 -7.23436475e-01 1.62865281e+00
3.61403316e-01 2.90150791e-01 1.70028389e-01 4.77083951e-01
1.06928730e+00 8.23651373e-01 -1.25587553e-01 -4.76197392e-01
1.23625350e+00 -1.23924315e+00 -1.12449551e+00 -2.06381276e-01
4.89506245e-01 -9.43143487e-01 1.06641090e+00 4.47527289e-01
-1.02545667e+00 -5.90570807e-01 -9.13516998e-01 2.83206582e-01
-3.49883795e-01 4.62394357e-02 3.28094184e-01 7.85032332e-01
-1.22478163e+00 2.87669778e-01 -8.72259557e-01 -7.83378303e-01
2.83399820e-01 5.09446003e-02 -3.47040653e-01 3.34949106e-01
-1.14485419e+00 7.27603853e-01 1.29356205e-01 -2.91877955e-01
-9.55931544e-01 -4.19498354e-01 -7.35178292e-01 -1.83741953e-02
4.45118934e-01 -1.46627739e-01 1.85423565e+00 -8.92534852e-01
-1.79684603e+00 9.12353992e-01 -4.81826067e-01 -5.98466814e-01
3.52366149e-01 -1.96812779e-01 -1.05634117e+00 -1.33391485e-01
-1.69477671e-01 3.78843099e-01 1.08412838e+00 -9.34745550e-01
-8.21554303e-01 -2.18357906e-01 -5.09016156e-01 1.65889338e-01
-6.20263755e-01 3.30796003e-01 -3.01775336e-01 -7.90859222e-01
-8.30766037e-02 -7.33746052e-01 1.56798899e-01 -6.38272285e-01
-4.12156910e-01 -6.35954738e-01 7.65901387e-01 -5.63056231e-01
1.46183777e+00 -2.47558403e+00 5.84885851e-03 -2.83257574e-01
-1.24565050e-01 6.59636855e-01 -1.25206515e-01 6.27259493e-01
-2.83233553e-01 -1.18888177e-01 -3.76920432e-01 -7.58413374e-01
6.50361180e-02 -3.80697325e-02 -1.95848718e-01 4.69859421e-01
-4.99206223e-02 5.92282355e-01 -7.91495681e-01 -2.33451664e-01
2.62039900e-01 4.41242993e-01 -2.86388040e-01 2.28114426e-01
1.19300514e-01 4.29118872e-01 9.27704107e-03 3.75784934e-01
3.74014795e-01 2.95931131e-01 -1.35312248e-02 9.91833135e-02
-4.36111659e-01 7.37336278e-01 -1.19191003e+00 1.85186303e+00
-5.40060937e-01 9.18781340e-01 5.21461293e-02 -1.20679283e+00
7.72211969e-01 7.94799030e-01 5.23784041e-01 -5.03736377e-01
8.00921768e-02 9.12731141e-02 1.06960416e-01 -6.39348567e-01
1.42152235e-01 7.07963705e-02 9.66761857e-02 3.93041909e-01
4.77867007e-01 -2.23404825e-01 2.04441503e-01 -1.09725244e-01
1.19954848e+00 -3.90260607e-01 5.11340916e-01 -1.75245360e-01
7.54401982e-01 -2.63935179e-01 4.49504048e-01 4.81851786e-01
-5.46999633e-01 6.59465015e-01 9.37616676e-02 -1.55641675e-01
-7.89671063e-01 -1.00509655e+00 -2.23949328e-01 1.34769237e+00
-4.03878540e-01 -4.77767199e-01 -7.96496511e-01 -4.41890746e-01
-1.13358237e-01 8.49564075e-01 -3.39221805e-01 7.66772926e-02
-4.24402833e-01 -6.17952228e-01 8.04984272e-01 2.51917154e-01
3.06459010e-01 -1.24703491e+00 -2.89224833e-01 3.98328125e-01
-3.72323483e-01 -1.24368083e+00 -4.41299319e-01 3.27904373e-01
-5.29805481e-01 -7.24124670e-01 -5.07198393e-01 -1.05880177e+00
-1.31534496e-02 3.43060642e-01 8.99339020e-01 -4.05712277e-01
-2.62154937e-01 5.24222314e-01 -6.09789014e-01 -6.46173775e-01
-6.37141526e-01 2.14381352e-01 3.54686588e-01 4.49241400e-01
4.35311079e-01 -6.69335127e-01 -2.96994150e-01 2.81315774e-01
-6.98390126e-01 -2.99323559e-01 9.52182338e-02 6.23561025e-01
4.15466398e-01 1.51418373e-01 9.21005845e-01 -5.64982057e-01
6.27287090e-01 -3.79395664e-01 -1.39844805e-01 -1.15622073e-01
-4.55145538e-01 -3.42048645e-01 3.41143161e-01 -4.89406615e-01
-1.14926016e+00 3.62999178e-02 -7.14857996e-01 -2.37642750e-01
-6.83183789e-01 4.09936309e-01 -1.50466055e-01 3.01549941e-01
4.77007508e-01 5.48374295e-01 -5.01380861e-02 -7.60902703e-01
4.08283949e-01 1.25231838e+00 5.13662457e-01 5.69022447e-02
5.61037719e-01 4.14023101e-01 -8.51075947e-01 -1.37895882e+00
-5.85722268e-01 -9.26469326e-01 -5.84853530e-01 -1.54210359e-01
7.09915698e-01 -1.14698815e+00 -1.82050377e-01 8.38155627e-01
-1.03052592e+00 -5.45879066e-01 -3.11134636e-01 6.28141582e-01
-5.48772991e-01 4.15672064e-01 -5.27911961e-01 -1.02823555e+00
-4.74174410e-01 -9.10406291e-01 9.29716289e-01 -1.71058923e-01
-3.69361728e-01 -9.97968078e-01 5.42113423e-01 4.31207269e-01
5.41947901e-01 -3.80062193e-01 4.40124363e-01 -1.21191859e+00
2.64902234e-01 -1.17663786e-01 4.40415859e-01 6.50804043e-01
5.91536522e-01 -2.06871137e-01 -1.50498617e+00 -4.44004595e-01
1.62626803e-01 -2.22608104e-01 1.08513761e+00 4.11194235e-01
9.80592012e-01 -1.77869890e-02 -1.47447690e-01 2.34836444e-01
8.53440583e-01 3.92790645e-01 3.09134722e-01 1.45506501e-01
4.10979837e-01 3.81955385e-01 4.01202142e-01 4.87894803e-01
4.56229448e-01 8.98120463e-01 1.79784119e-01 -1.54180184e-01
-4.22332674e-01 -1.12246210e-02 5.43302536e-01 1.49795318e+00
1.65982053e-01 -3.60522032e-01 -9.56187606e-01 8.21960449e-01
-1.83698106e+00 -1.17658710e+00 -1.60384700e-02 2.23390293e+00
8.97137284e-01 2.15727612e-01 4.85667020e-01 6.05913877e-01
8.56905878e-01 4.67712641e-01 -2.08632663e-01 -3.23336899e-01
-8.12771469e-02 2.00130269e-01 -6.94422126e-02 5.79595149e-01
-1.55750525e+00 9.84157383e-01 6.18610096e+00 8.05716872e-01
-1.16257763e+00 7.71068752e-01 3.55437845e-01 -2.56279737e-01
7.79772922e-02 -4.42650557e-01 -7.31918156e-01 5.24908662e-01
1.22792423e+00 -1.34377882e-01 2.98458129e-01 6.10625923e-01
5.28046906e-01 3.16196717e-02 -1.08308625e+00 1.18642282e+00
3.51754338e-01 -9.11037683e-01 -3.97398621e-01 -3.55377346e-01
4.90199119e-01 4.65491384e-01 5.41048832e-02 5.17215014e-01
-5.87244630e-02 -5.62183142e-01 7.46991396e-01 -4.12955023e-02
7.69015551e-01 -5.28517902e-01 6.07015133e-01 2.32145667e-01
-1.14492249e+00 -1.61704406e-01 2.36152653e-02 -8.25180113e-02
3.67119879e-01 7.29060709e-01 -9.26268458e-01 3.46249014e-01
7.73131013e-01 6.00149035e-01 -4.13364708e-01 1.11697173e+00
-1.78051934e-01 1.28066564e+00 -2.48908371e-01 8.11830088e-02
6.80593308e-03 3.54916602e-01 8.65543306e-01 1.60256457e+00
1.55828744e-01 -1.70478359e-01 7.16588870e-02 6.42654449e-02
8.80186409e-02 4.51316059e-01 -4.83418405e-01 1.74050429e-03
6.20622277e-01 1.16181707e+00 -4.55903113e-01 -4.53181446e-01
-7.16291547e-01 8.11600327e-01 3.75703901e-01 2.09476233e-01
-7.32096791e-01 -4.77163315e-01 8.66011560e-01 -2.92088669e-02
5.97190142e-01 -2.80577451e-01 2.91076023e-02 -1.30598915e+00
-4.82300576e-03 -8.85427952e-01 3.63371193e-01 -4.08414721e-01
-1.11461031e+00 8.46498907e-01 -1.00917019e-01 -1.18005633e+00
-5.55128455e-01 -1.61452249e-01 -8.90085936e-01 4.98079836e-01
-1.47302079e+00 -7.81648457e-01 -1.14694186e-01 7.84612298e-01
1.39195156e+00 -6.23690784e-01 1.01644754e+00 5.90050757e-01
-8.02961051e-01 6.60409212e-01 2.27895617e-01 1.26851944e-03
1.02401042e+00 -1.03128242e+00 8.52272451e-01 9.50819075e-01
6.01617098e-01 -8.87018815e-03 6.72759056e-01 -3.08373690e-01
-1.08873308e+00 -1.04074144e+00 1.18884444e+00 -2.03984559e-01
9.92497802e-01 -5.50090313e-01 -9.94863570e-01 6.63543999e-01
5.25783241e-01 -9.32090506e-02 8.99104834e-01 2.59544224e-01
-3.65763068e-01 -2.20504016e-01 -8.02426100e-01 3.64798009e-01
1.14851367e+00 -6.28117859e-01 -7.28660583e-01 4.03983980e-01
7.07574427e-01 -1.37987852e-01 -6.61759853e-01 2.12597862e-01
2.15559706e-01 -8.20106804e-01 7.06384540e-01 -5.00601351e-01
-1.38928413e-01 -1.57790288e-01 -3.59048486e-01 -1.77668571e+00
-2.91480631e-01 -8.35329652e-01 -1.82304502e-01 1.61669862e+00
5.57329655e-01 -7.79523969e-01 2.34555200e-01 -2.09277108e-01
-4.20449913e-01 -4.21960235e-01 -1.40747774e+00 -9.17468190e-01
-1.73412785e-01 -9.36383247e-01 3.83068353e-01 1.04841805e+00
4.12162244e-01 5.81763685e-01 -3.06346655e-01 4.68401946e-02
4.01209086e-01 -1.32215515e-01 7.11750269e-01 -1.18574107e+00
-1.92651376e-01 -4.10528451e-01 -5.31142890e-01 -7.38045990e-01
3.36320877e-01 -9.70519304e-01 2.08454147e-01 -1.33146703e+00
-1.19754486e-03 1.23660013e-01 -4.05993640e-01 6.69284582e-01
3.79756512e-03 2.29204878e-01 8.36657509e-02 1.70665219e-01
-5.84567845e-01 5.46780705e-01 5.63903630e-01 -3.05087417e-01
-5.01312673e-01 2.82832623e-01 -3.65574598e-01 6.07263267e-01
1.26846206e+00 -3.19603205e-01 -3.84644181e-01 -3.98177892e-01
-5.55060327e-01 -2.27434978e-01 9.92554650e-02 -1.22293735e+00
3.03329378e-01 4.49482203e-01 -1.91882312e-01 -3.96979541e-01
3.81001532e-01 -4.14212972e-01 -8.57249796e-02 2.20079318e-01
-3.79607260e-01 -7.51367211e-02 3.38869810e-01 3.83319557e-01
-4.59379554e-01 -6.23554811e-02 7.88622320e-01 1.69210076e-01
-8.90159428e-01 1.50062382e-01 -8.27919006e-01 7.33022094e-02
7.88161397e-01 2.41255492e-01 -3.07941347e-01 -6.18623257e-01
-1.03620899e+00 -3.43297087e-02 -9.97246727e-02 8.89995396e-01
4.60898042e-01 -1.22683430e+00 -1.02983761e+00 3.31220835e-01
2.74314851e-01 -5.71397364e-01 3.06961775e-01 8.52312386e-01
9.83919799e-02 5.44262826e-01 9.75918472e-02 -7.33222425e-01
-1.58367574e+00 4.63142157e-01 1.35992259e-01 7.29409531e-02
-5.17682970e-01 8.31956983e-01 -8.93394873e-02 -4.00107056e-01
5.83287239e-01 -1.95829257e-01 -3.44895810e-01 3.32905442e-01
6.99260175e-01 4.24940825e-01 4.75635052e-01 -8.71301949e-01
-6.01313472e-01 1.94846109e-01 -1.81701034e-01 -4.53770220e-01
1.42510080e+00 -3.25131625e-01 1.90939888e-01 1.05684805e+00
1.24074996e+00 4.22572456e-02 -9.13009703e-01 -5.43425798e-01
-5.20010926e-02 -7.39896670e-02 3.28499347e-01 -6.03532910e-01
-8.33097637e-01 8.63194227e-01 9.50773954e-01 6.98544085e-01
1.02832258e+00 2.65906125e-01 6.61466777e-01 2.34273091e-01
1.66108504e-01 -1.18605816e+00 -6.02779090e-02 6.73228264e-01
8.60122859e-01 -1.44239151e+00 -3.10901850e-01 -4.09627706e-01
-7.12068379e-01 7.66105652e-01 2.74990231e-01 3.00638407e-01
9.58827794e-01 2.00478002e-01 3.31674784e-01 1.14897691e-01
-8.76503229e-01 -6.54689372e-01 1.05419978e-01 6.71125889e-01
7.11581409e-01 2.38637492e-01 -2.66056567e-01 4.80885178e-01
-2.75071234e-01 -2.31626138e-01 4.17563230e-01 9.45202291e-01
-5.40100813e-01 -1.11231780e+00 -2.86693662e-01 2.24327087e-01
-5.54718077e-01 -1.36288032e-01 -3.45723212e-01 4.21177059e-01
-2.05530554e-01 1.44470716e+00 2.72152394e-01 -5.42479753e-01
6.53024256e-01 5.92100501e-01 1.87444568e-01 -6.83736980e-01
-5.29078424e-01 4.47822988e-01 3.38122070e-01 -3.34047526e-01
-6.77959025e-01 -1.12415767e+00 -8.81568193e-01 5.78921996e-02
-1.64363712e-01 2.40506038e-01 8.36654067e-01 8.34651649e-01
3.25688809e-01 6.54968739e-01 9.14995849e-01 -9.70169306e-01
-5.06414890e-01 -1.42945766e+00 -5.92098594e-01 2.68588632e-01
5.47762930e-01 -5.81882179e-01 -5.75130045e-01 1.77485391e-01] | [14.567239761352539, 6.241490364074707] |
41d3442e-7657-45a7-af56-c29afb22d6a3 | on-the-sample-complexity-of-estimation-in | 2307.04191 | null | https://arxiv.org/abs/2307.04191v1 | https://arxiv.org/pdf/2307.04191v1.pdf | On the sample complexity of estimation in logistic regression | The logistic regression model is one of the most popular data generation model in noisy binary classification problems. In this work, we study the sample complexity of estimating the parameters of the logistic regression model up to a given $\ell_2$ error, in terms of the dimension and the inverse temperature, with standard normal covariates. The inverse temperature controls the signal-to-noise ratio of the data generation process. While both generalization bounds and asymptotic performance of the maximum-likelihood estimator for logistic regression are well-studied, the non-asymptotic sample complexity that shows the dependence on error and the inverse temperature for parameter estimation is absent from previous analyses. We show that the sample complexity curve has two change-points (or critical points) in terms of the inverse temperature, clearly separating the low, moderate, and high temperature regimes. | ['Arya Mazumdar', 'Daniel Hsu'] | 2023-07-09 | null | null | null | null | ['generalization-bounds'] | ['methodology'] | [ 1.73938453e-01 -1.88229516e-01 -3.50472361e-01 -5.14699697e-01
-8.90405715e-01 -2.69132912e-01 4.51376796e-01 4.46415663e-01
-5.43132842e-01 9.55715477e-01 -3.16526711e-01 -4.06922221e-01
-5.24327338e-01 -5.51089227e-01 -5.94485879e-01 -1.24845970e+00
-9.37030464e-02 3.57498497e-01 -4.03491706e-02 9.32425261e-02
9.50070098e-02 2.25999132e-01 -1.36724007e+00 -3.47211808e-01
8.93895566e-01 1.22414088e+00 -2.91967899e-01 7.36838520e-01
2.18627095e-01 -1.06791504e-01 -6.97353661e-01 -1.81501910e-01
1.36048481e-01 -4.99619037e-01 -1.90623075e-01 -1.80658370e-01
-6.85635731e-02 2.85331219e-01 -8.25003907e-02 1.09385788e+00
4.99610573e-01 6.60129860e-02 1.17533076e+00 -1.35809386e+00
-4.91952568e-01 6.26984358e-01 -7.45800197e-01 3.40318173e-01
-8.77179503e-02 -3.43308877e-03 6.73013866e-01 -4.58841741e-01
3.43578488e-01 1.15582240e+00 7.84416318e-01 2.76961863e-01
-1.61093712e+00 -9.01500285e-01 -1.05725609e-01 -1.82996064e-01
-1.61332417e+00 -1.72544613e-01 4.64342415e-01 -6.58994615e-01
4.22605723e-01 6.80600628e-02 2.99141973e-01 1.20762694e+00
6.01187110e-01 1.45658761e-01 1.45686316e+00 -6.46815300e-01
6.97556913e-01 3.81003261e-01 7.52279103e-01 3.68001014e-01
8.43509912e-01 3.31618488e-01 -3.78517568e-01 -4.43928629e-01
5.34965634e-01 -3.46931070e-01 -2.04310000e-01 -2.34835267e-01
-5.25992274e-01 9.30328190e-01 1.02179594e-01 2.34242439e-01
7.88045824e-02 5.76492190e-01 1.65606827e-01 5.39365172e-01
7.16471612e-01 3.85108292e-01 -6.79869890e-01 -2.29973257e-01
-8.15314114e-01 2.46490136e-01 7.73444176e-01 9.39685047e-01
4.29338962e-01 -1.01986520e-01 -1.13070816e-01 9.10046935e-01
1.82387710e-01 7.31762826e-01 4.94086057e-01 -4.99308765e-01
5.79637468e-01 3.50617468e-01 1.52717546e-01 -4.48037267e-01
-7.42639780e-01 -7.67951131e-01 -1.02471697e+00 -2.42544934e-02
1.18095553e+00 -3.98444802e-01 -7.63072133e-01 1.72105384e+00
2.20181361e-01 -2.07957089e-01 1.79316506e-01 6.68969512e-01
2.68324941e-01 4.84594345e-01 1.79929882e-01 -6.76860154e-01
1.41979659e+00 -2.92613149e-01 -6.59197927e-01 -4.59383518e-01
5.96274257e-01 -4.09436047e-01 1.04892552e+00 4.70610559e-01
-7.69589484e-01 -2.98079252e-01 -1.19455838e+00 -9.71184224e-02
-1.66122109e-01 4.61058207e-02 3.13259691e-01 1.20568740e+00
-3.72407347e-01 6.21885657e-01 -8.27074170e-01 -5.43786250e-02
1.03330299e-01 3.63498539e-01 -1.88348621e-01 -2.09263191e-01
-1.06204629e+00 6.10118032e-01 1.04510821e-01 2.92929679e-01
-2.14208737e-01 -6.05286837e-01 -6.61395907e-01 7.93756247e-02
2.34146997e-01 -3.18786532e-01 9.48243320e-01 -6.54066980e-01
-1.36071503e+00 4.58664000e-01 -3.35902125e-01 -3.45602393e-01
5.38766563e-01 6.89847916e-02 -2.21382320e-01 -2.79848158e-01
-2.62680680e-01 2.51651853e-01 9.60274816e-01 -1.02993584e+00
-2.85259724e-01 -7.48833776e-01 -5.23035586e-01 -2.22135782e-01
9.37172249e-02 -8.61109272e-02 -3.88015062e-02 -4.94068414e-01
3.65527391e-01 -1.03209269e+00 -2.95182765e-01 -2.36926034e-01
-2.06748262e-01 -1.47213981e-01 3.84384930e-01 -4.45927471e-01
1.13356531e+00 -2.43798208e+00 -2.20952168e-01 4.13358837e-01
6.79065436e-02 -4.45170730e-01 1.41621187e-01 2.20378384e-01
-1.87130824e-01 4.24470693e-01 -4.95648831e-01 -8.04327130e-02
-8.00049398e-03 -1.31043524e-01 -1.01162478e-01 9.71357107e-01
3.30376953e-01 4.44273323e-01 -7.60308325e-01 -3.24486010e-02
-3.23760122e-01 3.94633442e-01 -3.82461041e-01 -1.26949146e-01
6.56586364e-02 2.45421067e-01 -3.21923822e-01 3.37489724e-01
7.97668099e-01 -1.41788602e-01 6.36545867e-02 2.73892254e-01
-5.19216135e-02 1.83030397e-01 -1.23659039e+00 7.81251848e-01
-2.99317807e-01 6.96473062e-01 1.64442971e-01 -9.80992377e-01
1.12478137e+00 1.84292182e-01 1.73638746e-01 -3.98657411e-01
2.79666930e-01 3.58241588e-01 2.11430311e-01 -3.65182787e-01
3.94111216e-01 -4.54326332e-01 -5.37728429e-01 2.27058277e-01
-1.07691817e-01 -2.75112033e-01 1.93311721e-01 -4.33815420e-01
8.50809157e-01 -3.27495724e-01 5.07714450e-01 -5.16432047e-01
-1.07136955e-02 -2.92847693e-01 4.57364857e-01 9.30522382e-01
-1.04564704e-01 5.85685194e-01 1.45400250e+00 1.29923925e-01
-1.13673520e+00 -7.55533218e-01 -8.94329846e-01 6.83332920e-01
1.61671355e-01 -2.07358763e-01 -6.58977270e-01 -3.08730572e-01
2.28535533e-01 8.42840195e-01 -8.43423665e-01 -5.94644308e-01
-3.75216424e-01 -1.63104153e+00 6.03470147e-01 3.96063775e-01
2.75339961e-01 -3.96156639e-01 -4.18904424e-01 -1.95883438e-01
2.47371607e-02 -1.11394560e+00 -2.59875029e-01 8.78330290e-01
-9.06496108e-01 -9.73727584e-01 -3.34304184e-01 -1.53510407e-01
6.30509317e-01 -3.06672633e-01 7.13316500e-01 -8.26372355e-02
-1.65875271e-01 -2.36904249e-02 -1.97462738e-01 -6.06459081e-01
-5.24896026e-01 2.83899665e-01 -1.90490000e-02 -2.58261561e-01
2.80439466e-01 -1.74174920e-01 -4.21786964e-01 5.51609099e-01
-7.52617300e-01 -5.63168406e-01 3.89589489e-01 1.14213955e+00
4.53387827e-01 2.60081619e-01 6.43845618e-01 -5.74489415e-01
7.45436907e-01 -8.40310276e-01 -9.70595956e-01 8.50407630e-02
-8.47767591e-01 5.26082933e-01 4.98712152e-01 -6.89275384e-01
-6.60646498e-01 7.62787685e-02 6.45920858e-02 3.17670815e-02
-1.66287184e-01 4.18313056e-01 -8.27826783e-02 2.64467508e-01
7.29493022e-01 -1.02335773e-01 -2.18961447e-01 -3.92913043e-01
1.15990631e-01 8.30497503e-01 2.88383484e-01 -6.71026111e-01
6.02921069e-01 1.15686104e-01 4.64051872e-01 -8.60684872e-01
-6.85533941e-01 -1.83782503e-01 -5.94518423e-01 1.86314806e-02
6.15998507e-01 -4.94196653e-01 -8.11631262e-01 4.31476146e-01
-7.39717245e-01 -1.72442257e-01 -5.45117557e-01 5.93369722e-01
-5.31579137e-01 1.56128779e-01 -3.60349119e-01 -1.28261411e+00
-6.88463822e-02 -1.34970510e+00 9.24571753e-01 1.19944304e-01
-1.21899117e-02 -8.50854218e-01 -2.59106040e-01 8.62377658e-02
2.03534395e-01 2.88971454e-01 1.22512937e+00 -6.55174851e-01
-1.18572026e-01 -6.20755494e-01 -1.98739186e-01 1.34409264e-01
-2.41455913e-01 1.42478451e-01 -8.72147262e-01 -3.18489403e-01
4.45962608e-01 -6.79912418e-02 9.82607007e-01 8.76104116e-01
1.12137735e+00 -1.68726608e-01 -2.96753645e-01 5.31149149e-01
1.44650388e+00 3.23517084e-01 3.82748425e-01 1.29032850e-01
-3.73273268e-02 4.88486469e-01 3.90912890e-01 3.28390509e-01
-1.13072865e-01 5.98143578e-01 8.78075659e-02 9.53902304e-02
3.07026207e-01 -7.92975426e-02 3.22155625e-01 3.12097818e-01
2.43808612e-01 -3.34063798e-01 -8.56561065e-01 5.96869625e-02
-1.44984913e+00 -4.78729665e-01 -4.82869178e-01 2.75378656e+00
8.82062674e-01 4.50934947e-01 1.73233896e-01 5.11944294e-01
7.02318728e-01 -1.60175651e-01 -5.75788975e-01 -4.82289910e-01
-8.76347199e-02 9.86228958e-02 1.11106050e+00 5.43748677e-01
-8.11489761e-01 3.47614139e-01 7.40496874e+00 8.75648260e-01
-1.07065690e+00 -5.50858825e-02 1.02687049e+00 -4.63347929e-03
1.27247602e-01 -1.72697264e-03 -1.05125237e+00 6.89404845e-01
1.42961240e+00 -2.62213335e-03 1.75376892e-01 9.41788912e-01
2.34668896e-01 -6.56133592e-01 -1.01102483e+00 6.85954034e-01
-3.14848006e-01 -4.81648564e-01 -7.09290266e-01 3.36648047e-01
5.06999910e-01 -3.59807342e-01 1.84611663e-01 2.49146476e-01
1.65716827e-01 -1.11951196e+00 6.77164674e-01 4.62120146e-01
1.15906203e+00 -6.93893075e-01 9.07496393e-01 5.53084850e-01
-7.45021105e-01 -4.84614789e-01 -4.00525928e-01 8.09246525e-02
-2.18783066e-01 1.23121798e+00 -7.47403741e-01 4.35511880e-02
6.15577459e-01 1.33693457e-01 -6.20378256e-01 9.54246223e-01
-4.92608454e-03 8.52549136e-01 -7.47327447e-01 -3.06346685e-01
-1.51391312e-01 -4.76844281e-01 3.32168847e-01 1.21134424e+00
4.91007686e-01 1.24862266e-03 -3.11356008e-01 7.82461643e-01
1.19658269e-01 -1.18538596e-01 -2.90284067e-01 3.78733650e-02
5.91351032e-01 7.88082063e-01 -9.26858068e-01 1.10926054e-01
2.09416244e-02 2.94185638e-01 9.86964926e-02 4.22322094e-01
-5.38195610e-01 -4.52093869e-01 4.70759988e-01 2.57221580e-01
2.94488907e-01 -5.72904527e-01 -9.02475595e-01 -8.49618256e-01
2.41482779e-01 -4.79048491e-01 1.97941378e-01 -2.74266273e-01
-1.27389848e+00 3.10751677e-01 4.22672063e-01 -8.41699839e-01
-2.84094065e-01 -6.71626985e-01 -1.10675231e-01 7.98063457e-01
-1.22040999e+00 -4.37892854e-01 -1.73122913e-01 1.92817613e-01
1.64488450e-01 1.37314647e-01 5.30447364e-01 1.73379872e-02
-9.96423125e-01 1.00966275e+00 8.41607869e-01 -2.64446829e-02
4.93876427e-01 -1.21148980e+00 3.19606692e-01 5.75615644e-01
-5.77240109e-01 5.78494608e-01 1.13233757e+00 -5.94166994e-01
-1.12683713e+00 -5.96672118e-01 6.73355997e-01 -3.44485700e-01
5.95458031e-01 -7.91985691e-01 -8.51180553e-01 2.90399969e-01
-5.40349066e-01 1.75674364e-01 5.49900889e-01 2.72802562e-01
-4.42297488e-01 -3.30068350e-01 -1.37344468e+00 3.66429389e-01
5.85335314e-01 -1.41783044e-01 1.79435555e-02 1.48127303e-01
5.50421715e-01 -1.53674543e-01 -1.04414725e+00 3.48677009e-01
6.21370256e-01 -7.18604147e-01 5.89422166e-01 -5.48967957e-01
3.20078671e-01 2.09377661e-01 -1.65315121e-01 -1.27196789e+00
-2.50783060e-02 -5.35828292e-01 3.14589381e-01 1.10205281e+00
7.84995973e-01 -9.39670801e-01 4.96765882e-01 7.37369359e-01
4.16030884e-01 -8.94706428e-01 -1.58192956e+00 -8.47203016e-01
4.76296991e-01 -7.40332961e-01 3.08496982e-01 3.87976915e-01
-3.34820896e-03 3.91618937e-01 -1.88186496e-01 -7.50854099e-03
4.56188858e-01 -2.29577839e-01 5.44315696e-01 -1.37278569e+00
-2.46387556e-01 -4.37990487e-01 -3.01735908e-01 -1.05592549e+00
-1.18564606e-01 -5.93959928e-01 2.57145196e-01 -8.85855913e-01
3.56722474e-02 -7.70701647e-01 -6.71463162e-02 -4.79362495e-02
-1.71577692e-01 -2.20567003e-01 -1.02833010e-01 7.79829696e-02
-3.38442884e-02 3.09931278e-01 7.71601081e-01 1.80884734e-01
-4.50942516e-01 4.15577054e-01 -7.69696414e-01 4.94873464e-01
8.06285679e-01 -1.09797049e+00 -1.83094889e-01 1.00424007e-01
4.00892884e-01 3.31708699e-01 6.37554154e-02 -7.42295504e-01
-2.04400402e-02 -2.10804164e-01 4.38561052e-01 -3.17824125e-01
3.89438421e-01 -7.13054299e-01 -6.29935553e-03 3.32781732e-01
-7.17904925e-01 1.16775140e-01 -8.58944561e-03 9.47113037e-01
1.71039253e-01 -6.65952384e-01 1.14822376e+00 2.37227798e-01
5.64414322e-01 -2.37688512e-01 -5.96296549e-01 1.00240432e-01
9.41243470e-01 -8.22684541e-02 -5.21926999e-01 -2.65030742e-01
-5.12688637e-01 1.04689375e-01 1.57102644e-01 2.67624885e-01
2.00254455e-01 -9.16055501e-01 -7.80464709e-01 4.47729617e-01
-1.49045978e-02 -4.27236483e-02 -2.28937040e-03 1.03085053e+00
-1.40616402e-01 1.04111865e-01 3.67895275e-01 -5.87796688e-01
-9.88418043e-01 4.61918324e-01 6.53129041e-01 -4.74306792e-01
-1.88003883e-01 5.90861082e-01 -2.27933198e-01 -1.56694829e-01
3.27003896e-01 -7.39467382e-01 1.95584610e-01 7.91802108e-02
3.06851059e-01 6.53099597e-01 1.76541075e-01 -1.72099650e-01
-1.24061167e-01 6.29341185e-01 2.85254270e-01 -1.76909909e-01
1.00478649e+00 -6.25750721e-02 -1.36690736e-01 9.52733934e-01
1.12594008e+00 -1.19382396e-01 -1.07414865e+00 -7.12735504e-02
1.73931792e-01 -3.55433583e-01 -5.52247511e-03 -5.85944772e-01
-9.10739720e-01 7.77716279e-01 8.73935878e-01 5.71135938e-01
8.43786716e-01 -2.13420372e-02 2.55724221e-01 1.54392213e-01
-6.20837808e-02 -1.15697134e+00 -3.96073967e-01 5.21477282e-01
8.66823435e-01 -1.10895753e+00 3.12302917e-01 -5.27870238e-01
-3.99246395e-01 1.00080681e+00 3.21355462e-01 -1.06896870e-01
1.01981902e+00 6.90072417e-01 -2.32645258e-01 1.82316244e-01
-9.41626191e-01 3.91192824e-01 2.27001056e-01 6.04051530e-01
3.59391391e-01 2.05965564e-01 -8.78720164e-01 9.63771105e-01
-6.24224603e-01 -3.34387362e-01 5.45508802e-01 5.45681059e-01
-2.83837855e-01 -8.46296668e-01 -7.11989284e-01 6.94897711e-01
-5.07110417e-01 -4.62482758e-02 -2.70363867e-01 8.87746274e-01
5.38860336e-02 1.28135622e+00 3.18711191e-01 -1.56417638e-01
2.35763922e-01 4.40238535e-01 2.17261627e-01 -1.02411121e-01
-4.28564548e-01 9.91761312e-02 -4.40613506e-03 9.44164917e-02
9.39307287e-02 -7.76806056e-01 -8.69627595e-01 -2.37569749e-01
-9.06418979e-01 2.42507502e-01 1.08287358e+00 1.05599916e+00
2.50326674e-02 2.26802707e-01 6.15409970e-01 -5.60228944e-01
-8.45731497e-01 -1.19636321e+00 -7.75614858e-01 -9.64210033e-02
5.68591475e-01 -6.17966652e-01 -9.72432435e-01 -3.30268383e-01] | [7.9209184646606445, 4.533568382263184] |
263dc5bd-8c65-4696-bbf4-b577e23a3fcc | a-scalable-reinforcement-learning-based | 2307.01599 | null | https://arxiv.org/abs/2307.01599v1 | https://arxiv.org/pdf/2307.01599v1.pdf | A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management | On-chain data (metrics) of blockchain networks, akin to company fundamentals, provide crucial and comprehensive insights into the networks. Despite their informative nature, on-chain data have not been utilized in reinforcement learning (RL)-based systems for cryptocurrency (crypto) portfolio management (PM). An intriguing subject is the extent to which the utilization of on-chain data can enhance an RL-based system's return performance compared to baselines. Therefore, in this study, we propose CryptoRLPM, a novel RL-based system incorporating on-chain data for end-to-end crypto PM. CryptoRLPM consists of five units, spanning from information comprehension to trading order execution. In CryptoRLPM, the on-chain data are tested and specified for each crypto to solve the issue of ineffectiveness of metrics. Moreover, the scalable nature of CryptoRLPM allows changes in the portfolios' cryptos at any time. Backtesting results on three portfolios indicate that CryptoRLPM outperforms all the baselines in terms of accumulated rate of return (ARR), daily rate of return (DRR), and Sortino ratio (SR). Particularly, when compared to Bitcoin, CryptoRLPM enhances the ARR, DRR, and SR by at least 83.14%, 0.5603%, and 2.1767 respectively. | ['Fumihide Tanaka', 'Zhenhan Huang'] | 2023-07-04 | null | null | null | null | ['reinforcement-learning-1', 'management'] | ['methodology', 'miscellaneous'] | [-5.69381297e-01 -1.18770629e-01 -5.43120086e-01 6.59147501e-02
-4.42289710e-01 -1.22673833e+00 8.49814594e-01 1.75798252e-01
-2.01295480e-01 8.05471659e-01 1.69079855e-01 -9.34983552e-01
-3.83356720e-01 -8.63441050e-01 -6.08064771e-01 -6.63049698e-01
-4.78245199e-01 5.43721914e-01 -1.91203728e-02 -4.36018944e-01
6.61041796e-01 2.69955307e-01 -5.45560539e-01 3.68103594e-01
4.62510467e-01 1.19753385e+00 -4.33989674e-01 4.26608711e-01
3.30215514e-01 1.38112891e+00 -6.84442878e-01 -5.98472595e-01
7.65561223e-01 -2.59170920e-01 -3.28954995e-01 -4.54777241e-01
-1.94128275e-01 -7.94572592e-01 -4.81394768e-01 9.37854767e-01
3.45691234e-01 -5.41676223e-01 2.97204375e-01 -1.31083846e+00
-2.94550985e-01 1.47600365e+00 -5.02875924e-01 4.04601872e-01
2.70301048e-02 6.90279186e-01 1.62816048e+00 -3.36643457e-01
7.22682953e-01 8.39028955e-01 3.98933113e-01 -7.54614174e-02
-1.07467294e+00 -8.85976911e-01 -1.86912417e-01 1.20989121e-01
-4.82054830e-01 -5.78981042e-02 7.11536229e-01 -3.90441775e-01
9.72886801e-01 1.99161187e-01 1.13451052e+00 8.33394766e-01
9.50155258e-01 8.56869936e-01 1.65349913e+00 -9.90356207e-02
3.17055345e-01 2.83477511e-02 1.71557963e-01 -1.01670302e-01
7.01688647e-01 6.86526537e-01 -6.24589682e-01 -2.03028738e-01
9.20174837e-01 -2.28903107e-02 4.84358855e-02 -9.60002989e-02
-1.40749419e+00 1.12966835e+00 3.70708823e-01 2.59021550e-01
-7.11658180e-01 5.30224919e-01 4.39088613e-01 1.13005161e+00
1.13621578e-01 9.70659494e-01 -5.69028556e-01 -4.32870597e-01
-8.60145509e-01 5.25436640e-01 1.21911812e+00 9.59810674e-01
4.56415296e-01 1.25045985e-01 -3.03775012e-01 -1.70478821e-01
3.71108085e-01 4.02511477e-01 4.56423223e-01 -1.30333018e+00
8.25236559e-01 5.88630676e-01 9.52890813e-02 -5.94243705e-01
-2.07235426e-01 -9.89833593e-01 -5.44622302e-01 1.96844682e-01
4.93160874e-01 -3.12502384e-01 -2.20017523e-01 1.13250053e+00
-1.74525931e-01 -4.70370561e-01 6.62155449e-02 6.38074100e-01
2.33571231e-03 5.15494883e-01 -4.90424037e-01 -4.59077030e-01
1.02117360e+00 -9.87993479e-01 -6.20401561e-01 1.39673844e-01
7.58714437e-01 -8.11640441e-01 5.14943361e-01 7.37797260e-01
-1.14091516e+00 1.37696430e-01 -9.05349612e-01 8.70667934e-01
-1.41237497e-01 -7.70982027e-01 7.43213713e-01 6.53717339e-01
-7.91113853e-01 8.05546224e-01 -6.30599856e-01 3.56580734e-01
2.68077552e-01 4.77517754e-01 7.41060823e-02 1.98496759e-01
-1.37830937e+00 7.48770416e-01 5.13213396e-01 -8.02312642e-02
-1.25228500e+00 -8.24940205e-01 -1.36180753e-02 1.06781408e-01
6.95658445e-01 -4.85009313e-01 1.46224570e+00 -6.72127068e-01
-1.64288306e+00 5.92121407e-02 1.12573063e+00 -1.10475791e+00
9.98935163e-01 -2.28303805e-01 -2.18081310e-01 1.29212961e-01
-2.40917847e-01 1.00118190e-01 4.70917076e-01 -7.68724322e-01
-7.43642569e-01 -9.35085341e-02 2.72657871e-01 6.68795928e-02
5.40271252e-02 -7.12476894e-02 3.73749912e-01 -9.16001201e-01
-1.84532106e-01 -1.03942347e+00 -6.86398745e-02 -8.83556724e-01
-1.64847642e-01 -2.27344066e-01 5.38342595e-01 -7.47249484e-01
1.23644066e+00 -1.66594791e+00 -4.26744103e-01 5.79857945e-01
3.84204723e-02 1.52548417e-01 6.44034669e-02 1.09856582e+00
1.37515441e-01 3.68118107e-01 1.99511677e-01 5.68346322e-01
1.75173432e-01 4.89149503e-02 -3.46389830e-01 5.01842082e-01
-6.82161450e-02 1.26763642e+00 -7.99919426e-01 -1.57631636e-01
3.54766399e-02 -3.30559045e-01 -9.73008454e-01 2.76188225e-01
-5.28680682e-01 6.76771820e-01 -4.20670152e-01 1.02091455e+00
3.01065087e-01 -1.93870038e-01 6.84963405e-01 2.51772404e-01
-1.62974268e-01 4.26597744e-01 -8.76680315e-01 9.81338382e-01
-2.30478212e-01 4.13225442e-01 -3.51932049e-01 -5.90782166e-01
9.65439677e-01 4.27502513e-01 7.28369296e-01 -1.20836484e+00
4.34155911e-02 6.51558459e-01 5.84262133e-01 -9.43890288e-02
4.18819070e-01 -8.79768729e-02 -1.03924967e-01 1.23876631e+00
-2.61896133e-01 1.22266531e-01 4.70339358e-01 6.09851293e-02
1.38441944e+00 -7.57625476e-02 3.41976732e-01 -4.12607342e-01
2.43231028e-01 -1.10751294e-01 7.77913511e-01 8.05510640e-01
-2.60968596e-01 -5.75467497e-02 1.16115820e+00 -5.23755312e-01
-1.24478698e+00 -7.92975187e-01 2.19597936e-01 7.83091784e-01
-4.64634113e-02 -2.74135262e-01 -5.02822638e-01 -8.77606809e-01
6.27879083e-01 7.67179370e-01 -1.48874685e-01 1.76086023e-01
-8.58532190e-01 -7.90648997e-01 5.09811461e-01 2.49688283e-01
6.86003804e-01 -1.27036440e+00 -7.20687985e-01 6.64816678e-01
3.63461971e-02 -6.79192364e-01 -5.46092689e-01 2.69781649e-02
-1.03099775e+00 -1.26670539e+00 -3.51622373e-01 2.54998100e-03
3.15036118e-01 -2.66541336e-02 1.12291253e+00 -1.33100003e-01
4.16044652e-01 -8.44884813e-02 -5.20647883e-01 -1.74312755e-01
-7.33321428e-01 6.12896532e-02 1.47350633e-03 -2.97848843e-02
4.55492325e-02 -6.91374719e-01 -1.15287614e+00 4.78938431e-01
-7.82860219e-01 -3.83273810e-01 1.04298055e+00 7.06009507e-01
2.07786158e-01 2.61334479e-02 1.00896859e+00 -1.10047972e+00
1.00795805e+00 -5.23514986e-01 -1.17950189e+00 3.15832973e-01
-1.50544190e+00 1.88661516e-01 4.90713447e-01 -2.62431979e-01
-6.97359979e-01 -8.56433332e-01 6.37232244e-01 -2.77239442e-01
7.68269956e-01 7.62101948e-01 3.01826090e-01 4.07658219e-02
1.57154739e-01 1.50589764e-01 1.97558671e-01 -2.13970423e-01
3.06369781e-01 7.10549176e-01 7.36956969e-02 -4.49702591e-01
6.38886094e-01 2.88717356e-02 -4.59596366e-02 2.32508242e-01
-1.83349758e-01 -1.92563474e-01 -2.54052621e-03 -2.60215044e-01
2.41889730e-02 -8.12204301e-01 -1.39421618e+00 5.22816062e-01
-7.51847982e-01 -4.54862654e-01 -4.93366122e-01 7.64914393e-01
-6.95140481e-01 1.62483588e-01 -1.06714368e+00 -7.32823133e-01
-7.44801641e-01 -1.11619759e+00 -7.54667819e-02 -3.21529284e-02
-2.13571385e-01 -8.72364938e-01 3.91133666e-01 8.23395848e-01
7.46711493e-01 4.44287002e-01 8.44097435e-01 -1.24361062e+00
-1.21210515e+00 -2.93923914e-01 -1.37157232e-01 5.89353859e-01
-2.66502965e-02 -1.20197415e-01 -3.37097436e-01 -9.32875514e-01
2.86992818e-01 -2.28873670e-01 3.20056081e-01 1.22033887e-01
5.65739870e-01 -1.02331030e+00 4.18795228e-01 3.38324606e-01
1.46958983e+00 5.27423859e-01 3.60559583e-01 1.02956724e+00
8.43976438e-02 5.65096438e-01 7.87155569e-01 9.78817403e-01
2.44858980e-01 2.90123105e-01 7.88756073e-01 7.32121289e-01
1.50127351e-01 -4.98597771e-01 8.84215653e-01 1.31602812e+00
-1.35273248e-01 -6.57977238e-02 -8.26389730e-01 3.33278298e-01
-1.71809149e+00 -8.25693965e-01 -1.14325760e-02 2.15829039e+00
9.68930900e-01 6.03595376e-01 3.13529938e-01 1.69357806e-01
5.21470249e-01 1.28725156e-01 -6.33921325e-01 -5.23165643e-01
-1.41135558e-01 -1.43119901e-01 1.20748687e+00 2.13142902e-01
-3.08534950e-01 5.13652563e-01 5.78209496e+00 5.83382905e-01
-9.68173981e-01 1.58162452e-02 7.13859141e-01 -1.28304884e-01
-6.65161014e-01 8.16917121e-01 -6.24058366e-01 8.87280643e-01
1.27847648e+00 -4.51100022e-01 9.26016688e-01 5.84303260e-01
2.34980106e-01 -2.42736284e-02 -1.29228652e+00 6.18873894e-01
-5.77398181e-01 -1.76176775e+00 -7.69059956e-02 6.05555236e-01
1.03475857e+00 2.73236901e-01 1.49656221e-01 3.89526576e-01
7.80657709e-01 -8.05291295e-01 9.24674988e-01 5.11634171e-01
2.94879079e-01 -1.00919724e+00 1.25004351e+00 4.16067749e-01
-5.37265658e-01 -5.31808972e-01 1.64873898e-01 -4.56671156e-02
-1.19509831e-01 6.25196815e-01 -1.20216954e+00 8.67654145e-01
4.67915744e-01 5.06144762e-01 -3.47344071e-01 7.12397039e-01
-4.53228116e-01 9.62746918e-01 -1.28691971e-01 -1.49920568e-01
6.19249940e-01 -4.67226982e-01 5.38593829e-01 7.56197691e-01
9.61743817e-02 -3.47386479e-01 -6.68061599e-02 7.99051762e-01
-5.16029179e-01 -1.83932036e-01 -2.72593409e-01 -3.83063912e-01
8.86013925e-01 1.15744233e+00 -3.43563884e-01 -2.15455890e-01
-1.15186505e-01 8.63003358e-03 -2.51086861e-01 2.90885061e-01
-5.18623948e-01 -3.84034723e-01 2.19541099e-02 4.35462773e-01
3.45029265e-01 -9.82944667e-02 -3.90881866e-01 -8.52169871e-01
8.17526504e-02 -1.59075904e+00 3.22697699e-01 -1.19783267e-01
-1.20609534e+00 1.36487827e-01 -1.49072021e-01 -1.34011543e+00
-5.58756053e-01 -2.41769105e-01 -6.42324686e-01 4.69682366e-01
-1.89084673e+00 -9.37372446e-01 3.39710593e-01 3.44206005e-01
1.46509856e-01 -7.35718966e-01 5.51827885e-02 2.62094270e-02
-3.32764685e-01 4.82525826e-01 6.94537699e-01 2.95099746e-02
5.27953863e-01 -1.30993474e+00 3.83970141e-01 6.23898268e-01
6.55910969e-02 5.68196118e-01 6.02002859e-01 -8.95888209e-01
-1.80966973e+00 -8.99631739e-01 6.00702703e-01 -3.62564027e-01
1.26391494e+00 -8.96292701e-02 -3.44884485e-01 8.08998942e-01
6.80379152e-01 -7.38388970e-02 3.06175053e-01 -2.03833386e-01
-3.21311355e-01 -7.13493347e-01 -1.13758576e+00 3.81320149e-01
3.77406955e-01 -3.30206752e-01 -4.29316640e-01 2.87387937e-01
7.49734759e-01 -8.55065957e-02 -1.38426340e+00 1.56916752e-01
5.86094975e-01 -1.31150377e+00 3.20770979e-01 -3.83926928e-01
3.49724293e-01 5.39006516e-02 -1.86801881e-01 -1.21528482e+00
-5.85224703e-02 -1.62853265e+00 -5.22518337e-01 1.16540563e+00
1.75356954e-01 -1.23378813e+00 9.11626160e-01 9.62021649e-02
3.69989544e-01 -8.87772679e-01 -1.04206634e+00 -1.08833599e+00
4.31736588e-01 7.16532290e-04 1.06845474e+00 8.95356894e-01
9.18271299e-03 -1.49491936e-01 -3.62180859e-01 -3.49202991e-01
1.03166234e+00 7.33664334e-01 8.63395810e-01 -7.40357518e-01
-6.09598100e-01 -5.58394730e-01 -5.13696438e-03 -6.37558937e-01
-4.46267009e-01 -9.90941048e-01 -7.40821302e-01 -8.93018067e-01
1.57672733e-01 -7.14155078e-01 -8.37750137e-01 2.87668824e-01
3.69273990e-01 -2.57582009e-01 7.61717021e-01 8.91674876e-01
-4.78473663e-01 3.77043784e-01 1.33036196e+00 -1.43547982e-01
-1.19456209e-01 2.90290773e-01 -6.15235209e-01 2.61060297e-01
1.04074347e+00 -7.13345289e-01 -3.03621031e-02 -6.39775619e-02
6.74136460e-01 7.47993767e-01 1.51038066e-01 -5.37587523e-01
3.18449348e-01 -3.25578064e-01 -1.10152252e-01 -8.27572286e-01
-5.47366858e-01 -6.27273321e-01 4.91235882e-01 1.19673920e+00
-2.82160640e-01 6.39797449e-01 -5.22619784e-01 5.57664037e-01
-3.91101956e-01 -1.62520945e-01 3.65997970e-01 -3.86098266e-01
-2.80929729e-02 1.77440509e-01 -2.43687466e-01 2.16426551e-01
1.16512692e+00 2.15115398e-02 -6.79900289e-01 -4.39726293e-01
-8.17042515e-02 6.98188961e-01 2.40657493e-01 1.87560514e-01
2.76067406e-01 -1.30346322e+00 -9.65317070e-01 8.87866765e-02
-2.78548241e-01 -1.68943703e-01 -1.75114125e-01 1.08606625e+00
-9.65914607e-01 9.27691460e-01 -5.48057199e-01 -3.02059501e-01
-6.49087250e-01 2.95344561e-01 3.60690117e-01 -1.30387056e+00
-3.50510776e-01 1.67951301e-01 -2.11373091e-01 -4.05670792e-01
4.61597182e-02 -3.84014934e-01 1.97897002e-01 3.36155325e-01
-7.82607421e-02 7.71987319e-01 3.35282274e-02 1.63784608e-01
-6.15077429e-02 1.51901525e-02 -3.63352656e-01 -3.82045805e-01
1.71196520e+00 2.67972052e-01 -4.18084621e-01 2.72919506e-01
6.94991708e-01 7.12430179e-02 -1.45497847e+00 -3.08712602e-01
6.96362257e-01 -5.05631328e-01 -6.14372641e-02 -1.03051424e+00
-1.35872650e+00 5.43050289e-01 1.95753992e-01 4.28720772e-01
5.98695040e-01 -4.80015218e-01 9.24708605e-01 3.33431453e-01
6.73518062e-01 -1.27255654e+00 2.58611888e-01 2.67991185e-01
9.08685565e-01 -8.39405596e-01 2.23967478e-01 5.77834189e-01
-7.58951426e-01 1.19633484e+00 1.54234901e-01 -3.51508319e-01
5.46039283e-01 8.60372558e-02 -1.14394322e-01 -7.35247135e-02
-1.27622366e+00 7.21872866e-01 -5.00457108e-01 -1.25331087e-02
5.47000021e-02 4.00427222e-01 -5.06895721e-01 6.17894828e-01
-4.19336468e-01 3.45813558e-02 1.01851439e+00 1.22079861e+00
-2.03269005e-01 -1.52098227e+00 -3.96797150e-01 4.63038862e-01
-9.45241690e-01 -1.41389817e-01 -1.45215943e-01 9.32382345e-01
-2.74028629e-01 8.06455970e-01 -1.09750360e-01 -3.05384159e-01
2.54986644e-01 -3.47389489e-01 1.26536295e-01 -1.13865510e-01
-1.40068078e+00 2.30696619e-01 1.34538114e-01 -4.85894144e-01
-1.56271458e-01 -8.77380073e-01 -9.23034012e-01 -8.81516695e-01
-5.71089506e-01 3.91759545e-01 7.12363005e-01 5.46764910e-01
1.18791066e-01 3.99466515e-01 1.46165729e+00 6.66477624e-03
-1.52347124e+00 -1.05550325e+00 -8.78676832e-01 1.71215296e-01
3.05200309e-01 -2.95512319e-01 -4.97628421e-01 -5.18222749e-01] | [4.667259693145752, 4.129644870758057] |
5f46cd0a-16bd-4034-9f45-8e494cdc0de2 | perspective-fields-for-single-image-camera | 2212.03239 | null | https://arxiv.org/abs/2212.03239v2 | https://arxiv.org/pdf/2212.03239v2.pdf | Perspective Fields for Single Image Camera Calibration | Geometric camera calibration is often required for applications that understand the perspective of the image. We propose perspective fields as a representation that models the local perspective properties of an image. Perspective Fields contain per-pixel information about the camera view, parameterized as an up vector and a latitude value. This representation has a number of advantages as it makes minimal assumptions about the camera model and is invariant or equivariant to common image editing operations like cropping, warping, and rotation. It is also more interpretable and aligned with human perception. We train a neural network to predict Perspective Fields and the predicted Perspective Fields can be converted to calibration parameters easily. We demonstrate the robustness of our approach under various scenarios compared with camera calibration-based methods and show example applications in image compositing. | ['David F. Fouhey', 'Matthew Sticha', 'Kevin Matzen', 'Oliver Wang', 'Yannick Hold-Geoffroy', 'Jianming Zhang', 'Linyi Jin'] | 2022-12-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jin_Perspective_Fields_for_Single_Image_Camera_Calibration_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jin_Perspective_Fields_for_Single_Image_Camera_Calibration_CVPR_2023_paper.pdf | cvpr-2023-1 | ['camera-calibration'] | ['computer-vision'] | [ 5.86646855e-01 -5.33478111e-02 -8.92592147e-02 -9.02188659e-01
-8.76756012e-02 -1.03973949e+00 8.73972714e-01 -9.18300897e-02
-5.25259972e-02 2.11546361e-01 1.72482207e-01 -2.39664048e-01
1.69050112e-01 -8.40074003e-01 -1.21003973e+00 -2.89805621e-01
3.22803497e-01 3.30955684e-01 1.29406050e-01 -2.35189259e-01
4.68423516e-01 9.72171724e-01 -1.05868351e+00 1.62168682e-01
2.75953829e-01 6.57905161e-01 2.31489807e-01 9.24493968e-01
4.56038177e-01 8.04466665e-01 -2.78121978e-01 -3.47235382e-01
6.10612631e-01 2.06757952e-02 -6.31962180e-01 5.31408429e-01
8.04643750e-01 -4.67544466e-01 -2.75112361e-01 1.18179297e+00
-3.28474157e-02 -2.39589468e-01 3.71414721e-01 -9.65022922e-01
-5.94996393e-01 1.21263616e-01 -5.98685563e-01 -2.24007815e-01
7.95343816e-01 -2.20429853e-01 6.50170147e-01 -8.83467436e-01
9.01873708e-01 1.09665799e+00 8.83587301e-01 -4.39212769e-02
-1.06965220e+00 6.32419214e-02 1.86237261e-01 -6.40678778e-02
-1.03342354e+00 -2.64238417e-01 9.46205020e-01 -4.70568329e-01
5.04248559e-01 4.66821849e-01 7.74129093e-01 8.47364962e-01
6.28214121e-01 3.34458441e-01 1.09632349e+00 -7.82497764e-01
5.62636517e-02 2.00191095e-01 -7.12350532e-02 5.45379817e-01
1.14789210e-01 7.64436200e-02 -2.01073900e-01 -3.13717723e-02
1.42220747e+00 1.33188874e-01 -5.10994673e-01 -9.37444508e-01
-1.64204741e+00 6.28250122e-01 5.25074840e-01 -2.21243918e-01
-2.00846344e-01 2.67883033e-01 -1.55778408e-01 3.28883678e-02
3.87774616e-01 5.63937485e-01 -4.55780417e-01 2.53865510e-01
-6.50833309e-01 2.36367628e-01 6.30470693e-01 1.49536145e+00
8.25140953e-01 1.69153772e-02 5.42336941e-01 5.78423858e-01
9.44060087e-02 7.99743831e-01 -1.02733640e-04 -1.52819860e+00
3.82676631e-01 4.20362741e-01 3.05612057e-01 -1.39400971e+00
-4.76116627e-01 -2.17340514e-01 -7.21502006e-01 4.96148348e-01
2.24980831e-01 7.80385658e-02 -7.43868291e-01 1.31420183e+00
1.20541438e-01 -1.33813590e-01 -8.51899087e-02 7.35438585e-01
3.41115743e-01 6.29985154e-01 -6.53533518e-01 1.18675195e-01
1.39573252e+00 -8.45256090e-01 -5.60958743e-01 -7.50017047e-01
1.04366176e-01 -1.09551191e+00 8.09950352e-01 2.88785219e-01
-1.06659436e+00 -5.56794703e-01 -1.10782337e+00 -3.54559273e-01
-5.03403544e-01 2.42483094e-01 4.91402417e-01 5.31480253e-01
-1.29014194e+00 2.42882222e-01 -5.90881944e-01 -5.46734869e-01
-2.77028173e-01 2.13559285e-01 -7.53514886e-01 -7.08386451e-02
-6.92797124e-01 1.00680983e+00 1.96570337e-01 8.64837542e-02
-2.56999075e-01 -6.19477093e-01 -1.14613736e+00 1.11262493e-01
1.25618055e-01 -9.33748901e-01 1.34581959e+00 -1.32261622e+00
-1.61468959e+00 8.52464378e-01 -2.12017134e-01 -2.19006673e-01
5.36582291e-01 -2.79838622e-01 -3.54047388e-01 1.05951108e-01
-4.86522987e-02 8.62041414e-01 9.29855824e-01 -1.51360822e+00
-5.23865521e-01 -2.79047787e-01 5.29153585e-01 5.57803392e-01
2.98438877e-01 2.25306023e-02 -7.56924093e-01 -7.57123768e-01
6.11837089e-01 -1.32879102e+00 -2.53099620e-01 2.55001396e-01
-3.08013320e-01 9.09609139e-01 9.85928416e-01 -6.30165100e-01
6.36169434e-01 -1.96219563e+00 1.28563691e-03 3.37401628e-01
-1.14100136e-01 -2.27675900e-01 5.53245060e-02 6.64778054e-01
-5.64107120e-01 -1.45083323e-01 -7.94956833e-02 6.29286990e-02
-4.75608468e-01 1.30398437e-01 -5.45270085e-01 6.85840607e-01
9.60649624e-02 6.65067792e-01 -7.32290983e-01 -2.28229742e-02
7.63749778e-01 7.41390109e-01 -5.37581086e-01 1.67120650e-01
-6.04200549e-02 5.53442478e-01 4.14646566e-02 5.46348870e-01
1.00628603e+00 -1.31370649e-01 3.33153278e-01 -5.01150250e-01
-3.19816679e-01 7.11007267e-02 -1.15503252e+00 1.44727588e+00
-6.19199395e-01 1.04924607e+00 -1.42666847e-01 -4.15292233e-01
9.57452476e-01 7.04450384e-02 2.43463784e-01 -2.13697329e-01
2.31069446e-01 -2.09271893e-01 -5.20090461e-01 -2.51963317e-01
8.50044668e-01 1.44061491e-01 6.65318072e-02 3.51065248e-01
-1.19959228e-01 -7.02582300e-01 -2.49720782e-01 1.04142584e-01
3.65768701e-01 5.07327199e-01 7.23709166e-01 -3.83396357e-01
5.58415711e-01 -1.03276409e-01 4.19125706e-01 3.78109723e-01
4.02238518e-01 1.17450511e+00 5.56819201e-01 -1.03566766e+00
-1.36228204e+00 -9.04658318e-01 -2.20859353e-03 4.80402648e-01
5.38185000e-01 -3.14424455e-01 -9.85423326e-01 -2.62760192e-01
-2.86139190e-01 7.75744677e-01 -6.37844443e-01 1.28424361e-01
-7.50597656e-01 -2.18736678e-01 -5.31769171e-02 8.85967314e-01
4.78209496e-01 -5.32703817e-01 -8.44605267e-01 -3.48775238e-01
-2.10787043e-01 -1.61162460e+00 -8.04199040e-01 -9.63093266e-02
-1.04121852e+00 -1.20792496e+00 -6.55591071e-01 -6.98205709e-01
1.33040655e+00 8.30661356e-01 1.26050127e+00 -3.59311402e-01
3.04334044e-01 8.04588139e-01 -1.37396783e-01 -4.70577598e-01
-3.71757627e-01 -3.43002379e-01 2.85756830e-02 2.67552465e-01
1.90177709e-01 -5.02132118e-01 -5.18781304e-01 5.08253157e-01
-9.00239527e-01 5.84715605e-01 3.51065248e-01 6.49671137e-01
6.47502184e-01 -2.24471867e-01 -7.34698713e-01 -1.17192841e+00
2.06454456e-01 6.49411827e-02 -1.12633753e+00 3.66951525e-01
-2.90723473e-01 -1.18691579e-01 6.10277116e-01 -2.30311930e-01
-1.22151685e+00 5.53258598e-01 4.05348778e-01 -3.70986849e-01
-3.08456630e-01 2.31704012e-01 -2.65509158e-01 -4.92465436e-01
5.88705659e-01 -1.52366281e-01 -2.43667483e-01 -2.44095042e-01
6.18186355e-01 1.90143362e-01 8.99940073e-01 -3.54198366e-01
1.06651366e+00 9.28749800e-01 1.07324950e-01 -9.72296000e-01
-5.66894472e-01 -3.36988449e-01 -1.37637305e+00 -3.84088188e-01
8.34694982e-01 -1.04300416e+00 -4.96879280e-01 5.43855071e-01
-1.49033821e+00 -2.69829072e-02 -1.16887443e-01 5.48503458e-01
-7.76412845e-01 4.66178685e-01 -2.19099373e-01 -2.50171334e-01
1.54320985e-01 -1.23471773e+00 1.20986342e+00 3.05631012e-01
-9.99629647e-02 -1.27112389e+00 -1.10435881e-01 6.36312291e-02
2.41163582e-01 4.91073400e-01 8.69155586e-01 9.89653170e-02
-9.25273836e-01 -2.71765202e-01 -2.11205870e-01 2.87961334e-01
1.35179818e-01 4.38106179e-01 -1.21658301e+00 -1.77205190e-01
1.31254822e-01 4.40398812e-01 1.73776925e-01 6.63134396e-01
1.06951284e+00 -2.96851426e-01 -2.32008204e-01 1.25588751e+00
1.66618645e+00 3.47299576e-01 6.70409739e-01 6.42493665e-01
7.92426586e-01 6.56280041e-01 4.49561983e-01 1.10791370e-01
4.14806843e-01 7.77661383e-01 5.42252779e-01 -3.87669444e-01
2.10698098e-01 -5.23135960e-01 1.43398255e-01 6.30931139e-01
-2.14927480e-01 -2.24168673e-01 -9.57771719e-01 2.87278861e-01
-1.59393632e+00 -7.08845079e-01 -2.07226098e-01 2.28340673e+00
2.44480409e-02 -4.37344462e-02 -1.99796572e-01 -2.04147086e-01
8.05717587e-01 3.18930119e-01 -3.14046502e-01 -6.42981291e-01
-3.12843978e-01 -1.62206650e-01 1.21636963e+00 9.03168440e-01
-1.13874257e+00 8.94043565e-01 7.85326672e+00 -1.14904404e-01
-1.28992546e+00 -3.11198980e-01 5.97030640e-01 4.81845498e-01
-3.92050356e-01 3.98490906e-01 -4.34650958e-01 -5.40549792e-02
2.89296180e-01 -2.32954808e-02 5.59231460e-01 9.59709644e-01
1.30093738e-01 -2.39157140e-01 -1.27615893e+00 1.05697131e+00
4.39467490e-01 -1.38932347e+00 1.03049673e-01 -3.18937451e-02
8.64595473e-01 3.49516943e-02 2.44315907e-01 -5.63972056e-01
3.18522304e-01 -6.84410214e-01 9.57077801e-01 5.95255792e-01
9.55604672e-01 -4.66197610e-01 6.58383846e-01 3.00151527e-01
-9.13937628e-01 1.88977513e-02 -5.05700469e-01 -1.02394931e-01
4.53277439e-01 3.80134046e-01 -9.83401418e-01 4.84523237e-01
6.86996281e-01 7.29943395e-01 -7.77256668e-01 6.61283970e-01
-3.11311990e-01 -8.45593512e-02 -2.83023596e-01 5.89430153e-01
7.86440745e-02 -8.61044168e-01 4.91982639e-01 9.48194802e-01
6.19241476e-01 -7.59308338e-02 -2.93401420e-01 7.22302616e-01
7.74224401e-02 -1.43980205e-01 -1.19678032e+00 3.36826622e-01
2.76918441e-01 1.16550779e+00 -7.72225857e-01 -1.72015429e-01
-5.77763021e-01 1.06097972e+00 -9.38785747e-02 5.89016974e-01
-7.05159903e-01 -1.28543600e-01 3.70861858e-01 3.17419708e-01
2.60545522e-01 -4.94239658e-01 -3.89557630e-01 -1.42056429e+00
2.26644471e-01 -8.43200386e-01 -1.21565372e-01 -1.63457525e+00
-7.13640690e-01 7.66574264e-01 3.83846402e-01 -1.64586568e+00
-3.11763018e-01 -1.09991074e+00 -5.98519266e-01 7.90853441e-01
-1.31345773e+00 -1.58603716e+00 -6.63501322e-01 3.46596390e-01
6.07680500e-01 -8.61555487e-02 8.30640316e-01 -3.01429480e-01
4.53066342e-02 1.87032782e-02 1.59292623e-01 1.33148417e-01
7.95985162e-01 -1.39685273e+00 9.37802613e-01 1.07118726e+00
1.21990353e-01 8.65719199e-01 7.98595846e-01 -3.67165864e-01
-1.39894283e+00 -9.67812061e-01 6.67480648e-01 -8.86304379e-01
2.72652596e-01 -4.26018566e-01 -5.16069412e-01 1.39954507e+00
5.57216883e-01 1.17323928e-01 3.28217179e-01 -2.53565937e-01
-4.73588616e-01 -2.94685006e-01 -7.61741042e-01 5.97029924e-01
6.46736443e-01 -7.35181868e-01 -3.81298482e-01 4.64454293e-01
4.58896935e-01 -1.08792794e+00 -7.67276525e-01 2.61783749e-01
8.20493102e-01 -1.48098028e+00 1.27571547e+00 -2.76956968e-02
2.31391653e-01 -4.42299992e-01 -3.47332537e-01 -1.38616049e+00
-4.93144304e-01 -4.91257519e-01 4.65564191e-01 8.07883143e-01
1.75245017e-01 -7.26330519e-01 5.67003787e-01 6.65454328e-01
2.68772811e-01 -3.60557318e-01 -4.18759078e-01 -3.90868038e-01
-1.35235325e-01 -1.56343937e-01 7.83739746e-01 1.20062792e+00
-4.27703112e-01 1.38743222e-01 -5.95340788e-01 6.87320292e-01
3.36856127e-01 7.23788515e-02 1.15173376e+00 -1.11944342e+00
-1.64475009e-01 -3.66714336e-02 -5.16841471e-01 -1.17341852e+00
-5.78812174e-02 -2.45315135e-01 -3.19416195e-01 -1.23493207e+00
-8.80036354e-02 -2.24716187e-01 4.20295656e-01 -1.00633400e-02
2.27998108e-01 1.98805451e-01 2.42409319e-01 2.32503623e-01
1.69957101e-01 -9.61517394e-02 1.06075990e+00 1.10108428e-01
1.02073792e-02 1.83659643e-01 -4.55571413e-01 1.31626558e+00
6.61661744e-01 -1.73571303e-01 -5.31121373e-01 -9.99520063e-01
6.09854937e-01 8.44995081e-02 3.27301174e-01 -9.15362298e-01
2.05702364e-01 -4.54544157e-01 5.44095993e-01 -7.32662082e-01
6.12937629e-01 -1.34388053e+00 6.83379650e-01 6.86173066e-02
-1.82485849e-01 8.91488314e-01 -9.58313420e-02 4.91778672e-01
-3.37849706e-01 -2.26943627e-01 8.07456374e-01 -3.43095839e-01
-7.88358092e-01 9.81889218e-02 -1.37068421e-01 -5.18219948e-01
1.02373755e+00 -5.66446245e-01 -3.72507602e-01 -9.42760587e-01
-2.25168347e-01 -2.68796712e-01 1.26438808e+00 5.35822034e-01
6.01290643e-01 -1.35476720e+00 -2.22809702e-01 7.47124434e-01
1.33016244e-01 1.95394963e-01 -5.46079315e-02 5.90022326e-01
-1.69795763e+00 5.03490329e-01 -4.05301720e-01 -8.86531770e-01
-1.34215713e+00 7.66934156e-01 5.72194934e-01 2.60965824e-01
-6.63780212e-01 3.40468049e-01 6.52334690e-01 -7.82049060e-01
-2.31813207e-01 -6.63037598e-01 -1.24917634e-01 -5.98383784e-01
3.93059254e-01 1.70918822e-01 -9.06066317e-03 -1.07782459e+00
-1.62512049e-01 1.27517879e+00 2.59232253e-01 -3.46989721e-01
1.21329582e+00 -3.40701401e-01 -2.43047416e-01 3.52919906e-01
1.04593706e+00 4.17004436e-01 -1.34771025e+00 -2.22467750e-01
-3.56521010e-01 -9.11826789e-01 5.01640812e-02 -6.04550719e-01
-9.82281148e-01 1.08067763e+00 4.94619995e-01 7.30228573e-02
1.14341593e+00 -4.25435245e-01 2.14357853e-01 5.79623759e-01
3.81675571e-01 -9.29086983e-01 -4.61787790e-01 5.76807737e-01
1.14205182e+00 -1.06885910e+00 3.62237960e-01 -7.83793449e-01
-6.94847524e-01 1.66646016e+00 4.59845662e-01 -1.87464967e-01
6.00906372e-01 3.50691319e-01 6.76325440e-01 1.60007197e-02
-2.68116385e-01 4.59849477e-01 3.16356719e-01 7.77714074e-01
3.81653041e-01 2.37210393e-02 3.08423638e-01 -3.69850338e-01
-3.90137166e-01 -2.52164066e-01 1.06309724e+00 7.40299165e-01
-1.74838170e-01 -1.09069073e+00 -8.16558301e-01 -3.62562746e-01
-1.38655648e-01 9.86047238e-02 -5.61519682e-01 1.01508915e+00
-5.91851659e-02 3.92468601e-01 2.96091914e-01 -3.17786932e-01
4.07170892e-01 -2.48637706e-01 6.65810108e-01 -4.10163075e-01
-4.12490629e-02 2.85394013e-01 -5.09386137e-02 -6.09355748e-01
-7.92981029e-01 -6.73958004e-01 -4.75572169e-01 -2.80929804e-01
-3.43945593e-01 -4.19440925e-01 9.45441604e-01 6.80668294e-01
3.57200235e-01 1.42959103e-01 7.75930524e-01 -1.23969185e+00
-8.50854814e-02 -6.29137039e-01 -2.95531392e-01 2.95855939e-01
4.65458304e-01 -4.80574667e-01 -1.75220743e-01 7.50101984e-01] | [8.907977104187012, -2.7787654399871826] |
cbb81a7b-836b-4c51-9066-a262c458e2db | outlier-galaxy-images-in-the-dark-energy | 2305.01720 | null | https://arxiv.org/abs/2305.01720v1 | https://arxiv.org/pdf/2305.01720v1.pdf | Outlier galaxy images in the Dark Energy Survey and their identification with unsupervised machine learning | The Dark Energy Survey is able to collect image data of an extremely large number of extragalactic objects, and it can be reasonably assumed that many unusual objects of high scientific interest are hidden inside these data. Due to the extreme size of DES data, identifying these objects among many millions of other celestial objects is a challenging task. The problem of outlier detection is further magnified by the presence of noisy or saturated images. When the number of tested objects is extremely high, even a small rate of noise or false positives leads to a very large number of false detections, making an automatic system impractical. This study applies an automatic method for automatic detection of outlier objects in the first data release of the Dark Energy Survey. By using machine learning-based outlier detection, the algorithm is able to identify objects that are visually different from the majority of the other objects in the database. An important feature of the algorithm is that it allows to control the false-positive rate, and therefore can be used for practical outlier detection. The algorithm does not provide perfect accuracy in the detection of outlier objects, but it reduces the data substantially to allow practical outlier detection. For instance, the selection of the top 250 objects after applying the algorithm to more than $2\cdot10^6$ DES images provides a collection of uncommon galaxies. Such collection would have been extremely time-consuming to compile by using manual inspection of the data. | ['Lior Shamir'] | 2023-05-02 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [-3.00276548e-01 -3.09408337e-01 5.65317512e-01 -1.83443204e-01
-4.26138610e-01 -4.80015606e-01 5.77677131e-01 2.69812196e-01
-4.53968108e-01 5.97084999e-01 -3.74738276e-01 -2.99715191e-01
1.08636923e-01 -9.03521061e-01 -5.64669669e-01 -8.85101318e-01
-2.42251649e-01 7.70810544e-01 8.37719738e-01 1.18032560e-01
4.43905294e-01 8.12382996e-01 -1.93046439e+00 2.38333777e-01
7.56007135e-01 1.04528570e+00 2.52640635e-01 5.88778913e-01
-2.32898280e-01 4.84767377e-01 -9.21660721e-01 -3.00566792e-01
5.99413633e-01 -5.03781736e-01 -2.55257964e-01 2.84537941e-01
4.44635242e-01 -4.05111611e-01 2.81313267e-02 1.29725051e+00
4.53070909e-01 2.12589264e-01 4.19196665e-01 -1.20204985e+00
-5.26425689e-02 -2.16963276e-01 -4.71817881e-01 5.53518891e-01
1.91787824e-01 3.24020237e-01 6.45461619e-01 -1.23060012e+00
6.58588946e-01 8.18118870e-01 4.21721190e-01 -2.65936881e-01
-1.23412228e+00 -5.18684268e-01 -2.93746740e-01 2.14592040e-01
-1.58101273e+00 -3.09045017e-01 5.94130933e-01 -5.33542514e-01
8.50323975e-01 5.62438428e-01 8.46141398e-01 5.29163599e-01
8.46095830e-02 2.70294309e-01 9.12745714e-01 -4.04075623e-01
6.68927789e-01 2.50531733e-01 -2.86247302e-02 3.84817570e-01
1.03120363e+00 7.66262189e-02 -1.77633092e-01 -5.22434235e-01
5.96682668e-01 2.48184040e-01 -6.36037886e-02 -4.67929423e-01
-1.01122344e+00 7.53083229e-01 3.72613609e-01 4.96197760e-01
-2.28849709e-01 -3.42573076e-01 4.72564012e-01 4.14878458e-01
5.08712471e-01 7.87077665e-01 -2.63836235e-01 -1.10722862e-01
-1.02786267e+00 2.89398611e-01 7.30040133e-01 6.04325056e-01
7.24958599e-01 1.75849035e-01 4.34513569e-01 6.45861745e-01
-7.42989257e-02 2.93423384e-01 5.91976941e-01 -5.81412315e-01
8.03708211e-02 1.03496897e+00 4.51562583e-01 -1.33771884e+00
-3.40476483e-01 -2.66728669e-01 -7.09827244e-01 8.88940275e-01
7.85315216e-01 3.21411908e-01 -8.68946433e-01 8.55357230e-01
4.35994536e-01 -1.70114964e-01 -1.72739595e-01 7.89094090e-01
6.41004026e-01 4.44525212e-01 -3.70456010e-01 -4.25801277e-01
1.13221359e+00 -1.71381056e-01 -5.73696971e-01 -6.34880885e-02
5.49718916e-01 -9.66662228e-01 1.02296686e+00 6.85098469e-01
-7.05573261e-01 -3.46064836e-01 -9.39424872e-01 4.36179072e-01
-5.78908205e-01 -2.05501746e-02 7.64920533e-01 4.81026560e-01
-5.97139359e-01 4.90685850e-01 -7.60143995e-01 -4.88241643e-01
1.86516210e-01 4.83808935e-01 -5.55604279e-01 -7.13419840e-02
-4.95953739e-01 7.54655957e-01 7.46748447e-01 1.04611060e-02
-4.54448104e-01 -7.21829310e-02 -5.73143899e-01 6.55272454e-02
5.80056071e-01 -1.80769190e-02 7.85935521e-01 -1.06504750e+00
-6.77303731e-01 9.58336830e-01 -1.39838376e-03 -2.54941434e-01
8.24950993e-01 1.49941623e-01 -6.69736207e-01 1.42674699e-01
6.32175878e-02 -1.24230735e-01 8.61386061e-01 -1.13848102e+00
-6.50393724e-01 -4.54554021e-01 -5.71253240e-01 -2.07190707e-01
-5.24587184e-02 2.59224296e-01 -4.33527976e-01 -6.36988342e-01
6.37163699e-01 -9.31416929e-01 -3.06531072e-01 -2.68127322e-01
-2.19949801e-02 -8.41013566e-02 9.92034912e-01 -4.77081090e-01
9.80739117e-01 -2.57352018e+00 -4.98411804e-01 4.68486369e-01
3.94967079e-01 2.66620964e-01 2.47420698e-01 2.94180095e-01
-2.95002490e-01 7.80951306e-02 3.21012980e-04 6.33934960e-02
-2.25172117e-01 3.20566416e-01 -9.79526043e-02 7.99842536e-01
4.40856256e-03 2.33727306e-01 -9.06359375e-01 -1.99854165e-01
4.16666418e-01 -1.00065030e-01 -4.94334161e-01 2.41899520e-01
4.50551473e-02 2.70398200e-01 -2.92664260e-01 7.59105921e-01
7.38203049e-01 -6.27324209e-02 -2.00435862e-01 7.65241534e-02
-3.31406325e-01 1.15369456e-02 -1.74724996e+00 7.37805784e-01
1.44401550e-01 6.03049815e-01 -2.40777031e-01 -9.70820665e-01
1.00503409e+00 2.02065378e-01 5.53951919e-01 -6.61493778e-01
4.36518304e-02 6.78577304e-01 2.99087852e-01 -4.91533369e-01
6.23756349e-01 -1.00578539e-01 1.06182955e-01 2.28792325e-01
-1.22868523e-01 -3.78192693e-01 5.51966667e-01 1.91249624e-01
1.33539081e+00 -4.99972820e-01 4.18276489e-01 -2.23146513e-01
1.89227790e-01 1.65199205e-01 7.25817263e-01 8.31190169e-01
-8.07770565e-02 8.09897006e-01 5.86831629e-01 -9.41717446e-01
-1.43351912e+00 -1.06747258e+00 -3.42118949e-01 5.20663857e-01
-7.06556514e-02 -2.01656088e-01 -3.26680988e-01 -5.68833351e-01
1.43573865e-01 6.01230025e-01 -3.57573062e-01 -1.99102044e-01
-1.57253131e-01 -1.28954923e+00 -4.68418673e-02 1.33451177e-02
4.09705609e-01 -1.08902347e+00 -5.52595794e-01 7.76410699e-02
1.15949415e-01 -8.49909961e-01 2.35568255e-01 2.84298331e-01
-9.28397834e-01 -1.27096152e+00 -1.71446055e-01 -4.02767032e-01
1.15646279e+00 3.03910524e-01 1.14693618e+00 4.47698176e-01
-7.40732014e-01 8.51212069e-03 -5.49645662e-01 -5.57511687e-01
-5.10861158e-01 -4.83119577e-01 4.10378098e-01 8.45657140e-02
7.99071431e-01 -2.73210078e-01 -2.43270472e-01 5.04384398e-01
-9.37339008e-01 -7.36041307e-01 3.40768695e-01 7.66992569e-01
6.65790081e-01 8.74039769e-01 3.21520001e-01 -5.61642408e-01
2.15247899e-01 -4.60173815e-01 -1.16963518e+00 -3.07365477e-01
-2.90270746e-01 -1.68091968e-01 7.40087688e-01 -3.29853714e-01
-6.84378386e-01 1.70129865e-01 7.32726008e-02 -2.73410141e-01
-3.19941878e-01 6.18235171e-02 -1.07946962e-01 -2.45759293e-01
7.60312676e-01 3.60008888e-02 -1.48052588e-01 -6.49764776e-01
-2.34519660e-01 6.80731595e-01 5.64174056e-01 -1.85097735e-02
9.23428178e-01 5.37065625e-01 -5.43735251e-02 -1.37020469e+00
-4.52398717e-01 -8.04912508e-01 -6.39949024e-01 -1.79538652e-01
4.96468782e-01 -7.19745994e-01 -3.74871582e-01 4.54158247e-01
-7.74508715e-01 2.08574757e-01 -5.55349886e-01 7.86371887e-01
-2.19957381e-01 5.40245473e-01 -8.63279179e-02 -8.86123300e-01
3.36500973e-01 -1.22911966e+00 5.80470741e-01 -5.21035083e-02
-3.03368092e-01 -5.35838425e-01 -7.01789409e-02 1.01808988e-01
1.25525028e-01 3.88400644e-01 6.50510430e-01 -1.23419607e+00
-8.14829648e-01 -9.94643211e-01 -1.41503155e-01 4.74707752e-01
1.82777300e-01 4.14145470e-01 -7.10331321e-01 -4.91836548e-01
4.98421669e-01 6.09355792e-02 6.83978021e-01 2.77646065e-01
1.15554917e+00 -1.46158695e-01 -1.87207282e-01 4.08448786e-01
1.46896982e+00 4.45554405e-01 8.73320997e-01 5.27317107e-01
3.84610981e-01 3.33481431e-01 8.04461241e-01 5.94416738e-01
-3.61689568e-01 6.41226113e-01 7.49973774e-01 -2.08620250e-01
4.67394441e-01 3.93476248e-01 6.70334026e-02 5.04602373e-01
-3.14502478e-01 -8.47925767e-02 -9.64704692e-01 6.82894588e-01
-1.59013021e+00 -1.27376223e+00 -7.53409147e-01 2.83365417e+00
2.35955149e-01 3.28381091e-01 2.20537320e-01 3.53656977e-01
7.45410502e-01 -2.82232583e-01 -2.21271440e-01 -3.68867099e-01
-1.84344780e-02 -1.79561779e-01 6.12416387e-01 1.15345165e-01
-1.00951266e+00 3.28325033e-01 6.59120846e+00 4.53372568e-01
-8.63761663e-01 -2.61947036e-01 5.53970218e-01 -3.68744105e-01
1.97845250e-01 -2.89239432e-03 -7.01722264e-01 9.38622832e-01
5.86402476e-01 -5.64011112e-02 1.44126967e-01 1.16018641e+00
3.79675329e-01 -9.06550765e-01 -7.94883728e-01 1.07388008e+00
8.88518095e-02 -9.88924563e-01 -1.23000048e-01 3.59055579e-01
7.78387606e-01 1.85369655e-01 -3.57290030e-01 -1.38073087e-01
3.76948237e-01 -7.90753841e-01 4.26795810e-01 3.88555825e-01
3.75970662e-01 -1.04827356e+00 1.15717256e+00 4.02353138e-01
-5.83535492e-01 -2.88843781e-01 -9.32341039e-01 -3.07321250e-01
-2.32158989e-01 1.30730259e+00 -8.84391248e-01 1.59156099e-01
1.05946076e+00 8.72473940e-02 -8.20101500e-01 1.53666711e+00
2.00298637e-01 4.33677793e-01 -8.90002310e-01 7.50981420e-02
7.85464272e-02 -4.36675251e-01 7.34344542e-01 9.12529767e-01
7.18916416e-01 4.16223444e-02 2.68236190e-01 4.91211355e-01
7.26817325e-02 2.73453444e-01 -1.04176104e+00 1.04142323e-01
2.89787173e-01 1.13197744e+00 -1.18776453e+00 -4.73282397e-01
-6.34989798e-01 8.90322268e-01 1.18677974e-01 3.38176414e-02
-4.51970816e-01 -3.52168411e-01 4.60019171e-01 4.55296636e-01
4.85852420e-01 -3.91080022e-01 -2.39473477e-01 -1.20215535e+00
2.83399284e-01 -9.85779345e-01 4.66479987e-01 -6.52778745e-01
-1.28759277e+00 4.62795317e-01 -3.25792342e-01 -1.45279491e+00
-2.52906501e-01 -7.13019788e-01 -8.62787783e-01 5.34684956e-01
-4.60339606e-01 -3.83974761e-01 -4.49363858e-01 3.62375438e-01
2.93985814e-01 -5.33918977e-01 4.85290587e-01 3.26961607e-01
-3.88141036e-01 7.34964684e-02 7.28647530e-01 1.08020596e-01
6.71896160e-01 -1.34019697e+00 2.57956654e-01 1.20414031e+00
2.33856440e-01 3.15705001e-01 1.08637309e+00 -9.71153140e-01
-9.46206629e-01 -8.91334534e-01 8.62111151e-01 -6.48552895e-01
6.58643067e-01 -4.00495142e-01 -1.17525554e+00 5.64748108e-01
-4.17440265e-01 4.19609755e-01 4.38289493e-01 -1.47151202e-01
1.38684452e-01 -4.56368476e-02 -1.21623170e+00 5.24099290e-01
5.84744096e-01 -5.70178367e-02 -6.61418200e-01 6.16949677e-01
2.15510391e-02 -1.21433288e-01 -5.15966833e-01 2.00989872e-01
6.24210462e-02 -1.48281157e+00 9.42346931e-01 -2.98282474e-01
5.92836328e-02 -6.47214651e-01 -3.43418703e-03 -1.17680073e+00
-1.86566293e-01 -3.49133462e-01 2.35037416e-01 1.06714845e+00
2.33271596e-04 -7.01572120e-01 7.02912331e-01 5.09650946e-01
3.24281119e-02 -4.57719378e-02 -1.16259122e+00 -1.18661273e+00
-4.51533407e-01 -3.92975420e-01 3.06183815e-01 9.79780793e-01
-2.59757072e-01 -2.53015906e-01 -2.07507312e-01 2.54719168e-01
6.76417112e-01 1.74909439e-02 1.07550025e+00 -1.55331755e+00
-2.03691542e-01 -9.29683223e-02 -1.04905725e+00 -3.01649153e-01
-5.74132085e-01 -5.74198246e-01 2.61030905e-03 -1.12065756e+00
-3.16539360e-03 -4.58178550e-01 -3.98863517e-02 1.56202987e-01
-1.74647018e-01 6.51827157e-01 -1.83015347e-01 3.83797139e-01
-4.67488348e-01 1.11062936e-01 6.62886202e-01 3.19042772e-01
-1.90083295e-01 2.24927992e-01 -9.56702605e-02 1.27732801e+00
6.57763183e-01 -6.86755300e-01 1.56842709e-01 5.25440238e-02
3.79548788e-01 -3.86671305e-01 5.74341059e-01 -1.13780558e+00
-2.00596526e-02 -5.77816088e-03 7.66753078e-01 -8.88787866e-01
4.76667359e-02 -1.11409044e+00 3.50797296e-01 5.08862376e-01
5.51770747e-01 3.10017228e-01 -6.52957186e-02 4.43192542e-01
-3.94558370e-01 -6.78911269e-01 1.03156960e+00 -6.33003414e-01
-8.83662045e-01 -2.14406140e-02 -6.42358780e-01 -1.25770718e-01
1.29273105e+00 -4.19082761e-01 5.11864312e-02 -1.98780790e-01
-6.34978950e-01 -1.94540054e-01 1.07794785e+00 5.46516515e-02
2.69503981e-01 -1.19489539e+00 -4.84589159e-01 5.68361759e-01
3.58345568e-01 1.07472934e-01 -2.81035472e-02 6.87209845e-01
-9.15721834e-01 -9.60996822e-02 -2.46151045e-01 -6.83093667e-01
-1.47499561e+00 7.80060887e-01 1.15048289e-01 -2.21308284e-02
-8.93969059e-01 5.94430625e-01 8.80409032e-02 5.22630513e-02
7.05945715e-02 -2.03490406e-01 8.84600729e-02 1.84908614e-01
7.65681863e-01 5.66980422e-01 4.00928259e-01 -5.19091010e-01
-2.67351776e-01 4.90572155e-02 -1.29613772e-01 3.86068761e-01
1.50297725e+00 1.10723913e-01 -4.99836355e-01 4.18118715e-01
9.30521131e-01 3.35292131e-01 -9.86027241e-01 3.37476544e-02
2.96098560e-01 -1.13353658e+00 -6.44541606e-02 -4.22258228e-01
-7.89288819e-01 4.90396053e-01 5.80072522e-01 7.24161148e-01
1.13446522e+00 9.21804905e-02 2.33819291e-01 5.48163176e-01
3.18660975e-01 -1.33093023e+00 1.48579329e-01 3.03849757e-01
7.37414062e-01 -1.64036238e+00 3.63497406e-01 -2.26835221e-01
-3.44794542e-01 1.06198180e+00 4.75452036e-01 -3.43094260e-01
1.74327537e-01 3.46795589e-01 -1.38978332e-01 -3.88351232e-01
-3.77337992e-01 -1.05872869e-01 8.36316347e-02 3.80387038e-01
-3.81807424e-02 -1.41401127e-01 -5.68911493e-01 6.39308542e-02
-1.14863165e-01 -2.16745019e-01 9.42931294e-01 8.80411267e-01
-8.87860239e-01 -8.03202391e-01 -9.67429996e-01 9.42282975e-01
-6.84221864e-01 2.69861761e-02 -3.86143625e-01 8.84108543e-01
3.07951659e-01 6.42270148e-01 5.03534853e-01 1.68189526e-01
3.41677874e-01 1.75540283e-01 5.49290478e-02 -5.02219856e-01
-3.11809123e-01 8.83444250e-02 5.81426872e-03 -4.93199944e-01
7.59821013e-02 -8.25786829e-01 -1.03775907e+00 -3.72180372e-01
-5.35201371e-01 3.35381925e-01 7.83672690e-01 7.67954767e-01
-1.45426439e-03 1.76273718e-01 6.35915697e-01 -8.24238718e-01
-4.23639029e-01 -6.92062676e-01 -9.47416902e-01 8.80912304e-01
1.70920298e-01 -7.68228114e-01 -9.00458455e-01 1.33929595e-01] | [7.605134963989258, 2.640451669692993] |
51908f0b-b7f5-4e3b-adde-178ca7fb2664 | attention-based-multi-modal-fusion-network | 2003.13910 | null | https://arxiv.org/abs/2003.13910v2 | https://arxiv.org/pdf/2003.13910v2.pdf | Attention-based Multi-modal Fusion Network for Semantic Scene Completion | This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the occupancy and semantic labels of a volumetric 3D scene from single-view RGB-D images. Compared with previous methods which use only the semantic features extracted from RGB-D images, the proposed AMFNet learns to perform effective 3D scene completion and semantic segmentation simultaneously via leveraging the experience of inferring 2D semantic segmentation from RGB-D images as well as the reliable depth cues in spatial dimension. It is achieved by employing a multi-modal fusion architecture boosted from 2D semantic segmentation and a 3D semantic completion network empowered by residual attention blocks. We validate our method on both the synthetic SUNCG-RGBD dataset and the real NYUv2 dataset and the results show that our method respectively achieves the gains of 2.5% and 2.6% on the synthetic SUNCG-RGBD dataset and the real NYUv2 dataset against the state-of-the-art method. | ['Yipeng Li', 'Yue Gao', 'Changqing Zou', 'Xibin Zhao', 'Siqi Li'] | 2020-03-31 | null | null | null | null | ['3d-semantic-scene-completion', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 0.27993727 0.429499 0.3401492 -0.5925499 -0.87773997 -0.29842946
0.46930563 -0.1870732 -0.5168587 0.31501403 0.08388811 -0.03601874
-0.01749409 -0.77968633 -0.8484009 -0.5068115 0.21681349 0.46259356
0.3608091 -0.07802644 0.13352542 0.5666377 -1.79825 0.05100548
0.67291 1.6522852 0.56451875 0.74875003 -0.24315718 0.7901402
0.05044211 0.1099536 0.56586266 -0.01728227 -0.76411754 0.4975658
0.4545853 -0.7652668 -0.5446376 0.7499961 0.56111604 0.22004662
0.5299871 -1.2448124 -0.34086674 -0.2554917 -0.7186636 0.06562313
0.41383794 -0.00982708 0.6710605 -0.9299747 0.44700035 1.2973812
0.45320582 0.35843328 -0.6997372 -0.3482267 0.22845328 -0.05446862
-1.2722602 -0.1854138 1.0324234 -0.3803411 1.1001755 0.02721693
0.77507186 0.6878338 -0.02857933 1.0242294 1.3109777 -0.20469292
0.39475402 -0.32992196 -0.03888563 0.8091795 -0.14250684 0.09044223
-0.5484494 0.2408394 1.1132035 0.39393774 -0.0765353 -0.4814242
-1.0628582 0.7382371 0.90271956 -0.12313423 -0.6790309 0.4721081
0.12508799 -0.35233706 0.8821159 -0.02347401 -0.5460933 0.24060492
-0.92628354 0.23994192 0.15956777 1.0756063 1.0502832 -0.0146089
0.05684988 0.40975636 0.82884353 0.96574634 0.16989644 -1.343584
0.26722062 0.9147258 0.21830618 -0.5836272 -0.6413954 -0.17068323
-0.649113 0.4302909 -0.07353475 0.22186515 -1.5908269 1.3829021
0.629643 0.22807884 0.20768896 1.1867626 1.2148608 0.41296992
-0.05743207 0.13002387 1.0840436 -1.1164068 -0.59959334 -0.52115774
0.23079962 -0.27815577 0.87273085 0.17989646 -1.017937 -0.6149953
-1.2201288 -0.6341751 -0.29778597 -0.17721951 0.954912 0.3245329
-1.0872347 0.03478286 -1.1924458 -0.27994314 0.9050373 0.11638252
-0.534417 -0.5058937 -0.7668967 0.5187141 0.4382889 0.2087103
-1.4033921 -0.6993543 -0.9935261 -0.28842682 0.31578395 -0.928792
1.1562607 -0.65541893 -1.3112469 1.0497247 -0.06895415 -0.16615388
0.46515018 -0.30957144 0.23846488 0.5530254 0.3664749 0.8951669
0.50419897 -1.5612539 -0.70395535 -0.98980075 0.23642686 0.6457069
0.34607378 -0.5725166 -0.64643955 -0.07995439 0.82302773 -0.64233357
-0.5130277 0.16476922 -0.69946706 0.1554241 0.93281585 -0.70207906
0.38637543 -1.9171038 0.41264427 -0.05476746 0.5036644 -0.16025075
0.0928909 -0.08310698 0.29560035 -0.15665327 -0.55456597 -1.0500444
-0.02274347 0.3789596 0.00573162 0.7058596 -0.05368156 1.0875396
-0.93886983 -0.38265818 0.77625215 0.7451931 -0.3500924 0.6100384
-0.51306814 0.7849877 -0.8005829 1.0173063 0.9881276 -0.36246717
-0.31669843 -0.1868164 -0.06231989 -0.09964111 -0.8456322 2.765577
-0.4082277 0.32707298 0.15905648 -0.77204674 0.78765553 0.00883688
0.6300376 -1.1371883 0.20027305 0.19805169 -0.97832 -0.5584469
0.5667133 -0.23065165 -0.29274496 0.22917962 0.16215152 -0.8087083
-0.5565247 0.3224546 1.0958161 0.5199119 -0.03452679 0.07444271
0.4329702 0.02140271 0.36631235 0.5070871 -0.12825324 0.84114206
0.10417311 -0.48276266 -1.2760277 -1.45658 0.03632458 0.53440046
0.54426545 -0.00768967 -0.5201133 -0.63803375 0.11597414 0.5876169
-0.77357715 0.09512318 -0.08567782 -0.4819173 0.36160263 0.6705435
1.1820424 -0.6221542 -1.0979227 -0.04569544 -0.30383974 -1.5878888
0.17613904 0.24582581 -0.94018155 -1.1846589 -0.75498736 -0.27992055
0.5327915 0.55012685 0.9522053 -0.2840373 -0.30061048 0.7687032
-0.43163663 -0.32437608 0.0761065 -0.15965168 -0.31562963 -0.02534242
0.06770607 -0.73398966 -0.9418797 0.04400881 -1.1361718 0.7215886
0.50441104 0.35990652 0.9978381 -0.10610395 0.27981836 -0.5940121
-0.3201773 -0.56123924 -0.58016324 -0.07179067 -0.33700335 -0.06082207
0.02476952 0.39698067 -1.1710887 0.45485812 -0.29470757 -0.9981543
-0.42310292 0.12413251 -0.31785405 -0.07861723 0.29911855 0.31775442
-0.06835181 -0.48863488 0.6002124 0.76979697 0.56290793 -0.27904698
0.41959202 1.0948546 0.21631436 -0.66118115 -1.171303 -0.70565516
-0.8142893 -0.4448641 1.4230789 -1.5307691 -0.6440761 0.78887236
-1.1582127 -0.5603118 -0.15254398 0.3670585 -1.0054743 0.29572704
-0.28345054 -0.9546528 -0.315412 -1.3164479 2.0121114 0.17861626
0.5474999 -0.86740977 -0.15964858 0.818392 0.2189947 0.9905849
0.7396999 -0.03147713 -1.0917724 0.05297941 -0.5080623 0.30443984
-0.11322536 -0.70410216 -1.5480195 0.02855467 0.04226395 -0.5452325
0.9456873 0.5549943 1.1408569 0.22336204 -0.20471339 1.0043917
1.9242384 -0.09005457 0.70805526 0.15923601 1.2155201 0.35851917
0.5586292 0.5045865 0.97220194 0.5126662 1.1979133 -0.44723135
-0.19930501 -0.23712 -0.17020631 0.44772813 -0.06366894 -0.19098292
-1.0209218 0.558507 -1.9830779 -0.26414895 -0.10967571 1.9306028
0.22521323 -0.08908824 -0.17603433 0.19321242 0.16213106 0.34829965
-0.97809607 0.13477837 -0.2974294 0.10788903 0.84927756 0.64492
-1.0221437 0.9219068 5.029146 0.6592283 -0.8044714 0.38766843
0.8197533 -0.19535033 -0.55042905 -0.17580634 -0.4201755 0.14859165
0.650268 0.514502 0.32709074 0.74843675 0.09122597 -0.6556769
-0.8414012 1.281986 0.26243046 -1.1861752 -0.15998995 0.11927562
0.9786319 0.6058425 -0.15393922 -0.01501928 0.25364074 -1.078058
1.0220387 0.89041084 0.9391116 -0.77763534 0.6986273 0.4251848
-1.2035968 -0.10571097 -0.17818974 -0.0215268 0.36442956 0.7353427
-0.4947021 1.0035508 0.90809506 1.0217172 -0.52771103 0.64920884
-0.29769197 -0.06104327 -0.41873345 0.36235043 0.54307425 -0.0348626
0.24953161 0.5036298 0.3453905 0.34820035 0.09721427 1.0541687
0.11183891 -0.41104236 -0.5940577 0.11892154 0.06933598 1.2069219
-0.8646134 -0.27893674 -0.2622648 1.3801892 0.1957848 0.49911946
-0.898224 -0.04451162 0.54358035 0.0196929 0.43989983 -0.53328764
-0.5924725 -0.9668983 -0.08639478 -0.02316435 0.02867343 -1.558036
-0.9226751 0.7428511 0.0335382 -0.78867596 0.06814344 -0.61354333
0.04160419 0.8984115 -1.9510753 -1.6297275 -1.0862893 0.78990936
0.40285823 0.25869733 0.68984234 0.21766527 -0.11357655 -0.09025304
-0.1208788 -0.16413231 -0.03184384 -1.185148 0.38523164 0.6055319
-0.2663886 -0.3406368 0.3254148 -0.5213232 -1.6361706 -1.3105577
0.39894435 -0.5152575 -0.09054772 -0.48206097 -0.3263765 0.6353567
-0.05743186 0.38596708 0.35559243 -0.58132726 -0.25381866 0.04714276
-1.4679012 0.05797495 1.5177531 -0.602144 -0.32813913 0.32450852
1.3145043 -0.8737215 -0.9089387 0.62783056 0.30970755 -1.3059651
1.2106079 -0.124956 0.67471075 -0.5779198 -1.0302662 -0.955123
0.15860917 0.00630705 -0.02730003 0.6659627 -0.06570895 -0.19448484
0.8457329 0.54404324 -0.6624867 -0.7465749 -1.1989837 -0.15873766
-0.3003626 -1.0819618 0.6936805 0.63744295 -0.7287904 0.26095837
-0.1016748 0.35725898 1.0716347 0.19846939 0.8553154 -1.1561748
0.04128806 0.03216367 -0.8309909 -1.2666115 0.23458137 -0.8923654
0.0701692 -2.048052 0.01146072 -0.42457023 -0.12981234 0.3880209
0.11515219 0.48130012 0.09136675 -0.05851656 -0.98200613 1.0564038
1.472382 -0.04249306 -0.13204344 -0.35284406 -0.5797508 0.5552409
0.3916252 -0.16335279 -0.63869 -0.6683211 0.25975415 0.43747166
0.8442317 -1.0245318 0.14362158 0.04448587 0.656614 -1.1117322
0.91786003 -0.9593232 -0.0859974 0.09403566 0.12349522 -0.33857754
0.2953026 0.92795503 0.08361288 0.5462178 0.6223722 -0.51238376
-1.1991392 0.58443946 0.05677106 -0.08550599 1.1362202 -0.4258354
-0.16941695 -0.21066563 -0.7653424 0.31053197 0.62392664 0.2189196
1.1598698 -1.4224064 -0.5278113 0.68344086 0.40187025 1.1531667
0.76845175 0.77146155 -0.70477426 0.5264927 -0.42120278 -1.0961446
-0.6709079 0.33788285 0.569197 0.07603889 -0.8069601 1.066836
0.35684437 -0.7226821 0.194283 -0.7156431 0.21520355 -0.3746289
0.24157728 0.20868134 0.17587635 -0.774711 -0.45522055 0.73103595
0.39963147 -0.22934777 1.5805349 -0.6055885 -0.02401112 0.55877256
1.3112782 -0.653747 -1.8526027 -0.34918702 -0.57408416 -0.67461395
0.63890666 -0.834717 -1.3036264 0.9311634 0.85251814 -0.22529954
1.3542945 0.3306921 0.80552506 0.02009452 0.7059706 -0.8341114
0.3298246 0.4239201 0.7961104 -1.4948348 0.10145958 -0.2423959
-0.61982983 0.7400691 0.52061814 -0.19776869 0.773368 -0.11849488
0.03490068 -0.5241925 -0.3104809 -0.4720015 0.15652353 0.595681
-0.1517735 0.05469974 0.5902695 0.5291848 0.12830748 0.05067697
0.39704177 0.9387926 -0.51940304 -0.39384094 -0.36770052 0.2808063
0.00876229 0.10301185 -0.13167469 0.71247464 0.27115077 0.983723
0.36866736 -0.5227124 0.3118049 -0.07366527 0.7413755 -0.48439205
-0.00635649 0.17088285 -0.19617727 -1.1500932 -0.93423295 -0.3155446
-1.6296161 -0.20792904 0.0607226 -0.51805586 1.1884965 1.0929213
0.41206428 0.9244877 0.7846181 -1.4124825 0.16354495 -0.94667757
-0.8756071 0.20315349 0.7305525 -0.94944185 -0.29795644 -0.11350199] | [8.52104377746582, -2.8607521057128906] |
d2ba01f6-06e8-461a-896a-6b1cd3ee1947 | data-poisoning-attacks-against-multimodal | 2209.15266 | null | https://arxiv.org/abs/2209.15266v2 | https://arxiv.org/pdf/2209.15266v2.pdf | Data Poisoning Attacks Against Multimodal Encoders | Recently, the newly emerged multimodal models, which leverage both visual and linguistic modalities to train powerful encoders, have gained increasing attention. However, learning from a large-scale unlabeled dataset also exposes the model to the risk of potential poisoning attacks, whereby the adversary aims to perturb the model's training data to trigger malicious behaviors in it. In contrast to previous work, only poisoning visual modality, in this work, we take the first step to studying poisoning attacks against multimodal models in both visual and linguistic modalities. Specially, we focus on answering two questions: (1) Is the linguistic modality also vulnerable to poisoning attacks? and (2) Which modality is most vulnerable? To answer the two questions, we propose three types of poisoning attacks against multimodal models. Extensive evaluations on different datasets and model architectures show that all three attacks can achieve significant attack performance while maintaining model utility in both visual and linguistic modalities. Furthermore, we observe that the poisoning effect differs between different modalities. To mitigate the attacks, we propose both pre-training and post-training defenses. We empirically show that both defenses can significantly reduce the attack performance while preserving the model's utility. | ['Yang Zhang', 'Pascal Berrang', 'Mathias Humbert', 'Michael Backes', 'Zheng Li', 'Xinlei He', 'Ziqing Yang'] | 2022-09-30 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 2.33511114e-03 -9.27287787e-02 -2.77607560e-01 3.91041525e-02
-9.52530026e-01 -1.30119562e+00 6.92070186e-01 1.49979100e-01
-5.10819793e-01 3.62640500e-01 3.76064107e-02 -5.66655219e-01
4.53131586e-01 -5.30503452e-01 -9.06967759e-01 -6.69964492e-01
-5.66140190e-03 -3.09144333e-02 3.13708693e-01 -1.47439599e-01
1.09989747e-01 7.20545948e-01 -1.00829136e+00 2.21853271e-01
5.47813356e-01 7.93296635e-01 -5.28886259e-01 7.70914972e-01
2.12732628e-01 1.23669255e+00 -6.52947724e-01 -9.25333738e-01
3.79068613e-01 -2.92573184e-01 -8.48823011e-01 1.32589405e-02
3.48903000e-01 -5.91158509e-01 -7.52015233e-01 1.46805799e+00
4.94962901e-01 -3.51685673e-01 5.19614935e-01 -1.81280470e+00
-6.01883709e-01 7.97227919e-01 -7.61476040e-01 4.30110320e-02
3.58697474e-01 7.10611999e-01 7.03878522e-01 -4.79086161e-01
2.80102849e-01 1.37186837e+00 3.68175209e-01 1.13122499e+00
-1.05841327e+00 -7.52907336e-01 1.30573004e-01 1.56067647e-02
-1.38633013e+00 -5.90657294e-01 7.25753844e-01 -2.98242748e-01
5.77867150e-01 3.34418982e-01 -2.43876755e-01 1.67533994e+00
-1.83354855e-01 9.00727749e-01 1.07556558e+00 -2.37570912e-01
1.80233687e-01 2.64507324e-01 1.17751047e-01 8.00742924e-01
2.64227659e-01 2.87038893e-01 -5.38045585e-01 -8.68514538e-01
2.25202248e-01 -1.85111091e-01 -3.39841127e-01 -2.08788484e-01
-9.02620375e-01 1.00065172e+00 1.73451543e-01 1.07671022e-02
-2.86775324e-02 3.79295796e-01 5.29166222e-01 2.41947547e-01
-1.68137118e-01 3.46672446e-01 -2.80419499e-01 6.00279868e-02
-3.10898930e-01 -1.83864638e-01 8.43210220e-01 5.83704352e-01
6.16422951e-01 1.76621720e-01 1.01390854e-01 1.88934982e-01
7.30453253e-01 9.20074761e-01 1.75831661e-01 -7.47624993e-01
6.92070842e-01 4.40583855e-01 -1.00889674e-03 -9.81078029e-01
-2.17674207e-02 1.80054724e-01 -6.94105983e-01 1.13928251e-01
5.60371578e-01 -5.41567206e-01 -7.55022943e-01 2.22844672e+00
4.40524995e-01 3.36727768e-01 3.27204853e-01 6.83836520e-01
6.14390731e-01 5.25489390e-01 5.00686228e-01 -8.46293047e-02
1.36694801e+00 -6.32466793e-01 -5.02124608e-01 2.21920256e-02
8.44528496e-01 -5.98830521e-01 1.02977896e+00 -1.77397225e-02
-1.04390430e+00 -1.31253779e-01 -1.15408957e+00 1.30710572e-01
-4.92967665e-01 -4.93775547e-01 2.52886474e-01 1.11992812e+00
-7.68673778e-01 -1.09285372e-03 -9.07252252e-01 -3.71233165e-01
4.93432850e-01 4.39010501e-01 -4.69041944e-01 -2.41981726e-02
-1.59090507e+00 9.37909245e-01 8.81028455e-03 -2.84384519e-01
-1.51537514e+00 -3.56239200e-01 -9.47182536e-01 9.91927758e-02
5.45233488e-01 -5.37855864e-01 1.16570449e+00 -7.45018601e-01
-1.19912577e+00 6.43108666e-01 -1.37639001e-01 -6.43171370e-01
5.68681657e-01 2.09984928e-02 -4.07829702e-01 4.51346040e-01
-2.78792262e-01 5.00304878e-01 1.16375625e+00 -1.81074774e+00
-5.04818380e-01 -3.81209224e-01 5.22043347e-01 3.34684625e-02
-9.79449153e-01 3.55603993e-01 -5.57319820e-01 -2.99981743e-01
-6.44982219e-01 -9.54546273e-01 -1.04619481e-01 2.50977892e-02
-9.58178461e-01 8.63758326e-02 1.23879790e+00 -2.91379541e-01
1.18578219e+00 -2.29472399e+00 -3.04677966e-03 3.44415277e-01
4.24114257e-01 6.30189061e-01 -3.74961734e-01 6.39212310e-01
2.45795220e-01 7.35378563e-01 -1.47115022e-01 -5.96536934e-01
2.62818575e-01 2.29300201e-01 -8.36340547e-01 5.66350579e-01
1.50587648e-01 1.01054561e+00 -6.00188017e-01 -5.36319435e-01
4.79819365e-02 5.75918078e-01 -3.86218339e-01 3.43536258e-01
-1.93102092e-01 3.60070437e-01 -5.35283804e-01 9.30132210e-01
7.37344086e-01 -2.45060295e-01 2.73990661e-01 -2.89498389e-01
4.08095658e-01 -1.60927713e-01 -6.84878230e-01 9.16817963e-01
-1.20381236e-01 3.95121604e-01 2.09640771e-01 -4.54878062e-01
2.94349790e-01 4.29755151e-01 1.49607718e-01 -5.39872169e-01
3.93518031e-01 -5.18939532e-02 -2.97677307e-03 -6.23733461e-01
1.69431642e-01 -8.01461935e-02 -2.82919258e-01 8.50376546e-01
-2.85984665e-01 4.13372844e-01 -1.48877025e-01 6.04994595e-01
1.20533586e+00 -4.83851254e-01 -1.06266893e-01 3.19249004e-01
7.35059023e-01 -1.99776635e-01 2.20481738e-01 1.01520944e+00
-5.90636492e-01 1.82207063e-01 8.13758552e-01 -6.99027926e-02
-7.84827471e-01 -1.13119042e+00 4.26760495e-01 1.06491339e+00
6.00409627e-01 -5.27762890e-01 -9.64905322e-01 -1.42198455e+00
3.72595340e-02 5.32093287e-01 -6.60870433e-01 -6.01374328e-01
-3.81576926e-01 -5.71226180e-01 1.64334643e+00 4.81853902e-01
5.74232697e-01 -6.91334128e-01 -5.63181043e-01 -4.44304317e-01
-2.99451053e-01 -1.25332880e+00 -5.57077408e-01 -1.45363342e-02
-4.46690410e-01 -1.26664829e+00 -2.73153007e-01 -5.04424572e-01
7.17345357e-01 3.60190719e-01 7.60331690e-01 4.98092920e-01
1.86332002e-01 8.03742886e-01 -3.07315528e-01 -4.23474133e-01
-7.93226719e-01 1.91087186e-01 1.39389783e-01 1.79997012e-01
5.20783901e-01 -2.36990348e-01 -1.59582347e-01 3.95696670e-01
-1.55241871e+00 -5.27945280e-01 3.40282857e-01 4.55494493e-01
1.31119236e-01 1.95568010e-01 3.04355741e-01 -8.35805953e-01
7.21970379e-01 -4.99900937e-01 -6.03149533e-01 5.45692801e-01
-1.69173464e-01 1.10683322e-01 7.36227453e-01 -9.55843389e-01
-5.67208827e-01 1.37797564e-01 -2.23001361e-01 -6.90987587e-01
-3.78863275e-01 4.89272833e-01 -5.81954062e-01 -3.78715158e-01
5.71947277e-01 2.62385964e-01 8.32773093e-03 -2.87521780e-01
3.97433251e-01 4.23118740e-01 2.93443620e-01 -6.79264188e-01
1.61778557e+00 7.09594846e-01 -3.62370536e-02 -7.43216932e-01
-5.59979975e-01 5.93160018e-02 -2.96717793e-01 -1.49598956e-01
8.68408322e-01 -7.11307943e-01 -1.16927016e+00 7.30695724e-01
-1.17147076e+00 -3.16136420e-01 2.03660101e-01 5.44197597e-02
-1.44439429e-01 1.02829969e+00 -8.11095655e-01 -8.87868285e-01
-1.62200630e-01 -1.50240171e+00 8.89801800e-01 1.62796527e-01
1.25767171e-01 -1.11518085e+00 -8.31623748e-03 3.38950574e-01
3.50307345e-01 2.47766718e-01 1.15398860e+00 -9.63930964e-01
-4.39778119e-01 -3.36716354e-01 -2.06710875e-01 2.13959485e-01
4.54609729e-02 6.92628026e-02 -1.11865962e+00 -5.64566791e-01
2.51301557e-01 -8.58836353e-01 6.46142364e-01 -2.74733007e-01
1.01075280e+00 -6.57363653e-01 -2.03269333e-01 5.76529086e-01
1.20923221e+00 1.08017325e-01 7.15488017e-01 3.03854048e-01
9.24228370e-01 4.85648453e-01 2.12989599e-01 3.62658709e-01
3.42816532e-01 4.18498039e-01 9.66987014e-01 -1.47513628e-01
3.37511897e-01 -5.86484730e-01 7.70843983e-01 3.41542244e-01
5.54640412e-01 -5.63981771e-01 -8.02827895e-01 1.61400601e-01
-1.75353491e+00 -1.01700091e+00 2.33075947e-01 2.22243714e+00
6.92214072e-01 6.60924762e-02 3.73207033e-01 -6.21239990e-02
6.24288201e-01 3.93611968e-01 -7.98082530e-01 -3.16084832e-01
-2.29932323e-01 -3.02780986e-01 6.91481948e-01 5.81237137e-01
-1.30914617e+00 1.02754450e+00 6.43507528e+00 6.63864017e-01
-1.40236723e+00 1.41906574e-01 5.80938458e-01 -2.35284880e-01
-2.44127199e-01 1.21915154e-01 -8.08966041e-01 5.20115495e-01
1.06041837e+00 -7.66681433e-02 4.83706027e-01 6.08581841e-01
-1.50387555e-01 2.85483062e-01 -1.03046370e+00 7.94673979e-01
3.03410828e-01 -9.34413671e-01 3.42844635e-01 3.75861466e-01
3.17891926e-01 -4.31452952e-02 5.09103656e-01 3.38725895e-01
5.83461404e-01 -1.21819746e+00 6.35087848e-01 -2.24010162e-02
6.62707448e-01 -1.16453660e+00 4.80867445e-01 4.53292131e-01
-1.07235503e+00 -1.46878883e-01 -1.22883461e-01 4.53718513e-01
1.57438517e-01 -2.71499515e-01 -3.71735066e-01 3.79989624e-01
4.13379908e-01 2.07604125e-01 -9.02558327e-01 4.50770497e-01
-3.96279633e-01 7.80812383e-01 -2.57150084e-01 2.25078762e-01
4.48980421e-01 4.43012923e-01 4.92105067e-01 9.14498627e-01
-1.38612568e-01 -4.92928363e-02 2.62488663e-01 6.25454366e-01
-4.40652400e-01 -3.51841599e-01 -9.33011770e-01 -3.70467484e-01
7.02516854e-01 9.58662271e-01 -2.11165935e-01 -2.66236305e-01
-6.02828383e-01 9.66803908e-01 2.71015197e-01 5.06985128e-01
-1.36896491e+00 -7.09655806e-02 6.48440242e-01 -1.07654735e-01
5.97529858e-02 -2.83353835e-01 -5.69463894e-02 -1.43779504e+00
-3.14852774e-01 -1.50420249e+00 6.26341999e-01 -3.54588658e-01
-1.42951703e+00 6.65726840e-01 -1.64218739e-01 -9.06453609e-01
-8.33652075e-03 -5.26893914e-01 -4.83408689e-01 6.92792892e-01
-1.57709289e+00 -1.44266891e+00 1.97164536e-01 1.12317085e+00
6.14632517e-02 -2.05777794e-01 7.10156977e-01 2.49505684e-01
-8.76501560e-01 1.26978040e+00 -1.68353125e-01 5.74248850e-01
7.40073442e-01 -8.97535026e-01 1.71877474e-01 1.16802454e+00
1.99830696e-01 8.64088655e-01 5.50702751e-01 -5.96223414e-01
-1.91417491e+00 -9.37504292e-01 5.34388959e-01 -7.23095775e-01
9.00921047e-01 -3.79615039e-01 -9.64967489e-01 7.65641034e-01
3.24093193e-01 -2.11519882e-01 9.51607466e-01 -3.19023520e-01
-1.13186049e+00 4.06199425e-01 -1.32752097e+00 9.96169865e-01
5.45954823e-01 -1.10466969e+00 -2.87556052e-01 1.91649154e-01
7.36112177e-01 -1.23141542e-01 -6.30184472e-01 4.39998478e-01
4.90509033e-01 -8.38695824e-01 9.68353152e-01 -1.10863924e+00
3.42577487e-01 -3.98490548e-01 -5.44638336e-01 -8.12859058e-01
1.68633908e-01 -7.15648532e-01 -6.35744870e-01 1.34578586e+00
4.17281747e-01 -6.19166315e-01 7.98129737e-01 8.25152397e-01
6.36425972e-01 -3.05774152e-01 -8.82637739e-01 -7.78331280e-01
3.28057706e-01 -3.44669312e-01 5.44097602e-01 1.08140326e+00
1.86207157e-03 3.18549991e-01 -8.61461520e-01 1.01459813e+00
7.62405396e-01 -3.09010446e-01 1.05655849e+00 -6.05384231e-01
-2.93704957e-01 -2.58506984e-01 -1.14600301e-01 -9.24961746e-01
4.23627883e-01 -5.47869384e-01 -2.23186940e-01 -8.13701212e-01
4.05590683e-01 1.03439562e-01 -4.05161053e-01 8.85980189e-01
-2.19411314e-01 4.83635545e-01 5.89796722e-01 4.18077707e-01
-7.51353085e-01 3.88448209e-01 6.36015356e-01 -3.69666934e-01
1.26432970e-01 -1.86336726e-01 -9.13474917e-01 6.76364481e-01
6.99021161e-01 -6.50621593e-01 -5.56885004e-01 -5.71076155e-01
1.56738356e-01 -1.45068094e-01 4.91249681e-01 -6.13845170e-01
3.18188161e-01 -2.86791980e-01 -7.45342448e-02 -3.08617681e-01
3.89986485e-01 -1.11925459e+00 -2.58509994e-01 8.35095882e-01
-4.48427498e-01 1.87298149e-01 3.63281101e-01 6.24295950e-01
-1.20653264e-01 -8.02038535e-02 1.06604111e+00 2.84724813e-02
-3.72408122e-01 4.57766533e-01 -6.75599575e-01 3.92864905e-02
1.21789920e+00 1.92622721e-01 -8.91074061e-01 -7.04323471e-01
-5.01262128e-01 4.64118421e-01 9.17350650e-01 2.72149056e-01
5.99618673e-01 -1.27246928e+00 -5.64068615e-01 1.84782762e-02
2.30301812e-01 -6.25317216e-01 1.52078182e-01 7.87609398e-01
-1.96584031e-01 -5.91665274e-03 7.74391368e-02 -3.98788184e-01
-1.67615294e+00 1.24781835e+00 4.64957267e-01 -2.78017938e-01
7.62315765e-02 6.11166000e-01 3.52842480e-01 -2.64473915e-01
6.17530346e-01 3.82250071e-01 -3.35879587e-02 -1.37857303e-01
7.96257555e-01 1.55768365e-01 -5.31022072e-01 -1.08101285e+00
-5.57158291e-01 2.71908730e-01 -3.23119074e-01 -2.23525614e-01
6.85826838e-01 -2.06456631e-01 1.27812950e-02 1.36123914e-02
1.27249479e+00 4.22797531e-01 -1.10830414e+00 -3.03415418e-01
-7.44635165e-02 -4.30037677e-01 -3.44154477e-01 -8.74027729e-01
-1.14638841e+00 1.19760919e+00 4.48075861e-01 6.48314655e-01
1.01441216e+00 -6.67533353e-02 1.03805172e+00 4.17471856e-01
2.63028979e-01 -5.57164431e-01 4.03152734e-01 6.09977603e-01
3.82829607e-01 -1.26201987e+00 -3.74428838e-01 -3.06998551e-01
-1.03647387e+00 6.93984091e-01 7.99125373e-01 2.13351160e-01
4.59481657e-01 4.30401295e-01 5.30943274e-01 -1.71024904e-01
-7.61524558e-01 -4.21082079e-02 5.33912852e-02 7.56788850e-01
-1.59080088e-01 -3.40907633e-01 3.24872673e-01 3.31957281e-01
3.82162541e-01 -5.32540798e-01 5.98426223e-01 1.21102393e+00
-3.33504021e-01 -1.22894108e+00 -6.83755457e-01 -5.98657802e-02
-7.23162115e-01 -2.93287709e-02 -1.10417986e+00 8.36630166e-01
-1.46292895e-01 1.29867601e+00 -5.38684785e-01 -8.02235901e-01
1.47249907e-01 6.12606294e-02 1.96098492e-01 -3.11920494e-01
-7.92034030e-01 3.30789201e-02 -5.23945913e-02 -4.79857951e-01
-2.01278046e-01 -1.95905447e-01 -1.01104367e+00 -7.84236789e-01
-3.73792738e-01 1.67568520e-01 3.67498010e-01 9.79945540e-01
3.39756578e-01 -8.35862905e-02 1.07580578e+00 -5.13669908e-01
-1.05766881e+00 -6.10231578e-01 -3.91073972e-01 5.61099231e-01
4.75716889e-01 -4.16478038e-01 -6.84571445e-01 2.13078503e-03] | [5.8301777839660645, 7.821191787719727] |
366fa215-6ce8-458d-a292-9158f0b1184c | multilingual-bottleneck-features-for | 2011.03118 | null | https://arxiv.org/abs/2011.03118v1 | https://arxiv.org/pdf/2011.03118v1.pdf | Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages | In this work, we explore the benefits of using multilingual bottleneck features (mBNF) in acoustic modelling for the automatic speech recognition of code-switched (CS) speech in African languages. The unavailability of annotated corpora in the languages of interest has always been a primary challenge when developing speech recognition systems for this severely under-resourced type of speech. Hence, it is worthwhile to investigate the potential of using speech corpora available for other better-resourced languages to improve speech recognition performance. To achieve this, we train a mBNF extractor using nine Southern Bantu languages that form part of the freely available multilingual NCHLT corpus. We append these mBNFs to the existing MFCCs, pitch features and i-vectors to train acoustic models for automatic speech recognition (ASR) in the target code-switched languages. Our results show that the inclusion of the mBNF features leads to clear performance improvements over a baseline trained without the mBNFs for code-switched English-isiZulu, English-isiXhosa, English-Sesotho and English-Setswana speech. | ['Thomas Niesler', 'Ewald van der Westhuizen', 'Febe De Wet', 'Astik Biswas', 'Trideba Padhi'] | 2020-10-31 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 6.34573847e-02 -1.09817259e-01 1.46237090e-01 -5.20029485e-01
-1.23766816e+00 -5.39900482e-01 4.46649134e-01 -9.28337798e-02
-6.54967785e-01 5.49653411e-01 3.70419264e-01 -1.03535795e+00
7.76949376e-02 -3.94229144e-02 -4.29806054e-01 -5.64777613e-01
-5.65669648e-02 3.97892982e-01 1.91597864e-01 -4.66307104e-01
1.77863479e-01 5.34567773e-01 -1.37874937e+00 3.33820343e-01
8.33193660e-01 3.90626490e-01 8.90270829e-01 9.80996609e-01
-5.15438989e-02 7.04352200e-01 -5.73163331e-01 -2.77920723e-01
9.94064733e-02 -4.36379761e-01 -9.01615202e-01 -2.51595557e-01
2.96501428e-01 -2.05590595e-02 -3.17872703e-01 7.47588098e-01
5.47792673e-01 1.70240015e-01 4.58291054e-01 -5.07988513e-01
-2.16301307e-01 9.73009229e-01 -2.29190774e-02 6.76102638e-01
3.43853027e-01 -1.00972772e-01 8.83569002e-01 -1.09591842e+00
4.88600522e-01 1.47493863e+00 4.19319421e-01 4.45204079e-01
-1.06014085e+00 -7.59120822e-01 1.04974164e-02 1.79641485e-01
-1.42109478e+00 -1.30349195e+00 4.29212987e-01 -3.80242348e-01
1.63851547e+00 3.40954900e-01 3.26445222e-01 8.94357502e-01
-1.93486750e-01 6.27246261e-01 1.15933335e+00 -1.07946515e+00
-1.62682403e-02 3.64113718e-01 -2.77399808e-01 3.62109959e-01
-3.27631593e-01 2.37677172e-01 -6.94078565e-01 8.64254832e-02
3.55773270e-01 -7.73202419e-01 -2.35886380e-01 2.90441304e-01
-1.14909601e+00 9.23597336e-01 -1.22040950e-01 7.72490621e-01
-9.05644372e-02 -1.63279131e-01 5.68954945e-01 6.54609799e-01
7.09427953e-01 1.41269907e-01 -9.30448830e-01 -7.26014316e-01
-1.02267373e+00 -1.59660220e-01 8.26265156e-01 9.25811231e-01
4.90943879e-01 6.40442729e-01 4.05663341e-01 1.64343047e+00
4.38852966e-01 8.62875879e-01 6.31891668e-01 -6.32397532e-01
6.08306706e-01 3.28015834e-02 -3.90166074e-01 -3.62537473e-01
-1.05993338e-01 -1.89539641e-02 -4.89828698e-02 -1.94099978e-01
2.41669223e-01 -2.53621966e-01 -1.12744176e+00 1.59237671e+00
1.50805622e-01 -2.71668792e-01 5.05735219e-01 6.29381299e-01
4.51697141e-01 1.07672930e+00 -9.67471581e-03 -1.76375493e-01
1.12191868e+00 -8.75879228e-01 -5.80415905e-01 -2.33047828e-01
8.19988012e-01 -1.38354301e+00 1.07713413e+00 3.01798671e-01
-9.06275034e-01 -6.09952331e-01 -8.63893092e-01 1.22786708e-01
-4.26377177e-01 2.48953596e-01 4.41026747e-01 1.10102296e+00
-1.24714053e+00 5.78179490e-03 -9.89401639e-01 -5.89522779e-01
-1.78390414e-01 4.46690708e-01 -4.61016119e-01 -3.24012220e-01
-1.27663970e+00 1.03329027e+00 3.92658800e-01 -1.74945697e-01
-7.84247637e-01 -3.89752448e-01 -1.08352172e+00 -2.50149190e-01
1.32074401e-01 5.53636551e-01 1.41340411e+00 -1.07898319e+00
-1.52993810e+00 7.90276229e-01 -2.69580096e-01 -2.66927272e-01
8.76094103e-02 1.41659826e-02 -1.02925014e+00 -7.80677376e-03
2.04817988e-02 4.87123191e-01 6.19277656e-01 -8.00662577e-01
-9.93871689e-01 -1.09032571e-01 -3.91051441e-01 2.07377970e-01
-1.80444255e-01 7.26421535e-01 -1.92389831e-01 -7.82470703e-01
-1.65504247e-01 -1.15977621e+00 1.06407359e-01 -9.07584488e-01
-1.45894021e-01 -1.65689811e-01 8.18788826e-01 -1.34246182e+00
1.27655029e+00 -2.37773156e+00 6.16261102e-02 1.66686580e-01
-8.52635741e-01 7.02000439e-01 -1.07634179e-01 6.79538667e-01
1.65811121e-01 -1.35646667e-02 -2.73499876e-01 -9.60242003e-02
-1.89504340e-01 6.41638041e-01 -6.85781464e-02 2.03771546e-01
5.49739778e-01 4.04994696e-01 -5.54620981e-01 -2.59204507e-01
3.76171201e-01 6.24719560e-01 -5.48965633e-01 1.35174826e-01
2.00755447e-01 6.10267699e-01 5.00020683e-02 6.39695585e-01
3.41404527e-01 8.46613169e-01 1.63413391e-01 4.53527480e-01
-7.01294243e-01 1.05670631e+00 -9.09848928e-01 1.57181025e+00
-7.96115398e-01 7.17085302e-01 4.78039354e-01 -9.79615688e-01
9.42557156e-01 6.26873195e-01 -1.29529491e-01 -7.67348170e-01
-4.03332412e-02 1.09184456e+00 8.79388452e-01 -4.95135069e-01
3.43899310e-01 -3.84970307e-01 -2.76118051e-02 8.68546218e-03
3.94358397e-01 -4.70051736e-01 1.72658339e-01 -1.16571054e-01
8.96832585e-01 -1.39425665e-01 1.86614349e-01 -7.85316288e-01
9.54311252e-01 -3.14356834e-02 4.40555274e-01 4.99673009e-01
-2.27101088e-01 5.08844018e-01 6.21369109e-02 6.31404445e-02
-1.10736823e+00 -8.87449086e-01 -4.94202703e-01 1.47524643e+00
-9.60411608e-01 -2.73177326e-01 -6.92710817e-01 -4.01790798e-01
-2.08192572e-01 9.20999169e-01 9.52192172e-02 2.44625494e-01
-9.94504690e-01 -3.67084235e-01 8.85666847e-01 1.16753690e-01
8.01410526e-02 -1.25770545e+00 -2.24816248e-01 4.89701837e-01
-1.39602125e-01 -1.24748063e+00 -5.16844392e-01 5.82220674e-01
-4.33388352e-01 -4.88110781e-01 -8.02119851e-01 -1.09709466e+00
-8.22838619e-02 -7.42757767e-02 6.62729263e-01 -1.29928663e-01
-4.86108325e-02 2.32697770e-01 -6.54947519e-01 -5.42825639e-01
-1.16132474e+00 3.96175623e-01 3.28263849e-01 -1.89264312e-01
5.63709974e-01 -1.34129450e-01 1.30549967e-01 1.49972871e-01
-6.67400539e-01 -3.49389553e-01 4.64052081e-01 6.55254006e-01
1.95440844e-01 -2.55303085e-01 9.89451766e-01 -6.71177983e-01
3.43531519e-01 -3.41600448e-01 -5.91090977e-01 8.24267045e-02
-2.05354363e-01 -6.92571625e-02 5.40819466e-01 -4.22010899e-01
-1.26850915e+00 1.24268822e-01 -1.06362498e+00 4.73526344e-02
-3.69643688e-01 7.75278270e-01 -3.94527078e-01 -6.27007335e-02
3.22225034e-01 2.05048919e-01 -2.08498761e-01 -5.81526041e-01
1.49572700e-01 1.55628431e+00 4.56343740e-01 -5.47342122e-01
3.56350720e-01 -2.94124544e-01 -6.33775711e-01 -1.75215816e+00
-1.38959453e-01 -8.21115255e-01 -7.79000342e-01 -1.87920257e-02
7.59894252e-01 -1.09060574e+00 3.94916954e-03 5.70987523e-01
-1.02330351e+00 -4.66543436e-01 1.69659108e-02 8.99002373e-01
-2.95634180e-01 1.83211103e-01 -6.79272115e-01 -1.05482244e+00
-5.81902117e-02 -1.50174415e+00 8.35823536e-01 -2.38384664e-01
-2.09912390e-01 -9.98194575e-01 2.63517737e-01 3.51235598e-01
7.85233617e-01 -6.02820218e-01 1.11680663e+00 -1.01523471e+00
-5.11226244e-02 5.17574251e-02 1.69154882e-01 9.18337941e-01
3.49326819e-01 4.50120494e-02 -1.15455616e+00 -4.78899717e-01
2.60014236e-02 -4.35703129e-01 5.86567521e-01 3.10909003e-01
3.47763330e-01 -1.91263303e-01 1.19559310e-01 3.30191404e-01
1.04345536e+00 8.30017924e-01 3.74673724e-01 1.34193599e-01
4.58072901e-01 7.89849639e-01 3.99709225e-01 8.75430331e-02
4.00997102e-01 6.39013231e-01 -2.95418948e-01 4.49764766e-02
-2.09679365e-01 8.04064199e-02 9.12301123e-01 2.06690955e+00
1.95713893e-01 -1.37622118e-01 -1.24388516e+00 1.08067584e+00
-1.15078056e+00 -4.10832494e-01 -2.12159380e-01 2.15900731e+00
1.06318343e+00 7.56798312e-02 7.19281435e-02 3.39675725e-01
6.09128952e-01 2.94528548e-02 3.34314853e-02 -1.06198692e+00
-7.87531286e-02 5.85063517e-01 3.34041148e-01 8.70524287e-01
-8.75939906e-01 1.31249678e+00 5.61628962e+00 1.05936754e+00
-1.33781052e+00 3.82896572e-01 3.95830274e-01 8.45506415e-02
-7.57994205e-02 1.26981914e-01 -8.73243570e-01 2.74336547e-01
2.00640798e+00 3.27652037e-01 7.18277931e-01 5.44808745e-01
3.51541907e-01 -1.20353520e-01 -8.13090086e-01 6.70977831e-01
9.23000351e-02 -6.95612133e-01 -3.31626028e-01 -3.79668139e-02
5.83569288e-01 7.48313248e-01 -5.05118594e-02 4.52434838e-01
2.96072185e-01 -9.11391139e-01 9.59098399e-01 -2.63328642e-01
1.14721441e+00 -1.01807106e+00 7.48535573e-01 3.38874131e-01
-1.15680563e+00 1.14422865e-01 -4.07953501e-01 6.25052303e-03
1.74647987e-01 2.45558277e-01 -1.16555822e+00 3.79353285e-01
6.20122612e-01 3.11096191e-01 -5.59260905e-01 7.90339768e-01
1.20825479e-02 1.49541402e+00 -4.21881020e-01 -1.68307442e-02
5.14489353e-01 1.09961748e-01 5.62404215e-01 1.79380155e+00
6.02576196e-01 -1.54980883e-01 4.50847298e-02 1.53440863e-01
2.98943251e-01 6.63016140e-01 -5.28988302e-01 -5.80319583e-01
5.34027398e-01 8.36743534e-01 -6.73010051e-01 -1.17106654e-01
-7.46510983e-01 8.35033834e-01 2.71613032e-01 2.56505609e-01
-1.14793487e-01 -7.32399166e-01 6.75521016e-01 1.61635466e-02
3.96375567e-01 -5.77565134e-01 2.86242932e-01 -8.78079951e-01
-2.76008129e-01 -1.34461308e+00 1.26303405e-01 -4.02441710e-01
-1.05340958e+00 1.07334495e+00 4.61548829e-04 -5.20402253e-01
-5.43169260e-01 -8.50105405e-01 -2.50944048e-01 1.52039039e+00
-1.51912737e+00 -1.25519538e+00 4.95281786e-01 4.36445981e-01
1.08827353e+00 -5.68136096e-01 1.03828144e+00 5.61332762e-01
-3.58248085e-01 4.61337566e-01 3.00075740e-01 1.46436185e-01
5.78681231e-01 -1.17848754e+00 7.17403471e-01 9.23398972e-01
4.68317837e-01 5.35910964e-01 5.57814717e-01 -6.04654789e-01
-9.62925911e-01 -7.98020065e-01 1.31952500e+00 -2.14938983e-01
8.31163645e-01 -7.96398282e-01 -1.07750833e+00 6.43260300e-01
4.38701719e-01 -3.56738210e-01 7.00255632e-01 4.81588803e-02
-8.98223966e-02 3.33419442e-02 -8.00581694e-01 2.44476616e-01
5.94860733e-01 -9.65353906e-01 -6.58549845e-01 6.50328696e-02
8.78425598e-01 -1.21162772e-01 -7.47536659e-01 1.36425123e-01
4.02622938e-01 -5.45088172e-01 5.89569747e-01 -3.53963286e-01
-1.09588601e-01 -3.47973257e-02 -7.10959494e-01 -1.68115675e+00
9.11340192e-02 -5.72559714e-01 7.58309543e-01 1.53416586e+00
7.35304177e-01 -6.98836982e-01 8.70719850e-02 1.89354755e-02
-6.41991377e-01 -6.61206543e-02 -1.61299157e+00 -8.19220424e-01
4.79189247e-01 -8.41436088e-01 3.63297731e-01 8.58204246e-01
7.74869472e-02 2.59169787e-01 -2.65211731e-01 1.39446646e-01
-8.26966092e-02 -6.27260447e-01 3.61271679e-01 -1.01806462e+00
-3.64017934e-01 -1.37049541e-01 -4.18402135e-01 -6.01760328e-01
3.65663737e-01 -1.10236073e+00 5.06997347e-01 -1.13018727e+00
-3.43649209e-01 -8.98062110e-01 -2.16657758e-01 5.01228154e-01
4.63469885e-03 -1.35848466e-02 2.23512530e-01 1.47159826e-02
1.01186417e-01 2.82279789e-01 6.72222972e-01 1.72567993e-01
-2.32691363e-01 1.11783750e-01 -1.23773284e-01 3.83512199e-01
6.66429102e-01 -7.24309683e-01 -3.53043079e-01 -5.45518935e-01
-2.15468302e-01 2.22138599e-01 -3.54238719e-01 -9.91225898e-01
-1.86391491e-02 -7.24492222e-02 -9.52702016e-02 -3.75908971e-01
3.07583898e-01 -6.20468795e-01 4.26336285e-03 5.20044625e-01
-2.13208392e-01 2.82010406e-01 7.02423096e-01 -2.16725990e-02
-3.95746678e-01 -6.65558338e-01 8.06037903e-01 -1.87946752e-01
-6.64333940e-01 -3.15155983e-01 -1.14623320e+00 1.45945996e-01
5.65340579e-01 2.99078338e-02 1.00389339e-01 -2.64126539e-01
-4.76631939e-01 -2.45411426e-01 2.32374176e-01 7.27800608e-01
3.52258474e-01 -9.35017586e-01 -9.48567629e-01 8.38693917e-01
1.10110261e-01 -3.97649139e-01 -4.74335551e-02 7.69941211e-01
-7.47476876e-01 1.04388821e+00 -1.98190317e-01 -4.15054917e-01
-1.21920502e+00 1.64436266e-01 1.64203942e-01 2.12676764e-01
-9.31268558e-02 1.02871132e+00 -1.01756327e-01 -1.07304180e+00
7.23275244e-02 -2.13926837e-01 -1.03548676e-01 -7.61231631e-02
1.69261307e-01 2.68089205e-01 5.90356767e-01 -1.47853386e+00
-6.02807045e-01 -2.71185916e-02 -2.04357043e-01 -7.78676033e-01
1.20664561e+00 -3.20879102e-01 -1.21211987e-02 8.34421933e-01
1.19240129e+00 6.69409394e-01 -7.41047859e-01 7.19589218e-02
4.36652809e-01 -2.72004783e-01 2.00922489e-01 -8.16909134e-01
-6.02362156e-01 1.17877066e+00 6.06834531e-01 1.47099011e-02
8.69115770e-01 1.03975658e-03 5.22261560e-01 9.33956057e-02
4.34246987e-01 -1.32618582e+00 -5.15226960e-01 1.06858218e+00
7.22554564e-01 -1.13647330e+00 -6.62803590e-01 -1.25311881e-01
-7.28318930e-01 1.01536548e+00 1.74453884e-01 3.08600515e-01
8.17461312e-01 3.43439758e-01 5.24993062e-01 1.45409644e-01
-7.55726516e-01 -4.04144824e-01 3.63497585e-01 6.55055225e-01
8.82101238e-01 3.21432680e-01 -4.08880800e-01 2.35880524e-01
-5.09354115e-01 -6.26389265e-01 5.87294519e-01 1.02353668e+00
-4.77769107e-01 -1.36586487e+00 -4.53813612e-01 5.98171651e-01
-9.79510546e-01 -5.79715312e-01 -2.88525850e-01 8.13173831e-01
3.27178910e-02 1.30428362e+00 -3.41813192e-02 -2.79434025e-01
1.86848804e-01 5.67184865e-01 5.53544909e-02 -9.90702629e-01
-5.91004550e-01 6.79017067e-01 4.51491863e-01 -2.44444571e-02
-4.60902512e-01 -1.07027352e+00 -1.13482785e+00 1.23001918e-01
-7.05654979e-01 3.84290159e-01 1.25961161e+00 1.03568542e+00
-8.03949758e-02 3.18851054e-01 4.35968310e-01 -6.52981400e-01
-3.57746333e-01 -1.36563051e+00 -5.30824125e-01 -1.30795538e-01
4.21996742e-01 -3.62123311e-01 -4.72285509e-01 5.14212660e-02] | [14.306580543518066, 6.958385944366455] |
1be8918e-0d4c-4531-980e-db8888b55c43 | understanding-neural-code-intelligence | 2106.03353 | null | https://arxiv.org/abs/2106.03353v2 | https://arxiv.org/pdf/2106.03353v2.pdf | Understanding Neural Code Intelligence Through Program Simplification | A wide range of code intelligence (CI) tools, powered by deep neural networks, have been developed recently to improve programming productivity and perform program analysis. To reliably use such tools, developers often need to reason about the behavior of the underlying models and the factors that affect them. This is especially challenging for tools backed by deep neural networks. Various methods have tried to reduce this opacity in the vein of "transparent/interpretable-AI". However, these approaches are often specific to a particular set of network architectures, even requiring access to the network's parameters. This makes them difficult to use for the average programmer, which hinders the reliable adoption of neural CI systems. In this paper, we propose a simple, model-agnostic approach to identify critical input features for models in CI systems, by drawing on software debugging research, specifically delta debugging. Our approach, SIVAND, uses simplification techniques that reduce the size of input programs of a CI model while preserving the predictions of the model. We show that this approach yields remarkably small outputs and is broadly applicable across many model architectures and problem domains. We find that the models in our experiments often rely heavily on just a few syntactic features in input programs. We believe that SIVAND's extracted features may help understand neural CI systems' predictions and learned behavior. | ['Mohammad Amin Alipour', 'Vincent J. Hellendoorn', 'Md Rafiqul Islam Rabin'] | 2021-06-07 | null | null | null | null | ['variable-misuse', 'method-name-prediction'] | ['computer-code', 'natural-language-processing'] | [-9.94901657e-02 -6.34745369e-03 -4.38608587e-01 -3.70254070e-01
2.96669770e-02 -5.65635800e-01 1.88839599e-01 1.25214398e-01
1.27388224e-01 2.02613801e-01 -8.75958428e-02 -8.11253667e-01
-6.00364320e-02 -7.11440384e-01 -8.60562444e-01 -1.23733461e-01
-1.78097069e-01 5.01589430e-03 2.24507973e-01 -4.35815990e-01
4.98863995e-01 4.02678967e-01 -1.52037454e+00 5.28763831e-01
8.63619685e-01 6.98375106e-01 8.67825523e-02 9.62476850e-01
-2.14214459e-01 1.51818752e+00 -7.77110994e-01 -2.66196787e-01
-1.11247050e-02 -2.22410426e-01 -8.27827752e-01 -6.92919433e-01
1.19135417e-01 -2.64613837e-01 -1.27555775e-02 1.09344530e+00
-7.49884993e-02 -3.36953312e-01 3.88287634e-01 -1.35691726e+00
-5.80578208e-01 9.68516588e-01 -5.81360579e-01 2.23697528e-01
-4.16087173e-02 2.18737707e-01 8.12983334e-01 -4.29097176e-01
3.25503379e-01 1.04894650e+00 1.06187224e+00 6.88694537e-01
-1.36463284e+00 -5.46619773e-01 1.05565771e-01 1.44241489e-02
-1.14751601e+00 -4.82802480e-01 6.60419703e-01 -9.03962433e-01
1.48045421e+00 2.97731072e-01 3.95725131e-01 9.69671845e-01
4.95285898e-01 4.45953667e-01 5.48507333e-01 -6.07657969e-01
2.96519399e-01 2.96973318e-01 5.70145071e-01 8.05210471e-01
3.83845687e-01 2.30063334e-01 -2.83732325e-01 -4.07356054e-01
6.24491930e-01 -2.68653333e-02 -1.22735798e-01 -2.68332422e-01
-9.20125127e-01 6.83027208e-01 3.55217695e-01 6.43446326e-01
1.54610313e-02 7.06155419e-01 7.10417092e-01 4.39953446e-01
2.73940474e-01 8.99159551e-01 -8.08780134e-01 -6.45166159e-01
-7.73347795e-01 2.25095406e-01 1.16327620e+00 9.90383983e-01
7.52857625e-01 3.13724399e-01 1.91661522e-01 6.62045777e-01
1.38362318e-01 -4.63663265e-02 5.33317268e-01 -1.20138156e+00
2.83556134e-01 9.30058658e-01 -8.53158385e-02 -1.25345016e+00
-3.54356766e-01 -4.46342707e-01 -6.30907059e-01 5.41298270e-01
3.04782778e-01 -1.69337988e-01 -5.71470559e-01 1.72245681e+00
-2.27663890e-01 -1.63214117e-01 -1.23450257e-01 4.37723875e-01
1.51170984e-01 4.72955108e-01 -6.62781894e-02 3.81519616e-01
7.56943524e-01 -9.39499974e-01 -1.74082145e-01 -5.79210699e-01
1.09286439e+00 -3.77783477e-01 1.28246272e+00 5.97094536e-01
-1.10144126e+00 -4.86867666e-01 -1.27838254e+00 4.50110957e-02
-2.77518958e-01 1.26325801e-01 9.76632059e-01 6.26893938e-01
-1.40733409e+00 1.06044960e+00 -1.15440214e+00 -8.71730447e-02
3.68006617e-01 6.33974731e-01 2.40917667e-03 1.82781979e-01
-4.80975658e-01 1.05770612e+00 3.82168859e-01 6.28316477e-02
-8.12890470e-01 -7.69323349e-01 -7.67864048e-01 5.60480654e-01
2.50697404e-01 -5.73744774e-01 1.78607666e+00 -1.61711025e+00
-1.33322227e+00 3.58145207e-01 -1.35806471e-01 -3.56399775e-01
1.44678146e-01 -2.44991198e-01 -4.55245525e-02 -4.17581081e-01
-2.63840884e-01 2.21126825e-01 7.86781192e-01 -1.27492917e+00
-3.64287525e-01 -1.90119162e-01 3.63399267e-01 -5.90609550e-01
-4.60964501e-01 4.03175026e-01 -2.07435131e-01 -3.24499041e-01
-4.10924822e-01 -8.58538270e-01 -2.43367434e-01 9.49484184e-02
-3.39987934e-01 8.60117842e-03 6.79121375e-01 -6.19661689e-01
1.36032712e+00 -2.27917480e+00 1.76314160e-01 2.44941860e-01
7.19018340e-01 3.87589246e-01 -2.86902208e-03 3.52708846e-01
-2.06654057e-01 5.33313096e-01 -1.13282479e-01 -1.39808536e-01
1.04543112e-01 2.14015692e-01 -3.89837712e-01 1.30760267e-01
3.26576412e-01 7.85523832e-01 -6.83381975e-01 -2.28023946e-01
-1.38308480e-01 1.25327617e-01 -8.91450107e-01 4.49160188e-01
-5.81993222e-01 -1.11191228e-01 -3.34239066e-01 6.11057699e-01
2.26414964e-01 -3.92965853e-01 2.11695626e-01 3.53816867e-01
-2.56993175e-01 3.63167465e-01 -5.40415168e-01 1.22876966e+00
-7.37173080e-01 1.01866436e+00 9.34544355e-02 -1.03425741e+00
8.29709113e-01 2.65658408e-01 -2.23880664e-01 -4.52114314e-01
9.74010229e-02 2.37897128e-01 5.71965516e-01 -6.80978894e-01
2.97571898e-01 1.09806031e-01 -2.54351571e-02 6.91051006e-01
-1.05322570e-01 9.81500670e-02 9.65146795e-02 -3.47883180e-02
1.47254729e+00 2.46628448e-01 4.46818799e-01 -3.27565491e-01
1.06087521e-01 2.58978814e-01 6.69445395e-01 1.07219958e+00
7.44971111e-02 3.39896262e-01 1.13048851e+00 -7.12981403e-01
-1.21830809e+00 -4.94272262e-01 2.32931972e-01 1.23817992e+00
-4.68509197e-01 -5.86513758e-01 -1.08793652e+00 -6.03208482e-01
-1.60973817e-01 8.73707652e-01 -7.99243152e-01 -6.13758683e-01
-7.59129465e-01 -3.18062961e-01 9.27879989e-01 9.91332889e-01
-3.46669257e-02 -1.02671933e+00 -8.76663446e-01 2.48809502e-01
5.23108006e-01 -3.78539681e-01 -6.00944050e-02 6.56634510e-01
-1.01532924e+00 -9.31575775e-01 -1.19611211e-01 -6.97634935e-01
6.60651743e-01 3.76221240e-02 1.42897868e+00 8.99834573e-01
-1.98739082e-01 -7.53669888e-02 -7.77480900e-02 -5.33049822e-01
-9.69351590e-01 1.85206369e-01 -1.98331684e-01 -6.40889883e-01
4.05929118e-01 -8.30112815e-01 -4.11728285e-02 4.91342619e-02
-7.34209776e-01 2.73772955e-01 6.16935790e-01 8.92628014e-01
-2.47997701e-01 -4.68114465e-02 3.06170553e-01 -1.27349555e+00
6.67509615e-01 -7.49627829e-01 -7.65607238e-01 2.64625341e-01
-7.73862839e-01 3.36285740e-01 1.16027153e+00 -5.95181823e-01
-8.06628525e-01 -2.01434508e-01 -1.55965030e-01 -4.41992939e-01
-2.41831303e-01 9.36530590e-01 -5.50460294e-02 -3.35473984e-01
1.06338048e+00 -1.32146373e-01 -8.31631571e-03 -4.70217675e-01
-7.74575993e-02 5.07487357e-01 5.48516095e-01 -9.72793281e-01
6.25092745e-01 -1.61711589e-01 -3.67574394e-01 -4.08689082e-01
-4.27771181e-01 1.20920934e-01 -4.10364300e-01 -1.06736422e-02
3.42804104e-01 -4.95325059e-01 -6.94645166e-01 4.46356028e-01
-1.46646369e+00 -7.51191020e-01 2.82379054e-02 -5.66698611e-02
-3.72182995e-01 7.76672587e-02 -8.53238583e-01 -6.04290366e-01
-2.02322036e-01 -1.47020197e+00 4.78847295e-01 2.31649011e-01
-7.47882724e-01 -9.74429011e-01 3.22438061e-01 -2.34313279e-01
9.88783360e-01 -1.60810463e-02 1.77643394e+00 -5.96637189e-01
-5.91693342e-01 -1.70942008e-01 -2.47007087e-01 6.60279214e-01
-6.62540644e-02 6.05700016e-01 -1.18560565e+00 6.98095039e-02
1.18936256e-01 -2.93704849e-02 3.29591334e-01 1.84292778e-01
1.66002011e+00 -5.52111208e-01 -3.54157656e-01 6.83607101e-01
1.35256982e+00 4.60756779e-01 4.72364008e-01 3.43640208e-01
7.92571127e-01 4.17619973e-01 -1.08447270e-02 2.51028180e-01
1.76375613e-01 4.48236674e-01 5.29049397e-01 -9.17556807e-02
3.57658237e-01 -1.33813247e-02 4.75818455e-01 8.32534015e-01
-1.52091324e-01 -4.60467562e-02 -1.50649142e+00 5.29322326e-01
-1.85349262e+00 -5.67885578e-01 -1.52691752e-02 1.91558373e+00
9.41158533e-01 3.97145569e-01 -8.12797472e-02 7.08222985e-02
3.11774045e-01 -2.68333018e-01 -5.13356805e-01 -9.30981576e-01
5.73625505e-01 3.58918279e-01 2.28558227e-01 2.95174927e-01
-5.60134232e-01 7.62435377e-01 6.64686441e+00 3.21183711e-01
-1.37273717e+00 3.27925682e-02 5.43806016e-01 2.65446126e-01
-4.52477634e-01 2.28330582e-01 -4.46312428e-01 2.66818911e-01
1.47725558e+00 -3.99103880e-01 6.41743302e-01 1.55647385e+00
5.02841473e-02 -1.63753361e-01 -1.80383921e+00 4.85890627e-01
-2.09378630e-01 -1.54281330e+00 -3.06870073e-01 -9.83478352e-02
4.15209800e-01 3.55306827e-02 5.25634848e-02 4.93574321e-01
4.76781964e-01 -1.43560636e+00 8.60694587e-01 4.67009336e-01
5.10024846e-01 -8.22466791e-01 7.95290828e-01 5.49020052e-01
-6.51625574e-01 -3.24389368e-01 -3.34436953e-01 -7.07061887e-01
-7.06106603e-01 3.63274932e-01 -9.77108836e-01 -2.04713538e-01
8.98229837e-01 6.29094541e-01 -9.07697320e-01 9.26277339e-01
-2.58259028e-02 8.99858117e-01 -5.18383011e-02 -2.57531047e-01
-2.31517479e-02 2.44583815e-01 3.23952064e-02 1.28753400e+00
2.82325745e-01 -2.97357827e-01 -2.39867270e-01 1.46928132e+00
7.04699978e-02 -2.88024813e-01 -9.48768675e-01 -4.34361607e-01
3.78274173e-01 9.68731344e-01 -5.17748535e-01 -2.70000458e-01
-5.58084607e-01 4.46147382e-01 6.42608047e-01 2.89309889e-01
-6.92762375e-01 -4.43445385e-01 8.69157314e-01 6.60799965e-02
3.89351621e-02 -4.03291106e-01 -8.29200387e-01 -1.00888276e+00
1.94963455e-01 -1.38063180e+00 -2.25659415e-01 -7.24228740e-01
-8.45849037e-01 6.49732530e-01 -1.03080440e-02 -7.86106646e-01
-6.70767963e-01 -7.17519403e-01 -1.08096969e+00 8.86253595e-01
-1.08875024e+00 -7.24329472e-01 -2.23112926e-01 1.34811640e-01
3.21719259e-01 -1.47139058e-01 8.99617910e-01 1.65543675e-01
-7.13116109e-01 6.90295458e-01 2.58028507e-02 6.73061460e-02
2.43324026e-01 -1.34061217e+00 8.26227784e-01 9.85167980e-01
-1.09634139e-01 1.31805789e+00 7.43255258e-01 -3.97428393e-01
-1.58453000e+00 -8.89026225e-01 7.82141924e-01 -4.66431618e-01
8.38052213e-01 -4.67716306e-01 -1.26308060e+00 1.07228494e+00
1.97655246e-01 -1.26044393e-01 5.18468440e-01 4.38520551e-01
-6.16843045e-01 -1.58485901e-02 -7.32796371e-01 6.23579621e-01
7.48754025e-01 -6.67614460e-01 -4.22176152e-01 7.86925405e-02
7.78078496e-01 -3.09628457e-01 -8.27771544e-01 6.08663298e-02
4.80397701e-01 -1.26912153e+00 6.88737571e-01 -8.61290932e-01
9.73111153e-01 -2.88297951e-01 4.09240201e-02 -1.29360366e+00
-3.09751332e-01 -4.52885836e-01 -3.59151423e-01 1.07130802e+00
6.39899433e-01 -5.77549100e-01 6.12142563e-01 9.76067305e-01
-4.30834174e-01 -6.79141402e-01 -3.06570619e-01 -4.90854174e-01
2.89192468e-01 -7.09922254e-01 7.53591299e-01 9.87925172e-01
1.97355226e-01 1.21543467e-01 -9.77438837e-02 1.52174547e-01
4.87516411e-02 1.97109953e-02 7.74085283e-01 -1.25539196e+00
-9.11595643e-01 -6.49327219e-01 -2.94236958e-01 -5.75383604e-01
4.06685829e-01 -6.66417539e-01 1.85035259e-01 -8.39197934e-01
2.62680113e-01 -6.84886336e-01 -1.16793752e-01 9.24490511e-01
6.56745061e-02 -3.47173482e-01 9.47802588e-02 2.53335088e-01
-1.31128281e-01 -3.65456156e-02 4.43240881e-01 -1.13262482e-01
-8.83217305e-02 5.57450727e-02 -9.08556998e-01 1.07358444e+00
9.17927504e-01 -7.40111053e-01 -3.51948857e-01 -8.86500895e-01
7.72735298e-01 1.02747753e-01 4.02677864e-01 -1.20914793e+00
3.06239665e-01 -3.39665145e-01 2.56508589e-01 6.25834167e-02
-1.48385927e-01 -6.62246525e-01 3.30720842e-01 5.34224689e-01
-6.36482716e-01 4.03988868e-01 4.49819475e-01 9.69423726e-02
-2.18505651e-01 -6.74986005e-01 6.74876928e-01 -3.12706411e-01
-7.70518064e-01 -1.34515092e-01 -5.87631524e-01 -3.80075127e-02
7.89788365e-01 5.43789417e-02 -5.82612872e-01 -1.59650549e-01
-3.03182334e-01 -8.09340850e-02 1.02399802e+00 4.00891513e-01
3.62896711e-01 -7.08509445e-01 -1.21676877e-01 3.29777569e-01
1.55064523e-01 -7.91027956e-03 -1.57821417e-01 6.90146148e-01
-8.16295445e-01 2.38852903e-01 -3.42546016e-01 -5.21188021e-01
-1.06708705e+00 7.33552933e-01 6.76207364e-01 -6.94798604e-02
-5.56282043e-01 7.44533181e-01 3.27135295e-01 -4.73244578e-01
1.39578372e-01 -9.28724647e-01 1.44837052e-01 -5.79236448e-01
6.80600762e-01 -6.78668469e-02 2.29331017e-01 1.62273422e-01
-1.78024039e-01 4.94583771e-02 -3.80801588e-01 3.03736478e-01
1.59468555e+00 4.42116559e-01 -5.78442931e-01 7.82968521e-01
1.02415645e+00 -1.27414525e-01 -1.13439929e+00 1.04603276e-01
4.89854932e-01 -3.11033726e-01 7.01964796e-02 -8.62487793e-01
-1.03126180e+00 1.27839541e+00 1.59803793e-01 3.79799455e-01
1.03142381e+00 -1.75192714e-01 3.70581985e-01 6.70897067e-01
3.79995108e-01 -7.16063619e-01 -2.17143372e-01 5.83734214e-01
6.16813123e-01 -9.56181765e-01 -3.34629834e-01 -1.00109488e-01
-2.32395142e-01 1.53586113e+00 9.98445332e-01 -1.49370641e-01
2.63450742e-01 9.73371625e-01 -9.20989588e-02 -1.79640174e-01
-1.21594787e+00 5.78114867e-01 -8.88750479e-02 6.91359162e-01
6.56603098e-01 -7.14156777e-02 3.25405747e-01 7.69550800e-01
-1.26851484e-01 3.54643524e-01 9.44121182e-01 1.31628597e+00
-3.17916423e-01 -1.13180661e+00 -2.94064492e-01 6.19455695e-01
-4.77472156e-01 -3.64501834e-01 -5.48410177e-01 9.90065098e-01
6.15312485e-03 4.98040944e-01 -1.62011057e-01 -6.99342906e-01
3.39982510e-02 9.67028067e-02 3.74481738e-01 -8.30047190e-01
-1.08194029e+00 -4.34899360e-01 2.63812155e-01 -7.18871653e-01
2.03020588e-01 -4.10291404e-01 -1.03836870e+00 -6.53695107e-01
-6.50309175e-02 -8.10049474e-02 7.10270166e-01 1.02483761e+00
5.80235064e-01 5.57928681e-01 2.81535357e-01 -8.30891788e-01
-6.51464164e-01 -7.38433957e-01 -3.07950526e-01 -1.72143787e-01
6.08331859e-01 -4.17817414e-01 -2.01076567e-01 1.76760405e-01] | [7.634208679199219, 7.693853378295898] |
af0d9eb0-81a0-49d7-91cd-014c006cc4c9 | sparse-video-representation-using-steered | 2209.05993 | null | https://arxiv.org/abs/2209.05993v1 | https://arxiv.org/pdf/2209.05993v1.pdf | Sparse Video Representation Using Steered Mixture-of-Experts With Global Motion Compensation | Steered-Mixtures-of Experts (SMoE) present a unified framework for sparse representation and compression of image data with arbitrary dimensionality. Recent work has shown great improvements in the performance of such models for image and light-field representation. However, for the case of videos the straight-forward application yields limited success as the SMoE framework leads to a piece-wise linear representation of the underlying imagery which is disrupted by nonlinear motion. We incorporate a global motion model into the SMoE framework which allows for higher temporal steering of the kernels. This drastically increases its capabilities to exploit correlations between adjacent frames by only adding 2 to 8 motion parameters per frame to the model but decreasing the required amount of kernels on average by 54.25%, respectively, while maintaining the same reconstruction quality yielding higher compression gains. | ['Thomas Sikora', 'Erik Bochinski', 'Rolf Jongebloed'] | 2022-09-13 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 2.58927196e-01 -1.65006742e-01 4.94937040e-02 -5.31733967e-02
-5.39211988e-01 -2.22062051e-01 5.97858787e-01 -3.61013800e-01
-3.77012372e-01 4.49363530e-01 3.83185297e-01 -4.95885946e-02
-1.48401961e-01 -4.92282182e-01 -5.22464633e-01 -9.43905175e-01
-1.00403979e-01 -1.37651011e-01 5.24729371e-01 4.53741141e-02
2.21858859e-01 5.01378894e-01 -1.55645132e+00 3.86544079e-01
4.69299406e-01 7.94001818e-01 5.16662240e-01 9.19103384e-01
1.34085819e-01 9.82261419e-01 9.44533125e-02 -1.72405675e-01
5.18269002e-01 -1.59505248e-01 -4.99201179e-01 5.31930506e-01
6.39272630e-01 -5.64033687e-01 -9.88328338e-01 7.40282655e-01
2.73024291e-01 3.26850116e-01 6.53954148e-01 -7.57272184e-01
-3.13652575e-01 -4.99822237e-02 -8.30922306e-01 2.15237677e-01
2.27000222e-01 6.00030497e-02 5.53140581e-01 -8.18739414e-01
6.02617919e-01 1.11678743e+00 4.98449862e-01 2.77121753e-01
-1.38914001e+00 -2.06909180e-01 -1.19560018e-01 4.12170351e-01
-1.43796480e+00 -8.78192425e-01 5.80945313e-01 -2.31441572e-01
1.06929088e+00 3.13847512e-01 4.47163075e-01 4.95876133e-01
2.77713954e-01 5.37575603e-01 7.67209589e-01 -4.79963511e-01
1.68071777e-01 6.94205835e-02 -1.03730008e-01 6.27377927e-01
2.84082979e-01 -5.16662970e-02 -6.60594583e-01 -9.15278420e-02
1.09700632e+00 4.19262126e-02 -3.47272545e-01 -6.77934170e-01
-1.16629839e+00 7.72093654e-01 2.11171836e-01 2.56163508e-01
-5.10362148e-01 2.05460951e-01 2.74687350e-01 8.15979317e-02
4.55402911e-01 1.10583030e-01 -1.58779174e-02 -1.36049584e-01
-1.28977847e+00 3.23123246e-01 5.74314594e-01 8.00448239e-01
7.98425734e-01 3.21488678e-01 -1.34632945e-01 8.18148315e-01
2.35373899e-01 5.03356040e-01 2.40081877e-01 -1.48022068e+00
2.62315989e-01 2.95758873e-01 3.37457418e-01 -1.31391168e+00
-1.64202362e-01 -5.35626888e-01 -9.35424328e-01 2.02811763e-01
2.57575423e-01 1.70992464e-01 -9.55085635e-01 1.37785411e+00
2.45848849e-01 5.77098012e-01 4.82306331e-02 8.75492334e-01
3.52393538e-01 8.45381856e-01 -1.86249256e-01 -2.56237596e-01
1.17438877e+00 -9.03867900e-01 -6.23121381e-01 -2.40315616e-01
4.96133983e-01 -1.06744099e+00 4.98644114e-01 5.63055634e-01
-1.21545112e+00 -4.58302468e-01 -9.65783060e-01 -1.02257438e-01
1.48169592e-01 2.77263820e-01 6.66741252e-01 5.44867456e-01
-1.19190168e+00 4.38605040e-01 -9.64035690e-01 -2.92507857e-01
3.93611044e-01 3.97753507e-01 -4.69148815e-01 -6.11217260e-01
-5.24982333e-01 8.67520332e-01 1.59350470e-01 -3.24153841e-01
-8.11368585e-01 -7.68538892e-01 -8.97420585e-01 1.25528201e-01
1.79707199e-01 -7.27431297e-01 7.51811385e-01 -5.38387775e-01
-1.52788544e+00 5.57485819e-01 -6.00003541e-01 -6.72171950e-01
2.81314731e-01 -2.49116406e-01 -2.91698277e-01 5.33550084e-01
-2.57440954e-01 8.03477883e-01 1.15453386e+00 -1.11934412e+00
-3.00859392e-01 3.01632192e-02 6.25975206e-02 1.91058695e-01
-3.79799753e-01 -5.43495007e-02 -6.66091084e-01 -7.93826938e-01
1.57098666e-01 -1.14578319e+00 -3.99989337e-01 1.74088001e-01
1.13234028e-01 3.90728831e-01 9.88548160e-01 -8.03620815e-01
1.35715449e+00 -2.48913813e+00 1.74749002e-01 -1.02486417e-01
1.96567535e-01 3.94053072e-01 -2.36832306e-01 4.84159708e-01
-7.35574663e-02 -5.42458117e-01 -3.58398795e-01 -4.96956497e-01
-4.37538773e-01 4.05520320e-01 -2.99354732e-01 6.84729278e-01
3.12581509e-02 5.66478670e-01 -6.58786654e-01 -2.20510498e-01
6.36261463e-01 8.84249687e-01 -9.38562632e-01 1.55245885e-01
1.86871782e-01 2.62478948e-01 -9.85998884e-02 1.85344771e-01
9.53844607e-01 -3.37561518e-01 4.19016704e-02 -4.18448299e-01
-7.19543770e-02 4.18462679e-02 -1.54894531e+00 1.86605418e+00
-5.48085809e-01 7.58627295e-01 3.19558203e-01 -9.61375356e-01
5.70928752e-01 3.03119421e-01 7.50010431e-01 -2.26264715e-01
-1.33741841e-01 2.71717887e-02 -5.65985925e-02 -2.96830326e-01
6.34015083e-01 -2.63650030e-01 4.37642872e-01 2.31883124e-01
2.08453298e-01 -9.37319547e-02 1.82315663e-01 3.38889092e-01
1.00701988e+00 1.44522473e-01 1.33091480e-01 -3.35108459e-01
5.40946722e-01 -1.34223282e-01 3.02305847e-01 5.28507352e-01
8.89117718e-02 8.52270007e-01 -6.87927306e-02 -4.65567678e-01
-1.25546563e+00 -8.52340221e-01 -2.13371307e-01 6.69737995e-01
1.20064780e-01 -6.54669583e-01 -5.37699521e-01 -1.32013932e-01
-1.85538888e-01 6.59219861e-01 -2.51906633e-01 -6.35301545e-02
-5.07916451e-01 -8.57941329e-01 1.24707237e-01 2.88180143e-01
4.39657420e-01 -5.07006645e-01 -8.30386698e-01 2.01387107e-01
-1.00021519e-01 -1.53205085e+00 -3.87721956e-01 -3.25611718e-02
-1.02766490e+00 -8.29024255e-01 -8.87052178e-01 -3.46195161e-01
6.21522486e-01 8.02831113e-01 7.59158373e-01 -1.72395334e-01
-4.98193353e-01 4.87365723e-01 -2.29713589e-01 1.02979556e-01
-2.01602444e-01 -4.91048694e-01 -3.59981391e-03 2.38116726e-01
5.92481717e-02 -8.00790906e-01 -8.44971538e-01 2.38670543e-01
-1.23016834e+00 1.91959336e-01 5.02980292e-01 7.77254581e-01
2.94740140e-01 2.18083203e-01 -8.52393806e-02 -5.49779832e-01
-2.03980189e-02 -3.93905371e-01 -4.84866083e-01 -1.69309482e-01
-6.00163519e-01 1.36352271e-01 5.22082508e-01 -3.78483504e-01
-1.19491792e+00 2.32308835e-01 8.11788887e-02 -7.82627821e-01
-6.00467250e-02 9.30385813e-02 3.85704368e-01 -6.92464530e-01
4.92507994e-01 5.39537728e-01 2.23139554e-01 -5.93128741e-01
5.88370025e-01 3.41229558e-01 6.62987769e-01 -3.04754138e-01
9.39938545e-01 8.33886027e-01 3.49917024e-01 -1.21619689e+00
-4.80792314e-01 -7.66770899e-01 -5.82321286e-01 3.62021700e-02
6.90773368e-01 -1.13079810e+00 -3.13859016e-01 3.04505825e-01
-9.75312710e-01 4.34887130e-03 -3.73859227e-01 7.54215240e-01
-7.22480178e-01 9.25666213e-01 -7.85600185e-01 -6.75882399e-01
-7.08360374e-02 -1.15888500e+00 9.93483841e-01 -3.83931771e-02
-4.47077826e-02 -7.73421466e-01 -1.01169512e-01 3.16937596e-01
7.23698676e-01 -9.24423859e-02 8.19212854e-01 1.05568610e-01
-8.93833339e-01 -2.01545358e-01 -2.63372511e-01 5.15919745e-01
-8.59488617e-04 -2.24661484e-01 -9.39695954e-01 -4.07748014e-01
2.22855285e-01 -1.54243223e-02 1.05494714e+00 6.45306408e-01
8.14280450e-01 -2.87105381e-01 -1.51455283e-01 9.11722898e-01
1.70973480e+00 5.82425203e-03 9.32436526e-01 1.81402817e-01
5.73944867e-01 3.83754611e-01 2.33224258e-01 7.65263915e-01
1.21076711e-01 9.31602120e-01 3.29520404e-01 -5.17935082e-02
-4.64602262e-01 3.62214670e-02 4.29174542e-01 8.38109553e-01
-2.17994615e-01 -4.81532328e-02 -4.56983566e-01 6.34063780e-01
-1.77391815e+00 -1.12001109e+00 -1.79458186e-01 2.39103794e+00
3.74348938e-01 -1.92242280e-01 -9.84340236e-02 7.56595135e-02
3.34016889e-01 4.84609574e-01 -2.53333747e-01 -1.34532377e-01
-5.20767383e-02 1.33301705e-01 7.15952694e-01 6.38753176e-01
-9.89055932e-01 6.85733080e-01 7.47124434e+00 1.06652582e+00
-9.91108775e-01 2.23424789e-02 2.84513861e-01 -3.35597724e-01
-1.52530789e-01 3.00717950e-01 -7.02037692e-01 3.60825449e-01
8.84253085e-01 -6.30489066e-02 6.44980669e-01 5.89628100e-01
3.43714803e-01 -4.12529558e-01 -5.94983876e-01 1.29078925e+00
7.25412890e-02 -1.46083462e+00 1.80513367e-01 2.24277005e-01
8.17739546e-01 4.38002385e-02 2.13721916e-02 -2.17910811e-01
-6.17954433e-02 -7.61816859e-01 3.72824192e-01 6.48994505e-01
7.12572575e-01 -7.11524606e-01 5.06812572e-01 3.88814688e-01
-9.96428311e-01 -3.44798863e-02 -7.42542744e-01 -1.70408159e-01
4.17066336e-01 8.47475052e-01 -5.55205524e-01 5.86711347e-01
4.99014258e-01 6.75316095e-01 -4.01336282e-01 1.02709341e+00
3.65014344e-01 5.87075353e-01 -6.01638436e-01 7.15324640e-01
3.60046774e-01 -4.28094417e-01 8.79040897e-01 1.29006100e+00
5.27234316e-01 1.43538222e-01 4.13757563e-02 4.47668761e-01
4.16166693e-01 -7.32049122e-02 -6.05706096e-01 2.23915383e-01
-2.27914155e-02 1.15975511e+00 -5.61853230e-01 -3.32167834e-01
-7.19150543e-01 1.25934136e+00 6.64162636e-02 5.01140714e-01
-5.04598200e-01 5.60479946e-02 8.39547098e-01 5.04529595e-01
7.88687825e-01 -5.73783994e-01 2.57172785e-03 -1.33795512e+00
-1.19631201e-01 -7.34363496e-01 1.07984468e-01 -7.52648413e-01
-8.28517258e-01 3.94684851e-01 2.56103307e-01 -1.29799867e+00
-4.39567566e-01 -5.98904431e-01 -3.98748934e-01 9.09571469e-01
-1.65016770e+00 -9.51131523e-01 -1.98367059e-01 7.47724354e-01
6.49480283e-01 -1.83601543e-01 9.20625210e-01 3.83735985e-01
-2.31008738e-01 2.76100069e-01 4.10905391e-01 -3.84201914e-01
6.82195127e-01 -7.52648234e-01 1.60043374e-01 1.03003478e+00
2.15491697e-01 6.25143111e-01 8.73863339e-01 -3.90158802e-01
-1.61591339e+00 -8.21207345e-01 6.58030093e-01 -1.39343277e-01
4.28483278e-01 -9.74577218e-02 -8.80264461e-01 4.19740438e-01
2.55694926e-01 3.80514503e-01 6.22089028e-01 -2.58359820e-01
-2.53762752e-01 -4.55102585e-02 -1.01245129e+00 4.37643200e-01
8.51639569e-01 -5.78546345e-01 -1.47434935e-01 2.96087474e-01
2.70115167e-01 -2.07274720e-01 -8.12127173e-01 3.54865044e-01
5.39472878e-01 -1.31381798e+00 1.22494161e+00 -1.78839162e-01
4.33249712e-01 -6.47680461e-01 -5.20176888e-01 -1.02722085e+00
-8.18546653e-01 -7.10103273e-01 -6.00217581e-01 7.03168571e-01
-1.55991092e-01 -3.29847962e-01 8.33938897e-01 5.30359566e-01
7.91161209e-02 -8.29471111e-01 -9.11573112e-01 -6.50307477e-01
-4.99897271e-01 -4.57165450e-01 8.68155155e-03 6.26936555e-01
-3.07558000e-01 1.21498130e-01 -8.05390537e-01 2.45790571e-01
8.40543687e-01 -1.54748908e-03 7.06599236e-01 -7.14947879e-01
-7.17097104e-01 -1.70938030e-01 -7.34385073e-01 -1.53980482e+00
-1.26449510e-01 -7.23105013e-01 -2.58530051e-01 -1.28689444e+00
3.24855298e-01 -1.45816758e-01 -1.10523745e-01 7.56479651e-02
-1.15616769e-01 5.42007267e-01 4.74824429e-01 2.43406728e-01
-3.54597330e-01 5.49632430e-01 1.02641034e+00 1.15493268e-01
1.25269331e-02 -7.57837743e-02 -4.58870441e-01 7.99093962e-01
4.27511513e-01 -2.51300126e-01 -6.05355203e-01 -6.20343626e-01
-1.40287459e-01 9.55328196e-02 4.26338047e-01 -1.16513777e+00
2.57452577e-01 6.09433502e-02 4.71784741e-01 -2.74020940e-01
6.86951935e-01 -9.83034670e-01 5.53820491e-01 2.94581741e-01
5.85212000e-02 -1.24559768e-01 2.32025996e-01 8.17385375e-01
-3.29127997e-01 -3.07110697e-01 1.07704628e+00 -1.33539855e-01
-7.96547055e-01 1.53624311e-01 -4.25694525e-01 -4.80180353e-01
9.26104128e-01 -4.27013844e-01 7.59774521e-02 -6.06615841e-01
-7.50999212e-01 -3.56151700e-01 6.82945073e-01 4.61856127e-02
5.62190890e-01 -1.13630021e+00 -7.77287126e-01 4.42795753e-01
-2.16503918e-01 -2.41141319e-01 6.59813464e-01 1.00055647e+00
-5.63006938e-01 6.65720582e-01 -1.76647037e-01 -6.29564583e-01
-1.21215427e+00 6.21249020e-01 1.15772024e-01 -3.32255661e-01
-9.43157911e-01 7.31935561e-01 4.62652206e-01 3.66344184e-01
-9.90418065e-03 -4.02594395e-02 -4.14960757e-02 -2.26942971e-01
7.37841964e-01 6.84954166e-01 1.06140757e-02 -8.94693792e-01
-3.42426807e-01 8.48335385e-01 -1.26125574e-01 -3.76382517e-03
1.41267145e+00 -2.87358165e-01 5.18729761e-02 4.44933027e-02
1.28729820e+00 2.08070815e-01 -1.62571287e+00 -1.39957279e-01
-4.27397579e-01 -8.36691558e-01 6.09072685e-01 -3.03197771e-01
-9.52201903e-01 7.77969480e-01 5.20584941e-01 -1.15380317e-01
1.29729688e+00 -2.13781223e-01 8.74550700e-01 1.43567488e-01
5.43361187e-01 -5.56178868e-01 -1.62628427e-01 3.81111056e-01
6.42169297e-01 -9.33506966e-01 4.60676104e-01 -7.30029821e-01
-5.68557978e-01 1.14491582e+00 -1.51916873e-02 -4.55912530e-01
7.89758146e-01 3.86607140e-01 -3.31644803e-01 -3.52324545e-02
-6.97072089e-01 -8.64105821e-02 5.28001130e-01 6.36283636e-01
2.38685653e-01 -1.24617852e-01 -2.92459577e-01 -6.34515285e-02
1.65895879e-01 1.29116371e-01 4.93602693e-01 7.43442059e-01
-4.47757751e-01 -1.04494560e+00 -3.90304565e-01 4.42618996e-01
-4.63078171e-01 -2.72313088e-01 5.19119561e-01 3.82661700e-01
-1.26204476e-01 8.46040726e-01 2.01157220e-02 -1.74355850e-01
7.39354491e-02 8.56961012e-02 6.92665219e-01 -4.78388071e-01
-5.72229996e-02 5.85360348e-01 -7.24158436e-02 -8.14774573e-01
-6.65968359e-01 -5.23843169e-01 -8.07376266e-01 -3.60198170e-01
-1.41248733e-01 -8.28536674e-02 4.69186723e-01 6.99407637e-01
7.88592935e-01 2.56061047e-01 5.48828721e-01 -1.19488168e+00
-5.20298541e-01 -8.08715045e-01 -8.24906528e-01 4.01484519e-01
3.97286326e-01 -7.37933874e-01 -3.54519546e-01 4.06590521e-01] | [11.351507186889648, -2.331185817718506] |
8f3f187f-b6e6-46c8-bb88-3561e8b06a2f | american-cultural-regions-mapped-through-the | 2208.07649 | null | https://arxiv.org/abs/2208.07649v2 | https://arxiv.org/pdf/2208.07649v2.pdf | American cultural regions mapped through the lexical analysis of social media | Cultural areas represent a useful concept that cross-fertilizes diverse fields in social sciences. Knowledge of how humans organize and relate their ideas and behavior within a society helps to understand their actions and attitudes towards different issues. However, the selection of common traits that shape a cultural area is somewhat arbitrary. What is needed is a method that can leverage the massive amounts of data coming online, especially through social media, to identify cultural regions without ad-hoc assumptions, biases or prejudices. This work takes a crucial step in this direction by introducing a method to infer cultural regions based on the automatic analysis of large datasets from microblogging posts. The approach presented here is based on the principle that cultural affiliation can be inferred from the topics that people discuss among themselves. Specifically, regional variations in written discourse are measured in American social media. From the frequency distributions of content words in geotagged Tweets, the regional hotspots of words' usage are found, and from there, principal components of regional variation are derived. Through a hierarchical clustering of the data in this lower-dimensional space, this method yields clear cultural areas and the topics of discussion that define them. It uncovers a manifest North-South separation, which is primarily influenced by the African American culture, and further contiguous (East-West) and non-contiguous divisions that provide a comprehensive picture of today's cultural areas in the US. | ['Jack Grieve', 'David Sanchez', 'Jose J. Ramasco', 'Bruno Gonçalves', 'Thomas Louf'] | 2022-08-16 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [-2.11635321e-01 -1.48055464e-01 -3.58969390e-01 -2.38566741e-01
-2.83228397e-01 -8.21800947e-01 1.10002053e+00 5.42390883e-01
-3.59109968e-01 3.85935545e-01 1.11369455e+00 -4.19515789e-01
-1.26151770e-01 -8.75350952e-01 -1.26639381e-01 -8.04666519e-01
8.95451233e-02 1.12419531e-01 2.33995374e-02 -4.60713238e-01
7.29337692e-01 8.29659477e-02 -1.34360409e+00 3.73610646e-01
8.69099617e-01 5.26359260e-01 4.56708148e-02 4.06433344e-01
-5.89772403e-01 5.59251428e-01 -4.21192676e-01 -2.61935055e-01
-1.15565173e-01 -6.62321270e-01 -6.57636523e-01 8.00478160e-02
2.03302145e-01 9.15098190e-02 2.39927202e-01 1.12193370e+00
-3.55415456e-02 -1.52233839e-01 7.51629233e-01 -6.20244503e-01
-6.41570032e-01 7.91605413e-01 -6.41803563e-01 3.24077159e-01
4.87814993e-01 -3.26396942e-01 1.04327607e+00 -5.06997108e-01
9.71245170e-01 1.39286602e+00 6.09407723e-01 -2.64071524e-01
-1.16377187e+00 -3.97371471e-01 3.48217815e-01 -3.82207423e-01
-1.61489010e+00 -9.57900956e-02 8.03220272e-01 -1.08007669e+00
2.88406730e-01 4.35961455e-01 9.71239507e-01 1.25864899e+00
1.48696259e-01 2.04222590e-01 1.46243405e+00 -4.81357157e-01
2.17582598e-01 5.86366534e-01 1.45159423e-01 4.29301977e-01
3.48498076e-01 -5.63686192e-01 -6.40761554e-01 -6.69013679e-01
2.67290652e-01 1.87776536e-01 -2.18866497e-01 -2.97632068e-01
-1.30754590e+00 1.09916043e+00 3.33630830e-01 9.59182620e-01
-3.39152873e-01 -6.03337526e-01 1.41331658e-01 1.68666154e-01
1.12212348e+00 4.78202760e-01 -1.98314697e-01 -1.94413990e-01
-1.04452860e+00 1.91150606e-01 9.07620370e-01 2.99901962e-01
9.33278263e-01 -4.14056391e-01 4.85049069e-01 9.53537524e-01
5.65139472e-01 5.62433302e-01 6.23025894e-01 -5.66419005e-01
2.35354885e-01 8.99498165e-01 4.92632687e-02 -1.71806812e+00
-4.84434932e-01 -2.43414819e-01 -3.77272189e-01 -2.21920654e-01
6.15771413e-01 -5.65557778e-01 -2.99260855e-01 1.62424433e+00
6.34310067e-01 -2.02548683e-01 -2.23151162e-01 7.75661051e-01
3.00440460e-01 6.60913408e-01 4.27463241e-02 -1.44391671e-01
1.63028026e+00 -1.10402657e-02 -5.66312969e-01 -5.31296320e-02
7.09241033e-01 -8.00108552e-01 8.87226999e-01 1.33603051e-01
-6.80144310e-01 -1.93861261e-01 -8.13472211e-01 1.32727697e-01
-9.59944665e-01 -4.45893168e-01 6.22413158e-01 8.49154592e-01
-1.16308403e+00 2.22301915e-01 -3.27458501e-01 -1.02098644e+00
1.78783312e-01 -1.95028350e-01 -1.58639207e-01 3.83739293e-01
-1.16295731e+00 5.93765080e-01 1.35375410e-01 -1.26105130e-01
1.25353888e-01 -5.48310876e-01 -6.89074636e-01 -4.19095635e-01
3.33110005e-01 -2.95861065e-01 5.44080079e-01 -1.43909240e+00
-1.15804458e+00 1.06527686e+00 -1.74431324e-01 -1.40887365e-01
4.46329892e-01 -7.48056471e-02 -7.95781851e-01 1.32294148e-01
4.35274750e-01 2.34652072e-01 5.04815578e-01 -1.35712039e+00
-9.03905988e-01 -6.15410507e-01 -2.78954774e-01 1.07054077e-02
-7.57126212e-01 3.96118492e-01 -2.24474728e-01 -8.14177692e-01
3.32543939e-01 -1.20210719e+00 -6.72791600e-02 -6.07757211e-01
-3.22562784e-01 -1.10046640e-01 4.95945543e-01 -7.25342572e-01
1.71267819e+00 -2.33979511e+00 1.79670990e-01 8.81595135e-01
5.28469980e-01 -3.45928699e-01 5.97590923e-01 7.93811619e-01
4.37497377e-01 5.48687041e-01 -5.27314320e-02 3.81977707e-01
8.13216716e-02 -1.05172485e-01 -4.16947126e-01 7.84189224e-01
-8.62676352e-02 4.11633104e-01 -1.05006337e+00 -4.86872345e-01
-1.04919270e-01 4.55346495e-01 -6.73649251e-01 -5.27518094e-01
1.49704330e-02 4.35803652e-01 -7.44877756e-01 4.46348190e-01
3.30855101e-01 -2.88036048e-01 7.25160122e-01 3.16318274e-01
-7.56428957e-01 3.31646711e-01 -7.47188747e-01 1.24785256e+00
-8.41872394e-02 1.23816681e+00 2.26362526e-01 -6.82356954e-01
9.82881844e-01 -4.09845896e-02 6.45623565e-01 -3.70970756e-01
4.76627052e-01 2.08501741e-01 1.62034169e-01 -5.10054708e-01
7.88956404e-01 -8.36477354e-02 -5.22225261e-01 8.00057590e-01
-4.73574311e-01 2.83782095e-01 5.13491854e-02 1.90194309e-01
5.75282335e-01 -1.01292551e-01 4.97409582e-01 -1.06364071e+00
3.50861430e-01 2.52673537e-01 4.41791624e-01 4.39619482e-01
-8.56766701e-02 3.07937205e-01 7.93798387e-01 -6.36231244e-01
-8.92701149e-01 -1.04763925e+00 -3.25979322e-01 1.20828164e+00
1.22269250e-01 -6.10368848e-01 -7.57666647e-01 -1.77912250e-01
1.42163530e-01 5.59684277e-01 -1.04191339e+00 3.05950195e-01
-3.72428238e-01 -1.01514399e+00 7.05706403e-02 -2.08904922e-01
3.29539210e-01 -7.14502454e-01 -4.85941947e-01 -1.66436613e-01
-2.69794643e-01 -8.22266698e-01 -8.42470154e-02 -3.91823798e-01
-6.31645739e-01 -1.00288486e+00 -7.20698595e-01 -5.40637493e-01
7.92989194e-01 4.46420789e-01 1.03958619e+00 -6.71945587e-02
5.85926920e-02 2.64152199e-01 -4.37387526e-01 -6.41895533e-01
-3.95065516e-01 3.29090446e-01 7.84897506e-02 4.96829808e-01
8.22895825e-01 -4.41514492e-01 -4.90056992e-01 3.16179246e-01
-7.07043529e-01 -8.53673294e-02 2.23467648e-01 7.32004344e-02
-1.21378258e-01 -1.38869226e-01 3.52530897e-01 -1.27690101e+00
6.98803902e-01 -1.36330462e+00 -3.29458177e-01 7.24499077e-02
-3.64953816e-01 -2.54405946e-01 2.17031583e-01 -2.85398483e-01
-1.03171444e+00 -5.56284964e-01 5.31824589e-01 3.85829449e-01
-2.38640413e-01 7.11331904e-01 2.48891234e-01 4.49049503e-01
6.28811538e-01 -8.30574036e-02 1.21138729e-01 -2.80466408e-01
2.46477410e-01 1.07443321e+00 -2.56904289e-02 -4.98275965e-01
6.48535371e-01 9.40335929e-01 -6.69265091e-01 -1.53570580e+00
-7.19535768e-01 -6.28397167e-01 -8.50425005e-01 -6.14017487e-01
1.18007100e+00 -9.38848674e-01 -5.96394181e-01 1.50735080e-01
-4.97800648e-01 -1.49607897e-01 2.22220272e-01 7.16544628e-01
5.36759943e-02 1.72162861e-01 -3.80333453e-01 -8.67356420e-01
3.56575906e-01 -6.58480942e-01 7.10957944e-01 7.53543526e-02
-1.19871044e+00 -1.55517995e+00 3.25511843e-01 5.29682875e-01
4.15728509e-01 5.46895564e-01 9.34270620e-01 -7.01395452e-01
-2.72979200e-01 1.59751117e-01 6.17788918e-02 -1.77834377e-01
5.25163591e-01 3.54331911e-01 -7.40796447e-01 -3.06472983e-02
-1.04146503e-01 1.34269595e-01 4.77421373e-01 4.16038811e-01
5.40619493e-01 -4.05704886e-01 -4.62489069e-01 1.83998555e-01
1.26442647e+00 1.86522841e-01 2.42725268e-01 6.70079827e-01
6.56804800e-01 1.17363071e+00 8.25604200e-02 4.75437969e-01
5.86593807e-01 2.86850572e-01 -1.81281134e-01 1.49868289e-03
5.62453330e-01 -8.16142410e-02 5.53045094e-01 1.03831971e+00
-2.35679328e-01 7.84964338e-02 -1.28165293e+00 7.11049497e-01
-1.58689690e+00 -1.06865132e+00 -3.67720962e-01 2.12202954e+00
6.24137282e-01 1.24836519e-01 4.84248370e-01 -1.88125476e-01
8.58605444e-01 4.60776836e-01 -2.11693700e-02 -5.30097663e-01
-2.62561768e-01 -2.92173088e-01 4.94067281e-01 7.01679766e-01
-8.55226278e-01 8.32603753e-01 6.76239109e+00 3.10967535e-01
-1.18248975e+00 -3.48590672e-01 8.81299436e-01 -1.59747362e-01
-5.58186054e-01 -1.86163351e-01 -6.97913170e-01 5.83891988e-01
1.03143525e+00 -1.84672117e-01 1.26282588e-01 4.96683240e-01
5.68410933e-01 -4.50231165e-01 -5.58324397e-01 5.95221162e-01
3.25451493e-01 -1.37256980e+00 -2.36893550e-01 5.38744390e-01
9.42065537e-01 -3.18828858e-02 2.08072573e-01 -1.16369300e-01
6.60996914e-01 -5.52759707e-01 8.17225993e-01 4.55293387e-01
2.39967078e-01 -6.15238547e-01 2.87514061e-01 1.07813776e-01
-7.35070407e-01 -5.90659976e-02 -1.28104249e-02 -5.12935638e-01
-1.86108649e-02 7.06675947e-01 -7.89553523e-01 1.37570441e-01
7.50161290e-01 8.01929712e-01 -5.66667497e-01 2.10743040e-01
1.54609680e-01 7.26230621e-01 -1.33185551e-01 -4.30591613e-01
5.86992681e-01 -9.67717469e-01 4.18770611e-01 1.23656023e+00
5.64993203e-01 -1.29034281e-01 -3.58228907e-02 6.93839014e-01
1.39524043e-01 4.12678957e-01 -9.33453023e-01 -3.01213741e-01
5.27526319e-01 1.27144206e+00 -1.26719093e+00 -2.36292258e-01
-6.62141323e-01 4.21124786e-01 2.31369436e-01 3.81275266e-01
-4.67860818e-01 6.99755782e-03 9.02090609e-01 4.56015229e-01
7.12201223e-02 -4.80814546e-01 -2.65171677e-01 -9.33708906e-01
-2.12198570e-01 -9.62910593e-01 3.62465352e-01 -4.06950414e-01
-1.31434071e+00 2.20766112e-01 -5.09322882e-02 -8.57969284e-01
-2.30051681e-01 -3.56045336e-01 -3.45558554e-01 7.86794841e-01
-1.00856638e+00 -7.64720201e-01 -1.15557224e-01 3.84500146e-01
1.34385973e-01 -1.98240533e-01 6.09430671e-01 3.47101944e-03
-5.34709692e-01 -3.92633267e-02 5.25541246e-01 3.72470707e-01
7.19081700e-01 -1.13343716e+00 1.03558838e-01 4.83047038e-01
1.76512137e-01 9.54057217e-01 7.03354955e-01 -8.52562547e-01
-1.04971206e+00 -6.48104250e-01 1.30770302e+00 -7.54124165e-01
1.51840627e+00 -6.98132336e-01 -5.66205919e-01 5.82489014e-01
4.96796429e-01 -7.78426111e-01 1.51497710e+00 7.20598042e-01
-3.55895609e-01 1.09797798e-01 -8.27938735e-01 1.15780568e+00
8.43439341e-01 -7.44974375e-01 -6.89901710e-01 3.59586090e-01
3.45444977e-01 3.28889847e-01 -9.47086036e-01 -4.66111988e-01
7.24287570e-01 -1.07239950e+00 6.45465553e-01 -5.16089916e-01
5.47536731e-01 3.92718278e-02 -2.18489140e-01 -1.43917084e+00
-4.65455681e-01 -8.29926431e-01 7.95158744e-01 1.40239465e+00
6.66341305e-01 -9.43426788e-01 6.09788179e-01 5.69035947e-01
2.07697153e-01 -2.72797972e-01 -3.70032430e-01 -1.18664637e-01
3.15166950e-01 -1.03121772e-01 4.52257842e-01 1.75239074e+00
3.57208729e-01 3.29005808e-01 3.51710916e-02 1.53889861e-02
2.56631166e-01 2.62514293e-01 8.42309952e-01 -1.51010931e+00
3.64546716e-01 -7.28343785e-01 -3.38930190e-01 -3.60676140e-01
4.22189422e-02 -6.50548279e-01 -4.80802357e-01 -1.20709705e+00
4.16487008e-02 -5.43127000e-01 7.35553503e-02 -3.16366732e-01
3.43048163e-02 7.20910802e-02 1.39596492e-01 5.09052336e-01
-4.57990587e-01 -9.93928239e-02 9.20230150e-01 1.92114770e-01
-4.89736766e-01 -5.43716252e-01 -1.17508173e+00 1.14096153e+00
9.36202466e-01 -1.68706164e-01 -1.25154540e-01 -2.37804994e-01
9.48401392e-01 -5.15586555e-01 8.48927647e-02 -8.35229576e-01
1.78840477e-02 -4.37117368e-01 3.36755484e-01 -4.35197413e-01
2.46734489e-02 -8.39464605e-01 1.19151466e-01 4.23378706e-01
-3.87705952e-01 3.19241405e-01 -1.96384862e-01 4.80644763e-01
-3.64052802e-01 3.38384688e-01 3.86602491e-01 -2.24698350e-01
-4.61108088e-01 -2.93342799e-01 -1.01263773e+00 2.17270210e-01
1.12063837e+00 -3.45416605e-01 -3.72382909e-01 -3.72861475e-01
-5.05749285e-01 -2.10129339e-02 8.51971090e-01 5.41887999e-01
-1.13412432e-01 -1.26512623e+00 -8.53950739e-01 1.12327971e-01
9.43748206e-02 -5.62150776e-01 1.65325910e-01 9.40328896e-01
-5.18817782e-01 3.42917800e-01 -2.02581808e-01 -5.22027791e-01
-1.17623973e+00 3.25458795e-01 -1.27726188e-02 2.17886165e-01
-4.69254792e-01 4.31296825e-01 3.13875645e-01 -2.92314351e-01
-4.04747665e-01 -2.62466252e-01 -6.49759054e-01 1.09890127e+00
4.69551355e-01 4.79140371e-01 -4.75389987e-01 -1.10430694e+00
-2.03318536e-01 5.91630638e-01 8.17617103e-02 -3.23695660e-01
1.47526777e+00 -5.35855234e-01 -4.55863088e-01 1.25849164e+00
1.31870675e+00 6.97575986e-01 -6.91496491e-01 -1.86244294e-01
5.03845394e-01 -7.48520017e-01 -8.97377506e-02 -3.40441912e-01
-6.39642835e-01 4.68679965e-01 1.01202257e-01 1.05810988e+00
5.76102614e-01 2.11167872e-01 4.16019112e-01 -3.13311107e-02
1.70220241e-01 -1.58092844e+00 -2.66872257e-01 4.54451919e-01
5.86304784e-01 -1.13917625e+00 1.39317811e-01 -4.43186104e-01
-7.09599853e-01 9.17262077e-01 4.77053337e-02 1.25598153e-02
1.06985664e+00 -1.10829867e-01 3.74412715e-01 -4.82924104e-01
-3.45297575e-01 -1.69290334e-01 2.18705013e-01 2.22867027e-01
7.33974755e-01 3.66935998e-01 -6.54072523e-01 2.07907975e-01
-7.00585186e-01 -5.84530592e-01 5.96212626e-01 7.71719396e-01
-6.25785112e-01 -7.49803185e-01 -9.34659004e-01 4.01292235e-01
-7.05170095e-01 5.10232747e-02 -1.10720515e+00 1.05248916e+00
1.11931406e-01 1.00324082e+00 6.21806085e-01 -3.70812953e-01
-3.73865038e-01 2.27197036e-01 -9.05437768e-02 -3.79864275e-01
-7.98028648e-01 2.87667692e-01 4.10860628e-01 -2.64059931e-01
-7.30000615e-01 -1.23269939e+00 -8.85001540e-01 -6.52977943e-01
1.90524444e-01 3.03882182e-01 8.30133975e-01 8.11411023e-01
4.16291535e-01 2.15276793e-01 7.66299546e-01 -6.05170071e-01
3.07155192e-01 -7.62255251e-01 -6.84121311e-01 6.52997017e-01
1.74610138e-01 -5.14775097e-01 -4.82663333e-01 4.15994301e-02] | [8.89621639251709, 9.892915725708008] |
f305f90f-c9f3-4263-afe9-d2cdc95ae2f3 | distilling-cross-temporal-contexts-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Guo_Distilling_Cross-Temporal_Contexts_for_Continuous_Sign_Language_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Guo_Distilling_Cross-Temporal_Contexts_for_Continuous_Sign_Language_Recognition_CVPR_2023_paper.pdf | Distilling Cross-Temporal Contexts for Continuous Sign Language Recognition | Continuous sign language recognition (CSLR) aims to recognize glosses in a sign language video. State-of-the-art methods typically have two modules, a spatial perception module and a temporal aggregation module, which are jointly learned end-to-end. Existing results in [9,20,25,36] have indicated that, as the frontal component of the overall model, the spatial perception module used for spatial feature extraction tends to be insufficiently trained. In this paper, we first conduct empirical studies and show that a shallow temporal aggregation module allows more thorough training of the spatial perception module. However, a shallow temporal aggregation module cannot well capture both local and global temporal context information in sign language. To address this dilemma, we propose a cross-temporal context aggregation (CTCA) model. Specifically, we build a dual-path network that contains two branches for perceptions of local temporal context and global temporal context. We further design a cross-context knowledge distillation learning objective to aggregate the two types of context and the linguistic prior. The knowledge distillation enables the resultant one-branch temporal aggregation module to perceive local-global temporal and semantic context. This shallow temporal perception module structure facilitates spatial perception module learning. Extensive experiments on challenging CSLR benchmarks demonstrate that our method outperforms all state-of-the-art methods. | ['ShengYong Chen', 'Tiantian Yuan', 'Kaihua Zhang', 'Bo Liu', 'Qing Guo', 'Wanli Xue', 'Leming Guo'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['sign-language-recognition'] | ['computer-vision'] | [ 3.58070955e-02 -4.00287271e-01 -3.28624278e-01 -4.90017682e-01
-7.60532320e-01 -4.41353947e-01 6.63293898e-01 -4.24391627e-01
-6.51709318e-01 3.18154931e-01 4.46888179e-01 -2.93759495e-01
-2.39631198e-02 -4.17136341e-01 -5.53180993e-01 -8.00161123e-01
6.26591686e-03 -1.49665177e-01 6.49111032e-01 -1.44249529e-01
3.01703244e-01 2.44599327e-01 -1.65010929e+00 6.80013359e-01
1.02256322e+00 9.93820131e-01 4.39087749e-01 6.06727242e-01
-2.34312877e-01 1.05298328e+00 -2.97803074e-01 7.47050568e-02
2.10318953e-01 -5.13401091e-01 -6.00320816e-01 -6.69516698e-02
7.97638118e-01 -4.98309344e-01 -5.35037577e-01 7.42206514e-01
5.71779668e-01 3.95076990e-01 3.85064870e-01 -1.07736683e+00
-6.46688223e-01 2.47108519e-01 -5.09508729e-01 1.23776503e-01
1.90236583e-01 7.13109076e-01 1.13646030e+00 -8.75862896e-01
5.07001579e-01 1.32213104e+00 3.71895999e-01 7.14469850e-01
-7.46913075e-01 -5.87179959e-01 9.86622870e-01 4.68893170e-01
-1.12838185e+00 -9.45475176e-02 8.73517394e-01 -3.91710252e-01
9.28381264e-01 -5.50846457e-02 9.62253749e-01 1.08758366e+00
4.14796770e-02 1.27934873e+00 1.49021053e+00 -2.74834782e-01
2.39991710e-01 -4.28100318e-01 1.38399303e-01 5.94892979e-01
-1.07647844e-01 5.42450309e-01 -8.38050842e-01 2.02822223e-01
8.87120247e-01 1.15295313e-01 -1.88137218e-01 -3.41677994e-01
-1.07820606e+00 3.79404128e-01 8.01926434e-01 2.84802496e-01
-3.80766392e-01 4.37668741e-01 2.82148898e-01 1.80548012e-01
-1.61026064e-02 -7.31672868e-02 -3.76128912e-01 -1.37061715e-01
-7.92587996e-01 1.05047591e-01 3.48758519e-01 8.46133947e-01
4.94191557e-01 -1.66037366e-01 -3.94351780e-01 7.40041673e-01
5.34442186e-01 4.87333268e-01 4.68414009e-01 -7.94111371e-01
6.31864071e-01 6.02585375e-01 -1.10335663e-01 -4.11514580e-01
-2.15911254e-01 -3.19210142e-01 -4.14529771e-01 4.74051535e-01
5.73824286e-01 8.93825740e-02 -1.45375836e+00 2.02287316e+00
1.01893023e-01 6.23187780e-01 5.13436198e-02 1.34155881e+00
6.99272037e-01 2.87869811e-01 5.32680869e-01 1.50604367e-01
1.28092313e+00 -1.26597393e+00 -6.19665563e-01 -3.84104520e-01
4.75484580e-01 -6.51782155e-01 1.29384160e+00 2.85589457e-01
-9.35213804e-01 -5.82048118e-01 -8.56815755e-01 -3.51818442e-01
-4.65014249e-01 5.10278463e-01 7.88564265e-01 1.90152481e-01
-9.42528069e-01 1.31216303e-01 -9.93449926e-01 -3.72177064e-01
3.40629667e-01 7.73447827e-02 -2.05940410e-01 -4.40336883e-01
-9.96996105e-01 8.85283113e-01 2.20695242e-01 4.91964340e-01
-7.94050217e-01 -4.12588805e-01 -9.19186950e-01 -3.18800062e-01
4.48808104e-01 -8.53124440e-01 1.33442390e+00 -8.06309700e-01
-1.54648960e+00 7.52019465e-01 -5.26683807e-01 -1.20647870e-01
4.16045189e-01 -4.38989341e-01 -3.18941087e-01 2.86932379e-01
1.20409932e-02 7.05172420e-01 8.08260202e-01 -1.50862777e+00
-1.14268327e+00 -3.73493403e-01 2.82151401e-01 3.44760537e-01
2.70033836e-01 -1.01656526e-01 -6.70438945e-01 -8.93708885e-01
4.57967371e-01 -7.44043827e-01 -2.28718549e-01 2.69374877e-01
5.38254902e-02 -3.06827396e-01 8.80419970e-01 -6.92652702e-01
1.24839830e+00 -2.16271544e+00 1.85730264e-01 1.98248386e-01
-5.77217788e-02 2.75539994e-01 -2.76981205e-01 2.02098116e-01
7.45228454e-02 -3.02938938e-01 -3.22439611e-01 -5.21230876e-01
1.13750011e-01 5.54955840e-01 -6.73248529e-01 1.30716980e-01
2.11265713e-01 1.08941150e+00 -1.35764813e+00 -3.96172464e-01
3.67901891e-01 6.65393353e-01 -7.22827315e-01 1.00267924e-01
-4.37892705e-01 6.47099137e-01 -3.58472019e-01 1.04259777e+00
5.10870636e-01 -3.76149900e-02 1.44051284e-01 -3.18153232e-01
-2.56781876e-01 5.95079362e-01 -9.25713778e-01 2.14469910e+00
-5.10818660e-01 6.46270514e-01 -3.59221436e-02 -6.79866731e-01
5.58067203e-01 2.93704480e-01 4.54297781e-01 -1.05398762e+00
6.67211115e-02 4.50030237e-01 7.37781590e-03 -6.95713878e-01
2.96434253e-01 -2.02896804e-01 4.67244126e-02 2.58917361e-01
-1.72778130e-01 1.63581461e-01 5.18791787e-02 -2.91672572e-02
9.00308132e-01 5.73315084e-01 -8.75414014e-02 1.91630259e-01
4.71424490e-01 -1.65183574e-01 7.15985954e-01 6.21081889e-01
-8.01896870e-01 5.43233335e-01 2.64607698e-01 -2.78870314e-01
-4.76338148e-01 -1.34669077e+00 2.96967447e-01 1.38273716e+00
4.88393098e-01 -2.49489561e-01 -3.92261356e-01 -8.47913802e-01
-1.73198413e-02 5.69693685e-01 -6.42141998e-01 -7.99984112e-02
-9.61417139e-01 -1.84764802e-01 5.76925159e-01 1.14336574e+00
9.00177956e-01 -1.18024135e+00 -9.49384153e-01 -4.57680710e-02
-2.45043814e-01 -1.15178764e+00 -8.77632856e-01 -1.30169958e-01
-8.51361990e-01 -1.01606846e+00 -7.36862004e-01 -1.07180083e+00
4.98797119e-01 4.03074831e-01 6.93228960e-01 5.19009121e-02
-1.91260189e-01 5.88183522e-01 -4.95478749e-01 -1.53707385e-01
2.22674057e-01 -3.72110546e-01 -7.64295459e-02 5.03248014e-02
6.64002657e-01 -8.52107525e-01 -1.02215600e+00 4.32788163e-01
-9.23593163e-01 1.21639036e-01 1.01267517e+00 1.00805545e+00
6.51758671e-01 -3.45503688e-01 2.97016054e-01 -8.18415880e-02
4.41885501e-01 -1.03596389e-01 -4.18991178e-01 5.11717498e-01
-2.46368319e-01 1.49918273e-01 2.06157759e-01 -5.74174643e-01
-1.24379027e+00 5.39589003e-02 -3.47477682e-02 -5.47566772e-01
-2.47199357e-01 6.02763712e-01 -2.71783769e-01 -2.47581787e-02
3.18362772e-01 6.20225787e-01 -7.63800666e-02 -5.29066682e-01
5.83343387e-01 4.31508690e-01 7.15462446e-01 -7.86559939e-01
5.01891375e-01 7.60552824e-01 -7.90313110e-02 -5.78786433e-01
-9.40079033e-01 -7.39313662e-01 -8.55756700e-01 -3.27334404e-01
1.04707170e+00 -8.94939661e-01 -7.55902708e-01 5.95912099e-01
-9.09502208e-01 -8.04225445e-01 -3.74138594e-01 6.76271915e-01
-7.69064844e-01 4.57064956e-01 -3.34273994e-01 -7.33900309e-01
9.21222270e-02 -1.07697737e+00 1.31569588e+00 2.61958778e-01
1.06819548e-01 -7.94997454e-01 6.18248917e-02 3.53814662e-01
3.21305662e-01 7.60476738e-02 5.61907470e-01 1.51291430e-01
-1.02627349e+00 1.02591544e-01 -5.10645628e-01 4.11543012e-01
2.81525105e-01 -3.42186362e-01 -1.01614618e+00 -2.54770964e-01
-3.59972715e-01 -3.89222741e-01 1.51972806e+00 5.28246343e-01
9.87405002e-01 -9.53909159e-02 -2.14524418e-01 6.36781514e-01
1.25056410e+00 3.31965804e-01 6.71322823e-01 1.17143638e-01
7.34004200e-01 5.51228642e-01 7.65127301e-01 3.67391706e-02
8.31033528e-01 7.29050875e-01 1.77358776e-01 -4.86612506e-03
-5.88189363e-01 -5.00788569e-01 5.22890687e-01 6.50912642e-01
-4.36617166e-01 1.45906299e-01 -9.36913490e-01 7.05410123e-01
-2.18768072e+00 -1.09162927e+00 4.93594259e-01 2.02233696e+00
9.83827412e-01 -6.60493448e-02 1.53946742e-01 -1.24815898e-03
1.70540646e-01 3.03565264e-01 -6.47426963e-01 -8.74513537e-02
-2.95132726e-01 -2.47394312e-02 2.58651912e-01 7.30648935e-01
-1.12838137e+00 1.27800238e+00 5.73027563e+00 5.59902847e-01
-1.38731933e+00 4.56481846e-03 -1.53208151e-01 -2.99407631e-01
-2.63874292e-01 2.01300994e-01 -5.32949209e-01 4.15702194e-01
1.12688757e-01 4.16330904e-01 3.01308453e-01 6.77323520e-01
3.30830008e-01 -2.48692617e-01 -1.23459923e+00 1.07553923e+00
3.44737731e-02 -9.43731368e-01 2.38807619e-01 1.53143510e-01
5.89774191e-01 1.80958539e-01 1.40870333e-01 4.93758023e-01
2.72397906e-01 -9.35671031e-01 8.01703453e-01 9.23784256e-01
7.30417609e-01 -3.07067364e-01 4.92208272e-01 2.65548557e-01
-1.79263020e+00 -4.43698287e-01 1.82543665e-01 -3.31870914e-02
5.53100109e-01 2.58172452e-02 -1.82245359e-01 3.68748188e-01
5.95923662e-01 8.38369727e-01 -4.28998977e-01 1.21012318e+00
-6.45292819e-01 5.87947369e-01 -3.36066276e-01 8.03713575e-02
6.39254749e-01 -1.24648891e-01 6.08149469e-01 1.36670268e+00
-3.17533575e-02 4.12769318e-01 4.26223695e-01 7.53699183e-01
4.58612144e-01 -2.50356436e-01 -3.95521939e-01 1.64944366e-01
3.27939183e-01 4.27926838e-01 -3.44643891e-01 -3.67168695e-01
-5.95096827e-01 1.11642087e+00 2.57143885e-01 9.44691956e-01
-6.41563833e-01 -2.08384439e-01 9.05700505e-01 -1.16446838e-01
4.81247008e-01 -6.77286267e-01 -5.79032898e-01 -1.30969799e+00
5.22601962e-01 -6.24108493e-01 6.18056297e-01 -7.93661833e-01
-1.27200842e+00 1.96485549e-01 -3.98371480e-02 -1.46273780e+00
-1.40529960e-01 -9.95231748e-01 -6.90733016e-01 1.03344738e+00
-2.12414527e+00 -1.71054101e+00 -4.97435063e-01 8.73030365e-01
6.41908884e-01 1.93427168e-02 4.43371415e-01 2.12503344e-01
-3.26357186e-01 7.57657051e-01 -4.52209026e-01 3.57896537e-01
7.06610620e-01 -1.25850368e+00 -8.24417844e-02 1.10703778e+00
-7.12912902e-02 9.53241646e-01 2.15920597e-01 -7.28887558e-01
-1.22130466e+00 -9.40055847e-01 9.64629114e-01 -4.37584281e-01
5.91978908e-01 -3.35938632e-02 -7.81321108e-01 6.19492471e-01
-2.40628514e-03 1.24179684e-01 4.48552400e-01 6.06995709e-02
-8.74739408e-01 -2.05939487e-01 -7.63592303e-01 8.51054132e-01
1.44509661e+00 -1.02286434e+00 -1.05560720e+00 -1.34561867e-01
5.68035364e-01 -3.58059913e-01 -5.23982108e-01 6.01667643e-01
1.08516419e+00 -8.91104698e-01 9.87100959e-01 -5.41817725e-01
3.46616089e-01 -6.63004696e-01 -4.80743915e-01 -1.04131901e+00
-2.14111671e-01 -2.62329012e-01 -4.32113379e-01 7.31889904e-01
9.67665315e-02 -6.88088655e-01 5.80642760e-01 3.75364482e-01
-3.32201153e-01 -9.36107397e-01 -1.03163600e+00 -1.06097722e+00
1.42838404e-01 -6.94397807e-01 2.89672524e-01 6.93878293e-01
9.28203389e-02 8.66072848e-02 -8.26832950e-02 2.22326145e-01
6.96587682e-01 4.41175133e-01 6.04924738e-01 -8.69896591e-01
-3.38109642e-01 -9.80269551e-01 -3.31944346e-01 -1.84628332e+00
-2.67270599e-02 -6.25491917e-01 3.72267812e-01 -1.82030749e+00
-4.22657803e-02 -3.70217830e-01 -5.27718782e-01 7.65623152e-01
-2.90360332e-01 -1.36665449e-01 2.35321194e-01 2.36783683e-01
-5.71248770e-01 7.80119121e-01 1.61113882e+00 -1.35634750e-01
-3.46272200e-01 -1.81873620e-01 -3.61854315e-01 7.72160709e-01
5.85434258e-01 2.08512604e-01 -6.37230277e-01 -6.19162560e-01
-1.67386726e-01 -2.41646975e-01 9.42143738e-01 -8.44769657e-01
5.29823720e-01 -4.74692881e-01 -9.81195197e-02 -9.82939184e-01
5.04994512e-01 -7.04309642e-01 -7.21478879e-01 2.66698927e-01
-2.51634389e-01 -4.20753330e-01 1.61529094e-01 6.08227432e-01
-6.19647026e-01 5.16281247e-01 7.07203627e-01 -1.46201596e-01
-1.38136733e+00 3.74538451e-01 -1.54651985e-01 -1.72247160e-02
7.47020602e-01 -4.55658108e-01 -3.72929215e-01 -1.99504554e-01
-8.86431158e-01 5.51511168e-01 7.64106587e-02 6.46992862e-01
9.21056330e-01 -1.46754587e+00 -3.98603618e-01 2.99934536e-01
2.93478280e-01 1.83159024e-01 4.43490386e-01 1.13041389e+00
-1.91769108e-01 5.14405787e-01 -1.42078519e-01 -8.38939965e-01
-1.08887184e+00 3.85298222e-01 5.73826909e-01 -8.56972337e-02
-8.57713640e-01 1.01374471e+00 4.51780677e-01 -3.35552990e-01
6.84139252e-01 -9.71991301e-01 -1.34153783e-01 -1.67473063e-01
5.62093794e-01 1.40404254e-01 -5.24398685e-01 -6.67330861e-01
-4.32304353e-01 1.16209185e+00 2.11614549e-01 -4.74701107e-01
9.02161181e-01 -1.58446625e-01 3.52702141e-02 4.86331075e-01
9.25631046e-01 -2.33731419e-01 -1.69100666e+00 -6.72441244e-01
-1.76480636e-02 -5.26778221e-01 5.97638264e-03 -1.06150699e+00
-8.70186031e-01 1.17944384e+00 6.05892181e-01 -3.67833346e-01
1.65709603e+00 1.35614509e-02 8.08996260e-01 4.18305784e-01
4.51079637e-01 -1.31988776e+00 3.42272252e-01 9.05340850e-01
1.17086172e+00 -1.35549450e+00 -3.89284760e-01 -4.14920211e-01
-6.86504602e-01 8.62454355e-01 8.76643836e-01 -3.82763520e-02
8.97909641e-01 4.96596396e-02 3.62367988e-01 -2.59857744e-01
-7.72261441e-01 -8.09426546e-01 7.55120039e-01 4.82855022e-01
2.93903351e-01 3.73070501e-02 -3.50577354e-01 6.77994728e-01
-5.01706973e-02 1.81031138e-01 -2.20491812e-01 1.38359964e+00
-4.30332273e-01 -1.01540852e+00 -1.21790595e-01 -1.11520521e-01
2.52762169e-01 -1.37216121e-01 -3.79328519e-01 7.25962043e-01
5.73624492e-01 7.44475067e-01 -1.17107928e-02 -5.44683516e-01
5.72006524e-01 2.08414540e-01 6.79333985e-01 -3.22085708e-01
-2.81707436e-01 1.99468881e-01 -1.14804618e-01 -9.96705711e-01
-6.81967914e-01 -7.08375275e-01 -1.47744131e+00 2.98435330e-01
4.88104634e-02 -3.99885327e-01 4.33041096e-01 1.25968504e+00
2.12656498e-01 5.52784741e-01 1.44887477e-01 -7.53789067e-01
-3.52367580e-01 -9.16775405e-01 -4.01543766e-01 4.15984303e-01
6.93193972e-01 -9.35879946e-01 -1.14839688e-01 1.88891813e-01] | [9.233084678649902, -6.527484893798828] |
99917ee2-8d10-41f3-b130-0e9923299a4c | 3d-surfel-map-aided-visual-relocalization | 2104.03856 | null | https://arxiv.org/abs/2104.03856v1 | https://arxiv.org/pdf/2104.03856v1.pdf | 3D Surfel Map-Aided Visual Relocalization with Learned Descriptors | In this paper, we introduce a method for visual relocalization using the geometric information from a 3D surfel map. A visual database is first built by global indices from the 3D surfel map rendering, which provides associations between image points and 3D surfels. Surfel reprojection constraints are utilized to optimize the keyframe poses and map points in the visual database. A hierarchical camera relocalization algorithm then utilizes the visual database to estimate 6-DoF camera poses. Learned descriptors are further used to improve the performance in challenging cases. We present evaluation under real-world conditions and simulation to show the effectiveness and efficiency of our method, and make the final camera poses consistently well aligned with the 3D environment. | ['Ming Liu', 'Timothy Sandy', 'Marco Hutter', 'Huaiyang Huang', 'Haoyang Ye'] | 2021-04-08 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [-4.12392646e-01 -5.07241249e-01 -4.19303536e-01 -1.36667088e-01
-3.65539879e-01 -9.70386267e-01 3.81115168e-01 -2.60278396e-02
-3.27786952e-01 1.30567282e-01 1.23823404e-01 1.80580959e-01
-2.72306800e-02 -4.75421786e-01 -7.70269096e-01 -2.11077109e-01
2.22818792e-01 4.11681950e-01 6.56101882e-01 -9.09249410e-02
6.13303721e-01 1.07998526e+00 -1.44048905e+00 -3.38586688e-01
4.06944484e-01 9.93308604e-01 5.73187113e-01 7.03431427e-01
1.72319442e-01 3.01504582e-01 -2.01363578e-01 1.30498290e-01
6.01678014e-01 2.01042339e-01 -5.97170852e-02 4.75793391e-01
8.43561590e-01 -5.71231306e-01 -4.30310339e-01 1.00110412e+00
2.54965246e-01 2.73284376e-01 3.93259794e-01 -1.14654648e+00
-2.93995589e-01 -3.32217693e-01 -7.49967515e-01 -2.16422141e-01
1.05353701e+00 -2.74024844e-01 8.50017965e-01 -1.57668960e+00
9.76744354e-01 1.24641085e+00 7.60086596e-01 -1.16157934e-01
-9.63119090e-01 -6.05946183e-01 1.57487556e-01 1.34226024e-01
-2.04882622e+00 -2.78970242e-01 9.11798060e-01 -4.64716762e-01
9.05776322e-01 2.46707186e-01 9.83251274e-01 5.29734373e-01
4.44341749e-01 1.16307013e-01 6.09020472e-01 -5.59861720e-01
-2.71452125e-02 1.65338784e-01 -1.16472453e-01 8.71432781e-01
3.56646121e-01 1.38195017e-02 -9.99873638e-01 -3.01913708e-01
1.49763167e+00 3.96893501e-01 -3.03327203e-01 -1.27109361e+00
-1.39770663e+00 6.88444257e-01 5.68669438e-01 -3.36775541e-01
-2.91663080e-01 7.31596053e-02 -1.55337512e-01 -1.79037705e-01
1.14800908e-01 2.10284963e-01 5.56883886e-02 2.00274155e-01
-4.98369336e-01 9.78214368e-02 4.74749058e-01 1.51441658e+00
1.16071773e+00 -1.40822634e-01 3.46207738e-01 6.06723845e-01
7.16974914e-01 1.06793487e+00 -9.37606767e-02 -1.23842061e+00
3.82976890e-01 7.46424675e-01 3.70357633e-01 -1.69831789e+00
-1.99505910e-01 1.70751452e-01 -4.26121652e-01 1.76718503e-01
-2.87463874e-01 4.72482383e-01 -6.84082150e-01 8.79766464e-01
5.56870580e-01 1.01547383e-01 -2.67266244e-01 1.10918009e+00
5.66578388e-01 6.10366762e-01 -6.74934268e-01 -5.31206168e-02
9.56795573e-01 -5.80715954e-01 -6.86621249e-01 -2.14018732e-01
7.36441910e-02 -1.04640424e+00 7.82994032e-01 1.48440301e-01
-8.51793170e-01 -6.63277864e-01 -1.27151525e+00 -1.88582480e-01
2.30841897e-02 2.23233163e-01 5.49343973e-02 1.56268224e-01
-1.24394047e+00 -1.40589565e-01 -9.77329433e-01 -6.04072452e-01
-2.72101611e-01 3.85792851e-01 -5.41541398e-01 5.03750816e-02
-7.01055348e-01 9.31039810e-01 4.02995229e-01 3.98181118e-02
-8.87794793e-01 -2.82255501e-01 -1.17515624e+00 -2.36551210e-01
3.29969287e-01 -5.73881567e-01 8.46460104e-01 -3.00277233e-01
-1.37844992e+00 8.71827126e-01 -4.38882083e-01 8.46850872e-02
3.23048562e-01 -3.50196660e-01 -7.74926245e-02 3.43764514e-01
2.24096268e-01 5.76863408e-01 8.48846674e-01 -1.70253563e+00
-6.62492692e-01 -4.02662605e-01 -7.86540285e-02 7.94041157e-01
9.54182993e-04 -2.26716936e-01 -1.35203993e+00 -5.37526965e-01
9.28202987e-01 -1.02634275e+00 -1.93242371e-01 2.04410389e-01
-2.23203897e-01 1.56124413e-01 9.50073957e-01 -3.83900374e-01
9.12875235e-01 -2.15013242e+00 2.76941627e-01 8.87779534e-01
2.80724853e-01 -3.30082685e-01 1.21795341e-01 4.82927412e-01
1.03906766e-01 -3.83778125e-01 5.81189513e-01 -3.76400203e-01
-3.54693174e-01 2.34601483e-01 -3.60267371e-01 7.94199169e-01
-4.01924133e-01 4.66850340e-01 -6.83099329e-01 -4.58877563e-01
7.20080376e-01 5.31912386e-01 -6.37708068e-01 3.54096621e-01
1.88650817e-01 3.13143998e-01 -6.29282176e-01 7.18578279e-01
7.81604826e-01 -4.46289480e-02 -1.09990917e-01 -5.76741934e-01
-4.33441371e-01 -1.37915790e-01 -1.58680880e+00 2.01910543e+00
-9.27209184e-02 4.26463336e-01 -1.15201389e-02 -1.33199930e-01
1.31002176e+00 -1.48632035e-01 4.57604021e-01 -5.19753158e-01
2.55316079e-01 4.68788818e-02 -9.75078404e-01 -4.97829095e-02
8.98355901e-01 4.83920991e-01 -7.65564442e-02 1.27751708e-01
-1.13561332e-01 -5.42015553e-01 -3.63168865e-01 3.67276430e-01
4.91019249e-01 2.26185888e-01 6.58645928e-01 -2.76323229e-01
5.88950038e-01 2.33109877e-01 5.23140192e-01 4.70079333e-01
2.33333066e-01 6.41156435e-01 -2.02612951e-02 -5.72239578e-01
-1.35695326e+00 -1.50212097e+00 -9.28878486e-02 5.21055043e-01
1.15458071e+00 -8.00525606e-01 -4.94199038e-01 -4.00265485e-01
2.14147195e-01 9.78638306e-02 -3.13683450e-01 5.64927384e-02
-4.29861635e-01 2.37137735e-01 -1.61125764e-01 3.33890945e-01
3.83155763e-01 -1.53201118e-01 -7.38585353e-01 -2.52254277e-01
8.83264989e-02 -1.21993291e+00 -8.46790910e-01 -2.43734047e-01
-7.04715312e-01 -1.08947861e+00 -3.43609542e-01 -8.10819447e-01
9.80330586e-01 1.01720023e+00 5.50506115e-01 -1.17828064e-02
-1.20640300e-01 8.87339950e-01 -3.90893012e-01 -4.09452915e-02
-4.92624044e-02 -3.27385843e-01 5.38809597e-01 -6.04694299e-02
1.57990932e-01 -2.36194372e-01 -5.85925400e-01 9.81839299e-01
-3.73568654e-01 2.25627065e-01 2.31275171e-01 5.39524376e-01
1.23505700e+00 -3.74869078e-01 -4.17984128e-01 -2.15767890e-01
6.60962015e-02 5.23364395e-02 -1.31055021e+00 3.31707507e-01
-3.59397203e-01 -1.26732916e-01 1.27940979e-02 -3.65926772e-01
-7.50053108e-01 7.16299057e-01 4.73316133e-01 -1.07429779e+00
3.26412857e-01 3.28246087e-01 -2.71523714e-01 -5.51265657e-01
5.68569124e-01 5.74178696e-02 4.62366641e-02 -3.85246962e-01
5.04260063e-01 6.21761560e-01 7.34644413e-01 -4.25570399e-01
1.12455797e+00 7.27745056e-01 1.45972475e-01 -8.79405618e-01
-7.83863366e-01 -7.33256698e-01 -1.20018589e+00 -6.58578217e-01
9.73715186e-01 -1.26075876e+00 -8.46931934e-01 1.50484562e-01
-1.30423224e+00 2.56247312e-01 1.54062822e-01 9.30188835e-01
-6.70807719e-01 5.31652510e-01 -2.45123044e-01 -5.09172618e-01
-4.54376489e-02 -1.34339404e+00 1.44046938e+00 2.05558673e-01
-1.00217670e-01 -9.35843706e-01 3.70687485e-01 -1.01024434e-01
-2.72800982e-01 2.19681367e-01 3.21289122e-01 1.06459625e-01
-1.14005876e+00 -3.42637688e-01 -2.27199927e-01 -4.12783101e-02
1.77119114e-02 1.48992375e-01 -5.86619318e-01 -6.40733719e-01
-1.60395578e-01 2.14095145e-01 3.28547299e-01 1.40268266e-01
7.23924637e-01 1.94439173e-01 -5.72424412e-01 1.12544835e+00
1.55283272e+00 3.60732287e-01 1.76546901e-01 5.22327244e-01
1.05198276e+00 1.62328184e-01 1.01740634e+00 6.07074201e-01
5.79331934e-01 1.10298109e+00 5.85578501e-01 -1.07012190e-01
-1.63538791e-02 -7.91770101e-01 2.99732059e-01 1.05593145e+00
-1.64079204e-01 3.89256552e-02 -1.10127211e+00 1.28823653e-01
-1.94694209e+00 -4.27687705e-01 -1.00267462e-01 2.39850855e+00
1.37023062e-01 -1.38600007e-01 -1.67580143e-01 -4.97688830e-01
1.06507015e+00 6.03603087e-02 -5.59277713e-01 2.16875970e-01
4.16055322e-02 -3.53945732e-01 1.05260694e+00 9.22881424e-01
-9.46131885e-01 1.24087942e+00 7.06699896e+00 3.41975331e-01
-9.20404077e-01 -1.63265646e-01 -2.59036869e-01 9.39129367e-02
-1.26467302e-01 3.02893251e-01 -1.24175024e+00 -7.00630620e-02
9.97310132e-02 -1.92293838e-01 4.84879524e-01 9.47498262e-01
5.77034168e-02 -1.35581955e-01 -1.02481687e+00 1.64760470e+00
6.23883069e-01 -1.44352949e+00 3.00993264e-01 2.52541006e-01
8.39741409e-01 2.17103407e-01 -6.20455556e-02 -4.43316221e-01
4.22557473e-01 -4.41883653e-01 1.15294945e+00 7.37944782e-01
1.07886529e+00 -8.95691574e-01 3.35622907e-01 2.56516606e-01
-1.69355631e+00 8.68653953e-02 -7.58941889e-01 1.26502186e-01
2.94276565e-01 1.06540561e-01 -9.25546288e-01 5.42836547e-01
9.07693505e-01 1.19377875e+00 -7.63006210e-01 1.03127360e+00
-2.81032264e-01 -4.01527345e-01 -3.92233253e-01 1.55148223e-01
-5.39976470e-02 -4.40266013e-01 7.70160854e-01 6.19790196e-01
5.66165090e-01 1.16316743e-01 6.05288684e-01 5.47387242e-01
2.53015637e-01 1.03261016e-01 -9.72167313e-01 5.74136317e-01
9.20064032e-01 1.19067848e+00 -7.99976110e-01 -1.50855154e-01
-3.75812620e-01 1.01507580e+00 2.98749715e-01 4.62996244e-01
-7.30773628e-01 -2.85568178e-01 6.70145869e-01 2.47992769e-01
1.41909152e-01 -9.26547766e-01 1.13240726e-01 -1.39143336e+00
1.36737479e-02 -4.15079653e-01 1.54512480e-01 -1.29188812e+00
-9.07433808e-01 4.02743548e-01 2.29486361e-01 -1.75188875e+00
-8.38560760e-02 -6.56101465e-01 -8.91163498e-02 7.42113769e-01
-9.37905490e-01 -1.09904265e+00 -8.09947491e-01 1.00200832e+00
3.82172167e-01 -2.49766156e-01 4.88203913e-01 -2.14463800e-01
-6.51628152e-02 2.87630588e-01 2.55751967e-01 2.98922230e-02
8.53461802e-01 -8.43624473e-01 4.13077682e-01 7.25051343e-01
3.37623477e-01 7.97269523e-01 4.75172073e-01 -8.80352497e-01
-2.01973104e+00 -9.40228581e-01 3.82117540e-01 -9.59353507e-01
6.15365028e-01 -8.80636632e-01 -5.55854380e-01 9.40684259e-01
-1.83597520e-01 2.32166722e-02 1.27533436e-01 -1.39226288e-01
-4.20056015e-01 -2.44769841e-01 -7.72327125e-01 6.53097272e-01
1.16244805e+00 -8.15243185e-01 -5.72286665e-01 1.07368074e-01
7.59642422e-01 -1.14256120e+00 -8.52223158e-01 2.99334884e-01
6.38914108e-01 -7.11883903e-01 1.45742643e+00 2.45851129e-01
-3.86401504e-01 -7.72364795e-01 -5.25552690e-01 -1.10657048e+00
-2.92386144e-01 -4.39826608e-01 2.51267016e-01 8.71498823e-01
-1.55154213e-01 -4.20038879e-01 7.41180599e-01 1.79825321e-01
-1.12956651e-02 -1.00009836e-01 -9.33849394e-01 -7.20829070e-01
-8.30387712e-01 -2.89756715e-01 4.05850768e-01 7.89982080e-01
-5.74336648e-02 3.48659307e-01 -4.69593316e-01 6.92887425e-01
8.28820705e-01 1.33906662e-01 1.26247418e+00 -1.08466601e+00
7.11531714e-02 -9.58216637e-02 -9.14165258e-01 -1.49419618e+00
1.14890270e-01 -7.47361183e-01 5.92868216e-02 -1.31567788e+00
2.50347555e-01 -3.41422796e-01 5.66610955e-02 1.66573122e-01
8.83454308e-02 3.33900988e-01 4.46217477e-01 6.30905807e-01
-8.40764344e-01 5.62278986e-01 1.05974042e+00 2.52883643e-01
-3.96345168e-01 -4.24021304e-01 -4.45372313e-02 8.62354040e-01
3.27430367e-01 -2.54688412e-01 -4.37520266e-01 -6.32905960e-01
4.25498575e-01 3.05235207e-01 4.44842756e-01 -9.57994223e-01
4.76048291e-01 -2.60438472e-01 7.22275019e-01 -1.31523728e+00
7.05839932e-01 -1.18227255e+00 4.65421498e-01 2.30411157e-01
-7.21106632e-03 6.15489304e-01 -3.92869264e-02 7.29968429e-01
-1.32469460e-01 3.42369825e-02 5.66366136e-01 1.82889864e-01
-9.21550572e-01 5.57886004e-01 2.32925955e-02 -5.01818597e-01
1.41147232e+00 -5.21571875e-01 -8.60797707e-03 -5.24380207e-01
-5.74131727e-01 2.85051167e-01 1.30872142e+00 5.69606602e-01
1.18697906e+00 -1.74057412e+00 -2.33191296e-01 8.19365263e-01
5.24936497e-01 1.88165173e-01 6.15865551e-02 5.79407692e-01
-1.11901963e+00 3.56720716e-01 -2.20825359e-01 -1.38165033e+00
-1.52780020e+00 7.56651044e-01 2.74386883e-01 5.25936365e-01
-7.10864186e-01 4.11215901e-01 4.54409450e-01 -4.06764656e-01
3.81592721e-01 -1.77414984e-01 1.94159988e-02 -1.86137572e-01
5.18736660e-01 3.34932297e-01 -1.79778486e-02 -1.21518850e+00
-4.93424863e-01 1.51115584e+00 1.18036352e-01 -3.09277117e-01
9.86940145e-01 -6.68964624e-01 -8.64313245e-02 4.01077479e-01
1.28515708e+00 4.36826408e-01 -1.62232780e+00 -3.66187274e-01
-1.44238517e-01 -1.18536103e+00 7.44496733e-02 1.10885529e-02
-6.71548724e-01 5.25532842e-01 6.28988147e-01 -4.15818512e-01
7.84817696e-01 2.19240054e-01 2.23992616e-01 5.85080266e-01
9.29951906e-01 -8.18540692e-01 2.74549484e-01 6.90232813e-01
1.04300189e+00 -8.99379790e-01 2.86668658e-01 -6.49682581e-01
-6.42341018e-01 1.22769105e+00 4.90818650e-01 -3.97995681e-01
5.73982775e-01 2.04524934e-01 1.65480450e-01 -2.50766605e-01
-1.55785084e-01 1.45538077e-01 3.84173751e-01 6.35784388e-01
-2.64199108e-01 -2.29281411e-01 3.26110721e-01 -1.15186088e-01
-6.76469132e-02 -4.36087906e-01 4.24539626e-01 8.62880766e-01
-7.60098159e-01 -9.81327832e-01 -9.79349315e-01 -2.68331647e-01
2.90554881e-01 2.91340828e-01 -4.55391049e-01 8.44377100e-01
-2.51219660e-01 5.59278548e-01 3.73132616e-01 -6.01313829e-01
5.93871057e-01 -5.58486044e-01 5.85781276e-01 -5.63704610e-01
1.43016679e-02 5.69068670e-01 -3.31578970e-01 -1.06242299e+00
-3.20680499e-01 -6.21930242e-01 -1.31389081e+00 -1.46347955e-01
-4.36495215e-01 2.45301845e-03 8.86385977e-01 3.37937891e-01
3.28827024e-01 -2.30174959e-01 9.19588923e-01 -1.32993555e+00
-4.90472429e-02 -1.49889395e-01 -6.67733490e-01 2.69672245e-01
4.66406673e-01 -1.11126089e+00 -2.41372690e-01 2.11326793e-01] | [7.4667067527771, -2.2127039432525635] |
6517a1fb-93b0-4489-ac20-abf9838dc8c1 | cssam-code-search-via-attention-matching-of | 2208.03922 | null | https://arxiv.org/abs/2208.03922v1 | https://arxiv.org/pdf/2208.03922v1.pdf | CSSAM:Code Search via Attention Matching of Code Semantics and Structures | Despite the continuous efforts in improving both the effectiveness and efficiency of code search, two issues remained unsolved. First, programming languages have inherent strong structural linkages, and feature mining of code as text form would omit the structural information contained inside it. Second, there is a potential semantic relationship between code and query, it is challenging to align code and text across sequences so that vectors are spatially consistent during similarity matching. To tackle both issues, in this paper, a code search model named CSSAM (Code Semantics and Structures Attention Matching) is proposed. By introducing semantic and structural matching mechanisms, CSSAM effectively extracts and fuses multidimensional code features. Specifically, the cross and residual layer was developed to facilitate high-latitude spatial alignment of code and query at the token level. By leveraging the residual interaction, a matching module is designed to preserve more code semantics and descriptive features, that enhances the adhesion between the code and its corresponding query text. Besides, to improve the model's comprehension of the code's inherent structure, a code representation structure named CSRG (Code Semantic Representation Graph) is proposed for jointly representing abstract syntax tree nodes and the data flow of the codes. According to the experimental results on two publicly available datasets containing 540k and 330k code segments, CSSAM significantly outperforms the baselines in terms of achieving the highest SR@1/5/10, MRR, and NDCG@50 on both datasets respectively. Moreover, the ablation study is conducted to quantitatively measure the impact of each key component of CSSAM on the efficiency and effectiveness of code search, which offers the insights into the improvement of advanced code search solutions. | ['Yaoxiang Yu', 'Bo Cai', 'Yi Hu'] | 2022-08-08 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-9.40103903e-02 -4.20490056e-01 -4.32042897e-01 -1.62359178e-01
-6.22636557e-01 -6.11362457e-01 7.39727169e-02 5.64603984e-01
-5.40591367e-02 -2.72280008e-01 4.91147578e-01 -4.01143491e-01
-3.13316286e-01 -5.62155902e-01 -3.36785018e-01 -2.54246980e-01
1.46518624e-03 -3.50644290e-01 2.47481957e-01 -1.64508045e-01
7.50203490e-01 -4.07319888e-02 -1.54585350e+00 3.38043749e-01
1.50926805e+00 8.11015725e-01 8.02649498e-01 1.75434336e-01
-7.65379250e-01 6.29685700e-01 -3.39639395e-01 -4.80486274e-01
-7.81392306e-02 -3.67362052e-01 -8.21437776e-01 -2.58429319e-01
2.13265315e-01 1.00749388e-01 -4.79812443e-01 1.52665520e+00
1.91282183e-01 -1.15242004e-01 3.07544380e-01 -1.11038470e+00
-9.83360171e-01 7.50059903e-01 -8.70749712e-01 2.22014546e-01
6.73819065e-01 -5.51335663e-02 1.34591806e+00 -9.77784872e-01
4.03028071e-01 8.33486080e-01 7.70893693e-01 1.44237563e-01
-9.63100016e-01 -6.51092052e-01 2.49298930e-01 1.71759084e-01
-1.64236546e+00 -9.54676419e-02 7.03605354e-01 -7.01642513e-01
1.31321657e+00 4.04283375e-01 5.22052646e-01 6.38288915e-01
1.69443309e-01 7.97442913e-01 1.54199332e-01 -3.95411462e-01
-4.38656937e-03 8.13506097e-02 4.16642010e-01 8.82943273e-01
1.49318933e-01 -2.02206239e-01 -4.32202429e-01 -3.40791166e-01
2.53835738e-01 2.81916678e-01 -3.06193829e-01 -5.95590234e-01
-1.06837118e+00 7.67305195e-01 6.61479115e-01 4.58559304e-01
8.25002939e-02 4.69605662e-02 8.54206145e-01 1.05900168e-01
1.24976588e-02 5.00213802e-01 -3.01331043e-01 -3.71516526e-01
-9.78568435e-01 1.49691269e-01 3.47907990e-01 1.52953875e+00
8.67395401e-01 -3.11460853e-01 -3.44711453e-01 9.78901267e-01
5.64263642e-01 5.12595952e-01 7.99297929e-01 -4.54113871e-01
1.09641075e+00 1.38474751e+00 -5.02983510e-01 -1.56222999e+00
-2.78147072e-01 -6.21690989e-01 -5.32671690e-01 -4.41181540e-01
-2.96954960e-01 4.02754962e-01 -4.38196272e-01 1.54896092e+00
5.33144623e-02 5.21486104e-02 -1.55179845e-02 6.51215911e-01
8.32101524e-01 4.58753586e-01 -9.42860916e-03 6.97404370e-02
1.54717505e+00 -1.04558551e+00 -4.39791232e-01 -5.04800797e-01
1.21466911e+00 -9.76841390e-01 1.30217838e+00 -3.36522758e-01
-6.43803596e-01 -6.40323460e-01 -1.01451504e+00 -1.24639809e-01
-2.37717301e-01 2.78246164e-01 4.71236706e-01 6.36465669e-01
-6.80579305e-01 1.41757965e-01 -6.00572765e-01 -4.93186355e-01
3.24822128e-01 1.60777286e-01 -1.11223035e-01 -3.22331637e-01
-1.01357973e+00 3.89093190e-01 4.25209284e-01 -2.50047952e-01
-3.00303668e-01 -8.13110352e-01 -1.10994673e+00 5.01071990e-01
2.36344084e-01 -2.30029628e-01 7.77946591e-01 -8.53526890e-01
-6.13603830e-01 6.89296782e-01 -3.13942283e-01 -1.25032023e-01
-1.66181326e-02 -9.83311757e-02 -6.24667704e-01 -1.79453045e-01
4.26340550e-01 2.38128692e-01 3.38601172e-01 -8.75371039e-01
-7.64005184e-01 -4.87684548e-01 -2.21959930e-02 2.10228354e-01
-7.05794752e-01 2.02724487e-01 -1.08727682e+00 -1.05328512e+00
2.52430022e-01 -8.87510717e-01 5.35699688e-02 -2.39811793e-01
-4.04899001e-01 -1.89730749e-01 8.16143870e-01 -7.86919236e-01
2.06174088e+00 -2.77367473e+00 3.50691408e-01 4.76985127e-01
2.09769741e-01 1.80214956e-01 -3.38633418e-01 6.80110514e-01
-8.80848765e-02 2.45908335e-01 -4.42052990e-01 3.45697328e-02
3.00481673e-02 -6.66943863e-02 -4.33483839e-01 2.96406865e-01
-8.74972045e-02 1.08790970e+00 -8.62343550e-01 -5.54877102e-01
-2.09489301e-01 1.58400610e-01 -1.01836145e+00 2.82509997e-02
-4.34010923e-02 -1.49829984e-01 -8.68601501e-01 6.34745359e-01
6.72564089e-01 -5.27054548e-01 2.19238818e-01 -2.19418146e-02
-1.29584461e-01 1.75537542e-01 -1.01234126e+00 2.24919510e+00
-5.92531621e-01 4.80197787e-01 -2.83975869e-01 -8.46645474e-01
1.06209385e+00 -5.43119721e-02 3.57833177e-01 -1.09269238e+00
-3.25569451e-01 2.36552939e-01 -2.58029789e-01 -8.40278029e-01
6.47053599e-01 7.52269268e-01 -6.14590108e-01 3.93181771e-01
-4.50010180e-01 2.33362898e-01 5.93787758e-03 5.14681220e-01
1.17667532e+00 3.83879356e-02 1.25885785e-01 -5.20733356e-01
8.08993816e-01 2.48974055e-01 4.84096229e-01 4.64765519e-01
9.09621492e-02 3.17829549e-01 5.11930048e-01 -2.29881540e-01
-7.89398193e-01 -6.42779648e-01 -2.88445503e-02 1.12303388e+00
7.59283185e-01 -1.10483122e+00 -8.54174554e-01 -7.34294653e-01
1.62592784e-01 4.87841338e-01 -6.35979295e-01 -5.79359949e-01
-7.28156090e-01 -4.09985244e-01 7.89100945e-01 5.93333721e-01
5.38845181e-01 -5.91981292e-01 -5.60859978e-01 3.50685716e-02
-4.85088825e-01 -7.28021264e-01 -1.10166669e+00 -1.11831918e-01
-5.72515011e-01 -1.29094076e+00 -3.27067375e-01 -9.47136223e-01
7.87473202e-01 6.68171227e-01 9.98275101e-01 7.71931231e-01
-3.05657059e-01 6.59654289e-02 -7.21244812e-01 1.42860591e-01
-3.52917075e-01 4.25539553e-01 -4.95631367e-01 -4.55804855e-01
5.58992863e-01 -3.76285344e-01 -6.95344865e-01 3.16494972e-01
-9.59375024e-01 8.66288170e-02 5.18823028e-01 7.10851610e-01
2.38219664e-01 4.81468327e-02 2.17915744e-01 -6.03381515e-01
6.83144748e-01 -7.89845884e-01 -6.93670571e-01 5.42663753e-01
-8.76968861e-01 3.17822367e-01 6.50517821e-01 -7.03350678e-02
-8.88781846e-01 7.86144808e-02 2.94856727e-03 -1.62087947e-01
2.92168498e-01 1.01274228e+00 -1.29217088e-01 7.79708624e-02
5.99209428e-01 6.81055963e-01 -2.01794133e-01 -6.24177217e-01
1.97528094e-01 8.62833261e-01 3.97171855e-01 -6.59243047e-01
7.76211262e-01 1.53607383e-01 -4.41713780e-01 -2.65244037e-01
-3.32188100e-01 -7.27193952e-01 -4.47038919e-01 2.42134884e-01
8.41780961e-01 -9.19145644e-01 -4.10337538e-01 1.84767812e-01
-1.00210798e+00 3.56858373e-01 2.68364638e-01 2.46790066e-01
-2.23620236e-01 8.63317490e-01 -3.91885787e-01 -2.84368485e-01
-3.20537925e-01 -1.65977216e+00 1.03361857e+00 1.25775427e-01
-4.80852842e-01 -6.81401014e-01 1.04475886e-01 2.81254590e-01
3.95573199e-01 -2.97426283e-01 1.66327345e+00 -5.96613526e-01
-6.63310409e-01 -8.69942531e-02 -4.49154675e-01 -1.49842560e-01
2.01537952e-01 5.83980978e-02 -5.61243773e-01 -5.57546735e-01
-2.74151713e-01 -1.78294610e-02 5.96040964e-01 -1.71428919e-01
1.28136277e+00 -2.35488519e-01 -7.09478021e-01 7.33680904e-01
1.67701495e+00 4.14376915e-01 5.19474268e-01 3.96442443e-01
8.41041625e-01 5.37626505e-01 6.02368534e-01 5.42698681e-01
5.03045797e-01 9.92338598e-01 5.87386072e-01 2.37098515e-01
8.26396327e-03 -5.01306236e-01 3.00014555e-01 1.18541670e+00
6.37781262e-01 1.53926924e-01 -1.15987861e+00 6.23896956e-01
-1.84572756e+00 -7.61455417e-01 -1.47575349e-01 2.01536727e+00
6.76974595e-01 -1.36039019e-01 -2.15759397e-01 -9.49591994e-02
6.38397694e-01 1.15971513e-01 -5.33862591e-01 -2.01810703e-01
-5.10024726e-02 -2.41563350e-01 4.46780503e-01 1.08337738e-01
-8.03392053e-01 6.54811263e-01 5.66081238e+00 1.26221049e+00
-9.67433333e-01 -5.56121990e-02 1.19195305e-01 1.44882575e-01
-6.70029342e-01 1.91142440e-01 -6.38459563e-01 9.46264923e-01
5.18519819e-01 -4.46391255e-01 6.11750126e-01 1.17822123e+00
-1.19260222e-01 1.14432134e-01 -9.43909824e-01 1.03916049e+00
1.15879336e-02 -1.42334223e+00 -1.54187158e-02 4.16961201e-02
6.98676109e-01 1.01448797e-01 9.03287157e-03 4.59476918e-01
1.48780107e-01 -9.61854696e-01 8.29345047e-01 2.32044339e-01
7.72595704e-01 -8.49349976e-01 6.76501632e-01 2.03480303e-01
-1.82893956e+00 -4.90951717e-01 -2.71024078e-01 3.67795169e-01
-4.27852243e-01 3.40708494e-01 -1.94146723e-01 8.52106571e-01
7.28938997e-01 9.38925445e-01 -1.05671799e+00 1.09273279e+00
1.56619102e-01 8.55443701e-02 2.35615164e-01 -6.88598752e-02
4.83542800e-01 -4.46352502e-03 2.63402015e-01 1.54808569e+00
6.30785942e-01 -2.52793342e-01 2.59224445e-01 1.16336477e+00
2.24433024e-03 5.10033607e-01 -5.40216804e-01 -5.10355383e-02
9.85837460e-01 9.77256238e-01 -6.56075835e-01 -1.38713837e-01
-9.26899791e-01 9.45742249e-01 3.95772427e-01 3.68067622e-01
-9.69079375e-01 -7.89611280e-01 9.75365818e-01 -1.50822416e-01
4.65652317e-01 1.30437938e-02 -3.61879259e-01 -1.20701742e+00
5.08646905e-01 -9.97207165e-01 6.10903203e-01 -5.55354238e-01
-7.18753636e-01 5.78430653e-01 -2.08729044e-01 -1.34001803e+00
7.47819841e-02 -2.06104487e-01 -4.72991437e-01 1.02618206e+00
-1.23125529e+00 -9.07318115e-01 -3.95088881e-01 4.75976616e-01
6.65368080e-01 -3.63572747e-01 6.28246725e-01 6.10596061e-01
-6.39788449e-01 1.00044847e+00 4.35163647e-01 4.69645232e-01
2.17718974e-01 -7.67961740e-01 6.65054202e-01 1.00858021e+00
2.01246589e-01 1.17199397e+00 3.38892996e-01 -6.69209898e-01
-1.75793827e+00 -1.10105133e+00 6.98407531e-01 -3.57465237e-01
5.84868491e-01 -3.52002263e-01 -1.15695000e+00 2.63224155e-01
-1.13204286e-01 -1.12358928e-01 5.90609550e-01 -3.78389768e-02
-8.48127425e-01 8.57977867e-02 -6.24512315e-01 5.23423731e-01
1.30842817e+00 -1.00967371e+00 -5.54734230e-01 -5.08471355e-02
1.02282572e+00 -3.47611070e-01 -8.54200721e-01 3.47411603e-01
3.89774859e-01 -9.26117301e-01 9.59814668e-01 -4.43062335e-01
5.83291650e-01 -5.11813283e-01 -4.85424817e-01 -9.77246463e-01
-4.00037766e-01 -1.40893415e-01 1.04287090e-02 1.28420925e+00
3.23839992e-01 -1.50291935e-01 5.96649349e-01 4.23582405e-01
-1.95858434e-01 -7.82649755e-01 -7.18531728e-01 -8.15749407e-01
-9.08609387e-03 -4.15164649e-01 1.00763214e+00 1.20683002e+00
4.97106373e-01 1.03657253e-01 6.59780726e-02 1.76909417e-01
1.20078854e-01 6.29472375e-01 4.77072895e-01 -9.98554826e-01
-1.52388722e-01 -8.89898300e-01 -4.22253221e-01 -1.34307742e+00
3.34053606e-01 -1.37540722e+00 -2.90816396e-01 -1.36885262e+00
6.14605188e-01 -6.17252052e-01 -2.36220479e-01 2.69137084e-01
-3.23603243e-01 -4.22618806e-01 4.26831581e-02 6.73665643e-01
-5.93517065e-01 5.83033085e-01 7.30502129e-01 -3.43115479e-01
1.52094867e-02 -3.54031533e-01 -9.43469048e-01 3.08946699e-01
3.74679714e-01 -5.11798561e-01 -7.24345446e-01 -9.07528579e-01
5.18600643e-01 7.38426745e-02 1.39649017e-02 -7.51606584e-01
6.25227213e-01 -1.13605037e-01 -9.42860544e-02 -5.22138298e-01
-3.34464431e-01 -9.34071839e-01 2.56864756e-01 7.38901496e-01
-3.96745890e-01 6.41888797e-01 2.86997497e-01 7.41681278e-01
-4.86605734e-01 -5.01122236e-01 5.03626943e-01 -2.97577605e-02
-1.01928115e+00 1.15678772e-01 -1.18397094e-01 3.61812890e-01
9.35819149e-01 -4.03085887e-01 -3.85217577e-01 2.67265409e-01
-1.96944531e-02 4.26831692e-01 8.45397413e-01 8.97876561e-01
6.62568331e-01 -1.47368407e+00 -3.16281259e-01 4.20368731e-01
8.92904580e-01 -2.48458534e-01 4.08936799e-01 5.88231027e-01
-4.71296936e-01 6.72852278e-01 4.97257747e-02 -4.29190516e-01
-1.38476169e+00 8.24010849e-01 1.27457693e-01 -1.26620829e-01
-6.08192146e-01 6.56282485e-01 3.27970713e-01 -5.77365160e-01
3.06393057e-01 -3.91102284e-01 -2.40517497e-01 -1.23936020e-01
6.08111084e-01 1.91841438e-01 2.45798081e-01 -7.80866861e-01
-6.86120391e-01 8.69694948e-01 -8.13791677e-02 6.05498731e-01
1.01611519e+00 -3.47469270e-01 -5.09631157e-01 -2.35987186e-01
1.54452789e+00 4.28025067e-01 -8.42904747e-01 -4.83139038e-01
6.06483698e-01 -7.88990021e-01 -1.85030103e-01 -4.66664344e-01
-1.26641715e+00 6.79937303e-01 4.38125968e-01 9.12619606e-02
1.09536707e+00 3.48790325e-02 7.64467359e-01 2.17773676e-01
1.86212018e-01 -8.45783591e-01 1.94049761e-01 5.82834423e-01
5.09560108e-01 -9.99140143e-01 -2.07961693e-01 -5.79502106e-01
-4.34657514e-01 1.02931106e+00 7.79884636e-01 3.35778207e-01
4.03486758e-01 3.23001802e-01 -6.34850860e-02 -5.59846520e-01
-5.39960861e-01 1.54918745e-01 5.14772534e-01 2.02037886e-01
4.88099962e-01 -2.97571450e-01 -1.85962841e-01 6.38962924e-01
2.10020877e-02 -4.42566276e-01 2.88718417e-02 1.09281266e+00
-5.46935856e-01 -1.06023192e+00 -1.96232311e-02 4.30596560e-01
-3.37052882e-01 -6.20784760e-01 -2.47344896e-01 5.45873940e-01
1.68887638e-02 7.11078525e-01 5.53786643e-02 -7.86898315e-01
4.24793690e-01 -1.90744653e-01 -2.49791965e-01 -6.24592185e-01
-6.68729961e-01 -2.68976629e-01 -4.40837890e-01 -6.92212045e-01
-5.46874665e-02 -5.09124219e-01 -1.49917459e+00 -2.64256239e-01
-4.90907788e-01 4.41849589e-01 5.45500696e-01 6.69086814e-01
9.33007061e-01 5.04951656e-01 6.54800534e-01 1.64636150e-01
-3.81350338e-01 -4.79232520e-01 -3.17074448e-01 5.66706598e-01
1.01656541e-01 -4.61580694e-01 -9.17615965e-02 -1.61720604e-01] | [7.488550662994385, 8.065035820007324] |
75e7b3a9-9263-41a5-99ad-101a8ef2b5d9 | a-robust-framework-for-deep-learning | 2201.12705 | null | https://arxiv.org/abs/2201.12705v1 | https://arxiv.org/pdf/2201.12705v1.pdf | A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation | Facial emotion recognition is a vast and complex problem space within the domain of computer vision and thus requires a universally accepted baseline method with which to evaluate proposed models. While test datasets have served this purpose in the academic sphere real world application and testing of such models lacks any real comparison. Therefore we propose a framework in which models developed for FER can be compared and contrasted against one another in a constant standardized fashion. A lightweight convolutional neural network is trained on the AffectNet dataset a large variable dataset for facial emotion recognition and a web application is developed and deployed with our proposed framework as a proof of concept. The CNN is embedded into our application and is capable of instant real time facial emotion recognition. When tested on the AffectNet test set this model achieves high accuracy for emotion classification of eight different emotions. Using our framework the validity of this model and others can be properly tested by evaluating a model efficacy not only based on its accuracy on a sample test dataset, but also on in the wild experiments. Additionally, our application is built with the ability to save and store any image captured or uploaded to it for emotion recognition, allowing for the curation of more quality and diverse facial emotion recognition datasets. | ['Mitchell Hanson', 'Dylan Black', 'Thomas Reither', 'Tyler Bauer', 'Rushit Dave', 'Nyle Siddiqui'] | 2022-01-30 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 6.33344427e-02 -7.42992386e-03 3.21858317e-01 -7.12252855e-01
-2.52171308e-01 -3.83729249e-01 6.56801939e-01 -7.91027471e-02
-6.84236884e-01 3.94719332e-01 -3.91381353e-01 1.57253236e-01
-5.56861013e-02 -6.33604288e-01 -3.83584410e-01 -6.33987069e-01
-1.66715026e-01 3.17294240e-01 -3.37089419e-01 -3.61369342e-01
-1.28104046e-01 7.12229669e-01 -2.15968037e+00 3.07187766e-01
3.21007043e-01 1.69336545e+00 -2.92842090e-01 5.62685072e-01
1.68113843e-01 5.92374384e-01 -7.78044701e-01 -6.58995211e-01
3.39677513e-01 -1.54987425e-01 -6.18816078e-01 -5.83956987e-02
5.75016916e-01 -1.73516959e-01 2.96115786e-01 8.53323758e-01
8.64490688e-01 -7.13826492e-02 2.97898799e-01 -1.84043610e+00
-1.20193034e-01 -1.02854371e-01 5.24241030e-02 -2.28985623e-01
4.76401716e-01 1.96405455e-01 5.27058244e-01 -7.14415729e-01
8.25763106e-01 1.09094465e+00 5.57950795e-01 7.45578885e-01
-9.73616660e-01 -8.25434327e-01 -3.03456932e-01 2.92557180e-01
-1.10350180e+00 -5.71931601e-01 8.29628646e-01 -1.38914257e-01
9.80599165e-01 3.57778519e-01 8.28448355e-01 1.51545489e+00
8.34412500e-03 4.22972530e-01 1.44171858e+00 -3.07829261e-01
3.80468220e-01 5.81216514e-01 7.96833038e-02 5.42955756e-01
-1.92213267e-01 2.10711416e-02 -4.69823569e-01 5.66863939e-02
2.01083526e-01 -2.21988961e-01 -1.57800004e-01 -5.08103818e-02
-3.98865223e-01 5.76156378e-01 2.16224313e-01 5.76902926e-01
-6.82218015e-01 6.98555782e-02 8.34718525e-01 7.36149251e-01
6.39233112e-01 2.10178211e-01 -7.91950881e-01 -5.38988233e-01
-9.18953240e-01 -6.95978524e-03 9.19271290e-01 1.77933335e-01
6.66391253e-01 7.22028911e-02 2.17548192e-01 8.30235243e-01
1.97648361e-01 5.39834678e-01 7.49199748e-01 -6.02198362e-01
-3.38465452e-01 8.06895912e-01 -1.36742055e-01 -1.22260582e+00
-5.37989914e-01 -1.52482331e-01 -6.71463251e-01 8.30929220e-01
2.09416032e-01 -1.67472169e-01 -6.41331971e-01 1.83148146e+00
4.70263392e-01 2.56993443e-01 1.86527655e-01 8.53756309e-01
9.74501073e-01 4.30304438e-01 2.80219913e-01 -2.85490662e-01
1.26991212e+00 -5.38423002e-01 -6.65261388e-01 2.07069874e-01
5.45804083e-01 -7.05720782e-01 1.02238691e+00 8.40841711e-01
-7.66615152e-01 -5.32447875e-01 -8.54779720e-01 2.46448934e-01
-7.99662769e-01 2.78351009e-01 6.90847695e-01 1.10402107e+00
-1.46714067e+00 5.44002593e-01 -5.87514520e-01 -6.90163016e-01
5.30125976e-01 6.10716105e-01 -1.10985720e+00 1.74100995e-01
-1.00708807e+00 1.12872183e+00 8.12746063e-02 3.89155358e-01
-7.54753351e-01 -5.01851201e-01 -7.46775389e-01 -1.00534949e-02
-2.59398818e-01 -2.23182425e-01 9.14519131e-01 -2.07441354e+00
-1.69168627e+00 1.32458496e+00 6.77725226e-02 -3.66317034e-01
5.11216760e-01 -3.02591641e-02 -8.17665040e-01 2.34076396e-01
-2.70056427e-01 6.77420855e-01 9.04206753e-01 -1.08508027e+00
-2.97634423e-01 -6.73337638e-01 -8.26525465e-02 -3.40137720e-01
-6.21989846e-01 3.49600583e-01 -1.83413655e-01 -3.63725610e-02
-5.22442579e-01 -8.24049354e-01 2.44268253e-01 5.50735146e-02
2.52187520e-01 -7.61311576e-02 1.24674773e+00 -5.40150106e-01
8.71994257e-01 -2.12948680e+00 -2.53496051e-01 5.13010383e-01
-4.23030891e-02 5.09187162e-01 -3.46327841e-01 5.97084723e-02
-7.09111333e-01 8.21129829e-02 1.64966300e-01 -2.73578525e-01
8.43080580e-02 3.80396955e-02 1.32418066e-01 5.42935908e-01
5.32454669e-01 6.63568437e-01 -5.16244173e-01 -2.38032877e-01
2.85421610e-01 9.09642041e-01 -3.22749734e-01 5.02491236e-01
4.84776683e-02 1.60287336e-01 -1.79676712e-01 7.94438839e-01
8.06014359e-01 2.05851406e-01 1.10501803e-01 -2.82967359e-01
8.67369547e-02 -4.89762634e-01 -1.02745605e+00 1.36653256e+00
-7.62006640e-01 8.88726294e-01 1.87121674e-01 -1.11732817e+00
1.30586445e+00 6.49157047e-01 5.45581758e-01 -1.00348759e+00
7.09150672e-01 9.31743309e-02 -6.32874221e-02 -8.34043145e-01
3.36546123e-01 -1.69983417e-01 1.58363089e-01 3.61206383e-01
5.33140898e-01 1.56597838e-01 1.66364461e-01 -2.23789990e-01
1.21002531e+00 2.24349782e-01 -1.00374529e-02 -2.06116870e-01
6.80828035e-01 -2.68647701e-01 1.60871521e-01 5.36241531e-02
-6.05329037e-01 1.65352330e-01 4.82885331e-01 -6.93703473e-01
-8.40824008e-01 -6.22234702e-01 -3.28223258e-01 1.19597006e+00
-4.29107964e-01 -3.28573853e-01 -9.08707976e-01 -7.36649334e-01
-2.28723049e-01 3.84336174e-01 -1.19381535e+00 -1.36473000e-01
6.69005439e-02 -6.11062586e-01 6.60259485e-01 4.79622856e-02
6.32110238e-01 -1.54340160e+00 -1.21676838e+00 -8.10292736e-02
1.59835368e-01 -9.25741553e-01 5.98482788e-01 9.01413932e-02
-4.30772632e-01 -1.30790591e+00 -4.33751851e-01 -5.43435812e-01
4.70722049e-01 -3.87997210e-01 1.31743276e+00 1.52875379e-01
-5.45999587e-01 8.53957176e-01 -5.21081030e-01 -8.74678969e-01
-4.34225529e-01 -2.84909427e-01 -7.32316524e-02 4.98988748e-01
5.87822258e-01 -6.74738109e-01 -6.42001092e-01 2.51267999e-01
-1.14661825e+00 -4.61898834e-01 3.01447242e-01 5.46988010e-01
1.65905476e-01 -2.42357373e-01 6.33650839e-01 -5.10695934e-01
7.49948561e-01 -4.83557910e-01 -3.99495542e-01 2.85041451e-01
-5.82697809e-01 -4.32857752e-01 4.56815243e-01 -3.16826582e-01
-8.74857306e-01 -7.61242360e-02 -5.21097898e-01 -4.97949451e-01
-5.49797058e-01 4.85958129e-01 -3.50771099e-01 -2.70157695e-01
7.47141838e-01 -8.95613804e-02 4.42112207e-01 -1.21983357e-01
1.58032969e-01 7.16859102e-01 2.41762206e-01 -3.86575192e-01
2.20144421e-01 3.27334046e-01 7.20210522e-02 -8.05058002e-01
-1.56282037e-01 -1.04975231e-01 -5.20126641e-01 -9.22760904e-01
6.73752785e-01 -6.48222864e-01 -1.16957152e+00 6.97246671e-01
-9.85211134e-01 -4.09715414e-01 -5.39781451e-02 1.04764022e-01
-4.79048401e-01 -2.10879162e-01 -2.09995717e-01 -1.05430210e+00
-4.44902480e-01 -1.02346480e+00 1.13444865e+00 2.69782066e-01
-3.28645974e-01 -1.04767299e+00 9.50371325e-02 5.78568093e-02
6.28212690e-01 7.57608354e-01 4.74030465e-01 -6.94330513e-01
2.50915408e-01 -6.01395309e-01 -4.95827291e-03 7.58762717e-01
7.34639838e-02 5.77870369e-01 -1.41819239e+00 -1.20099671e-01
1.16639674e-01 -9.63808835e-01 4.35756922e-01 -7.18531385e-02
1.10411310e+00 -8.53600632e-03 3.85330878e-02 3.65907878e-01
1.57389617e+00 3.06521859e-02 1.04982436e+00 5.36241591e-01
3.03024799e-02 8.96930993e-01 5.78738749e-01 3.46060187e-01
-2.98406184e-02 6.59383416e-01 7.44039834e-01 -2.58127987e-01
3.16040814e-01 4.27434564e-01 4.12500679e-01 2.23470211e-01
-2.37880513e-01 -8.15672353e-02 -8.67515624e-01 2.68352568e-01
-1.54366958e+00 -1.04813695e+00 4.61863503e-02 2.06444645e+00
5.61695397e-01 -2.04776853e-01 1.77252248e-01 3.37013334e-01
4.01952595e-01 -1.75619140e-01 -2.12316349e-01 -1.02826667e+00
-2.12814882e-01 8.47504675e-01 -3.33941638e-01 -1.51924063e-02
-9.96937335e-01 8.06276083e-01 5.82418299e+00 5.52000046e-01
-2.05523229e+00 9.50940549e-02 1.03765082e+00 -2.59088010e-01
2.86189318e-01 -6.69306517e-01 -1.53920159e-01 2.92129189e-01
1.38650501e+00 -1.86332234e-03 2.05204949e-01 1.26826978e+00
4.11531806e-01 -1.63149953e-01 -9.71807301e-01 1.36446035e+00
3.19087088e-01 -1.02644110e+00 -9.13280845e-02 -1.38174266e-01
3.14001650e-01 -1.24918975e-01 1.98929936e-01 4.49123412e-01
-2.76641279e-01 -1.45184863e+00 5.74594200e-01 6.53793633e-01
8.63716662e-01 -8.65905643e-01 9.97027159e-01 -8.27513561e-02
-6.47968948e-01 5.89828007e-02 -1.36901706e-01 -1.33697212e-01
-3.35658938e-01 2.80457377e-01 -6.38562500e-01 4.53271359e-01
9.89540398e-01 5.29839337e-01 -7.50598431e-01 6.84102952e-01
1.94728836e-01 6.21959984e-01 -3.34622085e-01 -8.26110840e-02
1.75635457e-01 -1.60244659e-01 1.66211072e-02 1.37394381e+00
3.89294446e-01 -2.54283339e-01 -4.07031894e-01 4.21672553e-01
-9.42429993e-03 6.42802894e-01 -7.04580605e-01 -5.55636212e-02
-2.23893285e-01 1.96531582e+00 -6.54225588e-01 -1.97665051e-01
-2.74639994e-01 1.02862167e+00 3.80689681e-01 1.57203287e-01
-9.95708585e-01 -8.20172280e-02 9.43464875e-01 -2.20785514e-01
4.21113940e-03 3.13502789e-01 1.41936705e-01 -9.30254757e-01
1.03609107e-01 -1.11490309e+00 1.75182611e-01 -1.01195514e+00
-1.19135439e+00 1.23149645e+00 -3.27090442e-01 -1.00028694e+00
-3.00176471e-01 -1.16170013e+00 -6.96794391e-01 7.68161297e-01
-1.28912330e+00 -1.27176678e+00 -8.15791965e-01 1.06678104e+00
-2.02880204e-02 -4.00481224e-01 1.40535426e+00 4.45675880e-01
-6.78229690e-01 6.45758808e-01 -2.77490497e-01 1.76941141e-01
9.35773075e-01 -9.01519299e-01 -5.17524183e-01 4.40231472e-01
1.22626334e-01 3.36734653e-01 7.84388304e-01 -8.77586082e-02
-1.26521719e+00 -1.04370499e+00 5.00223041e-01 -3.62249166e-01
4.54212546e-01 -4.07440275e-01 -8.46153378e-01 3.12023401e-01
4.75663841e-01 2.31030211e-01 1.03384912e+00 8.50299522e-02
-5.44688702e-01 -4.77882206e-01 -1.57491434e+00 3.03943247e-01
4.94218916e-01 -3.39883238e-01 -2.09898114e-01 1.29149020e-01
-9.21623483e-02 4.61183079e-02 -9.74701703e-01 4.67114091e-01
9.65305686e-01 -1.53485262e+00 3.31106931e-01 -8.23068798e-01
7.95544147e-01 1.40412763e-01 -1.57113791e-01 -1.24556971e+00
4.26967323e-01 -2.93723017e-01 3.19444984e-01 1.42532861e+00
3.21834505e-01 -6.60392463e-01 8.23659897e-01 9.22278285e-01
3.57408851e-01 -9.14415956e-01 -1.07163775e+00 -4.91136521e-01
-1.79596305e-01 -9.87475634e-01 4.55223143e-01 1.08907342e+00
-1.30208358e-01 2.01438501e-01 1.30479112e-02 -1.36017293e-01
1.15178332e-01 -1.36129811e-01 1.13131642e+00 -1.23094320e+00
1.76173940e-01 -6.63129866e-01 -1.19625700e+00 3.11211616e-01
5.95164478e-01 -8.05055618e-01 -2.79408813e-01 -9.50057328e-01
-2.00255471e-03 -2.04911038e-01 -3.99054646e-01 5.89164495e-01
2.33418494e-01 7.70890057e-01 1.78725675e-01 -3.41978937e-01
-5.13717234e-01 5.50613463e-01 5.95086098e-01 3.37278098e-02
1.23258978e-01 -3.12950283e-01 -3.06568682e-01 6.40066206e-01
7.35076368e-01 -2.67010093e-01 -2.69454390e-01 1.88759267e-01
2.30427250e-01 -2.23645359e-01 7.26026356e-01 -1.24676383e+00
-2.53692776e-01 2.71182775e-01 6.79614842e-01 2.14857891e-01
4.18780804e-01 -1.35546851e+00 2.51166105e-01 2.15071440e-01
-1.37709782e-01 1.31939530e-01 6.01010501e-01 -3.66055267e-03
-4.15943891e-01 2.62019373e-02 7.99765468e-01 1.18168868e-01
-9.46669996e-01 3.48326802e-01 -1.70642838e-01 -3.83813828e-01
1.25607324e+00 -3.95327002e-01 -5.94089478e-02 -5.94838798e-01
-1.00423872e+00 -1.44023508e-01 4.41126406e-01 5.87727726e-01
4.85499203e-01 -1.26227295e+00 -6.21733308e-01 3.11013877e-01
3.56219679e-01 -8.18972111e-01 3.02938968e-01 6.89513028e-01
-4.63164389e-01 6.70287311e-02 -9.05461967e-01 -6.06793225e-01
-1.86384892e+00 4.34626639e-01 7.26551831e-01 -2.17176042e-02
3.24897282e-02 6.02579594e-01 -2.94843405e-01 -5.00023007e-01
1.57135367e-01 7.77872130e-02 -4.25916404e-01 2.67993152e-01
7.31382728e-01 -7.82656223e-02 4.08718348e-01 -8.63833845e-01
-4.05253530e-01 2.26279616e-01 4.08513099e-01 -1.63975626e-01
1.64739001e+00 2.19253734e-01 -4.81021315e-01 4.76840436e-01
1.49123454e+00 -2.96665490e-01 -9.10239458e-01 5.15132129e-01
-1.48166686e-01 -3.28297377e-01 -1.30648771e-02 -1.21875536e+00
-1.45867503e+00 7.84230709e-01 1.38944817e+00 2.67396659e-01
1.64771688e+00 -4.13544387e-01 9.79200378e-02 3.53689432e-01
3.70183170e-01 -1.13057113e+00 1.05323486e-01 2.52963424e-01
1.11578572e+00 -1.41613555e+00 -4.98317033e-01 -4.07052645e-03
-6.19142532e-01 1.45955873e+00 6.61896884e-01 -1.66302830e-01
9.17637527e-01 3.33137423e-01 6.23494327e-01 -4.46655542e-01
-8.85341823e-01 -1.95438147e-01 1.29425183e-01 7.02869296e-01
7.32026696e-01 -1.75637946e-01 -3.77156526e-01 6.86801374e-01
-2.82040685e-01 3.95606190e-01 2.45235085e-01 6.71876431e-01
5.14483033e-03 -1.20032728e+00 -3.37780386e-01 2.54521310e-01
-6.60492599e-01 2.72838086e-01 -6.22014701e-01 1.11061776e+00
3.87781918e-01 9.90892828e-01 8.76422077e-02 -4.38556105e-01
4.19235617e-01 5.98941267e-01 5.13724327e-01 -8.11438635e-02
-1.07881904e+00 -3.78893226e-01 2.23604023e-01 -9.28547382e-01
-7.69499004e-01 -4.07626122e-01 -8.13594162e-01 -2.49192700e-01
2.63744667e-02 9.53883752e-02 1.30959463e+00 8.74517798e-01
5.38010359e-01 2.97696471e-01 7.59444416e-01 -9.02841449e-01
-1.80356093e-02 -1.11112583e+00 -6.48220301e-01 1.00974762e+00
-1.12779312e-01 -6.62403405e-01 -1.92791671e-01 1.54135570e-01] | [13.545973777770996, 1.94833242893219] |
d1f5bd26-1b07-48bb-9c44-46a6d02b7641 | learning-high-fidelity-depths-of-dressed | 2103.03319 | null | https://arxiv.org/abs/2103.03319v3 | https://arxiv.org/pdf/2103.03319v3.pdf | Self-supervised 3D Representation Learning of Dressed Humans from Social Media Videos | A key challenge of learning a visual representation for the 3D high fidelity geometry of dressed humans lies in the limited availability of the ground truth data (e.g., 3D scanned models), which results in the performance degradation of 3D human reconstruction when applying to real-world imagery. We address this challenge by leveraging a new data resource: a number of social media dance videos that span diverse appearance, clothing styles, performances, and identities. Each video depicts dynamic movements of the body and clothes of a single person while lacking the 3D ground truth geometry. To learn a visual representation from these videos, we present a new self-supervised learning method to use the local transformation that warps the predicted local geometry of the person from an image to that of another image at a different time instant. This allows self-supervision by enforcing a temporal coherence over the predictions. In addition, we jointly learn the depths along with the surface normals that are highly responsive to local texture, wrinkle, and shade by maximizing their geometric consistency. Our method is end-to-end trainable, resulting in high fidelity depth estimation that predicts fine geometry faithful to the input real image. We further provide a theoretical bound of self-supervised learning via an uncertainty analysis that characterizes the performance of the self-supervised learning without training. We demonstrate that our method outperforms the state-of-the-art human depth estimation and human shape recovery approaches on both real and rendered images. | ['Hyun Soo Park', 'Yasamin Jafarian'] | 2021-03-04 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Jafarian_Learning_High_Fidelity_Depths_of_Dressed_Humans_by_Watching_Social_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Jafarian_Learning_High_Fidelity_Depths_of_Dressed_Humans_by_Watching_Social_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 2.66118377e-01 1.49390921e-01 3.05341333e-02 -3.92338246e-01
-5.60048401e-01 -3.42746198e-01 3.86882603e-01 -1.21465452e-01
-1.41250670e-01 4.03880507e-01 2.87114978e-01 6.18253767e-01
2.96674401e-01 -7.84442127e-01 -1.04531705e+00 -4.56928521e-01
4.58630174e-02 7.20166743e-01 7.97205865e-02 -2.57112414e-01
-1.09950736e-01 4.03077930e-01 -1.63638973e+00 2.69221544e-01
3.91381770e-01 1.13049352e+00 8.35377350e-02 6.40524328e-01
4.48946774e-01 6.33571446e-01 -1.47183225e-01 -2.81938285e-01
5.95590293e-01 -4.63422179e-01 -5.54587305e-01 7.55081356e-01
9.70348597e-01 -6.47753775e-01 -6.87296152e-01 6.78378701e-01
3.49333704e-01 1.62678733e-01 6.94036126e-01 -1.02530062e+00
-4.81148899e-01 -1.84821174e-01 -6.96467519e-01 -2.60463685e-01
9.11848247e-01 2.79593915e-01 7.60016680e-01 -6.78181231e-01
1.04365921e+00 1.19320464e+00 8.60612154e-01 6.10417604e-01
-1.44344437e+00 -3.25410008e-01 6.87433928e-02 2.22124364e-02
-1.40486169e+00 -3.83302778e-01 1.02774274e+00 -6.01741135e-01
7.21096218e-01 3.97346914e-03 1.15199232e+00 1.27544808e+00
1.95217252e-01 6.49526596e-01 1.14331937e+00 -5.85297465e-01
1.97215274e-01 -9.18533653e-02 -6.27855301e-01 1.35450566e+00
-1.29902542e-01 3.37366074e-01 -9.17918265e-01 -8.08707327e-02
1.39027512e+00 6.12689257e-02 -3.15619409e-01 -9.17436302e-01
-1.19870389e+00 4.95969445e-01 5.32931745e-01 -3.24070632e-01
-3.03222954e-01 2.10249081e-01 4.55711447e-02 5.67587018e-02
5.95351338e-01 3.57345715e-02 -2.85499334e-01 8.46855789e-02
-8.41714799e-01 3.78906995e-01 6.32037044e-01 8.85424793e-01
8.83438945e-01 3.88904326e-02 1.58236057e-01 5.23506582e-01
3.99110705e-01 9.25211310e-01 1.58777595e-01 -1.26660180e+00
2.97559500e-01 5.05070746e-01 2.04773590e-01 -1.16787064e+00
-2.98506439e-01 -1.17483191e-01 -7.55881369e-01 3.42878163e-01
6.26507342e-01 7.88385421e-02 -8.22636724e-01 1.71286809e+00
6.72821760e-01 2.21371606e-01 -3.51318955e-01 1.31255519e+00
5.22546470e-01 3.80346417e-01 -2.21626282e-01 -3.71222943e-02
1.06660080e+00 -8.40709865e-01 -3.51314664e-01 -4.84930634e-01
1.37271419e-01 -5.75339139e-01 1.10126305e+00 3.25426370e-01
-1.14052832e+00 -5.61274588e-01 -9.79143500e-01 -1.74930274e-01
2.77678043e-01 -8.21415484e-02 4.44287568e-01 3.08759868e-01
-6.23197377e-01 8.37740958e-01 -1.06981683e+00 -5.11513710e-01
2.57089585e-01 7.51364380e-02 -6.35188699e-01 -2.15433404e-01
-7.62328804e-01 6.33540869e-01 -5.26167601e-02 3.27182449e-02
-9.99944270e-01 -7.71589398e-01 -1.18320012e+00 -4.66184378e-01
4.16997313e-01 -9.61108685e-01 9.70646977e-01 -1.09818053e+00
-1.61535835e+00 1.28537023e+00 6.42727688e-02 -1.70940787e-01
9.32522595e-01 -3.18379074e-01 -2.20278800e-02 4.06175762e-01
1.57215983e-01 6.04054749e-01 9.75396335e-01 -1.70274591e+00
-1.88305840e-01 -6.12118721e-01 -1.32458925e-01 4.24804956e-01
2.03087796e-02 -4.99697685e-01 -6.52060866e-01 -7.06480384e-01
3.43350381e-01 -1.02977300e+00 1.84643664e-03 6.53629839e-01
-3.64129782e-01 4.22361165e-01 5.32739580e-01 -7.57741392e-01
6.27931952e-01 -1.95291793e+00 5.53300083e-01 3.27738762e-01
3.05103008e-02 -4.72456366e-01 1.09559596e-02 1.98980629e-01
3.03407282e-01 -4.07267362e-01 -2.13058606e-01 -7.54553020e-01
-1.08497187e-01 3.86655629e-01 -1.54510364e-01 9.34659123e-01
-1.04098909e-01 8.08913887e-01 -9.76288140e-01 -7.22085059e-01
4.55257922e-01 7.17357993e-01 -5.78030527e-01 6.35301948e-01
-2.90551871e-01 7.94542253e-01 -2.48445839e-01 8.06525886e-01
3.50574672e-01 -4.51899022e-01 3.21573943e-01 -6.36502504e-01
1.87707260e-01 -1.92901134e-01 -1.18571067e+00 2.30686235e+00
-3.48583668e-01 1.54065266e-01 6.31536022e-02 -6.21905088e-01
7.93839693e-01 1.64243594e-01 7.46265054e-01 -7.15649188e-01
1.14226952e-01 -6.71310648e-02 -7.24121630e-01 -4.88887131e-01
1.84483871e-01 -4.07330155e-01 -4.44488786e-02 5.58281064e-01
1.14855915e-01 -4.24665362e-01 -4.31202710e-01 -3.85134146e-02
8.25266600e-01 7.35343695e-01 1.11777447e-01 6.97776750e-02
4.89467382e-03 -9.88586806e-03 5.28301895e-01 2.90661067e-01
6.64734170e-02 9.89858389e-01 4.44018282e-02 -6.94232643e-01
-1.55159760e+00 -1.31288874e+00 9.51442197e-02 7.23139882e-01
3.78133386e-01 -4.25058067e-01 -7.60553181e-01 -4.21145469e-01
9.23514590e-02 1.11478440e-01 -9.26927686e-01 -1.56312823e-01
-6.81010723e-01 -1.97729945e-01 2.65021056e-01 5.59817672e-01
5.67656994e-01 -8.06846857e-01 -9.63243604e-01 -2.78112255e-02
-3.69831383e-01 -1.44778275e+00 -7.17551589e-01 -1.84318841e-01
-8.12494934e-01 -1.16723228e+00 -5.43455660e-01 -6.55197859e-01
8.20987165e-01 8.20533186e-02 1.11403608e+00 6.63143815e-03
-5.29190540e-01 8.15128684e-01 -1.58701822e-01 2.80474305e-01
-2.97394514e-01 -3.93764049e-01 3.18404227e-01 2.66808242e-01
-2.64128655e-01 -7.02959955e-01 -7.90637791e-01 5.04756391e-01
-5.77481091e-01 3.79099041e-01 1.58175856e-01 8.22077751e-01
1.08990920e+00 -1.49002299e-01 -3.50533485e-01 -6.21705234e-01
-7.28628561e-02 -1.27630651e-01 -3.78117949e-01 3.36394131e-01
-2.81090647e-01 5.27136996e-02 4.11458850e-01 -5.54024279e-01
-1.05061448e+00 5.86013377e-01 2.28955746e-01 -7.75802910e-01
-1.17534764e-01 1.64970215e-02 1.32730296e-02 -1.83688611e-01
8.21815193e-01 7.52576813e-02 3.36764514e-01 -4.66773778e-01
2.31890589e-01 1.58682570e-01 9.18092012e-01 -8.57681811e-01
8.98765862e-01 8.22437525e-01 1.86182082e-01 -8.86560559e-01
-1.12024271e+00 -3.23805332e-01 -1.02174807e+00 -5.61062932e-01
8.87292802e-01 -1.19939303e+00 -6.78604960e-01 5.15224218e-01
-9.70145345e-01 -7.80289233e-01 -4.78247374e-01 3.82388979e-01
-1.10674703e+00 6.09918594e-01 -7.17408359e-01 -6.13911271e-01
-1.79285958e-01 -6.89751863e-01 1.56802773e+00 -1.45034894e-01
-3.47468108e-01 -9.60720003e-01 1.76604927e-01 7.10100889e-01
-2.40830258e-01 9.66944277e-01 7.13113368e-01 4.04567122e-01
-5.94210505e-01 -4.48851027e-02 1.12307526e-01 3.00924391e-01
1.79244086e-01 -2.43027642e-01 -8.04516315e-01 -4.84591663e-01
-7.45539889e-02 -7.60382831e-01 5.07691324e-01 3.09903562e-01
8.04288149e-01 -3.71214509e-01 -1.01951383e-01 8.90777051e-01
1.44610536e+00 -6.02380574e-01 3.42723846e-01 2.22342178e-01
1.07750094e+00 7.17438459e-01 5.55310547e-01 7.93347776e-01
4.60409343e-01 1.02139306e+00 4.61552471e-01 -2.21193790e-01
-2.96813756e-01 -7.72597551e-01 3.67762327e-01 4.82097358e-01
-5.04982173e-01 1.84118673e-01 -7.85951376e-01 2.27474183e-01
-1.87477720e+00 -8.74745488e-01 2.43635654e-01 2.56457305e+00
8.02056909e-01 -6.31972477e-02 3.30920428e-01 7.17637222e-03
3.17310274e-01 4.78913300e-02 -7.69920111e-01 2.16166586e-01
-1.48980945e-01 8.35880935e-02 4.51283514e-01 5.50185740e-01
-9.08449113e-01 8.52566838e-01 6.43347645e+00 2.12014526e-01
-9.75588858e-01 3.59594524e-02 3.98672551e-01 -2.79144436e-01
-2.28053287e-01 -2.20334679e-01 -3.25437337e-01 1.27773792e-01
4.28332001e-01 2.28978097e-01 7.27209449e-01 6.78804278e-01
1.96471870e-01 -4.03825343e-02 -1.30338645e+00 1.28774762e+00
5.22298157e-01 -1.22889972e+00 -4.99239080e-02 1.15938544e-01
9.57134426e-01 -2.73683518e-01 -8.96621421e-02 -2.62873530e-01
1.55504018e-01 -1.04440284e+00 1.19798505e+00 8.53694856e-01
1.15272927e+00 -5.64815998e-01 2.53622323e-01 4.18862820e-01
-1.14437068e+00 1.13494366e-01 -2.00637475e-01 -2.53620148e-02
3.77750278e-01 3.48748833e-01 -5.27133584e-01 3.18972379e-01
7.93631613e-01 7.89935529e-01 -4.64817315e-01 5.23203254e-01
-2.57583201e-01 1.65768027e-01 -5.17917335e-01 5.82570255e-01
-2.18695775e-01 -1.40935943e-01 3.82523715e-01 7.38567054e-01
1.66019022e-01 2.85954505e-01 5.22201657e-01 9.98605549e-01
2.92353351e-02 -3.42952721e-02 -5.28351367e-01 3.13188732e-01
1.84585899e-01 9.02530849e-01 -4.99892414e-01 -3.56676169e-02
-1.90471232e-01 1.56859469e+00 4.51751858e-01 3.55803818e-01
-6.88921928e-01 4.87393558e-01 5.35474420e-01 8.93228889e-01
1.18477426e-01 -4.98199552e-01 -6.06457591e-02 -1.34707642e+00
2.60202646e-01 -7.85092592e-01 2.57516056e-01 -1.01939356e+00
-1.49410343e+00 5.03030062e-01 1.05143979e-01 -1.31433105e+00
-3.56321752e-01 -4.84751195e-01 -1.31742507e-01 4.93117779e-01
-1.06875718e+00 -1.55846345e+00 -7.55090833e-01 8.34773242e-01
2.85348296e-01 6.48302678e-03 9.05471385e-01 -4.99874987e-02
-1.64181978e-01 5.97993851e-01 -2.34387264e-01 1.75528139e-01
9.04865921e-01 -1.17753100e+00 2.70315289e-01 4.90856826e-01
2.90442407e-01 1.39718533e-01 8.10301423e-01 -6.95478737e-01
-1.66756475e+00 -9.29529011e-01 3.52990508e-01 -5.94522595e-01
2.83557624e-01 -5.19722700e-01 -6.50402009e-01 7.87644982e-01
-3.98355007e-01 4.57673758e-01 3.58074278e-01 -4.28735539e-02
-5.21845341e-01 -1.14889689e-01 -1.31432366e+00 4.82030421e-01
1.38199198e+00 -7.61376500e-01 -3.83172512e-01 3.60386908e-01
3.81837219e-01 -9.42109764e-01 -9.88364875e-01 2.85611004e-01
9.12807405e-01 -1.15413654e+00 1.23991704e+00 -4.00685579e-01
5.89047134e-01 -2.11984023e-01 -5.10363758e-01 -1.09721518e+00
-9.20667276e-02 -4.77339983e-01 -4.63613272e-01 7.04442561e-01
-2.33809248e-01 4.24314290e-02 1.11574352e+00 7.49906540e-01
2.46996865e-01 -8.61971617e-01 -8.64921868e-01 -6.47055984e-01
-1.75944060e-01 -3.09953779e-01 3.08446765e-01 7.93369055e-01
-1.96937695e-01 1.01567216e-01 -8.53871584e-01 2.07192838e-01
1.21212304e+00 3.10172230e-01 9.96691883e-01 -1.12594593e+00
-5.81729591e-01 1.63815558e-01 -6.97040200e-01 -1.19248760e+00
2.02612936e-01 -6.08115852e-01 1.43885389e-01 -1.22642875e+00
2.20381364e-01 -3.03780138e-01 3.91532391e-01 5.21830618e-01
2.91564375e-01 6.83532178e-01 1.35177210e-01 2.77633309e-01
-5.19962847e-01 6.92059815e-01 1.42405558e+00 -2.78590545e-02
-2.24807084e-01 -2.29197845e-01 -1.48018107e-01 9.61668074e-01
3.38141233e-01 -2.64779001e-01 -2.86547840e-01 -5.69291353e-01
1.73290923e-01 3.54278117e-01 9.00503516e-01 -1.07382309e+00
1.02444753e-01 -2.60973573e-01 8.34332764e-01 -2.78299570e-01
6.91522002e-01 -8.37564468e-01 4.11693573e-01 2.29924425e-01
-2.47510627e-01 -8.71126205e-02 -2.08469313e-02 9.42811072e-01
2.44864777e-01 3.45406532e-01 8.88979077e-01 -4.06008005e-01
-5.21603465e-01 6.09280169e-01 1.96827754e-01 2.28670508e-01
7.69034922e-01 -4.69217330e-01 2.63711840e-01 -5.48686922e-01
-7.93236077e-01 -5.26241288e-02 1.20372474e+00 2.74169207e-01
8.87181222e-01 -1.58354533e+00 -6.96538568e-01 4.69596863e-01
2.32394353e-01 3.18243802e-01 4.25563723e-01 4.11849260e-01
-6.87596858e-01 -1.54714718e-01 -5.02631128e-01 -9.34511125e-01
-1.27589822e+00 3.31338018e-01 5.84625661e-01 -4.31512343e-03
-1.15959704e+00 5.46410143e-01 1.61903873e-01 -4.45768237e-01
2.15313852e-01 -1.25948384e-01 4.77728128e-01 -5.64880490e-01
3.58897299e-01 2.40698114e-01 -8.85582119e-02 -1.11404371e+00
-2.19133765e-01 1.14007807e+00 3.53604674e-01 -2.80691534e-01
1.38814270e+00 -2.38107175e-01 2.16533959e-01 6.15811288e-01
1.15529430e+00 -2.24085711e-02 -2.08242226e+00 -5.09348929e-01
-6.24669492e-01 -8.74298990e-01 -1.13240099e-02 -6.86875939e-01
-1.11034119e+00 5.39570451e-01 6.71775758e-01 -4.89516199e-01
8.99063528e-01 2.09353924e-01 8.67111385e-01 1.91138312e-01
8.99038672e-01 -1.14695907e+00 6.51838720e-01 1.55027166e-01
1.00820553e+00 -1.30310643e+00 4.60222155e-01 -5.56290209e-01
-7.29879439e-01 1.00241268e+00 6.77079141e-01 -2.47883230e-01
5.36485493e-01 8.58038664e-02 1.15858894e-02 -3.86315823e-01
-4.54315990e-01 5.79090491e-02 4.88099933e-01 7.76954234e-01
1.44695386e-01 9.61716995e-02 4.21961010e-01 3.19632113e-01
-4.19965833e-01 -9.87771004e-02 2.62786090e-01 8.29862773e-01
-7.26935193e-02 -8.07840884e-01 -4.55376446e-01 4.22570221e-02
8.16142410e-02 3.75879377e-01 -3.69263411e-01 6.64414406e-01
2.38521144e-01 5.92522740e-01 1.69585437e-01 -4.43466485e-01
5.64207792e-01 -1.71940148e-01 1.31034446e+00 -7.04915285e-01
-5.43797761e-02 1.38492778e-01 -1.46902159e-01 -8.99363220e-01
-6.11149073e-01 -7.18810439e-01 -1.33713067e+00 -4.37604666e-01
1.39906526e-01 -4.48063552e-01 3.04959476e-01 9.17639256e-01
1.05225042e-01 8.03403184e-02 6.06939614e-01 -1.34735823e+00
-3.65915388e-01 -5.25464654e-01 -8.19799602e-01 1.00666845e+00
3.57515842e-01 -9.94127929e-01 -3.17650259e-01 4.36191410e-01] | [7.214889049530029, -1.262578010559082] |
760f2f1d-7c20-4b93-a6be-ef06e918c336 | tactical-rewind-self-correction-via | 1903.02547 | null | http://arxiv.org/abs/1903.02547v2 | http://arxiv.org/pdf/1903.02547v2.pdf | Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation | We present the Frontier Aware Search with backTracking (FAST) Navigator, a
general framework for action decoding, that achieves state-of-the-art results
on the Room-to-Room (R2R) Vision-and-Language navigation challenge of Anderson
et. al. (2018). Given a natural language instruction and photo-realistic image
views of a previously unseen environment, the agent was tasked with navigating
from source to target location as quickly as possible. While all current
approaches make local action decisions or score entire trajectories using beam
search, ours balances local and global signals when exploring an unobserved
environment. Importantly, this lets us act greedily but use global signals to
backtrack when necessary. Applying FAST framework to existing state-of-the-art
models achieved a 17% relative gain, an absolute 6% gain on Success rate
weighted by Path Length (SPL). | ['Jianfeng Gao', 'Yejin Choi', 'Liyiming Ke', 'Jingjing Liu', 'Zhe Gan', 'Ari Holtzman', 'Yonatan Bisk', 'Xiujun Li', 'Siddhartha Srinivasa'] | 2019-03-06 | tactical-rewind-self-correction-via-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ke_Tactical_Rewind_Self-Correction_via_Backtracking_in_Vision-And-Language_Navigation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ke_Tactical_Rewind_Self-Correction_via_Backtracking_in_Vision-And-Language_Navigation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['vision-language-navigation'] | ['computer-vision'] | [ 3.06641340e-01 7.04709217e-02 -2.19329566e-01 -2.42806673e-01
-1.15135539e+00 -8.47627878e-01 7.57003188e-01 1.21607587e-01
-7.96596646e-01 5.69658816e-01 6.17551565e-01 -7.42492497e-01
-1.51953176e-01 -4.71928358e-01 -1.08663034e+00 -6.15051448e-01
-2.18621433e-01 4.23472643e-01 4.24252361e-01 -2.89588302e-01
6.64804995e-01 3.15263391e-01 -1.49024689e+00 2.98419625e-01
5.25584340e-01 5.24612606e-01 7.99940169e-01 1.24967539e+00
1.54557481e-01 1.44167471e+00 2.21686102e-02 3.59976500e-01
2.85513788e-01 -3.54927868e-01 -1.11208975e+00 -4.62945074e-01
7.18411624e-01 -5.01003265e-01 -7.77445972e-01 8.79652083e-01
5.86571097e-01 8.54629755e-01 2.64203727e-01 -7.48217285e-01
-7.10094273e-01 6.98158741e-01 -2.96764016e-01 4.65140343e-01
7.36032426e-01 8.61284256e-01 6.86127543e-01 -9.68667924e-01
8.94743562e-01 1.22655261e+00 2.49836192e-01 7.00325191e-01
-9.89394546e-01 -3.44842106e-01 7.70103276e-01 4.63436037e-01
-9.75624621e-01 -7.17476368e-01 1.82311803e-01 -3.58651072e-01
1.39923227e+00 7.38706365e-02 7.03106582e-01 1.68021178e+00
2.82570064e-01 1.13495636e+00 1.08830357e+00 -2.85874248e-01
3.65869075e-01 -7.15755343e-01 9.59231928e-02 1.04750276e+00
-3.09104711e-01 4.20919687e-01 -1.18247306e+00 4.40955311e-01
5.83157539e-01 -1.82821929e-01 -4.55005497e-01 -7.07396150e-01
-1.79598784e+00 4.13216919e-01 6.57113016e-01 5.57706244e-02
-4.61260527e-01 7.53350616e-01 1.34175606e-02 1.77533358e-01
-2.15180904e-01 5.90271115e-01 -3.57119501e-01 -9.76347029e-01
-6.17422163e-01 3.18863988e-01 2.71523386e-01 1.30492997e+00
3.93919051e-01 2.47709807e-02 -7.31416106e-01 2.29892060e-01
5.32639205e-01 5.36750674e-01 3.74027014e-01 -1.51264763e+00
7.98197567e-01 1.70155138e-01 5.42748511e-01 -4.47707385e-01
-4.94174391e-01 -4.12056267e-01 2.08088607e-02 4.17140514e-01
5.94525874e-01 1.53937384e-01 -1.33275068e+00 1.86741662e+00
4.50608432e-01 2.99122453e-01 2.01928377e-01 9.13034260e-01
6.12408280e-01 5.83057344e-01 2.64798962e-02 3.39388281e-01
9.74689782e-01 -1.88917029e+00 -7.57722616e-01 -8.25894892e-01
7.98887074e-01 -3.84326071e-01 1.27601135e+00 6.93288028e-01
-1.21474302e+00 -4.61903751e-01 -7.13034809e-01 -1.94870934e-01
-2.15952516e-01 9.40551423e-03 4.86445278e-01 1.69578403e-01
-1.49648225e+00 3.84445280e-01 -1.38660204e+00 -4.07903403e-01
4.53990579e-01 5.69193624e-02 -6.30008459e-01 -4.67836380e-01
-5.51709592e-01 1.01878834e+00 1.70529820e-02 1.49264917e-01
-1.99941730e+00 -5.77753425e-01 -8.46296370e-01 -1.76813483e-01
7.40484357e-01 -6.34972572e-01 1.77108943e+00 1.90737974e-02
-1.73575914e+00 5.57308137e-01 -5.07465780e-01 -5.93149722e-01
5.96713185e-01 -8.23456824e-01 7.40247592e-03 -2.07689837e-01
3.11174870e-01 8.28628600e-01 2.20541254e-01 -9.36289608e-01
-9.18377638e-01 -4.60984975e-01 3.64422463e-02 6.14296556e-01
4.09520894e-01 -2.92133242e-01 -6.85653508e-01 -3.94014195e-02
5.20764947e-01 -9.39533114e-01 -4.60249603e-01 -2.11893305e-01
-2.47870356e-01 -2.06328049e-01 1.94231704e-01 -5.16032636e-01
1.21341813e+00 -1.76701510e+00 4.55285043e-01 -3.35449606e-01
3.34552974e-02 -1.96199596e-01 -5.81737518e-01 6.24871075e-01
2.61784524e-01 -2.08055168e-01 1.45391181e-01 -5.08356869e-01
-2.43226681e-02 3.28594260e-02 -4.86584306e-01 5.32609761e-01
-4.26068544e-01 1.01048040e+00 -1.46842980e+00 -8.04978982e-02
4.69238102e-01 1.46985799e-01 -6.58781111e-01 1.29059866e-01
-3.46966386e-01 9.45718884e-01 -6.47230029e-01 6.53424978e-01
1.91981494e-01 -4.95049506e-02 -6.58965930e-02 5.70079803e-01
-5.70112944e-01 7.24482536e-01 -9.50361967e-01 2.64671922e+00
-5.59655547e-01 8.02161396e-01 1.39650330e-01 -4.70332950e-01
4.55358952e-01 -1.00033320e-01 1.85593188e-01 -1.22822750e+00
-2.66826451e-01 1.13933973e-01 -1.63167715e-01 -5.53060412e-01
6.20300889e-01 8.35133016e-01 1.09423906e-01 2.17611760e-01
-2.46329643e-02 2.11528376e-01 -1.66616172e-01 1.72220349e-01
1.79934013e+00 1.01791036e+00 2.07007229e-01 -4.54264581e-02
2.35408768e-01 4.42552239e-01 9.56156403e-02 1.53932214e+00
-6.87998354e-01 3.85720044e-01 -8.43144804e-02 -4.87490952e-01
-4.59198385e-01 -1.24137950e+00 3.96632135e-01 1.66828299e+00
3.34859878e-01 -3.27543944e-01 -6.92657590e-01 -7.15238750e-01
-3.92051727e-01 1.30015159e+00 -7.52319574e-01 -1.40325250e-02
-1.02400553e+00 1.11613542e-01 4.26590621e-01 5.97481251e-01
3.32059950e-01 -1.35511875e+00 -1.25636148e+00 2.47242406e-01
-3.44314754e-01 -1.02520072e+00 -4.90024596e-01 4.91542220e-01
-6.22043610e-01 -1.00578618e+00 -4.03875887e-01 -5.52279234e-01
5.88074327e-01 6.24385953e-01 1.19263017e+00 -3.29653978e-01
-1.17930397e-01 8.03590178e-01 -3.89717400e-01 3.48707475e-02
-2.13117555e-01 -1.85335830e-01 -7.26270629e-03 -5.89865744e-01
1.91345677e-01 -2.69339718e-02 -9.44207609e-01 9.80509967e-02
-7.14536756e-02 2.05345199e-01 4.10787433e-01 7.05171108e-01
7.81470776e-01 -4.98231262e-01 2.82105152e-02 -4.37302232e-01
5.32054901e-01 -4.09459859e-01 -8.63332033e-01 2.60698706e-01
-7.57584512e-01 2.26247817e-01 2.05426291e-01 -2.11906940e-01
-1.09536421e+00 1.75968274e-01 3.61206718e-02 -2.37536773e-01
-4.38565850e-01 2.12676466e-01 2.41467133e-01 -1.81016892e-01
7.42592871e-01 6.03448927e-01 -5.42545676e-01 -2.78030366e-01
7.96530724e-01 2.03081802e-01 8.36341679e-01 -6.09838784e-01
2.37867102e-01 3.70493293e-01 9.56469029e-02 -1.10565439e-01
-7.37361908e-01 -7.26963222e-01 -6.17740750e-01 -4.02057827e-01
1.26471126e+00 -1.22026670e+00 -1.27434003e+00 1.66626289e-01
-8.93017650e-01 -1.17901075e+00 -4.92395610e-02 5.49725473e-01
-1.08463800e+00 -1.92754656e-01 -2.42640465e-01 -9.86207366e-01
2.07085788e-01 -1.39205515e+00 1.24675333e+00 2.50970215e-01
-1.09282561e-01 -7.47238755e-01 2.75145024e-01 6.90472722e-01
5.78302264e-01 -1.33327588e-01 3.47183198e-01 -5.72881222e-01
-1.41335690e+00 1.21922433e-01 2.06489772e-01 -6.59799397e-01
-2.05125362e-01 -6.79518163e-01 -7.75101602e-01 -3.12067181e-01
-3.37794095e-01 -4.38652545e-01 1.13493991e+00 4.40580696e-01
1.13033855e+00 -1.69597477e-01 -6.81558907e-01 5.79734087e-01
1.19996011e+00 4.67566192e-01 3.14842820e-01 9.11957800e-01
6.24913216e-01 3.57048422e-01 1.07338393e+00 2.24745914e-01
9.57386792e-01 7.73174405e-01 9.72353101e-01 5.52631140e-01
-3.05016786e-01 -9.24357116e-01 7.36626267e-01 3.72503936e-01
1.36840701e-01 -5.77359736e-01 -1.27881598e+00 9.35819864e-01
-2.27456856e+00 -9.88351643e-01 5.46989664e-02 2.37112975e+00
6.44150376e-01 -1.53492531e-02 -3.64635170e-01 -6.53234184e-01
-7.76631162e-02 2.86802024e-01 -7.20214784e-01 -2.69516647e-01
3.36905032e-01 -1.14339150e-01 7.75950015e-01 1.07822037e+00
-9.40370321e-01 1.59216905e+00 6.40548706e+00 5.94538689e-01
-7.48539925e-01 1.95663452e-01 2.55974233e-01 -4.76755291e-01
-2.89028347e-01 1.17996633e-01 -1.13080907e+00 7.01889955e-03
9.39171433e-01 4.30822700e-01 1.08904862e+00 8.99430931e-01
3.15025955e-01 -5.27476668e-01 -1.32449663e+00 8.99860740e-01
1.11406175e-02 -1.29264927e+00 -3.96450698e-01 -2.46505573e-01
8.09615672e-01 6.82442307e-01 4.50629473e-01 6.96980357e-01
1.24704564e+00 -1.52476025e+00 1.07247496e+00 7.77014256e-01
2.75747150e-01 -3.17912161e-01 9.65251699e-02 8.36464047e-01
-1.13876164e+00 -4.65648741e-01 -8.62150826e-03 -7.84164146e-02
3.07953030e-01 -4.33598161e-01 -9.46397960e-01 2.56786823e-01
1.07897985e+00 6.18328571e-01 -5.67643821e-01 1.04199898e+00
-6.54794395e-01 5.25719404e-01 -1.68165922e-01 -1.99157715e-01
8.70332062e-01 -2.19427809e-01 7.77112424e-01 1.01683986e+00
4.56761509e-01 1.64489850e-01 4.40950990e-01 8.44681203e-01
3.23600531e-01 -3.65474194e-01 -9.35540020e-01 1.10913947e-01
6.26700878e-01 9.14071977e-01 -5.30382931e-01 -4.21295203e-02
-8.36256817e-02 1.05733800e+00 6.90816045e-01 7.52070785e-01
-7.59644210e-01 7.78890327e-02 7.16844916e-01 -1.83958441e-01
4.83593851e-01 -5.87662756e-01 -1.78242296e-01 -7.85329998e-01
-2.49147713e-01 -8.39527965e-01 7.60448352e-02 -1.20683765e+00
-3.03114712e-01 2.88612068e-01 -1.55285180e-01 -9.85049546e-01
-3.12025756e-01 -4.64090407e-01 -4.37530756e-01 7.66057432e-01
-1.65521741e+00 -1.06809676e+00 -4.36465412e-01 3.74673367e-01
1.08706403e+00 -1.39557213e-01 9.08199608e-01 -2.36097902e-01
-2.78424591e-01 2.80888677e-01 5.92007581e-03 -2.23271564e-01
7.00486839e-01 -1.46082580e+00 7.59621561e-01 1.01179230e+00
3.56557548e-01 9.72199976e-01 7.78917968e-01 -7.17069030e-01
-1.82343626e+00 -8.21671605e-01 6.57316506e-01 -1.21513879e+00
5.26129127e-01 -3.27145666e-01 -4.09431070e-01 1.26371574e+00
4.26306874e-01 1.06063552e-01 1.92194417e-01 1.26836732e-01
-1.96473420e-01 3.78723472e-01 -8.06667805e-01 1.20945263e+00
1.78387284e+00 -3.93222958e-01 -3.54296029e-01 3.42592746e-01
8.59483004e-01 -1.08778656e+00 -2.63743550e-01 1.88197896e-01
4.30732727e-01 -1.21630096e+00 1.13312507e+00 -8.20586801e-01
2.16413066e-01 -5.97818732e-01 -5.01853228e-01 -1.26632059e+00
-3.87378037e-01 -1.00309134e+00 -3.60780388e-01 4.31775689e-01
4.70804632e-01 -2.94244647e-01 7.39108622e-01 3.38146240e-01
-5.57369888e-01 -9.55193341e-01 -1.21250427e+00 -4.92183656e-01
-2.10597470e-01 -6.90989912e-01 3.91705781e-01 3.05068195e-01
-1.38580382e-01 1.62747666e-01 -3.08955044e-01 5.23820579e-01
4.63085562e-01 -8.17450434e-02 9.84097779e-01 -4.67500478e-01
-1.14580706e-01 -4.97607946e-01 1.49366155e-01 -1.95216513e+00
9.56575796e-02 -7.68926978e-01 7.09609568e-01 -2.12862134e+00
2.40156669e-02 -2.41704762e-01 -5.03459811e-01 6.35755002e-01
7.51140043e-02 -4.54380065e-01 2.74276614e-01 -3.71080562e-02
-1.28134036e+00 6.14702880e-01 1.59555566e+00 -8.05302188e-02
-4.06570256e-01 -6.84498996e-02 -4.62010115e-01 5.68046212e-01
3.79723758e-01 -2.88783580e-01 -6.81866229e-01 -1.02979016e+00
3.53232622e-01 3.95355403e-01 3.22016805e-01 -9.96881187e-01
9.14469719e-01 -5.12934923e-01 2.22612709e-01 -8.48511636e-01
6.06299460e-01 -7.26245224e-01 -3.00422192e-01 5.39852560e-01
-8.07736993e-01 3.54944915e-01 2.07736537e-01 1.01756656e+00
2.10154667e-01 -7.87958130e-02 2.92349130e-01 -2.56780356e-01
-1.35330701e+00 1.27969965e-01 -5.07134378e-01 2.53497586e-02
1.10810709e+00 -3.70061100e-01 -7.07079113e-01 -6.35570586e-01
-7.70324230e-01 6.90832853e-01 2.46733218e-01 7.16174543e-01
7.71334469e-01 -1.09101069e+00 -2.69724548e-01 2.65820473e-01
3.45063329e-01 2.40769386e-01 2.75707901e-01 1.00940037e+00
-4.78752345e-01 1.00283778e+00 2.65988652e-02 -7.18933105e-01
-1.04251075e+00 5.04714608e-01 3.08025986e-01 -1.98687315e-01
-7.75676429e-01 1.39540637e+00 1.19256005e-01 -8.45149636e-01
5.89080751e-01 -6.22355640e-01 -4.45865899e-01 -4.29037631e-01
4.65711534e-01 4.93094593e-01 -2.26932049e-01 -3.82601619e-01
-6.08478308e-01 6.09391153e-01 5.87217659e-02 -5.71667433e-01
1.05485928e+00 -4.01186496e-01 4.76202756e-01 5.70244014e-01
5.68110704e-01 1.71340048e-01 -1.85649002e+00 -1.39101639e-01
-2.00537704e-02 -5.22223234e-01 3.69916826e-01 -1.37056208e+00
-3.29444468e-01 9.24846709e-01 7.54792035e-01 -4.35240984e-01
5.52319467e-01 -1.16598094e-02 4.71894056e-01 9.44168508e-01
8.57873797e-01 -9.71403301e-01 5.01466036e-01 1.10946953e+00
7.46529341e-01 -1.48165679e+00 -3.69908988e-01 2.90601373e-01
-8.03698242e-01 8.94166052e-01 9.22984660e-01 2.76944488e-01
1.18710943e-01 -3.47588062e-02 8.96988660e-02 -2.50631511e-01
-1.20186424e+00 -3.29040527e-01 2.33800754e-01 7.94839323e-01
2.84980655e-01 -3.13014090e-02 4.16841149e-01 5.51962033e-02
-2.19862640e-01 -7.66487271e-02 4.00238067e-01 1.13587749e+00
-8.83065999e-01 -5.33437490e-01 -1.51992366e-01 -2.25325719e-01
-3.30241360e-02 -2.13780224e-01 -3.18553559e-02 6.05845273e-01
-2.71995157e-01 1.16724455e+00 2.15060581e-02 -2.81775355e-01
3.30898523e-01 4.10412662e-02 6.13701463e-01 -5.82946241e-01
-4.00838882e-01 -1.38896823e-01 8.61669332e-02 -1.55092704e+00
6.12881966e-02 -6.60245359e-01 -1.70403087e+00 6.15431555e-02
6.87836409e-02 -3.90330255e-01 8.95093381e-01 7.86169767e-01
6.72667444e-01 9.45302844e-01 5.18044503e-03 -7.97139645e-01
-8.51788104e-01 -8.47771883e-01 3.32678109e-01 -1.92553431e-01
8.98079515e-01 -6.80836856e-01 -7.17314035e-02 -3.37909520e-01] | [4.506296157836914, 0.560080885887146] |
61c14d18-82cc-4d7d-aedb-e167addc383b | spatially-varying-exposure-with-2-by-2 | 2306.17367 | null | https://arxiv.org/abs/2306.17367v1 | https://arxiv.org/pdf/2306.17367v1.pdf | Spatially Varying Exposure with 2-by-2 Multiplexing: Optimality and Universality | The advancement of new digital image sensors has enabled the design of exposure multiplexing schemes where a single image capture can have multiple exposures and conversion gains in an interlaced format, similar to that of a Bayer color filter array. In this paper, we ask the question of how to design such multiplexing schemes for adaptive high-dynamic range (HDR) imaging where the multiplexing scheme can be updated according to the scenes. We present two new findings. (i) We address the problem of design optimality. We show that given a multiplex pattern, the conventional optimality criteria based on the input/output-referred signal-to-noise ratio (SNR) of the independently measured pixels can lead to flawed decisions because it cannot encapsulate the location of the saturated pixels. We overcome the issue by proposing a new concept known as the spatially varying exposure risk (SVE-Risk) which is a pseudo-idealistic quantification of the amount of recoverable pixels. We present an efficient enumeration algorithm to select the optimal multiplex patterns. (ii) We report a design universality observation that the design of the multiplex pattern can be decoupled from the image reconstruction algorithm. This is a significant departure from the recent literature that the multiplex pattern should be jointly optimized with the reconstruction algorithm. Our finding suggests that in the context of exposure multiplexing, an end-to-end training may not be necessary. | ['Stanley H. Chan', 'Yiheng Chi', 'Xiangyu Qu'] | 2023-06-30 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 1.01235223e+00 -2.08402351e-01 3.18391204e-01 -8.97827223e-02
-5.46465337e-01 -5.87206304e-01 2.48418897e-01 -2.60225058e-01
-5.84550917e-01 5.38333058e-01 2.71610301e-02 -4.00314987e-01
-3.63545418e-01 -6.96603000e-01 -6.92117751e-01 -9.59489226e-01
1.34970784e-01 -1.93187341e-01 3.44005764e-01 3.88139077e-02
3.55876297e-01 7.81322956e-01 -1.62382269e+00 1.04293711e-01
5.35127878e-01 1.21469247e+00 4.07533675e-01 1.05188394e+00
2.99712479e-01 5.52395999e-01 -4.01125729e-01 -2.58040786e-01
6.90674841e-01 -7.80074656e-01 -4.16231036e-01 2.57579356e-01
3.83800119e-01 -8.09915543e-01 -4.16271776e-01 9.24925864e-01
6.41224384e-01 1.61680002e-02 5.45003772e-01 -7.43522286e-01
-3.16754997e-01 1.45481750e-01 -5.46750188e-01 3.99247169e-01
1.15728416e-01 2.63295650e-01 5.75852334e-01 -5.81556439e-01
5.10919392e-01 7.42892861e-01 4.63483244e-01 2.67535985e-01
-1.33376527e+00 -4.63594109e-01 -2.84184277e-01 5.58509678e-02
-1.28842390e+00 -5.32365680e-01 5.99753618e-01 -4.16984320e-01
4.80972290e-01 4.19605106e-01 4.21539903e-01 5.13879418e-01
3.45445156e-01 3.57535109e-02 1.63422430e+00 -7.95709491e-01
1.59142703e-01 6.71686903e-02 -2.09315181e-01 6.45585656e-01
4.50972080e-01 4.61044639e-01 -4.18010920e-01 9.25690755e-02
1.07361460e+00 -2.37270191e-01 -4.64730084e-01 -2.52733797e-01
-1.00188482e+00 5.67060053e-01 1.59864455e-01 1.82816759e-01
-1.57267854e-01 1.87749386e-01 -7.31734112e-02 4.49767709e-01
-1.20683340e-02 7.98969567e-01 8.04168880e-02 3.43477219e-01
-7.74296522e-01 -1.28635079e-01 6.29931331e-01 5.05663753e-01
6.84551179e-01 -4.73136157e-02 -1.03877425e-01 5.08034110e-01
2.29056597e-01 5.96100032e-01 8.00859407e-02 -1.29738581e+00
1.22245803e-01 4.03702408e-02 3.21532756e-01 -7.76963055e-01
-3.23454648e-01 -5.75287879e-01 -5.84178329e-01 5.86351514e-01
6.19548440e-01 -2.35294476e-01 -6.33292675e-01 1.54460156e+00
1.68667197e-01 7.87832960e-02 7.66773224e-02 9.29140389e-01
1.55152440e-01 5.67910790e-01 -2.11494952e-01 -5.74882746e-01
1.22724080e+00 -3.02579403e-01 -5.84153533e-01 6.76140655e-03
-1.62803039e-01 -8.67375970e-01 9.25172329e-01 5.55387735e-01
-1.29788899e+00 -3.91508669e-01 -1.53763568e+00 2.44141072e-01
2.86730342e-02 7.28020743e-02 3.03598583e-01 9.90885198e-01
-9.62050259e-01 6.19300127e-01 -3.51549029e-01 -2.80033529e-01
7.92674646e-02 3.88170242e-01 -3.19770686e-02 -2.04305485e-01
-7.16469526e-01 7.96065271e-01 1.83275089e-01 6.17015660e-02
-6.18281782e-01 -6.74175858e-01 -2.06624925e-01 -8.97364765e-02
2.98714370e-01 -5.78975797e-01 8.94130349e-01 -1.14441025e+00
-1.82175100e+00 8.28478754e-01 7.06504881e-02 -3.49604756e-01
4.50965732e-01 2.68845886e-01 -3.51938635e-01 6.38177097e-01
-3.34997714e-01 3.21073383e-01 9.42954779e-01 -1.48445606e+00
-6.40243590e-01 -1.44805729e-01 1.35241166e-01 1.01834573e-01
-1.04249716e-01 4.94795665e-02 -2.52448738e-01 -3.67728591e-01
3.40541154e-01 -7.22949326e-01 -2.77623177e-01 4.05693263e-01
-1.50816455e-01 6.04387164e-01 4.82609391e-01 -4.75926161e-01
1.13547862e+00 -2.25818706e+00 -1.30208641e-01 1.52869791e-01
9.35123637e-02 -6.39449758e-03 -1.47501558e-01 4.70334470e-01
7.15716109e-02 -2.72640973e-01 -4.91899669e-01 -6.74602389e-02
-3.38464499e-01 -4.90822196e-02 -5.38560092e-01 9.11897242e-01
2.56375745e-02 3.72535139e-01 -7.83818185e-01 -4.12917972e-01
4.67274994e-01 3.12397927e-01 -4.27937627e-01 3.70631993e-01
1.22081093e-01 4.80180711e-01 -1.03328608e-01 5.19750297e-01
8.78935993e-01 -1.50135651e-01 3.99025679e-01 -5.94229519e-01
-6.44658685e-01 -1.73284233e-01 -1.26075327e+00 1.05999684e+00
-1.63270041e-01 7.64560401e-01 1.25334650e-01 -7.29150295e-01
6.55975103e-01 9.25673991e-02 6.61194205e-01 -9.91527200e-01
5.99824041e-02 3.58251393e-01 -1.59759328e-01 -5.54877341e-01
5.88260591e-01 -5.76472223e-01 2.33438145e-02 4.84206587e-01
-1.11707106e-01 4.10908759e-02 6.64704293e-02 -1.74349576e-01
1.05178130e+00 -6.45888597e-02 4.16511893e-01 -4.02824849e-01
2.66542792e-01 -2.84169018e-01 2.14084387e-01 9.49046254e-01
-3.37141752e-02 8.08711052e-01 4.61099416e-01 7.26453364e-02
-1.35971558e+00 -1.37436092e+00 -4.13646489e-01 6.63079560e-01
5.48104465e-01 1.83841512e-01 -4.94058758e-01 1.46556497e-01
-4.45187390e-01 3.87820125e-01 -2.89758503e-01 1.39184102e-01
-5.58529258e-01 -1.06076431e+00 3.92331898e-01 1.17224157e-01
5.72490335e-01 -5.08452177e-01 -1.29270887e+00 1.48619547e-01
5.32348417e-02 -1.38229680e+00 -1.73786655e-01 3.12584490e-01
-7.81895459e-01 -1.06051946e+00 -5.22555947e-01 -2.64816284e-01
7.87943423e-01 3.64409566e-01 7.64031112e-01 -6.84378520e-02
-3.46061230e-01 8.87781203e-01 -3.84956807e-01 -6.72244951e-02
-4.65389848e-01 -3.74955058e-01 -1.73937887e-01 1.81521252e-01
-1.91636056e-01 -6.21763110e-01 -1.06525457e+00 4.88093883e-01
-1.30884814e+00 7.44900256e-02 6.73862815e-01 5.08091807e-01
7.79011428e-01 1.85233161e-01 4.48013484e-01 -6.19665504e-01
2.16754198e-01 -1.96156293e-01 -8.54295731e-01 3.57752532e-01
-7.53208160e-01 1.57121465e-01 5.91477454e-01 -3.89167398e-01
-1.01386130e+00 2.48061463e-01 1.17100492e-01 -1.09455518e-01
1.83297768e-01 1.62497591e-02 -2.07170993e-01 -5.49458802e-01
5.61240196e-01 1.34049729e-01 8.14605653e-02 -1.61605775e-01
2.82668054e-01 5.01180112e-01 7.17691243e-01 -5.63709140e-01
7.28582382e-01 8.47800314e-01 4.85930055e-01 -1.08325350e+00
-2.47388482e-01 -4.02831316e-01 -2.09765717e-01 -6.20520353e-01
7.71872461e-01 -6.51570857e-01 -7.28974223e-01 4.56797808e-01
-7.89654851e-01 -4.19546217e-01 -4.59022075e-01 5.00725687e-01
-6.36914253e-01 4.95156109e-01 -4.92640555e-01 -8.57069492e-01
4.35031168e-02 -1.15982747e+00 7.24777699e-01 3.84416401e-01
1.43822581e-01 -6.38044953e-01 -2.00747445e-01 9.79848430e-02
5.55415750e-01 3.28101605e-01 8.60082567e-01 1.90114424e-01
-1.08007455e+00 -4.17744741e-02 -4.94891137e-01 3.46731633e-01
-1.18118316e-01 -7.55967349e-02 -1.15384662e+00 -2.91101128e-01
3.85442257e-01 1.10035457e-01 8.47778857e-01 6.02494299e-01
1.13673198e+00 -2.27586165e-01 -1.08843274e-01 7.82223821e-01
2.23378181e+00 3.03901970e-01 1.24604738e+00 2.30425149e-01
3.64696950e-01 5.96365333e-01 1.66042745e-01 5.31444728e-01
-1.15763627e-01 7.66426444e-01 3.82190347e-01 -7.36569837e-02
-3.21021467e-01 -4.27236483e-02 3.49276155e-01 2.71080375e-01
1.62291244e-01 -6.47101820e-01 -2.18825594e-01 1.79929405e-01
-1.18804955e+00 -9.73352075e-01 3.15694101e-02 2.64743066e+00
5.89081705e-01 -2.05011502e-01 1.19814403e-01 1.82724014e-01
6.12675011e-01 1.56330943e-01 -4.32593346e-01 -2.40028188e-01
-3.97205919e-01 2.61714011e-01 1.22183251e+00 7.10194111e-01
-6.91015422e-01 8.57231840e-02 7.37971306e+00 6.88086152e-01
-1.23728192e+00 1.32135510e-01 6.50626123e-01 -1.36183456e-01
-5.83047628e-01 1.31405115e-01 -7.44534254e-01 6.69904053e-01
7.37352073e-01 1.50109410e-01 5.04523337e-01 -1.82556063e-02
1.82769358e-01 -6.27544284e-01 -9.34266925e-01 8.74258459e-01
1.85813561e-01 -1.00371361e+00 -8.21643695e-02 2.62567729e-01
4.89479721e-01 -4.90585119e-01 3.77729267e-01 -5.17158747e-01
-1.75949126e-01 -7.90833533e-01 6.54805005e-01 8.13161016e-01
1.01274419e+00 -4.33079034e-01 1.91714570e-01 -1.74906582e-01
-9.33348298e-01 -3.88841331e-01 -2.54813254e-01 1.40298903e-01
2.87984699e-01 7.17114747e-01 -5.28490305e-01 4.22752559e-01
4.09633458e-01 -8.67321864e-02 -3.40143770e-01 1.13190997e+00
1.58959985e-01 3.58379811e-01 -4.20897603e-01 2.22578958e-01
-1.14525437e-01 -3.49452496e-01 7.52032459e-01 1.06770098e+00
6.48884356e-01 4.37014878e-01 -3.53728980e-01 8.35985959e-01
1.06502824e-01 -2.17430592e-01 -2.84714729e-01 2.18876973e-01
4.01637971e-01 1.00506246e+00 -1.04227221e+00 9.37109515e-02
-4.25939530e-01 9.82308209e-01 -3.30940723e-01 4.90958720e-01
-5.95974922e-01 -2.45132938e-01 2.20794216e-01 4.85077411e-01
5.89295745e-01 -1.89623103e-01 -3.09257448e-01 -7.22377241e-01
8.96086767e-02 -6.09475911e-01 3.09853315e-01 -8.07546079e-01
-1.22886002e+00 1.68819398e-01 2.18373865e-01 -1.31613195e+00
2.64923543e-01 -8.70996535e-01 -3.88153732e-01 7.21837997e-01
-1.59298861e+00 -5.68713129e-01 -7.07019866e-02 2.11961910e-01
7.44084343e-02 1.97971061e-01 3.38040113e-01 5.45251131e-01
-3.61147612e-01 4.80035931e-01 4.41016436e-01 -2.44931147e-01
6.18282437e-01 -9.50658739e-01 -4.52139467e-01 1.34628499e+00
-3.00191462e-01 2.75608003e-01 9.83696222e-01 -2.76891887e-01
-1.62833035e+00 -6.72137380e-01 3.49725425e-01 -2.04927504e-01
3.05193901e-01 2.84580290e-02 -5.31487525e-01 2.00071752e-01
8.00342411e-02 -1.72459856e-01 6.83030963e-01 -5.58300614e-01
-6.05200492e-02 -5.55224597e-01 -1.33275521e+00 4.75812763e-01
8.15911353e-01 -4.49438900e-01 8.48435089e-02 -7.40506500e-02
5.39716125e-01 -3.02895248e-01 -7.11662292e-01 4.25559700e-01
8.89668167e-01 -1.21626949e+00 1.10267067e+00 2.39672258e-01
4.88556355e-01 -5.84307790e-01 -6.34870768e-01 -7.46827841e-01
-1.24351934e-01 -6.01141214e-01 1.09156556e-01 8.40192199e-01
1.51204839e-01 -7.32422352e-01 4.22762990e-01 6.12881958e-01
-1.56254753e-01 -5.93279600e-01 -1.00961590e+00 -7.17082024e-01
-2.99830437e-01 -1.15876168e-01 3.75053704e-01 4.44188327e-01
-3.66856724e-01 1.70169771e-02 -4.70185280e-01 4.71626997e-01
9.94538903e-01 6.85451999e-02 2.25622758e-01 -9.03119028e-01
-7.27792680e-01 -3.57879817e-01 -3.41482282e-01 -1.27544165e+00
-4.59609568e-01 -3.16585809e-01 1.96402863e-01 -1.19587576e+00
3.41310129e-02 -5.62357306e-01 -2.29257092e-01 -3.03501844e-01
6.51223138e-02 2.65903413e-01 1.47742093e-01 1.49984583e-01
-3.39364707e-01 2.10356843e-02 1.16365874e+00 1.03382163e-01
-2.13508740e-01 5.22586424e-03 -7.68764675e-01 2.07537726e-01
5.67379832e-01 -3.27229887e-01 -3.50668311e-01 -3.43904197e-01
6.24124527e-01 2.76447743e-01 6.63904846e-01 -1.23062813e+00
2.99530268e-01 -1.64223671e-01 4.19341385e-01 -2.75115371e-01
2.90147990e-01 -1.10496747e+00 6.34065509e-01 5.11822820e-01
-2.73935467e-01 -2.38962859e-01 -5.94524927e-02 7.24240303e-01
4.04149413e-01 -5.99195063e-01 1.11334682e+00 -8.01781341e-02
-7.31453657e-01 1.09935686e-01 -5.98436892e-01 -3.74673814e-01
9.50268865e-01 -6.08136654e-01 -4.16899413e-01 -1.59703434e-01
-1.79496765e-01 -3.73268247e-01 6.57359660e-01 -2.80579180e-01
5.38632929e-01 -9.11028445e-01 -4.67647374e-01 1.72747135e-01
-1.14767857e-01 -5.14818609e-01 5.20084560e-01 7.65702188e-01
-7.32367635e-01 7.81200081e-02 -2.45383725e-01 -3.77753764e-01
-1.05204201e+00 4.64893162e-01 7.85383224e-01 -8.12930241e-02
-5.60594738e-01 5.63717067e-01 1.07096650e-01 4.57274556e-01
-6.27815425e-02 -2.06325408e-02 1.99889690e-01 -9.08807144e-02
6.36547923e-01 5.65939546e-01 -8.81972760e-02 -3.03612798e-01
1.85565576e-02 7.04990745e-01 3.21499139e-01 -4.09684241e-01
1.14069295e+00 -6.63041294e-01 -8.80409032e-02 3.41583520e-01
1.07389450e+00 6.36043847e-02 -1.59701920e+00 -1.27102703e-01
-6.15399778e-01 -7.77213275e-01 2.14476898e-01 -6.18829548e-01
-1.00310326e+00 6.21134937e-01 1.17517221e+00 2.07027376e-01
1.69545996e+00 -1.62300974e-01 5.35756290e-01 1.79141790e-01
2.19905481e-01 -1.13917875e+00 7.97991157e-02 -1.77175347e-02
5.38028121e-01 -9.17906523e-01 4.27461386e-01 -3.75018090e-01
-2.67235965e-01 1.17470956e+00 2.47521162e-01 1.78293902e-02
4.99683440e-01 3.98242563e-01 -2.85859674e-01 -1.57001659e-01
-1.71341643e-01 -2.81967342e-01 6.85180421e-04 5.68354726e-01
1.67973399e-01 8.92916992e-02 -4.39611673e-01 1.68224335e-01
2.88394243e-01 3.79544273e-02 7.34161913e-01 7.06303477e-01
-7.14302957e-01 -1.04080510e+00 -5.45130014e-01 4.87839639e-01
-4.50196475e-01 1.04308024e-01 1.90818176e-01 4.68003362e-01
2.01139167e-01 9.69894290e-01 7.48654529e-02 -2.38192797e-01
3.87220621e-01 -2.23056108e-01 1.10217083e+00 -2.20417514e-01
-1.91350639e-01 -1.68436952e-02 -1.06813319e-01 -3.34337592e-01
-8.18589389e-01 -5.21806479e-01 -7.83778071e-01 -3.73533875e-01
-3.11356187e-01 -2.45126247e-01 5.45600355e-01 8.74092102e-01
1.31878227e-01 3.73136938e-01 9.80334282e-01 -6.12342179e-01
-4.98987168e-01 -2.52077520e-01 -8.09750736e-01 -5.16537912e-02
5.47717154e-01 -3.54432106e-01 -5.85261405e-01 1.84053019e-01] | [10.888848304748535, -2.4356725215911865] |
4c44738a-5a66-4792-84c8-131d88d3fceb | wind-farm-layout-optimisation-using-set-based | 2203.17065 | null | https://arxiv.org/abs/2203.17065v2 | https://arxiv.org/pdf/2203.17065v2.pdf | Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation | Wind energy is one of the cleanest renewable electricity sources and can help in addressing the challenge of climate change. One of the drawbacks of wind-generated energy is the large space necessary to install a wind farm; this arises from the fact that placing wind turbines in a limited area would hinder their productivity and therefore not be economically convenient. This naturally leads to an optimisation problem, which has three specific challenges: (1) multiple conflicting objectives (2) computationally expensive simulation models and (3) optimisation over design sets instead of design vectors. The first and second challenges can be addressed by using surrogate-assisted e.g.\ Bayesian multi-objective optimisation. However, the traditional Bayesian optimisation cannot be applied as the optimisation function in the problem relies on design sets instead of design vectors. This paper extends the applicability of Bayesian multi-objective optimisation to set based optimisation for solving the wind farm layout problem. We use a set-based kernel in Gaussian process to quantify the correlation between wind farms (with a different number of turbines). The results on the given data set of wind energy and direction clearly show the potential of using set-based Bayesian multi-objective optimisation. | ['Endi Ymeraj', 'Tinkle Chugh'] | 2022-03-31 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [-6.17323071e-02 -3.14292878e-01 3.06040615e-01 9.33737531e-02
-3.82849306e-01 -6.35972440e-01 5.43134570e-01 8.56860802e-02
-3.58702362e-01 1.09652436e+00 -9.07234028e-02 -5.29824376e-01
-9.60862458e-01 -1.05875409e+00 -2.53284603e-01 -1.23171234e+00
-1.51473150e-01 6.35949910e-01 3.09109390e-02 -1.44783944e-01
2.18918487e-01 5.63463211e-01 -1.82257628e+00 -6.75237358e-01
9.18669462e-01 5.95623255e-01 6.62949204e-01 8.02382529e-01
-2.26532929e-02 -3.60636085e-01 -7.50675440e-01 1.17780164e-01
3.58089268e-01 -2.75525331e-01 -2.77926266e-01 -3.18506926e-01
-3.20029736e-01 -8.71379524e-02 9.28663135e-01 1.07611620e+00
9.04819846e-01 5.60185552e-01 8.65635455e-01 -1.07621121e+00
4.66859806e-03 3.16708088e-01 -5.78394532e-01 3.70517671e-01
-1.88029960e-01 2.43435521e-02 6.87870383e-01 -5.90278804e-01
-7.16271028e-02 1.31093121e+00 4.03031588e-01 -1.03344269e-01
-1.39779210e+00 -2.39673883e-01 -1.05534829e-01 -1.22506365e-01
-1.36309814e+00 -2.29170203e-01 4.54349399e-01 -7.00264275e-01
9.51881051e-01 4.56794053e-01 7.57370830e-01 5.56492090e-01
3.10991704e-01 -6.26196489e-02 1.43875027e+00 -5.44179320e-01
8.35090458e-01 -4.88515198e-02 -3.81398737e-01 6.31450070e-03
8.42943907e-01 6.75670505e-01 -8.66425782e-02 -1.91943586e-01
4.00839984e-01 -2.94962138e-01 -2.28315428e-01 -3.65775377e-01
-8.88212025e-01 1.28144288e+00 -4.45470512e-02 4.06684518e-01
-4.05199438e-01 3.45798224e-01 1.86205298e-01 -1.08997785e-02
4.67465997e-01 5.78448117e-01 -7.87139475e-01 -2.25692287e-01
-1.16259181e+00 3.01261663e-01 6.95287049e-01 3.02226722e-01
4.59536761e-01 6.16571248e-01 2.29412913e-01 5.86883307e-01
1.01579607e+00 1.20466423e+00 2.83589840e-01 -4.94554579e-01
1.01819105e-01 -1.25575498e-01 4.15793717e-01 -8.60455453e-01
-4.14285630e-01 -6.51365817e-01 -9.53205168e-01 8.53217840e-01
3.74881566e-01 -6.20145142e-01 -8.52732122e-01 1.25303864e+00
4.53367621e-01 -1.33697391e-01 -8.24083835e-02 8.16814184e-01
3.20625842e-01 7.33297706e-01 4.01629806e-02 -4.57460612e-01
1.40709245e+00 -4.25977260e-01 -9.07567263e-01 -1.33856177e-01
4.48528200e-01 -9.42925453e-01 1.87730938e-01 4.80254889e-01
-7.00320303e-01 -4.79914665e-01 -1.23821402e+00 8.36969793e-01
-8.63753796e-01 1.87232733e-01 4.24267709e-01 1.28733897e+00
-1.02592432e+00 6.26638412e-01 -7.93214619e-01 -1.76410943e-01
5.52803911e-02 3.56350631e-01 8.92660022e-02 3.45028132e-01
-1.02478766e+00 1.40017140e+00 7.16351211e-01 6.03661656e-01
-4.24598038e-01 -6.32771373e-01 -7.32457221e-01 2.57076710e-01
3.32788616e-01 -7.36012816e-01 7.66665936e-01 -2.50059932e-01
-1.78498542e+00 -1.96834892e-01 -1.15253262e-01 -2.77207613e-01
3.74349773e-01 -3.15069705e-01 -3.63955379e-01 -2.13810325e-01
1.07043728e-01 1.38621792e-01 9.93105888e-01 -1.31703126e+00
-7.88782716e-01 -1.71750247e-01 -3.59371245e-01 8.80625844e-02
1.30759299e-01 3.83200198e-02 6.44928038e-01 -6.98181748e-01
-3.40479523e-01 -8.19273174e-01 -5.73964238e-01 -6.50246441e-01
-1.04628257e-01 3.82664078e-03 6.71181679e-01 -7.51455009e-01
1.37193513e+00 -1.78076303e+00 2.93247074e-01 3.76645058e-01
-3.73755604e-01 3.93651158e-01 2.69522607e-01 5.60848057e-01
-1.54571339e-01 5.43429136e-01 -2.40589350e-01 -1.25107411e-02
2.01624811e-01 6.43942595e-01 -1.96636245e-01 2.96870679e-01
2.98301220e-01 4.52501744e-01 -1.09900749e+00 -1.60106435e-01
7.53865540e-01 4.52100635e-01 -7.00876638e-02 -1.57993138e-01
7.14357523e-03 3.38901639e-01 -4.47630584e-01 1.40989900e-01
9.77501750e-01 3.49359810e-01 -1.92847326e-02 -2.15621851e-02
-5.91443717e-01 -1.59224465e-01 -1.87287474e+00 1.16426277e+00
-7.72386491e-01 1.96861401e-01 2.35695243e-01 -1.16422808e+00
9.16581988e-01 5.93193233e-01 2.73965836e-01 1.41574554e-02
-1.02298804e-01 3.39834452e-01 3.50670546e-01 -2.98715323e-01
3.01666975e-01 -5.65620899e-01 3.23564619e-01 3.64326775e-01
4.48256955e-02 -7.06925809e-01 3.95615280e-01 -6.42224312e-01
5.64627588e-01 3.86518091e-01 5.61124682e-01 -9.71103787e-01
3.31253111e-01 -2.71031111e-01 6.86962843e-01 4.61675912e-01
3.24838609e-01 3.28524411e-01 1.24813154e-01 -1.28430367e-01
-8.41564238e-01 -1.09103203e+00 -4.30500001e-01 5.37211657e-01
-3.17815840e-01 -6.53727800e-02 -1.01883024e-01 -3.50581348e-01
2.25587144e-01 1.10908496e+00 -3.63241434e-01 3.76366347e-01
-1.54885620e-01 -1.54548931e+00 2.17357418e-03 3.83231848e-01
1.59696430e-01 -3.39614868e-01 -1.08895743e+00 4.19532359e-01
2.03406185e-01 -3.76481771e-01 2.10782066e-01 8.80067766e-01
-1.07467067e+00 -7.19679534e-01 -6.10886872e-01 -1.15459658e-01
6.06743932e-01 6.99952021e-02 1.00616884e+00 -2.18492091e-01
-2.60998845e-01 5.27689606e-02 -3.73409361e-01 -9.89054084e-01
-2.76695311e-01 -2.05364510e-01 1.89761609e-01 -3.88938487e-01
-1.23155184e-01 -6.67830169e-01 -5.49649358e-01 4.75820810e-01
-8.47371876e-01 -3.97378474e-01 7.22946703e-01 1.12840700e+00
2.91479617e-01 1.13721049e+00 9.35146749e-01 -3.08216810e-01
5.32152712e-01 -5.19836426e-01 -1.10440576e+00 1.77998677e-01
-7.90090799e-01 3.24068069e-01 4.69281942e-01 -1.09430484e-01
-1.07517231e+00 -2.62791663e-02 1.60483807e-01 -2.26044841e-02
-3.00814897e-01 9.15860832e-01 -2.03789741e-01 2.13829085e-01
2.83491671e-01 -2.68999398e-01 -3.02669220e-02 -5.26451826e-01
3.31012130e-01 4.13752794e-01 -1.24880075e-01 -3.97350997e-01
1.19084251e+00 2.73942411e-01 7.94421673e-01 -1.16973460e+00
-3.24862599e-01 -6.50616169e-01 -7.08180964e-01 -2.80128628e-01
9.41786587e-01 -7.45168924e-01 -4.07094687e-01 1.40660062e-01
-8.23984802e-01 -9.84349325e-02 -4.72037464e-01 8.07797253e-01
-3.60310137e-01 3.54700029e-01 3.90257835e-01 -1.54636395e+00
-2.37466693e-01 -1.19890332e+00 8.16274405e-01 2.45751292e-01
-1.80578738e-01 -1.47164881e+00 2.12939277e-01 7.20156208e-02
5.40026009e-01 6.12704813e-01 7.90605128e-01 -2.01489031e-01
-2.34600544e-01 -7.03465864e-02 2.12769940e-01 5.13682008e-01
4.98742163e-01 4.67993379e-01 -8.25214148e-01 -3.94491106e-01
2.60596544e-01 1.58224806e-01 4.22052771e-01 8.42984617e-01
3.43275994e-01 -2.30900981e-02 -2.12018654e-01 2.36989230e-01
1.82803845e+00 3.19610238e-01 3.60320389e-01 2.69135594e-01
2.24309117e-01 5.96368909e-01 8.25150073e-01 5.58655918e-01
-1.28323361e-01 6.50651276e-01 5.69724798e-01 -1.74492504e-02
3.55859041e-01 2.53609896e-01 1.95407659e-01 6.35444224e-01
-5.22860944e-01 -4.00846481e-01 -7.57748067e-01 8.44723761e-01
-1.95654988e+00 -1.11067319e+00 -5.20443261e-01 2.53854752e+00
3.97589773e-01 -1.15566038e-01 -2.20025592e-02 3.40804994e-01
6.14342451e-01 1.41191915e-01 7.62149617e-02 -7.88243055e-01
1.88908875e-02 6.48157716e-01 1.00616097e+00 5.20822465e-01
-1.12566900e+00 -6.27565607e-02 5.77915907e+00 1.06916690e+00
-7.01976895e-01 1.19265400e-01 2.09808186e-01 8.58855397e-02
-2.69245833e-01 2.60448933e-01 -8.74696851e-01 8.42971981e-01
1.12616491e+00 -1.95336193e-01 4.09530312e-01 5.09381652e-01
9.54998553e-01 -9.46932137e-01 -6.62239313e-01 4.65336025e-01
-5.24755478e-01 -7.17774630e-01 -2.82047331e-01 5.79991817e-01
8.49976420e-01 -4.53180494e-03 -2.54609585e-01 -1.34467602e-01
6.02931321e-01 -1.30401766e+00 5.10466337e-01 6.03965044e-01
4.07728702e-01 -8.84531200e-01 1.27212012e+00 3.66256267e-01
-1.24573445e+00 -5.08313850e-02 -4.22155499e-01 -1.93644717e-01
6.08869851e-01 1.25627661e+00 -7.86017895e-01 1.30126977e+00
1.03743601e+00 1.86419502e-01 -2.27237791e-02 1.38746428e+00
-4.27419692e-01 7.02662528e-01 -1.13200939e+00 -2.33691320e-01
4.87153418e-02 -6.84738576e-01 7.77774036e-01 9.43229198e-01
9.28293765e-01 -2.67678171e-01 -7.56230354e-02 7.77763844e-01
9.81708586e-01 -9.63720009e-02 -5.95352471e-01 7.44154677e-02
5.28968990e-01 1.30977070e+00 -9.69966352e-01 2.99556166e-01
-1.62046671e-01 2.81641275e-01 -5.65080881e-01 1.93639278e-01
-4.55836892e-01 -3.78025204e-01 6.86114490e-01 -2.05432087e-01
8.70556235e-01 -5.04310906e-01 -4.49972540e-01 -5.23888230e-01
-6.60300702e-02 -3.21660519e-01 1.61837891e-01 -6.92202270e-01
-1.15711010e+00 -9.88940150e-03 5.91051579e-01 -1.09930933e+00
-5.42317688e-01 -8.78079772e-01 -9.19377387e-01 1.66728008e+00
-1.52358377e+00 -7.78414726e-01 1.60705745e-01 -3.69358957e-01
4.31546748e-01 7.97399729e-02 1.19918418e+00 -7.63413087e-02
-3.54942292e-01 -2.47289076e-01 8.34243298e-01 -4.57713813e-01
2.73622125e-01 -1.80778146e+00 9.33137015e-02 1.06821930e+00
-2.05293253e-01 4.18876082e-01 1.14573443e+00 -7.17961013e-01
-1.10208070e+00 -7.99365759e-01 6.77472949e-01 -4.77779627e-01
7.78352678e-01 4.40929234e-02 -3.64878833e-01 -1.66828051e-01
2.96482056e-01 -3.94149989e-01 7.93741882e-01 9.25721377e-02
5.35718381e-01 2.33958829e-02 -1.22547936e+00 2.81584322e-01
3.38013977e-01 9.55644920e-02 -6.62614882e-01 1.55440360e-01
4.63647721e-03 1.22053809e-01 -1.30417705e+00 6.94489360e-01
4.19740945e-01 -5.94713748e-01 9.89828646e-01 -1.61696762e-01
4.29436361e-04 -8.91849279e-01 -8.66554771e-03 -2.11911058e+00
-2.26481840e-01 -6.39009714e-01 3.48082930e-02 1.46892560e+00
4.70595211e-01 -8.56786609e-01 2.02230826e-01 1.56989172e-01
1.42751306e-01 -7.07841992e-01 -1.36568403e+00 -1.15948462e+00
2.03743801e-01 -1.00282691e-01 6.02748573e-01 5.99136174e-01
-4.93565351e-01 2.43479684e-01 -2.88158655e-02 6.60056591e-01
7.31771529e-01 -6.53322181e-03 5.27857900e-01 -1.59595430e+00
-3.93080026e-01 -5.53869426e-01 -1.98961437e-01 -4.60856855e-01
-3.60913396e-01 -2.04410270e-01 3.90928745e-01 -1.89232850e+00
-5.88058710e-01 -5.68639815e-01 1.46401778e-01 1.40034899e-01
-3.02620620e-01 -2.97081709e-01 -3.48562263e-02 -2.29495853e-01
4.63584632e-01 7.63922930e-01 9.38602984e-01 1.38944864e-01
-7.74290413e-02 4.40192163e-01 -2.98906147e-01 6.87282324e-01
1.00940216e+00 -5.18383384e-01 -6.91016316e-01 -1.57463387e-01
6.46990478e-01 -7.73026124e-02 2.65261859e-01 -8.22111487e-01
-1.75763637e-01 -3.65355343e-01 2.93623179e-01 -8.39771032e-01
1.35721594e-01 -1.10368049e+00 7.41640210e-01 5.76858521e-01
5.97372293e-01 3.47892374e-01 3.28774571e-01 7.26633608e-01
-2.63350084e-02 -1.00803447e+00 5.88187754e-01 -1.03543192e-01
-3.86834800e-01 -4.07936662e-01 -7.15031862e-01 -3.69122416e-01
1.00311732e+00 -3.78096581e-01 -1.65388122e-01 -3.09550613e-02
-8.16435695e-01 5.50060511e-01 2.37920612e-01 2.05576882e-01
2.29159489e-01 -8.49016309e-01 -8.67919147e-01 3.86109320e-03
-4.62694824e-01 3.40276182e-01 1.34816602e-01 7.66116202e-01
-6.69147253e-01 5.04510224e-01 -8.07281286e-02 -5.60254097e-01
-1.01203763e+00 3.95179063e-01 3.80156606e-01 -5.56093574e-01
-1.06374118e-02 6.46558464e-01 -2.53771633e-01 -3.62898141e-01
-5.21769166e-01 -4.10267651e-01 -3.70455593e-01 5.40802717e-01
1.40473142e-01 7.31041133e-01 2.48266667e-01 -3.80006164e-01
-3.14171106e-01 6.93661511e-01 6.74286783e-01 -4.64998573e-01
1.44650817e+00 -3.53759557e-01 -1.60760775e-01 5.28130710e-01
4.83729541e-01 2.52847642e-01 -9.35838282e-01 4.84530389e-01
6.51555285e-02 -4.82671946e-01 6.48758769e-01 -9.76065159e-01
-7.89913833e-01 7.08219528e-01 6.87004983e-01 5.08649230e-01
9.58074450e-01 -8.19269419e-01 -3.25728357e-02 1.49748489e-01
4.63077515e-01 -1.33134687e+00 -7.89549232e-01 2.39544064e-01
7.55310297e-01 -1.03820646e+00 4.73619998e-01 -3.38431716e-01
-7.98602626e-02 1.18039501e+00 -4.51909304e-02 1.07847743e-01
1.19615698e+00 3.67468059e-01 -6.74878713e-03 -1.44950598e-01
-4.14184034e-01 -5.87835073e-01 1.06041268e-01 8.67428601e-01
2.00474784e-01 3.59140784e-01 -7.29968250e-01 2.59334683e-01
-2.30607480e-01 -5.37267812e-02 4.13638443e-01 1.03812981e+00
-3.46557409e-01 -1.36370301e+00 -8.65130424e-01 6.93163812e-01
-4.81051654e-01 -1.43563911e-01 4.05132830e-01 6.32360518e-01
4.66461599e-01 1.43640220e+00 -1.38724402e-01 9.63297412e-02
1.40093058e-01 1.17593877e-01 1.94214135e-01 -7.59464860e-01
-5.55258811e-01 1.81581035e-01 2.99764901e-01 8.23385194e-02
-7.33754039e-01 -1.07257140e+00 -6.86181366e-01 -7.76580572e-02
-1.10679519e+00 6.91784203e-01 1.22620988e+00 9.50606465e-01
1.42920047e-01 6.84624791e-01 7.51809835e-01 -1.00908494e+00
-9.40300941e-01 -1.07565784e+00 -7.87463129e-01 -3.32845598e-01
1.06830657e-01 -1.19563627e+00 -9.21635687e-01 -1.49009272e-01] | [6.1404290199279785, 3.4539601802825928] |
e50d795b-dc2d-4a29-aab3-e1e77699625e | confidence-preserving-machine-for-facial | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zeng_Confidence_Preserving_Machine_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zeng_Confidence_Preserving_Machine_ICCV_2015_paper.pdf | Confidence Preserving Machine for Facial Action Unit Detection | Varied sources of error contribute to the challenge of facial action unit detection. Previous approaches address specific and known sources. However, many sources are unknown. To address the ubiquity of error, we propose a Confident Preserving Machine (CPM) that follows an easy-to-hard classification strategy. During training, CPM learns two confident classifiers. A confident positive classifier separates easily identified positive samples from all else; a confident negative classifier does same for negative samples. During testing, CPM then learns a person-specific classifier using ``virtual labels'' provided by confident classifiers. This step is achieved using a quasi-semi-supervised (QSS) approach. Hard samples are typically close to the decision boundary, and the QSS approach disambiguates them using spatio-temporal constraints. To evaluate CPM, we compared it with a baseline single-margin classifier and state-of-the-art semi-supervised learning, transfer learning, and boosting methods in three datasets of spontaneous facial behavior. With few exceptions, CPM outperformed baseline and state-of-the art methods. | ['Jeffrey F. Cohn', 'Fernando de la Torre', 'Wen-Sheng Chu', 'Jiabei Zeng', 'Zhang Xiong'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 5.87985635e-01 3.82228613e-01 -6.45605087e-01 -6.80666149e-01
-1.13078749e+00 -3.12931985e-01 7.23871350e-01 -1.17697775e-01
-3.49964589e-01 9.82054949e-01 -1.32179543e-01 2.64987379e-01
2.20038727e-01 -2.65413344e-01 -5.78310668e-01 -9.45588291e-01
-2.58765459e-01 4.41515028e-01 2.87835747e-01 1.18373416e-01
5.46100065e-02 2.31448472e-01 -1.91947198e+00 9.32756007e-01
5.89668691e-01 1.40010321e+00 -4.19598818e-01 4.67391968e-01
2.22821146e-01 9.10248399e-01 -7.08098054e-01 -4.54314053e-01
1.62259668e-01 -4.77741390e-01 -7.42870331e-01 1.26781270e-01
5.70527971e-01 -1.85443431e-01 2.02900410e-01 9.39208210e-01
3.41786116e-01 -1.72607750e-01 1.01730394e+00 -1.85880184e+00
-3.10130864e-01 3.35677005e-02 -7.44379818e-01 -3.45754698e-02
4.28306907e-01 -1.22056432e-01 8.06314945e-01 -1.12135208e+00
7.03298390e-01 1.14649367e+00 8.75074923e-01 9.72995400e-01
-1.30253828e+00 -1.01138306e+00 2.33847529e-01 2.08038226e-01
-1.57741690e+00 -8.23421061e-01 6.35185778e-01 -7.60224223e-01
6.73888147e-01 1.48578852e-01 5.57745516e-01 1.46118379e+00
6.69052005e-02 1.09105110e+00 1.58500588e+00 -5.83557010e-01
5.90273619e-01 5.45727670e-01 5.80348484e-02 7.21942604e-01
-6.73456937e-02 4.61926639e-01 -9.04259503e-01 -5.08555830e-01
2.39605427e-01 -1.28457963e-01 -1.01945311e-01 -3.95820051e-01
-5.91835618e-01 7.68608987e-01 1.34981377e-02 9.60677937e-02
-1.79637849e-01 -1.99445233e-01 3.14384222e-01 7.37423226e-02
7.04383373e-01 -1.73100352e-01 -4.46800411e-01 -1.45332843e-01
-1.23064101e+00 7.50587210e-02 6.73044980e-01 7.86582530e-01
8.11955929e-01 -3.81295085e-01 -2.30274111e-01 7.35698879e-01
1.45832270e-01 2.78554827e-01 3.88897687e-01 -7.88644195e-01
5.52307107e-02 4.62760001e-01 5.07048406e-02 -7.28137791e-01
-1.97487578e-01 -1.18722223e-01 -7.30872631e-01 6.71179414e-01
4.26347405e-01 -1.14953347e-01 -9.59089279e-01 1.63721967e+00
5.49160719e-01 5.37794232e-01 -1.22295186e-01 6.75823510e-01
3.85304511e-01 3.25129062e-01 1.88462332e-01 -4.70369935e-01
1.01900351e+00 -8.96393597e-01 -5.91630399e-01 -3.58006805e-01
5.52570462e-01 -4.20578063e-01 3.37126046e-01 6.35665834e-01
-6.35915399e-01 -5.73073924e-01 -9.82582271e-01 4.61750001e-01
-4.33744848e-01 3.77605379e-01 4.21891391e-01 7.29393423e-01
-8.64164829e-01 8.14486504e-01 -6.78770542e-01 -2.90345281e-01
8.69494796e-01 2.26382583e-01 -8.70292068e-01 -4.87550162e-02
-9.64270234e-01 9.34767365e-01 1.13017537e-01 6.47341684e-02
-8.67950737e-01 -5.36425531e-01 -8.98485899e-01 -5.29358208e-01
1.57167420e-01 -1.04487456e-01 1.31811237e+00 -1.77003658e+00
-1.33367264e+00 1.40777826e+00 -4.74926829e-01 -2.85159826e-01
6.62185609e-01 -2.57156760e-01 -5.07646680e-01 2.74680078e-01
2.18840927e-01 8.87040496e-01 1.54312122e+00 -1.35407150e+00
-8.90801251e-01 -5.36517918e-01 -6.19323850e-01 -6.91962838e-02
-3.36179137e-02 3.07640582e-01 -1.45903930e-01 -5.62027097e-01
-2.15560302e-01 -9.70075488e-01 2.61452019e-01 5.93617260e-01
-2.47850806e-01 -5.56755304e-01 9.30515707e-01 -5.35553932e-01
9.67889190e-01 -2.42027688e+00 -4.75115813e-02 2.18618155e-01
1.57410383e-01 2.93488413e-01 2.44262125e-02 6.51331898e-03
-4.37450081e-01 -8.83402377e-02 -1.98727533e-01 -8.49066913e-01
-7.12164566e-02 2.29025885e-01 -7.43249282e-02 8.41705918e-01
6.51146591e-01 5.18091321e-01 -9.13087785e-01 -8.63155723e-01
-5.81965931e-02 4.80860323e-01 -2.81979620e-01 3.53740960e-01
-5.31605966e-02 1.93585500e-01 3.90786603e-02 1.11537600e+00
7.42298186e-01 -1.33937553e-01 1.54863194e-01 -6.19670702e-03
2.74624914e-01 -1.57537058e-01 -1.18283880e+00 1.14027703e+00
1.16029419e-01 6.49868846e-01 1.78423807e-01 -1.20240545e+00
8.70288968e-01 4.06052858e-01 3.96471530e-01 -3.71754736e-01
2.12498568e-03 1.80656582e-01 -4.88501102e-01 -3.63935530e-01
8.64388943e-02 -2.17431247e-01 1.95664451e-01 3.03507626e-01
2.55682766e-01 3.31744909e-01 -3.00403744e-01 -9.57252160e-02
9.93245304e-01 5.64371049e-01 5.84591389e-01 -9.90832299e-02
3.00118983e-01 -1.21059790e-01 9.04187977e-01 5.34335554e-01
-1.04389489e+00 7.80451357e-01 4.08949375e-01 -3.03239316e-01
-4.32881266e-01 -1.08651364e+00 -3.83657455e-01 1.29332316e+00
-5.18156774e-02 -3.71769905e-01 -9.68839109e-01 -1.22175467e+00
2.31518954e-01 3.11021000e-01 -1.17689860e+00 -2.41143569e-01
-2.14770213e-01 -3.69772553e-01 3.52057725e-01 6.84653461e-01
3.16071302e-01 -9.50338721e-01 -3.32476974e-01 -1.16400726e-01
-1.91761672e-01 -1.09193611e+00 -3.59361112e-01 3.41913372e-01
-4.20848817e-01 -1.28526700e+00 -6.60080194e-01 -7.87939489e-01
8.41103971e-01 1.59216762e-01 1.05494881e+00 1.98585670e-02
-5.24863601e-01 2.50007391e-01 -3.38400662e-01 -5.65050185e-01
-2.92850822e-01 -3.52312028e-01 3.99108469e-01 5.30906200e-01
8.94799590e-01 -2.96325117e-01 -4.81428325e-01 6.33511961e-01
-2.66727865e-01 -2.24172279e-01 6.73680067e-01 9.75136280e-01
6.85416877e-01 -4.20063287e-02 5.83302140e-01 -7.79192448e-01
1.07161820e-01 -5.26671171e-01 -2.64710426e-01 2.16402799e-01
-5.78348756e-01 -1.60058752e-01 1.35879904e-01 -8.91150057e-01
-8.95395935e-01 5.36383450e-01 2.58888394e-01 -5.71253896e-01
-5.43844461e-01 -8.14833492e-02 -2.19886024e-02 -1.75798193e-01
8.29793632e-01 1.74657896e-01 9.78963450e-02 -1.94576994e-01
-2.06435919e-02 1.06552887e+00 5.15009463e-01 -4.96907562e-01
5.90627670e-01 7.50499904e-01 -2.52309024e-01 -9.04229820e-01
-1.05462408e+00 -5.84242404e-01 -9.78025317e-01 -5.74606121e-01
8.76680017e-01 -9.87164438e-01 -4.32393938e-01 6.16397500e-01
-1.01966596e+00 -2.87844151e-01 -3.27849269e-01 2.90316194e-01
-6.65012538e-01 2.10713878e-01 -3.66259962e-01 -1.27961004e+00
-7.70887658e-02 -6.85328424e-01 1.61542964e+00 1.36484727e-01
-7.25702107e-01 -6.13875210e-01 2.68067330e-01 4.19374347e-01
7.26914257e-02 5.44597268e-01 1.70053229e-01 -6.97081029e-01
-7.68197253e-02 -5.06441832e-01 -9.32469815e-02 5.91318667e-01
8.52000192e-02 1.21874154e-01 -1.53803039e+00 -3.41136456e-01
-3.28084618e-01 -8.89407218e-01 9.68213141e-01 7.55014941e-02
9.95594740e-01 -3.27035487e-01 -6.87951624e-01 3.78552288e-01
8.88798118e-01 -4.31115441e-02 6.02627873e-01 1.77433208e-01
7.42905810e-02 7.98073292e-01 9.43993628e-01 5.89031756e-01
1.79279312e-01 7.55693913e-01 2.75918871e-01 -8.73374417e-02
6.05505295e-02 -3.84568989e-01 6.55918479e-01 -1.87600002e-01
-1.73868850e-01 3.44184935e-01 -5.57294309e-01 3.61130476e-01
-1.90241873e+00 -1.25542617e+00 3.73618752e-01 2.09152412e+00
1.15170479e+00 2.32520595e-01 3.69987100e-01 5.41369557e-01
8.72045100e-01 7.70126935e-03 -3.84064913e-01 -5.73127158e-02
-1.12507403e-01 1.44656301e-01 1.11545391e-01 2.85199881e-01
-1.56510043e+00 8.58844757e-01 6.64156961e+00 9.80283797e-01
-1.08959556e+00 1.35520563e-01 7.85395741e-01 -5.05481847e-02
4.35707301e-01 -1.87788665e-01 -1.13993049e+00 4.71974552e-01
7.03207076e-01 3.84480864e-01 3.75519469e-02 1.32803047e+00
-1.25130489e-01 -3.93775910e-01 -1.50685537e+00 1.20593345e+00
4.95823383e-01 -1.10112596e+00 -4.53236073e-01 -2.42643487e-02
7.56547868e-01 -2.34526187e-01 -1.17318161e-01 3.62817943e-01
9.53587964e-02 -1.11791468e+00 9.07951117e-01 6.29395843e-01
9.07480001e-01 -5.50799251e-01 6.22018993e-01 4.87098455e-01
-1.14024806e+00 -6.22663200e-02 -1.36144415e-01 3.33061926e-02
-1.98463529e-01 4.78353590e-01 -6.54344380e-01 -7.89997168e-03
9.67728674e-01 8.80009472e-01 -6.09847248e-01 6.73941910e-01
-4.38549429e-01 6.48884952e-01 -2.45542303e-01 1.29222035e-01
-1.66179642e-01 1.25807852e-01 2.37949103e-01 1.53167164e+00
-8.97987783e-02 2.15162143e-01 1.17380403e-01 5.45455754e-01
8.71749297e-02 -5.50136305e-02 -5.77977836e-01 1.99233234e-01
3.66962135e-01 1.27118611e+00 -3.89748186e-01 -2.66300678e-01
-3.03797632e-01 1.33495009e+00 6.75132275e-01 2.50508308e-01
-7.64734268e-01 -1.11963414e-01 7.25390851e-01 1.97948977e-01
2.60400712e-01 1.42577872e-01 -4.56295423e-02 -1.07980931e+00
2.76010156e-01 -8.25283170e-01 7.24360764e-01 -7.20861018e-01
-1.45641172e+00 5.62821269e-01 -3.58102508e-02 -1.38716292e+00
-3.34095299e-01 -6.74277127e-01 -5.29960871e-01 5.32038987e-01
-1.60439348e+00 -1.30925083e+00 -3.57668608e-01 7.55745173e-01
2.83970445e-01 -3.07184279e-01 1.08106053e+00 7.17531592e-02
-5.59430718e-01 9.51673388e-01 -1.51199311e-01 4.15515274e-01
1.22909617e+00 -1.02897203e+00 -2.02480942e-01 5.70851982e-01
-4.26607653e-02 2.76294291e-01 4.33989942e-01 -5.87580562e-01
-8.30071092e-01 -1.28209400e+00 1.08553791e+00 -5.66004217e-01
4.22092497e-01 -6.33436441e-01 -7.88773775e-01 6.99541926e-01
-1.84438586e-01 6.63876593e-01 1.00081897e+00 1.13620937e-01
-7.55402446e-01 -2.41397053e-01 -1.38400412e+00 2.29242384e-01
9.93456185e-01 -7.58113980e-01 -5.03617644e-01 3.85772407e-01
8.71024132e-02 -1.30353823e-01 -6.22449934e-01 4.11339194e-01
8.77136648e-01 -1.05299580e+00 6.96820199e-01 -7.71767318e-01
2.83431590e-01 -1.52419150e-01 -2.09177122e-01 -1.21302009e+00
-2.01368302e-01 -8.30600142e-01 -4.43642884e-01 1.15772438e+00
4.18364435e-01 -5.40411413e-01 1.11620593e+00 6.03074133e-01
2.71946371e-01 -8.18087995e-01 -1.36356437e+00 -9.13338363e-01
-2.18158867e-02 -4.80863571e-01 3.53776552e-02 1.05004466e+00
4.14495319e-01 2.47969646e-02 -6.41555846e-01 1.02723181e-01
9.89327431e-01 -1.86964702e-02 6.46249831e-01 -1.37420249e+00
-2.32060894e-01 -2.43495226e-01 -8.87160599e-01 -6.25153720e-01
5.48950255e-01 -4.62447882e-01 2.88441837e-01 -7.09344208e-01
3.43103290e-01 -3.70667517e-01 -2.36944817e-02 9.05611396e-01
-2.72217184e-01 6.09435439e-01 -2.45169669e-01 2.42195308e-01
-7.99215019e-01 4.75377083e-01 5.93288958e-01 -1.66092634e-01
2.10789740e-02 2.74044484e-01 -4.96542990e-01 8.31580281e-01
7.09831774e-01 -5.89137197e-01 -4.40263376e-02 2.72560954e-01
-2.15709835e-01 -1.67636171e-01 4.06496584e-01 -9.31983054e-01
2.74454296e-01 -3.32586795e-01 7.76095510e-01 -5.11648536e-01
6.38935924e-01 -6.63238406e-01 -2.67818749e-01 2.72980660e-01
-2.49373645e-01 -5.52869976e-01 5.15203066e-02 6.84260905e-01
-2.75653988e-01 8.41417611e-02 1.38747883e+00 1.48198292e-01
-6.65215671e-01 2.77971536e-01 -2.91738153e-01 5.15556186e-02
1.53426600e+00 -5.67541838e-01 -2.63141751e-01 -4.07018304e-01
-9.80390966e-01 2.50880327e-02 2.60517538e-01 5.16880989e-01
7.17994928e-01 -1.38604212e+00 -6.90821886e-01 3.93100083e-01
5.26592076e-01 -3.38401735e-01 -8.70698988e-02 8.11147213e-01
1.92417264e-01 1.09194323e-01 -4.36110087e-02 -9.77520943e-01
-1.70952260e+00 4.94182318e-01 6.88800573e-01 2.38228962e-01
-1.99029803e-01 9.69312847e-01 -5.71185015e-02 -3.53632599e-01
6.48220718e-01 6.60849735e-02 -1.42480344e-01 2.38942504e-01
1.02426636e+00 2.53170520e-01 6.49069622e-02 -9.32903111e-01
-5.96116185e-01 6.10696137e-01 4.41051163e-02 -4.87724729e-02
1.16347647e+00 1.82075500e-01 1.35836929e-01 4.74731505e-01
1.30433679e+00 -2.53651381e-01 -1.48996699e+00 -4.43443686e-01
6.90291598e-02 -5.92425048e-01 -2.66649753e-01 -7.82901883e-01
-7.24677026e-01 7.94124603e-01 8.20836008e-01 -2.50719666e-01
1.15544689e+00 1.91902354e-01 1.28396049e-01 1.09056741e-01
4.48784292e-01 -1.36710072e+00 2.00931892e-01 1.53410748e-01
8.20145965e-01 -1.83290553e+00 -2.67507392e-03 -6.44344509e-01
-8.58780682e-01 1.01285207e+00 8.33691180e-01 1.81813762e-01
9.93612528e-01 4.06461954e-01 9.52139199e-02 1.07788697e-01
-8.76055479e-01 -1.47741020e-01 4.29526031e-01 1.01586556e+00
1.63439825e-01 -3.66834775e-02 2.82791644e-01 7.05273688e-01
7.08662271e-02 3.58478576e-01 -6.90112710e-02 1.32819438e+00
-3.97611886e-01 -8.37868392e-01 -3.91140789e-01 1.97517604e-01
-2.50324696e-01 2.47126743e-01 -7.88599133e-01 7.19210923e-01
4.87121224e-01 1.00267696e+00 1.67951882e-01 -6.28020227e-01
9.89047959e-02 3.21352452e-01 4.16776597e-01 -5.34751594e-01
-2.52626061e-01 -1.37047186e-01 2.44519487e-01 -8.24938416e-01
-6.46185815e-01 -1.05759192e+00 -1.02384126e+00 -2.52230298e-02
-4.82169628e-01 6.55301958e-02 4.28179115e-01 1.08435869e+00
2.85163164e-01 -1.61847785e-01 8.79818559e-01 -1.05749881e+00
-7.64465034e-01 -9.90515530e-01 -6.27932250e-01 6.52241468e-01
5.30007005e-01 -8.72409165e-01 -6.41925693e-01 3.15719008e-01] | [13.581794738769531, 1.5928034782409668] |
79f9e8c9-fca6-40ff-9642-5ab2dd1bc2cb | fully-test-time-adaptation-by-entropy | 2006.10726 | null | https://arxiv.org/abs/2006.10726v3 | https://arxiv.org/pdf/2006.10726v3.pdf | Tent: Fully Test-time Adaptation by Entropy Minimization | A model must adapt itself to generalize to new and different data during testing. In this setting of fully test-time adaptation the model has only the test data and its own parameters. We propose to adapt by test entropy minimization (tent): we optimize the model for confidence as measured by the entropy of its predictions. Our method estimates normalization statistics and optimizes channel-wise affine transformations to update online on each batch. Tent reduces generalization error for image classification on corrupted ImageNet and CIFAR-10/100 and reaches a new state-of-the-art error on ImageNet-C. Tent handles source-free domain adaptation on digit recognition from SVHN to MNIST/MNIST-M/USPS, on semantic segmentation from GTA to Cityscapes, and on the VisDA-C benchmark. These results are achieved in one epoch of test-time optimization without altering training. | ['Shaoteng Liu', 'Evan Shelhamer', 'Bruno Olshausen', 'Trevor Darrell', 'Dequan Wang'] | 2020-06-18 | tent-fully-test-time-adaptation-by-entropy | https://openreview.net/forum?id=uXl3bZLkr3c | https://openreview.net/pdf?id=uXl3bZLkr3c | iclr-2021-1 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 3.08832318e-01 -5.93194403e-02 1.17920049e-01 -7.53279388e-01
-1.05344975e+00 -8.82568777e-01 4.32245940e-01 -2.43749052e-01
-1.03412688e+00 9.02517557e-01 -5.67924023e-01 -4.46913838e-01
1.06731161e-01 -4.47983712e-01 -9.02940094e-01 -4.91231263e-01
-3.33228558e-02 7.89510548e-01 1.38655186e-01 4.44850624e-02
-4.43297289e-02 4.14618760e-01 -8.64624500e-01 4.21715796e-01
8.48220468e-01 1.53981483e+00 -2.14384240e-03 1.10902977e+00
3.03474873e-01 4.64321375e-01 -7.85117626e-01 -8.38476479e-01
6.18998110e-01 -5.23957014e-01 -9.24779892e-01 2.59883702e-01
6.95542037e-01 -1.14197031e-01 -2.26212725e-01 1.31621683e+00
6.47763371e-01 2.24114895e-01 7.21299469e-01 -1.00720680e+00
-8.15790355e-01 7.37548470e-01 3.03722266e-02 4.93615031e-01
-3.04037213e-01 5.72167158e-01 4.12402421e-01 -9.19130206e-01
4.98151660e-01 1.03314078e+00 6.77325249e-01 6.57495439e-01
-1.57842362e+00 -6.83065712e-01 3.73176247e-01 3.58098805e-01
-1.24196613e+00 -4.76954669e-01 1.51292473e-01 -3.11855137e-01
1.26541007e+00 1.50799707e-01 4.71089154e-01 1.41296637e+00
6.73238412e-02 8.30777109e-01 1.09972966e+00 -1.16917744e-01
7.41382301e-01 3.04322243e-01 1.00070700e-01 4.42312300e-01
1.78260222e-01 1.26260281e-01 -3.14609170e-01 2.76440859e-01
5.72280824e-01 -5.86683571e-01 -1.78390339e-01 -1.12752818e-01
-9.64652121e-01 6.07421219e-01 2.73402721e-01 8.10417254e-03
-2.46789619e-01 5.93513250e-04 6.31954372e-01 8.69314075e-01
5.78612924e-01 5.93671501e-01 -1.12729073e+00 -2.70156622e-01
-9.41936731e-01 2.90077012e-02 8.57450366e-01 9.08554912e-01
5.21375656e-01 5.87688446e-01 -2.19800800e-01 1.02526987e+00
-9.42855552e-02 9.15672243e-01 1.03906250e+00 -7.87468016e-01
7.36280859e-01 2.71947414e-01 -4.14493322e-01 -2.65510269e-02
-3.68544281e-01 -1.14187300e+00 -8.75190139e-01 3.90472740e-01
3.89512271e-01 -3.66303176e-01 -1.55058753e+00 1.75538170e+00
4.67646308e-02 4.45750982e-01 3.25375140e-01 6.34445906e-01
3.54350716e-01 1.61546513e-01 8.80958736e-02 2.22307101e-01
7.23748505e-01 -9.59367633e-01 -2.05339789e-01 -7.18815088e-01
6.29903674e-01 -3.31986994e-01 1.24418521e+00 7.59718657e-01
-1.03103375e+00 -8.57834637e-01 -1.21800900e+00 3.04280281e-01
-5.56310654e-01 1.46228105e-01 1.18945330e-01 9.65709746e-01
-9.64568436e-01 8.50264013e-01 -8.29547405e-01 -3.39861475e-02
7.95777440e-01 5.42454898e-01 -4.31872070e-01 -9.19682533e-02
-9.30401504e-01 8.98380041e-01 8.05387616e-01 -3.05177774e-02
-1.24640274e+00 -8.07319045e-01 -7.79842317e-01 1.69132918e-01
2.00000510e-01 -4.46885258e-01 1.36919081e+00 -1.29623139e+00
-1.74993432e+00 8.40797424e-01 3.38036805e-01 -1.11151743e+00
7.51450419e-01 -1.06715024e-01 -8.33864987e-01 -1.74221426e-01
-2.10994948e-02 7.69877493e-01 8.82653713e-01 -8.67678642e-01
-5.61017931e-01 -3.89369160e-01 -5.41248977e-01 5.09979576e-02
-3.59973162e-01 -4.01882768e-01 -3.67916614e-01 -7.25110233e-01
1.51418313e-01 -9.39398110e-01 -1.95739076e-01 -4.29456174e-01
-5.38507223e-01 2.79047251e-01 5.69816828e-01 -8.08614016e-01
7.66276002e-01 -2.18627834e+00 1.59509256e-02 5.33266485e-01
-4.07513410e-01 5.63983023e-01 -7.95194030e-01 -4.64935690e-01
-2.40113899e-01 2.13815019e-01 -6.54113114e-01 -5.45095563e-01
4.85026510e-03 2.47596741e-01 -3.10129046e-01 4.69364673e-01
5.13319135e-01 9.02060449e-01 -4.50065792e-01 -9.26488489e-02
1.41827483e-02 2.11325556e-01 -8.50068569e-01 -8.54797661e-02
-3.18774015e-01 5.70490420e-01 -2.57761270e-01 6.28132105e-01
8.90300512e-01 -1.39228851e-01 -5.65491393e-02 2.96881467e-01
2.60507792e-01 2.63095722e-02 -1.10649562e+00 1.71734786e+00
-4.42440569e-01 5.78091919e-01 -2.10553139e-01 -1.13829231e+00
8.45774055e-01 1.25526130e-01 1.11530408e-01 -9.19276953e-01
1.29240543e-01 3.60588580e-01 2.23470375e-01 -2.33388677e-01
1.37629911e-01 3.90439004e-01 2.30044052e-02 -1.96840689e-02
7.47995317e-01 -2.50141382e-01 2.44896859e-01 -2.49169111e-01
1.31812978e+00 -6.53482676e-02 2.78337188e-02 -1.84354797e-01
4.75740701e-01 -1.81474999e-01 4.49614465e-01 1.11744666e+00
-1.50727674e-01 6.37041092e-01 3.02672535e-01 -2.54693925e-01
-1.10072839e+00 -1.39182305e+00 -3.46277267e-01 1.03813183e+00
-5.75312614e-01 2.63003439e-01 -1.03767800e+00 -1.10920370e+00
1.74558684e-01 1.13130581e+00 -6.96415424e-01 -4.35074866e-01
-5.45898855e-01 -7.43616939e-01 7.70156503e-01 4.73984778e-01
9.95283186e-01 -1.05395103e+00 -1.68566361e-01 3.87656152e-01
1.96477875e-01 -1.35866606e+00 -4.87660050e-01 7.85347223e-01
-1.08428860e+00 -9.64026034e-01 -8.55556786e-01 -6.73492670e-01
6.41393661e-01 -6.89358890e-01 1.01381946e+00 -5.59981644e-01
-1.42610610e-01 2.69011885e-01 -2.07971796e-01 -4.14174229e-01
-5.66016138e-01 5.22597611e-01 6.46162704e-02 1.74336836e-01
1.19896188e-01 -4.14133430e-01 -2.84640878e-01 5.35422802e-01
-8.37084115e-01 -4.67443496e-01 6.65834725e-01 1.01700747e+00
8.78786266e-01 -2.25619480e-01 6.64823949e-01 -9.70193803e-01
3.94905090e-01 -4.19392526e-01 -9.51117873e-01 4.12262052e-01
-8.17014754e-01 2.35499293e-01 8.37764025e-01 -8.24469090e-01
-8.35840940e-01 1.10020019e-01 -2.65236259e-01 -5.04169583e-01
-2.19376236e-01 4.00509059e-01 -6.68489113e-02 -1.77708298e-01
1.18620872e+00 4.34675753e-01 -4.87505019e-01 -5.69875956e-01
2.08562374e-01 3.85772705e-01 1.01050842e+00 -4.04359430e-01
7.31797159e-01 9.21884254e-02 -2.95020908e-01 -5.63436985e-01
-6.59092665e-01 -6.02582246e-02 -7.84325719e-01 -4.14601527e-02
8.28590572e-01 -8.22864294e-01 -4.59046721e-01 8.13914359e-01
-8.40480268e-01 -1.06039023e+00 -7.86688268e-01 7.23760307e-01
-6.24365628e-01 -1.07821472e-01 -4.74045038e-01 -3.87588501e-01
-2.39112139e-01 -1.16491711e+00 5.97917616e-01 -9.62848589e-03
1.62868440e-01 -9.59004641e-01 1.13748319e-01 -1.05389819e-01
6.40082240e-01 2.71101333e-02 5.99441588e-01 -1.23454976e+00
-3.16750199e-01 -3.54602635e-01 -6.28001243e-02 1.37421346e+00
-3.01680148e-01 -3.97587508e-01 -1.25219822e+00 -4.42174882e-01
1.32122383e-01 -5.09399474e-01 1.23822236e+00 5.42811692e-01
1.67923069e+00 -3.85474920e-01 2.62988150e-01 1.21884513e+00
1.25822234e+00 3.16978753e-01 6.30913615e-01 5.29119611e-01
3.80371213e-01 -1.43665850e-01 2.04621866e-01 3.71015936e-01
-1.31357491e-01 4.10582781e-01 3.15329254e-01 1.00622721e-01
-3.31134826e-01 9.73942429e-02 5.95228493e-01 4.83055025e-01
1.36934116e-01 -3.00193816e-01 -1.05072939e+00 4.15023208e-01
-1.30809307e+00 -6.05058789e-01 2.40194932e-01 2.35004663e+00
8.43995512e-01 5.18592358e-01 -2.82286078e-01 2.25855913e-02
4.76980388e-01 -3.07211041e-01 -1.12759364e+00 -3.81124139e-01
-5.18559277e-01 7.31177032e-01 9.90948498e-01 4.45336491e-01
-1.21547389e+00 1.12401509e+00 6.43264961e+00 9.74058032e-01
-1.30613542e+00 4.07571197e-01 1.29247665e+00 -3.13126922e-01
1.19063772e-01 -4.20858055e-01 -7.49876738e-01 4.82190937e-01
1.33405066e+00 1.45047665e-01 7.25804687e-01 1.26162469e+00
-3.20824832e-01 8.34685713e-02 -1.29967487e+00 1.03098428e+00
4.23623733e-02 -1.20992434e+00 -2.22762495e-01 -5.79713583e-02
1.03497136e+00 8.40072811e-01 6.54406071e-01 8.07560086e-01
5.06089270e-01 -9.95836675e-01 9.55877960e-01 2.84087807e-01
1.04967117e+00 -6.95930839e-01 7.82110870e-01 5.68688996e-02
-6.06417298e-01 -2.35410169e-01 -4.58588064e-01 4.23397690e-01
-1.18347690e-01 5.63535750e-01 -1.06161213e+00 -4.71382141e-02
6.57958210e-01 5.27760744e-01 -9.79140341e-01 1.15143299e+00
-2.35694379e-01 1.00602841e+00 -3.59949350e-01 2.07043096e-01
2.78459191e-01 -1.07489023e-02 5.19983172e-01 1.38300705e+00
4.77877676e-01 -1.74838647e-01 6.68508187e-02 6.73452795e-01
-4.90152389e-01 -1.41269386e-01 -2.42327049e-01 2.40632385e-01
1.97036654e-01 8.08928251e-01 -6.11762643e-01 -5.68948090e-01
1.21189095e-01 1.35486209e+00 3.48608762e-01 7.12846279e-01
-8.88783813e-01 -1.93193346e-01 6.47980809e-01 -1.96849525e-01
7.15845883e-01 -7.66786560e-02 -6.05318844e-01 -1.23874652e+00
8.10243934e-02 -1.06844306e+00 3.67058754e-01 -4.06626314e-01
-1.25831389e+00 1.03332388e+00 -1.27153441e-01 -1.05604184e+00
-3.61700118e-01 -1.05696559e+00 -3.29984844e-01 7.32722342e-01
-1.52287710e+00 -6.12318695e-01 5.01913801e-02 7.97154665e-01
6.08224154e-01 -8.58339727e-01 8.47483814e-01 3.93129528e-01
-6.49054706e-01 1.23256552e+00 4.91064042e-01 4.02092725e-01
6.78218544e-01 -1.23378658e+00 1.11688745e+00 9.78400588e-01
2.35948041e-01 -1.88596010e-01 4.16408360e-01 -5.57632089e-01
-6.40519500e-01 -1.46694708e+00 5.05839109e-01 -3.08015406e-01
6.01553679e-01 -3.41771752e-01 -8.95856678e-01 8.14893782e-01
-1.37753770e-01 3.15470725e-01 3.20308000e-01 -1.21809386e-01
-7.35428870e-01 -3.25430363e-01 -1.40770137e+00 3.86693358e-01
9.34921443e-01 -5.39074659e-01 -1.10264391e-01 4.93734598e-01
5.55529892e-01 -6.66559756e-01 -7.39206016e-01 3.01821560e-01
2.43152827e-01 -7.23702252e-01 7.78131008e-01 -8.24976742e-01
1.66095570e-02 2.34238714e-01 -5.92691541e-01 -1.76510775e+00
-1.84409112e-01 -3.48982543e-01 2.77617574e-01 8.31147075e-01
9.48803604e-01 -1.00549805e+00 7.75231004e-01 4.74911869e-01
-5.04932165e-01 -4.77828056e-01 -1.37680411e+00 -1.29739916e+00
2.63660342e-01 -9.99246299e-01 5.65013945e-01 8.49266291e-01
-7.02451825e-01 -1.92184299e-01 1.30920008e-01 2.15288311e-01
5.81016660e-01 -7.77379036e-01 5.60328960e-01 -1.08465755e+00
-6.66374564e-01 -6.19550169e-01 -6.55857265e-01 -7.15290308e-01
2.86824554e-01 -1.05645037e+00 1.73503011e-02 -8.02784085e-01
-2.10849598e-01 -2.92527705e-01 -6.66490257e-01 6.22461319e-01
7.79579207e-02 4.07602280e-01 2.23405167e-01 -1.18466169e-01
-6.08556628e-01 3.89023662e-01 9.32392836e-01 -4.93564308e-01
-1.67094439e-01 2.20285252e-01 -2.99543023e-01 5.06909549e-01
1.05363703e+00 -7.94836879e-01 -2.94400424e-01 -6.19304419e-01
-3.74510214e-02 -2.46674806e-01 4.71311748e-01 -1.62852800e+00
5.66975474e-02 7.82407522e-02 8.14932585e-01 -2.28056982e-01
2.29407340e-01 -6.30177736e-01 -1.01207525e-01 6.08163655e-01
-7.13809252e-01 1.74498446e-02 5.07701993e-01 4.61928010e-01
-1.85490668e-01 -3.52864861e-01 1.16416216e+00 8.14983323e-02
-7.30332792e-01 4.51774836e-01 -1.11708306e-01 4.13134158e-01
6.48160875e-01 -1.51810348e-01 -3.19546759e-01 -1.54396430e-01
-1.07373345e+00 1.41616881e-01 1.06108695e-01 2.04274908e-01
6.01964891e-01 -1.24438488e+00 -1.15000713e+00 6.10518336e-01
5.15741110e-02 -9.43606645e-02 3.35081905e-01 5.47721803e-01
-3.76127422e-01 2.21405119e-01 -3.20235759e-01 -6.69333518e-01
-9.67831731e-01 1.86083198e-01 7.65154898e-01 -5.45486212e-01
-1.26118690e-01 1.34995568e+00 6.01927675e-02 -6.11415386e-01
3.06385458e-01 -6.41493201e-01 1.57599106e-01 -3.33940357e-01
3.51774663e-01 1.73397467e-01 6.52978301e-01 -1.92164585e-01
-1.97886199e-01 2.30045050e-01 -1.67845175e-01 -5.27795374e-01
1.36069107e+00 1.41015187e-01 3.90838414e-01 3.21226805e-01
1.51130188e+00 -5.14172077e-01 -1.69335353e+00 -3.05209279e-01
7.55808204e-02 -1.14457265e-01 9.81988087e-02 -1.53223348e+00
-1.36242211e+00 7.79132545e-01 1.08376420e+00 -1.45583600e-01
1.08221757e+00 -2.61620194e-01 4.43510026e-01 9.49294448e-01
3.55511606e-02 -1.59881139e+00 7.49290511e-02 9.91323292e-01
9.45456624e-01 -1.35638130e+00 -3.15714240e-01 2.96022385e-01
-8.70301962e-01 1.00660813e+00 4.99535948e-01 -1.51555583e-01
7.15018630e-01 1.73814520e-01 3.77526321e-02 3.46612751e-01
-6.66648269e-01 -8.70989934e-02 5.59820592e-01 8.20730209e-01
-6.50447682e-02 1.13487296e-01 5.22638142e-01 6.14325225e-01
-3.61147910e-01 -9.13175866e-02 1.67853877e-01 3.80417049e-01
-3.28541309e-01 -8.53453815e-01 -2.41591990e-01 6.56676769e-01
-4.39318389e-01 -3.86160463e-01 -1.13892272e-01 8.94120693e-01
3.93162929e-02 7.37911999e-01 2.22445250e-01 -4.67114508e-01
5.11730015e-01 4.84140754e-01 4.01876479e-01 -4.21474248e-01
-7.68093169e-01 -1.75619155e-01 -8.39779153e-02 -5.96747279e-01
2.27026641e-01 -8.24716568e-01 -1.09370267e+00 -7.80664086e-02
-8.03536326e-02 -9.53656733e-02 1.18186963e+00 1.01595283e+00
1.99848548e-01 7.86405027e-01 5.27560234e-01 -5.94228327e-01
-1.06552076e+00 -1.13618100e+00 -6.26868963e-01 3.72696161e-01
3.39080065e-01 -1.17695861e-01 -3.21632028e-01 1.66548446e-01] | [9.77973461151123, 2.990612506866455] |
0d3fbc20-cfe7-49d2-958c-4c68beba804d | affirmative-algorithms-the-legal-grounds-for | 2012.14285 | null | https://arxiv.org/abs/2012.14285v1 | https://arxiv.org/pdf/2012.14285v1.pdf | Affirmative Algorithms: The Legal Grounds for Fairness as Awareness | While there has been a flurry of research in algorithmic fairness, what is less recognized is that modern antidiscrimination law may prohibit the adoption of such techniques. We make three contributions. First, we discuss how such approaches will likely be deemed "algorithmic affirmative action," posing serious legal risks of violating equal protection, particularly under the higher education jurisprudence. Such cases have increasingly turned toward anticlassification, demanding "individualized consideration" and barring formal, quantitative weights for race regardless of purpose. This case law is hence fundamentally incompatible with fairness in machine learning. Second, we argue that the government-contracting cases offer an alternative grounding for algorithmic fairness, as these cases permit explicit and quantitative race-based remedies based on historical discrimination by the actor. Third, while limited, this doctrinal approach also guides the future of algorithmic fairness, mandating that adjustments be calibrated to the entity's responsibility for historical discrimination causing present-day disparities. The contractor cases provide a legally viable path for algorithmic fairness under current constitutional doctrine but call for more research at the intersection of algorithmic fairness and causal inference to ensure that bias mitigation is tailored to specific causes and mechanisms of bias. | ['Alice Xiang', 'Daniel E. Ho'] | 2020-12-18 | null | null | null | null | ['jurisprudence'] | ['miscellaneous'] | [ 3.74356210e-01 2.84750581e-01 -9.58559513e-01 -8.00241411e-01
-5.56330562e-01 -6.82202399e-01 5.18375039e-01 3.76330107e-01
-8.51068974e-01 6.82793081e-01 9.29956198e-01 -1.27500987e+00
-5.54650605e-01 -7.88424790e-01 -8.95063728e-02 -3.47589821e-01
6.23898029e-01 1.29756210e-02 -7.07108974e-01 -5.08765541e-02
9.45946991e-01 6.57658815e-01 -1.19976377e+00 3.94593400e-04
1.35080576e+00 9.75014567e-02 -9.89727974e-01 4.14516240e-01
-3.26315850e-01 1.12729979e+00 -5.15774846e-01 -1.01906645e+00
6.27948642e-01 -6.18784189e-01 -9.64876354e-01 -3.83369952e-01
8.67129385e-01 -7.95423031e-01 -3.53233904e-01 1.03795338e+00
5.34254134e-01 1.83742926e-01 5.90398252e-01 -1.05693924e+00
-1.01434195e+00 9.42715526e-01 -5.99697709e-01 2.99837947e-01
-8.42063874e-02 5.32494843e-01 9.25664842e-01 -8.69149938e-02
3.89152825e-01 1.44997489e+00 4.80192542e-01 8.98041606e-01
-1.29119873e+00 -1.10498536e+00 3.36105704e-01 -2.77495980e-01
-9.98877168e-01 -7.58120418e-01 4.72079009e-01 -8.88109803e-01
2.10204735e-01 8.25628936e-01 8.10591280e-01 5.65914333e-01
2.74015576e-01 2.01308262e-02 1.30548692e+00 -4.52594280e-01
1.53820783e-01 -3.31201345e-01 5.12978852e-01 6.11745179e-01
1.20961320e+00 3.77521634e-01 -2.52061665e-01 -6.24784768e-01
5.54999590e-01 -5.53051978e-02 -9.31974128e-02 -1.48390889e-01
-7.62006879e-01 9.24315929e-01 1.22084528e-01 3.41303021e-01
-9.63364393e-02 4.17904973e-01 5.64168155e-01 -2.02450715e-02
2.94194818e-01 5.61374068e-01 -7.40472004e-02 -1.88980341e-01
-8.11442912e-01 6.06806934e-01 4.05212522e-01 9.95154306e-02
3.99254650e-01 2.77848504e-02 -2.97254205e-01 4.34975177e-01
3.64465475e-01 3.41735035e-01 -1.58747047e-01 -1.67690599e+00
5.10364175e-01 4.14470524e-01 3.89154583e-01 -1.12140858e+00
-9.29649174e-02 -4.52072263e-01 -5.29788256e-01 7.40455389e-01
9.67712522e-01 -2.14426756e-01 -5.91074944e-01 1.82452476e+00
2.44714320e-01 -5.81951559e-01 -1.78385913e-01 9.45359468e-01
4.15604897e-02 8.12495351e-02 8.32186460e-01 -1.63081750e-01
1.26194525e+00 2.78717149e-02 -6.23209655e-01 -4.24776226e-03
7.43226111e-01 -6.16250038e-01 1.04329050e+00 1.43438980e-01
-1.05660534e+00 5.15270866e-02 -6.07129991e-01 -4.70441550e-01
-4.45396751e-02 -5.09021103e-01 1.04686165e+00 1.62302685e+00
-3.79534751e-01 5.38405478e-01 -4.13307428e-01 -1.89220652e-01
8.87586892e-01 6.22708872e-02 -3.14245969e-02 1.56236202e-01
-9.71465945e-01 8.88499081e-01 -1.13200292e-01 4.91018653e-01
-2.41144791e-01 -1.14248657e+00 -7.59409845e-01 3.35026354e-01
3.02520871e-01 -5.81405699e-01 9.08267200e-01 -1.25109291e+00
-7.15782166e-01 1.19266796e+00 1.52179241e-01 -3.26827347e-01
9.29966271e-01 -2.19665933e-03 -5.54252088e-01 -1.15168400e-01
4.33780938e-01 5.27181208e-01 2.03846067e-01 -1.23525906e+00
-6.43841028e-01 -7.36759365e-01 3.74109715e-01 2.27752432e-01
-2.35148013e-01 5.27220845e-01 8.52685690e-01 -6.10406041e-01
-1.56891838e-01 -7.66887784e-01 -4.45250213e-01 1.99157432e-01
-2.16219485e-01 -1.04437947e-01 3.92035037e-01 -3.98443133e-01
1.33277941e+00 -2.03423333e+00 -1.09528506e+00 2.23690242e-01
2.13495046e-01 2.20566049e-01 2.17711702e-01 7.14257583e-02
5.23925014e-02 9.48395848e-01 -2.44960696e-01 4.96883899e-01
4.08969671e-01 -6.10120222e-02 -5.51682770e-01 8.70966375e-01
-3.22039902e-01 8.07115138e-01 -9.31221545e-01 -5.07330000e-01
1.06046483e-01 1.54409647e-01 -8.51967931e-01 -5.34447551e-01
4.14481491e-01 1.78391472e-01 -3.41990143e-01 6.77001357e-01
6.37037456e-01 1.20731920e-01 5.59106350e-01 3.90145421e-01
-7.89850175e-01 5.44430971e-01 -9.29064810e-01 1.13048446e+00
1.68909550e-01 5.60174108e-01 2.52028137e-01 -6.47522509e-01
5.33748388e-01 1.79696694e-01 3.66596282e-01 -5.64740598e-01
2.89209068e-01 3.13655078e-01 6.53519690e-01 -4.64384615e-01
6.67783856e-01 -6.60204768e-01 -8.57391283e-02 6.71891809e-01
-7.79090643e-01 -1.56951100e-01 -5.76540120e-02 1.31205827e-01
7.76711762e-01 1.58302769e-01 4.09370333e-01 -8.11679184e-01
1.67718887e-01 3.35394233e-01 1.21377790e+00 9.82547820e-01
-9.73564744e-01 1.99525118e-01 5.63115776e-01 -5.98677397e-01
-8.87858808e-01 -9.02652919e-01 -3.30754101e-01 1.08551502e+00
7.25195836e-03 2.41154462e-01 -6.24054551e-01 -8.94709468e-01
4.99461174e-01 1.18544602e+00 -6.94985390e-01 -2.08189368e-01
-4.16547447e-01 -7.90072858e-01 7.90414572e-01 7.24877492e-02
2.79523820e-01 -3.64564389e-01 -1.19877839e+00 -7.91013762e-02
1.98867634e-01 -4.30651009e-01 -5.93563974e-01 -4.26129967e-01
-7.69126058e-01 -1.30573320e+00 -2.95253307e-01 2.46722531e-03
5.52384555e-01 2.03681231e-01 6.50870085e-01 5.45346498e-01
-2.93526262e-01 7.62674212e-01 1.41928822e-01 -9.45665658e-01
-5.77917099e-01 -2.52016425e-01 -3.27604651e-01 -3.98397684e-01
8.43527019e-01 -4.32200968e-01 -7.77076721e-01 -2.44543925e-01
-7.35950232e-01 -2.20984891e-01 1.18953250e-01 5.10983944e-01
-1.04228124e-01 -4.37869668e-01 1.04313183e+00 -1.42359936e+00
7.01282799e-01 -4.05573010e-01 -6.93954408e-01 2.67371655e-01
-1.39850986e+00 -3.57945293e-01 2.61889935e-01 -7.35665709e-02
-1.58101726e+00 -7.75278211e-01 1.56541169e-01 3.74374092e-01
-3.33494872e-01 1.26715049e-01 -3.06140389e-02 -2.71702975e-01
1.15521514e+00 -5.25492013e-01 7.08626658e-02 -2.25641832e-01
3.21863800e-01 5.37902355e-01 4.07766879e-01 -1.12357795e+00
5.17152727e-01 6.92699373e-01 -8.50363821e-02 -3.01786304e-01
-9.81966019e-01 1.32004758e-02 -1.48543388e-01 -2.99754620e-01
1.00311828e+00 -6.16763532e-01 -1.09718800e+00 -5.11770174e-02
-8.28600526e-01 -2.51304984e-01 -4.22818065e-01 8.19256783e-01
-2.15663478e-01 4.60072577e-01 -5.43281257e-01 -1.30820179e+00
-2.17797443e-01 -8.95591497e-01 -5.29365651e-02 1.27960667e-01
-6.03293717e-01 -6.74737990e-01 2.81734318e-02 1.01039064e+00
3.39909613e-01 4.87094462e-01 1.14806437e+00 -5.88762760e-01
-2.75803655e-01 -2.27343500e-01 -1.42941669e-01 1.04399160e-01
3.23607951e-01 4.78330016e-01 -1.04616272e+00 3.26260626e-01
-5.84914833e-02 -1.11203976e-01 4.54406768e-01 2.49141559e-01
8.84798229e-01 -7.84897804e-01 7.85281509e-02 1.79114416e-01
1.52069354e+00 5.80572784e-01 5.64510942e-01 1.32030949e-01
6.04611158e-01 1.05808556e+00 3.20401937e-01 4.22041446e-01
5.35864353e-01 2.44281128e-01 3.34989727e-02 -4.76243384e-02
-6.49006218e-02 -3.16050649e-01 1.72517337e-02 1.83834627e-01
-1.55219287e-01 4.03272033e-01 -1.21504021e+00 6.58121645e-01
-1.61761618e+00 -1.64300144e+00 -4.75724965e-01 2.38335371e+00
8.48344862e-01 1.88940555e-01 2.73393512e-01 5.10672890e-02
8.71488333e-01 2.98625857e-01 -5.23962736e-01 -1.09158444e+00
2.89484233e-01 1.06850505e-01 6.85296416e-01 8.13638687e-01
-4.75859910e-01 6.39838874e-01 7.10473442e+00 3.84720355e-01
-9.33284819e-01 8.91050845e-02 1.17430115e+00 -2.12788701e-01
-1.20982361e+00 3.99518490e-01 -1.77490115e-01 5.48113167e-01
3.97763073e-01 -7.92393267e-01 6.66635558e-02 5.03746212e-01
5.80550909e-01 -4.08679187e-01 -6.18336737e-01 2.73734242e-01
-3.83268148e-01 -1.38858449e+00 -1.21266050e-02 3.77948880e-01
9.17629480e-01 -5.93580365e-01 3.19240421e-01 1.03522800e-01
7.17723966e-01 -1.20045137e+00 1.07773495e+00 3.41347158e-01
6.04187131e-01 -9.23511803e-01 2.47297704e-01 7.24204630e-02
-3.47254336e-01 -1.66618630e-01 -3.27619314e-01 -7.82442033e-01
-1.47937030e-01 8.40541780e-01 -1.53286040e-01 1.48096934e-01
2.06562057e-01 -1.11226276e-01 2.37204079e-02 8.56282532e-01
-1.97549939e-01 5.97603798e-01 4.33252722e-01 2.36225262e-01
3.02274615e-01 -3.28200221e-01 3.74788493e-01 1.05858946e+00
-3.18564475e-02 6.90640688e-01 -4.47442293e-01 1.03786397e+00
-2.67494015e-05 2.91152894e-01 -6.64254189e-01 -1.59490973e-01
8.87110412e-01 1.00925326e+00 -6.90354228e-01 -1.77844599e-01
-4.78055209e-01 3.09150536e-02 1.67899311e-03 4.63604420e-01
-5.45219719e-01 9.91855375e-03 1.09862661e+00 3.63945991e-01
-5.32081187e-01 9.81402304e-03 -1.21628523e+00 -1.05007827e+00
-5.33027112e-01 -1.08400273e+00 5.60513616e-01 -1.74789876e-01
-8.90952766e-01 -5.48019886e-01 -1.26345843e-01 -4.28623974e-01
3.10260981e-01 -4.13644642e-01 -5.69435537e-01 1.22944212e+00
-1.31156194e+00 -8.27845395e-01 3.11123163e-01 8.73552635e-02
-1.35015875e-01 2.84254998e-01 5.15752614e-01 1.64898038e-01
-2.54206985e-01 7.04469144e-01 -2.59163290e-01 9.55222249e-02
1.04645753e+00 -9.75694716e-01 1.01787567e-01 1.17572486e+00
-3.09206307e-01 1.10122883e+00 7.35198081e-01 -8.01551163e-01
-8.97938967e-01 -7.48958170e-01 1.13698328e+00 -5.65514982e-01
4.72544789e-01 -1.90389026e-02 -8.48985493e-01 9.34086680e-01
2.55037807e-02 -4.61445361e-01 1.16337895e+00 6.34347081e-01
-7.87028491e-01 -4.38081436e-02 -1.61682165e+00 9.37541544e-01
1.25398517e+00 -6.35769963e-01 -4.31717277e-01 2.18940470e-02
4.49575782e-01 -8.09907764e-02 -7.42280781e-01 1.69825673e-01
9.91289616e-01 -1.01868176e+00 5.08601487e-01 -8.57319891e-01
4.84087676e-01 -1.81936011e-01 -5.64575419e-02 -5.83774626e-01
-4.93220806e-01 -6.78460836e-01 8.73927236e-01 1.25313008e+00
3.68759096e-01 -1.02051568e+00 8.43254626e-01 1.67045057e+00
-1.15608759e-01 -6.01983547e-01 -7.17014134e-01 -4.63082582e-01
7.81690061e-01 -5.55522561e-01 9.39626217e-01 2.04983711e+00
2.05587864e-01 -1.47588894e-01 -2.93960690e-01 2.45621540e-02
6.51531518e-01 6.45838529e-02 7.71578074e-01 -1.15581501e+00
1.34779736e-01 -1.02166569e+00 -1.42711520e-01 -2.82220710e-02
2.49088511e-01 -7.62119770e-01 -3.32720429e-01 -1.38709629e+00
3.53325248e-01 -6.65767789e-01 -1.51667118e-01 3.83861333e-01
-5.16054630e-01 -3.64320159e-01 4.78212178e-01 5.91579126e-03
2.16249391e-01 1.58345386e-01 1.27993000e+00 -1.62382901e-01
-1.84457973e-02 -3.10947090e-01 -1.90207720e+00 6.63271070e-01
7.75362968e-01 -6.38620257e-01 -4.06181753e-01 -4.14527386e-01
5.04506111e-01 -1.98393285e-01 5.50915420e-01 -5.06658673e-01
2.17419609e-01 -1.26031303e+00 1.90958276e-01 1.54395252e-01
-3.87618840e-01 -6.91559553e-01 4.26842719e-01 8.52391064e-01
-9.24257100e-01 -1.99834689e-01 6.87304363e-02 5.28482720e-02
3.46622020e-01 -3.18066984e-01 9.58371818e-01 -2.19072253e-02
-1.42676190e-01 1.54676542e-01 -4.29532498e-01 4.73322213e-01
9.16824281e-01 -4.19592381e-01 -8.94505918e-01 -2.37618953e-01
-1.75133616e-01 1.33966744e-01 8.76604021e-01 -8.91157389e-02
-4.38350486e-04 -1.05347323e+00 -9.21374321e-01 -5.31775236e-01
-1.61036626e-01 -4.96990323e-01 4.32775646e-01 7.47082293e-01
-4.40045774e-01 3.55254829e-01 -9.98675898e-02 3.78035069e-01
-9.69581187e-01 6.05301917e-01 7.66898692e-01 3.26614738e-01
-5.29077649e-01 5.77508867e-01 4.24276143e-01 -2.91438431e-01
6.63450360e-02 1.05667792e-01 1.75570294e-01 -1.25823587e-01
4.83189702e-01 9.22686696e-01 -4.86864239e-01 -3.82402986e-01
-4.52986002e-01 3.67876887e-02 8.31461176e-02 -3.67281348e-01
8.15877318e-01 5.49472263e-03 -4.73801345e-01 3.25965703e-01
5.43280125e-01 8.82135034e-01 -1.16910958e+00 5.59613347e-01
2.15009786e-02 -1.20991814e+00 -2.37038434e-01 -1.14964116e+00
-5.86950004e-01 7.33303189e-01 2.70100832e-01 1.88743457e-01
8.48158181e-01 -7.41025209e-01 1.19723687e-02 -8.98397639e-02
2.19005063e-01 -1.26739407e+00 -8.09191227e-01 -1.89688995e-01
5.41721284e-01 -8.95061851e-01 4.26607281e-01 -2.48547688e-01
-4.14294660e-01 6.80980742e-01 5.98959565e-01 9.69628468e-02
1.67272836e-01 -8.73914137e-02 3.12711954e-01 -7.42667839e-02
-4.61224377e-01 1.60170004e-01 -7.77546642e-03 4.34697837e-01
8.77672017e-01 6.57769084e-01 -1.69486511e+00 4.09752190e-01
-1.96142927e-01 1.08255386e-01 6.74952567e-01 7.06644058e-01
-6.69242144e-01 -1.12656260e+00 -8.52387369e-01 6.57780707e-01
-9.98766124e-01 -2.76212007e-01 -7.17236519e-01 1.15845287e+00
5.40380299e-01 8.92050683e-01 1.21709071e-02 3.72806817e-01
1.22709215e-01 8.31967220e-02 3.70874375e-01 -5.73791385e-01
-9.78190124e-01 -1.34737119e-01 4.65580583e-01 -3.31449330e-01
-3.37801874e-01 -6.64342105e-01 -1.20094180e+00 -1.07840061e+00
7.43862838e-02 3.38284820e-01 3.62180620e-01 9.08549488e-01
3.59871358e-01 -5.66538377e-03 2.87436396e-01 1.51043966e-01
-7.60555744e-01 -8.28185976e-02 -5.92217445e-01 1.97427273e-01
2.81929225e-01 -1.09362155e-01 -3.17214310e-01 -4.06230271e-01] | [8.86178970336914, 5.620482921600342] |
aad94f79-8652-40c8-90a5-769c5565c422 | differencing-based-self-supervised | 2208.05838 | null | https://arxiv.org/abs/2208.05838v1 | https://arxiv.org/pdf/2208.05838v1.pdf | Differencing based Self-supervised pretraining for Scene Change Detection | Scene change detection (SCD), a crucial perception task, identifies changes by comparing scenes captured at different times. SCD is challenging due to noisy changes in illumination, seasonal variations, and perspective differences across a pair of views. Deep neural network based solutions require a large quantity of annotated data which is tedious and expensive to obtain. On the other hand, transfer learning from large datasets induces domain shift. To address these challenges, we propose a novel \textit{Differencing self-supervised pretraining (DSP)} method that uses feature differencing to learn discriminatory representations corresponding to the changed regions while simultaneously tackling the noisy changes by enforcing temporal invariance across views. Our experimental results on SCD datasets demonstrate the effectiveness of our method, specifically to differences in camera viewpoints and lighting conditions. Compared against the self-supervised Barlow Twins and the standard ImageNet pretraining that uses more than a million additional labeled images, DSP can surpass it without using any additional data. Our results also demonstrate the robustness of DSP to natural corruptions, distribution shift, and learning under limited labeled data. | ['Bahram Zonooz', 'Elahe Arani', 'Vijaya Raghavan T. Ramkumar'] | 2022-08-11 | null | null | null | null | ['scene-change-detection'] | ['computer-vision'] | [ 4.41770226e-01 -7.00451553e-01 2.19621316e-01 -6.92587674e-01
-6.24014199e-01 -7.74532139e-01 5.86254656e-01 -1.37206465e-01
-5.71586072e-01 6.76691532e-01 1.75626308e-01 2.22101286e-01
1.97122380e-01 -5.36971688e-01 -1.06001186e+00 -6.12920880e-01
9.28448215e-02 -7.32152956e-03 3.96132886e-01 -2.14972153e-01
4.39500101e-02 4.59276974e-01 -1.81015670e+00 3.50233823e-01
8.79556596e-01 1.04308414e+00 3.54648978e-01 4.99230504e-01
2.25720793e-01 7.69192219e-01 -4.69081551e-01 4.90471460e-02
6.66978836e-01 -3.46872896e-01 -4.89563942e-01 3.85931224e-01
1.14237058e+00 -5.71885526e-01 -3.82269025e-01 1.23973048e+00
6.04384184e-01 3.54346216e-01 3.34062815e-01 -1.18943000e+00
-7.76692152e-01 -8.81582350e-02 -6.66646183e-01 5.75597227e-01
1.70957729e-01 3.98724139e-01 5.28009951e-01 -8.63683820e-01
6.49799228e-01 1.08084536e+00 8.23867261e-01 3.20360154e-01
-1.34032929e+00 -8.08283389e-01 4.66497421e-01 4.07833278e-01
-1.14632404e+00 -6.23947561e-01 9.66725111e-01 -5.95033944e-01
9.05893385e-01 -6.06009387e-04 4.58390862e-01 1.33011293e+00
-9.81868654e-02 4.75840598e-01 1.34784937e+00 -1.98705137e-01
3.42309415e-01 -4.74644303e-02 -1.62583366e-01 4.69192445e-01
1.45076230e-01 2.88902938e-01 -4.50858295e-01 2.80057907e-01
7.25751817e-01 2.85332739e-01 -5.16302049e-01 -5.71333468e-01
-1.26936460e+00 4.84674692e-01 5.42149961e-01 -3.58829796e-02
-6.96559250e-02 8.45342427e-02 7.02738106e-01 5.87139130e-01
6.64394081e-01 4.31104630e-01 -8.11861336e-01 4.02074493e-02
-7.23593235e-01 -8.85343850e-02 4.01780844e-01 8.61604452e-01
9.13415194e-01 2.88810045e-01 1.33784354e-01 8.52770388e-01
-2.41352469e-01 6.42067313e-01 6.51957631e-01 -1.06235743e+00
4.62283522e-01 3.23717296e-01 1.76286846e-01 -1.24787307e+00
-3.28735560e-01 -3.55234236e-01 -1.13166583e+00 3.09661210e-01
3.34169090e-01 -7.09332004e-02 -1.06863630e+00 1.95959389e+00
2.97822535e-01 3.41695786e-01 -7.50750303e-02 9.45906281e-01
6.96709037e-01 4.97593194e-01 -3.02714378e-01 -1.75454363e-01
9.39590216e-01 -9.09669876e-01 -5.38060725e-01 -3.91592205e-01
1.32123858e-01 -5.54810822e-01 1.16673791e+00 2.86656171e-01
-6.50066376e-01 -9.58914578e-01 -1.05612004e+00 -4.27574925e-02
-4.36028063e-01 1.04628205e-02 3.39172363e-01 2.30747849e-01
-1.13780367e+00 4.81828362e-01 -8.28788936e-01 -6.31868541e-01
4.75531459e-01 2.98918068e-01 -6.27202451e-01 -3.29708695e-01
-1.00206506e+00 7.07458735e-01 2.73023218e-01 5.78471161e-02
-1.03748715e+00 -6.58114851e-01 -1.12667203e+00 -1.77313685e-01
2.98533648e-01 -5.08741796e-01 1.00780249e+00 -1.62373888e+00
-1.43420255e+00 9.71624434e-01 -1.48199245e-01 -4.77497607e-01
6.45466506e-01 -4.06970799e-01 -5.99540949e-01 1.66958988e-01
1.82627320e-01 7.44923055e-01 1.08190155e+00 -1.37399471e+00
-5.80160141e-01 -3.38503301e-01 -5.39883897e-02 4.23037767e-01
-2.18393788e-01 -6.92790076e-02 -4.21893209e-01 -7.19124138e-01
1.34918615e-01 -8.50912869e-01 -8.88899416e-02 2.36409143e-01
-2.34348755e-02 2.39545152e-01 1.00786173e+00 -7.71490633e-01
4.93053079e-01 -2.34894848e+00 -1.54901043e-01 -1.33649141e-01
3.37040909e-02 2.63683826e-01 -3.16261411e-01 1.02689095e-01
-1.92756861e-01 -3.31899226e-01 -3.93819064e-01 -1.10931933e-01
-2.21163332e-01 3.89282405e-01 -3.99322987e-01 8.21815431e-01
2.86866248e-01 5.71173310e-01 -1.02788723e+00 -1.12115212e-01
4.31636244e-01 3.60969782e-01 -4.89236593e-01 2.23304123e-01
-1.90931097e-01 8.12977076e-01 2.53932774e-01 5.50882161e-01
9.30765271e-01 -1.29457563e-01 -2.40782034e-02 -4.09909695e-01
1.10217312e-03 1.20236158e-01 -1.24251640e+00 1.84016645e+00
-3.79201561e-01 9.99557972e-01 -1.54581547e-01 -1.21958745e+00
8.68215322e-01 -4.37139571e-02 4.10038054e-01 -9.55588222e-01
1.35570727e-02 4.21248302e-02 -2.70097196e-01 -6.82014525e-01
3.61051679e-01 -1.39085613e-02 -8.80657732e-02 2.36439124e-01
9.42713842e-02 -1.83295473e-01 -1.47737172e-02 -1.32235765e-01
1.01214004e+00 3.38714749e-01 2.89285213e-01 -5.78111643e-03
2.98274636e-01 -1.67907953e-01 1.06052434e+00 6.60409033e-01
-6.16228521e-01 7.35121727e-01 1.46375643e-02 -8.19175184e-01
-8.99713933e-01 -1.17223203e+00 6.19657338e-02 9.77328062e-01
4.63518322e-01 1.93692207e-01 -3.11404645e-01 -7.13125169e-01
1.84028223e-02 4.68628675e-01 -5.42105734e-01 -2.35853791e-01
-5.16084194e-01 -6.87332630e-01 3.75031501e-01 6.66234076e-01
1.06384087e+00 -7.68716514e-01 -8.00544083e-01 -7.06298500e-02
-3.63633215e-01 -1.49951088e+00 -5.94406009e-01 3.29616606e-01
-5.90985000e-01 -1.03021669e+00 -4.68326300e-01 -1.02306294e+00
7.81963050e-01 6.61426425e-01 1.11522472e+00 -5.65371633e-01
-2.42968202e-01 4.80976641e-01 -1.34208947e-01 -2.53492653e-01
-1.43384382e-01 -4.47941959e-01 3.32977504e-01 1.30113527e-01
2.35056996e-01 -7.91983902e-01 -8.49001646e-01 3.78700107e-01
-9.70274746e-01 -7.06973150e-02 3.28274935e-01 9.45477605e-01
5.59211910e-01 3.81564975e-01 4.25597787e-01 -8.36270392e-01
6.89639673e-02 -3.26126158e-01 -6.83344543e-01 8.24514404e-02
-4.07903105e-01 -1.30727291e-01 7.50388563e-01 -5.34974813e-01
-1.35605931e+00 2.69936293e-01 4.08053607e-01 -6.08063638e-01
-5.80249131e-01 1.40309945e-01 -1.58941656e-01 -1.12630814e-01
7.65678346e-01 3.44055802e-01 6.94874208e-04 -3.01525801e-01
3.83802027e-01 3.34983081e-01 1.03037512e+00 -2.78972149e-01
9.61416423e-01 9.13431942e-01 -4.02614534e-01 -6.46424949e-01
-9.99267697e-01 -5.32473266e-01 -9.97234821e-01 -2.15870857e-01
8.00182998e-01 -1.37782550e+00 -1.23353273e-01 8.11030865e-01
-1.01643133e+00 -4.69906121e-01 -3.92342627e-01 4.60853964e-01
-5.18627524e-01 5.34733713e-01 -4.41192091e-01 -3.53364646e-01
-4.98411320e-02 -7.93896258e-01 9.73602533e-01 1.25052691e-01
2.16979720e-02 -9.72959340e-01 2.50057459e-01 3.48804742e-01
3.58898222e-01 7.36953497e-01 6.91239297e-01 -3.13082337e-01
-4.16383147e-01 6.76738378e-03 -3.39728296e-01 8.09065580e-01
7.37194717e-01 -1.18324317e-01 -1.07817388e+00 -6.14650905e-01
1.46735281e-01 -4.92473036e-01 1.03181243e+00 3.78687084e-01
1.27842152e+00 -2.53436863e-01 5.46324030e-02 9.65039909e-01
1.67787242e+00 1.79380089e-01 3.64415824e-01 4.69705790e-01
8.89401317e-01 5.64965487e-01 5.45199692e-01 4.18972343e-01
4.87421393e-01 4.83731627e-01 5.79848170e-01 -6.03937149e-01
-3.02757591e-01 -9.07629281e-02 5.06537497e-01 7.12630272e-01
1.80933595e-01 -2.56888047e-02 -8.55881572e-01 8.36174309e-01
-1.65238082e+00 -1.17421019e+00 1.74663261e-01 2.33743072e+00
9.91596818e-01 1.01254527e-02 -1.03548132e-01 -7.62607455e-02
8.32416296e-01 3.93096805e-01 -1.04880118e+00 -7.38187432e-02
-4.72480327e-01 3.81273068e-02 6.76648855e-01 1.71965122e-01
-1.40947926e+00 8.98568332e-01 6.36551285e+00 1.63227022e-01
-1.53444088e+00 1.28873035e-01 5.52154303e-01 -3.12692851e-01
1.47569580e-02 -2.64258593e-01 -3.36700380e-01 6.31838858e-01
5.83424449e-01 1.68791309e-01 5.99913776e-01 6.25089109e-01
3.18484694e-01 -2.03692213e-01 -1.21547496e+00 1.26384175e+00
6.31936610e-01 -1.03318393e+00 -9.28676054e-02 -4.03706729e-01
1.27269733e+00 5.16332626e-01 1.53676629e-01 2.63882697e-01
6.79540575e-01 -7.15919793e-01 6.52414382e-01 4.24586952e-01
8.93362820e-01 -4.80989009e-01 5.77447355e-01 2.20778421e-01
-1.00671422e+00 -2.62765735e-01 -4.58144575e-01 -2.22793609e-01
-1.20850183e-01 6.20725513e-01 -7.68385351e-01 4.39295113e-01
1.17893970e+00 1.13894498e+00 -6.91766858e-01 7.50182390e-01
-7.25797862e-02 4.45649773e-01 -2.89029837e-01 6.53225720e-01
-6.27606809e-02 -1.14901558e-01 4.25095499e-01 1.15039301e+00
1.72153145e-01 -2.84477264e-01 4.12077755e-01 5.93216717e-01
-1.46611333e-01 -4.25228447e-01 -8.62644255e-01 4.32198465e-01
4.25970286e-01 9.35935795e-01 -5.13846874e-01 -2.97195256e-01
-5.10889769e-01 1.60955834e+00 3.16942751e-01 6.55626118e-01
-8.75050306e-01 -1.85536936e-01 9.31225181e-01 -8.98029283e-02
6.49498224e-01 -3.73421311e-01 -1.46114632e-01 -1.48113179e+00
1.83320552e-01 -9.68346179e-01 4.79998738e-01 -8.97272110e-01
-1.51865947e+00 4.30561244e-01 -4.05928642e-02 -1.56557822e+00
-2.23477557e-02 -4.22836483e-01 -5.46933115e-01 5.62486470e-01
-2.01277614e+00 -1.04388940e+00 -8.31160009e-01 1.04447865e+00
7.54058957e-01 -7.85265565e-02 5.02684474e-01 3.32614303e-01
-6.62079334e-01 4.44762796e-01 4.51730907e-01 2.02596948e-01
1.21484947e+00 -1.27129495e+00 3.71723980e-01 1.26802289e+00
-1.37298731e-02 1.62487194e-01 5.74585438e-01 -2.48123139e-01
-1.07186651e+00 -1.61632264e+00 3.59059185e-01 -3.65312696e-01
5.28616428e-01 -3.41590732e-01 -9.05679226e-01 7.01774478e-01
2.92984903e-01 4.30075109e-01 7.15277553e-01 -1.24007523e-01
-7.82654226e-01 -6.52807713e-01 -1.19772744e+00 3.83957177e-01
1.27155173e+00 -7.57511079e-01 -5.23267090e-01 3.79153877e-01
7.81709313e-01 -4.57237870e-01 -6.26993656e-01 4.78815496e-01
2.28195608e-01 -1.11496580e+00 8.88092816e-01 -4.71317649e-01
3.66452664e-01 -5.14206886e-01 -3.96655887e-01 -1.56750166e+00
-3.86690170e-01 -4.41317350e-01 2.51015991e-01 1.23482347e+00
-1.85010865e-01 -8.22983444e-01 3.59647393e-01 4.93857950e-01
-6.01169765e-02 -2.25815382e-02 -8.85625482e-01 -1.08551729e+00
-1.01240605e-01 -2.82817602e-01 4.77829605e-01 1.44769287e+00
-6.38875246e-01 1.23299368e-01 -4.53420490e-01 6.09128237e-01
5.27014852e-01 2.41271883e-01 9.51236367e-01 -1.06897616e+00
-3.17656070e-01 -3.02774962e-02 -5.65488577e-01 -8.58397186e-01
2.60388732e-01 -6.70592129e-01 3.78754765e-01 -1.35980797e+00
9.69247520e-02 -2.08720371e-01 -5.44984221e-01 5.27865469e-01
-2.20820487e-01 4.04685766e-01 2.90672034e-02 2.57773519e-01
-7.78510809e-01 7.07970142e-01 1.02756238e+00 -3.23737442e-01
-1.75052106e-01 -3.69840652e-01 -5.24009347e-01 9.16364551e-01
8.47641230e-01 -3.07820350e-01 -5.84643066e-01 -9.37975049e-01
5.29226139e-02 -4.51065838e-01 6.25087678e-01 -1.28215432e+00
1.07653014e-01 -3.59337479e-01 6.86015844e-01 -5.07517934e-01
2.27306589e-01 -9.36070561e-01 5.61924651e-02 3.38672251e-01
-2.01197401e-01 2.31145084e-01 3.86990339e-01 9.22197759e-01
-4.22285467e-01 3.53836596e-01 9.81086493e-01 -2.73676842e-01
-1.28952157e+00 2.91021675e-01 -1.58807144e-01 2.55475998e-01
1.06770051e+00 -3.91370803e-01 -4.74132299e-01 -4.61247057e-01
-3.27264577e-01 2.12630317e-01 6.47039413e-01 5.00752985e-01
5.82265198e-01 -1.29200733e+00 -5.96617281e-01 4.69224274e-01
2.60034144e-01 2.86573857e-01 3.99880826e-01 6.47682667e-01
-5.20259798e-01 -2.11817715e-02 -5.02511442e-01 -8.94543767e-01
-1.30318320e+00 5.23733139e-01 4.83516186e-01 6.78995624e-02
-6.05912805e-01 8.42343569e-01 4.04636562e-01 -6.70438588e-01
1.30048871e-01 -5.28009117e-01 6.13958091e-02 4.79509607e-02
3.09192926e-01 2.36134574e-01 1.47426903e-01 -4.98455614e-01
-3.04402322e-01 6.01177871e-01 -1.19402170e-01 1.10856496e-01
1.48126245e+00 -4.36225235e-01 1.06014013e-01 5.89010358e-01
1.43113589e+00 -1.45037755e-01 -2.01815557e+00 -6.97165132e-01
-3.26641142e-01 -7.16915190e-01 6.87936917e-02 -8.31367970e-01
-1.21945822e+00 6.87408984e-01 1.04763544e+00 -1.92477435e-01
1.61453378e+00 -3.99708033e-01 8.21402192e-01 5.60681462e-01
2.97691166e-01 -1.30081511e+00 3.66368264e-01 5.58704257e-01
9.18886721e-01 -1.71767867e+00 -5.90031147e-02 -1.65584400e-01
-7.86495805e-01 9.11121190e-01 9.33795094e-01 -2.44670391e-01
5.52498758e-01 5.23976833e-02 3.54172438e-01 5.26396520e-02
-6.23354375e-01 -2.53841043e-01 5.72369471e-02 9.77591038e-01
-1.80437058e-01 -1.77636310e-01 3.92181754e-01 -8.92099738e-02
3.49089480e-03 -1.82571173e-01 4.44636405e-01 9.85316396e-01
-2.31988713e-01 -6.46339238e-01 -3.21204543e-01 2.02026591e-01
-7.68105984e-02 -1.96464732e-02 -3.17859471e-01 6.61689401e-01
4.12316531e-01 8.58432174e-01 4.59230572e-01 -3.99396330e-01
3.82210940e-01 -2.15342432e-01 3.71995389e-01 -5.65803707e-01
-3.48188013e-01 -1.89244837e-01 -1.98363557e-01 -7.28915334e-01
-8.95334899e-01 -9.20288444e-01 -8.98335099e-01 -1.07090570e-01
2.64741909e-02 -6.11677170e-01 3.60517681e-01 7.59975910e-01
5.99747241e-01 5.21887362e-01 1.11278379e+00 -8.82361472e-01
-5.47426820e-01 -7.76210904e-01 -4.45706517e-01 8.97145152e-01
8.11774671e-01 -6.70056224e-01 -5.22823274e-01 6.86779678e-01] | [9.527865409851074, -1.3389488458633423] |
f29c8a44-d52c-40a8-bc95-f8af95920d97 | a-robust-feature-downsampling-module-for | null | null | https://ieeexplore.ieee.org/document/10142024 | https://ieeexplore.ieee.org/document/10142024 | A Robust Feature Downsampling Module for Remote Sensing Visual Tasks | Remote-sensing (RS) images present unique challenges for computer vision (CV) due to lower resolution, smaller objects, and fewer features. Mainstream backbone networks show promising results for traditional visual tasks. However, they use convolution to reduce feature map dimensionality, which can result in information loss for small objects in RS images and decreased performance. To address this problem, we propose a new and universal downsampling module named robust feature downsampling (RFD). RFD fuses multiple feature maps extracted by different downsampling techniques, creating a more robust feature map with a complementary set of features. Leveraging this, we overcome the limitations of conventional convolutional downsampling, resulting in a more accurate and robust analysis of RS images. We develop two versions of the RFD module, shallow RFD (SRFD) and deep RFD (DRFD), tailored to adapt to different stages of feature capture and improve feature robustness. We replace the downsampling layers (DSL) of existing mainstream backbones with the RFD module and conduct comparative experiments on several public RS image datasets. The results show significant improvements compared to baseline approaches in RS image classification, object detection, and semantic segmentation. Specifically, our RFD module achieved an average performance gain of 1.5% on the NWPU-RESISC45 classification dataset without utilizing any additional pretraining data, resulting in state-of-the-art performance on this dataset. Moreover, in detection and segmentation tasks on dataset for object detection in aerial images (DOTA) and instance segmentation in aerial images dataset (iSAID), our RFD module outperforms the baseline approaches by 2%–7% when utilizing pretraining data from NWPU-RESISC45. These results highlight the value of the RFD module in enhancing the performance of RS visual tasks. The code is available at https://github.com/lwCVer/RFD. | ['and Bin Luo', 'Chris H. Q. Ding', 'Jin Tang', 'Si-Bao Chen', 'Wei Lu'] | 2023-06-01 | null | null | null | ieee-transactions-on-geoscience-and-remote-19 | ['object-detection-in-aerial-images', 'object-detection', 'instance-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.07766074e-01 -4.39088106e-01 -2.57456694e-02 -3.47940356e-01
-3.88506979e-01 -5.42666197e-01 4.09409612e-01 -2.76528031e-01
-4.44970608e-01 2.54074901e-01 -7.72962198e-02 -3.23377639e-01
4.26135771e-02 -1.08233035e+00 -7.09390163e-01 -6.02643490e-01
3.97936180e-02 -4.03030962e-01 4.30751711e-01 -3.20986301e-01
-1.58888295e-01 8.33459139e-01 -1.72502577e+00 2.49896765e-01
7.80996323e-01 1.17190552e+00 6.54191673e-01 5.34552574e-01
8.90501514e-02 2.50021547e-01 -4.48559850e-01 1.00847781e-01
7.43013561e-01 -1.43406959e-02 -7.49246299e-01 2.80909296e-02
5.88239729e-01 -7.37341881e-01 -3.18359166e-01 9.74027872e-01
5.97833216e-01 1.41265050e-01 3.39428663e-01 -9.48828518e-01
-7.93417931e-01 5.12210190e-01 -9.86632824e-01 3.00804526e-01
-2.27524400e-01 1.33438438e-01 6.05957091e-01 -9.83378887e-01
3.57043624e-01 1.06273520e+00 9.70555544e-01 4.84123141e-01
-1.24904263e+00 -9.18446541e-01 2.68987983e-01 5.88577241e-04
-1.49511397e+00 -3.50826502e-01 3.63090366e-01 -3.74815106e-01
9.30457115e-01 4.03448343e-01 6.59525335e-01 7.43912876e-01
-2.70795971e-01 6.78779542e-01 1.12797058e+00 -1.18129514e-01
6.91783130e-02 -5.16895540e-02 8.37322697e-02 6.08091772e-01
3.55513096e-01 1.27993420e-01 -1.43818200e-01 2.00666711e-01
8.38504255e-01 5.15133083e-01 -5.33846855e-01 -1.29284561e-01
-9.72746372e-01 7.85434723e-01 1.14611363e+00 1.03252223e-02
-3.08786958e-01 -9.21032652e-02 2.35448241e-01 1.68333426e-01
5.41740954e-01 4.12255734e-01 -7.09541321e-01 5.28776705e-01
-8.50681961e-01 2.13525649e-02 2.91183203e-01 8.04779768e-01
8.19927514e-01 1.32301733e-01 -3.18948150e-01 1.07994974e+00
2.93784533e-02 8.82870674e-01 3.37195396e-01 -7.75778055e-01
4.28556770e-01 6.96454287e-01 -7.91847333e-02 -8.65685642e-01
-6.85583889e-01 -6.19421542e-01 -9.13774967e-01 2.68544644e-01
1.06723383e-02 -2.00828716e-01 -1.28984308e+00 1.65316403e+00
4.30177063e-01 -7.55824968e-02 7.57972971e-02 1.06624091e+00
1.20194423e+00 8.23659956e-01 1.12127841e-01 3.49210024e-01
1.28778005e+00 -8.24084461e-01 -1.88645616e-01 -4.17915314e-01
4.80679423e-01 -6.32714450e-01 1.20085728e+00 1.55299744e-02
-5.09594202e-01 -8.41416776e-01 -1.25122023e+00 -7.01685473e-02
-5.78400314e-01 6.83116198e-01 6.91501319e-01 4.52021986e-01
-8.98501694e-01 5.95696390e-01 -6.44314349e-01 -5.93606293e-01
9.94410753e-01 1.98043272e-01 -4.91052270e-01 -2.55142808e-01
-9.03467357e-01 6.30150378e-01 2.59593189e-01 3.07100505e-01
-9.64772522e-01 -1.09875822e+00 -8.59980166e-01 1.35395765e-01
3.71679395e-01 -5.00181615e-01 1.05353892e+00 -7.55771458e-01
-1.31884480e+00 7.45954990e-01 2.22145274e-01 -3.92295957e-01
4.52747315e-01 -4.70294088e-01 -3.53927225e-01 2.90553093e-01
2.85948366e-01 8.68916690e-01 1.00909162e+00 -1.10542893e+00
-7.15761900e-01 -4.16328758e-01 1.40179724e-01 8.12048614e-02
-3.26904625e-01 -1.37977734e-01 -3.13201070e-01 -6.98285758e-01
8.22866634e-02 -8.29675376e-01 -2.21407041e-01 2.49433860e-01
-3.33977729e-01 2.24100828e-01 1.26943624e+00 -5.45966446e-01
8.60763252e-01 -2.25968432e+00 -1.14837050e-01 -7.99507797e-02
2.65424430e-01 1.03335869e+00 -4.14447755e-01 -4.67607863e-02
-2.25923494e-01 2.76386291e-01 -4.94029373e-01 -3.31365503e-02
-3.57888699e-01 -8.28608349e-02 -3.45129132e-01 5.97305954e-01
5.92842996e-01 8.64452899e-01 -6.66663945e-01 -7.73608088e-02
5.85009515e-01 5.70958436e-01 -2.48510405e-01 3.10668480e-02
4.03124727e-02 3.56606722e-01 -5.24333894e-01 9.67805862e-01
1.18015349e+00 -2.44450212e-01 -1.79844022e-01 -4.82674688e-01
-4.25237894e-01 -1.60321608e-01 -1.06946361e+00 1.37930620e+00
-4.84272808e-01 6.67829216e-01 4.65396345e-02 -8.64096642e-01
1.01093054e+00 -2.05253363e-01 2.26534292e-01 -8.02614450e-01
6.08420558e-02 -7.30561838e-02 -2.42406085e-01 -3.71110678e-01
3.49476725e-01 5.26844859e-02 5.40373921e-02 1.49080411e-01
-4.17685099e-02 -1.85506180e-01 1.96809575e-01 4.28058207e-02
7.97247589e-01 2.42363736e-01 2.15580344e-01 -2.04267487e-01
4.25439149e-01 1.09473117e-01 4.65957314e-01 8.70149195e-01
-1.16693713e-01 8.30473602e-01 1.11820064e-01 -7.63085127e-01
-8.54299009e-01 -1.00061989e+00 -6.10214055e-01 1.17614007e+00
2.33976498e-01 -2.34377503e-01 -5.75772583e-01 -7.37205207e-01
1.82420611e-01 2.15233043e-01 -7.42591083e-01 -1.04343809e-01
-3.33557278e-01 -1.17049110e+00 7.01490223e-01 7.23162234e-01
1.23700702e+00 -9.21065390e-01 -8.75876427e-01 -9.04471055e-02
-3.02391052e-02 -1.34966218e+00 -1.22507684e-01 2.05461159e-01
-8.82722557e-01 -1.07069778e+00 -7.95569003e-01 -4.36643034e-01
6.50323689e-01 9.55403626e-01 6.61739826e-01 2.37835795e-02
-7.98003197e-01 -2.35707778e-02 -5.22552013e-01 -4.55141395e-01
5.24553955e-02 2.00764596e-01 -2.04484642e-01 -2.38413572e-01
2.12599352e-01 -2.87954926e-01 -8.71800363e-01 4.06130105e-01
-1.05179381e+00 2.07916737e-01 9.17057514e-01 7.12530911e-01
4.84490991e-01 -1.62474647e-01 5.22724390e-01 -6.45641744e-01
1.98599193e-02 -3.65782380e-01 -7.70164907e-01 1.08007051e-01
-4.85751778e-01 -1.52484968e-01 8.02433431e-01 -9.90669131e-02
-1.03977418e+00 2.95482546e-01 -8.65866765e-02 -3.40740353e-01
-3.70607138e-01 2.86466748e-01 -2.17437744e-02 -3.18502814e-01
8.68495464e-01 1.03348136e-01 7.93169737e-02 -6.46451414e-01
3.55991274e-01 8.54510069e-01 3.39408040e-01 -4.85574603e-02
9.16072071e-01 7.25014687e-01 4.14824346e-03 -1.00649452e+00
-1.18575323e+00 -5.38885236e-01 -5.33674061e-01 3.26766111e-02
6.78523600e-01 -1.35504472e+00 -2.66973495e-01 7.09663808e-01
-6.14920199e-01 -4.14686054e-01 -3.38202626e-01 4.00029510e-01
-6.62053889e-03 2.22281575e-01 -4.21509594e-01 -4.16790813e-01
-6.58491015e-01 -1.04488993e+00 1.16041982e+00 5.72043955e-01
3.17513138e-01 -3.48836631e-01 -3.80509257e-01 2.07460836e-01
7.95906067e-01 3.85282636e-01 6.08856976e-01 -1.95292041e-01
-3.59191567e-01 -1.54250070e-01 -8.72345984e-01 7.38123417e-01
2.17548668e-01 6.16261661e-02 -1.16004491e+00 -3.59195322e-01
-4.88133430e-01 -2.75177896e-01 1.52419281e+00 4.87592459e-01
1.29652965e+00 2.24747453e-02 -4.34393525e-01 1.20494759e+00
1.62058961e+00 -1.17614716e-01 5.08973300e-01 5.50851405e-01
8.41508627e-01 4.22681808e-01 6.34590805e-01 6.38486564e-01
2.55291551e-01 4.86996502e-01 6.24573767e-01 -5.71027696e-01
-4.48603302e-01 -9.46316719e-02 4.02879149e-01 -6.97043836e-02
-3.68503571e-01 -1.98621172e-02 -7.62387991e-01 5.40334702e-01
-1.55440271e+00 -7.40072310e-01 -1.34953901e-01 2.02647424e+00
5.67049503e-01 -3.35783124e-01 -1.77230814e-03 -8.47438872e-02
8.67705584e-01 4.12081569e-01 -7.22523153e-01 -2.18918677e-02
-2.89063841e-01 4.39027786e-01 9.91434872e-01 -2.16250122e-02
-1.59851992e+00 1.19665611e+00 5.73328781e+00 7.28840172e-01
-1.41587925e+00 -7.13150427e-02 4.21139628e-01 -1.30949691e-01
2.01557323e-01 -1.85964867e-01 -1.01853645e+00 2.38370970e-01
5.47669530e-01 2.89962083e-01 3.87282699e-01 9.82521534e-01
1.71718851e-01 -9.98503938e-02 -4.75770593e-01 8.26980412e-01
-1.03556700e-01 -1.43349075e+00 2.37831175e-01 -1.45018160e-01
7.78931141e-01 5.92986107e-01 1.80087730e-01 4.04794484e-01
1.79938555e-01 -1.03595483e+00 5.65568447e-01 1.09885216e-01
1.10731459e+00 -7.17808485e-01 8.45898628e-01 -1.18402606e-02
-1.55354702e+00 -4.18781281e-01 -8.27402771e-01 -1.29790278e-02
-2.23359466e-01 5.99995553e-01 -5.79479992e-01 6.37488663e-01
1.32289851e+00 1.00065124e+00 -9.34918404e-01 9.21701908e-01
-2.62665004e-01 4.01736528e-01 -2.89588660e-01 3.90349269e-01
2.41485551e-01 -1.21444143e-01 4.34056312e-01 1.19667304e+00
3.28377753e-01 5.75467609e-02 5.44724911e-02 9.61222470e-01
-2.43273556e-01 -1.83913544e-01 -5.08512318e-01 4.28438969e-02
4.59474832e-01 1.77806771e+00 -7.21779346e-01 -2.28163719e-01
-3.73601675e-01 8.59047413e-01 1.84361771e-01 3.46273988e-01
-9.50373352e-01 -7.44837701e-01 8.65742743e-01 4.59146798e-02
6.96309090e-01 -5.48243523e-02 -6.32240549e-02 -1.06109703e+00
-6.92390576e-02 -6.65923655e-01 3.82412195e-01 -6.00751758e-01
-1.05142677e+00 7.53787041e-01 -7.86065962e-03 -1.22972476e+00
4.30227846e-01 -8.44847381e-01 -4.71218973e-01 8.76152694e-01
-2.14362860e+00 -1.40945923e+00 -1.11170757e+00 5.44268191e-01
5.63250065e-01 -2.40703803e-02 7.57917583e-01 1.97710276e-01
-8.88468444e-01 2.88431793e-01 1.67967081e-01 2.18299583e-01
6.41283572e-01 -1.08361733e+00 6.75384998e-01 1.19125438e+00
-2.81031400e-01 3.82562876e-01 1.58629075e-01 -4.33986425e-01
-1.19359767e+00 -1.78260899e+00 6.62963614e-02 -1.52049556e-01
3.40482831e-01 -2.49196723e-01 -7.77526200e-01 5.22337914e-01
-4.39321876e-01 5.83915710e-01 6.19051158e-01 -3.10829610e-01
-5.06928146e-01 -3.50707859e-01 -1.19900942e+00 3.33300561e-01
1.16788352e+00 -3.36677164e-01 -3.68974298e-01 1.70431793e-01
8.30644429e-01 -3.39570045e-01 -7.37782657e-01 7.80221224e-01
6.53086960e-01 -1.04617512e+00 1.26657522e+00 -2.97816634e-01
5.36635876e-01 -6.93723381e-01 -3.00822467e-01 -1.24944985e+00
-4.68054384e-01 -1.89427868e-01 1.55960098e-01 1.06304181e+00
3.31896722e-01 -6.66471124e-01 3.68465275e-01 1.71259448e-01
-3.92400026e-01 -5.56123018e-01 -4.97083753e-01 -8.39416206e-01
-1.97448745e-01 -3.55116248e-01 8.09010506e-01 8.20387661e-01
-7.61988461e-01 2.84144375e-02 -2.32763693e-01 5.04408896e-01
5.57652771e-01 5.13634145e-01 7.81777084e-01 -1.26512909e+00
6.42781565e-03 -3.19188952e-01 -2.50346750e-01 -8.88901711e-01
-1.36614919e-01 -9.37114537e-01 1.74229499e-02 -1.73028219e+00
2.47871295e-01 -4.40312058e-01 -2.22458050e-01 8.84477317e-01
-3.29208404e-01 7.46962309e-01 3.95896226e-01 1.51038453e-01
-3.89341682e-01 5.47168136e-01 1.18236148e+00 -6.73020259e-02
-2.76870608e-01 -7.94877335e-02 -8.54271591e-01 7.30886042e-01
1.05160952e+00 -4.17472392e-01 -7.69049227e-02 -6.86376333e-01
-2.32978657e-01 -5.88413179e-01 7.97854722e-01 -1.11011636e+00
-9.15236250e-02 1.13488637e-01 9.46347237e-01 -6.18828416e-01
1.57662511e-01 -6.22620344e-01 -2.77951658e-02 6.65354133e-01
1.57668456e-01 -3.70386362e-01 6.20750725e-01 3.70474815e-01
-9.20172874e-03 4.54763137e-02 1.08234465e+00 -1.68205485e-01
-1.15365577e+00 3.67623895e-01 -2.05490291e-01 -2.07998753e-01
1.04736423e+00 -2.44613841e-01 -7.76386261e-01 2.17057109e-01
-4.00905639e-01 2.46311113e-01 4.08833742e-01 5.51529467e-01
7.17969239e-01 -9.00280714e-01 -8.08436871e-01 5.91913283e-01
3.61744881e-01 4.25392956e-01 6.32867098e-01 8.34969521e-01
-7.67682493e-01 2.39666745e-01 -5.74003875e-01 -6.12080812e-01
-1.11712885e+00 2.91048825e-01 2.99260587e-01 1.49110451e-01
-8.61586750e-01 9.51195300e-01 3.14381987e-01 -5.38039088e-01
-6.45898953e-02 -6.01548612e-01 -4.40617085e-01 1.67752907e-01
7.68394172e-01 5.69699049e-01 9.83943045e-02 -6.01019740e-01
-4.93984193e-01 8.68140399e-01 -1.45913601e-01 5.53607047e-01
1.74071503e+00 -1.60262838e-01 1.12610720e-01 -9.26190093e-02
9.81499970e-01 -3.22301060e-01 -1.64214456e+00 -4.13380414e-01
-4.30427819e-01 -6.02810681e-01 5.25401890e-01 -7.82930374e-01
-1.37176895e+00 7.12000132e-01 1.04443789e+00 9.77338757e-03
1.37488353e+00 7.19062164e-02 7.07045972e-01 3.99617672e-01
1.67847514e-01 -9.41357076e-01 -3.00327450e-01 3.88254017e-01
8.97764981e-01 -1.53428614e+00 2.75465786e-01 -3.64964575e-01
-7.02936471e-01 1.24675357e+00 7.07786620e-01 -1.68891743e-01
4.57604527e-01 2.63243288e-01 1.17663652e-01 -2.16103122e-01
-1.72877297e-01 -6.77922547e-01 3.12119454e-01 6.99173748e-01
9.08361673e-02 1.32309422e-01 1.53417781e-01 5.11312187e-01
-4.77063246e-02 -2.19319761e-02 3.21530253e-01 9.30771172e-01
-5.34132302e-01 -6.06428802e-01 -2.64335483e-01 5.28549969e-01
-3.81040126e-01 -2.87696391e-01 -2.54162371e-01 9.79677856e-01
2.48929814e-01 8.63665640e-01 2.97021642e-02 -4.96759027e-01
5.35939038e-01 -4.63605165e-01 1.34709820e-01 -5.51892817e-01
-4.54625607e-01 -1.64176598e-01 -2.04470694e-01 -8.46780002e-01
-5.67528009e-01 -4.55395132e-01 -1.05543244e+00 -2.41539881e-01
-4.93212223e-01 -2.73266941e-01 7.87741661e-01 6.84333742e-01
5.43736160e-01 6.42653942e-01 6.95747614e-01 -1.00436378e+00
-5.92630684e-01 -9.48248565e-01 -6.38518155e-01 7.88569078e-02
4.79880929e-01 -5.81582308e-01 -3.70177597e-01 -4.24082279e-02] | [9.031492233276367, -0.9071186184883118] |
40e02ec5-a20d-480c-95a6-ad808b97cfc3 | mcae-masked-contrastive-autoencoder-for-face | 2302.08674 | null | https://arxiv.org/abs/2302.08674v2 | https://arxiv.org/pdf/2302.08674v2.pdf | EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing | The face anti-spoofing (FAS) method performs well under intra-domain setups. However, its cross-domain performance is unsatisfactory. As a result, the domain generalization (DG) method has gained more attention in FAS. Existing methods treat FAS as a simple binary classification task and propose a heuristic training objective to learn domain-invariant features. However, there is no theoretical explanation of what a domain-invariant feature is. Additionally, the lack of theoretical support makes domain generalization techniques such as adversarial training lack training stability. To address these issues, this paper proposes the EnfoMax framework, which uses information theory to analyze cross-domain FAS tasks. This framework provides theoretical guarantees and optimization objectives for domain-generalized FAS tasks. EnfoMax maximizes the domain entropy and mutual information of live samples in source domains without using adversarial learning. Experimental results demonstrate that our approach performs well on extensive public datasets and outperforms state-of-the-art methods. | ['Tianyi Zheng'] | 2023-02-17 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 4.20132458e-01 -4.73739766e-02 -7.32615471e-01 -4.27855641e-01
-7.03881562e-01 -6.90865517e-01 6.48679912e-01 -1.77778482e-01
6.68899566e-02 9.50916231e-01 -1.06070966e-01 -2.25094974e-01
-1.68170542e-01 -6.82537317e-01 -6.49276137e-01 -8.24775100e-01
-2.56707221e-01 2.16540471e-01 1.44791707e-01 -2.10631117e-01
6.39100894e-02 4.57953632e-01 -1.34932911e+00 2.82704890e-01
9.69472408e-01 1.19980812e+00 -4.00990009e-01 1.66281778e-02
-2.51445640e-03 4.87357855e-01 -7.97003090e-01 -8.71957600e-01
4.93314385e-01 -4.49239939e-01 -8.85622621e-01 -4.43608463e-02
2.10794643e-01 -4.14058447e-01 -5.65172553e-01 1.23251438e+00
3.88930380e-01 -6.87876940e-02 7.14502454e-01 -1.89782107e+00
-8.70292068e-01 2.84028828e-01 -4.92789805e-01 1.05401225e-01
4.15593296e-01 -2.31935382e-01 9.36309993e-01 -6.03739202e-01
5.57207942e-01 1.37173402e+00 6.21472180e-01 7.50210643e-01
-1.23285985e+00 -1.15960085e+00 -1.00598432e-01 1.52712420e-01
-1.36821425e+00 -5.11310101e-01 1.07011223e+00 -3.62195998e-01
5.21974504e-01 -5.11685796e-02 6.05333857e-02 1.46376967e+00
1.05888523e-01 8.20257902e-01 1.28552234e+00 -2.25635216e-01
3.42460543e-01 4.10508454e-01 -2.83294082e-01 3.69922876e-01
4.02892292e-01 4.79875654e-01 -7.49161482e-01 -5.25878668e-01
5.02498150e-01 -1.39746174e-01 -1.96102887e-01 -6.46987736e-01
-7.11294591e-01 1.10677946e+00 2.80336976e-01 1.39520958e-01
-6.72575831e-02 -4.43851709e-01 6.45898700e-01 7.17679858e-01
7.50141859e-01 9.02667120e-02 -6.02857471e-01 8.76203477e-02
-8.44208360e-01 2.84136564e-01 7.99359858e-01 9.45364952e-01
6.77819550e-01 4.10293415e-02 5.46859801e-01 8.69693279e-01
1.58727914e-01 6.01697028e-01 3.26701015e-01 -9.50839162e-01
5.22617102e-01 3.00339848e-01 -2.05427527e-01 -1.16170645e+00
4.65662032e-02 -1.70482680e-01 -9.32709336e-01 1.09700203e-01
5.69214702e-01 -1.56508207e-01 -4.97084409e-01 2.03960800e+00
4.14331019e-01 3.51271033e-01 2.67894655e-01 5.16522110e-01
3.48220170e-01 3.48794729e-01 5.92706352e-02 -4.12305623e-01
1.09031415e+00 -4.32514101e-01 -7.60719359e-01 -2.64319628e-01
4.14913654e-01 -6.25133038e-01 8.22325230e-01 3.19447398e-01
-6.90699041e-01 -3.44802260e-01 -1.05381465e+00 3.82331997e-01
-4.58864242e-01 -3.76288116e-01 6.06723666e-01 1.27374279e+00
-7.93064177e-01 6.11730993e-01 -5.30063808e-01 -3.19813579e-01
8.21259022e-01 5.63067973e-01 -7.96141744e-01 -2.33808875e-01
-1.44082355e+00 5.17894089e-01 4.66333926e-01 -6.91038072e-01
-8.86863708e-01 -8.46435547e-01 -1.03084064e+00 -1.48807675e-01
2.10510954e-01 -4.04169977e-01 1.01361990e+00 -1.21142077e+00
-1.50571454e+00 1.11261332e+00 -1.66216671e-01 -6.20042622e-01
4.87648100e-01 -7.14959055e-02 -8.12801600e-01 4.30750549e-01
1.72601894e-01 3.90129626e-01 1.30251753e+00 -1.42691326e+00
-5.22184491e-01 -5.60379446e-01 1.63368732e-02 -3.32812458e-01
-5.88780940e-01 1.32471442e-01 9.00742505e-03 -9.55829620e-01
-5.28819449e-02 -9.01353359e-01 3.14722598e-01 2.34495744e-01
-2.22838074e-01 -1.53984338e-01 1.32376361e+00 -7.29403198e-01
1.17568219e+00 -2.43569970e+00 -2.07005247e-01 2.28535026e-01
6.53994530e-02 5.16627967e-01 -5.23590297e-02 2.37074256e-01
-2.40357548e-01 3.75062346e-01 -4.99331385e-01 -2.11434215e-01
-1.52504608e-01 3.22763652e-01 -5.04021704e-01 7.80284345e-01
4.96471494e-01 3.00262839e-01 -9.21464443e-01 -6.08314097e-01
-1.80633611e-03 5.18048525e-01 -8.96878600e-01 2.45483294e-01
3.21211666e-02 6.14907146e-01 -3.90311897e-01 9.71330225e-01
1.27098942e+00 -2.35602751e-01 3.72204095e-01 3.51566970e-02
4.71140027e-01 2.81075627e-01 -9.48635697e-01 1.49024367e+00
-2.48802558e-01 5.45734942e-01 2.35804871e-01 -1.46385455e+00
9.96876717e-01 3.89792532e-01 7.93743074e-01 -5.91373563e-01
1.08337393e-02 3.79514813e-01 -2.56034285e-01 -3.78088355e-01
-9.59767774e-03 -4.64546055e-01 -1.62936464e-01 2.67151415e-01
2.18145430e-01 1.46391407e-01 -3.09452534e-01 1.02025054e-01
1.09794402e+00 -1.27322316e-01 5.53898096e-01 -3.28588128e-01
7.69094348e-01 -3.46129119e-01 8.35369408e-01 4.36004162e-01
-7.24795997e-01 4.11227167e-01 5.49893856e-01 -1.34701654e-01
-9.06087935e-01 -1.22994912e+00 -4.07419413e-01 1.02456141e+00
4.99511510e-01 -2.47948438e-01 -7.77846038e-01 -1.31602645e+00
4.00158852e-01 3.48818094e-01 -5.76405764e-01 -3.92835885e-01
-5.16050756e-01 -5.14166832e-01 8.87058318e-01 2.37489760e-01
8.46810460e-01 -6.74410939e-01 4.45335917e-02 -3.62301953e-02
-3.73403966e-01 -1.29213667e+00 -3.11501145e-01 -8.91390517e-02
-7.07463920e-01 -1.09465015e+00 -6.30505025e-01 -8.32985103e-01
3.51739854e-01 2.73897976e-01 8.87658000e-01 -1.86447605e-01
5.74176833e-02 2.78401554e-01 -4.38117325e-01 -2.03066498e-01
-6.82592034e-01 -4.16726992e-03 4.41990554e-01 2.73568064e-01
6.48392379e-01 -8.03924739e-01 -4.87519413e-01 5.82089543e-01
-9.47495282e-01 -7.07240820e-01 2.70937622e-01 1.05272007e+00
2.28332445e-01 4.12948489e-01 1.07873690e+00 -8.28559816e-01
5.51514208e-01 -8.27973247e-01 -3.41164231e-01 2.36865729e-01
-6.51167333e-01 9.82839465e-02 6.55724049e-01 -6.47520125e-01
-1.13807786e+00 -1.46005347e-01 6.03988133e-02 -5.07749498e-01
-2.73385346e-01 3.25885303e-02 -6.76499009e-01 -3.65318656e-01
6.58477366e-01 2.02145144e-01 4.27298009e-01 -3.94473016e-01
-3.72971892e-02 8.44042182e-01 3.93556356e-01 -8.09086859e-01
1.06436265e+00 7.57164598e-01 -3.07572708e-02 -8.83187890e-01
-6.40359759e-01 -4.07039016e-01 -4.00236487e-01 1.12222634e-01
4.68683243e-01 -9.19149578e-01 -6.96489453e-01 4.87878680e-01
-1.00586832e+00 -2.61458866e-02 -2.42243148e-02 2.81296849e-01
-6.94167495e-01 7.70450056e-01 -2.60357946e-01 -8.39991033e-01
-2.10958552e-02 -1.01295984e+00 9.76663888e-01 -4.42054542e-03
-1.09656610e-01 -1.11614931e+00 -1.49174228e-01 3.76328439e-01
2.69770265e-01 4.23785239e-01 7.25134552e-01 -8.99352551e-01
-1.64381042e-01 -1.85159758e-01 -2.62258440e-01 7.92374372e-01
4.35452610e-01 -2.89252192e-01 -1.19226015e+00 -5.90813935e-01
2.03012228e-01 -3.94450128e-01 6.40728474e-01 1.56829819e-01
1.45575452e+00 -6.75343812e-01 -5.12938201e-01 8.22465420e-01
1.29339707e+00 2.26387262e-01 5.57514906e-01 3.48732293e-01
1.33445933e-01 7.89612412e-01 7.80128956e-01 7.54630268e-01
1.86258614e-01 6.86786890e-01 4.42327142e-01 1.75165921e-01
5.03771752e-02 -4.23500299e-01 5.77627242e-01 6.44256407e-03
3.08378518e-01 -4.76731062e-01 -8.13423872e-01 6.11926973e-01
-1.26966918e+00 -1.25706065e+00 5.51820576e-01 2.20001721e+00
8.31727326e-01 2.70941183e-02 3.53623718e-01 5.00773072e-01
9.74022508e-01 3.06912214e-01 -5.79182506e-01 -2.66003370e-01
-2.99410284e-01 3.11839879e-01 5.11752665e-01 2.28785306e-01
-1.43828070e+00 9.13438618e-01 6.16829872e+00 1.05878294e+00
-1.06334567e+00 2.04631567e-01 5.57857990e-01 4.08245087e-01
-1.22950703e-01 -1.68196350e-01 -6.85140669e-01 7.02223420e-01
7.34093785e-01 -4.78322178e-01 6.14762247e-01 1.01198113e+00
-3.02960843e-01 4.18806732e-01 -1.03879082e+00 1.06844342e+00
8.18368234e-03 -9.62344885e-01 -1.42332643e-01 4.25249130e-01
7.44226396e-01 -1.97270721e-01 5.44929206e-01 2.28855446e-01
3.51806939e-01 -8.57304156e-01 3.50104302e-01 -2.81982452e-01
1.34332156e+00 -7.81161726e-01 5.12655139e-01 2.28874639e-01
-1.03991592e+00 -3.18218768e-01 -3.75664294e-01 1.88687429e-01
-5.02493866e-02 4.82994109e-01 -8.57836187e-01 6.26027465e-01
6.69346869e-01 7.94484377e-01 -1.49418145e-01 5.38874805e-01
3.90729420e-02 7.27681458e-01 -3.41981679e-01 4.81391042e-01
-1.30327716e-01 1.12521641e-01 6.58342481e-01 1.08967578e+00
3.49151462e-01 -5.78730442e-02 7.85981305e-03 4.59227353e-01
-3.63739789e-01 -7.60936290e-02 -9.89643931e-01 -2.22517952e-01
7.86705315e-01 6.02560699e-01 -2.22426653e-01 7.18325377e-03
-4.48965222e-01 1.10672081e+00 3.75476889e-02 3.58020484e-01
-8.11359644e-01 -3.46288711e-01 1.45773351e+00 2.47715458e-01
2.44660050e-01 -1.72801106e-03 -3.08722645e-01 -1.31903946e+00
1.00583360e-01 -1.25942111e+00 7.21677959e-01 1.15456805e-01
-1.76730192e+00 3.99275869e-01 7.66542368e-03 -1.53646553e+00
-3.41587305e-01 -6.98928356e-01 -2.47090414e-01 4.24811393e-01
-1.87362635e+00 -1.04540205e+00 3.71975601e-02 1.00901639e+00
2.99402028e-01 -6.18346930e-01 8.17202270e-01 5.02311289e-01
-2.82036155e-01 1.24283338e+00 4.31335151e-01 2.91602671e-01
9.89209414e-01 -8.72005224e-01 1.82507575e-01 7.10899115e-01
-2.94351161e-01 6.36275709e-01 6.08106136e-01 -6.50174797e-01
-1.17585301e+00 -1.00177813e+00 8.35864604e-01 -1.73852935e-01
7.18571544e-01 -2.87341446e-01 -9.66790497e-01 5.16531646e-01
-5.85560463e-02 4.75846618e-01 1.01719224e+00 -6.58828095e-02
-9.67805386e-01 -2.95330882e-01 -1.95596707e+00 4.88048866e-02
1.33503067e+00 -8.65014374e-01 -4.20911998e-01 3.46020907e-01
7.03802943e-01 -1.54249305e-02 -1.04673016e+00 6.56159699e-01
5.84316552e-01 -1.00646782e+00 1.34063101e+00 -6.56100810e-01
3.96770775e-01 7.52052069e-02 -5.56600809e-01 -1.09215951e+00
3.87079976e-02 -8.16251278e-01 -3.22137326e-01 1.50411928e+00
-6.60228496e-03 -1.07680595e+00 8.89042675e-01 3.00472409e-01
4.15546834e-01 -3.58469427e-01 -1.25568593e+00 -1.38967860e+00
4.90552396e-01 -2.52752572e-01 1.00154114e+00 1.33040726e+00
1.03776351e-01 -2.02693895e-01 -4.98375326e-01 4.72950101e-01
9.77246940e-01 -9.70546007e-02 7.31626868e-01 -1.45632160e+00
-1.57489896e-01 -2.55311251e-01 -5.98108470e-01 -9.54975128e-01
8.90783012e-01 -8.42015028e-01 -3.89828026e-01 -5.33603191e-01
-1.00300595e-01 -6.37933552e-01 -3.20675313e-01 1.84275299e-01
-2.00654846e-02 1.10008143e-01 9.85718295e-02 1.84923753e-01
-2.96744138e-01 5.59283793e-01 9.70596075e-01 -2.77004898e-01
8.65372345e-02 1.74886584e-01 -7.51503050e-01 4.48828936e-01
1.14957678e+00 -6.30221725e-01 -4.84153241e-01 -1.13891087e-01
-3.32544297e-01 9.28228945e-02 3.43930840e-01 -1.01170456e+00
-1.55865669e-01 -4.54175591e-01 1.53044730e-01 -1.64712831e-01
3.46392959e-01 -1.06236076e+00 -6.66098893e-02 4.86283094e-01
-2.08311960e-01 -4.30304319e-01 9.56873596e-02 8.10357153e-01
-4.32653129e-01 1.87247217e-01 1.10024500e+00 2.64013320e-01
-5.23197711e-01 5.27263701e-01 4.71397415e-02 3.74549478e-01
1.16853881e+00 -3.23452532e-01 -3.53146136e-01 -4.16302651e-01
-3.87561560e-01 -9.46976915e-02 6.35523438e-01 5.08526444e-01
5.20180523e-01 -1.37840641e+00 -6.66122913e-01 5.93872130e-01
2.07882315e-01 -3.31343949e-01 1.41478390e-01 2.35290572e-01
-1.66694149e-01 3.66240442e-01 -2.09148735e-01 -4.99931425e-01
-1.39370167e+00 8.92096698e-01 1.80582792e-01 -3.15723985e-01
-1.43917710e-01 8.59060347e-01 4.70104963e-01 -4.97757047e-01
1.26434311e-01 4.34863061e-01 1.11367419e-01 1.31968865e-02
5.67371905e-01 3.56199682e-01 -7.25720674e-02 -8.96807313e-01
-6.22578979e-01 3.67640138e-01 1.68096833e-02 -8.12660456e-02
1.19677031e+00 -1.84633836e-01 8.75780061e-02 -3.00031364e-01
1.74622238e+00 -5.29974513e-03 -1.23750067e+00 -5.48391044e-01
1.79957952e-02 -1.00084174e+00 -2.96468198e-01 -5.14968157e-01
-1.02730882e+00 9.91113067e-01 6.17365897e-01 3.84764761e-01
1.47481894e+00 2.84238681e-02 8.83721828e-01 8.62385854e-02
5.90994537e-01 -9.75286961e-01 3.00924182e-01 3.06002885e-01
6.87363744e-01 -1.53432059e+00 -2.10474193e-01 -8.07541966e-01
-5.79369605e-01 8.71798933e-01 6.12148166e-01 -2.00250316e-02
9.65497553e-01 1.47464231e-01 -1.88750252e-01 2.42487416e-01
-3.47583383e-01 7.93712661e-02 2.81416625e-02 1.24661255e+00
9.32449028e-02 1.33225754e-01 -2.09148973e-01 5.74010611e-01
-3.29466105e-01 1.75144169e-02 -2.94913184e-02 9.22844529e-01
-1.42891988e-01 -1.53091681e+00 -4.36242640e-01 1.45849898e-01
-9.11208630e-01 2.60515839e-01 -3.95235330e-01 7.85668194e-01
2.75885105e-01 1.15083098e+00 -1.49430886e-01 -6.65524244e-01
-1.24053054e-01 1.53983429e-01 3.36561710e-01 -1.94852397e-01
-1.53012753e-01 -4.54342425e-01 -1.35690764e-01 -4.62564260e-01
-4.87747669e-01 -8.24198902e-01 -9.05975342e-01 -9.07183349e-01
-3.25953960e-01 8.49526674e-02 4.92028922e-01 7.75952101e-01
5.25526226e-01 -1.02719106e-01 1.28694725e+00 -4.38562602e-01
-9.16050732e-01 -4.61480975e-01 -6.22590065e-01 7.19070375e-01
7.10261345e-01 -9.81425822e-01 -5.72739124e-01 1.11482121e-01] | [13.060860633850098, 1.185052514076233] |
5bb65abd-59a2-474b-b71a-68dcb4c6f40f | facial-expression-recognition-based-on-multi | 2203.13235 | null | https://arxiv.org/abs/2203.13235v1 | https://arxiv.org/pdf/2203.13235v1.pdf | Facial Expression Recognition based on Multi-head Cross Attention Network | Facial expression in-the-wild is essential for various interactive computing domains. In this paper, we proposed an extended version of DAN model to address the VA estimation and facial expression challenges introduced in ABAW 2022. Our method produced preliminary results of 0.44 of mean CCC value for the VA estimation task, and 0.33 of the average F1 score for the expression classification task. | ['Jin-Woo Jeong', 'Yuchul Jung', 'Daun Kim', 'Yeong-Gi Hong', 'Jae-Yeop Jeong'] | 2022-03-24 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-1.64024442e-01 8.33195597e-02 -2.42883161e-01 -1.07536089e+00
-7.84200907e-01 -2.46832758e-01 3.85643184e-01 -9.79048371e-01
-2.43552834e-01 9.49219882e-01 1.00330170e-02 1.96268588e-01
6.35751724e-01 -1.84895009e-01 -1.54296666e-01 -5.64867139e-01
-2.93785006e-01 -1.55082196e-01 -5.42453825e-01 -4.52261209e-01
-4.37482178e-01 8.46875966e-01 -1.93651938e+00 6.42865837e-01
4.52259451e-01 1.85215223e+00 -6.32764935e-01 8.00518334e-01
-1.41644657e-01 9.44761872e-01 -8.64857018e-01 -8.84298205e-01
7.17718825e-02 -6.16538942e-01 -5.64903617e-01 1.21916868e-01
9.49140072e-01 -4.41002458e-01 -1.67069465e-01 9.19876873e-01
8.15298736e-01 1.00802675e-01 6.03757799e-01 -2.04100347e+00
-1.22148469e-01 -6.85597882e-02 -8.80753160e-01 -1.60578564e-01
4.17419851e-01 2.13266946e-02 7.60258794e-01 -1.20537615e+00
9.51861739e-01 1.27818799e+00 5.65432072e-01 1.13692427e+00
-1.02099609e+00 -1.24063754e+00 -7.25366697e-02 1.01635695e-01
-1.80313039e+00 -1.16102707e+00 6.95626318e-01 -2.39021897e-01
7.29241252e-01 3.82239521e-01 7.94518173e-01 1.20452535e+00
-9.01358947e-02 1.12328005e+00 1.10702348e+00 -2.30840832e-01
-8.76091570e-02 3.79486270e-02 -2.48069212e-01 6.25837803e-01
-6.72155380e-01 1.25526991e-02 -1.00657105e+00 -2.53689826e-01
4.25471604e-01 -1.11120629e+00 -3.86994481e-02 2.18423396e-01
-2.18654081e-01 8.66994739e-01 2.18870640e-01 -1.41907349e-01
-4.90936697e-01 6.32289290e-01 6.03851438e-01 3.71054351e-01
8.99469197e-01 -3.84616926e-02 -2.55906045e-01 -8.36071372e-01
-1.11020505e+00 6.34257615e-01 3.68884832e-01 9.83242154e-01
2.59608239e-01 4.90434885e-01 -3.78098190e-01 1.24083614e+00
3.26249152e-01 7.39624560e-01 -2.68304259e-01 -1.65529788e+00
-3.74620929e-02 -1.01676837e-01 2.53103618e-02 -8.71628582e-01
-4.87095535e-01 -8.17543119e-02 -5.88112652e-01 5.40610850e-01
3.17492515e-01 -6.56378388e-01 -1.03543413e+00 2.12858891e+00
1.98108107e-01 -6.55119866e-02 -2.60961443e-01 9.41263497e-01
9.13445175e-01 5.66437125e-01 4.58796203e-01 -4.38892841e-01
1.03577673e+00 -8.53880227e-01 -1.31337619e+00 1.95849121e-01
5.63908517e-01 -8.69833827e-01 9.61440682e-01 7.27841496e-01
-1.33217740e+00 -4.54311043e-01 -7.94700980e-01 9.11201984e-02
1.36102974e-01 3.59297216e-01 9.04127300e-01 1.15897751e+00
-1.38042414e+00 1.04420021e-01 -1.29249573e-01 -3.10817473e-02
1.05209005e+00 2.46352479e-01 -3.61641735e-01 2.17854217e-01
-9.77929890e-01 7.94163167e-01 -3.90382588e-01 3.02928537e-01
-3.60549510e-01 -6.38125777e-01 -7.14410603e-01 -3.50870311e-01
-2.84856349e-01 1.27727360e-01 1.62931252e+00 -1.84677887e+00
-1.99161303e+00 1.30869222e+00 -4.80999857e-01 4.13836129e-02
7.23181903e-01 -4.18334097e-01 -8.72697473e-01 -8.24072435e-02
-3.82244647e-01 1.18771636e+00 7.34723449e-01 -1.20848262e+00
-4.11357880e-01 -3.07462513e-01 -2.35606641e-01 2.91500594e-02
1.19253397e-01 4.83344465e-01 -5.39372504e-01 -4.59054947e-01
-3.30531090e-01 -9.90913868e-01 1.78890288e-01 9.08489406e-01
1.35545045e-01 -9.77386087e-02 9.18327451e-01 -8.71995568e-01
1.09606588e+00 -2.46706438e+00 -1.81462988e-01 5.80318511e-01
1.06918655e-01 2.51787245e-01 -4.13491011e-01 -3.49377990e-01
-2.87939161e-01 -1.47955865e-01 1.30012602e-01 -5.80100417e-01
-7.92194344e-03 -4.03245017e-02 -1.61834329e-01 5.23850918e-01
4.14746433e-01 8.29111576e-01 -4.12676722e-01 -6.52770281e-01
-3.04535776e-01 5.67121625e-01 -6.31680667e-01 3.89648080e-01
-6.59583956e-02 2.60359079e-01 3.65780555e-02 1.09732950e+00
1.18173873e+00 5.25817096e-01 7.26012439e-02 -2.75675714e-01
1.69688582e-01 -4.75524664e-01 -6.97939277e-01 1.79485965e+00
-3.69527996e-01 1.40390182e+00 6.14891887e-01 -2.73927450e-01
1.03552413e+00 3.81110758e-01 6.86385036e-01 -9.98045564e-01
6.39494717e-01 6.63008913e-02 1.38877988e-01 -4.38040972e-01
4.41376776e-01 -2.88381755e-01 6.06589504e-02 4.05763313e-02
2.90164411e-01 -3.50841105e-01 -1.05233565e-01 -1.77106217e-01
4.92034048e-01 4.91159052e-01 4.35075946e-02 -2.54553050e-01
4.16070372e-01 -2.85641432e-01 4.73324597e-01 1.35987565e-01
-8.34918976e-01 5.27087390e-01 8.80156338e-01 -2.83130944e-01
-6.37713790e-01 -1.19989312e+00 -4.16960806e-01 1.38019466e+00
-3.94671857e-01 -4.94730413e-01 -1.04135716e+00 -6.73203886e-01
-3.19673270e-02 7.66516089e-01 -8.36125672e-01 -3.92676033e-02
-6.97307885e-02 -4.62009907e-01 1.19609284e+00 5.29502273e-01
5.67564011e-01 -1.09221363e+00 -2.97212452e-01 -3.64292800e-01
-2.49532729e-01 -1.19516492e+00 -4.20460850e-01 -2.67862976e-01
-2.36501172e-01 -7.46923566e-01 -7.70775378e-01 -3.49157751e-01
3.54978621e-01 -5.93877316e-01 1.28362501e+00 6.86889738e-02
-4.16131496e-01 1.83234364e-01 -2.33234793e-01 -8.16688418e-01
-1.41290948e-01 -4.03251350e-01 7.08863735e-02 2.65022576e-01
4.64955986e-01 -2.21914858e-01 -2.84826249e-01 3.64749730e-01
-3.06902111e-01 -7.35603496e-02 -1.18357418e-02 6.82832837e-01
3.95679712e-01 -7.24785209e-01 6.57835364e-01 -6.38447106e-01
7.29811311e-01 -2.48737007e-01 -4.20412153e-01 2.44617742e-02
-2.71769345e-01 -4.64161783e-01 -1.67419150e-01 -5.10498643e-01
-1.28262579e+00 2.84577429e-01 -7.43956566e-01 -6.02781296e-01
8.21454152e-02 1.20646954e-01 -3.59188169e-01 -2.95608997e-01
5.88519156e-01 -3.75746161e-01 2.69661099e-01 7.35140070e-02
4.23875332e-01 1.13477492e+00 5.16573548e-01 -8.20472062e-01
8.80443603e-02 7.58614391e-02 1.60018146e-01 -1.00561130e+00
-5.33207417e-01 -2.66423523e-02 -4.99402612e-01 -9.77198601e-01
6.49146736e-01 -1.21189249e+00 -1.00172102e+00 8.76589477e-01
-1.19267941e+00 -5.53092778e-01 -1.71145648e-01 6.37752563e-02
-7.97555685e-01 -2.26130575e-01 -2.53592908e-01 -1.35051143e+00
-4.71234709e-01 -1.05351889e+00 1.18115079e+00 2.20976502e-01
-7.70957649e-01 -3.56058478e-01 2.31113434e-02 1.59901708e-01
6.58553779e-01 6.34498000e-01 2.60082543e-01 2.76187181e-01
4.34340775e-01 -2.15316206e-01 -5.33616424e-01 6.62384987e-01
-1.64568037e-01 4.80667979e-01 -1.72084081e+00 1.73289087e-02
-5.42576551e-01 -9.05532539e-01 4.71488863e-01 4.59243596e-01
1.61105514e+00 3.36595297e-01 9.82238203e-02 7.87027657e-01
8.51152599e-01 2.65018702e-01 1.15193951e+00 -3.29637110e-01
2.49765351e-01 7.28578389e-01 8.39356065e-01 7.35132992e-01
-1.45677522e-01 1.05036128e+00 2.00700194e-01 -3.56955856e-01
-3.06005567e-01 4.34254259e-02 3.73844802e-01 -1.00374959e-01
-4.63262260e-01 -1.77863881e-01 -6.90390706e-01 1.89623326e-01
-1.32580721e+00 -1.04004598e+00 -1.02503099e-01 1.47456646e+00
8.32368851e-01 -3.64994466e-01 4.15529072e-01 -3.53730351e-01
3.34842354e-01 1.46637276e-01 -3.96335840e-01 -1.23803830e+00
-4.07552540e-01 7.65767217e-01 3.29166348e-03 4.57777023e-01
-9.34038162e-01 1.05558896e+00 7.89336395e+00 1.02158296e+00
-1.20726204e+00 2.49810461e-02 1.02278209e+00 -5.39766729e-01
2.54508555e-02 -7.24690795e-01 -4.42220300e-01 2.82588065e-01
1.17446268e+00 -7.16206711e-03 3.67933095e-01 1.14138579e+00
3.26796234e-01 -2.86807686e-01 -6.74702048e-01 1.44644976e+00
3.74501050e-01 -8.51283371e-01 -1.18941106e-01 1.41963676e-01
6.73555017e-01 -2.47672558e-01 3.73098522e-01 4.53718275e-01
-2.08338305e-01 -1.52794766e+00 8.17016959e-01 5.56392372e-01
1.73005903e+00 -1.05212331e+00 8.47051322e-01 -3.85804951e-01
-9.78587508e-01 3.73303443e-01 5.51971653e-03 -3.28282788e-02
2.76674360e-01 1.03520967e-01 -4.90418822e-01 -1.90877646e-01
7.33243644e-01 4.33531463e-01 -2.38441199e-01 4.20417607e-01
-1.11153841e-01 5.76800823e-01 -3.11380744e-01 -6.77553862e-02
-7.84134679e-03 2.53282283e-02 1.41723394e-01 1.50142646e+00
3.25688839e-01 2.84859329e-01 -4.20213223e-01 5.36541760e-01
-4.51210111e-01 2.56240547e-01 -3.73158336e-01 2.31131557e-02
1.12343810e-01 1.25975776e+00 8.21540207e-02 -1.51024848e-01
-2.66427279e-01 1.19406009e+00 2.97494382e-01 5.25673032e-01
-1.20040441e+00 -2.78036803e-01 1.56866717e+00 3.56140248e-02
4.77113314e-02 5.05077019e-02 -2.22317249e-01 -8.86415482e-01
-6.76894113e-02 -9.82985377e-01 1.21937111e-01 -1.22143114e+00
-1.19182038e+00 8.85907352e-01 -5.86746410e-02 -1.01358926e+00
-4.99775499e-01 -9.60094810e-01 -4.65606749e-01 9.68520761e-01
-1.18157160e+00 -1.32405484e+00 -7.29910195e-01 7.10294366e-01
3.99727762e-01 -3.75056595e-01 1.17798615e+00 4.13040072e-01
-3.67036104e-01 1.32805467e+00 -2.35194534e-01 2.12746114e-01
9.17757988e-01 -7.36900330e-01 1.69021666e-01 4.62022960e-01
-1.58788413e-01 3.19793932e-02 8.10193419e-01 2.56990045e-02
-9.76374805e-01 -7.68505812e-01 5.79878926e-01 -2.66922593e-01
3.37466925e-01 -4.70337093e-01 -4.07224000e-01 5.48360705e-01
3.89143586e-01 2.98327267e-01 1.06417048e+00 2.89917618e-01
-4.21523392e-01 -5.18317893e-02 -1.70297778e+00 6.05811954e-01
1.16394007e+00 -5.20497382e-01 3.60136032e-01 -2.08643958e-01
1.62206501e-01 -7.17941880e-01 -9.46078122e-01 4.03122514e-01
1.45322502e+00 -8.11494231e-01 4.97531354e-01 -9.52493966e-01
5.37329495e-01 1.85251474e-01 -7.36015379e-01 -1.23693800e+00
1.46626800e-01 -6.28785491e-01 -2.15866998e-01 1.21823609e+00
4.95928377e-01 -2.76013110e-02 1.16247904e+00 1.01704621e+00
1.13679096e-01 -8.39625955e-01 -1.28486729e+00 -4.41603810e-01
6.29521757e-02 -8.77726555e-01 4.59969521e-01 6.99112773e-01
-5.77826723e-02 7.79418498e-02 -6.82737052e-01 -3.94404560e-01
5.24069607e-01 -3.73744726e-01 1.09477496e+00 -8.88614655e-01
7.82359168e-02 -5.21276236e-01 -7.38605797e-01 -5.97480059e-01
6.87856972e-01 -5.90889215e-01 4.31075618e-02 -7.06338286e-01
2.74122171e-02 -2.01075934e-02 -1.55902535e-01 5.76512516e-01
1.90320373e-01 6.62018239e-01 5.45296371e-01 -5.84281802e-01
-2.94805259e-01 7.06584215e-01 9.72001553e-01 -1.43164277e-01
-1.48331020e-02 -2.15603840e-02 -4.41033453e-01 5.75437903e-01
9.40775752e-01 -4.84991670e-02 -3.18655163e-01 -1.90407917e-01
-1.68737128e-01 -6.58848882e-02 -1.61359571e-02 -7.21020103e-01
-5.63225210e-01 -3.99123490e-01 7.63083637e-01 -4.39592540e-01
1.08487475e+00 -7.33178914e-01 1.08027078e-01 -2.31094845e-02
-3.16929996e-01 -3.23935337e-02 7.95345962e-01 -1.92679673e-01
-2.06451654e-01 2.48706460e-01 1.09783721e+00 5.94526410e-01
-9.95562911e-01 2.49684766e-01 -4.99510765e-01 4.17560712e-02
1.01904416e+00 -5.43534160e-02 -2.70007432e-01 -9.70306396e-01
-8.70083511e-01 1.55856043e-01 2.10620165e-01 4.58640575e-01
8.42146099e-01 -1.65250397e+00 -9.74038303e-01 3.43567491e-01
4.74666119e-01 -8.12773824e-01 1.91349477e-01 7.48898864e-01
-6.66040361e-01 -2.73186743e-01 -7.44095802e-01 -4.22254711e-01
-2.00065494e+00 -4.13422287e-01 8.09902847e-01 3.53287280e-01
2.00923532e-01 1.32443953e+00 -9.84527916e-02 -1.91403955e-01
2.45031908e-01 3.27417940e-01 7.95486718e-02 2.47005463e-01
8.41367722e-01 3.60413134e-01 -1.28546745e-01 -1.21899700e+00
-4.65142429e-01 4.58542645e-01 3.40205550e-01 -4.28570807e-01
1.08462739e+00 -1.06482729e-01 -1.64315462e-01 3.29037130e-01
1.67813563e+00 -8.76371861e-02 -1.03124774e+00 2.81908691e-01
-5.37041068e-01 -6.81614697e-01 3.43974650e-01 -1.14298010e+00
-1.33619082e+00 9.11606908e-01 1.18805420e+00 -5.51821649e-01
1.61549342e+00 -1.87235743e-01 4.63470548e-01 5.43857329e-02
2.76526600e-01 -1.29372680e+00 -3.07302892e-01 3.02695751e-01
1.21877539e+00 -1.21142268e+00 9.44893584e-02 -6.23044610e-01
-1.16916335e+00 1.09272730e+00 9.62663054e-01 2.94960111e-01
8.59963596e-01 6.96210682e-01 7.39546716e-01 -2.26721808e-01
-7.33346462e-01 -1.10216498e-01 5.09716928e-01 9.55056190e-01
9.19323146e-01 1.27314925e-01 -2.39880949e-01 6.59264743e-01
-2.52863884e-01 4.24998432e-01 8.27007890e-02 6.74329340e-01
-8.65645707e-03 -7.50032246e-01 -3.40895317e-02 3.16592276e-01
-5.08953750e-01 3.15400422e-01 -7.68021703e-01 8.57929409e-01
9.62523073e-02 8.49960446e-01 3.30866009e-01 -5.97433686e-01
5.76849639e-01 4.91566181e-01 7.53051758e-01 8.10727030e-02
-3.54190111e-01 -6.62599057e-02 8.76620412e-01 -1.12918723e+00
-5.53912640e-01 -7.70859897e-01 -1.20408869e+00 -5.69901586e-01
-4.59910557e-02 -1.41219646e-01 8.75011623e-01 4.97105837e-01
2.93872029e-01 1.89041942e-01 7.66719222e-01 -8.63552272e-01
3.73925529e-02 -1.09283423e+00 -9.69107032e-01 4.41038996e-01
2.46508509e-01 -7.99337268e-01 -2.41056874e-01 8.64589363e-02] | [13.574628829956055, 1.86173677444458] |
a8ebc66b-16bb-45e6-8427-5bf7f0907290 | co-occurrence-feature-learning-from-skeleton | 1804.06055 | null | http://arxiv.org/abs/1804.06055v1 | http://arxiv.org/pdf/1804.06055v1.pdf | Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation | Skeleton-based human action recognition has recently drawn increasing
attentions with the availability of large-scale skeleton datasets. The most
crucial factors for this task lie in two aspects: the intra-frame
representation for joint co-occurrences and the inter-frame representation for
skeletons' temporal evolutions. In this paper we propose an end-to-end
convolutional co-occurrence feature learning framework. The co-occurrence
features are learned with a hierarchical methodology, in which different levels
of contextual information are aggregated gradually. Firstly point-level
information of each joint is encoded independently. Then they are assembled
into semantic representation in both spatial and temporal domains.
Specifically, we introduce a global spatial aggregation scheme, which is able
to learn superior joint co-occurrence features over local aggregation. Besides,
raw skeleton coordinates as well as their temporal difference are integrated
with a two-stream paradigm. Experiments show that our approach consistently
outperforms other state-of-the-arts on action recognition and detection
benchmarks like NTU RGB+D, SBU Kinect Interaction and PKU-MMD. | ['ShiLiang Pu', 'Di Xie', 'Chao Li', 'Qiaoyong Zhong'] | 2018-04-17 | null | null | null | null | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 9.36920717e-02 -4.38557357e-01 -1.39001772e-01 -4.01214093e-01
-7.23185301e-01 -4.18272838e-02 7.12969780e-01 1.84528783e-01
-6.11116588e-01 5.53811014e-01 5.55416763e-01 5.23944736e-01
-1.34129688e-01 -7.04537988e-01 -5.50928473e-01 -6.42610788e-01
-2.74644643e-01 1.48316503e-01 7.34817982e-01 -3.00554693e-01
8.84196311e-02 5.04435718e-01 -1.78549576e+00 4.67014194e-01
5.04721761e-01 1.30742514e+00 5.48234619e-02 6.81209862e-01
-1.06393158e-01 8.28702092e-01 -2.68510252e-01 -2.58070648e-01
3.78978252e-01 -5.64191878e-01 -5.78518927e-01 3.15680772e-01
3.77741843e-01 -3.79777998e-01 -5.74947894e-01 7.10389256e-01
5.57071865e-01 1.40146360e-01 4.21876043e-01 -1.05575466e+00
-1.34264633e-01 4.19804491e-02 -9.90999579e-01 4.25843358e-01
6.65653586e-01 4.28540200e-01 1.10047925e+00 -6.80520475e-01
6.02738082e-01 1.30708230e+00 5.08643627e-01 3.11667264e-01
-8.77923667e-01 -2.91770369e-01 2.86117017e-01 5.63532889e-01
-1.02320802e+00 -2.71419615e-01 8.89249682e-01 -4.75169659e-01
9.78025794e-01 -2.97837448e-03 9.87646341e-01 1.09849775e+00
1.64471313e-01 1.28305209e+00 8.33863139e-01 -2.73467779e-01
8.58206302e-02 -7.92212605e-01 -1.34514853e-01 7.65520394e-01
-2.22069681e-01 4.43834346e-03 -1.08181334e+00 1.11375675e-01
1.13712180e+00 4.66180712e-01 4.31471989e-02 -6.78411663e-01
-1.50427902e+00 4.96457368e-01 5.44602215e-01 4.46191847e-01
-6.96997285e-01 4.99810934e-01 6.18971407e-01 -5.36713451e-02
5.02856970e-01 -2.76718736e-01 -6.04263723e-01 -6.25033975e-01
-8.29428732e-01 2.16585174e-01 4.16291542e-02 6.83982909e-01
6.28573418e-01 -2.46449500e-01 -3.37193102e-01 7.67419934e-01
3.01023602e-01 1.65599823e-01 8.29875290e-01 -8.33456397e-01
7.60330975e-01 8.43975663e-01 -2.66075227e-02 -9.65640545e-01
-5.00528812e-01 -1.15228333e-01 -6.19133115e-01 3.21097255e-01
6.22431278e-01 1.77369818e-01 -8.04731190e-01 1.52411890e+00
6.70595109e-01 4.87353057e-01 -9.98597369e-02 9.80500758e-01
4.96163666e-01 2.92032540e-01 2.29595125e-01 -4.43658000e-03
1.51131296e+00 -1.14211166e+00 -5.85206628e-01 1.32561997e-02
4.65412766e-01 -4.32380348e-01 9.38206077e-01 1.45518854e-01
-1.12026334e+00 -8.92539680e-01 -8.59024525e-01 -2.75796920e-01
-2.79978693e-01 2.90417194e-01 6.42566681e-01 2.37578884e-01
-6.74786508e-01 7.59628415e-01 -1.41486681e+00 -4.29751158e-01
5.69104970e-01 1.65104330e-01 -6.29377723e-01 3.91400047e-02
-1.04988182e+00 5.05437315e-01 4.39225316e-01 1.51299268e-01
-5.22593260e-01 -4.12992805e-01 -1.01569390e+00 -2.38434479e-01
3.40243310e-01 -7.33148575e-01 1.12998176e+00 -6.91822231e-01
-1.68267941e+00 7.39744246e-01 -1.57604516e-01 -5.03040910e-01
9.03104305e-01 -5.11816323e-01 -1.76275983e-01 5.66950738e-01
2.65166283e-01 4.58957165e-01 7.58305788e-01 -6.91186905e-01
-1.16068983e+00 -9.01600718e-01 -1.23178132e-01 5.14224052e-01
-4.39711332e-01 1.15851115e-03 -7.86777258e-01 -7.48651564e-01
3.29262972e-01 -5.94351113e-01 -3.12978745e-01 4.25342172e-01
-5.44099510e-02 -4.06424195e-01 9.82370436e-01 -5.83425105e-01
1.13795781e+00 -2.08291149e+00 5.43221951e-01 -1.58650041e-01
1.85459659e-01 1.13215998e-01 2.41079822e-01 2.71124840e-01
2.06644088e-03 -5.30785501e-01 -2.67147690e-01 -6.52677238e-01
-4.22507478e-03 3.37199181e-01 1.75455660e-01 5.77902257e-01
3.34947914e-01 1.05002809e+00 -1.13818598e+00 -7.86801934e-01
6.70730889e-01 5.77049077e-01 -2.54666060e-01 9.26842690e-02
-1.36569634e-01 8.05906177e-01 -6.90746427e-01 9.29134548e-01
1.61039367e-01 -4.70813029e-02 -2.56995380e-01 -2.55330384e-01
-1.38131857e-01 -1.21497273e-01 -1.14073169e+00 2.47243881e+00
-2.06200212e-01 2.17072010e-01 -2.54378766e-01 -9.25201893e-01
7.75929630e-01 2.16616675e-01 1.01601911e+00 -8.39729249e-01
4.26649494e-04 2.53006309e-01 -3.34991306e-01 -6.07231736e-01
3.05744976e-01 3.05089094e-02 -3.93204205e-02 1.88419268e-01
8.54342431e-02 3.73309851e-01 4.33269292e-01 2.82412451e-02
1.33829153e+00 6.87696874e-01 4.60164577e-01 1.73840374e-01
7.46238112e-01 -3.31782639e-01 5.26715875e-01 2.41712332e-01
-5.47659695e-01 8.12392235e-01 5.15904307e-01 -6.89861417e-01
-7.68135369e-01 -1.02249408e+00 -1.93335414e-02 1.06873262e+00
2.25112766e-01 -5.64681351e-01 -4.76097852e-01 -8.91980767e-01
1.51584163e-01 1.09746242e-02 -9.27090943e-01 8.41015354e-02
-7.73116291e-01 -3.93605232e-01 3.38362753e-01 1.09393907e+00
7.90068150e-01 -1.13919640e+00 -1.37218952e+00 3.09236258e-01
-1.02264985e-01 -1.10206342e+00 -4.48440701e-01 4.60536554e-02
-1.12539768e+00 -1.10398042e+00 -1.08442211e+00 -2.22557575e-01
2.39877939e-01 1.00571521e-01 7.87083864e-01 -3.09025824e-01
-6.24657691e-01 6.48252308e-01 -6.33403420e-01 1.47982314e-01
3.41877937e-01 -2.00613186e-01 -7.72141218e-02 4.85121310e-01
4.28887665e-01 -9.53516424e-01 -9.96613503e-01 3.15866679e-01
-8.62351894e-01 3.00370567e-02 8.58232439e-01 7.00274467e-01
8.66388738e-01 -3.14891964e-01 1.22362085e-01 -1.83608398e-01
1.29829392e-01 -3.66093814e-01 -7.88027197e-02 2.35788465e-01
1.12476446e-01 1.12831064e-01 2.61162788e-01 -2.56343365e-01
-1.00119150e+00 5.45222521e-01 -5.81691191e-02 -7.14309275e-01
-4.03678536e-01 3.29697371e-01 -3.25186670e-01 2.74406850e-01
4.84722704e-01 3.68869394e-01 -4.76118550e-02 -7.08519995e-01
3.68507981e-01 3.13671917e-01 8.49634469e-01 -8.28174710e-01
4.58246142e-01 7.57934272e-01 1.59849599e-01 -8.71001363e-01
-6.47221684e-01 -8.75951231e-01 -1.32656705e+00 -4.99010026e-01
1.16324210e+00 -9.48077083e-01 -3.94478887e-01 8.95099044e-01
-1.06392324e+00 -1.50853261e-01 -5.90928316e-01 5.83054721e-01
-8.74297798e-01 7.19811499e-01 -5.85210621e-01 -7.67778337e-01
2.73917373e-02 -1.19156802e+00 1.75690734e+00 2.84477383e-01
-5.76748215e-02 -7.26132989e-01 3.10737997e-01 4.07973558e-01
-8.04665983e-02 8.46845150e-01 2.79340237e-01 -2.23912343e-01
-3.61597925e-01 -2.82861352e-01 -2.18369395e-01 1.72896832e-01
2.84349829e-01 -1.49297744e-01 -7.02611506e-01 7.74587691e-03
-2.21512213e-01 -4.44577038e-01 1.07040489e+00 3.63163829e-01
1.11733913e+00 1.05331026e-01 -2.77611732e-01 5.41143060e-01
1.11229634e+00 -6.49277717e-02 7.17659116e-01 4.13598865e-01
9.50817287e-01 5.12095392e-01 8.65753293e-01 8.35451365e-01
4.06479955e-01 1.07417214e+00 4.81094390e-01 6.13805465e-02
-1.80594310e-01 -1.80096582e-01 3.63353580e-01 4.14386690e-01
-7.00845063e-01 4.36096489e-01 -8.72811615e-01 3.60587686e-01
-2.40194082e+00 -1.13260949e+00 -2.02033490e-01 2.07394886e+00
7.78728783e-01 9.45932791e-02 6.16915464e-01 2.73677170e-01
5.50617158e-01 4.28576052e-01 -6.43883586e-01 2.98080921e-01
-9.98257920e-02 2.10045785e-01 2.58792758e-01 1.09328292e-01
-1.39348149e+00 7.00300336e-01 4.61301231e+00 8.21918786e-01
-8.03507686e-01 1.82929307e-01 4.70568955e-01 -1.92642108e-01
3.60450417e-01 -2.91709840e-01 -4.78701413e-01 5.84900618e-01
4.73639190e-01 1.50019020e-01 -3.88317972e-01 9.46528912e-01
1.28015792e-02 -3.80083025e-01 -1.13700736e+00 1.25104547e+00
7.36312335e-03 -9.41204369e-01 -2.57891834e-01 1.12082049e-01
5.38024187e-01 -5.50489128e-02 -1.67186439e-01 1.66391745e-01
3.87701392e-02 -6.97072208e-01 8.90103459e-01 7.40880668e-01
5.16299665e-01 -7.31825113e-01 5.46673357e-01 2.52574682e-01
-1.76753438e+00 -1.09741122e-01 -1.92513585e-01 -1.26813680e-01
4.23526675e-01 4.44601506e-01 -4.93116826e-02 9.64208305e-01
8.16160798e-01 1.40823650e+00 -7.09822953e-01 1.02996480e+00
-3.02869409e-01 7.70144016e-02 -4.18574542e-01 1.45996630e-01
4.15608674e-01 -1.93278238e-01 2.39772603e-01 1.27500021e+00
3.25918198e-01 2.52661288e-01 3.15509230e-01 2.67229259e-01
3.54141533e-01 -6.42455462e-03 -3.15673679e-01 2.15824068e-01
-1.30670875e-01 1.14426970e+00 -8.35076153e-01 -3.01414877e-01
-5.30501485e-01 1.41949832e+00 2.98558593e-01 6.57244399e-02
-9.71409321e-01 -1.39971122e-01 8.95791054e-01 1.20425373e-01
5.32074034e-01 -5.50297439e-01 -2.08123520e-01 -1.30424881e+00
3.81443530e-01 -4.75291461e-01 7.07122803e-01 -4.87389266e-01
-1.15849352e+00 3.34670514e-01 5.25641181e-02 -1.62634325e+00
-2.37169519e-01 -5.90534925e-01 -5.12901366e-01 5.16179919e-01
-1.46125758e+00 -1.38309336e+00 -5.26669085e-01 1.12618363e+00
8.97264600e-01 -5.73639497e-02 6.55102611e-01 3.39155465e-01
-5.77472508e-01 2.78181225e-01 -1.69529825e-01 3.39371294e-01
4.82360423e-01 -1.30026841e+00 2.79279649e-01 8.99342716e-01
4.33089197e-01 2.84055501e-01 3.57317954e-01 -6.24824345e-01
-1.25573933e+00 -8.73117745e-01 6.38863266e-01 -6.56117320e-01
7.25995004e-01 -5.82901873e-02 -6.29144967e-01 3.70122105e-01
-2.03727797e-01 6.37391150e-01 4.43931639e-01 -8.81056562e-02
-4.38395619e-01 -2.40102306e-01 -8.81548226e-01 3.66321534e-01
1.42033517e+00 -4.66108769e-01 -6.02338612e-01 3.58907670e-01
3.54576468e-01 -5.74114621e-01 -8.21294308e-01 4.21129256e-01
8.30816805e-01 -1.45042002e+00 1.08288777e+00 -6.06504798e-01
7.88813293e-01 -5.75936556e-01 -3.01128238e-01 -8.95304501e-01
-1.05867304e-01 -3.54938775e-01 -4.60597366e-01 9.11266029e-01
-1.88188836e-01 -1.32983282e-01 8.88390422e-01 3.58269274e-01
-9.28374976e-02 -1.10145843e+00 -1.29036677e+00 -7.49175608e-01
-3.49336296e-01 -5.91201425e-01 2.55142152e-01 5.28875887e-01
1.37741059e-01 1.21305697e-01 -4.37014014e-01 -1.99977607e-01
7.14609504e-01 1.40734822e-01 8.98310959e-01 -1.18858695e+00
-5.01331329e-01 -6.19533539e-01 -1.23902416e+00 -1.21232474e+00
-1.72898635e-01 -4.44494426e-01 -1.10297278e-01 -1.47372866e+00
8.34018961e-02 7.44204968e-02 -5.29362917e-01 5.26120484e-01
-3.32870275e-01 2.70606637e-01 1.87580585e-01 3.93969029e-01
-1.00602496e+00 8.81559849e-01 1.13584685e+00 1.09283492e-01
-8.67605433e-02 2.20321976e-02 1.09923728e-01 7.03542054e-01
4.90766346e-01 -1.50627062e-01 -2.33856842e-01 -2.99460858e-01
-3.72064501e-01 1.19662941e-01 6.70845985e-01 -1.53482819e+00
3.60660344e-01 -9.98553038e-02 6.58050895e-01 -7.51057088e-01
5.87522149e-01 -8.25212002e-01 -4.10502441e-02 4.72887397e-01
-2.58342445e-01 6.79676980e-03 -1.91402197e-01 9.36281025e-01
-4.97062266e-01 2.73327023e-01 7.71345675e-01 -2.54971862e-01
-1.06153452e+00 6.26021981e-01 1.42805576e-01 -1.07330516e-01
1.44712067e+00 -5.26811063e-01 2.75214434e-01 -7.45275021e-02
-8.82180214e-01 1.25228405e-01 2.54502982e-01 5.99210262e-01
6.18280768e-01 -1.56880808e+00 -5.21272659e-01 1.09905012e-01
3.53155941e-01 1.16970494e-01 5.06961167e-01 1.24414563e+00
-3.99345934e-01 2.58345127e-01 -5.36999941e-01 -1.01062441e+00
-1.25264323e+00 2.68212736e-01 1.46759540e-01 -4.98371363e-01
-8.86621535e-01 8.76834333e-01 1.21339053e-01 4.38035168e-02
4.03667778e-01 -4.57519233e-01 -1.53985679e-01 2.76224375e-01
6.72266483e-01 4.92908865e-01 -7.51327425e-02 -9.69988883e-01
-4.59449232e-01 9.46438909e-01 1.51865527e-01 -1.99854448e-01
1.46765256e+00 1.04546268e-02 1.15463026e-01 5.99720478e-01
1.22572148e+00 -3.75651717e-01 -1.82879508e+00 -4.08437103e-01
2.25608200e-01 -8.74220490e-01 -2.45400637e-01 -4.53476638e-01
-1.24930227e+00 1.03236771e+00 5.30767262e-01 1.01844948e-02
1.17591238e+00 2.00622991e-01 9.55183387e-01 6.70969859e-02
5.91340303e-01 -1.24459076e+00 6.39973402e-01 3.54357928e-01
8.65830243e-01 -1.16445017e+00 7.04775900e-02 -9.35200006e-02
-7.57339597e-01 1.30028236e+00 5.69604695e-01 -1.47693276e-01
4.89649951e-01 -5.35897575e-02 -2.09961325e-01 -3.18624139e-01
-2.93385684e-01 -6.88117921e-01 3.18370730e-01 5.43587565e-01
3.95801157e-01 -3.30245607e-02 -4.51267809e-01 5.17825663e-01
2.00588688e-01 5.55086881e-02 -1.94258064e-01 1.21844482e+00
-3.91829252e-01 -1.22222209e+00 -2.57460654e-01 1.63976997e-01
-2.85362095e-01 4.92738366e-01 -2.48866171e-01 7.80073047e-01
3.49811971e-01 6.16448343e-01 -9.50795561e-02 -4.56035048e-01
6.38405681e-01 1.03978328e-01 6.46247804e-01 -5.03423572e-01
-3.38172436e-01 1.83991998e-01 -1.55729264e-01 -1.21675503e+00
-8.37514877e-01 -1.23884523e+00 -1.31499159e+00 2.15969473e-01
1.73295245e-01 -3.05964142e-01 5.31923592e-01 1.02839112e+00
3.44220430e-01 5.92550457e-01 4.18923974e-01 -1.31533909e+00
-4.50051457e-01 -8.49441051e-01 -6.79288685e-01 7.30636954e-01
2.70900965e-01 -9.67253506e-01 -4.16481569e-02 2.68515319e-01] | [7.838171005249023, 0.3969959020614624] |
b4d7eb3c-db8d-4874-9e4c-fc66ec299d4f | mestereo-du2cnn-a-novel-dual-channel-cnn-for | 2206.10375 | null | https://arxiv.org/abs/2206.10375v1 | https://arxiv.org/pdf/2206.10375v1.pdf | MEStereo-Du2CNN: A Novel Dual Channel CNN for Learning Robust Depth Estimates from Multi-exposure Stereo Images for HDR 3D Applications | Display technologies have evolved over the years. It is critical to develop practical HDR capturing, processing, and display solutions to bring 3D technologies to the next level. Depth estimation of multi-exposure stereo image sequences is an essential task in the development of cost-effective 3D HDR video content. In this paper, we develop a novel deep architecture for multi-exposure stereo depth estimation. The proposed architecture has two novel components. First, the stereo matching technique used in traditional stereo depth estimation is revamped. For the stereo depth estimation component of our architecture, a mono-to-stereo transfer learning approach is deployed. The proposed formulation circumvents the cost volume construction requirement, which is replaced by a ResNet based dual-encoder single-decoder CNN with different weights for feature fusion. EfficientNet based blocks are used to learn the disparity. Secondly, we combine disparity maps obtained from the stereo images at different exposure levels using a robust disparity feature fusion approach. The disparity maps obtained at different exposures are merged using weight maps calculated for different quality measures. The final predicted disparity map obtained is more robust and retains best features that preserve the depth discontinuities. The proposed CNN offers flexibility to train using standard dynamic range stereo data or with multi-exposure low dynamic range stereo sequences. In terms of performance, the proposed model surpasses state-of-the-art monocular and stereo depth estimation methods, both quantitatively and qualitatively, on challenging Scene flow and differently exposed Middlebury stereo datasets. The architecture performs exceedingly well on complex natural scenes, demonstrating its usefulness for diverse 3D HDR applications. | ['Rithvik Anil', 'Uma T V', 'Mansi Sharma', 'Rohit Choudhary'] | 2022-06-21 | null | null | null | null | ['stereo-depth-estimation', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 3.48188788e-01 -5.11811197e-01 4.30521578e-01 -5.33270001e-01
-5.30094504e-01 -2.08346084e-01 4.73687887e-01 -2.92848349e-01
-6.07629418e-01 6.21777952e-01 2.34523579e-01 4.86993678e-02
1.23725019e-01 -9.23424363e-01 -6.09262884e-01 -6.56612575e-01
2.13304237e-01 -3.07302084e-02 5.92506528e-01 -4.79809463e-01
5.86453617e-01 6.12695932e-01 -2.07585096e+00 2.20814273e-01
7.53714263e-01 1.28311205e+00 6.66777015e-01 9.20783341e-01
4.86756079e-02 9.65349197e-01 -3.20082575e-01 -2.52774924e-01
6.45398676e-01 -2.68584311e-01 -4.71972555e-01 2.70090997e-02
9.94149566e-01 -1.10014606e+00 -6.63459003e-01 8.51211548e-01
9.30515110e-01 1.60888940e-01 3.20199966e-01 -8.70307922e-01
-1.45723134e-01 -1.88860983e-01 -7.27383494e-01 4.94180977e-01
5.96488118e-01 3.11505258e-01 4.91557688e-01 -7.43001819e-01
6.59828961e-01 1.15736008e+00 4.73362148e-01 4.24698442e-01
-1.00218534e+00 -6.10277057e-01 -2.34260708e-01 3.40708643e-01
-1.06815696e+00 -3.79043281e-01 8.46103847e-01 -6.14425004e-01
1.29609275e+00 -1.05146002e-02 8.18934262e-01 6.77488983e-01
6.22974277e-01 4.16470438e-01 1.38909411e+00 -1.65699482e-01
1.85270570e-02 -2.10888892e-01 -1.71138793e-01 6.78942680e-01
-2.56882198e-02 5.58875561e-01 -7.72014558e-01 4.85492289e-01
1.02493882e+00 9.99437124e-02 -6.32711053e-01 -3.49238366e-01
-8.14768612e-01 5.17887950e-01 3.85741949e-01 2.07561821e-01
-1.70238808e-01 -1.51340608e-02 3.37484300e-01 4.97508138e-01
6.25508189e-01 6.56635761e-02 -1.94094881e-01 -2.83491969e-01
-9.77326572e-01 2.77592957e-01 3.99535716e-01 7.26277471e-01
1.03383327e+00 2.34594867e-01 1.82456538e-01 8.82383823e-01
2.56274134e-01 4.57448781e-01 4.27014917e-01 -1.14077246e+00
5.57734430e-01 3.33573163e-01 -1.78606227e-01 -9.79618430e-01
-3.70398462e-01 -1.55248433e-01 -1.09427857e+00 8.24389756e-01
2.43528515e-01 6.41804188e-02 -9.76239860e-01 1.37695813e+00
2.48851135e-01 1.39361575e-01 7.87101239e-02 1.22236431e+00
9.14764106e-01 7.40029931e-01 -2.61401504e-01 9.02844593e-03
9.73982155e-01 -6.38002217e-01 -6.14318311e-01 -2.25567907e-01
9.28527564e-02 -8.20754051e-01 7.51793623e-01 4.59079564e-01
-1.38364446e+00 -8.64407361e-01 -1.48779488e+00 -6.44993424e-01
-1.29245713e-01 -4.41051304e-01 3.28904301e-01 4.95487273e-01
-1.32950044e+00 6.75894082e-01 -4.61137295e-01 -7.12324381e-02
2.62312323e-01 4.60359573e-01 -4.75145400e-01 -4.32307571e-01
-1.26138616e+00 1.00522459e+00 2.22474813e-01 1.15849897e-01
-7.05912769e-01 -8.95886064e-01 -1.19462705e+00 -8.03069919e-02
-1.74974799e-01 -1.00520265e+00 1.00512671e+00 -7.49600828e-01
-1.81711161e+00 1.05660200e+00 -1.23904422e-01 -3.34617555e-01
6.77578211e-01 -3.49498272e-01 -2.28884980e-01 2.97490776e-01
-5.15846238e-02 7.81668723e-01 9.20624077e-01 -1.11824965e+00
-1.15447688e+00 -3.74005854e-01 1.44936457e-01 6.29846394e-01
2.20708221e-01 -2.74281383e-01 -4.49916840e-01 -2.83988565e-01
2.40946248e-01 -4.85755980e-01 -8.53773355e-02 3.89044993e-02
5.31166680e-02 4.52948898e-01 8.53199184e-01 -6.66403890e-01
8.66336465e-01 -2.06956053e+00 1.73854277e-01 -4.03617993e-02
4.42428589e-01 2.52522051e-01 1.95843786e-01 9.82258692e-02
1.72266066e-02 -5.21645248e-01 -2.09233359e-01 -4.42918867e-01
-3.03423494e-01 -1.83290809e-01 -1.44581348e-01 5.68320811e-01
-6.17327802e-02 5.91208458e-01 -6.46486282e-01 -4.01360035e-01
1.16089952e+00 7.78488696e-01 -6.07497096e-01 5.51495552e-01
1.96776852e-01 7.46033549e-01 1.26668289e-01 4.13467109e-01
1.11653423e+00 7.03970641e-02 -1.78658292e-01 -4.33986276e-01
-4.73778814e-01 4.78385016e-02 -1.19070745e+00 1.98850214e+00
-7.92935252e-01 1.12096405e+00 -1.20440565e-01 -6.04037583e-01
1.10582566e+00 3.81399877e-02 5.81055522e-01 -1.30590057e+00
2.05970809e-01 4.36460495e-01 -2.77608901e-01 -5.60433090e-01
8.18301320e-01 -3.23860228e-01 1.18611932e-01 1.38608128e-01
7.59785920e-02 -5.34856558e-01 8.80399570e-02 -1.34042099e-01
8.47607255e-01 2.17169285e-01 3.72357160e-01 -4.28467691e-02
9.26003635e-01 -3.96909624e-01 5.35315692e-01 3.43309730e-01
-1.54042080e-01 1.09310663e+00 1.00805737e-01 -6.83396995e-01
-1.41822517e+00 -1.14614952e+00 -2.36853927e-01 4.97300625e-01
6.04989171e-01 1.03728198e-01 -3.78490925e-01 2.41347607e-02
-1.91662893e-01 1.44444615e-01 -3.75080496e-01 6.87672347e-02
-8.15471828e-01 -2.59376645e-01 2.07656577e-01 3.80153745e-01
1.28418231e+00 -8.95624161e-01 -1.13447571e+00 3.61604899e-01
-2.30874062e-01 -1.19337881e+00 -4.01080430e-01 1.50150299e-01
-9.40320611e-01 -9.45884705e-01 -9.95088577e-01 -7.95443535e-01
9.86735597e-02 4.95402336e-01 1.10272658e+00 -2.32519954e-01
-3.79042059e-01 1.25549823e-01 1.28667038e-02 1.42727047e-01
-1.87766492e-01 -1.13357499e-01 -3.91804218e-01 -1.64226443e-01
2.64284164e-01 -7.82376587e-01 -1.14570677e+00 2.90300190e-01
-9.90015388e-01 3.94105554e-01 3.91171455e-01 7.33970165e-01
4.80775267e-01 -6.70489818e-02 2.17174292e-01 -4.36331928e-01
2.79604912e-01 -1.05373837e-01 -8.41031909e-01 -1.72809631e-01
-4.43839550e-01 7.48252729e-03 6.36238873e-01 -2.70709638e-02
-1.49808192e+00 1.14363786e-02 -5.08967876e-01 -4.68631327e-01
-1.96141809e-01 -1.31736174e-01 -1.95501953e-01 -2.86576867e-01
4.52214569e-01 2.58633047e-01 6.92038089e-02 -2.49184489e-01
8.48772079e-02 7.00945735e-01 6.24863029e-01 -4.08061557e-02
6.25713170e-01 8.35041821e-01 2.50327140e-01 -8.78565013e-01
-4.09996897e-01 -5.66441178e-01 -6.07448399e-01 -5.42075336e-01
1.14718914e+00 -1.23531091e+00 -7.88745224e-01 1.02413762e+00
-1.15539432e+00 -3.08903545e-01 -1.16223656e-01 5.13881385e-01
-7.84024119e-01 4.31759059e-01 -8.48356068e-01 -4.11504328e-01
-4.13708448e-01 -1.40098500e+00 1.21692979e+00 4.36913192e-01
6.52575940e-02 -9.83457744e-01 3.27571690e-01 2.90791452e-01
5.61712861e-01 2.78160989e-01 6.69852614e-01 4.17087078e-01
-1.01566017e+00 1.50584176e-01 -3.92168820e-01 5.01883984e-01
1.91396505e-01 -1.78129822e-01 -1.33982003e+00 -2.60559738e-01
1.71947002e-01 -3.17109585e-01 9.71235752e-01 7.51346409e-01
1.03055286e+00 3.23933184e-01 1.50195926e-01 1.22276044e+00
2.01387501e+00 4.82234567e-01 1.12413633e+00 7.16252208e-01
7.85787702e-01 6.23935044e-01 5.70101917e-01 4.44428831e-01
3.65088999e-01 7.04310536e-01 3.76675665e-01 -3.91939968e-01
-3.41928661e-01 -1.62112206e-01 2.11202815e-01 5.97745717e-01
-1.43472850e-01 -1.65347353e-01 -7.07605243e-01 2.09726572e-01
-1.47961164e+00 -1.18884826e+00 -1.60833076e-02 2.25472498e+00
5.56685150e-01 2.02267677e-01 -2.91244119e-01 3.39256048e-01
5.83088398e-01 4.35515225e-01 -5.48543811e-01 -6.64547205e-01
-4.14955229e-01 4.70462888e-01 5.58326185e-01 7.57249534e-01
-9.46910977e-01 6.72346830e-01 5.45038509e+00 5.39316237e-01
-1.48826337e+00 -9.92382020e-02 6.05579376e-01 -1.63163632e-01
-2.97178417e-01 -2.48459667e-01 -6.94021046e-01 4.15733337e-01
5.43324590e-01 -8.71358663e-02 2.70841300e-01 5.38064361e-01
3.41623127e-01 -5.88371992e-01 -1.07490706e+00 1.57598686e+00
2.58148938e-01 -1.34001505e+00 -1.48256138e-01 5.34006767e-02
9.33236778e-01 5.66275716e-02 3.18356633e-01 -8.24071616e-02
-8.55421126e-02 -8.56395721e-01 6.16685569e-01 4.45086777e-01
1.04340327e+00 -8.48920465e-01 7.90734649e-01 -4.27232543e-03
-1.35347402e+00 -1.51210636e-01 -3.45485449e-01 -7.22169653e-02
6.13796532e-01 5.61592937e-01 -3.41660202e-01 6.18598342e-01
9.44330573e-01 1.05521226e+00 -4.61290061e-01 1.10446477e+00
1.01695180e-01 -3.72808784e-01 -9.26754847e-02 5.33950448e-01
1.42650753e-01 -2.61116087e-01 3.93126547e-01 9.71041322e-01
5.21475017e-01 3.45994644e-02 -4.28335369e-01 4.80502754e-01
5.92559651e-02 -1.78850710e-01 -9.86259460e-01 8.16383183e-01
1.42265469e-01 9.29517150e-01 -3.20446163e-01 -3.57874781e-01
-5.68124413e-01 1.18404925e+00 1.35791942e-01 2.66926199e-01
-7.04311967e-01 -4.52484667e-01 8.02098095e-01 1.81537747e-01
2.83259273e-01 -1.98643237e-01 -3.80832672e-01 -1.22676945e+00
-1.29494164e-02 -7.67114043e-01 1.36364341e-01 -1.03761351e+00
-1.07450831e+00 8.68043303e-01 -5.54546937e-02 -1.67949522e+00
-5.34568071e-01 -4.78930891e-01 -3.80906403e-01 1.11043143e+00
-2.03282070e+00 -6.05941176e-01 -8.65954876e-01 7.96743274e-01
8.67281318e-01 -2.86347475e-02 2.34399036e-01 7.17041969e-01
-1.58887893e-01 1.95846468e-01 -1.88291376e-03 -3.16706598e-01
7.84684718e-01 -1.06352735e+00 3.70612681e-01 9.86995518e-01
-5.79665065e-01 1.82943508e-01 5.44537663e-01 -4.82798100e-01
-9.98018444e-01 -8.87854695e-01 6.95444286e-01 5.03798500e-02
-6.85433671e-03 -3.05912167e-01 -8.35119128e-01 2.28616953e-01
3.78608555e-01 -2.09702685e-01 3.33198398e-01 -4.79979038e-01
-6.39289469e-02 -5.27759373e-01 -1.26713204e+00 4.49005663e-01
1.05480671e+00 -7.38620996e-01 -3.36863518e-01 -3.43212724e-01
5.37105918e-01 -7.10342169e-01 -7.95697391e-01 5.17467141e-01
8.23608577e-01 -1.84450567e+00 1.00424623e+00 3.01056921e-01
8.54222476e-01 -3.38466734e-01 -3.33416164e-01 -1.17856479e+00
-5.20721897e-02 -3.88788700e-01 1.30974799e-02 8.43311906e-01
-5.76125383e-02 -6.16989255e-01 7.59500802e-01 5.66563964e-01
-3.66219848e-01 -6.03994846e-01 -9.58466649e-01 -4.37236637e-01
-2.06626222e-01 -2.81665355e-01 5.76564431e-01 6.07225299e-01
-4.23779964e-01 2.78848946e-01 -5.40846348e-01 -6.08867127e-03
1.01382470e+00 1.85326904e-01 8.16454768e-01 -9.85675335e-01
-2.70058513e-01 -3.90931696e-01 -7.77741492e-01 -1.43155360e+00
-1.74584799e-02 -3.67129028e-01 6.65429980e-02 -1.47119904e+00
-2.32592970e-02 -2.10777849e-01 1.34394854e-01 -3.67099822e-01
-6.36286568e-03 4.71127689e-01 2.20169351e-01 1.08025752e-01
-1.37680620e-01 6.53266072e-01 1.45460904e+00 1.02828696e-01
-3.97287905e-01 -2.94042349e-01 -4.78180312e-02 6.01345241e-01
8.62354100e-01 -2.36013886e-02 -5.79236746e-01 -6.49544537e-01
7.25027770e-02 4.63343292e-01 3.71275663e-01 -1.49563575e+00
3.99619699e-01 2.60471553e-01 6.64161146e-01 -8.75103652e-01
7.05039978e-01 -9.27095115e-01 2.78469026e-01 4.12136465e-01
-9.57691595e-02 2.74817467e-01 1.55798897e-01 3.42102319e-01
-5.07325053e-01 1.47631973e-01 1.25351453e+00 -1.52595133e-01
-1.27438855e+00 5.16091108e-01 -2.51335919e-01 -3.99351045e-02
9.92990196e-01 -8.91376674e-01 -3.42731327e-01 -4.02828366e-01
-3.16745758e-01 -3.00857835e-02 7.39047527e-01 3.35601479e-01
1.19645941e+00 -1.12384617e+00 -5.41077018e-01 6.06642067e-01
-5.91982976e-02 1.53027564e-01 8.63237977e-01 4.60837066e-01
-1.08437276e+00 4.55849648e-01 -9.13747609e-01 -9.30440724e-01
-1.17040277e+00 2.22121954e-01 6.51759982e-01 -1.57541379e-01
-8.61247838e-01 5.08857548e-01 4.70303863e-01 7.01982602e-02
9.46376473e-02 -3.03063065e-01 -3.13819736e-01 -1.29868388e-01
5.48094809e-01 5.09875655e-01 2.04264045e-01 -7.31038928e-01
-3.92599739e-02 1.12847948e+00 -1.87930930e-02 -3.35388035e-01
1.48444152e+00 -6.60838008e-01 2.61583477e-01 4.05500650e-01
1.62890494e+00 -3.98875296e-01 -1.77828217e+00 -1.89152844e-02
-6.37281775e-01 -1.06494927e+00 4.34410036e-01 -3.67503017e-01
-1.29280066e+00 1.12119019e+00 1.17156899e+00 -3.37775469e-01
1.52125049e+00 -4.99741167e-01 1.04881835e+00 -1.31781876e-01
5.28382480e-01 -9.81760144e-01 6.88848719e-02 4.35097307e-01
5.90644479e-01 -1.47731757e+00 -1.09541491e-01 -2.66416520e-01
-5.24656177e-01 1.21214640e+00 9.01972950e-01 -1.94885105e-01
5.57372451e-01 3.15427929e-01 1.05074450e-01 -1.82456970e-01
-5.43504536e-01 -3.22582901e-01 1.87069759e-01 7.43210018e-01
4.64935124e-01 -5.95858872e-01 -3.85900550e-02 -4.39310968e-01
-8.95740539e-02 1.60812065e-01 5.88504672e-01 8.21443677e-01
-4.85581160e-01 -8.37937236e-01 -1.37476891e-01 2.42653653e-01
-2.85262078e-01 -1.52207896e-01 2.46512085e-01 7.83846736e-01
2.57552266e-01 8.48572850e-01 3.68766963e-01 -5.04592299e-01
5.59299231e-01 -3.88602942e-01 7.02911496e-01 -1.65250778e-01
-5.87475419e-01 -9.63046327e-02 -1.21916339e-01 -9.16119933e-01
-7.33324885e-01 -3.33622605e-01 -8.68111312e-01 -7.39041448e-01
1.64946944e-01 -4.74916071e-01 6.95328057e-01 5.67593753e-01
1.55900270e-01 4.24388975e-01 9.08196330e-01 -1.23985445e+00
5.40055037e-02 -6.33637488e-01 -6.65413022e-01 4.14031208e-01
7.38752663e-01 -7.20464051e-01 -4.76410955e-01 9.86166075e-02] | [8.99902057647705, -2.415095329284668] |
3ea6adb7-3b28-449e-93ac-e56896000cbd | clip-count-towards-text-guided-zero-shot | 2305.07304 | null | https://arxiv.org/abs/2305.07304v1 | https://arxiv.org/pdf/2305.07304v1.pdf | CLIP-Count: Towards Text-Guided Zero-Shot Object Counting | Recent advances in visual-language models have shown remarkable zero-shot text-image matching ability that is transferable to down-stream tasks such as object detection and segmentation. However, adapting these models for object counting, which involves estimating the number of objects in an image, remains a formidable challenge. In this study, we conduct the first exploration of transferring visual-language models for class-agnostic object counting. Specifically, we propose CLIP-Count, a novel pipeline that estimates density maps for open-vocabulary objects with text guidance in a zero-shot manner, without requiring any finetuning on specific object classes. To align the text embedding with dense image features, we introduce a patch-text contrastive loss that guides the model to learn informative patch-level image representations for dense prediction. Moreover, we design a hierarchical patch-text interaction module that propagates semantic information across different resolution levels of image features. Benefiting from the full exploitation of the rich image-text alignment knowledge of pretrained visual-language models, our method effectively generates high-quality density maps for objects-of-interest. Extensive experiments on FSC-147, CARPK, and ShanghaiTech crowd counting datasets demonstrate that our proposed method achieves state-of-the-art accuracy and generalizability for zero-shot object counting. Project page at https://github.com/songrise/CLIP-Count | ['Changwen Chen', 'Lingbo Liu', 'Ruixiang Jiang'] | 2023-05-12 | null | null | null | null | ['object-counting', 'crowd-counting'] | ['computer-vision', 'computer-vision'] | [-1.74892750e-02 -2.86892742e-01 -9.26935077e-02 -5.95399857e-01
-8.37094367e-01 -4.72145468e-01 7.42958367e-01 2.82543302e-01
-6.90066695e-01 1.71584487e-01 1.15710430e-01 -5.04643023e-02
3.20439249e-01 -8.64339173e-01 -1.03546035e+00 -3.79734904e-01
1.80631831e-01 8.90213609e-01 4.91096377e-01 6.59513101e-02
2.75935203e-01 2.11625874e-01 -1.70430267e+00 4.39378291e-01
7.62700498e-01 9.82119143e-01 5.28706074e-01 8.92964184e-01
-6.59479380e-01 1.09904552e+00 -1.93751037e-01 -7.30633140e-01
1.31333724e-01 -2.65756510e-02 -6.05336070e-01 -9.29270834e-02
1.13951910e+00 -6.62572861e-01 -5.15498638e-01 1.13704824e+00
2.88338840e-01 -2.98959445e-02 1.00623763e+00 -1.19181073e+00
-9.88595545e-01 3.23361993e-01 -9.64234591e-01 4.83924180e-01
-5.20585626e-02 5.33215702e-01 9.59977508e-01 -1.16271722e+00
4.58086789e-01 1.41579163e+00 6.26508117e-01 6.09516978e-01
-1.25353706e+00 -8.16561341e-01 1.80056423e-01 8.22511166e-02
-1.40095758e+00 -2.81402171e-01 3.87756139e-01 -8.74150991e-01
1.09260929e+00 -8.88596997e-02 6.92881823e-01 8.79215598e-01
-1.22285508e-01 9.59948897e-01 7.09502637e-01 -2.01490492e-01
7.14930147e-02 2.21631899e-01 2.23393604e-01 1.04790914e+00
5.25899589e-01 -2.74210125e-01 -6.16493464e-01 3.72033492e-02
7.91785121e-01 2.53672898e-01 1.81630880e-01 -4.46189880e-01
-1.16737819e+00 8.19029093e-01 7.46603489e-01 1.74926460e-01
-1.23082660e-01 6.97880864e-01 3.65175009e-01 -3.22434694e-01
7.17825890e-01 -6.44493327e-02 -6.78484365e-02 -6.03527874e-02
-1.19786394e+00 2.34953359e-01 6.09963298e-01 1.28061366e+00
1.01546705e+00 -1.25563681e-01 -8.11009943e-01 7.64118671e-01
2.39913508e-01 1.09193373e+00 -7.91559070e-02 -8.67064774e-01
8.43178451e-01 7.88302124e-01 7.59471729e-02 -1.08188176e+00
-1.39319614e-01 -8.41461346e-02 -8.20948005e-01 -7.54217356e-02
5.67605793e-01 2.93886662e-01 -1.15131927e+00 1.50293350e+00
4.44798738e-01 3.42880189e-01 -4.32235926e-01 1.03918052e+00
8.27139318e-01 6.42278194e-01 5.42317450e-01 4.75128651e-01
1.66212392e+00 -1.02125275e+00 -3.08343202e-01 -6.11787200e-01
4.24931049e-01 -4.72909302e-01 1.27885067e+00 -3.03089470e-01
-9.98660743e-01 -4.93362755e-01 -7.41075635e-01 -6.80208683e-01
-5.42730629e-01 2.71823704e-01 6.64918840e-01 4.58496362e-01
-7.29778349e-01 2.77093202e-01 -8.93834829e-01 -3.91270280e-01
1.11609483e+00 9.68331769e-02 -1.01210080e-01 -1.71820581e-01
-6.41090691e-01 5.78953803e-01 3.05762082e-01 -3.39452773e-01
-1.05064285e+00 -1.06174803e+00 -1.01779342e+00 2.59872019e-01
2.42781714e-01 -8.58943582e-01 1.12611520e+00 -6.42402112e-01
-7.19083428e-01 1.28415823e+00 -4.51937944e-01 -4.60493922e-01
6.75121605e-01 -5.56973144e-02 3.01106632e-01 3.62261981e-01
7.02817678e-01 1.31371748e+00 8.33605826e-01 -1.15133560e+00
-8.45890164e-01 -4.97424424e-01 -1.06442824e-01 3.76607664e-02
-5.26608646e-01 -1.04448728e-01 -8.73621225e-01 -4.60397750e-01
-3.16129297e-01 -4.67668444e-01 3.39846723e-02 6.10627353e-01
-1.63963318e-01 -3.44837606e-01 5.80567241e-01 -5.01040578e-01
9.97394502e-01 -2.08963180e+00 -1.32605046e-01 -1.86183378e-01
4.98296440e-01 9.52696875e-02 -2.59930581e-01 1.19985953e-01
5.40544391e-01 -1.00470632e-01 -3.53762537e-01 -8.70079219e-01
1.16832361e-01 2.73076873e-02 -3.51989895e-01 5.17800212e-01
5.55478692e-01 1.36199868e+00 -1.01682198e+00 -1.08447993e+00
5.44472158e-01 6.15816772e-01 -7.44742572e-01 2.34123364e-01
-4.57648367e-01 2.90768534e-01 -4.20980603e-01 8.84648740e-01
7.91177154e-01 -8.26628625e-01 -2.57997900e-01 -6.88056573e-02
-6.03062846e-03 -2.17520401e-01 -8.24413359e-01 1.75228345e+00
-4.66125697e-01 6.60673380e-01 -1.94616631e-01 -7.90318668e-01
6.22842371e-01 -3.30446869e-01 2.06581801e-01 -8.67846191e-01
3.31033587e-01 5.06894700e-02 -5.36308587e-01 -3.59310061e-01
6.73712909e-01 2.48779356e-01 -2.31570065e-01 3.75067264e-01
4.24730122e-01 -2.09042564e-01 4.25166816e-01 6.04756474e-01
7.54200876e-01 -1.53274402e-01 1.37806401e-01 -1.65931076e-01
2.32444555e-01 1.72225997e-01 4.83029820e-02 1.06117547e+00
-3.46392930e-01 8.46043229e-01 3.81600827e-01 -3.75266045e-01
-1.39214230e+00 -1.07465017e+00 -2.98228443e-01 1.37827051e+00
2.48630866e-01 -2.41768152e-01 -8.30065548e-01 -4.89455432e-01
3.30371678e-01 4.50533867e-01 -9.17516172e-01 2.99736768e-01
-5.17169297e-01 -4.24382746e-01 6.16358936e-01 8.02478611e-01
5.84186077e-01 -8.25685501e-01 -5.94253421e-01 2.14291066e-02
-3.22572529e-01 -1.44275153e+00 -9.75851238e-01 2.48723966e-03
-5.27794003e-01 -1.10393119e+00 -9.84720707e-01 -8.36782336e-01
6.97204292e-01 6.79632366e-01 1.38332939e+00 2.33409822e-01
-8.56272280e-01 8.05209398e-01 -3.04355333e-03 -4.78911698e-01
-4.81384993e-02 1.06451146e-01 -3.53920281e-01 5.36957849e-03
8.08915555e-01 -2.62382030e-01 -8.99234176e-01 1.29993737e-01
-6.96780801e-01 1.87848613e-01 3.16787869e-01 5.75008690e-01
7.53753960e-01 -7.63650715e-01 2.51711398e-01 -6.30488455e-01
2.44926825e-01 -5.95467925e-01 -9.30632472e-01 4.04156148e-01
-2.57891297e-01 9.72467586e-02 3.33468050e-01 -4.67281997e-01
-8.61989200e-01 2.51803011e-01 2.67473787e-01 -7.40123570e-01
8.22703913e-03 -2.81876206e-01 1.70322791e-01 3.05143278e-02
5.43447912e-01 5.44987619e-01 -1.45519599e-01 -1.63623199e-01
7.73016274e-01 7.07364500e-01 6.33802056e-01 -6.22399092e-01
7.47740090e-01 9.43228722e-01 -2.51507849e-01 -7.99109757e-01
-1.09681165e+00 -1.07320249e+00 -6.88699245e-01 -2.35898033e-01
1.40396702e+00 -1.37506151e+00 -9.09648001e-01 4.99739200e-01
-1.31223583e+00 -4.40345913e-01 -2.66993910e-01 1.14853419e-01
-5.74228108e-01 2.84637243e-01 -5.93382061e-01 -7.86854923e-01
-6.90093815e-01 -9.01370645e-01 1.88025844e+00 3.40240061e-01
2.03288004e-01 -7.89054155e-01 1.74628105e-02 6.07292771e-01
3.30909789e-01 -2.58013099e-01 6.20602548e-01 -4.32150453e-01
-1.17827964e+00 -7.46662244e-02 -9.72853661e-01 1.78748578e-01
-4.19855684e-01 -2.45805666e-01 -1.13090360e+00 -1.35423318e-01
-6.26815259e-01 -6.52722538e-01 1.35113537e+00 4.56867009e-01
1.51530826e+00 -2.02272296e-01 -4.33820814e-01 9.08142507e-01
1.51653290e+00 -6.01298094e-01 4.85030711e-01 5.92955053e-02
1.08096802e+00 3.28819543e-01 5.31075954e-01 5.79209030e-01
8.64819586e-01 5.64582050e-01 3.71194661e-01 -3.46484333e-02
-3.63037854e-01 -7.17242897e-01 -1.44544899e-01 4.87961233e-01
6.32187799e-02 -2.12874383e-01 -9.75667417e-01 8.69365990e-01
-1.75410652e+00 -1.12257850e+00 -5.68343066e-02 1.87299705e+00
7.62576342e-01 -1.87748492e-01 1.58742040e-01 -5.27202010e-01
7.33187735e-01 3.32112312e-01 -5.10594785e-01 9.59259719e-02
3.10297180e-02 2.68116035e-02 7.95131981e-01 4.36140358e-01
-1.22340989e+00 1.31352186e+00 4.87547874e+00 1.16645551e+00
-7.43083239e-01 4.08185810e-01 8.40127409e-01 -2.77962476e-01
-1.27658769e-01 -2.29547501e-01 -1.24017286e+00 6.02573872e-01
4.76848006e-01 -5.18918149e-02 4.62647974e-01 1.00987554e+00
-9.16569680e-02 -2.72144794e-01 -1.08034456e+00 1.25691140e+00
2.91309953e-01 -1.68472111e+00 2.87409127e-01 -1.26311257e-01
7.29458034e-01 3.16843837e-01 4.00414281e-02 4.96358186e-01
2.33508036e-01 -1.08306885e+00 1.03789854e+00 6.08227611e-01
1.17180037e+00 -5.20710349e-01 3.65486652e-01 3.94173682e-01
-1.57214248e+00 -3.93817462e-02 -7.68831074e-01 7.80460611e-02
2.24090576e-01 4.38550353e-01 -6.91879690e-01 -4.49767672e-02
8.35094392e-01 7.12185383e-01 -7.89232492e-01 1.01913035e+00
4.99193519e-02 3.83267522e-01 -3.05564880e-01 -2.16046959e-01
2.50955135e-01 7.49870390e-02 1.50261030e-01 1.62056065e+00
2.03800380e-01 1.16239846e-01 2.00984761e-01 1.41903198e+00
-3.19516599e-01 7.18646310e-03 -5.96734524e-01 7.28127137e-02
7.05815017e-01 1.27431822e+00 -8.77319515e-01 -6.77253067e-01
-4.66870159e-01 1.11936867e+00 8.98202837e-01 1.66616693e-01
-1.15439165e+00 -1.46850199e-01 5.29651165e-01 4.16639954e-01
6.17951334e-01 -1.65801466e-01 -4.01410013e-01 -1.32562423e+00
5.86637259e-02 -3.56376678e-01 1.45140722e-01 -7.66917408e-01
-1.53241646e+00 1.29921794e-01 1.53627172e-01 -1.00311863e+00
1.39683411e-01 -6.44895971e-01 -5.91204464e-01 7.29711831e-01
-1.48349452e+00 -1.56951857e+00 -7.54909873e-01 6.63689792e-01
7.35808492e-01 -1.26723439e-01 4.04938966e-01 4.63019133e-01
-2.56311625e-01 6.64465785e-01 -1.24886982e-01 5.84698975e-01
4.36430871e-01 -1.14072716e+00 6.40867233e-01 6.33227408e-01
3.98647249e-01 3.86551023e-01 2.77686834e-01 -6.40182197e-01
-1.17087173e+00 -1.65181661e+00 7.46704757e-01 -8.67313981e-01
6.91424251e-01 -8.86555552e-01 -8.60302269e-01 5.62326789e-01
-1.64210454e-01 5.33358097e-01 1.26080856e-01 -1.74120381e-01
-6.29538238e-01 1.33207096e-02 -9.49937284e-01 3.48781973e-01
1.17384934e+00 -8.06681573e-01 -3.91505808e-01 4.61707324e-01
8.16714942e-01 -3.27087551e-01 -4.29135084e-01 -1.56110907e-02
4.85174954e-01 -8.09680879e-01 1.33856952e+00 -3.15007746e-01
5.89060187e-01 -1.20077990e-01 -2.06542626e-01 -5.58441043e-01
-3.91875029e-01 2.42506936e-01 -9.97797847e-02 1.36229420e+00
1.49265289e-01 -2.74221122e-01 7.76532650e-01 4.29423124e-01
1.58287674e-01 -5.18342674e-01 -1.02035260e+00 -6.35195971e-01
1.71498373e-01 -4.71095920e-01 4.65048790e-01 6.68905616e-01
-3.03252399e-01 2.61224061e-01 -4.20612127e-01 7.27475733e-02
9.98601794e-01 -5.08929752e-02 1.07915080e+00 -9.58992004e-01
-2.13100508e-01 -4.51785624e-01 -6.11858785e-01 -1.36722231e+00
1.97588995e-01 -1.10616684e+00 9.52798054e-02 -1.64570045e+00
9.73760486e-01 -3.98292959e-01 1.49053305e-01 3.07608753e-01
-4.29207772e-01 5.47620356e-01 5.23628652e-01 2.51214057e-01
-1.15668190e+00 7.10899889e-01 1.14681995e+00 -5.86311221e-01
2.70962656e-01 -3.10688019e-01 -2.80732960e-01 6.42456889e-01
6.23224020e-01 -7.36340046e-01 -2.16650441e-01 -7.67279208e-01
1.06638260e-01 -7.67573267e-02 1.00384223e+00 -1.01830888e+00
4.47984785e-01 -5.91255054e-02 4.80116725e-01 -7.81656802e-01
4.47189212e-01 -5.97567439e-01 -4.82865661e-01 2.86431342e-01
-2.42126092e-01 -3.60517263e-01 2.64225394e-01 1.03186178e+00
4.58921082e-02 -1.98032856e-01 9.31228638e-01 -2.50345379e-01
-7.58205056e-01 6.74375653e-01 3.79944853e-02 5.92049658e-01
9.09814298e-01 -2.67408520e-01 -6.59827650e-01 -4.76632304e-02
-2.73255885e-01 4.99647707e-01 5.09102821e-01 4.64222163e-01
5.52571595e-01 -1.12005186e+00 -7.50504375e-01 1.85374677e-01
5.81065536e-01 2.79719114e-01 5.03214359e-01 7.13220119e-01
-5.43624640e-01 5.25905907e-01 6.30741566e-02 -1.17168570e+00
-1.10479379e+00 5.69943368e-01 3.60327661e-01 -1.62423491e-01
-8.02227318e-01 1.10227215e+00 7.49420166e-01 -4.43696618e-01
3.12985659e-01 -5.83647490e-01 1.04639024e-01 2.18575839e-02
8.06133986e-01 2.12776825e-01 -2.72088975e-01 -6.29504979e-01
-4.69732642e-01 9.04697895e-01 -3.26195247e-02 1.52755171e-01
1.17230904e+00 -1.92718580e-01 1.03976168e-01 5.00814021e-01
1.19919503e+00 -4.03694570e-01 -1.66976237e+00 -4.31076735e-01
-1.99065000e-01 -8.38158190e-01 -1.11655869e-01 -4.56446588e-01
-1.05182004e+00 1.27581847e+00 7.47626007e-01 -4.49401766e-01
5.42390645e-01 5.06480038e-01 5.98844528e-01 3.42949778e-01
6.06150329e-01 -1.00976539e+00 4.68608379e-01 5.34367979e-01
6.41797125e-01 -1.83142173e+00 -2.19574854e-01 -1.23235270e-01
-7.07176864e-01 7.52955258e-01 8.76944125e-01 -2.36640498e-01
4.64252740e-01 3.12455624e-01 -3.12870502e-01 -4.09126550e-01
-6.69710994e-01 -5.32519937e-01 4.35824215e-01 6.76895559e-01
2.27448314e-01 2.11166278e-01 2.51567066e-01 4.37260628e-01
1.93825096e-01 7.84250274e-02 2.12091297e-01 5.11679173e-01
-8.64743948e-01 -2.14777187e-01 -3.59919667e-01 6.39061809e-01
-1.50056094e-01 -5.03561199e-01 -2.24530902e-02 6.83180451e-01
6.86389357e-02 5.75245500e-01 5.90620458e-01 1.14605352e-01
1.35898218e-01 -1.50088519e-01 5.68671405e-01 -6.59619629e-01
-1.76446706e-01 -3.22223067e-01 -3.99370879e-01 -5.58779955e-01
-2.62173951e-01 -4.30323333e-01 -1.32351124e+00 -5.73841512e-01
-1.91114277e-01 -4.51288790e-01 5.20425260e-01 7.22778618e-01
3.65647972e-01 4.52589869e-01 -4.67158444e-02 -1.11433697e+00
-4.27413195e-01 -9.63339448e-01 -5.62034428e-01 5.13504326e-01
2.64241487e-01 -7.30386555e-01 -2.26123244e-01 1.21097431e-01] | [9.099711418151855, 0.5496980547904968] |
6d35144b-7277-4291-9d8f-2c016d80fdc8 | textit-what-textit-when-and-textit-how-to | 2306.03361 | null | https://arxiv.org/abs/2306.03361v3 | https://arxiv.org/pdf/2306.03361v3.pdf | WHAT, WHEN, and HOW to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue | This paper presents a method for building a personalized open-domain dialogue system to address the WWH (WHAT, WHEN, and HOW) problem for natural response generation in a commercial setting, where personalized dialogue responses are heavily interleaved with casual response turns. The proposed approach involves weighted dataset blending, negative persona information augmentation methods, and the design of personalized conversation datasets to address the challenges of WWH in personalized, open-domain dialogue systems. Our work effectively balances dialogue fluency and tendency to ground, while also introducing a response-type label to improve the controllability and explainability of the grounded responses. The combination of these methods leads to more fluent conversations, as evidenced by subjective human evaluations as well as objective evaluations. | ['Eric Davis', 'Taeyoon Kim', 'Seojin Lee', 'Ki Hyun Kim', 'Sunwoo Lee', 'Deuksin Kwon'] | 2023-06-06 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 3.16352881e-02 9.18478608e-01 1.08207442e-01 -5.88556588e-01
-5.21418989e-01 -5.52397311e-01 8.39106798e-01 -1.47913948e-01
-7.35891983e-02 9.84808147e-01 1.07383215e+00 1.03904158e-01
-2.39226446e-02 -6.49123311e-01 3.18540305e-01 -1.91192031e-01
4.84624863e-01 9.74238992e-01 -2.09634021e-01 -1.02799416e+00
2.27202922e-01 -9.18953568e-02 -1.03805828e+00 6.52762055e-01
1.28601420e+00 6.64588571e-01 2.93091424e-02 9.05465484e-01
-6.06303632e-01 1.05841708e+00 -6.74751699e-01 -7.42818356e-01
-1.86645743e-02 -7.26724625e-01 -1.32317865e+00 1.49630532e-01
1.16480693e-01 -4.65318859e-01 -2.01824844e-01 4.58191395e-01
8.45301688e-01 6.60808206e-01 4.95478272e-01 -1.16931915e+00
-8.05874646e-01 1.18758035e+00 7.13970959e-02 -1.44143075e-01
9.83118355e-01 5.32954216e-01 1.15728688e+00 -4.09969389e-01
6.36487961e-01 1.50489402e+00 6.60504520e-01 1.13400042e+00
-1.44165874e+00 -3.54547411e-01 3.38503309e-02 -1.91536739e-01
-5.60087323e-01 -5.66637337e-01 8.94930243e-01 -4.98522341e-01
7.76592374e-01 5.56221187e-01 5.73386610e-01 1.72570765e+00
-4.12961006e-01 7.07650304e-01 1.12115502e+00 -3.68145227e-01
-1.65476017e-02 6.96959615e-01 5.75436354e-01 2.29357734e-01
-4.65049744e-01 -2.28570893e-01 -6.51768982e-01 -3.83524030e-01
5.03810465e-01 -2.27812260e-01 -4.29513335e-01 2.47313440e-01
-1.18798387e+00 1.04012787e+00 1.00877203e-01 3.09039325e-01
-4.90042120e-01 -6.18712187e-01 6.32780612e-01 4.45063174e-01
7.16051102e-01 1.33420897e+00 -4.67666775e-01 -4.79658782e-01
-4.16727662e-01 7.42637277e-01 1.41763663e+00 1.03443718e+00
5.96329153e-01 -1.44569516e-01 -1.19086277e+00 1.24528241e+00
-3.63801643e-02 2.76506513e-01 6.60271049e-01 -1.05487800e+00
5.17904222e-01 8.81172240e-01 5.32038569e-01 -1.01630425e+00
-7.29020357e-01 1.25022441e-01 -6.69075012e-01 -6.36539876e-01
5.17674327e-01 -7.99835801e-01 -1.58174008e-01 1.94499362e+00
3.63411307e-01 -6.31257236e-01 3.50916982e-01 8.94278049e-01
1.17543471e+00 6.66679561e-01 2.24701598e-01 -2.77417779e-01
1.31684995e+00 -1.19816458e+00 -1.32220399e+00 -1.30647302e-01
4.87429291e-01 -8.44117105e-01 1.70693099e+00 -1.38803143e-02
-9.97094631e-01 -5.08118808e-01 -4.91507977e-01 -1.98036373e-01
-5.43521307e-02 -7.35493898e-02 5.92979431e-01 5.76473534e-01
-7.30640352e-01 3.65379006e-01 1.65861666e-01 -5.16084135e-01
-1.97520420e-01 8.32910761e-02 -1.03429064e-01 2.59357691e-01
-1.72009563e+00 1.05031645e+00 1.20571472e-01 -2.22466350e-01
-1.41121954e-01 -8.16855013e-01 -7.12944627e-01 -5.39919510e-02
2.14352146e-01 -7.58530080e-01 1.59280205e+00 -9.20923531e-01
-2.25334668e+00 6.42659605e-01 4.04681675e-02 -2.10399583e-01
7.50306726e-01 -3.55908960e-01 -3.22817594e-01 -1.89759359e-01
-4.71455697e-03 6.10648990e-01 3.18456650e-01 -1.18356073e+00
-3.59998196e-01 -1.35704026e-01 2.81386048e-01 5.52180231e-01
-5.46988368e-01 -9.71069634e-02 1.40304893e-01 -5.35660148e-01
-4.22516972e-01 -8.49277377e-01 -3.12505811e-01 -7.52398252e-01
-6.45560563e-01 -6.71933770e-01 5.27396500e-01 -8.36829126e-01
1.18239796e+00 -1.90157187e+00 2.15678349e-01 -2.91731255e-03
4.96559918e-01 2.40055710e-01 -1.53658673e-01 9.51989651e-01
3.32845151e-01 1.02162370e-02 2.55567759e-01 -3.25961232e-01
3.73899668e-01 -4.65097576e-02 -3.50032419e-01 -2.91852981e-01
-6.37868196e-02 8.19006681e-01 -1.05094302e+00 -3.03930104e-01
-2.10853815e-02 -1.25382170e-01 -7.41662383e-01 9.45269763e-01
-6.19052887e-01 7.67039061e-01 -4.42648023e-01 -9.50111076e-02
3.27114850e-01 -1.31478847e-03 2.47601211e-01 -1.17812879e-01
-9.92366001e-02 6.00139320e-01 -7.38305867e-01 1.38908684e+00
-6.15272224e-01 3.68186265e-01 8.51689838e-03 -1.76896393e-01
1.38794148e+00 5.41702867e-01 2.97300369e-01 -7.18433380e-01
2.10523367e-01 -1.37243167e-01 1.16030246e-01 -7.99532056e-01
1.10968041e+00 -2.32258797e-01 -5.19323349e-01 8.81501794e-01
5.51127344e-02 -4.25503135e-01 1.19542152e-01 5.23256481e-01
8.40361834e-01 -2.54132658e-01 2.18881071e-01 -3.24327767e-01
3.21364224e-01 4.89117876e-02 4.68126357e-01 8.01790893e-01
-3.41897905e-01 3.55719179e-01 6.62689149e-01 -2.25575432e-01
-1.10656035e+00 -4.10247862e-01 3.08029443e-01 1.38437080e+00
-8.83780867e-02 -3.39491338e-01 -1.07318068e+00 -5.48933446e-01
-2.23974064e-01 1.00189710e+00 -4.88528877e-01 -2.28726551e-01
-3.73816252e-01 -2.59845078e-01 7.65299559e-01 2.22527251e-01
5.85349262e-01 -1.18385792e+00 -7.98643846e-03 4.74990547e-01
-1.04313231e+00 -1.11901438e+00 -8.37729394e-01 -1.87024742e-01
-3.92141491e-01 -8.14432859e-01 -5.68135083e-01 -5.13476551e-01
2.42898434e-01 -3.32058631e-02 1.06450069e+00 -5.32698184e-02
2.47366771e-01 3.20426255e-01 -7.47639477e-01 -1.12395853e-01
-9.33356643e-01 2.18771487e-01 1.12320244e-01 8.45708996e-02
1.39939308e-01 -2.75182277e-01 -5.49826145e-01 5.61507642e-01
-5.20832002e-01 5.20374238e-01 -1.40543645e-02 1.16673231e+00
-3.91776323e-01 -7.91827738e-01 1.02960742e+00 -1.24066949e+00
1.80409849e+00 -4.49739099e-01 2.69692808e-01 3.99873227e-01
-6.12768471e-01 -2.82043666e-02 7.50582039e-01 -7.13826478e-01
-1.67221117e+00 -4.14296657e-01 -2.93369174e-01 2.57005334e-01
-9.98800769e-02 1.57775059e-01 -2.27325395e-01 1.31335720e-01
1.13695705e+00 -8.18452239e-02 4.31168199e-01 -1.69361800e-01
8.24195683e-01 1.12092412e+00 3.09055001e-01 -8.81374836e-01
2.85763055e-01 -2.27496341e-01 -9.09613252e-01 -7.54035175e-01
-6.20999277e-01 -3.76831621e-01 -3.10146362e-01 -5.04876196e-01
7.34340668e-01 -6.23486400e-01 -9.07290697e-01 4.15961385e-01
-1.25495017e+00 -6.71857119e-01 -4.42071915e-01 8.64219517e-02
-6.76850796e-01 3.55634511e-01 -9.91355717e-01 -9.05791402e-01
-7.05627859e-01 -8.84160459e-01 5.31719208e-01 4.38776851e-01
-1.25549364e+00 -9.72708166e-01 2.47158751e-01 9.41799045e-01
7.52182066e-01 6.69284835e-02 1.00189972e+00 -1.39379776e+00
1.47107869e-01 -1.65961489e-01 4.60666791e-03 1.67342573e-01
1.85073093e-01 -1.26142994e-01 -8.98230314e-01 2.92379528e-01
-7.11608492e-03 -8.47907186e-01 7.74945915e-02 -1.71618998e-01
4.51406360e-01 -1.02803111e+00 1.64489344e-01 -1.27071381e-01
4.40291107e-01 5.62149435e-02 6.38368249e-01 9.32211149e-03
4.53511387e-01 1.18829787e+00 7.63139963e-01 9.64390337e-01
7.78039515e-01 8.43201458e-01 -2.80039370e-01 -9.61191282e-02
-1.37957573e-01 -5.48134089e-01 1.19445257e-01 7.84104407e-01
1.29743591e-01 -4.83076036e-01 -7.09484041e-01 3.81499887e-01
-1.93449438e+00 -1.08242464e+00 -2.96203703e-01 1.73878181e+00
1.42074549e+00 -2.84313112e-01 2.75076985e-01 -1.79637462e-01
7.04873145e-01 1.94820017e-01 -1.46833658e-01 -9.04332995e-01
-7.51250312e-02 -1.96919248e-01 -8.07140991e-02 7.69912958e-01
-4.45276886e-01 1.08921611e+00 6.54495430e+00 4.87908721e-01
-7.25846291e-01 7.46643245e-02 6.56966865e-01 2.39356741e-01
-7.71314979e-01 -1.30206287e-01 -6.27118528e-01 4.16560620e-01
8.01055491e-01 -4.50730354e-01 5.21670401e-01 5.87544203e-01
5.62849283e-01 2.90207386e-01 -1.13526070e+00 5.02509892e-01
-2.53306869e-02 -1.16430688e+00 5.52603789e-02 -2.19599694e-01
7.22146213e-01 -7.40493178e-01 -2.05332875e-01 7.08754122e-01
6.82179391e-01 -6.48958206e-01 3.58046651e-01 6.54636383e-01
4.72017616e-01 -3.62963319e-01 6.53743565e-01 6.19516253e-01
-3.75116974e-01 -8.73001069e-02 1.87765837e-01 -2.95283943e-01
2.67134428e-01 1.91127628e-01 -1.30555844e+00 2.95816183e-01
1.84064776e-01 2.80996505e-02 -9.41145793e-02 3.96275401e-01
-1.63935900e-01 2.88418651e-01 5.26887886e-02 -5.25619209e-01
5.08285500e-02 -1.82390749e-01 5.00410497e-01 1.25780070e+00
-1.80287659e-01 5.70948482e-01 3.79082978e-01 9.75293517e-01
-1.27826735e-01 3.93960387e-01 -5.46257496e-01 -2.51028359e-01
7.49621034e-01 1.33183026e+00 -6.85349032e-02 -3.07360142e-01
2.74501909e-02 9.17836845e-01 4.22370791e-01 2.82912910e-01
-4.32661653e-01 -2.45092750e-01 6.00302577e-01 7.39941597e-02
-5.91841936e-01 2.45526969e-01 -5.84093273e-01 -9.56824183e-01
-3.67369056e-01 -1.38820410e+00 1.88494489e-01 -4.83302057e-01
-1.59586036e+00 7.74496257e-01 -1.76462859e-01 -7.62744904e-01
-6.34899199e-01 3.87759320e-02 -8.63121629e-01 1.08989871e+00
-7.34223723e-01 -9.81293559e-01 -5.47570646e-01 5.33858240e-01
9.53254104e-01 -1.64895505e-01 1.13697636e+00 2.37048402e-01
-6.20127618e-01 8.18361759e-01 -3.33816886e-01 -9.50020775e-02
1.05232453e+00 -1.25261247e+00 3.51029545e-01 1.85787261e-01
-6.48202777e-01 6.66780651e-01 1.04047394e+00 -6.76044464e-01
-9.71935272e-01 -6.97724402e-01 1.14381444e+00 -3.10748935e-01
6.11928880e-01 -3.69364619e-01 -8.43710840e-01 3.18022162e-01
7.54958391e-01 -9.12422776e-01 1.04495883e+00 6.24355078e-01
-5.02360538e-02 1.43038511e-01 -1.41433239e+00 8.45625460e-01
6.77624643e-01 -4.54665929e-01 -8.21073771e-01 5.15689373e-01
1.05367064e+00 -5.14455259e-01 -1.00320172e+00 1.21809319e-01
3.85164857e-01 -6.90578222e-01 5.53784609e-01 -8.27172577e-01
6.66776896e-01 3.95126194e-01 1.23401538e-01 -1.75281572e+00
-4.03416783e-01 -1.25921309e+00 4.43474315e-02 1.80802536e+00
6.04229152e-01 -4.98355329e-01 4.03730065e-01 1.35353863e+00
-2.05186158e-01 -4.49250370e-01 -3.85606617e-01 -1.17827252e-01
-1.09106340e-01 1.67748407e-01 7.10699975e-01 1.17445505e+00
8.70676160e-01 9.21500385e-01 -9.25038874e-01 -4.55503196e-01
8.78794491e-02 -6.90547526e-02 1.27839661e+00 -1.05425894e+00
-1.53957039e-01 -5.24441063e-01 2.45165199e-01 -1.17265558e+00
1.78179920e-01 -3.93194020e-01 3.22751671e-01 -1.53627181e+00
1.82048663e-01 -5.04862368e-01 6.63089752e-01 2.18713462e-01
-5.26754856e-01 -3.58167887e-01 2.31191128e-01 1.14740930e-01
-3.76074851e-01 7.45629787e-01 1.46036386e+00 1.28595391e-02
-6.73002303e-01 2.27774739e-01 -1.16003811e+00 4.16019946e-01
7.92499423e-01 1.68525092e-02 -5.47802925e-01 -9.05821174e-02
-3.22700106e-02 5.28104544e-01 -3.70990597e-02 -4.23722923e-01
2.10361376e-01 -3.77367437e-01 -1.95175037e-01 -1.29372016e-01
3.34279537e-01 -3.72721583e-01 -1.55282035e-01 2.43619289e-02
-1.16007197e+00 -7.94415772e-02 1.10546984e-02 3.68805408e-01
-1.59641448e-02 -4.90789786e-02 6.37402296e-01 -2.18881309e-01
-3.83546531e-01 -3.47858630e-02 -4.98094976e-01 4.34130907e-01
8.54543269e-01 -4.98840995e-02 -6.77507341e-01 -1.00836945e+00
-7.60678530e-01 6.88511312e-01 5.69743477e-02 6.86414003e-01
4.55929965e-01 -1.10942400e+00 -8.65118504e-01 -8.91396925e-02
2.42239088e-01 -3.46763045e-01 5.68561673e-01 5.72997510e-01
-1.63933948e-01 2.69767463e-01 -2.76761651e-01 -3.32305804e-02
-9.82126951e-01 4.68222387e-02 3.77022445e-01 -5.91923356e-01
-5.54112673e-01 8.28727841e-01 -3.00809052e-02 -1.02188003e+00
5.08771420e-01 1.66186169e-01 -6.33759677e-01 3.97237659e-01
5.72948754e-01 4.58604872e-01 -2.15007871e-01 -2.93659449e-01
2.86865562e-01 -4.36361313e-01 -1.94943741e-01 -3.52946162e-01
8.45602155e-01 -4.54994678e-01 -2.30095126e-02 5.49153566e-01
6.49505794e-01 5.06544374e-02 -9.90847230e-01 -3.65108788e-01
-1.02656327e-01 -3.57536227e-01 -4.84920591e-01 -1.35281181e+00
-3.81843537e-01 4.06885415e-01 4.69700359e-02 6.56514704e-01
5.76976359e-01 -1.33887872e-01 1.05950809e+00 4.09712225e-01
1.17217138e-01 -1.36203527e+00 4.14912641e-01 8.86545718e-01
1.19669616e+00 -1.25769746e+00 -4.66574162e-01 -3.16416115e-01
-1.61017513e+00 9.86350238e-01 1.16194010e+00 4.61821795e-01
-1.42844515e-02 -3.73350037e-03 3.68982553e-01 -5.79654723e-02
-1.06190598e+00 4.40525785e-02 3.07278279e-02 6.42311990e-01
7.07307696e-01 3.00731152e-01 -7.38341391e-01 1.06411338e+00
-4.91092145e-01 -1.76800445e-01 4.95355576e-01 3.67501706e-01
-2.76045829e-01 -1.08741558e+00 -1.87982783e-01 3.47424060e-01
-3.31581384e-02 -6.31836429e-02 -1.17270720e+00 4.39153880e-01
-3.42551351e-01 1.42067504e+00 -2.26298407e-01 -7.61765778e-01
7.78101325e-01 3.69846940e-01 4.09523249e-02 -6.68959796e-01
-1.27548337e+00 -3.10968608e-01 9.96754766e-01 -2.83945382e-01
-1.11311503e-01 -3.28990906e-01 -1.14207888e+00 -6.78434312e-01
-4.39665765e-01 5.08167088e-01 3.28342915e-01 9.08114910e-01
3.44635665e-01 5.28604329e-01 1.12841582e+00 -6.64739490e-01
-1.01657784e+00 -1.58168471e+00 -3.61627370e-01 8.43113542e-01
1.06786191e-01 -3.54336172e-01 -2.51874745e-01 -3.36894274e-01] | [12.737165451049805, 8.1053466796875] |
f7700559-4a6b-44f1-b863-03ab07be17e5 | adaptive-bi-recommendation-and-self-improving | 2303.14317 | null | https://arxiv.org/abs/2303.14317v1 | https://arxiv.org/pdf/2303.14317v1.pdf | Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection | As Internet of Things devices become prevalent, using intrusion detection to protect IoT from malicious intrusions is of vital importance. However, the data scarcity of IoT hinders the effectiveness of traditional intrusion detection methods. To tackle this issue, in this paper, we propose the Adaptive Bi-Recommendation and Self-Improving Network (ABRSI) based on unsupervised heterogeneous domain adaptation (HDA). The ABRSI transfers enrich intrusion knowledge from a data-rich network intrusion source domain to facilitate effective intrusion detection for data-scarce IoT target domains. The ABRSI achieves fine-grained intrusion knowledge transfer via adaptive bi-recommendation matching. Matching the bi-recommendation interests of two recommender systems and the alignment of intrusion categories in the shared feature space form a mutual-benefit loop. Besides, the ABRSI uses a self-improving mechanism, autonomously improving the intrusion knowledge transfer from four ways. A hard pseudo label voting mechanism jointly considers recommender system decision and label relationship information to promote more accurate hard pseudo label assignment. To promote diversity and target data participation during intrusion knowledge transfer, target instances failing to be assigned with a hard pseudo label will be assigned with a probabilistic soft pseudo label, forming a hybrid pseudo-labelling strategy. Meanwhile, the ABRSI also makes soft pseudo-labels globally diverse and individually certain. Finally, an error knowledge learning mechanism is utilised to adversarially exploit factors that causes detection ambiguity and learns through both current and previous error knowledge, preventing error knowledge forgetfulness. Holistically, these mechanisms form the ABRSI model that boosts IoT intrusion detection accuracy via HDA-assisted intrusion knowledge transfer. | ['Kenneth B. Kent', 'Chengzhong Xu', 'Hao Dai', 'Yang Wang', 'Jiashu Wu'] | 2023-03-25 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 3.97934228e-01 7.03216344e-02 -4.76073742e-01 -3.52094918e-01
-1.84083804e-02 -4.67314005e-01 3.75132978e-01 -7.99730942e-02
-4.60203111e-01 7.40200937e-01 -1.31999150e-01 -1.30931750e-01
-7.37057507e-01 -1.25140500e+00 -3.70572358e-01 -7.52379537e-01
3.25966090e-01 6.45361900e-01 3.91771108e-01 -2.33203039e-01
1.43138126e-01 6.88396633e-01 -1.45294523e+00 1.59510046e-01
9.93027806e-01 1.33831489e+00 -2.48713434e-01 7.39692673e-02
-3.61797929e-01 5.33323765e-01 -7.13375151e-01 -3.21112335e-01
6.23183489e-01 -3.23229320e-02 -6.76110804e-01 -4.05433297e-01
-3.95687431e-01 -2.90211886e-02 -2.52306372e-01 1.08805525e+00
4.06063557e-01 2.83461571e-01 7.54465878e-01 -1.58270383e+00
-5.66374123e-01 7.23973691e-01 -1.90052241e-01 2.45591834e-01
1.97817639e-01 8.84596854e-02 5.29752433e-01 -2.91614056e-01
3.27143997e-01 1.10350525e+00 5.34130216e-01 8.10753405e-01
-9.58041847e-01 -1.31298018e+00 4.22263980e-01 3.27583194e-01
-1.39102924e+00 -1.08687639e-01 9.20912385e-01 -1.90737113e-01
6.06819928e-01 3.27425241e-01 5.19289553e-01 1.10823035e+00
1.21243447e-01 2.58085728e-01 9.42282200e-01 -9.98071432e-02
6.00059509e-01 6.11403286e-01 2.91055053e-01 1.57050326e-01
4.68662739e-01 4.33061123e-01 -1.45191059e-01 -2.41146177e-01
3.27208579e-01 6.42189026e-01 1.78974658e-01 -1.20319925e-01
-5.89782119e-01 7.01482475e-01 7.32439220e-01 6.57998562e-01
-6.73575819e-01 -6.78844929e-01 2.58503437e-01 7.19769299e-01
1.18848436e-01 4.86807138e-01 -1.01290250e+00 2.50801027e-01
-3.97162706e-01 -4.89176840e-01 9.86897349e-01 7.16575325e-01
1.10399675e+00 2.00915903e-01 6.69041723e-02 5.96006751e-01
5.88231325e-01 7.76135981e-01 7.22747445e-01 -4.72115248e-01
-2.22688746e-02 1.18071926e+00 -3.16858858e-01 -9.77765203e-01
-6.84758246e-01 -6.53945029e-01 -9.61138427e-01 2.40007311e-01
1.32267013e-01 -1.38075396e-01 -7.36530662e-01 1.87733543e+00
8.16390574e-01 4.40573514e-01 4.47367311e-01 4.67817485e-01
3.81940991e-01 4.62669432e-01 5.54283977e-01 -4.61272299e-01
1.43296671e+00 -3.61490428e-01 -4.44161624e-01 -3.99171971e-02
4.58730370e-01 -4.36007500e-01 4.48623598e-01 4.31615561e-01
-2.87891626e-01 -5.91404378e-01 -1.00283039e+00 8.46894741e-01
-7.58531868e-01 -6.58936143e-01 5.05978286e-01 9.43720877e-01
-4.88563836e-01 3.44235480e-01 -2.75385261e-01 -4.45497125e-01
7.76585281e-01 8.58197272e-01 -1.24487720e-01 -1.77821383e-01
-1.58913147e+00 6.53987050e-01 8.45947146e-01 -3.71716142e-01
-3.79906297e-01 -8.27221215e-01 -4.09447163e-01 -2.41856977e-01
4.70670968e-01 -9.00474429e-01 5.93421400e-01 -1.26680303e+00
-1.84613204e+00 2.96968549e-01 4.20806825e-01 -5.04536688e-01
-1.43561913e-02 1.53540507e-01 -1.34030676e+00 -3.27558368e-02
-1.67451367e-01 5.85262477e-01 1.31011724e+00 -1.25095868e+00
-9.68142927e-01 -6.85862064e-01 -7.61052668e-02 2.13428125e-01
-9.01808202e-01 -2.23865330e-01 2.64603406e-01 -6.04167938e-01
2.94118486e-02 -8.39238942e-01 -2.78192937e-01 -2.59985119e-01
-1.95811763e-01 -3.69611263e-01 1.40078223e+00 -1.86432540e-01
1.27556098e+00 -2.14749193e+00 -9.11686122e-02 9.28424060e-01
2.35773385e-01 7.92735219e-01 -1.30425498e-01 -4.50460874e-02
-9.39458832e-02 1.15627743e-01 -1.01523280e-01 3.04366738e-01
1.00984506e-01 4.22573835e-01 -3.99347216e-01 -5.36844507e-02
1.49587125e-01 7.84407079e-01 -8.64078522e-01 -2.19854087e-01
3.32356423e-01 5.16612589e-01 -5.49566031e-01 2.30947390e-01
-2.25662872e-01 7.47849822e-01 -9.48131144e-01 6.74276769e-01
7.83935428e-01 -1.86136544e-01 8.68068784e-02 -4.63635296e-01
1.84301838e-01 -1.42964303e-01 -1.34624112e+00 1.10551214e+00
-4.25896913e-01 -6.02956474e-01 -4.99989390e-02 -1.06481230e+00
1.37128258e+00 9.80930030e-02 8.56629848e-01 -1.15847874e+00
4.20130700e-01 2.18979552e-01 5.11381403e-02 -1.09838590e-01
-1.60518158e-02 -2.64592439e-01 -1.62792087e-01 6.39809728e-01
-9.01829079e-02 2.49490052e-01 -3.67827952e-01 3.37873846e-02
1.17411685e+00 -2.52488643e-01 5.09921849e-01 -4.76645716e-02
9.36245024e-01 -3.05190265e-01 8.71474147e-01 7.49828458e-01
-4.53341872e-01 -2.12767974e-01 -3.86126250e-01 -5.78072548e-01
-3.86027157e-01 -1.05883217e+00 -2.15522364e-01 1.21137905e+00
4.00259554e-01 1.55458272e-01 -3.51553023e-01 -1.37166786e+00
1.49010375e-01 7.86731303e-01 -5.76025724e-01 -9.80181396e-01
-2.90201455e-01 -6.47883415e-01 5.71784019e-01 2.75549710e-01
7.63943136e-01 -1.34934247e+00 -1.66843802e-01 3.04027706e-01
2.59544343e-01 -6.99844003e-01 -3.47427905e-01 4.15833294e-01
-5.67981780e-01 -1.14828253e+00 6.36094883e-02 -4.49770659e-01
5.54297924e-01 2.31345728e-01 6.51849031e-01 2.92441752e-02
-1.73208833e-01 6.04127526e-01 -6.32494271e-01 -4.54063624e-01
-5.43090582e-01 1.18267357e-01 5.70963919e-01 3.33640605e-01
8.89541030e-01 -8.81499648e-01 -6.05390668e-01 8.51598740e-01
-1.20986748e+00 -7.37865269e-01 7.11317182e-01 5.77122927e-01
4.93847698e-01 6.00921273e-01 1.11517644e+00 -8.10411513e-01
2.69187748e-01 -1.03018153e+00 -3.68133485e-01 2.17776805e-01
-1.03001094e+00 -5.02303243e-02 9.46651757e-01 -1.02060676e+00
-1.25098264e+00 -1.86231792e-01 -1.45191014e-01 -4.25580531e-01
-5.38502574e-01 5.31867379e-04 -6.42990828e-01 -3.83902520e-01
8.10887635e-01 4.56334054e-02 -1.20337099e-01 -1.30346715e-01
4.05663192e-01 8.80169690e-01 1.06107034e-01 -5.30416727e-01
1.16617525e+00 4.78288621e-01 -1.89668298e-01 -4.27584141e-01
-8.47144246e-01 -4.38529849e-01 -3.55744243e-01 -1.45934626e-01
6.44470334e-01 -5.73752880e-01 -8.88774633e-01 4.16956782e-01
-5.22901237e-01 3.79436370e-03 -8.62835228e-01 5.51792800e-01
-2.46441457e-02 1.92566276e-01 -1.65445939e-01 -7.02679396e-01
-6.63486004e-01 -9.05981302e-01 4.46186781e-01 7.22954750e-01
-1.29440680e-01 -8.87069583e-01 3.87596004e-02 3.14157575e-01
7.80172050e-01 -1.49825379e-01 7.30086029e-01 -1.55795968e+00
-3.32633078e-01 -7.78777078e-02 -3.13881576e-01 4.60938603e-01
3.59917760e-01 -4.37445849e-01 -8.76889229e-01 -3.03386062e-01
3.45019549e-01 -1.76263496e-01 5.08296549e-01 -5.06993905e-02
9.73039508e-01 -4.40576345e-01 -3.89509887e-01 3.79463464e-01
1.15972197e+00 8.46782267e-01 6.23762250e-01 5.58667719e-01
7.42467940e-01 5.46112359e-01 6.86795890e-01 5.91025114e-01
5.27638257e-01 4.70027924e-01 5.14544964e-01 1.50430590e-01
-3.22319083e-02 -6.99243546e-02 2.75118023e-01 6.94758892e-01
3.12279522e-01 -8.10410753e-02 -4.67109412e-01 -7.98377171e-02
-1.70258904e+00 -8.43442321e-01 3.85313869e-01 2.24328685e+00
6.56205595e-01 3.39639992e-01 1.48580313e-01 3.85601401e-01
7.95525730e-01 -3.61442149e-01 -1.11446834e+00 -2.38776222e-01
-1.46329135e-01 3.66252601e-01 5.31132221e-01 2.40699798e-01
-1.07352269e+00 8.76408398e-01 4.17373610e+00 1.07286406e+00
-1.10082948e+00 2.51909614e-01 3.14215124e-01 5.24296343e-01
-3.67546171e-01 -2.27949947e-01 -8.67101014e-01 5.69342911e-01
9.62499321e-01 2.37843953e-02 6.26654804e-01 7.99360812e-01
-2.40582272e-01 3.20418268e-01 -5.26535451e-01 7.61662245e-01
-9.04289037e-02 -8.35188985e-01 2.11786300e-01 2.62350708e-01
7.30977714e-01 -5.16795628e-02 2.32764259e-01 6.44752502e-01
7.96487629e-01 -3.71690273e-01 2.01289684e-01 9.33416843e-01
5.61581314e-01 -9.57298756e-01 6.38435602e-01 2.56855845e-01
-1.29473066e+00 -8.69321406e-01 -2.03018472e-01 2.18861088e-01
-1.42594546e-01 6.20507121e-01 -6.28849328e-01 1.06982040e+00
6.62717342e-01 4.93951410e-01 -3.64728302e-01 7.44458497e-01
-1.47635654e-01 4.65881288e-01 -5.76256454e-01 3.01116556e-01
-2.21305206e-01 -1.45898849e-01 6.85919523e-01 7.14014232e-01
1.20879412e-01 2.88379341e-01 6.17310584e-01 3.89315516e-01
-9.72926989e-02 9.54321697e-02 -4.59539145e-01 1.14672028e-01
8.32762420e-01 1.37596810e+00 -6.82514548e-01 -2.10327744e-01
-8.91982689e-02 6.12074912e-01 -1.37222884e-02 1.61748767e-01
-6.95151329e-01 -2.86201090e-01 8.31135154e-01 -2.15620801e-01
1.70899972e-01 5.11479080e-01 -4.27816838e-01 -9.74212289e-01
-5.27279913e-01 -9.07773972e-01 9.56943810e-01 -1.98808745e-01
-1.95987368e+00 7.26517677e-01 -2.79473305e-01 -1.36368799e+00
2.16380820e-01 -3.06897640e-01 -5.13006628e-01 3.53234470e-01
-1.74839854e+00 -1.26017773e+00 -1.42904848e-01 1.12459052e+00
1.08392529e-01 -4.87883568e-01 1.04277670e+00 6.16356373e-01
-6.84115350e-01 9.80911314e-01 -1.82013527e-01 -3.76873016e-01
8.29273164e-01 -6.94151700e-01 -1.71645060e-01 4.95998561e-01
-2.63946891e-01 6.70914233e-01 2.21674025e-01 -9.92373109e-01
-1.08841395e+00 -1.71348798e+00 2.72268444e-01 -1.67398080e-01
6.27944827e-01 1.72625557e-01 -1.12743986e+00 3.34047556e-01
-4.42681581e-01 -3.75029035e-02 1.01348531e+00 -1.32453397e-01
-8.17628860e-01 -7.69363165e-01 -1.94919538e+00 3.44428599e-01
1.07277644e+00 -2.90761590e-01 -5.30934811e-01 1.10920466e-01
1.02896237e+00 4.21221793e-01 -1.16592491e+00 8.30874443e-01
3.78101408e-01 -3.73628587e-01 1.06017518e+00 -4.91373658e-01
-5.31078100e-01 -7.56815851e-01 -2.62338907e-01 -9.26622808e-01
-5.86345971e-01 -5.02066135e-01 -4.06381637e-01 1.41356742e+00
1.28375262e-01 -1.28630018e+00 6.43020093e-01 4.78859007e-01
2.31173765e-02 -3.54860693e-01 -9.63564813e-01 -7.89897025e-01
-1.16886966e-01 -4.44389135e-01 1.04936767e+00 1.26786757e+00
-1.30240276e-01 2.19187438e-01 -1.77294016e-01 4.18718547e-01
7.69616067e-01 -3.78625393e-01 4.71207470e-01 -1.93951201e+00
-1.20060131e-01 -4.78914410e-01 -4.39874440e-01 -4.99540597e-01
2.33263597e-01 -1.10443699e+00 -4.53898758e-01 -8.23298991e-01
-1.82274580e-01 -1.09112513e+00 -1.08018446e+00 7.98898339e-01
9.40107107e-02 4.09488916e-01 7.62979016e-02 3.59610051e-01
-1.03115451e+00 4.74976987e-01 9.29895401e-01 -1.50259122e-01
-5.58908641e-01 4.10919964e-01 -1.06289554e+00 6.42480552e-01
1.22233236e+00 -7.03759432e-01 -5.40599406e-01 3.05926234e-01
1.07533999e-01 -4.10404980e-01 7.02011911e-03 -1.08702862e+00
4.19706613e-01 -2.29628727e-01 4.24032271e-01 -1.88170195e-01
4.63376716e-02 -1.53614509e+00 3.81469488e-01 7.21033871e-01
1.14173293e-01 -4.21800673e-01 1.24156117e-01 9.02456462e-01
3.22816312e-01 1.40501812e-01 9.93382931e-01 1.38305888e-01
-6.71923816e-01 7.02758014e-01 -3.58147144e-01 -7.58605078e-02
1.37944341e+00 -5.31090021e-01 -2.19874889e-01 1.64830193e-01
-7.86903203e-01 1.62858963e-01 3.52823347e-01 7.32045054e-01
5.63366890e-01 -1.24684095e+00 -1.97115675e-01 6.86596334e-01
2.21056655e-01 -2.91964710e-01 5.87109983e-01 6.08346581e-01
4.66976404e-01 2.84711760e-03 -2.83191949e-01 -3.02678376e-01
-7.91808248e-01 8.58250141e-01 1.05410941e-01 -6.52368844e-01
-2.86414295e-01 5.97514451e-01 4.11997456e-03 -9.03424799e-01
2.57379174e-01 4.42275941e-01 -5.76342523e-01 6.12200871e-02
8.65596771e-01 5.24407923e-01 1.00724250e-01 -6.11870646e-01
-5.47347963e-01 5.14497638e-01 -3.43551785e-01 4.36818868e-01
1.05813289e+00 -3.97095531e-01 -7.54521862e-02 1.57155097e-02
7.77087033e-01 -1.49800256e-01 -8.38513434e-01 -6.40381277e-01
-5.89473732e-03 -1.01129167e-01 -1.77527949e-01 -1.42096126e+00
-1.14009690e+00 1.50165975e-01 1.02355933e+00 3.94440800e-01
1.50558543e+00 -2.04314321e-01 8.43964458e-01 4.51465279e-01
5.49218714e-01 -9.42254305e-01 2.51513153e-01 6.22874081e-01
1.43686563e-01 -1.05237138e+00 -4.35714543e-01 -3.08183491e-01
-4.97514337e-01 8.88341427e-01 8.57620358e-01 1.97863542e-02
1.06828117e+00 2.46055439e-01 2.24292185e-02 5.29318824e-02
-2.80529112e-01 -2.52633810e-01 2.61352420e-01 1.10885203e+00
-7.12415755e-01 1.01796165e-01 -1.29520983e-01 1.00727320e+00
1.08964592e-01 -5.97248748e-02 -1.38458893e-01 9.09352064e-01
-6.48456633e-01 -1.45468402e+00 -4.77441877e-01 5.40908217e-01
-1.35604531e-01 1.66393071e-01 -2.22836658e-01 2.54516065e-01
6.32980108e-01 1.34486020e+00 -1.79178327e-01 -1.07767832e+00
4.40974414e-01 1.46760553e-01 1.32588381e-02 -3.94329488e-01
-8.18063378e-01 -3.82109404e-01 -4.04781222e-01 -4.67874259e-01
-3.13024372e-01 -2.26189405e-01 -1.42065489e+00 -2.96369016e-01
-5.69236517e-01 2.73903787e-01 5.93937814e-01 1.19259930e+00
7.06794202e-01 7.69463480e-01 1.15921891e+00 -4.17059243e-01
-5.60677290e-01 -6.33015454e-01 -4.64656770e-01 4.07440752e-01
-2.09780946e-01 -8.32189679e-01 -5.23887813e-01 -4.21230584e-01] | [5.285915374755859, 7.133382320404053] |
dcbcbf4d-5be2-49e5-ab82-2277c0893001 | understanding-and-constructing-latent | 2303.05952 | null | https://arxiv.org/abs/2303.05952v1 | https://arxiv.org/pdf/2303.05952v1.pdf | Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning | Contrastive loss has been increasingly used in learning representations from multiple modalities. In the limit, the nature of the contrastive loss encourages modalities to exactly match each other in the latent space. Yet it remains an open question how the modality alignment affects the downstream task performance. In this paper, based on an information-theoretic argument, we first prove that exact modality alignment is sub-optimal in general for downstream prediction tasks. Hence we advocate that the key of better performance lies in meaningful latent modality structures instead of perfect modality alignment. To this end, we propose three general approaches to construct latent modality structures. Specifically, we design 1) a deep feature separation loss for intra-modality regularization; 2) a Brownian-bridge loss for inter-modality regularization; and 3) a geometric consistency loss for both intra- and inter-modality regularization. Extensive experiments are conducted on two popular multi-modal representation learning frameworks: the CLIP-based two-tower model and the ALBEF-based fusion model. We test our model on a variety of tasks including zero/few-shot image classification, image-text retrieval, visual question answering, visual reasoning, and visual entailment. Our method achieves consistent improvements over existing methods, demonstrating the effectiveness and generalizability of our proposed approach on latent modality structure regularization. | ['Trishul Chilimbi', 'Belinda Zeng', 'Yi Xu', 'Son Dinh Tran', 'Qing Ping', 'Liqun Chen', 'Han Zhao', 'Changyou Chen', 'Qian Jiang'] | 2023-03-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_Understanding_and_Constructing_Latent_Modality_Structures_in_Multi-Modal_Representation_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_Understanding_and_Constructing_Latent_Modality_Structures_in_Multi-Modal_Representation_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-reasoning', 'few-shot-image-classification', 'open-question', 'visual-reasoning', 'visual-entailment'] | ['computer-vision', 'computer-vision', 'natural-language-processing', 'reasoning', 'reasoning'] | [ 5.45885623e-01 -3.72077674e-02 -5.66984892e-01 -3.56089950e-01
-1.13211477e+00 -4.16728079e-01 8.29174995e-01 1.64196149e-01
-2.31157750e-01 2.94133365e-01 5.68829000e-01 -2.01217920e-01
-3.03572625e-01 -4.03404027e-01 -8.43898356e-01 -8.25789392e-01
3.86436433e-01 9.32267830e-02 -4.50619236e-02 -7.06737265e-02
3.27201098e-01 1.88508227e-01 -1.56157911e+00 6.33774936e-01
7.73987472e-01 1.17641294e+00 2.83954106e-02 4.76885051e-01
-2.74995379e-02 1.32636428e+00 5.16318344e-02 -4.57226902e-01
2.13528395e-01 -5.38920224e-01 -9.79354501e-01 2.92198330e-01
5.60798943e-01 -2.23618165e-01 -4.79465514e-01 1.02054977e+00
4.12595958e-01 3.63909662e-01 1.01984370e+00 -1.40898764e+00
-9.14957583e-01 4.37313437e-01 -8.54622781e-01 7.39508644e-02
5.05725205e-01 6.86291233e-02 1.47564054e+00 -9.92745042e-01
6.03876293e-01 1.48151827e+00 5.37042320e-01 4.62555707e-01
-1.24520028e+00 -2.10536242e-01 2.95575380e-01 3.60361218e-01
-1.21972120e+00 -6.10856831e-01 8.69017541e-01 -5.15142024e-01
7.28831172e-01 1.93413317e-01 1.24389298e-01 1.26885271e+00
1.20063640e-01 9.70387995e-01 1.21791339e+00 -8.24561596e-01
8.80505797e-03 1.06566176e-01 1.56543344e-01 8.11571658e-01
-2.57347614e-01 9.72179845e-02 -8.01338971e-01 -3.08798999e-01
7.16261685e-01 2.36821637e-01 -2.59122401e-01 -7.27968812e-01
-1.22414088e+00 1.02797151e+00 4.05543804e-01 1.33389026e-01
-2.79783159e-01 3.62920165e-01 5.57100832e-01 3.31728846e-01
4.67913896e-01 -9.47390944e-02 -6.61709206e-03 1.94493040e-01
-7.18302727e-01 -1.91632286e-02 3.45699400e-01 8.31125677e-01
5.30318797e-01 -1.75060615e-01 -4.88202542e-01 1.02820230e+00
7.10407138e-01 3.81861925e-01 2.80355275e-01 -1.21540689e+00
5.63324869e-01 3.49712074e-01 -1.25884518e-01 -7.27372468e-01
-6.22625761e-02 3.08675244e-02 -8.32457900e-01 -4.27987203e-02
3.55358332e-01 2.21167058e-01 -9.87868488e-01 2.28395486e+00
1.58581451e-01 1.81410089e-01 6.20573908e-02 1.04538643e+00
7.55099714e-01 6.33451819e-01 3.25865805e-01 -2.52893567e-01
1.50317717e+00 -1.09896576e+00 -7.73245335e-01 -6.83806688e-02
4.73793119e-01 -8.91451538e-01 1.29188943e+00 -1.22206274e-03
-1.23963845e+00 -3.46058786e-01 -7.83513784e-01 -5.34170449e-01
-1.44432604e-01 -1.14370264e-01 6.64285541e-01 2.87713647e-01
-8.04274142e-01 2.58742273e-01 -5.90062797e-01 -5.24229527e-01
3.25230658e-01 1.65860429e-02 -5.22659600e-01 -3.14577490e-01
-1.12707770e+00 8.07925642e-01 1.89032838e-01 -2.63234284e-02
-8.02639186e-01 -7.22494006e-01 -1.01609099e+00 1.15560129e-01
3.62126797e-01 -1.21668971e+00 1.11125183e+00 -1.10103452e+00
-1.32287765e+00 1.14915323e+00 -3.57296407e-01 -2.78057516e-01
4.60566014e-01 -2.56561309e-01 -8.62813443e-02 4.21580613e-01
9.44717452e-02 7.15275407e-01 1.22794700e+00 -1.47515345e+00
-4.54968810e-01 -3.98489863e-01 3.20439398e-01 5.45518517e-01
-2.47837752e-01 -1.23057969e-01 -4.42672610e-01 -6.24402881e-01
2.18742520e-01 -8.47977996e-01 1.17980562e-01 3.66698205e-01
-3.96681041e-01 -3.07528645e-01 5.07397115e-01 -4.79399174e-01
9.82434690e-01 -2.24191570e+00 4.54420924e-01 1.75555483e-01
2.56620735e-01 -2.24135831e-01 -3.94505531e-01 5.15875995e-01
-1.90481767e-01 -9.67951640e-02 -1.09096713e-01 -5.97392499e-01
1.92978352e-01 1.00370958e-01 -7.12818623e-01 5.31237423e-01
6.86176568e-02 1.06319940e+00 -5.65502346e-01 -6.03282392e-01
1.19275622e-01 5.33850133e-01 -5.52531838e-01 1.84155256e-01
-6.40736073e-02 3.13316673e-01 -1.70258030e-01 8.56648743e-01
4.15630847e-01 -6.40288293e-01 8.80333185e-02 -4.98702943e-01
2.18861267e-01 6.76656589e-02 -8.36776197e-01 2.02812219e+00
-4.35282588e-01 5.42704225e-01 4.67479080e-02 -1.17408383e+00
3.31651658e-01 3.73575568e-01 3.96754861e-01 -8.48145604e-01
8.78644809e-02 -7.78639242e-02 -4.11749959e-01 -5.21882176e-01
4.61630702e-01 -6.30925179e-01 -1.61712289e-01 4.38228756e-01
2.03570232e-01 2.48884633e-01 4.38614488e-02 5.16597033e-01
8.02175224e-01 1.18641168e-01 2.67985374e-01 -3.01369876e-02
4.63710099e-01 -3.89270663e-01 3.18490148e-01 8.97266865e-01
-2.73541600e-01 7.24163294e-01 6.50090516e-01 8.27244148e-02
-1.07138145e+00 -1.46002555e+00 -6.92925900e-02 1.58115339e+00
3.57883066e-01 -2.14562327e-01 -3.37905765e-01 -6.63433671e-01
2.54421085e-02 5.70105851e-01 -6.36910558e-01 -3.58202398e-01
-1.51636362e-01 -3.61659974e-01 5.84885061e-01 6.05058491e-01
2.46231213e-01 -8.13606858e-01 -2.55096853e-01 -3.38078141e-01
-5.40038943e-01 -1.10677552e+00 -6.53114736e-01 6.23575896e-02
-8.31759691e-01 -8.21557105e-01 -8.02220881e-01 -8.34371090e-01
6.08709276e-01 6.11461401e-01 8.62411678e-01 -1.35920733e-01
-9.68217850e-03 1.04068398e+00 -2.34935045e-01 -1.39271058e-02
-1.43154979e-01 -2.20197380e-01 -1.00129262e-01 1.37637675e-01
2.21799791e-01 -5.37003994e-01 -6.43348277e-01 8.78284201e-02
-1.17124581e+00 2.14169994e-01 5.95372438e-01 1.09064126e+00
6.55707717e-01 -4.05107290e-01 4.24849510e-01 -8.05377960e-01
6.28560364e-01 -5.38265109e-01 -1.98394582e-01 8.00749660e-01
-4.93765295e-01 2.29366675e-01 2.32685283e-01 -4.58555877e-01
-1.28190064e+00 -1.21346004e-02 1.52891502e-01 -9.13974226e-01
5.95572144e-02 6.78402364e-01 -1.33814260e-01 1.47097468e-01
4.45338786e-01 2.21597359e-01 2.06820607e-01 -2.87931830e-01
8.92685831e-01 2.84812182e-01 4.53990281e-01 -6.84283733e-01
7.02521205e-01 7.72328377e-01 7.95088857e-02 -7.99929976e-01
-1.10203660e+00 -5.53646266e-01 -3.89938354e-01 -8.98380950e-02
9.58584428e-01 -1.10112441e+00 -7.09132552e-01 3.13977867e-01
-1.10705614e+00 -9.38604623e-02 -3.46678257e-01 3.71812493e-01
-8.24963748e-01 8.65742922e-01 -8.23117197e-01 -7.62890935e-01
-3.09385151e-01 -1.04705584e+00 1.18127501e+00 4.15358692e-02
7.27277920e-02 -1.23219943e+00 1.24072395e-01 8.03755462e-01
2.25848466e-01 -7.57766142e-02 1.18525481e+00 -2.72476703e-01
-5.53370297e-01 1.19989917e-01 -5.87676704e-01 3.26925963e-01
-4.10113744e-02 -1.87149018e-01 -1.09591424e+00 -2.45012343e-01
-4.63589989e-02 -7.95898438e-01 1.36264420e+00 4.73257661e-01
1.00219429e+00 -1.26848191e-01 -9.42305997e-02 5.20963013e-01
1.38693750e+00 -2.67842889e-01 6.75113857e-01 -5.99067938e-03
8.48799884e-01 7.43247390e-01 5.03364205e-01 3.82090330e-01
4.84965503e-01 5.58374166e-01 2.89903492e-01 1.02109155e-02
-1.42110482e-01 -4.13262904e-01 4.24221426e-01 7.90092885e-01
2.10416764e-02 -2.56561577e-01 -6.89858794e-01 5.14265954e-01
-2.27443314e+00 -1.20507741e+00 1.18748277e-01 2.07490301e+00
6.74614668e-01 -7.81209469e-02 1.34778917e-01 -1.78055674e-01
6.79912925e-01 2.73374468e-01 -5.00466108e-01 -3.25102806e-01
-3.46098661e-01 -2.62615055e-01 1.50730178e-01 4.74198997e-01
-1.24866664e+00 6.69811487e-01 6.15453625e+00 1.07139969e+00
-9.67834890e-01 3.82905483e-01 5.68273008e-01 -1.24500096e-01
-6.90459609e-01 2.22899690e-01 -4.35485005e-01 2.72652686e-01
5.25318205e-01 4.08368511e-03 3.62291932e-01 6.51694655e-01
-1.16298953e-02 -2.23689198e-01 -1.25235736e+00 1.20514762e+00
3.41507435e-01 -1.37830544e+00 3.25005680e-01 2.00433284e-02
7.15454817e-01 -2.22981855e-01 4.02432829e-01 2.28467837e-01
-2.37112820e-01 -9.32303905e-01 9.43663895e-01 7.44262099e-01
8.82011414e-01 -5.88356018e-01 3.37319642e-01 1.67594448e-01
-1.22094870e+00 -1.38399169e-01 -1.08891703e-01 1.75102457e-01
2.75054008e-01 3.12396377e-01 -6.53894544e-02 5.66582382e-01
2.60635555e-01 5.40972054e-01 -4.97979522e-01 7.23367393e-01
-6.64648712e-02 3.45546305e-01 -1.19302951e-01 4.95421708e-01
1.94972858e-01 -9.43573788e-02 4.84837830e-01 1.04914129e+00
4.08905856e-02 -7.69639090e-02 7.95862973e-02 8.08506608e-01
-2.15932116e-01 -6.17165193e-02 -6.68784976e-01 -1.92086741e-01
4.30876464e-01 1.03210652e+00 -5.00027597e-01 -1.93876341e-01
-6.96389914e-01 1.00592184e+00 5.09727716e-01 6.09047055e-01
-9.41092968e-01 -3.72118922e-03 4.36739713e-01 -8.82025957e-02
3.11516553e-01 5.68096451e-02 -4.19291764e-01 -1.51981568e+00
7.41054714e-02 -7.56487072e-01 7.75679410e-01 -8.77053261e-01
-1.98182702e+00 2.30698928e-01 2.07592353e-01 -1.24855077e+00
-2.70975351e-01 -4.38328028e-01 -5.29693365e-01 7.49577761e-01
-1.74078453e+00 -1.59916234e+00 -3.86859290e-02 9.18642521e-01
4.30432826e-01 -3.39943394e-02 5.75383067e-01 3.55891168e-01
-3.87953222e-01 6.62696064e-01 1.04955301e-01 -1.71214312e-01
8.65748107e-01 -1.02328634e+00 -4.44275796e-01 7.42605090e-01
1.63897380e-01 7.48145640e-01 6.22538090e-01 -3.04589331e-01
-1.45587957e+00 -6.33846521e-01 7.66564131e-01 -4.51444477e-01
8.27010334e-01 -1.27455428e-01 -9.38821256e-01 8.30199957e-01
4.36510593e-01 5.37790684e-03 9.42198396e-01 2.53233671e-01
-8.36959124e-01 6.06304146e-02 -9.19038415e-01 5.92290878e-01
9.38475907e-01 -1.08626556e+00 -7.77696311e-01 4.25734133e-01
6.17129982e-01 -8.40931609e-02 -7.60384083e-01 4.96762186e-01
6.74910963e-01 -9.05509233e-01 1.12331152e+00 -7.25257456e-01
8.82966042e-01 -4.02711667e-02 -5.48516929e-01 -6.76174462e-01
-3.32797378e-01 -4.10264313e-01 -3.58820409e-01 1.18122518e+00
2.40118027e-01 -3.38345021e-01 4.65487927e-01 6.74133301e-01
7.00093135e-02 -7.87051857e-01 -1.05753422e+00 -5.99731266e-01
2.89436460e-01 -4.89135891e-01 -1.13641813e-01 9.81971204e-01
8.39902163e-02 7.72631347e-01 -7.16296256e-01 -2.74601132e-02
8.64307523e-01 3.48331541e-01 4.66602832e-01 -9.59595203e-01
-5.00382185e-01 -5.57515264e-01 -1.37662798e-01 -1.26962793e+00
3.72528017e-01 -8.95633221e-01 -9.48936194e-02 -1.52173162e+00
8.45529079e-01 -3.91285159e-02 -5.40954828e-01 4.23316330e-01
-1.44131452e-01 1.90270558e-01 4.95896846e-01 4.96357381e-01
-1.01582551e+00 8.01888824e-01 1.08794904e+00 -1.24975227e-01
2.35728808e-02 -2.60804594e-01 -7.16181517e-01 7.38747120e-01
4.11287665e-01 -2.61794269e-01 -6.63814247e-01 -4.08451259e-01
4.07071024e-01 2.09826797e-01 6.91498280e-01 -3.98522109e-01
2.08371639e-01 -2.78675199e-01 7.69772902e-02 -4.95284557e-01
6.68201327e-01 -7.27899909e-01 -2.51221001e-01 5.34403063e-02
-8.03824544e-01 -2.45988414e-01 -1.24219477e-01 9.39395666e-01
-3.20001066e-01 -1.85129732e-01 8.49711299e-01 1.25942543e-01
-8.93271685e-01 1.77802637e-01 -2.40429610e-01 1.46107957e-01
7.97132850e-01 -1.12035871e-01 -5.39190054e-01 -6.00182950e-01
-7.64651299e-01 1.82606295e-01 3.39421183e-01 4.93519247e-01
7.45530903e-01 -1.74050748e+00 -4.51701880e-01 -5.16602164e-03
4.79595989e-01 -6.38922632e-01 6.25183105e-01 1.36449373e+00
-9.50315446e-02 4.09603626e-01 -6.45744428e-02 -8.36805284e-01
-1.40223396e+00 6.85730278e-01 1.37242705e-01 -3.10594529e-01
-4.72895563e-01 7.91993499e-01 7.51162052e-01 -2.52951473e-01
3.58314574e-01 -3.76234353e-02 2.99212635e-02 2.31315345e-01
3.58542532e-01 2.73545265e-01 -3.85313243e-01 -8.42784941e-01
-3.83216500e-01 5.90587676e-01 -1.84993863e-01 -3.96095008e-01
9.69383001e-01 -4.88206595e-01 -1.01257958e-01 7.70217240e-01
1.43026543e+00 -2.76574254e-01 -1.24768996e+00 -5.65021396e-01
-1.21365540e-01 -4.33813840e-01 -4.76792513e-04 -5.87242424e-01
-7.62223899e-01 1.00849354e+00 7.23197281e-01 2.59227633e-01
1.31476355e+00 3.66633475e-01 6.50900900e-01 3.00482631e-01
4.64450754e-02 -1.04527819e+00 5.55805206e-01 4.87598896e-01
8.60130310e-01 -1.42635417e+00 3.50450836e-02 -2.83304930e-01
-9.79449749e-01 8.80275428e-01 3.86085302e-01 1.02879472e-01
6.66657090e-01 -2.76381165e-01 -1.02431953e-01 -2.60725200e-01
-1.02518225e+00 -4.19399619e-01 7.67667174e-01 3.15269053e-01
4.81596380e-01 -1.96073845e-01 -2.41153553e-01 3.34467947e-01
4.65328246e-01 -1.11800671e-01 8.67023990e-02 1.01045585e+00
-3.63062263e-01 -9.25181746e-01 -3.85545075e-01 2.41618440e-01
-3.66633594e-01 -6.10525906e-02 -2.65493870e-01 5.07411420e-01
-1.25348583e-01 9.29572999e-01 -8.26409683e-02 -1.83434375e-02
1.06501788e-01 2.73949236e-01 8.34002495e-01 -4.17298824e-01
-7.87144974e-02 4.18088138e-01 -1.65629506e-01 -4.79331344e-01
-8.27816129e-01 -5.69090009e-01 -1.04741800e+00 -1.98448077e-01
-4.27684486e-01 -2.19140366e-01 4.33714241e-01 1.09533930e+00
3.84482324e-01 2.95029163e-01 4.61083740e-01 -7.07390070e-01
-8.45749021e-01 -7.25399494e-01 -5.36890805e-01 8.34849358e-01
2.75642306e-01 -8.34191024e-01 -5.45813978e-01 2.15274006e-01] | [10.684000968933105, 1.4462110996246338] |
39c31d64-6641-4934-a4cf-702ea9ba371a | hierarchical-graph-neural-networks-for-1 | 2306.15858 | null | https://arxiv.org/abs/2306.15858v1 | https://arxiv.org/pdf/2306.15858v1.pdf | Hierarchical Graph Neural Networks for Proprioceptive 6D Pose Estimation of In-hand Objects | Robotic manipulation, in particular in-hand object manipulation, often requires an accurate estimate of the object's 6D pose. To improve the accuracy of the estimated pose, state-of-the-art approaches in 6D object pose estimation use observational data from one or more modalities, e.g., RGB images, depth, and tactile readings. However, existing approaches make limited use of the underlying geometric structure of the object captured by these modalities, thereby, increasing their reliance on visual features. This results in poor performance when presented with objects that lack such visual features or when visual features are simply occluded. Furthermore, current approaches do not take advantage of the proprioceptive information embedded in the position of the fingers. To address these limitations, in this paper: (1) we introduce a hierarchical graph neural network architecture for combining multimodal (vision and touch) data that allows for a geometrically informed 6D object pose estimation, (2) we introduce a hierarchical message passing operation that flows the information within and across modalities to learn a graph-based object representation, and (3) we introduce a method that accounts for the proprioceptive information for in-hand object representation. We evaluate our model on a diverse subset of objects from the YCB Object and Model Set, and show that our method substantially outperforms existing state-of-the-art work in accuracy and robustness to occlusion. We also deploy our proposed framework on a real robot and qualitatively demonstrate successful transfer to real settings. | ['Nawid Jamali', 'Soshi Iba', 'Snehal Dikhale', 'Alireza Rezazadeh'] | 2023-06-28 | null | null | null | null | ['pose-estimation', '6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.04058200e-01 -1.94747373e-01 -1.65034756e-01 -3.01226061e-02
-4.07594770e-01 -6.43743634e-01 2.83389956e-01 1.70776322e-01
-1.94873422e-01 4.65195209e-01 -1.23549737e-01 8.41255710e-02
-4.03882146e-01 -6.39573634e-01 -9.97150421e-01 -3.55882734e-01
-1.23045132e-01 6.51572645e-01 3.47222000e-01 -9.09752175e-02
4.66648370e-01 9.14590836e-01 -1.72026932e+00 7.12568015e-02
3.98804218e-01 1.45310080e+00 5.20589054e-01 7.06599891e-01
5.58551550e-02 5.63373864e-01 -4.88983363e-01 2.60297239e-01
5.51888347e-01 5.45019954e-02 -5.73224664e-01 3.40165555e-01
5.57550728e-01 -8.48438978e-01 -6.31756902e-01 8.95535946e-01
3.76463979e-01 1.24845341e-01 7.16858625e-01 -1.38422906e+00
-5.60270905e-01 1.84092253e-01 -5.32237947e-01 -4.64710355e-01
6.41712189e-01 2.91034907e-01 6.67582393e-01 -9.79189754e-01
7.74579167e-01 1.42628360e+00 3.42304349e-01 4.53782350e-01
-1.05674982e+00 -3.11118245e-01 4.11265880e-01 1.29102185e-01
-1.21045601e+00 -1.78868383e-01 9.21460450e-01 -4.92833704e-01
1.05295050e+00 1.01490535e-01 7.90772915e-01 1.08471727e+00
1.21730499e-01 6.72680736e-01 1.06380022e+00 -5.20269632e-01
1.44052222e-01 -9.29390192e-02 -3.15427966e-02 6.99096262e-01
3.96927655e-01 2.06171244e-01 -8.35981488e-01 -1.29676312e-01
1.30045033e+00 2.55421191e-01 -2.82440662e-01 -1.16156971e+00
-1.43524373e+00 2.86904067e-01 8.08924317e-01 -5.04642799e-02
-5.22530079e-01 4.61920112e-01 4.43997942e-02 9.44801718e-02
1.30445305e-02 4.29184198e-01 -2.22563520e-01 -2.08459064e-01
-6.96947873e-02 3.05729359e-01 9.27429616e-01 1.46291113e+00
7.19864428e-01 -2.21342489e-01 7.43460581e-02 5.64012885e-01
6.95114553e-01 6.56182766e-01 1.19213946e-03 -1.22979105e+00
7.44523525e-01 6.84809148e-01 4.43164080e-01 -1.09752262e+00
-4.12028909e-01 9.32944417e-02 -3.14327478e-01 7.52932250e-01
5.75795293e-01 2.17094928e-01 -1.15261364e+00 1.58917439e+00
4.11253244e-01 -3.61794740e-01 -1.74320817e-01 1.27010858e+00
7.69032359e-01 1.45871341e-01 -1.97660312e-01 2.32276171e-01
1.12944269e+00 -6.54502630e-01 -6.27945423e-01 -2.04560369e-01
1.73097868e-02 -5.40016830e-01 8.88921261e-01 5.20067394e-01
-1.03346932e+00 -5.11203051e-01 -1.21315956e+00 -2.18692556e-01
-6.44779205e-01 4.67580147e-02 7.12534785e-01 2.23393992e-01
-8.27215374e-01 5.56799889e-01 -1.08378720e+00 -6.55416787e-01
1.11307748e-01 7.34115481e-01 -5.37180603e-01 -2.01470017e-01
-6.01161778e-01 1.25980103e+00 4.56952095e-01 4.07446057e-01
-9.15182650e-01 -1.04048297e-01 -8.19637835e-01 -2.02462137e-01
6.39304698e-01 -7.75389194e-01 1.08413780e+00 -3.43148649e-01
-1.76274288e+00 5.85567415e-01 1.20824546e-01 8.92520323e-02
5.86360276e-01 -4.81562048e-01 3.90538990e-01 4.10270900e-01
-3.77087831e-01 7.90979505e-01 1.01082826e+00 -1.84452009e+00
-2.09561750e-01 -8.75225902e-01 4.05823559e-01 5.33686996e-01
-2.38702789e-01 -5.07037401e-01 -5.46239376e-01 -3.23220521e-01
6.99647427e-01 -1.05771327e+00 3.27482149e-02 6.72334611e-01
-5.05276263e-01 -6.72098324e-02 1.08885252e+00 -4.10680890e-01
3.64080071e-01 -1.92545927e+00 5.82967579e-01 3.79259169e-01
2.92979568e-01 -1.76525973e-02 -6.86417744e-02 6.47902131e-01
5.31914413e-01 -2.81952888e-01 2.42060155e-01 -3.42416704e-01
1.65398046e-01 3.82600635e-01 -7.22269788e-02 4.83550817e-01
1.09823674e-01 7.87451327e-01 -8.21269810e-01 -3.63045305e-01
7.07300425e-01 6.28147364e-01 -4.32045043e-01 3.78853470e-01
-3.61444116e-01 5.96722722e-01 -6.85690641e-01 9.11031544e-01
4.75876927e-01 -1.92097679e-01 2.32493028e-01 -5.02845168e-01
6.25138730e-02 2.39508048e-01 -1.34192348e+00 2.14154911e+00
-3.05372924e-01 1.65488228e-01 2.91273773e-01 -7.65478790e-01
7.98314393e-01 2.44696394e-01 5.43175936e-01 -3.07877749e-01
2.17698783e-01 3.05609375e-01 -8.39715675e-02 -6.61639810e-01
4.29802656e-01 3.63667637e-01 1.09862849e-01 2.33690456e-01
4.78099994e-02 -6.71189964e-01 -2.31717229e-01 -1.17922112e-01
1.04128706e+00 6.05685353e-01 1.95341274e-01 3.43102753e-01
2.10634694e-02 7.62317702e-02 -7.53777623e-02 9.06471789e-01
-1.79614797e-01 7.74670184e-01 7.87812546e-02 -9.92980134e-03
-1.01902378e+00 -1.29110789e+00 5.98318577e-02 7.37644792e-01
6.52123034e-01 -6.30464777e-02 -2.66962320e-01 -3.60854059e-01
6.28290057e-01 2.28489101e-01 -5.34095943e-01 -3.31943296e-02
-5.78345060e-01 -1.09442718e-01 9.27944854e-02 8.70278776e-01
3.53594363e-01 -9.98792171e-01 -1.01273346e+00 1.13866642e-01
1.57508887e-02 -1.23361742e+00 9.71682668e-02 1.50813624e-01
-1.10055852e+00 -1.03379953e+00 -4.92140025e-01 -5.80433488e-01
8.17741930e-01 4.53007370e-01 6.12467468e-01 -2.01350734e-01
-3.64076644e-01 1.00006711e+00 -3.82514656e-01 -4.22774851e-01
-1.58599973e-01 -1.44503132e-01 1.91384867e-01 -4.14204121e-01
-2.36379877e-02 -5.38313210e-01 -5.86540163e-01 2.73084283e-01
-5.70171177e-01 -3.15427296e-02 6.86385810e-01 5.97359359e-01
4.81616944e-01 -4.16941553e-01 1.78654850e-01 -5.82015291e-02
4.02091920e-01 -7.87956864e-02 -6.01846814e-01 8.58280659e-02
-1.94904432e-01 -4.87330817e-02 -2.64564324e-02 -7.59175301e-01
-8.62403095e-01 3.21611702e-01 3.09685379e-01 -7.91336954e-01
-2.47250393e-01 5.89046121e-01 -5.99275641e-02 -5.68257570e-01
4.25210059e-01 -1.94172859e-01 3.27474594e-01 -5.37101567e-01
3.84172320e-01 7.64746845e-01 5.36029458e-01 -7.14188755e-01
5.70429564e-01 5.77653587e-01 4.40793335e-01 -6.12831652e-01
-2.54729360e-01 -3.47564906e-01 -9.04036164e-01 -4.49683279e-01
6.15828753e-01 -6.75516844e-01 -1.41567743e+00 5.87953687e-01
-1.33517003e+00 -2.38271698e-01 -7.67525807e-02 8.79897356e-01
-9.37287271e-01 2.85794675e-01 -6.97950959e-01 -1.21225226e+00
6.90625831e-02 -1.19114006e+00 1.44133091e+00 -4.13167290e-02
-1.76985458e-01 -5.24046540e-01 -5.42974055e-01 2.46524036e-01
2.82888889e-01 5.01647770e-01 8.60875666e-01 -1.78260833e-01
-1.12836599e+00 -5.17896116e-01 -3.46529961e-01 -3.42288427e-02
4.42776173e-01 -2.77382821e-01 -8.05237055e-01 -3.82510245e-01
-1.66950539e-01 -6.64629817e-01 4.55719411e-01 2.17540845e-01
1.07523644e+00 -5.05963601e-02 -5.10802746e-01 2.25564018e-01
1.39608729e+00 1.27164155e-01 2.03773245e-01 3.06248516e-01
1.10414362e+00 8.09138656e-01 8.38558555e-01 4.35611486e-01
2.88928926e-01 8.98862898e-01 1.18829167e+00 3.02978218e-01
-1.55410990e-01 -2.76113749e-01 1.17366329e-01 6.41782880e-01
-4.11156327e-01 -2.88292497e-01 -8.18444550e-01 2.73794889e-01
-2.05492806e+00 -3.89877230e-01 1.96500942e-01 2.33324242e+00
5.75710535e-01 1.70857653e-01 -8.50434899e-02 1.60253659e-01
4.93316442e-01 -1.61010146e-01 -9.19383705e-01 3.31854932e-02
2.71608442e-01 -8.89764801e-02 5.28489590e-01 4.09470618e-01
-7.99423516e-01 7.71120131e-01 6.09451818e+00 1.64043903e-01
-1.07071722e+00 -2.45468572e-01 -5.05002320e-01 -1.64192736e-01
1.50496766e-01 -2.14710563e-01 -4.79932427e-01 -6.81998357e-02
4.23629582e-02 3.39797914e-01 6.75819457e-01 8.43474746e-01
-1.83315679e-01 -3.80617291e-01 -1.64309382e+00 1.01124120e+00
2.77859002e-01 -7.54958510e-01 -3.51172984e-02 1.06734842e-01
2.23876446e-01 7.66451238e-03 6.85434267e-02 -7.19075128e-02
1.60347894e-01 -9.00651157e-01 9.67876434e-01 6.18505359e-01
6.37431860e-01 -1.92084879e-01 3.57434750e-01 5.46810389e-01
-9.71267402e-01 -1.60376027e-01 -1.72792733e-01 -3.62733722e-01
1.75729189e-02 5.60940728e-02 -1.04930162e+00 5.25091231e-01
8.00606668e-01 4.80864763e-01 -2.85816878e-01 1.06752765e+00
-1.74462572e-01 -1.68767348e-01 -7.27988839e-01 -3.15930009e-01
-4.87788357e-02 1.46589041e-01 7.86691368e-01 5.57238102e-01
1.81965292e-01 9.59951505e-02 3.77035201e-01 9.75293338e-01
1.61006272e-01 -3.71992290e-01 -8.61072540e-01 -4.99119200e-02
5.91187596e-01 7.84753680e-01 -6.56668842e-01 -1.43564925e-01
-3.34964901e-01 8.86286795e-01 4.44970548e-01 6.69491112e-01
-3.41760665e-01 -3.07748079e-01 5.02358556e-01 -1.48495674e-01
4.73902196e-01 -9.15046513e-01 -4.12413418e-01 -9.58464324e-01
5.26536644e-01 -6.16956532e-01 -9.87685323e-02 -1.18357396e+00
-1.29917490e+00 1.63706079e-01 2.87302941e-01 -1.35130143e+00
-4.28164303e-01 -1.17160356e+00 2.15974852e-01 8.85482907e-01
-1.24797368e+00 -1.34255672e+00 -7.86123812e-01 5.01191318e-01
2.72660792e-01 3.07204694e-01 9.40906584e-01 -1.82193145e-01
1.96747363e-01 2.81154383e-02 -2.37552822e-01 1.15858885e-02
6.74482107e-01 -1.02971089e+00 7.87836872e-03 1.21080466e-01
-1.39509439e-01 7.89553761e-01 5.57421863e-01 -7.28572071e-01
-2.27501631e+00 -4.26550686e-01 1.05683975e-01 -7.41848826e-01
5.17994821e-01 -6.45321548e-01 -6.65204346e-01 8.43040228e-01
-2.52768904e-01 1.12758934e-01 -2.96118241e-02 2.04547960e-02
-3.21987629e-01 3.13410945e-02 -1.22567976e+00 5.52199781e-01
1.39474607e+00 -6.44954383e-01 -7.75319219e-01 2.94743627e-01
5.49299896e-01 -1.13229084e+00 -9.50824738e-01 6.67586386e-01
1.18765450e+00 -7.28469789e-01 1.15880287e+00 -5.21040440e-01
3.09485883e-01 -4.33399677e-01 -4.09125656e-01 -1.09966063e+00
-9.79862511e-02 -8.24472159e-02 -5.02195835e-01 7.69246757e-01
-5.97283095e-02 -6.26022458e-01 7.28523016e-01 6.29073322e-01
2.28489023e-02 -6.52518988e-01 -9.80826259e-01 -8.45886528e-01
-2.52487123e-01 -4.69449252e-01 4.11821574e-01 5.55145800e-01
1.78161591e-01 -5.63883446e-02 -2.52964467e-01 4.75044161e-01
6.78477168e-01 3.25914562e-01 1.02998102e+00 -1.24652100e+00
-2.02082545e-01 -1.73910260e-01 -7.84263372e-01 -1.25941861e+00
-2.58055539e-03 -4.46288645e-01 4.54999745e-01 -1.62525320e+00
6.70574084e-02 -4.67356086e-01 -1.12214848e-01 5.46993375e-01
1.96043670e-01 3.32479328e-01 5.06831408e-01 3.69596064e-01
-3.44862580e-01 5.49739361e-01 1.56217051e+00 -1.24558106e-01
-2.10673392e-01 -2.50556201e-01 -1.26128504e-02 6.69239104e-01
4.90476787e-01 -1.63408756e-01 -8.93862024e-02 -7.52271950e-01
1.54472375e-02 3.30544174e-01 8.92214179e-01 -9.17956769e-01
2.84712434e-01 -2.36948967e-01 6.40957117e-01 -6.63110077e-01
1.03667724e+00 -1.34730005e+00 -2.74756383e-02 4.71741647e-01
-2.63261408e-01 -6.98155239e-02 2.26254910e-01 8.05871606e-01
1.02346219e-01 -1.04045026e-01 1.86103746e-01 -2.66973585e-01
-6.69973493e-01 2.47071847e-01 -2.04802871e-01 -5.77428341e-01
8.33575726e-01 -5.65213084e-01 -2.99924403e-01 -4.06504959e-01
-7.45365858e-01 1.63202852e-01 6.98976040e-01 6.88454449e-01
7.65213192e-01 -1.37585533e+00 -1.46242186e-01 2.61091918e-01
4.45348144e-01 2.95123219e-01 -1.96249351e-01 8.59578192e-01
-4.74311531e-01 3.28161687e-01 -4.24627274e-01 -1.09806383e+00
-1.12404132e+00 5.23200512e-01 1.40204236e-01 3.27299446e-01
-5.19905925e-01 4.91187692e-01 -2.59604622e-02 -5.92159271e-01
6.73939288e-01 -6.23324811e-01 1.54927433e-01 -4.20509547e-01
-9.15837139e-02 3.23315263e-01 -3.64352390e-02 -5.18449962e-01
-3.56563061e-01 9.79680419e-01 1.85012221e-01 -3.10163826e-01
1.12520421e+00 -1.83317870e-01 -4.94732969e-02 7.35018611e-01
9.52240288e-01 -2.80453175e-01 -1.49606848e+00 -2.87683129e-01
-3.14046413e-01 -7.34071493e-01 -1.30857959e-01 -8.63872647e-01
-6.45324111e-01 9.44279611e-01 5.92439592e-01 8.93658698e-02
7.92124212e-01 1.45126775e-01 4.11223352e-01 9.44444299e-01
9.73858953e-01 -9.79813159e-01 4.06609952e-01 5.41629791e-01
1.30317140e+00 -1.44646168e+00 1.32379666e-01 -8.35250020e-01
-1.63994297e-01 1.33629513e+00 6.96144521e-01 -2.78389528e-02
4.66088980e-01 2.70924419e-01 4.16877586e-03 -3.41947198e-01
-3.39571655e-01 -2.32361093e-01 3.71717691e-01 7.96235383e-01
-5.54527622e-03 -3.64833176e-02 2.24074647e-01 -3.71306250e-03
5.15674092e-02 1.25396937e-01 5.62012419e-02 1.75212288e+00
-3.61130953e-01 -7.19598114e-01 -5.26325107e-01 2.56403953e-01
-1.10667916e-02 4.27415401e-01 -6.86788440e-01 9.79381084e-01
-1.26383439e-01 8.06300879e-01 -9.15726498e-02 -3.57011110e-01
5.98832488e-01 -1.18327849e-01 1.45493174e+00 -6.73653185e-01
-2.14960694e-01 -5.05444705e-02 -8.81010145e-02 -8.31345141e-01
-6.17091835e-01 -5.47706842e-01 -1.23405528e+00 8.07276219e-02
-5.59671104e-01 -5.69349408e-01 1.15856898e+00 9.00267959e-01
3.40544522e-01 4.38313693e-01 8.44369829e-02 -1.78271008e+00
-8.15681994e-01 -1.07611990e+00 -5.92812419e-01 4.43839580e-01
4.88510221e-01 -1.38653791e+00 -2.07669884e-01 -2.28883088e-01] | [5.9403839111328125, -0.8989889621734619] |
5c06c566-8072-49cc-826e-cfa488ec634e | exploring-techniques-for-the-analysis-of | 2205.13963 | null | https://arxiv.org/abs/2205.13963v1 | https://arxiv.org/pdf/2205.13963v1.pdf | Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications | This paper studies the utility of using data analytics and machine learning techniques for identifying, classifying, and characterizing the dynamics of large-scale parallel (MPI) programs. To this end, we run microbenchmarks and realistic proxy applications with the regular compute-communicate structure on two different supercomputing platforms and choose the per-process performance and MPI time per time step as relevant observables. Using principal component analysis, clustering techniques, correlation functions, and a new "phase space plot," we show how desynchronization patterns (or lack thereof) can be readily identified from a data set that is much smaller than a full MPI trace. Our methods also lead the way towards a more general classification of parallel program dynamics. | ['Stefano Markidis', 'Gerhard Wellein', 'Georg Hager', 'Ayesha Afzal'] | 2022-05-27 | null | null | null | null | ['classification'] | ['methodology'] | [-4.47772861e-01 -8.22508812e-01 -2.13995293e-01 -8.25099945e-02
-6.24023438e-01 -5.91343462e-01 8.21624875e-01 6.85609937e-01
-1.72312275e-01 5.27265370e-01 1.34719342e-01 -4.68326896e-01
-4.13051277e-01 -6.95242465e-01 -2.12142214e-01 -1.12406957e+00
-8.50119233e-01 8.62138748e-01 4.28324461e-01 -6.41037896e-02
6.28796279e-01 5.87114275e-01 -1.05297995e+00 1.94028124e-01
3.84764105e-01 3.78067911e-01 -1.24756634e-01 9.73377526e-01
5.39513528e-02 9.49571371e-01 -4.19871539e-01 5.26712060e-01
-6.72207400e-02 -2.09077582e-01 -1.08347154e+00 -3.99909407e-01
-2.03186333e-01 1.91595539e-01 -4.48476017e-01 8.19076777e-01
3.42165709e-01 1.73292216e-02 3.19931626e-01 -1.30087197e+00
2.57405460e-01 5.33975184e-01 -6.91755712e-01 8.15104723e-01
1.98554933e-01 2.27770492e-01 5.57364762e-01 -3.03045422e-01
6.97164595e-01 9.38660920e-01 7.37943769e-01 -1.81306869e-01
-1.99108720e+00 -2.98903346e-01 -4.62747127e-01 -2.98092160e-02
-1.56609690e+00 -2.95339972e-01 8.27487528e-01 -9.57236767e-01
9.91671622e-01 6.00058556e-01 5.52667797e-01 6.45840108e-01
7.98128426e-01 8.91830698e-02 1.69288671e+00 -3.72975111e-01
5.67172766e-01 -8.37231278e-02 9.82589185e-01 3.07578772e-01
5.78785781e-03 3.72232676e-01 -5.17312586e-01 -1.03763020e+00
4.28742677e-01 -1.53958738e-01 -2.63709188e-01 -5.30252695e-01
-1.75287592e+00 5.80192149e-01 -1.57892585e-01 3.53869528e-01
-2.42252633e-01 1.50912508e-01 8.55569601e-01 5.99609315e-01
4.28552389e-01 6.91056967e-01 -7.80319333e-01 -6.74817860e-01
-1.01295841e+00 3.78390580e-01 1.35054255e+00 2.86646396e-01
1.03488708e+00 -1.16304725e-01 2.39766240e-01 1.69265553e-01
-7.64576122e-02 2.61255592e-01 5.21491110e-01 -1.21763241e+00
-2.38110095e-01 3.62879097e-01 1.23344012e-01 -8.10537219e-01
-8.24440062e-01 -2.34470498e-02 -7.87055492e-01 3.33956540e-01
4.73070025e-01 -1.83580682e-01 -3.23047265e-02 1.15912747e+00
4.69269753e-01 3.51966530e-01 3.27108912e-02 5.35054922e-01
1.95432201e-01 7.63480425e-01 -6.69058859e-02 -4.04871225e-01
1.15713799e+00 -5.95541298e-01 -1.18461750e-01 4.61923629e-01
8.42656732e-01 -8.06844473e-01 8.68172824e-01 1.87129587e-01
-6.00734293e-01 -4.74194705e-01 -6.96440816e-01 4.05971199e-01
5.83454855e-02 -3.24075341e-01 1.07504225e+00 4.46428806e-01
-1.15595150e+00 1.13095319e+00 -1.84655213e+00 -2.83915967e-01
-4.77305859e-01 3.33901972e-01 -1.82424217e-01 4.64753509e-01
-2.63379425e-01 3.87358040e-01 2.38002598e-01 -6.21826708e-01
-7.25782156e-01 -1.02572417e+00 -2.25125015e-01 3.17512043e-02
-5.40094543e-03 -6.00157142e-01 9.74197388e-01 -6.50508881e-01
-1.32822871e+00 7.65190780e-01 -2.15303138e-01 -3.30614932e-02
1.17100313e-01 2.50942498e-01 -2.99240589e-01 -1.74371496e-01
9.96146817e-03 -3.96495074e-01 4.75250244e-01 -1.11101913e+00
-5.37863895e-02 -4.92747754e-01 -3.90787303e-01 -1.86039582e-01
-3.40742432e-02 6.17418706e-01 5.01368232e-02 -1.64546594e-01
1.77341774e-01 -1.19141793e+00 -2.73501694e-01 -1.04819882e+00
-5.71847916e-01 -1.88057885e-01 7.00034976e-01 -4.18199450e-01
1.21833622e+00 -2.17328787e+00 5.77305436e-01 5.36289215e-01
6.97731674e-01 -6.13374054e-01 -8.34579580e-03 9.20488119e-01
-2.29265824e-01 1.88988000e-01 3.11975181e-01 5.25942892e-02
-5.36700934e-02 9.49276984e-02 -2.45875701e-01 8.26468289e-01
-3.28884304e-01 1.73453525e-01 -7.49515057e-01 -2.40718260e-01
-5.14132790e-02 -9.11394283e-02 -4.64713275e-01 3.09393525e-01
-6.25011250e-02 1.00899875e+00 -6.01368546e-01 4.19788867e-01
4.48226094e-01 -6.78951263e-01 5.79072714e-01 2.16289669e-01
-7.39581704e-01 4.68392432e-01 -9.84571338e-01 1.16594493e+00
-2.57002562e-01 8.63414586e-01 1.83089077e-01 -9.43612158e-01
6.23355329e-01 1.21356070e-01 9.17050779e-01 -4.40829515e-01
-2.28625555e-02 -6.28980324e-02 2.82335043e-01 -3.99922282e-01
4.37134683e-01 1.85412824e-01 -1.85636923e-01 1.02078700e+00
-3.38841110e-01 -1.66738015e-02 2.49055430e-01 2.31034487e-01
1.86604130e+00 -2.71614939e-01 2.22303271e-01 -1.13168049e+00
2.35977009e-01 4.97592360e-01 5.01563251e-01 6.76118016e-01
-1.68118045e-01 -8.24721232e-02 1.31448913e+00 -7.13342667e-01
-1.34966850e+00 -9.54089224e-01 -3.39250445e-01 1.36897576e+00
4.12281156e-02 -9.81823921e-01 -6.05262518e-01 6.06070496e-02
2.44608045e-01 3.39387715e-01 -4.88059640e-01 2.63140500e-01
-8.95063758e-01 -1.43693364e+00 2.83596069e-01 1.55746043e-01
-1.38165742e-01 -6.93477213e-01 -3.91886204e-01 1.01015262e-01
3.08461905e-01 -6.36735678e-01 -7.86864981e-02 2.91608810e-01
-1.05540776e+00 -1.31199801e+00 1.24111727e-01 -4.09526050e-01
2.69351244e-01 1.74242184e-01 1.43258393e+00 6.37734383e-02
-1.93013415e-01 2.91119188e-01 1.77999884e-02 3.69442344e-01
-8.04213941e-01 9.45611820e-02 2.57936716e-01 -3.34397823e-01
1.78121060e-01 -1.12187493e+00 -3.80985856e-01 3.74281615e-01
-4.67234433e-01 -1.54672442e-02 5.88226914e-02 7.38500118e-01
6.89451337e-01 2.71282732e-01 -2.09127277e-01 -8.43256712e-01
8.43430281e-01 -1.01400375e+00 -1.07248104e+00 -1.04972735e-01
-9.87207234e-01 1.57019466e-01 7.30066657e-01 -5.06581485e-01
-7.21154690e-01 -1.48139983e-01 2.99724489e-01 -2.59805679e-01
-2.73165464e-01 6.87952578e-01 2.67156720e-01 -2.95954734e-01
8.29441488e-01 4.12078112e-01 8.50098506e-02 -7.13412344e-01
-3.51259150e-02 3.73023123e-01 3.54218572e-01 -1.21660876e+00
4.82390523e-01 4.65101719e-01 1.39829859e-01 -1.02165341e+00
2.76691049e-01 -6.95319533e-01 -6.52084649e-01 1.25923857e-01
5.16965985e-01 -7.20255911e-01 -1.35909212e+00 3.96452188e-01
-8.45338047e-01 -6.36087179e-01 -1.10068042e-02 5.83888829e-01
-5.72698295e-01 4.94920194e-01 -1.21301222e+00 -4.33317035e-01
-6.07763343e-02 -1.06930792e+00 8.67185116e-01 8.58919472e-02
-4.85472798e-01 -1.02070963e+00 1.18908942e+00 1.17801033e-01
8.45536232e-01 2.27140546e-01 1.14722109e+00 -7.89086223e-01
-7.41405487e-01 4.65075634e-02 -1.73245475e-01 -4.86845255e-01
-3.82296503e-01 5.53237140e-01 -6.18407547e-01 -6.39049888e-01
4.24868464e-01 2.23812237e-01 2.82237887e-01 1.81984335e-01
1.11762655e+00 -2.73486704e-01 -5.51846445e-01 8.43378663e-01
1.51679587e+00 1.15665697e-01 1.60093591e-01 5.99064589e-01
7.48707950e-01 4.61031049e-01 -1.04798831e-01 5.99654019e-01
2.77661413e-01 7.64778733e-01 -3.51743579e-01 3.61716896e-01
2.85069585e-01 3.07865530e-01 4.29982007e-01 1.38481367e+00
-3.23044181e-01 4.00197834e-01 -1.74059367e+00 4.12849724e-01
-1.71329796e+00 -1.07460427e+00 -6.83490634e-01 2.17893147e+00
7.89330781e-01 7.45180249e-03 3.33665043e-01 -1.60093471e-01
5.22728622e-01 1.60664678e-01 -3.09158683e-01 -3.13740581e-01
1.68503433e-01 2.79835552e-01 5.61555326e-01 3.30057353e-01
-1.05032158e+00 4.82768714e-01 7.25616741e+00 6.44019485e-01
-1.42200208e+00 3.18465620e-01 4.81542557e-01 1.48580879e-01
2.41098851e-02 5.02922237e-01 -2.99452066e-01 5.62353432e-01
1.67571414e+00 -6.32272422e-01 8.41827691e-01 9.71796930e-01
5.01325011e-01 -4.41884488e-01 -1.39839220e+00 7.53863454e-01
-6.44249499e-01 -1.47357762e+00 -8.63927543e-01 2.47115254e-01
7.60851502e-01 8.68892610e-01 -2.80672848e-01 1.16821602e-01
6.97538733e-01 -7.23461270e-01 2.99615294e-01 4.21680391e-01
3.63013297e-01 -3.78865063e-01 5.28986573e-01 5.02520740e-01
-1.07151294e+00 1.50712296e-01 -8.61460567e-02 -6.03928626e-01
-3.15059096e-01 7.39118397e-01 -4.26299870e-01 3.36111188e-01
6.30413830e-01 4.79917020e-01 -6.67203307e-01 9.05804873e-01
4.59436983e-01 9.80641425e-01 -4.11516368e-01 4.63795029e-02
-2.73058265e-01 -4.58569497e-01 6.58119023e-01 1.22119880e+00
5.92943914e-02 7.24760257e-03 3.45777988e-01 8.77830684e-01
2.88648993e-01 5.86025640e-02 -3.34962875e-01 -1.38027087e-01
5.35600543e-01 1.26625836e+00 -1.07659960e+00 -4.31832105e-01
-3.03550154e-01 4.13607150e-01 1.30105585e-01 4.69737142e-01
-2.67232031e-01 -4.48064990e-02 1.08776057e+00 1.31098390e-01
-2.92881221e-01 -7.69633830e-01 -3.67084831e-01 -1.46171260e+00
-2.93060780e-01 -8.62340033e-01 4.42490950e-02 -3.59132499e-01
-1.48428178e+00 4.34445024e-01 2.10265014e-02 -6.43501937e-01
-4.53528047e-01 -1.67936996e-01 -1.13897693e+00 8.90920579e-01
-7.76661515e-01 -4.66897488e-01 -9.99359488e-02 5.14859438e-01
-7.89556280e-02 -2.35741660e-01 9.75517452e-01 1.51146101e-02
-8.23458254e-01 -6.19551502e-02 1.17980587e+00 -1.10886842e-02
4.41100538e-01 -1.41825628e+00 3.71367395e-01 4.46828604e-01
-2.18973190e-01 8.65549088e-01 1.06401217e+00 -7.23688662e-01
-2.19828510e+00 -7.41537511e-01 3.71188730e-01 -8.06930661e-01
1.52142131e+00 -2.25899577e-01 -1.13144004e+00 7.28118598e-01
2.91186959e-01 2.72718787e-01 6.26035690e-01 6.90300226e-01
-2.42399201e-01 -1.07213527e-01 -5.41414082e-01 9.45006013e-02
4.01902735e-01 -9.47492182e-01 -2.59111702e-01 7.90124059e-01
2.77322412e-01 -2.83326536e-01 -1.51390588e+00 -3.68950889e-02
3.00976366e-01 -1.10552216e+00 7.61608064e-01 -7.28189826e-01
2.26695508e-01 -1.72137007e-01 -1.22487545e-01 -1.15944386e+00
-4.76131260e-01 -1.15128565e+00 1.49937078e-01 8.97351921e-01
-1.48928285e-01 -5.94071269e-01 8.14826250e-01 5.93677044e-01
1.68209806e-01 -3.93560410e-01 -9.51516211e-01 -7.71151602e-01
3.60743880e-01 -1.27763554e-01 4.10088360e-01 1.38922298e+00
5.28329551e-01 3.60506177e-01 -2.72723529e-02 2.49654159e-01
7.61449218e-01 6.80296898e-01 1.04125893e+00 -1.26035821e+00
-7.01482296e-01 -6.15610719e-01 -4.43992406e-01 -5.35644531e-01
2.45687917e-01 -8.25942278e-01 -3.29742193e-01 -2.53501534e-01
7.29988277e-01 -8.94967318e-01 -1.60891250e-01 -9.65524931e-03
6.57776445e-02 -3.74060452e-01 3.83495651e-02 1.07800174e+00
-7.47458756e-01 4.44402657e-02 4.65537459e-01 1.36911109e-01
-2.85642624e-01 1.06973156e-01 7.69266933e-02 7.03429639e-01
6.81312919e-01 -6.75970376e-01 1.83179393e-01 -3.77125340e-03
4.20836598e-01 7.56551087e-01 4.36775267e-01 -1.00817525e+00
2.68318117e-01 -3.21078748e-01 -1.14248537e-01 -1.53751954e-01
-1.91952258e-01 -1.69171646e-01 7.76876330e-01 5.56631505e-01
-2.16189876e-01 7.86233902e-01 1.53448045e-01 6.46338284e-01
-2.77907938e-01 2.54138559e-01 5.32928348e-01 6.50089681e-02
-5.52874982e-01 -1.82282124e-02 -6.62281752e-01 -5.83730489e-02
1.02640975e+00 5.06528854e-01 -6.21385574e-01 1.18866362e-01
-8.27225983e-01 9.97590199e-02 1.27222872e+00 -1.02013737e-01
-1.41497254e-01 -9.85313475e-01 -6.14996850e-01 2.54799157e-01
-8.07044581e-02 -6.66172564e-01 2.02496752e-01 1.44418287e+00
-9.36835587e-01 2.96984553e-01 -2.64130205e-01 -8.20855379e-01
-1.30878079e+00 8.14739406e-01 2.02608854e-01 -5.83727717e-01
-6.65998399e-01 7.95722678e-02 -8.16946104e-02 -4.42397863e-01
-5.30265331e-01 5.65061055e-04 2.47978210e-01 -2.16410607e-01
3.61886650e-01 6.91882551e-01 2.73800194e-02 -3.80487591e-01
-3.07772338e-01 4.72570688e-01 3.91717672e-01 2.07198724e-01
1.31999874e+00 -1.87742323e-01 -1.04691434e+00 9.19555008e-01
1.24455166e+00 4.35729414e-01 -9.08894479e-01 -1.13478154e-01
5.58572590e-01 -4.47187960e-01 -8.05000663e-02 -1.66027695e-01
-7.07151949e-01 6.80388331e-01 5.06151080e-01 9.22589123e-01
6.69405401e-01 2.74921000e-01 3.89433146e-01 3.23872000e-01
5.21761358e-01 -7.18065798e-01 -3.21639806e-01 4.94525671e-01
2.04817370e-01 -1.15249300e+00 4.97883372e-02 -1.04654059e-01
-3.07870302e-02 1.31566298e+00 2.81386793e-01 -3.05980384e-01
8.81045163e-01 8.54766488e-01 -3.05297315e-01 -4.60325986e-01
-1.04842842e+00 5.94480991e-01 -3.36273521e-01 1.82789877e-01
4.03204918e-01 5.37418485e-01 -4.19756889e-01 2.52127498e-01
-1.58134758e-01 -1.76517785e-01 8.46079409e-01 1.05720758e+00
-2.68358946e-01 -1.21557641e+00 -7.02259779e-01 4.77712423e-01
-4.09626335e-01 4.16157544e-02 1.02542140e-01 7.87416101e-01
-4.13070679e-01 4.83387172e-01 3.48049849e-01 -5.64093947e-01
-2.44307369e-01 8.87473673e-02 2.82484323e-01 -4.26340610e-01
-6.15977168e-01 2.30822824e-02 -6.78806985e-03 -8.64347398e-01
-1.34998739e-01 -8.65207136e-01 -9.71142888e-01 -1.25619650e+00
1.12377428e-01 4.61409152e-01 7.61607707e-01 7.42074132e-01
6.38084471e-01 3.48010689e-01 8.16081345e-01 -1.18745923e+00
-6.88245118e-01 -7.55614996e-01 -8.03062618e-01 3.55142266e-01
2.71240145e-01 -1.90846518e-01 -4.59236085e-01 2.21418552e-02] | [5.998636245727539, 4.594879150390625] |
a5f3e929-2ac1-46b6-9ff3-a9de5215da01 | a-cryptanalysis-of-two-cancelable-biometric | 1910.01389 | null | https://arxiv.org/abs/1910.01389v3 | https://arxiv.org/pdf/1910.01389v3.pdf | A Cryptanalysis of Two Cancelable Biometric Schemes based on Index-of-Max Hashing | Cancelable biometric schemes generate secure biometric templates by combining user specific tokens and biometric data. The main objective is to create irreversible, unlinkable, and revocable templates, with high accuracy in matching. In this paper, we cryptanalyze two recent cancelable biometric schemes based on a particular locality sensitive hashing function, index-of-max (IoM): Gaussian Random Projection-IoM (GRP-IoM) and Uniformly Random Permutation-IoM (URP-IoM). As originally proposed, these schemes were claimed to be resistant against reversibility, authentication, and linkability attacks under the stolen token scenario. We propose several attacks against GRP-IoM and URP-IoM, and argue that both schemes are severely vulnerable against authentication and linkability attacks. We also propose better, but not yet practical, reversibility attacks against GRP-IoM. The correctness and practical impact of our attacks are verified over the same dataset provided by the authors of these two schemes. | ['Koray Karabina', 'Loubna Ghammam', 'Patrick Lacharme', 'Kevin Atighehchi'] | 2019-10-03 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 6.49117649e-01 4.48273383e-02 -2.22379491e-02 -8.32747668e-02
-5.38584769e-01 -1.42916644e+00 7.94841588e-01 -1.33209080e-01
-2.52911508e-01 8.38501871e-01 -1.23679750e-02 -4.85828757e-01
-2.87035435e-01 -9.48447704e-01 -6.23981833e-01 -8.16964924e-01
-9.82576236e-02 2.93809891e-01 1.36862367e-01 -1.64253771e-01
7.12827325e-01 7.58770943e-01 -1.29709792e+00 8.20530131e-02
4.54758644e-01 5.96610963e-01 -7.04539061e-01 8.47793162e-01
4.21913266e-01 1.71042085e-01 -2.43717983e-01 -1.24257386e+00
9.30922627e-01 -1.61756471e-01 -9.70744252e-01 -8.64633262e-01
4.77738321e-01 -2.95352221e-01 -4.43632573e-01 1.06352091e+00
6.46153569e-01 -4.64748353e-01 7.14741409e-01 -1.54360485e+00
-8.07581425e-01 5.30080378e-01 -3.59689564e-01 -4.93614465e-01
6.81862533e-01 1.33589476e-01 6.93949699e-01 -9.09780324e-01
6.51825607e-01 1.02752376e+00 9.90053654e-01 1.00144267e+00
-9.30252731e-01 -9.18766618e-01 -9.92858827e-01 4.48808111e-02
-1.91364288e+00 -6.41327500e-01 2.21865028e-01 -3.59011851e-02
3.49278003e-01 8.93347561e-01 1.51630074e-01 7.89256811e-01
6.50914431e-01 1.96609050e-01 1.58158588e+00 -4.82195050e-01
-1.09244313e-03 9.14343223e-02 -1.90633684e-01 5.77764928e-01
1.10362816e+00 3.65956396e-01 -4.87596154e-01 -8.91135514e-01
9.57651019e-01 7.55942846e-03 -2.60962486e-01 -1.83093101e-01
-1.07966626e+00 4.51593548e-01 -3.67857456e-01 4.18789983e-01
-1.97829038e-01 1.98435485e-01 -1.36939883e-01 3.80676538e-01
-5.04358351e-01 4.03860003e-01 -4.29114074e-01 -1.45408913e-01
-5.34496129e-01 2.93561906e-01 1.11150587e+00 9.88327026e-01
4.53582644e-01 -3.51519465e-01 -5.60639575e-02 2.52217025e-01
4.00918663e-01 1.11777711e+00 3.47425163e-01 -5.59285641e-01
2.58386284e-01 2.07679302e-01 5.49756289e-01 -1.05321527e+00
-1.99075893e-01 3.57488632e-01 -8.91023517e-01 5.10528870e-02
9.48310137e-01 -2.81131780e-03 -6.34822547e-01 1.70442271e+00
7.17931539e-02 1.63264349e-01 3.24881732e-01 3.57266694e-01
4.71967101e-01 1.92899987e-01 4.80323061e-02 3.75392810e-02
1.64106345e+00 -8.27708393e-02 -4.72657949e-01 7.06574082e-01
3.67645085e-01 -1.17145467e+00 4.51839656e-01 9.68565792e-02
-1.34686100e+00 -8.77284706e-02 -1.10795069e+00 2.21187100e-01
-4.92757618e-01 -1.88315302e-01 5.44870138e-01 1.67551661e+00
-1.19532382e+00 4.86972690e-01 -2.72609293e-01 -2.08467305e-01
2.21814632e-01 1.08934247e+00 -8.73401940e-01 6.00823499e-02
-1.54504776e+00 7.83159614e-01 1.07287899e-01 1.83650032e-01
-4.33446705e-01 -2.55735487e-01 -4.90952730e-01 -9.43098292e-02
-2.19903916e-01 -3.19195449e-01 6.75413847e-01 -1.54091045e-01
-1.49930310e+00 1.22050452e+00 -9.70055629e-03 -4.81284589e-01
4.61439967e-01 5.41387260e-01 -5.86913466e-01 6.38339520e-02
-2.43885949e-01 1.63270891e-01 8.47324967e-01 -1.04113650e+00
-1.77989557e-01 -5.97332776e-01 -1.52461588e-01 -2.20079824e-01
-1.87708646e-01 7.11975247e-02 2.35567659e-01 -4.56648380e-01
3.33224207e-01 -1.40707672e+00 8.93582702e-02 -5.14326811e-01
-6.02216661e-01 1.58676151e-02 7.95475185e-01 -6.34684563e-01
7.37874269e-01 -1.98349571e+00 -5.11157036e-01 9.20789719e-01
-1.29361361e-01 7.69339204e-01 -3.50563973e-01 8.04860890e-01
1.38149410e-01 4.31369126e-01 -1.20629489e-01 1.07672982e-01
1.60334215e-01 9.85831115e-03 -5.31394780e-01 1.00766444e+00
-2.82283366e-01 1.30360377e+00 -8.65397513e-01 -1.37576014e-01
-1.64668426e-01 5.75434446e-01 -5.23445494e-02 -3.69044721e-01
6.02251291e-01 4.22015071e-01 -3.30122501e-01 1.25949931e+00
1.44020617e+00 1.89094558e-01 6.08622253e-01 -6.37335032e-02
-3.54394205e-02 1.86047092e-01 -1.40950549e+00 9.51422274e-01
9.60364938e-02 1.03062643e-02 -3.18455875e-01 8.26296676e-03
1.07869363e+00 6.63801730e-01 3.59665126e-01 -6.31400704e-01
1.79841742e-01 7.82596409e-01 -6.28803894e-02 -3.08106150e-02
8.72027934e-01 -4.77296859e-02 -3.75567675e-01 7.55810380e-01
-2.30278119e-01 3.89254570e-01 -5.78763068e-01 -1.12759039e-01
9.88867283e-01 -5.19054532e-02 3.56304914e-01 -5.46503544e-01
1.16427994e+00 -8.75605941e-01 5.80730557e-01 1.16622043e+00
-4.14166451e-01 6.48541570e-01 1.80612594e-01 -2.14218751e-01
-1.08239722e+00 -1.12906575e+00 -3.33906978e-01 3.75476658e-01
6.49373353e-01 -2.08246171e-01 -7.56436050e-01 -6.52292132e-01
4.63521272e-01 2.13728175e-02 -3.89546633e-01 -1.53703958e-01
-6.58306420e-01 -5.73804021e-01 1.77347028e+00 2.34883770e-01
5.98380685e-01 -8.07685435e-01 -2.05369964e-01 1.18522644e-01
-1.80064067e-01 -8.70227993e-01 -6.22305632e-01 -5.42277515e-01
-5.57052791e-01 -1.39571726e+00 -6.76301181e-01 -6.08419299e-01
7.83349991e-01 2.27594584e-01 5.44409513e-01 3.89911592e-01
-3.28896344e-01 7.12295234e-01 1.63747028e-01 -2.28808239e-01
-5.35713613e-01 2.44378775e-01 5.51179111e-01 5.49727678e-01
5.14100730e-01 -3.51620883e-01 -8.77257705e-01 6.02838635e-01
-9.76018071e-01 -5.46145141e-01 3.86009485e-01 7.24595666e-01
1.42086327e-01 -2.17637405e-01 4.18435723e-01 -8.95259857e-01
4.42612648e-01 1.42556271e-02 -4.61945653e-01 6.16432846e-01
-9.92651463e-01 2.56889135e-01 4.07750756e-01 -4.99846011e-01
-8.48320127e-01 1.19871177e-01 -1.83086812e-01 2.21081570e-01
8.36330801e-02 -2.16175720e-01 -6.55413449e-01 -1.22898960e+00
4.13662523e-01 5.39467335e-01 -2.00787529e-01 -2.70066082e-01
5.31784117e-01 9.63570297e-01 9.22869980e-01 -6.02153242e-01
1.47538459e+00 7.02286899e-01 5.73028743e-01 -5.27725101e-01
4.57287043e-01 -1.69770315e-01 -4.51826036e-01 1.30280182e-01
2.55385339e-01 -4.09188896e-01 -1.68691504e+00 1.42660034e+00
-9.68849599e-01 3.31086755e-01 -9.74008292e-02 2.60338634e-01
-4.63414133e-01 9.07950878e-01 -6.34814441e-01 -1.12327838e+00
-6.59941673e-01 -7.44196892e-01 8.05602133e-01 4.95711446e-01
5.36071286e-02 -8.86338532e-01 2.16402579e-02 2.98817098e-01
7.97891796e-01 3.88408273e-01 4.27611113e-01 -8.44349146e-01
-7.62403250e-01 -8.68615806e-01 -1.08001232e-01 -2.09290579e-01
3.46980602e-01 -1.65759325e-01 -9.82726336e-01 -6.58642530e-01
-5.23148775e-02 6.93854541e-02 4.05828029e-01 -1.57394037e-01
6.38582468e-01 -8.21050406e-01 -2.80005962e-01 5.99836588e-01
1.56479216e+00 2.87978977e-01 1.52837992e+00 1.91540480e-01
3.92797709e-01 4.67302084e-01 3.73451352e-01 3.28894973e-01
5.29253900e-01 6.76419318e-01 2.56134152e-01 2.05160171e-01
1.76660970e-01 -4.09821600e-01 3.04077983e-01 3.53270143e-01
-5.61104953e-01 -1.40274584e-01 -4.14314151e-01 3.94143373e-01
-1.59983587e+00 -1.11016703e+00 -4.00495738e-01 2.73013139e+00
9.65873837e-01 -3.56436849e-01 1.28015755e-02 2.37267271e-01
8.94361258e-01 -1.71684787e-01 -4.94550616e-02 -5.23766458e-01
-4.63244021e-01 7.22057045e-01 1.45679092e+00 5.99794149e-01
-8.37041497e-01 1.06431568e+00 5.85592890e+00 5.76769114e-01
-1.04859042e+00 2.17609897e-01 3.74108732e-01 5.73072255e-01
-5.28370559e-01 6.06692791e-01 -9.91560400e-01 5.78995883e-01
8.26393247e-01 -5.00443392e-02 3.32390279e-01 2.18364105e-01
-6.05964124e-01 1.03595115e-01 -6.71459794e-01 8.40347350e-01
5.43859191e-02 -1.24899924e+00 -4.70060296e-03 5.30403674e-01
6.08874619e-01 -8.28503609e-01 4.97385502e-01 -1.62562981e-01
3.00253108e-02 -1.01553154e+00 2.80376762e-01 7.76291549e-01
1.17760956e+00 -7.33321786e-01 7.61974931e-01 -2.00425237e-01
-1.28294361e+00 2.14655429e-01 -6.83609545e-01 3.08877558e-01
-2.77808338e-01 -3.54248323e-02 -6.63186729e-01 8.90228271e-01
1.68521091e-01 -3.03088039e-01 -5.89976609e-01 9.25389230e-01
-2.40321130e-01 4.43986326e-01 -4.65720206e-01 -1.19519405e-01
-2.18721479e-01 -1.16997898e-01 4.98483002e-01 9.99088109e-01
3.78745615e-01 3.03167403e-01 -5.60333312e-01 4.21665311e-01
-2.46565193e-01 1.69812977e-01 -6.57976985e-01 1.30674303e-01
8.37213933e-01 1.19223225e+00 -6.33586943e-01 -1.19112954e-01
1.06911100e-01 1.09469306e+00 -5.74248612e-01 2.62067821e-02
-4.60018426e-01 -7.49037564e-01 6.72056973e-01 8.07760656e-02
1.08023971e-01 -2.85177439e-01 -5.88214219e-01 -1.31285369e+00
-5.59969470e-02 -8.03501725e-01 3.27336669e-01 -9.02376398e-02
-1.16775322e+00 2.07157448e-01 -3.53793532e-01 -1.13460326e+00
-2.23395266e-02 -2.48474658e-01 -2.14663848e-01 1.25324321e+00
-1.38292658e+00 -1.56961048e+00 2.24278763e-01 8.89734805e-01
-8.03543508e-01 -3.62605512e-01 1.31797326e+00 1.68660149e-01
2.96011358e-01 1.52673328e+00 1.93808779e-01 1.90477028e-01
7.94734716e-01 -9.00287867e-01 9.80436683e-01 7.77764142e-01
2.92344671e-02 1.38339293e+00 3.03589135e-01 -8.50646555e-01
-1.99544156e+00 -7.38971233e-01 1.64411902e+00 -6.87983572e-01
5.14363386e-02 -6.14233971e-01 -7.36323535e-01 5.22100866e-01
-1.46151021e-01 -3.89865994e-01 9.61576998e-01 -4.29186910e-01
-8.38635802e-01 2.44092313e-03 -2.02117372e+00 5.84321439e-01
8.74641299e-01 -9.13728297e-01 -3.74458432e-01 2.20741421e-01
-1.73200611e-02 -3.71602625e-01 -1.03761756e+00 4.41841096e-01
1.46846151e+00 -7.57919610e-01 1.13508523e+00 -2.41832390e-01
-5.34396470e-01 -6.07534707e-01 -1.16678357e-01 -4.40515652e-02
-1.22911520e-01 -1.33947551e+00 -1.23997629e-02 1.54088855e+00
1.61448389e-01 -1.32511044e+00 7.88839877e-01 8.29372704e-01
9.31948006e-01 -1.35982692e-01 -1.28218031e+00 -1.04586136e+00
9.09281746e-02 2.10901842e-01 1.13285673e+00 1.02153373e+00
5.35766780e-02 -5.19088745e-01 -1.04598832e+00 3.90360594e-01
1.10346794e+00 -1.39155552e-01 9.93743002e-01 -1.31392252e+00
-3.04366406e-02 8.41057375e-02 -9.39123631e-01 -5.71309805e-01
-2.22195059e-01 -7.87548125e-01 -4.01706040e-01 -6.87450469e-01
1.78015128e-01 -8.13725889e-01 -5.50303340e-01 7.89965212e-01
1.46411106e-01 1.16240311e+00 2.76072383e-01 6.42190695e-01
1.57713220e-01 -1.32458672e-01 9.31758881e-01 -4.40950366e-03
-1.70302317e-01 3.01652431e-01 -8.28296065e-01 1.15546755e-01
7.40291595e-01 -4.82951701e-01 -1.15188211e-01 5.22274137e-01
3.27968508e-01 2.67639440e-02 3.50896567e-01 -8.25019300e-01
4.04860228e-01 -1.84583679e-01 1.44209281e-01 -4.92694944e-01
1.72316253e-01 -6.60323501e-01 8.21578026e-01 9.18763518e-01
-6.60429616e-03 -5.65047227e-02 -1.91797510e-01 2.17643142e-01
4.16367322e-01 -2.76171625e-01 6.60174727e-01 1.10177636e-01
-2.83105701e-01 4.93399680e-01 -2.18886316e-01 -6.13523304e-01
8.65576923e-01 -4.89732087e-01 -6.82945490e-01 -3.23349208e-01
-4.51813698e-01 -3.84157717e-01 9.16059136e-01 3.81185442e-01
7.29868412e-01 -1.36586022e+00 -8.02942336e-01 6.66353703e-01
6.29152581e-02 -1.01036119e+00 1.97694406e-01 7.12606072e-01
-6.87266946e-01 6.02951705e-01 -4.69305664e-01 -2.35353917e-01
-1.64324951e+00 3.75913531e-01 2.37420753e-01 -7.65364915e-02
-1.52975529e-01 3.95214438e-01 -3.81675690e-01 -5.46697319e-01
-2.80415397e-02 1.98577240e-01 1.90537646e-01 -4.37268317e-01
4.98586595e-01 4.16094005e-01 2.11295411e-02 -9.65149939e-01
-5.93437374e-01 8.97094429e-01 -6.53717592e-02 -4.40058857e-01
6.29259348e-01 -1.79554522e-01 -5.97795486e-01 -4.99482661e-01
1.04224169e+00 7.44631946e-01 -5.43300331e-01 -1.26322061e-01
2.23757789e-01 -8.37263644e-01 -1.07011878e+00 -5.66527307e-01
-6.13238454e-01 2.48998344e-01 8.45870912e-01 2.36379564e-01
7.43840098e-01 -5.59753716e-01 1.28511906e+00 2.28413254e-01
9.86271977e-01 -6.43206477e-01 -7.04221547e-01 4.34881091e-01
2.75485456e-01 -5.71367025e-01 -4.68020774e-02 -4.72717255e-01
-1.04934566e-01 1.11610079e+00 5.34344278e-02 9.04566236e-03
7.34055817e-01 8.09041634e-02 1.00679053e-02 2.04738960e-01
-1.78426176e-01 1.83527395e-01 2.38465026e-01 9.10895586e-01
4.61182930e-02 2.31432721e-01 -1.12085783e+00 2.59474874e-01
-2.07982138e-01 8.35392997e-02 7.98257113e-01 1.27995086e+00
7.83471391e-02 -2.15381098e+00 -7.04194307e-01 -1.73422068e-01
-8.23054731e-01 -7.18266517e-02 -5.85564971e-01 5.07633030e-01
1.38096847e-02 6.87757254e-01 -5.95439017e-01 -7.73947001e-01
1.46409159e-03 2.27365375e-01 7.64302433e-01 2.48054519e-01
-8.65040898e-01 -4.61087942e-01 -3.33850056e-01 -1.81931645e-01
-3.81850541e-01 -6.91803873e-01 -1.07686079e+00 -9.53274667e-01
-6.02947712e-01 2.88976103e-01 9.07638311e-01 5.42711496e-01
4.99012053e-01 -6.66295886e-01 1.16809642e+00 -2.10218728e-01
-8.05906653e-01 -3.94635051e-01 -1.01306903e+00 3.94173443e-01
7.24665895e-02 -1.33232415e-01 -2.31222272e-01 -1.54880866e-01] | [13.02570915222168, 1.1091883182525635] |
b5ad567e-bb06-4c4b-a290-00dfd2466582 | an-unsupervised-segmentation-of-vocal-breath | 2304.03758 | null | https://arxiv.org/abs/2304.03758v1 | https://arxiv.org/pdf/2304.03758v1.pdf | An unsupervised segmentation of vocal breath sounds | Breathing is an essential part of human survival, which carries information about a person's physiological and psychological state. Generally, breath boundaries are marked by experts before using for any task. An unsupervised algorithm for breath boundary detection has been proposed for breath sounds recorded at the mouth also referred as vocal breath sounds (VBS) in this work. Breath sounds recorded at the mouth are used in this work because they are easy and contactless to record than tracheal breath sounds and lung breath sounds. The periodic nature of breath signal energy is used to segment the breath boundaries. Dynamic programming with the prior information of the number of breath phases($P$) and breath phase duration($d$) is used to find the boundaries. In this work, 367 breath boundaries from 60 subjects (31 healthy, 29 patients) having 307 breaths are predicted. With the proposed method, M ($89\%$), I ($13\%$), D ($11\%$) and S ($79\%$) is found. The proposed method shows better performance than the baselines used in this work. Even the classification performance between asthmatic and healthy subjects using estimated boundaries by the proposed method is comparable with the ground truth boundaries. | ['Prasanta Kumar Ghosh', 'Uma Maheswari K.', 'Dipanjan Gope', 'Shivani Yadav'] | 2023-04-07 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.41527448e-02 7.02896789e-02 5.88365644e-02 -9.78182033e-02
-3.62339735e-01 -2.23258182e-01 -2.21015334e-01 1.41826928e-01
-3.30619544e-01 7.32709944e-01 -1.14669643e-01 -5.53381555e-02
-1.44893199e-01 -5.05774140e-01 6.71890154e-02 -8.60121012e-01
-2.92085499e-01 5.86255156e-02 4.32178676e-01 1.46979734e-01
3.85198623e-01 1.74681291e-01 -1.35337961e+00 -1.03159152e-01
6.83131576e-01 9.74165499e-01 2.19284564e-01 1.08219743e+00
-2.89340913e-01 4.53543186e-01 -7.75860548e-01 1.56276058e-02
3.65021706e-01 -9.89148796e-01 -4.98855799e-01 -8.94601345e-02
2.66289204e-01 -2.21357539e-01 8.61547291e-02 8.76991093e-01
7.26897359e-01 7.43083119e-01 9.19129312e-01 -9.98885036e-01
1.33885980e-01 3.01046073e-01 -3.74986678e-01 7.07530499e-01
3.58774960e-01 1.41073719e-01 8.20163846e-01 -5.86880028e-01
2.54296094e-01 7.13290393e-01 9.53333378e-01 6.19426310e-01
-7.27847636e-01 -9.11252975e-01 -4.15619731e-01 3.94057810e-01
-1.51565731e+00 -4.78731185e-01 8.73733699e-01 -6.81462348e-01
1.11349893e+00 6.80979371e-01 8.66408587e-01 4.02295589e-01
5.11562288e-01 -6.17465563e-03 1.06310916e+00 -4.58266735e-01
2.39011288e-01 4.34630722e-01 3.82397801e-01 6.52349532e-01
1.63637078e-03 -1.48046076e-01 -3.03215712e-01 -5.98720722e-02
2.93413877e-01 3.06208096e-02 -4.90463436e-01 6.47509813e-01
-8.01339507e-01 7.17245281e-01 1.34425774e-01 6.80694938e-01
-4.56483573e-01 2.11739317e-01 2.14127034e-01 -6.06000014e-02
-6.89708292e-02 3.31106603e-01 7.04595819e-02 -2.96482503e-01
-1.31567788e+00 7.27531090e-02 9.63309884e-01 5.78349710e-01
5.63522518e-01 1.54772520e-01 -2.44122595e-01 8.23438466e-01
5.63092887e-01 4.24148321e-01 7.51089931e-01 -1.26467061e+00
1.58919439e-01 3.44866157e-01 2.23506972e-01 -1.22557545e+00
-5.58865547e-01 -3.40045951e-02 -9.16781604e-01 -2.84112573e-01
4.10687983e-01 -1.26958326e-01 -6.63672268e-01 1.21722293e+00
4.45702225e-01 2.94693887e-01 4.79620621e-02 7.93580115e-01
1.20078754e+00 8.82977366e-01 -7.93867782e-02 -1.03307366e+00
1.36341274e+00 -7.53478944e-01 -1.11450326e+00 -6.09268956e-02
2.47698743e-02 -7.99883664e-01 8.22111368e-01 4.05577540e-01
-9.61314499e-01 -8.30490410e-01 -1.02238619e+00 4.10320610e-01
-1.07566565e-01 -3.95951450e-01 9.20852274e-02 9.39890325e-01
-7.72010088e-01 8.05269063e-01 -7.34626472e-01 -3.60562295e-01
-7.94456080e-02 3.14028084e-01 1.98602840e-01 6.08951986e-01
-1.17825067e+00 5.33652544e-01 9.69458446e-02 2.82730728e-01
-4.57961261e-01 -4.11643118e-01 -5.28532505e-01 -2.40607902e-01
-3.23154256e-02 -3.89256656e-01 1.03852963e+00 -4.06102538e-01
-1.56050277e+00 6.28468156e-01 -3.83355349e-01 -4.23233241e-01
4.86181200e-01 2.50141360e-02 -3.52583051e-01 7.72018671e-01
-1.72196150e-01 4.96666163e-01 8.89981747e-01 -8.06292117e-01
-2.77174711e-01 -2.31782477e-02 -7.27480531e-01 1.87994644e-01
-1.18187219e-01 1.98150992e-01 4.65573519e-02 -3.73923451e-01
3.28099757e-01 -7.09016919e-01 1.19674727e-01 -3.05127978e-01
-5.70569158e-01 -4.66038883e-01 5.86487174e-01 -1.25281465e+00
1.62541306e+00 -1.99638939e+00 -3.80811542e-01 2.70455182e-01
2.66394407e-01 3.05767357e-01 7.28199124e-01 6.21055484e-01
1.64990857e-01 3.88297707e-01 -6.03715003e-01 -1.85261548e-01
-5.01020104e-02 1.54201671e-01 -3.40424627e-02 5.97727895e-01
-5.27202249e-01 2.64082462e-01 -7.19763458e-01 -1.01298773e+00
3.21496189e-01 4.32605773e-01 -4.26908642e-01 6.67201936e-01
1.57710925e-01 6.71735406e-01 -3.14476699e-01 5.54642141e-01
2.08674163e-01 2.88544714e-01 -2.42122874e-01 -1.86144680e-01
-2.33735278e-01 5.08701801e-02 -1.24032485e+00 9.06733513e-01
-7.74047300e-02 7.31264889e-01 7.34829754e-02 -7.78195381e-01
1.26997948e+00 9.02767360e-01 6.49265110e-01 -2.48914436e-01
3.02417189e-01 3.29044491e-01 4.54680741e-01 -1.29778600e+00
-5.91224469e-02 -7.84668446e-01 3.50178570e-01 2.52494365e-01
-2.81257540e-01 -7.26686120e-01 2.30296165e-01 -3.33789945e-01
1.00346637e+00 -3.62475485e-01 4.84947115e-01 -3.09660345e-01
8.05479646e-01 -2.51636416e-01 7.03503549e-01 4.42239821e-01
-8.16044271e-01 6.93261862e-01 8.80770758e-02 2.28920355e-02
-6.41368926e-01 -8.40840101e-01 -3.99094939e-01 4.26298171e-01
-8.35108850e-03 -1.89484805e-01 -1.04587650e+00 -1.93320602e-01
-2.00638130e-01 7.67312527e-01 -2.70227104e-01 -5.51508106e-02
-8.52400422e-01 -5.06817102e-01 5.97481608e-01 3.44732106e-01
7.79100597e-01 -1.63701499e+00 -1.09801328e+00 2.02211887e-01
-2.63156652e-01 -8.12193155e-01 -7.24676907e-01 1.72564179e-01
-8.37715209e-01 -1.11915755e+00 -7.68979311e-01 -6.06099725e-01
3.88313890e-01 -1.97149128e-01 6.85877025e-01 1.83753371e-01
-8.98098826e-01 3.06613892e-01 -2.99492776e-01 -5.19355953e-01
-5.12288988e-01 -4.19746697e-01 2.41223112e-01 -1.34556651e-01
3.24374996e-02 -8.35719466e-01 -1.21761489e+00 3.66526902e-01
-3.83136421e-01 -6.21520102e-01 8.76734331e-02 4.42168474e-01
8.47710788e-01 3.42800170e-01 8.30884576e-01 -6.27005875e-01
7.67280102e-01 -4.75756496e-01 -1.80403024e-01 -2.57881254e-01
-7.45319843e-01 -5.65994322e-01 8.69191766e-01 -3.64990532e-01
-4.01643306e-01 -4.77661133e-01 -2.93137789e-01 -5.58440149e-01
-4.39179420e-01 -2.93401014e-02 3.84994559e-02 3.10919613e-01
6.57586992e-01 1.51768565e-01 -7.23412409e-02 -5.57266116e-01
-1.01423964e-01 1.01150596e+00 6.28703475e-01 1.29448265e-01
5.36425948e-01 6.75396547e-02 9.19461921e-02 -1.22618747e+00
-3.74024928e-01 -9.19682384e-01 -6.55391812e-01 -4.11380887e-01
1.23921561e+00 -1.78350180e-01 -8.25898528e-01 4.11137193e-01
-7.70702422e-01 -4.03214633e-01 -3.67880911e-01 8.49112153e-01
-4.27887142e-01 7.85819888e-01 -3.93945009e-01 -1.59292829e+00
-9.66288447e-01 -7.07849860e-01 3.82434219e-01 6.51132703e-01
-7.31275558e-01 -1.17094958e+00 1.30219519e-01 6.67309999e-01
3.94388258e-01 6.77023947e-01 7.51218200e-01 -8.70614469e-01
2.06030775e-02 -2.82834589e-01 3.34525436e-01 7.01153278e-01
5.70581615e-01 2.11997926e-01 -9.42723215e-01 -9.87968445e-02
5.86286962e-01 9.46612433e-02 6.24760747e-01 4.75498080e-01
8.59029412e-01 -6.54953659e-01 -1.84093535e-01 5.32557070e-01
1.08496821e+00 1.09356868e+00 4.20036435e-01 -1.57104105e-01
3.58028859e-01 8.45212460e-01 5.53851664e-01 3.75396013e-01
-2.12270290e-01 1.31666332e-01 2.91912973e-01 2.86721230e-01
-2.18278751e-01 1.20772980e-01 4.35702562e-01 1.24138224e+00
-5.40454052e-02 -3.81928504e-01 -8.20934892e-01 5.94961762e-01
-1.01676798e+00 -1.10237420e+00 -4.70483959e-01 2.32184839e+00
9.10000622e-01 2.97659729e-02 3.79359007e-01 7.63858616e-01
9.78692055e-01 9.29308981e-02 -5.42321026e-01 -9.00725126e-01
7.18706310e-01 5.72283268e-01 1.33557186e-01 6.49191618e-01
-7.47895002e-01 2.57951081e-01 6.79171896e+00 5.19431949e-01
-1.13939691e+00 -9.29599479e-02 3.45323682e-01 1.84498221e-01
-3.50267403e-02 -3.80850822e-01 -8.03313851e-01 8.57930601e-01
1.15363085e+00 8.71873647e-02 3.33449692e-01 4.83514965e-01
6.45416558e-01 -4.53811467e-01 -9.69284952e-01 1.21093917e+00
2.53673404e-01 -5.99337220e-01 -7.14881837e-01 -2.19504431e-01
2.41540328e-01 -5.30192494e-01 -1.68269262e-01 -1.77108675e-01
-6.19633973e-01 -1.20171106e+00 4.32145894e-01 6.30327702e-01
7.18283653e-01 -5.03401637e-01 7.10117996e-01 7.05526054e-01
-1.16428387e+00 1.15149796e-01 -2.81758517e-01 1.42316520e-01
9.66940522e-02 5.73388934e-01 -1.46828902e+00 2.32076749e-01
5.76689303e-01 2.37682238e-01 -2.57075965e-01 1.31216919e+00
-1.76536769e-01 1.21508658e+00 -7.40071058e-01 -3.40310365e-01
-6.51776865e-02 -3.74882489e-01 8.89179051e-01 1.29187906e+00
5.07189631e-01 3.40668499e-01 -5.53455427e-02 1.03603196e+00
3.85559380e-01 5.49495518e-01 -4.72569436e-01 -2.24403694e-01
5.46991050e-01 1.06932175e+00 -1.02759528e+00 -2.34705806e-02
1.19226687e-01 5.89123666e-01 -6.83966935e-01 1.15850240e-01
-7.80306041e-01 -8.10876131e-01 6.86703101e-02 6.06264412e-01
-5.74642494e-02 7.22155347e-02 1.60003424e-01 -2.05190212e-01
-1.84104875e-01 -4.74238425e-01 4.88134533e-01 -6.28856897e-01
-9.38225448e-01 5.43030679e-01 3.08317721e-01 -1.00461507e+00
-4.26489174e-01 -1.80864781e-01 -1.16767180e+00 1.03381491e+00
-1.20445275e+00 -6.18714020e-02 -6.16339147e-01 4.33476746e-01
6.13085747e-01 1.60803482e-01 7.25219369e-01 2.37810850e-01
-6.32081509e-01 5.45410752e-01 -1.61549166e-01 -6.64106384e-03
4.84189361e-01 -1.39265192e+00 -2.66717792e-01 4.64603186e-01
-1.24820441e-01 6.31646156e-01 9.96689737e-01 -6.62181139e-01
-9.44476843e-01 -8.53285015e-01 8.56995106e-01 -9.68602225e-02
1.50891826e-01 1.99350134e-01 -9.48109031e-01 1.47995040e-01
3.37580681e-01 8.91333595e-02 1.13853467e+00 -8.11252892e-01
4.34724540e-01 -3.48935992e-01 -1.64259160e+00 7.45576248e-02
4.42107916e-01 -1.44323632e-01 -8.38759124e-01 2.01419115e-01
4.08493638e-01 -2.27707297e-01 -1.09047198e+00 2.77694106e-01
5.55830419e-01 -1.37315285e+00 7.92585194e-01 1.24700822e-01
-3.55769128e-01 -2.04355925e-01 -4.23948728e-02 -5.75998366e-01
6.86130673e-02 -1.10908496e+00 -1.05009722e-02 1.38350654e+00
8.11693966e-02 -9.01361644e-01 6.56087399e-01 4.14324313e-01
-2.20769823e-01 -7.71392822e-01 -1.06215310e+00 -7.19028592e-01
6.46918714e-02 -1.22832559e-01 8.35486874e-02 5.22985339e-01
-7.33531788e-02 1.44544095e-01 -1.43430894e-02 -2.36509129e-01
5.34232914e-01 1.70634575e-02 2.33823270e-01 -9.73334849e-01
-3.38812560e-01 -2.62034565e-01 -1.21743485e-01 -5.43020368e-01
-3.02190870e-01 -7.83165514e-01 4.10407245e-01 -1.74250913e+00
-3.40122804e-02 -4.38851714e-01 -3.69331926e-01 8.55431706e-02
-2.61360735e-01 3.22994947e-01 5.75011484e-02 2.66158730e-01
1.08023081e-02 6.60383329e-02 1.29127467e+00 2.08314762e-01
-6.22547984e-01 2.86947519e-01 -9.36517939e-02 1.01707125e+00
9.68613327e-01 -5.77737331e-01 -4.63972092e-01 6.33941650e-01
-1.95774794e-01 4.43770677e-01 1.21449299e-01 -1.11724985e+00
2.57963105e-03 -2.38767341e-01 2.09960669e-01 -7.76923120e-01
5.40174305e-01 -6.35696709e-01 4.84447300e-01 7.98031867e-01
-1.57739565e-01 -6.17652126e-02 -1.00346394e-01 3.86131972e-01
-7.75311291e-02 -8.94517183e-01 1.10237348e+00 -4.04672831e-01
4.51746620e-02 2.59092096e-02 -4.43424016e-01 2.90405810e-01
1.05972421e+00 -7.08444655e-01 5.72008863e-02 -4.66193169e-01
-9.33500290e-01 1.65793598e-02 -3.98440920e-02 -2.03093931e-01
7.11364686e-01 -8.82923186e-01 -3.46213400e-01 5.77347353e-02
-4.86591637e-01 9.28240120e-02 2.06289619e-01 1.37302363e+00
-9.07216311e-01 3.60431641e-01 1.88915476e-01 -5.55679381e-01
-1.32006359e+00 3.13340694e-01 6.28987491e-01 -2.12851726e-02
-6.21932447e-01 1.20057547e+00 -2.49411881e-01 3.83074611e-01
4.67735678e-01 -6.26846731e-01 -5.92841685e-01 3.93198907e-01
2.09811479e-01 1.03865337e+00 -4.00252163e-01 -5.69244802e-01
-4.90783930e-01 9.25350845e-01 6.26807630e-01 -1.99092463e-01
7.72058368e-01 -2.16958329e-01 -2.73622960e-01 9.69020545e-01
1.30132425e+00 5.20559311e-01 -7.56329656e-01 6.20590687e-01
-1.92095876e-01 -2.25283548e-01 -3.65964323e-01 -4.65434611e-01
-7.51379669e-01 1.16493595e+00 7.64341712e-01 6.38916194e-01
1.14423668e+00 -2.98553944e-01 1.32245052e+00 -1.34745892e-03
-8.27076286e-02 -1.14445925e+00 3.46829087e-01 -3.02038372e-01
8.33202600e-01 -8.08422029e-01 -1.31668881e-01 -5.77344060e-01
-5.07026851e-01 1.11080003e+00 3.76392692e-01 -3.12514931e-01
8.73255193e-01 -8.24258402e-02 1.44797891e-01 -4.03224528e-02
-4.11152273e-01 -5.10713644e-02 2.80065775e-01 5.97983003e-01
4.27879721e-01 -6.93512112e-02 -7.19657540e-01 8.76046896e-01
-5.34452081e-01 -1.84208795e-01 2.65165359e-01 6.63998783e-01
-9.43927646e-01 -7.62666702e-01 -6.33723915e-01 5.27528882e-01
-1.08268309e+00 -4.47662733e-02 -3.46863449e-01 5.59515417e-01
5.71560681e-01 1.46482265e+00 6.33115554e-03 -2.34828338e-01
1.46956339e-01 7.23842382e-01 4.30464983e-01 -6.56135678e-01
-9.63101447e-01 2.90802836e-01 -7.73664713e-02 -1.13978073e-01
-3.76089275e-01 -6.05487049e-01 -2.09927177e+00 2.08639354e-01
-3.53674293e-01 8.21288884e-01 4.53401119e-01 6.60072923e-01
-3.26743215e-01 5.39968967e-01 8.31050754e-01 -2.28085950e-01
-4.07551050e-01 -1.17915213e+00 -8.80276442e-01 2.47053966e-01
4.33780342e-01 -4.32084918e-01 -1.15773821e+00 3.05119246e-01] | [14.232559204101562, 3.4679455757141113] |
3a643f89-58de-471d-941d-accf9f38c82e | a-hierarchical-bayesian-model-for-deep-few | 2306.09702 | null | https://arxiv.org/abs/2306.09702v1 | https://arxiv.org/pdf/2306.09702v1.pdf | A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning | We propose a novel hierarchical Bayesian model for learning with a large (possibly infinite) number of tasks/episodes, which suits well the few-shot meta learning problem. We consider episode-wise random variables to model episode-specific target generative processes, where these local random variables are governed by a higher-level global random variate. The global variable helps memorize the important information from historic episodes while controlling how much the model needs to be adapted to new episodes in a principled Bayesian manner. Within our model framework, the prediction on a novel episode/task can be seen as a Bayesian inference problem. However, a main obstacle in learning with a large/infinite number of local random variables in online nature, is that one is not allowed to store the posterior distribution of the current local random variable for frequent future updates, typical in conventional variational inference. We need to be able to treat each local variable as a one-time iterate in the optimization. We propose a Normal-Inverse-Wishart model, for which we show that this one-time iterate optimization becomes feasible due to the approximate closed-form solutions for the local posterior distributions. The resulting algorithm is more attractive than the MAML in that it is not required to maintain computational graphs for the whole gradient optimization steps per episode. Our approach is also different from existing Bayesian meta learning methods in that unlike dealing with a single random variable for the whole episodes, our approach has a hierarchical structure that allows one-time episodic optimization, desirable for principled Bayesian learning with many/infinite tasks. The code is available at \url{https://github.com/minyoungkim21/niwmeta}. | ['Timothy Hospedales', 'Minyoung Kim'] | 2023-06-16 | null | null | null | null | ['bayesian-inference', 'meta-learning'] | ['methodology', 'methodology'] | [-7.01895803e-02 2.16008708e-01 -1.29080757e-01 -3.05591166e-01
-1.00167680e+00 -1.79232702e-01 9.41410005e-01 -5.67026772e-02
-5.95941246e-01 1.18575704e+00 6.97417408e-02 9.66947302e-02
-6.00339830e-01 -1.04058933e+00 -8.72961760e-01 -1.14596713e+00
-2.23277528e-02 9.76120532e-01 1.97112098e-01 3.32678139e-01
1.48182482e-01 2.05063090e-01 -1.74081457e+00 -2.82156050e-01
8.20186138e-01 6.56519413e-01 6.17829740e-01 8.98498654e-01
-1.24437556e-01 5.79655230e-01 -3.69084448e-01 -2.86054909e-01
-5.34757823e-02 -5.59229255e-01 -5.51178515e-01 -6.12979010e-02
-2.34371349e-02 -6.50661215e-02 -9.72618237e-02 9.85452294e-01
5.17412424e-01 7.78008580e-01 1.04921150e+00 -9.67784584e-01
-9.22483802e-02 6.19231820e-01 -3.88795555e-01 2.16054946e-01
-1.92999225e-02 -1.74665954e-02 9.10574973e-01 -7.07571030e-01
6.15060866e-01 1.20697987e+00 5.55619895e-01 3.46869707e-01
-1.22257292e+00 -2.94355094e-01 3.45955878e-01 3.39826018e-01
-1.24472916e+00 -4.80511844e-01 7.36438811e-01 -4.85172778e-01
8.42482269e-01 6.30004779e-02 9.98751104e-01 1.35931575e+00
4.96762842e-01 8.32864285e-01 1.00676739e+00 -4.96672243e-01
9.21977878e-01 1.67727679e-01 3.75887126e-01 7.43454158e-01
2.91222632e-02 2.87913997e-02 -9.16289985e-01 -2.95653313e-01
6.33579135e-01 1.96969017e-01 -3.99251357e-02 -5.21732390e-01
-8.57807457e-01 1.06630147e+00 -1.27212062e-01 1.94683075e-01
-6.09312356e-01 5.38784325e-01 7.14007691e-02 2.31899083e-01
8.01650345e-01 -5.62651567e-02 -4.72603440e-01 -5.97655952e-01
-1.23601151e+00 5.24599135e-01 1.12342203e+00 6.51799917e-01
9.27892327e-01 1.77870635e-02 -2.57140011e-01 8.87834966e-01
6.33809090e-01 5.66533864e-01 3.45468193e-01 -1.19817519e+00
6.53651953e-02 -1.76035106e-01 3.47840220e-01 -4.49450046e-01
-1.47636414e-01 -5.90687633e-01 -8.47833872e-01 2.38673270e-01
4.67752337e-01 -2.32112765e-01 -8.90443146e-01 1.97414672e+00
5.30203223e-01 3.34323794e-01 -1.29307315e-01 3.56952965e-01
2.07127735e-01 9.62081552e-01 -3.72007899e-02 -9.13194537e-01
1.12494183e+00 -7.70299017e-01 -8.95007491e-01 -2.96337217e-01
1.52441427e-01 -3.47071290e-01 9.01855826e-01 5.83447099e-01
-1.36301959e+00 -4.43560302e-01 -7.75120437e-01 1.15781248e-01
-4.08770472e-01 -4.15936947e-01 6.84365511e-01 6.01837158e-01
-9.85649407e-01 7.12174892e-01 -1.16406953e+00 -2.82814294e-01
1.81566671e-01 4.98556197e-02 2.71172732e-01 -5.48856184e-02
-1.25574541e+00 9.21040893e-01 3.56718838e-01 1.40641227e-01
-1.25440156e+00 -6.93116248e-01 -7.18516469e-01 1.04983836e-01
5.71349919e-01 -9.23851311e-01 1.44284689e+00 -5.13324678e-01
-1.66944635e+00 4.72398639e-01 -8.06069434e-01 -4.50325370e-01
7.30977237e-01 -2.10060120e-01 1.20182842e-01 -1.17266484e-01
3.26568447e-02 3.42208326e-01 1.25424826e+00 -1.11105072e+00
-4.91007417e-01 -4.19847101e-01 -1.32412255e-01 4.40493375e-01
-4.05246578e-02 -5.13250172e-01 -6.30558014e-01 -4.83298391e-01
1.10630147e-01 -9.02907968e-01 -2.07862586e-01 -2.65088886e-01
1.15725182e-01 -4.09058958e-01 3.62308383e-01 -3.78231376e-01
1.20898080e+00 -1.90304923e+00 5.94068289e-01 3.97328511e-02
-3.31919342e-02 -2.99836695e-01 3.12092692e-01 5.01058340e-01
4.30774838e-01 -1.75414070e-01 -3.94666463e-01 -7.88522542e-01
2.08780736e-01 4.89989281e-01 -3.12150300e-01 4.33824241e-01
-3.72644722e-01 7.03204989e-01 -1.07131755e+00 -5.31187534e-01
2.08471656e-01 4.71625924e-01 -3.86522800e-01 2.36894995e-01
-5.28166533e-01 3.25389087e-01 -6.11710966e-01 2.98517644e-01
4.84387130e-01 -2.28822589e-01 1.07737802e-01 2.60983586e-01
-1.87852636e-01 1.20218083e-01 -1.61628819e+00 1.77317071e+00
-5.94094574e-01 3.91852379e-01 9.66095775e-02 -1.07717860e+00
5.28168201e-01 4.81986105e-01 4.01837528e-01 -2.84004122e-01
-1.89057454e-01 5.24845393e-03 -4.99775440e-01 -4.44274187e-01
3.79078180e-01 -5.22614598e-01 -7.45798871e-02 6.07596517e-01
3.21634799e-01 -4.17853326e-01 4.43707049e-01 2.75810122e-01
9.44610476e-01 5.02892017e-01 3.18757683e-01 -2.10469976e-01
1.46631464e-01 -3.18999469e-01 7.92476177e-01 1.40588760e+00
1.41743183e-01 3.11995864e-01 4.80645269e-01 -2.85070270e-01
-8.93244505e-01 -1.38897073e+00 -4.04293954e-01 1.27693987e+00
-2.46094912e-01 -3.02339882e-01 -5.37019432e-01 -1.29373029e-01
-2.83317626e-01 1.10720849e+00 -6.57314599e-01 -4.53521572e-02
-2.50738740e-01 -1.16738212e+00 -7.65873268e-02 1.34320721e-01
3.50170910e-01 -1.21277928e+00 -8.53205442e-01 5.88935196e-01
-1.28694341e-01 -3.30590099e-01 -1.37203485e-01 6.40348911e-01
-1.14040399e+00 -6.12797618e-01 -8.14162493e-01 -1.95875555e-01
2.74934918e-01 -1.91571116e-01 1.06379306e+00 -5.29103637e-01
-1.76843762e-01 8.70312572e-01 -5.26416227e-02 -4.69300717e-01
-1.65570691e-01 7.75797889e-02 8.53358284e-02 1.72664672e-02
2.51516104e-01 -8.32205534e-01 -3.88211787e-01 -1.73809022e-01
-8.07437658e-01 1.15010835e-01 3.55575323e-01 9.13340151e-01
7.47177005e-01 2.37459302e-01 5.13929784e-01 -1.00795269e+00
5.11268258e-01 -6.82208359e-01 -7.37172127e-01 3.93266827e-01
-6.61202490e-01 3.33819002e-01 1.50653124e-01 -5.75137436e-01
-1.59184897e+00 -2.43002743e-01 1.25372678e-01 -2.03278616e-01
-6.99818432e-02 7.87561834e-01 1.80699453e-02 4.90344048e-01
3.62913400e-01 4.01730537e-01 -6.15667850e-02 -5.05957663e-01
4.12132263e-01 3.39751244e-01 1.00046054e-01 -8.41706514e-01
4.73028123e-01 4.21242446e-01 1.43983662e-01 -9.63072360e-01
-1.08121383e+00 -3.08467984e-01 -7.09612966e-01 -5.54621100e-01
8.41333032e-01 -8.86484683e-01 -8.09732080e-01 7.20160484e-01
-9.79853570e-01 -7.19650567e-01 -5.87765753e-01 5.95713615e-01
-1.00251734e+00 1.02101967e-01 -5.24574399e-01 -1.37489378e+00
-9.19823274e-02 -9.38615143e-01 8.15830827e-01 2.88126945e-01
-2.19396636e-01 -1.56030345e+00 5.23078740e-01 1.47986978e-01
3.15099061e-01 1.17132962e-02 8.22232962e-01 -3.21238786e-01
-7.43393958e-01 -4.11542095e-02 5.18363237e-01 7.48334751e-02
-2.10079998e-01 1.06364796e-02 -8.41108978e-01 -3.94961804e-01
6.39447033e-01 -3.42733443e-01 1.12075639e+00 8.09562981e-01
8.99905145e-01 -2.06905663e-01 -3.74198109e-01 4.32281137e-01
1.37413692e+00 7.36738667e-02 3.73774827e-01 5.80927692e-02
3.92250568e-01 4.63804454e-01 5.60960650e-01 7.21401453e-01
3.87195528e-01 4.87409115e-01 1.20584406e-01 4.90475774e-01
3.16113353e-01 -1.65252835e-01 5.05492806e-01 8.48605335e-01
-1.62049264e-01 -2.45872661e-01 -7.95462847e-01 7.27779269e-01
-2.26312733e+00 -1.34206307e+00 -7.19383433e-02 2.50148892e+00
9.47500050e-01 8.12836289e-02 -1.93147257e-01 -2.46905521e-01
4.57894892e-01 3.62008929e-01 -7.53878355e-01 -1.17392115e-01
1.71638310e-01 3.27604741e-01 1.94990680e-01 9.81638849e-01
-8.58361304e-01 7.08789408e-01 6.21397495e+00 1.01693547e+00
-4.36636001e-01 6.05323255e-01 3.50432605e-01 -6.57045901e-01
-2.97064692e-01 1.69452786e-01 -1.14643598e+00 5.89539587e-01
1.25017512e+00 -1.62984192e-01 5.52838326e-01 6.54039621e-01
4.41798836e-01 -6.74928010e-01 -1.04538226e+00 7.36898363e-01
-4.43532951e-02 -1.14772010e+00 -1.94834456e-01 2.05821767e-01
6.88047886e-01 1.65921040e-02 -2.30762158e-02 4.78538424e-01
6.28555655e-01 -6.92958951e-01 7.21940398e-01 1.15349412e+00
1.89016640e-01 -7.61534572e-01 2.55886436e-01 8.58895898e-01
-8.53861928e-01 -6.45901589e-03 -5.15168786e-01 -1.63714200e-01
4.34133917e-01 1.01862502e+00 -3.31274927e-01 3.55764061e-01
7.76128590e-01 6.98841393e-01 -1.19477518e-01 9.59668875e-01
-3.66588712e-01 7.08097279e-01 -5.79106510e-01 -9.89538953e-02
2.18217924e-01 -6.45205915e-01 7.85176873e-01 1.02041602e+00
5.55334091e-01 1.88711453e-02 1.21925855e-02 8.66724730e-01
3.80871952e-01 -2.25072697e-01 -5.24062932e-01 2.76792049e-01
5.08915186e-01 1.08711386e+00 -5.78192592e-01 -5.01690865e-01
-2.31653005e-01 8.22515786e-01 5.86795032e-01 6.88773930e-01
-7.97589123e-01 7.63034821e-03 2.89510936e-01 -1.27390742e-01
6.47326112e-01 -2.71275192e-01 2.76623722e-02 -1.32948124e+00
-1.18248994e-02 -5.11631846e-01 5.24445295e-01 -6.58614099e-01
-1.28570354e+00 1.34094715e-01 4.93432581e-01 -6.66498899e-01
-7.39146888e-01 -1.61247894e-01 -6.58763945e-01 9.42561269e-01
-1.21927094e+00 -9.61420774e-01 1.10433824e-01 7.11047113e-01
7.64352083e-01 5.53495511e-02 7.81093836e-01 -2.29387850e-01
-4.60347891e-01 2.71820761e-02 5.90929747e-01 -6.03174806e-01
4.81645346e-01 -1.39637876e+00 -2.85760105e-01 7.44785368e-01
1.37198687e-01 7.33483195e-01 1.13903534e+00 -8.08629632e-01
-1.29099870e+00 -7.26191401e-01 9.16060030e-01 -4.01746511e-01
6.49140656e-01 -5.61903358e-01 -1.08414900e+00 8.01714003e-01
1.52188137e-01 -3.75245929e-01 6.44140780e-01 5.53576648e-01
1.81165725e-01 -7.19863698e-02 -9.32120979e-01 4.85837549e-01
7.33065724e-01 -3.03812712e-01 -7.11974442e-01 5.95764816e-01
3.77846569e-01 -1.37332976e-01 -8.71233582e-01 2.77751721e-02
5.23526192e-01 -8.76122713e-01 8.24461162e-01 -2.26621360e-01
1.08364567e-01 -1.40909359e-01 -9.38996151e-02 -1.27340031e+00
-3.54219556e-01 -7.87471354e-01 -7.54327416e-01 1.17420864e+00
2.39682168e-01 -7.89415598e-01 6.40206277e-01 8.10846865e-01
9.47810933e-02 -6.57503366e-01 -1.33308911e+00 -7.00875521e-01
1.77765369e-01 -6.50641203e-01 4.88694683e-02 5.82353771e-01
-2.27767184e-01 3.08457375e-01 -6.29519820e-01 6.47790655e-02
1.20494008e+00 1.92433834e-01 5.16543031e-01 -1.27986753e+00
-8.66418719e-01 -2.18293533e-01 3.10607165e-01 -1.06776631e+00
3.23032737e-01 -7.16745257e-01 3.60939622e-01 -1.52271795e+00
5.72875321e-01 -3.69224131e-01 -1.58235729e-01 1.57764733e-01
-2.64839113e-01 -3.29593152e-01 -3.11001446e-02 2.30395064e-01
-6.35839045e-01 8.99606705e-01 8.40041757e-01 1.12922363e-01
-4.09189016e-01 4.14656907e-01 -1.44610867e-01 8.25970471e-01
5.79260945e-01 -9.00659561e-01 -5.21553576e-01 -1.93950698e-01
4.39367235e-01 4.25789773e-01 3.62417579e-01 -8.29019547e-01
6.60976887e-01 -3.55133474e-01 3.35772306e-01 -7.30765462e-01
8.66937280e-01 -4.07694459e-01 6.79008484e-01 3.54312301e-01
-3.40093851e-01 -3.66033614e-01 -2.85608321e-01 1.00872552e+00
-3.26807424e-03 -8.98151338e-01 8.20105076e-01 -6.39299572e-01
-3.97572994e-01 3.11853588e-01 -7.80041695e-01 -1.46596700e-01
8.73795390e-01 -9.43631530e-02 2.66269863e-01 -4.96284038e-01
-1.23865855e+00 2.44273037e-01 3.84596616e-01 -1.10518873e-01
3.81728411e-01 -1.02813506e+00 -5.71490765e-01 -1.72975019e-01
-3.35486650e-01 2.28844941e-01 6.27419055e-01 7.93814957e-01
1.31136775e-01 8.47840607e-02 1.11693293e-01 -6.11532569e-01
-8.40890229e-01 5.05816698e-01 3.28301042e-01 -6.33647442e-01
-4.91950542e-01 8.58150184e-01 2.20536478e-02 -2.96203345e-01
2.42169663e-01 2.07735509e-01 -3.37890722e-02 6.18002534e-01
4.50726062e-01 5.96861959e-01 -3.30527663e-01 3.54038514e-02
8.32097679e-02 5.45350790e-01 -2.33723000e-01 -7.58769870e-01
1.71676064e+00 -4.19065416e-01 -3.71550739e-01 1.43539822e+00
8.38923752e-01 -3.31250370e-01 -1.61535680e+00 -5.32946169e-01
-1.92038283e-01 -2.56725043e-01 3.65497947e-01 -5.40523767e-01
-5.83249509e-01 9.47413385e-01 3.96305412e-01 1.46819532e-01
7.73098290e-01 1.20753825e-01 2.30144769e-01 4.96610403e-01
5.18476486e-01 -1.36033916e+00 5.55475019e-02 7.66200006e-01
6.92369819e-01 -1.01585174e+00 2.25981861e-01 1.98256686e-01
-4.03879464e-01 1.00465965e+00 1.85200468e-01 2.90870219e-02
1.00383234e+00 4.55815643e-02 -4.53908503e-01 -2.97361910e-01
-1.15825546e+00 -1.37468860e-01 1.13110565e-01 4.00977820e-01
2.54628450e-01 -2.15685982e-02 -2.84582227e-01 2.88952529e-01
-4.52755019e-02 5.71383014e-02 4.69419450e-01 9.44893777e-01
-6.71347320e-01 -1.19453263e+00 -2.41615117e-01 5.76517701e-01
-3.36132824e-01 -1.14180207e-01 3.86145115e-01 5.09360790e-01
-1.39517382e-01 7.20446289e-01 1.98174596e-01 5.76578200e-01
-2.16855347e-01 7.30008543e-01 6.74356639e-01 -7.50730336e-01
-1.52191699e-01 3.70108962e-01 -2.37799570e-01 -3.63698304e-01
-4.52146411e-01 -1.21080995e+00 -9.52321589e-01 -1.98264852e-01
4.68172394e-02 2.78040975e-01 7.35676646e-01 1.05866396e+00
6.13210946e-02 4.78492826e-01 2.88951755e-01 -1.12284791e+00
-6.31224871e-01 -1.14140594e+00 -9.77057755e-01 9.65296105e-02
2.34880701e-01 -8.76591384e-01 -6.57453895e-01 8.06210116e-02] | [6.993046283721924, 3.8269379138946533] |
5c76de77-231c-4c2e-b755-c5555ef65342 | toward-unifying-text-segmentation-and-long | 2210.16422 | null | https://arxiv.org/abs/2210.16422v1 | https://arxiv.org/pdf/2210.16422v1.pdf | Toward Unifying Text Segmentation and Long Document Summarization | Text segmentation is important for signaling a document's structure. Without segmenting a long document into topically coherent sections, it is difficult for readers to comprehend the text, let alone find important information. The problem is only exacerbated by a lack of segmentation in transcripts of audio/video recordings. In this paper, we explore the role that section segmentation plays in extractive summarization of written and spoken documents. Our approach learns robust sentence representations by performing summarization and segmentation simultaneously, which is further enhanced by an optimization-based regularizer to promote selection of diverse summary sentences. We conduct experiments on multiple datasets ranging from scientific articles to spoken transcripts to evaluate the model's performance. Our findings suggest that the model can not only achieve state-of-the-art performance on publicly available benchmarks, but demonstrate better cross-genre transferability when equipped with text segmentation. We perform a series of analyses to quantify the impact of section segmentation on summarizing written and spoken documents of substantial length and complexity. | ['Dong Yu', 'Fei Liu', 'Xiaoyang Wang', 'Kaiqiang Song', 'Sangwoo Cho'] | 2022-10-28 | null | null | null | null | ['extractive-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.35608339e-01 2.69985884e-01 -2.79064059e-01 -4.80631948e-01
-1.59472489e+00 -9.29931879e-01 5.38515925e-01 6.04233265e-01
-4.32034135e-01 6.41438425e-01 7.84763396e-01 -3.01224887e-01
1.29119962e-01 -1.66354224e-01 -6.40382707e-01 -4.09359664e-01
1.26144290e-01 2.51950234e-01 2.10630894e-03 -6.14524782e-02
5.27344346e-01 1.47864476e-01 -1.35766387e+00 6.90977454e-01
1.09150577e+00 5.96807301e-01 2.90245980e-01 9.66772139e-01
-5.61826766e-01 7.32999086e-01 -1.17076886e+00 -2.07548842e-01
-2.37589717e-01 -7.55402327e-01 -1.14834046e+00 6.12900376e-01
7.02510655e-01 -2.05186903e-01 6.52036071e-02 8.77628922e-01
4.74695802e-01 2.05817834e-01 7.26637244e-01 -5.70524931e-01
-2.82074064e-01 1.19642889e+00 -6.85333192e-01 4.39456254e-01
6.64331794e-01 -8.34263489e-02 1.50489867e+00 -5.49175918e-01
7.47766256e-01 1.04518938e+00 3.52684826e-01 5.85012794e-01
-1.24278104e+00 -2.51310378e-01 5.26489198e-01 -2.95939445e-01
-9.23653543e-01 -7.76867449e-01 7.10565388e-01 -3.54157299e-01
1.05408800e+00 5.93034923e-01 4.47678715e-01 1.09115601e+00
1.91263616e-01 1.06665587e+00 5.21908998e-01 -4.61799085e-01
1.37939274e-01 3.35128680e-02 6.50146306e-01 4.38069701e-01
1.79857105e-01 -8.97820115e-01 -7.83394098e-01 3.55056673e-02
4.90185209e-02 -5.60602903e-01 -4.99947041e-01 4.14556354e-01
-1.18907750e+00 7.09528446e-01 -2.14336276e-01 2.72758573e-01
-3.30885381e-01 -1.95446134e-01 7.14803338e-01 2.77171701e-01
6.90386832e-01 8.77335787e-01 -3.21234286e-01 -4.60946202e-01
-1.56751394e+00 2.18022734e-01 1.06218755e+00 7.66705334e-01
3.84732664e-01 -1.60145164e-02 -5.37162662e-01 1.00209510e+00
-3.79489139e-02 2.19608292e-01 6.13180220e-01 -9.98672783e-01
9.12667036e-01 4.21141595e-01 -6.12668432e-02 -7.05175459e-01
-3.06381375e-01 -3.55030447e-01 -7.74394691e-01 -4.73102659e-01
4.10966903e-01 -4.79810268e-01 -8.62358809e-01 1.59869909e+00
-1.06499135e-01 -3.64934742e-01 2.09400401e-01 5.02557278e-01
1.17670584e+00 1.13667405e+00 -1.24217579e-02 -6.46411657e-01
1.34662259e+00 -1.05124891e+00 -7.93697417e-01 -4.03883398e-01
5.52063882e-01 -9.60618913e-01 1.25682116e+00 5.09693503e-01
-1.40240037e+00 -4.00722682e-01 -1.02679813e+00 -4.08686608e-01
1.85215697e-01 2.23893777e-01 2.27521375e-01 3.49347979e-01
-8.78444612e-01 4.80117649e-01 -7.72162318e-01 -3.99693578e-01
4.32850868e-01 1.23417854e-01 -1.75167844e-01 1.95804209e-01
-7.99532473e-01 5.01311719e-01 2.79659569e-01 -1.48997992e-01
-5.39790452e-01 -7.40478992e-01 -8.11874986e-01 3.23523432e-01
2.70400852e-01 -4.83579725e-01 1.63536501e+00 -9.81155217e-01
-1.48868525e+00 8.10207725e-01 -6.24205828e-01 -4.63916808e-01
4.47298616e-01 -3.86092901e-01 4.03237354e-04 6.56568050e-01
2.44819045e-01 5.97138047e-01 8.03690195e-01 -1.14264596e+00
-6.33054733e-01 -2.21044749e-01 -5.46100996e-02 5.07060885e-01
-4.53151703e-01 2.74926752e-01 -6.19657755e-01 -7.21695006e-01
1.10878097e-02 -6.46442413e-01 -1.89990997e-01 -6.16187871e-01
-9.69400167e-01 -3.99349570e-01 5.33675969e-01 -9.83455300e-01
1.50271952e+00 -2.12112212e+00 3.17182690e-01 -5.16503677e-02
7.50645697e-02 -2.03589387e-02 -2.89528459e-01 7.42158890e-01
2.71567702e-01 4.14019287e-01 -3.48150611e-01 -7.53172398e-01
-8.13146681e-02 -7.00213760e-02 -6.72776639e-01 1.07194453e-01
2.76840568e-01 7.54553020e-01 -6.37026668e-01 -6.21221781e-01
-3.74517173e-01 2.40269676e-01 -6.02625132e-01 1.61994040e-01
-5.88773072e-01 4.17755127e-01 -5.37101746e-01 3.86382461e-01
2.80933648e-01 -2.53567129e-01 1.72182202e-01 1.09583110e-01
-2.27205575e-01 8.89023662e-01 -8.30758691e-01 1.84594238e+00
-2.38635659e-01 1.01664674e+00 2.06613496e-01 -1.13335931e+00
6.83043838e-01 2.80798614e-01 3.77351195e-01 -4.28877473e-01
8.31151903e-02 -5.00307269e-02 -2.00051039e-01 -5.56268692e-01
1.04371119e+00 1.09155506e-01 -4.51848894e-01 8.37845922e-01
-4.92980471e-04 -4.48541075e-01 7.26020217e-01 6.18865073e-01
9.67932761e-01 -3.14955264e-01 1.39059156e-01 -3.80573809e-01
3.13967586e-01 2.40802959e-01 2.65247881e-01 9.44203317e-01
1.91021651e-01 8.98698747e-01 1.01451325e+00 1.71645820e-01
-8.18344295e-01 -8.62347841e-01 6.92771673e-02 1.29817319e+00
-1.22808553e-01 -7.99471736e-01 -1.03873813e+00 -6.38406813e-01
-2.70850480e-01 9.20482337e-01 -3.78918111e-01 2.04308461e-02
-5.58306098e-01 -6.02689564e-01 7.13370800e-01 3.65806222e-01
2.33016670e-01 -9.41013396e-01 -5.51769853e-01 2.41920248e-01
-5.99195302e-01 -1.15365660e+00 -7.33815908e-01 2.10049853e-01
-8.25532675e-01 -7.80954599e-01 -7.02156007e-01 -8.70820820e-01
6.45235360e-01 2.77557015e-01 1.13513422e+00 -8.87158960e-02
1.67098048e-03 5.25069654e-01 -4.62387383e-01 -5.31404734e-01
-7.58698404e-01 7.67528594e-01 -2.48899236e-01 -1.72558591e-01
-1.32968947e-02 -3.44664484e-01 -3.03975791e-01 -4.05452810e-02
-1.05938601e+00 9.79404077e-02 4.71531361e-01 5.90843618e-01
4.70546842e-01 -6.27679825e-02 7.56929636e-01 -1.10640156e+00
1.19438875e+00 -2.63893813e-01 -1.62987113e-01 2.76773185e-01
-1.82442933e-01 1.42727762e-01 6.74976051e-01 -2.74464965e-01
-1.25725937e+00 -1.32793143e-01 -2.24504083e-01 3.15618426e-01
-1.20719343e-01 9.64431822e-01 -7.72296079e-03 5.90737522e-01
5.68778813e-01 4.46948409e-01 -1.11983232e-01 -5.68271697e-01
2.61528134e-01 9.15453970e-01 5.82695305e-01 -6.65075302e-01
3.68834615e-01 1.50490850e-01 -5.21539867e-01 -1.43810713e+00
-1.28393030e+00 -6.50591731e-01 -5.07677376e-01 -6.59536421e-02
8.28706801e-01 -8.39544594e-01 -1.89069405e-01 1.96350753e-01
-1.34220219e+00 -2.88001448e-01 -3.61178428e-01 1.82436600e-01
-3.08168024e-01 6.53625965e-01 -8.40152919e-01 -5.52710891e-01
-6.33684218e-01 -9.55940843e-01 1.28927565e+00 2.75369078e-01
-7.69902349e-01 -7.41034627e-01 -4.32665343e-04 7.05658257e-01
-6.42891005e-02 -1.37523413e-02 8.80545974e-01 -9.46084440e-01
-3.29660267e-01 -1.35554612e-01 -2.09220424e-02 4.85319376e-01
1.72127053e-01 3.40122610e-01 -1.03988492e+00 -3.09630007e-01
-1.60991624e-01 -3.73805553e-01 1.29871440e+00 5.30977249e-01
1.16430354e+00 -7.07972944e-01 -3.97958867e-02 3.76975507e-01
8.73198450e-01 3.24896276e-02 4.53629404e-01 1.42927438e-01
6.66113675e-01 8.43237102e-01 4.66609001e-01 4.28351134e-01
2.76410758e-01 1.22425832e-01 -1.10451438e-01 1.31049082e-01
6.69767484e-02 -9.40680727e-02 4.85243946e-01 1.30426729e+00
1.45117775e-01 -6.42733932e-01 -8.31870317e-01 5.70099771e-01
-1.53491855e+00 -9.96862054e-01 -1.18577229e-02 1.79494202e+00
1.32288432e+00 3.77361655e-01 1.90699711e-01 1.76065788e-01
4.62282747e-01 5.36223471e-01 -3.17053765e-01 -7.10890830e-01
-9.93288979e-02 1.12512223e-01 1.07967146e-01 5.55912793e-01
-1.03673756e+00 9.06892419e-01 6.83870173e+00 8.17357183e-01
-1.18001688e+00 -4.01430815e-01 7.67592072e-01 -3.50141972e-01
-5.27173281e-01 -2.50062734e-01 -9.55776513e-01 3.44912410e-01
1.07639873e+00 -4.70856130e-01 7.45141953e-02 5.26642203e-01
5.01820207e-01 -2.33615920e-01 -1.25661993e+00 5.40741861e-01
2.29978681e-01 -1.52735972e+00 3.28693539e-01 -1.85251594e-01
8.82636726e-01 -1.41160265e-01 -6.71664253e-02 2.22111449e-01
4.65857377e-03 -9.57681298e-01 7.79101729e-01 1.09719239e-01
6.46992862e-01 -7.28233039e-01 4.54226375e-01 4.54865068e-01
-8.06006610e-01 1.45658135e-01 -2.27853969e-01 -6.14179224e-02
1.36912927e-01 5.53657591e-01 -1.01008129e+00 5.17362773e-01
3.76918405e-01 6.95348918e-01 -6.14621341e-01 7.88868546e-01
-3.55981439e-01 1.11080050e+00 -1.55585751e-01 -2.08086878e-01
5.05584240e-01 -4.45610620e-02 7.21507132e-01 1.80707765e+00
1.48320258e-01 9.87445638e-02 3.30721766e-01 5.73393047e-01
-3.86255503e-01 3.60469460e-01 -2.82596678e-01 -5.80361009e-01
4.70247447e-01 1.05099761e+00 -9.50409830e-01 -5.14512777e-01
-2.47876436e-01 9.47154224e-01 1.73911139e-01 5.70078850e-01
-4.23878312e-01 -5.29479146e-01 3.66373211e-01 -8.60539898e-02
3.17396671e-01 -3.77990425e-01 -4.81593132e-01 -1.25173426e+00
1.69181257e-01 -1.22290874e+00 4.75784838e-01 -4.53211874e-01
-9.64757383e-01 5.49313962e-01 -9.62719247e-02 -8.47308934e-01
-3.73450845e-01 -7.52832294e-02 -8.42537940e-01 6.30808294e-01
-1.34999347e+00 -7.99085975e-01 -6.31258786e-02 7.16636255e-02
1.24456215e+00 1.92633588e-02 6.89247727e-01 -4.13969904e-02
-8.55313122e-01 5.37452757e-01 2.01356128e-01 1.44109026e-01
8.03131938e-01 -1.28582942e+00 4.45830315e-01 9.83063340e-01
3.85412127e-01 6.63447738e-01 1.04683888e+00 -5.10375261e-01
-1.21716475e+00 -9.39646304e-01 1.11555135e+00 -3.75948936e-01
5.58095217e-01 -4.08660799e-01 -1.03642333e+00 7.27921963e-01
6.37196302e-01 -8.19783866e-01 8.79598260e-01 2.69632041e-01
-2.69965470e-01 4.71677817e-02 -5.58002710e-01 6.75388813e-01
6.19118035e-01 -5.31466961e-01 -9.63543236e-01 2.57767946e-01
1.05876184e+00 -4.31547880e-01 -5.43459892e-01 2.58248635e-02
4.64496702e-01 -8.08654785e-01 5.91821432e-01 -6.92097723e-01
9.82726812e-01 1.68333173e-01 -5.38824163e-02 -1.36774325e+00
7.70744383e-02 -9.87259805e-01 1.76579267e-01 1.69743347e+00
7.14085460e-01 -2.51210034e-01 7.04767406e-01 3.93224418e-01
-4.10204858e-01 -5.66236675e-01 -6.77817225e-01 -5.39989710e-01
1.84995353e-01 -3.79709333e-01 1.44656494e-01 7.33799875e-01
4.09546852e-01 8.18478107e-01 -3.52756232e-02 -6.53706789e-02
3.45963836e-01 3.24763179e-01 7.17511773e-01 -1.01991248e+00
-7.77854919e-02 -7.28067696e-01 1.22518905e-01 -1.31014681e+00
3.97230297e-01 -8.60100091e-01 4.06556785e-01 -1.94778621e+00
3.34571689e-01 2.56007820e-01 -3.71359885e-02 2.12449729e-01
-3.52685630e-01 -9.45416987e-02 1.86619505e-01 1.65082440e-01
-9.65577304e-01 3.69638592e-01 1.20769596e+00 -4.62286115e-01
-5.11323929e-01 1.38087317e-01 -1.20085216e+00 6.32758379e-01
8.44597876e-01 -3.07679325e-01 -5.30610681e-01 -5.90682447e-01
1.04130536e-01 1.44719481e-01 -1.81529880e-01 -7.85227418e-01
2.93943495e-01 -7.29660764e-02 1.52667105e-01 -7.65078425e-01
1.54752716e-01 6.63717790e-03 -6.70714259e-01 4.87544835e-02
-1.06173074e+00 -9.43462402e-02 4.33021158e-01 3.59014392e-01
-4.28197026e-01 -3.17459941e-01 4.93804783e-01 -1.05813615e-01
-3.12483907e-01 -6.33200333e-02 -8.65131974e-01 5.31520903e-01
4.50563967e-01 -1.23090170e-01 -3.63937199e-01 -5.89405358e-01
-4.80697185e-01 3.97072822e-01 3.12988937e-01 2.87696570e-01
5.12427032e-01 -6.83158696e-01 -9.88692403e-01 -7.31694559e-03
-7.04661608e-02 2.10921884e-01 1.58940673e-01 6.80306852e-01
-4.92124796e-01 5.45151174e-01 1.36557296e-01 -6.38065696e-01
-1.59800959e+00 -7.44146034e-02 -2.57860571e-01 -4.41306919e-01
-6.90756023e-01 9.79250491e-01 8.69050398e-02 -5.98532483e-02
5.98082900e-01 -7.18310118e-01 -3.70801628e-01 6.65714085e-01
7.27216959e-01 1.55391917e-01 1.83622688e-02 -3.85284185e-01
-8.44786763e-02 4.07032609e-01 -4.20515209e-01 -3.44115347e-01
1.31533122e+00 -3.69565666e-01 -1.22523218e-01 6.91359699e-01
1.16804171e+00 4.30314034e-01 -1.11302292e+00 7.40901530e-02
2.81524777e-01 5.73891588e-02 -7.54097253e-02 -7.00113773e-01
-5.14748752e-01 8.92709911e-01 -4.73714441e-01 5.62482476e-01
9.68575001e-01 8.95107016e-02 9.69653308e-01 7.49523878e-01
-3.66146743e-01 -1.24918330e+00 2.67724782e-01 7.33937204e-01
1.00351822e+00 -1.15011227e+00 1.07017443e-01 -3.42059404e-01
-9.04805362e-01 1.11857224e+00 2.94527501e-01 9.93583277e-02
6.55604824e-02 2.43549839e-01 1.75731838e-01 -1.53272197e-01
-9.46669638e-01 1.86651833e-02 5.57564020e-01 1.96723640e-01
7.93179452e-01 -2.74688095e-01 -2.34534949e-01 7.29041278e-01
-4.85354722e-01 -4.81182188e-01 8.83044124e-01 9.01758134e-01
-7.81232476e-01 -8.87576282e-01 -2.54597276e-01 6.37810349e-01
-9.39613581e-01 -1.60172418e-01 -8.78328085e-01 5.78097045e-01
-6.46984041e-01 1.19755268e+00 1.93219185e-01 5.56317018e-03
2.62812048e-01 2.63392806e-01 3.71564955e-01 -1.13333619e+00
-7.01722622e-01 4.35227424e-01 5.17805338e-01 -2.80319721e-01
-3.41148466e-01 -9.27811444e-01 -1.37103319e+00 -3.00616212e-02
2.76536774e-03 6.00342155e-01 5.94279826e-01 1.03375804e+00
3.30906153e-01 8.80626380e-01 4.86502141e-01 -8.09954703e-01
-8.23691726e-01 -1.02971780e+00 -4.18448865e-01 3.50202560e-01
6.08900309e-01 2.08996773e-01 -3.94175649e-01 4.31415021e-01] | [12.499053001403809, 9.417186737060547] |
a6eb1f60-4a66-4222-acc7-1ac98ccb2bdd | hydramix-net-a-deep-multi-task-semi | 2008.04753 | null | https://arxiv.org/abs/2008.04753v1 | https://arxiv.org/pdf/2008.04753v1.pdf | HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification | Semi-supervised techniques have removed the barriers of large scale labelled set by exploiting unlabelled data to improve the performance of a model. In this paper, we propose a semi-supervised deep multi-task classification and localization approach HydraMix-Net in the field of medical imagining where labelling is time consuming and costly. Firstly, the pseudo labels are generated using the model's prediction on the augmented set of unlabelled image with averaging. The high entropy predictions are further sharpened to reduced the entropy and are then mixed with the labelled set for training. The model is trained in multi-task learning manner with noise tolerant joint loss for classification localization and achieves better performance when given limited data in contrast to a simple deep model. On DLBCL data it achieves 80\% accuracy in contrast to simple CNN achieving 70\% accuracy when given only 100 labelled examples. | ['Nasir M. Rajpoot', 'Talha Qaiser', 'Shan E Ahmed Raza', 'R. M. Saad Bashir'] | 2020-08-11 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 6.24819160e-01 8.06667626e-01 -1.10598795e-01 -5.60883999e-01
-1.47766495e+00 -1.63820624e-01 4.61684048e-01 3.49242598e-01
-6.92285061e-01 1.30953455e+00 1.64671019e-01 1.26140058e-01
-1.58299968e-01 -4.11385030e-01 -4.64337498e-01 -1.05236793e+00
2.51389146e-01 9.27507341e-01 -1.92859359e-02 2.12511435e-01
-1.18299976e-01 -3.80223133e-02 -1.12941480e+00 6.69813156e-01
5.16198158e-01 1.18467593e+00 1.74864098e-01 8.06466103e-01
3.09281617e-01 1.13891149e+00 -3.86069328e-01 -2.09944710e-01
1.25697106e-01 -4.81455147e-01 -1.31499982e+00 7.74969533e-02
1.58840135e-01 6.73999712e-02 -2.29159705e-02 8.73934627e-01
8.15819204e-01 -5.28837554e-02 7.75763571e-01 -7.58297563e-01
-1.22159302e-01 3.79849434e-01 -4.43355173e-01 2.29099318e-02
-4.11084592e-02 1.07277399e-02 9.09635901e-01 -5.47314286e-01
6.97304368e-01 8.47272515e-01 9.13783789e-01 7.78174281e-01
-1.29736507e+00 -3.61576706e-01 -5.78851700e-01 -2.11267009e-01
-1.37679029e+00 -3.04215699e-01 4.51232821e-01 -2.62336105e-01
1.04052722e+00 1.78419128e-01 4.86511141e-01 8.42156947e-01
4.88671571e-01 8.21280479e-01 1.53304076e+00 -5.48528373e-01
1.15345024e-01 4.38792408e-01 1.38022732e-02 9.62806284e-01
-2.26436660e-01 1.52757376e-01 -1.95823357e-01 -1.60624981e-01
3.28638107e-01 4.23509218e-02 4.62928191e-02 -7.41048828e-02
-1.21217632e+00 8.97687018e-01 5.76672077e-01 3.76051098e-01
-3.68976563e-01 2.25644678e-01 6.32543445e-01 1.75055087e-01
9.29439664e-01 3.48397911e-01 -6.31436646e-01 1.26082614e-01
-1.35979617e+00 1.24660030e-03 4.83919680e-01 4.83787537e-01
6.53529048e-01 -3.41184080e-01 -2.99165100e-01 1.03073502e+00
2.70979971e-01 -2.18972359e-02 7.77583659e-01 -9.22373176e-01
-9.43480432e-03 5.80173254e-01 -4.54313964e-01 -2.43908316e-01
-7.70670414e-01 -8.67231011e-01 -1.15593469e+00 2.76846260e-01
4.61033076e-01 -2.16400370e-01 -1.28765118e+00 1.54755938e+00
2.49765456e-01 -9.32376236e-02 2.73304939e-01 4.01971281e-01
7.89679408e-01 3.86689186e-01 2.67504275e-01 -1.95996597e-01
1.32348883e+00 -1.16409612e+00 -6.55646861e-01 -2.67627150e-01
1.21076691e+00 -5.45693994e-01 4.07237977e-01 4.68269736e-01
-1.02123976e+00 -4.58234638e-01 -9.01070595e-01 -1.63345665e-01
-4.49108034e-01 2.50592709e-01 5.00459313e-01 4.20833707e-01
-1.30762935e+00 8.02087963e-01 -5.76185048e-01 -2.39963993e-01
1.17202497e+00 6.90090418e-01 -4.86030579e-01 -1.72145531e-01
-1.12873888e+00 1.21424186e+00 8.28768075e-01 -2.37185687e-01
-1.35800290e+00 -5.12739480e-01 -8.11872721e-01 -2.53540158e-01
-1.75764170e-02 -6.46765351e-01 1.12609744e+00 -1.06599534e+00
-1.32516265e+00 1.30958641e+00 1.40683249e-01 -7.94454098e-01
9.31509674e-01 2.34048903e-01 -4.27735709e-02 1.42917648e-01
1.35899708e-01 1.30511296e+00 7.35052824e-01 -1.10070658e+00
-6.58533573e-01 -2.47707322e-01 -4.58722532e-01 3.75704527e-01
1.15328275e-01 -2.30434239e-01 1.29041106e-01 -2.30474710e-01
-8.05049241e-02 -1.01285946e+00 -5.39718688e-01 -1.59229144e-01
-5.52678883e-01 -1.81050941e-01 9.13273096e-01 -7.28216052e-01
6.55445158e-01 -2.05948639e+00 -5.06742522e-02 1.83665268e-02
4.81698453e-01 2.37518355e-01 6.35635257e-02 -1.47389239e-02
-4.22940344e-01 1.10313157e-02 -3.40364486e-01 -5.95317543e-01
-3.95095646e-01 3.25781196e-01 2.72010595e-01 6.90904319e-01
1.47051573e-01 1.12195492e+00 -9.61165607e-01 -9.62018073e-01
4.38352793e-01 6.03131175e-01 -2.35657066e-01 -2.73328293e-02
-5.44397421e-02 7.15486884e-01 -1.13898203e-01 5.74281037e-01
3.72739673e-01 -5.16922474e-01 1.13766246e-01 1.74427573e-02
5.22461712e-01 9.19341296e-02 -5.19958615e-01 1.81020856e+00
-6.95071638e-01 5.28991640e-01 -1.96760163e-01 -1.26653600e+00
7.62984276e-01 7.12150812e-01 6.40641153e-01 -3.81077290e-01
1.77184448e-01 2.11353436e-01 5.21215191e-03 -1.38490856e-01
-1.28645629e-01 -6.22759044e-01 -7.45629296e-02 3.30439627e-01
4.94784027e-01 -3.22602779e-01 -2.43393689e-01 7.22527653e-02
1.37438512e+00 1.67108908e-01 7.22132087e-01 -3.10616612e-01
5.31858861e-01 -3.29967923e-02 4.71466422e-01 6.96236014e-01
-4.79418010e-01 8.05135429e-01 3.79202485e-01 -6.25129402e-01
-1.21931326e+00 -7.26440728e-01 -5.50279319e-01 7.92759776e-01
-5.25968075e-01 -1.42019689e-01 -8.44368577e-01 -1.32730365e+00
-2.24704519e-01 5.49510896e-01 -9.60086405e-01 -2.76533931e-01
-2.63862431e-01 -1.35274267e+00 6.38590991e-01 2.79483914e-01
6.37509048e-01 -1.25243270e+00 -5.74381769e-01 2.83498555e-01
-2.51107126e-01 -7.78713882e-01 -7.76155945e-03 9.76089716e-01
-9.12635028e-01 -7.97274113e-01 -9.20590341e-01 -9.08552170e-01
9.13842320e-01 -6.20638430e-01 9.66165066e-01 -5.86132891e-02
-5.66758394e-01 -2.48616710e-01 -2.04765871e-02 -4.82425869e-01
-7.10232019e-01 9.21088159e-02 -3.04019392e-01 -2.11161643e-01
2.23333180e-01 -2.99151838e-01 -7.36827016e-01 7.10842833e-02
-7.68379986e-01 2.05704167e-01 9.68800008e-01 1.49445546e+00
9.27115083e-01 2.71198660e-01 9.17889237e-01 -1.19200003e+00
2.88128436e-01 -4.06325161e-01 -3.99427861e-02 1.49146527e-01
-6.56954825e-01 2.30709873e-02 6.18966639e-01 -2.60107428e-01
-9.42968190e-01 6.43287301e-01 -2.60556877e-01 -1.70172259e-01
-4.11043972e-01 1.74398616e-01 3.06283981e-01 -3.59283030e-01
7.92007625e-01 1.18776873e-01 9.38511193e-02 -2.14570150e-01
9.15076956e-02 8.24287176e-01 2.28162825e-01 2.80585624e-02
7.23591968e-02 6.18669868e-01 2.60939211e-01 -3.65802348e-01
-1.44150996e+00 -4.32155579e-01 -7.83940613e-01 -2.73621619e-01
1.08061004e+00 -7.87752688e-01 -5.00749588e-01 4.86652017e-01
-7.86363780e-01 -4.34540361e-01 -7.91434169e-01 4.61521685e-01
-8.49123299e-01 2.17524514e-01 -8.81413758e-01 -6.64299488e-01
-6.70641303e-01 -1.07476854e+00 1.34715354e+00 -4.03548032e-02
-3.91298622e-01 -1.32184815e+00 2.43018687e-01 6.67484403e-01
3.03801715e-01 3.67286831e-01 8.14074814e-01 -1.20670605e+00
-1.39085054e-02 -7.68026948e-01 -1.80625483e-01 8.33507061e-01
-6.12083531e-04 -5.44885516e-01 -1.43745124e+00 -2.90528923e-01
1.70470878e-01 -1.02773166e+00 1.12774277e+00 6.39622927e-01
1.37407374e+00 -1.43514462e-02 -6.59179866e-01 5.28776050e-01
1.50692189e+00 -5.00070080e-02 4.90685821e-01 1.87084928e-01
4.53834325e-01 4.92290527e-01 4.30181950e-01 1.48006707e-01
3.05669699e-02 2.61039108e-01 3.47342670e-01 -3.70883197e-01
-2.86953419e-01 2.36201398e-02 -1.61397561e-01 5.97293735e-01
2.32185632e-01 -2.76203722e-01 -1.02178967e+00 5.05803823e-01
-1.70516753e+00 -6.75409734e-01 2.57027615e-02 1.93065441e+00
1.01601160e+00 4.31677192e-01 -2.87680507e-01 3.91265243e-01
6.48061693e-01 4.57033440e-02 -3.98519665e-01 -3.04198354e-01
-1.33432940e-01 4.43323851e-01 7.30421841e-01 4.96754616e-01
-1.38893771e+00 7.49246180e-01 6.64005518e+00 1.34313142e+00
-7.73525536e-01 7.27255225e-01 1.39135158e+00 -2.07582578e-01
2.96826363e-01 -1.64026484e-01 -5.91259241e-01 3.79477620e-01
1.34185696e+00 3.15284163e-01 -1.24457575e-01 7.79028714e-01
-1.42056525e-01 -5.41882098e-01 -1.08895433e+00 9.37833965e-01
5.35958186e-02 -1.29415083e+00 -4.06607807e-01 3.31999272e-01
8.43631983e-01 3.33552182e-01 1.70944422e-01 2.46158555e-01
6.48998737e-01 -1.30418551e+00 2.11049229e-01 4.79216307e-01
9.58073139e-01 -7.56253719e-01 1.23631465e+00 7.14107573e-01
-5.66400588e-01 1.22925499e-02 -3.05856496e-01 1.46378860e-01
2.12224305e-01 9.22349930e-01 -1.29846597e+00 4.19034749e-01
3.17256182e-01 5.52158952e-01 -5.10295928e-01 8.18360627e-01
4.13549989e-02 5.57754278e-01 -5.46900570e-01 1.41295731e-01
5.40251911e-01 2.25853577e-01 -1.82302278e-02 1.22262275e+00
4.15272228e-02 -2.11561754e-01 1.74168110e-01 3.27499568e-01
-2.42545485e-01 9.50797051e-02 -5.90803802e-01 2.14588523e-01
-2.53548205e-01 1.58601987e+00 -9.62828100e-01 -6.21597052e-01
-7.99502358e-02 1.17338705e+00 4.97701615e-01 -1.21772550e-01
-6.59317374e-01 -1.65031627e-01 -4.40170109e-01 -1.06385946e-01
-1.06951341e-01 6.43265665e-01 -4.62772250e-01 -6.79610372e-01
-4.86871749e-01 -4.18789208e-01 5.75822353e-01 -6.91577137e-01
-1.00200832e+00 8.50644708e-01 -3.42787623e-01 -9.49975729e-01
-2.06304535e-01 -3.99498552e-01 -3.72130901e-01 7.50858545e-01
-1.37837505e+00 -1.35700643e+00 -1.74169932e-02 2.20888361e-01
5.08279443e-01 -1.33698002e-01 1.19558012e+00 2.34748647e-01
-2.70258397e-01 5.04986703e-01 3.77137363e-01 2.06237826e-02
7.06604779e-01 -1.60628819e+00 -1.51047647e-01 1.55836090e-01
-1.67703941e-01 -2.34699473e-01 3.97786200e-01 -6.47082388e-01
-2.12310225e-01 -1.35765684e+00 1.00340152e+00 -5.75277150e-01
2.48559356e-01 -3.21875781e-01 -7.20793128e-01 5.89441180e-01
2.94425249e-01 4.93016005e-01 1.10701072e+00 -4.01845336e-01
1.88132718e-01 -5.87179139e-02 -1.80936301e+00 -5.81419058e-02
5.30011296e-01 -5.76404512e-01 -4.07961011e-01 1.02145135e+00
3.37044895e-01 -2.25385725e-01 -1.09813941e+00 5.17692506e-01
2.14876413e-01 -6.49474859e-01 7.75129378e-01 -4.34800953e-01
4.80312526e-01 1.92065135e-01 -3.48754078e-02 -1.43878841e+00
-2.47950241e-01 -3.77410918e-01 3.43876511e-01 7.64472187e-01
7.48367667e-01 -4.29910451e-01 1.07231224e+00 2.47094169e-01
-8.21071044e-02 -1.20288408e+00 -1.25355446e+00 -2.95894712e-01
2.15343118e-01 -1.67821646e-01 -1.06164716e-01 9.05569136e-01
1.01808369e-01 4.24789548e-01 -4.58167672e-01 -4.27218080e-01
9.06740546e-01 -3.27299625e-01 -3.88568267e-02 -1.19642889e+00
-2.26781905e-01 8.59796181e-02 -5.08986175e-01 -3.24973315e-01
2.91286021e-01 -1.29313850e+00 1.62551448e-01 -1.49861896e+00
6.01211548e-01 -5.18047094e-01 -5.59344411e-01 6.98479950e-01
1.09250627e-01 7.58737326e-01 -2.32271239e-01 2.18701348e-01
-1.01702178e+00 2.88081884e-01 1.26858473e+00 -8.55543315e-02
2.37354711e-01 1.08899146e-01 -4.19655472e-01 8.83404851e-01
8.14793050e-01 -8.11357319e-01 -2.68765837e-01 9.19033885e-02
-5.71888238e-02 2.98682719e-01 2.67181039e-01 -1.14377677e+00
3.88141343e-04 3.89511734e-01 8.74221981e-01 -4.54282790e-01
5.25261700e-01 -7.02403188e-01 1.68162584e-01 7.75030375e-01
-8.36970389e-01 -4.87124294e-01 -6.41136542e-02 5.26012540e-01
-2.44621888e-01 -3.84693801e-01 1.36116362e+00 -5.81457496e-01
-8.64133313e-02 2.17053607e-01 -2.16148064e-01 -4.97201346e-02
1.38254762e+00 -1.21655412e-01 -8.74534249e-02 -2.71091163e-01
-1.53639030e+00 2.22191215e-01 2.18002006e-01 -2.31575742e-01
5.61587095e-01 -1.16159773e+00 -7.58562446e-01 2.83669196e-02
-8.74976143e-02 2.97522306e-01 3.41294080e-01 9.69434023e-01
-5.28375447e-01 7.21459687e-01 -5.14243059e-02 -6.19909883e-01
-1.19893992e+00 5.71258783e-01 7.59288549e-01 -9.07318294e-01
-3.82877976e-01 1.10584474e+00 2.76449621e-01 -6.86819553e-01
1.15993507e-01 -1.05742123e-02 -1.20022409e-01 8.81229416e-02
2.56791115e-01 1.97839752e-01 3.28776598e-01 -6.29461110e-01
-2.47350410e-01 2.74947077e-01 -5.84175467e-01 -1.49433598e-01
1.49128091e+00 2.62550432e-02 -8.98303911e-02 3.67321640e-01
1.61408913e+00 -7.14202285e-01 -1.25849819e+00 -2.31850460e-01
1.66423917e-02 1.20480768e-01 5.05104303e-01 -1.29517233e+00
-9.56394672e-01 1.02241004e+00 1.06901097e+00 -1.54973224e-01
1.06585741e+00 3.14167798e-01 6.28699303e-01 3.34854662e-01
8.49044025e-02 -1.35032439e+00 4.75158729e-02 1.98787212e-01
3.06626171e-01 -1.55895841e+00 2.52134670e-02 -6.40120208e-02
-8.73396933e-01 8.44742715e-01 3.46732974e-01 -1.20032430e-01
7.15543270e-01 3.69203448e-01 -3.63502167e-02 -4.65702504e-01
-9.22431767e-01 -1.93020970e-01 9.86225978e-02 6.25425696e-01
3.29648495e-01 1.29636824e-01 -8.67114365e-02 2.11524531e-01
1.14811309e-01 4.15358484e-01 1.89103991e-01 8.45872879e-01
-4.94654864e-01 -8.98438215e-01 1.44977430e-02 9.94620740e-01
-1.10025048e+00 -2.96635389e-01 -2.61146992e-01 4.50099915e-01
3.72380823e-01 5.29818475e-01 -1.97645366e-01 -1.84636638e-01
-3.01101387e-01 5.19735157e-01 3.93608868e-01 -7.50566304e-01
-7.07509935e-01 2.53864855e-01 1.77530557e-01 -1.85311526e-01
-3.65756065e-01 -7.61508346e-01 -1.26213896e+00 2.88595527e-01
-5.77212036e-01 1.71878070e-01 5.72511196e-01 1.09030461e+00
7.82488510e-02 6.32443547e-01 6.16933107e-01 -6.38520241e-01
-5.61539531e-01 -1.24910319e+00 -6.33677721e-01 3.60435307e-01
4.20006365e-01 -5.02734721e-01 -4.50419754e-01 4.95813079e-02] | [14.736019134521484, -2.2614588737487793] |
90519a52-dbe1-4ab0-96fa-c6da815359d2 | genetic-algorithm-optimized-neural-networks | 2010.04340 | null | https://arxiv.org/abs/2010.04340v2 | https://arxiv.org/pdf/2010.04340v2.pdf | Genetic-algorithm-optimized neural networks for gravitational wave classification | Gravitational-wave detection strategies are based on a signal analysis technique known as matched filtering. Despite the success of matched filtering, due to its computational cost, there has been recent interest in developing deep convolutional neural networks (CNNs) for signal detection. Designing these networks remains a challenge as most procedures adopt a trial and error strategy to set the hyperparameter values. We propose a new method for hyperparameter optimization based on genetic algorithms (GAs). We compare six different GA variants and explore different choices for the GA-optimized fitness score. We show that the GA can discover high-quality architectures when the initial hyperparameter seed values are far from a good solution as well as refining already good networks. For example, when starting from the architecture proposed by George and Huerta, the network optimized over the 20-dimensional hyperparameter space has 78% fewer trainable parameters while obtaining an 11% increase in accuracy for our test problem. Using genetic algorithm optimization to refine an existing network should be especially useful if the problem context (e.g. statistical properties of the noise, signal model, etc) changes and one needs to rebuild a network. In all of our experiments, we find the GA discovers significantly less complicated networks as compared to the seed network, suggesting it can be used to prune wasteful network structures. While we have restricted our attention to CNN classifiers, our GA hyperparameter optimization strategy can be applied within other machine learning settings. | ['Gaurav Khanna', 'Collin D. Capano', 'Scott E. Field', 'Dwyer S. Deighan'] | 2020-10-09 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [ 3.37330878e-01 -3.97573337e-02 3.46573710e-01 -9.60532650e-02
-4.55859721e-01 -6.87803805e-01 2.50040174e-01 -1.80255428e-01
-7.26894379e-01 6.16665244e-01 -1.49963170e-01 -4.34602261e-01
-5.67362547e-01 -8.65970373e-01 -5.17600656e-01 -9.00803506e-01
-3.76244724e-01 3.15734535e-01 4.58149493e-01 -4.23615128e-01
3.50668103e-01 7.94690013e-01 -1.48501539e+00 1.14204548e-03
5.19799948e-01 9.16086435e-01 -1.19011685e-01 8.38113248e-01
3.65627944e-01 1.34149730e-01 -8.02139878e-01 -4.24437135e-01
5.49746275e-01 -6.65138423e-01 -6.18307412e-01 -4.67820108e-01
2.63932467e-01 2.96021461e-01 -1.81464866e-01 1.21865022e+00
7.95124710e-01 5.22062838e-01 2.16311485e-01 -1.03862226e+00
9.88910627e-03 7.71937132e-01 -7.36777410e-02 5.80455840e-01
-1.08559079e-01 3.87765825e-01 8.67680192e-01 -3.92990381e-01
4.20348823e-01 7.90037096e-01 1.05192256e+00 6.13879859e-01
-1.36816192e+00 -7.26001203e-01 -1.80223361e-01 2.48367935e-01
-1.47180033e+00 -4.46013302e-01 6.90995038e-01 -2.08073750e-01
9.90805387e-01 3.93657386e-01 9.53641593e-01 8.18028629e-01
1.34913012e-01 2.20736161e-01 6.27309978e-01 -6.84742391e-01
4.43484962e-01 -6.73576593e-02 -2.43473068e-01 7.89393306e-01
4.91733044e-01 3.38180751e-01 -3.41376424e-01 -3.07080805e-01
8.58458102e-01 -7.30836987e-01 -5.16846180e-01 -1.83113605e-01
-1.01341820e+00 8.96583378e-01 5.61566114e-01 7.10091531e-01
-1.81894124e-01 2.67976046e-01 1.20061949e-01 4.36189324e-01
1.08515583e-01 1.64843178e+00 -6.63923442e-01 -1.77208602e-01
-1.01488340e+00 3.50827724e-01 8.73660862e-01 2.49516174e-01
7.40675032e-01 2.98502505e-01 1.17901176e-01 8.76919329e-01
6.09560777e-03 -1.49710789e-01 5.06438494e-01 -9.70483541e-01
1.25905991e-01 4.99140382e-01 -1.97764963e-01 -1.26563716e+00
-9.53538716e-01 -1.10538208e+00 -7.12838173e-01 3.05242836e-01
8.30887139e-01 -6.33431375e-01 -9.01240170e-01 1.62886488e+00
7.83719495e-02 4.65433955e-01 2.53093559e-02 8.87749434e-01
6.50278389e-01 2.27752686e-01 -2.89101273e-01 4.33164723e-02
1.19217181e+00 -4.80166405e-01 -8.09574872e-02 -4.45146501e-01
6.41993701e-01 -5.29140174e-01 7.08064735e-01 6.14032626e-01
-1.01994884e+00 -1.86067894e-01 -1.35528731e+00 5.61136663e-01
-2.73807496e-01 -1.24905249e-02 6.32611871e-01 1.07200336e+00
-1.15679240e+00 1.08933151e+00 -7.41983235e-01 -2.20882505e-01
4.50585216e-01 8.14847708e-01 -7.80040473e-02 2.11279720e-01
-1.27341378e+00 8.15340102e-01 6.86584055e-01 4.38456327e-01
-4.58738029e-01 -5.54507315e-01 -4.32826877e-01 2.74861962e-01
4.45340186e-01 -7.12103963e-01 8.69244039e-01 -1.03345561e+00
-1.65277815e+00 5.06298423e-01 4.07030016e-01 -6.34464324e-01
2.45039657e-01 4.20927793e-01 -3.91795248e-01 8.14250037e-02
-6.02339268e-01 5.42760909e-01 9.85504031e-01 -8.11160088e-01
-5.09408355e-01 3.09716556e-02 1.18558355e-01 1.79879293e-02
-2.22170755e-01 2.16475219e-01 -3.44215244e-01 -6.12295032e-01
5.15204132e-01 -1.13944840e+00 -5.12975335e-01 -3.84594530e-01
-1.87619984e-01 8.47472250e-02 9.03374180e-02 -2.32672304e-01
1.12630856e+00 -2.00141001e+00 1.01531044e-01 8.59669566e-01
2.45131865e-01 4.57500130e-01 -1.57443047e-01 1.35538615e-02
-3.56514186e-01 3.12913507e-01 -2.54868269e-01 2.40244076e-01
-2.31386080e-01 -3.81989330e-02 2.93890715e-01 4.46187854e-01
1.85091823e-01 7.57447541e-01 -6.04472995e-01 -1.08251520e-01
-2.02142507e-01 4.75350648e-01 -9.56699014e-01 -1.08272746e-01
-1.02575403e-02 3.51278663e-01 -3.09705794e-01 3.62213075e-01
3.48935634e-01 -2.16842487e-01 1.02141187e-01 -3.74106646e-01
8.91334116e-02 1.48933336e-01 -1.47253478e+00 1.21230423e+00
-1.91269919e-01 9.26967740e-01 1.20452091e-01 -1.24103367e+00
1.09795094e+00 1.32274061e-01 3.73811245e-01 -5.13863266e-01
6.18169963e-01 1.85849637e-01 7.59249568e-01 -4.71318662e-01
2.79428273e-01 -2.67632812e-01 2.82387376e-01 4.04248953e-01
7.70509467e-02 -1.12267278e-01 2.21903175e-01 -3.44264537e-01
1.56188774e+00 -2.20606014e-01 8.87270924e-03 -2.26034179e-01
2.49717295e-01 3.62161994e-02 5.89871109e-01 1.09838545e+00
-7.56569654e-02 7.94660509e-01 5.89748919e-01 -5.99242985e-01
-9.24702764e-01 -4.73783463e-01 -2.62926817e-01 9.01071846e-01
-3.35350335e-01 -2.93079555e-01 -7.35507429e-01 -3.90054345e-01
-3.09305787e-01 4.19018149e-01 -4.68187839e-01 -4.76804852e-01
-9.68549609e-01 -1.34206843e+00 9.09485102e-01 2.04797298e-01
5.66806793e-01 -1.17827559e+00 -7.06049204e-01 3.26954484e-01
1.24869332e-01 -7.91166842e-01 1.42280668e-01 6.06042087e-01
-9.07722592e-01 -1.03733861e+00 -4.55373138e-01 -5.47173381e-01
6.01275086e-01 -2.61903435e-01 1.03776407e+00 6.26732707e-01
-2.92541653e-01 3.24054733e-02 -4.15407538e-01 -3.22779119e-01
-4.45607513e-01 3.99230808e-01 -9.12075117e-02 -1.76239118e-01
2.56039530e-01 -6.78060234e-01 -5.47324002e-01 3.35017115e-01
-7.78296828e-01 -3.38659853e-01 5.92511892e-01 9.36762750e-01
1.75763443e-01 6.08022690e-01 5.84563017e-01 -6.09550059e-01
9.75615323e-01 -3.08571726e-01 -7.86509216e-01 7.36919940e-02
-5.92855871e-01 3.59389901e-01 4.12956357e-01 -6.86361253e-01
-4.91614789e-01 1.49123460e-01 -2.70345747e-01 -3.29914242e-01
-1.39832795e-01 5.77116370e-01 1.16978988e-01 -7.12840080e-01
1.20782268e+00 -2.69151747e-01 5.12546003e-02 -2.48849824e-01
-1.29548714e-01 8.33166093e-02 4.04960096e-01 -4.82228011e-01
8.73777866e-01 -6.29340634e-02 2.64361739e-01 -6.95038795e-01
-4.59870130e-01 -5.42086065e-02 -3.57591718e-01 -2.70588249e-01
5.41303694e-01 -3.69221479e-01 -9.09691036e-01 3.08168977e-01
-8.27466309e-01 -2.73515582e-01 -1.32099718e-01 6.42151535e-01
-1.97818056e-01 -4.87186313e-02 -1.44527376e-01 -5.18400013e-01
-1.49697468e-01 -1.29636359e+00 3.34347844e-01 3.39636207e-01
-3.43258470e-01 -9.02050436e-01 -9.24411938e-02 -2.30795741e-02
7.02849865e-01 2.54125595e-01 9.74827111e-01 -8.19883645e-01
-5.29807150e-01 -4.20383841e-01 -3.76387313e-02 2.81774312e-01
-1.67182505e-01 -9.24436674e-02 -9.26344752e-01 -3.84631634e-01
-3.34435552e-02 1.87388420e-01 8.90490949e-01 5.84738255e-01
1.14571226e+00 -2.58654982e-01 -1.09237641e-01 9.66767490e-01
1.29904306e+00 3.92486840e-01 8.67494643e-01 9.29910243e-01
3.62931103e-01 4.47012573e-01 -1.08141243e-01 1.26170129e-01
-4.37250465e-01 6.53597474e-01 4.73523378e-01 4.61549610e-02
9.80933383e-02 3.45365584e-01 -7.13158026e-02 1.88782305e-01
-2.45178223e-01 -1.79516613e-01 -1.00471771e+00 3.03859025e-01
-1.53208899e+00 -9.88101900e-01 7.80860782e-02 2.17780566e+00
7.73890853e-01 5.01619279e-01 2.01835319e-01 4.02227491e-01
5.79938412e-01 -1.53827369e-01 -4.76688355e-01 -2.88713098e-01
-2.01251552e-01 5.41096270e-01 6.68675184e-01 3.63588154e-01
-8.97928238e-01 7.06939042e-01 6.35045862e+00 5.97928226e-01
-1.21446729e+00 -3.82115632e-01 5.14130294e-01 -3.75698507e-01
3.99375521e-02 -7.25705689e-03 -7.17850804e-01 2.24504247e-01
8.89743686e-01 7.87943080e-02 7.63833582e-01 4.61147338e-01
1.12562239e-01 -1.38215333e-01 -7.51775920e-01 1.00094807e+00
-2.11143699e-02 -1.56135106e+00 -4.72907722e-01 -6.13315366e-02
5.94839096e-01 4.96165864e-02 -2.47038215e-01 1.48795292e-01
1.66620627e-01 -1.19037712e+00 4.68934983e-01 3.75625193e-01
2.73777694e-01 -9.56270099e-01 8.11975062e-01 1.89056829e-01
-7.88842142e-01 -3.63483489e-01 -4.11744654e-01 5.01771979e-02
-2.43213251e-01 5.88407457e-01 -1.01197898e+00 1.40580744e-01
8.52756262e-01 4.18366119e-02 -8.66491437e-01 1.78588653e+00
-5.74342124e-02 8.86008143e-01 -6.99252665e-01 -4.56029624e-01
1.61136165e-01 -4.65123504e-02 8.09269786e-01 9.71083283e-01
4.32426095e-01 1.22518338e-01 -3.19873154e-01 7.19700217e-01
2.36902479e-02 -1.05080158e-02 -3.14427495e-01 -1.62749905e-02
4.92140621e-01 1.24845886e+00 -1.17795885e+00 1.32635877e-01
6.74149320e-02 2.48431861e-01 1.89430460e-01 4.72649127e-01
-5.02566159e-01 -6.89337671e-01 6.46515965e-01 2.09679808e-02
4.93659794e-01 -1.79468870e-01 -4.36056554e-01 -8.89537036e-01
-1.76179230e-01 -1.12702203e+00 3.31371456e-01 -5.35608590e-01
-9.23011601e-01 1.00777638e+00 5.83657250e-02 -1.14618051e+00
-3.68441045e-01 -6.51684821e-01 -5.58390200e-01 7.57040024e-01
-1.00946164e+00 -3.73380363e-01 -2.38319114e-01 2.60625154e-01
3.34088318e-02 -5.12331188e-01 6.49941266e-01 2.35091567e-01
-6.38244867e-01 7.55505025e-01 -1.08585894e-01 1.25022247e-01
4.30042714e-01 -8.24510515e-01 2.55197793e-01 1.02633131e+00
2.97437936e-01 6.55783534e-01 1.16620982e+00 -3.92213762e-01
-1.18410587e+00 -7.37522721e-01 4.52964664e-01 -5.61989509e-02
6.59920871e-01 -3.28149408e-01 -1.04360092e+00 3.57641071e-01
-3.98721062e-02 -9.34275612e-02 3.33606780e-01 2.46968225e-01
-4.03414592e-02 -3.33610848e-02 -1.12474787e+00 7.27708638e-01
9.94163930e-01 1.79664958e-02 -1.61058649e-01 1.53325602e-01
3.56634259e-01 -6.25654817e-01 -6.03688061e-01 5.48963964e-01
5.38437545e-01 -9.56889570e-01 9.66343820e-01 -6.03917181e-01
-5.75661659e-02 -2.97100693e-01 1.37266934e-01 -1.63475382e+00
-5.49559116e-01 -8.28866303e-01 2.22799137e-01 7.92916656e-01
8.02519262e-01 -8.21815372e-01 1.16802931e+00 7.30220616e-01
-1.36397943e-01 -8.94057333e-01 -1.01712918e+00 -7.33699799e-01
-6.94803596e-02 -6.80569232e-01 8.30656707e-01 8.47191334e-01
-2.48709247e-01 1.23268366e-01 5.62603353e-03 3.68719488e-01
4.11788464e-01 -2.69888401e-01 5.56697905e-01 -1.49727476e+00
-5.86899042e-01 -9.47142482e-01 -6.02880239e-01 -6.05955482e-01
-4.13688235e-02 -8.06529701e-01 1.50208488e-01 -1.01792002e+00
-4.15734619e-01 -8.49828839e-01 -1.65002674e-01 6.75425112e-01
3.85707687e-03 4.56406683e-01 5.38245849e-02 -1.93616152e-01
-1.96179450e-02 2.90704649e-02 9.85136151e-01 4.06501219e-02
-5.70675313e-01 3.46196502e-01 -8.11307430e-01 6.31113768e-01
1.12467742e+00 -7.35104084e-01 -2.24120989e-01 -2.93051302e-01
9.34260726e-01 -2.53151536e-01 5.01671255e-01 -1.43164527e+00
4.35198694e-01 2.34509259e-02 5.95864058e-01 -5.24263531e-02
2.37140879e-01 -7.03882158e-01 3.48144323e-01 3.39601606e-01
-2.98777431e-01 8.89041573e-02 5.21184742e-01 2.78720409e-01
1.48356229e-01 -9.13683891e-01 9.50391650e-01 -1.38453171e-01
-4.29548115e-01 -1.44499913e-01 -5.48999846e-01 2.11549569e-02
4.99043524e-01 -4.66070652e-01 -2.76745945e-01 -2.11293638e-01
-9.72511113e-01 7.11916685e-02 1.80825323e-01 2.16321304e-01
4.78067189e-01 -1.05883515e+00 -6.04962111e-01 4.54470277e-01
-2.93767005e-01 -1.74379453e-01 -1.32079914e-01 7.21132755e-01
-5.55110037e-01 8.58662203e-02 -9.88091007e-02 -4.50517803e-01
-1.34756434e+00 -9.11673307e-02 1.04034400e+00 -2.33822651e-02
-7.27611423e-01 1.26151156e+00 -5.27851939e-01 -1.89303413e-01
2.79183805e-01 -3.55235606e-01 -3.02753717e-01 7.05304518e-02
4.45922315e-01 3.61840010e-01 6.54674232e-01 -1.82852596e-01
-3.19279343e-01 4.23464626e-01 2.12764889e-01 -6.83419853e-02
1.61453080e+00 2.84411848e-01 -5.73386475e-02 -1.26497284e-01
1.14268136e+00 -2.39110395e-01 -9.60101545e-01 -3.34715620e-02
9.44329873e-02 -4.09037143e-01 5.67077935e-01 -8.41239512e-01
-1.48327947e+00 3.28618497e-01 7.96083748e-01 5.67074478e-01
1.31630182e+00 -1.11921974e-01 3.76348585e-01 7.89821625e-01
2.76887923e-01 -1.02639556e+00 -7.61673525e-02 6.52680397e-01
7.99584448e-01 -8.35394502e-01 1.71458915e-01 3.50129716e-02
-2.02760324e-01 1.41452599e+00 4.21176583e-01 -3.49886537e-01
7.05769002e-01 4.59600031e-01 4.67097759e-03 -4.85222399e-01
-6.23838365e-01 -2.49039933e-01 3.85960400e-01 5.59423387e-01
1.35005027e-01 -3.94592762e-01 -7.78264999e-02 3.84193957e-01
-7.43189991e-01 -2.01156810e-01 6.11143768e-01 7.34730542e-01
-6.60221994e-01 -1.01723635e+00 -5.46351016e-01 6.99119210e-01
-4.58918005e-01 -1.71569884e-01 -2.80151635e-01 7.05823421e-01
2.48689994e-01 9.65129614e-01 3.17323022e-02 -4.81236190e-01
3.99841189e-01 1.82329312e-01 6.97400928e-01 -3.67001832e-01
-1.11666656e+00 2.52121519e-02 3.27533871e-01 -4.12426651e-01
-3.52077842e-01 -6.48300886e-01 -1.18601608e+00 -1.97471246e-01
-7.17518032e-01 2.18080461e-01 8.01206410e-01 1.11215568e+00
2.12786093e-01 6.83607459e-01 4.11567867e-01 -9.37759042e-01
-2.17264831e-01 -8.18137884e-01 -3.64639819e-01 -1.50758415e-01
2.77344733e-01 -8.06006491e-01 -7.04135239e-01 -2.83407688e-01] | [8.311131477355957, 3.2378203868865967] |
f4974866-432c-4a17-acfb-aac5ce8324a7 | pvrnet-point-view-relation-neural-network-for | 1812.00333 | null | http://arxiv.org/abs/1812.00333v1 | http://arxiv.org/pdf/1812.00333v1.pdf | PVRNet: Point-View Relation Neural Network for 3D Shape Recognition | Three-dimensional (3D) shape recognition has drawn much research attention in
the field of computer vision. The advances of deep learning encourage various
deep models for 3D feature representation. For point cloud and multi-view data,
two popular 3D data modalities, different models are proposed with remarkable
performance. However the relation between point cloud and views has been rarely
investigated. In this paper, we introduce Point-View Relation Network (PVRNet),
an effective network designed to well fuse the view features and the point
cloud feature with a proposed relation score module. More specifically, based
on the relation score module, the point-single-view fusion feature is first
extracted by fusing the point cloud feature and each single view feature with
point-singe-view relation, then the point-multi-view fusion feature is
extracted by fusing the point cloud feature and the features of different
number of views with point-multi-view relation. Finally, the point-single-view
fusion feature and point-multi-view fusion feature are further combined
together to achieve a unified representation for a 3D shape. Our proposed
PVRNet has been evaluated on ModelNet40 dataset for 3D shape classification and
retrieval. Experimental results indicate our model can achieve significant
performance improvement compared with the state-of-the-art models. | ['Yue Gao', 'Changqing Zou', 'Yifan Feng', 'Xibin Zhao', 'Rongrong Ji', 'Haoxuan You'] | 2018-12-02 | null | null | null | null | ['3d-shape-retrieval', '3d-shape-recognition'] | ['computer-vision', 'computer-vision'] | [-6.36853456e-01 -7.65569806e-01 1.06509589e-01 -4.74014461e-01
-5.14865279e-01 -3.63168746e-01 8.36510956e-01 -8.40810835e-02
1.31015703e-01 -1.81058556e-01 1.02245890e-01 2.37424478e-01
-3.65583181e-01 -8.41723800e-01 -4.34820175e-01 -7.19320536e-01
3.14800680e-01 4.50123668e-01 3.18467140e-01 -3.18757713e-01
1.14985973e-01 1.05157995e+00 -1.52833962e+00 4.58798170e-01
2.00190097e-01 1.37183166e+00 1.55007899e-01 1.08811796e-01
-4.32877719e-01 -4.66498025e-02 -2.37822488e-01 -1.96843833e-01
5.25976956e-01 2.93885708e-01 -1.42340541e-01 2.16902822e-01
5.77136636e-01 -5.31173289e-01 -4.75242764e-01 9.54560459e-01
8.73926163e-01 -2.21611992e-01 5.76214671e-01 -1.28992760e+00
-8.65025043e-01 -1.13291502e-01 -8.45399976e-01 1.20247558e-01
3.62127125e-01 -2.21509367e-01 7.40827978e-01 -1.59804273e+00
5.18571854e-01 1.53013718e+00 4.14805204e-01 1.13147676e-01
-5.75159073e-01 -5.97116888e-01 1.92356199e-01 2.03017756e-01
-1.33121240e+00 -7.50729209e-03 1.20538843e+00 -2.63111770e-01
1.24290705e+00 1.55353010e-01 1.02083778e+00 6.24674082e-01
2.62903422e-01 9.09683645e-01 8.74445498e-01 2.71165743e-02
-3.32184702e-01 -7.92524144e-02 -1.18900239e-01 6.57468975e-01
1.58906057e-01 2.60866225e-01 -4.02330160e-01 -2.10398674e-01
9.98546839e-01 8.53822052e-01 -2.95863710e-02 -8.55953574e-01
-1.22287416e+00 5.44449151e-01 9.45995390e-01 5.33875406e-01
-4.75729287e-01 -3.02293599e-01 3.62149686e-01 2.85973608e-01
5.95522463e-01 -2.56496400e-01 -5.42658508e-01 2.33894661e-01
-5.18646657e-01 1.47406071e-01 4.85918790e-01 1.18910325e+00
5.96022189e-01 4.21203524e-02 1.78832456e-01 9.60523486e-01
8.03058624e-01 1.01994348e+00 2.13425115e-01 -4.46525216e-01
6.11896515e-01 1.44194806e+00 -3.15817237e-01 -1.34854233e+00
-3.62933248e-01 -4.51687694e-01 -1.16661811e+00 1.01798899e-01
-3.36540163e-01 4.56810862e-01 -8.49451303e-01 1.04136014e+00
5.00999510e-01 1.22109234e-01 5.83777837e-02 1.14623272e+00
1.63209450e+00 5.51798463e-01 -4.14305329e-01 -1.62902046e-02
1.42424715e+00 -6.03386879e-01 -2.65357465e-01 2.96019524e-01
2.06955239e-01 -8.71429801e-01 4.99931097e-01 2.22647950e-01
-1.12663245e+00 -8.46064866e-01 -1.20485222e+00 -5.01999334e-02
-5.62428892e-01 2.42729411e-01 4.13541347e-01 1.76883757e-01
-8.33305538e-01 2.02335536e-01 -6.89529181e-01 -3.03871006e-01
6.09618247e-01 5.05356729e-01 -8.84627461e-01 -4.00919795e-01
-7.65408039e-01 8.38988245e-01 1.18532225e-01 1.79928795e-01
-4.80036229e-01 -6.25591278e-01 -7.55742371e-01 1.38654947e-01
1.07918143e-01 -1.06425202e+00 8.32998276e-01 -8.81740600e-02
-1.03309691e+00 9.13322568e-01 -7.87678063e-02 3.55046391e-01
4.69944179e-02 2.45886035e-02 -5.81655502e-01 2.06221491e-01
8.21083114e-02 4.21268672e-01 5.65752506e-01 -1.59313238e+00
-6.11546695e-01 -1.11215508e+00 -8.54332224e-02 6.96541965e-01
-2.21435018e-02 3.80074116e-03 -6.95193410e-01 -2.66622063e-02
1.06984830e+00 -6.58962727e-01 2.06943184e-01 5.79189658e-02
-6.54076561e-02 -6.25821233e-01 1.44473767e+00 -1.86273217e-01
4.56309378e-01 -2.35329032e+00 2.39619240e-01 1.74665391e-01
4.06891257e-01 3.92971128e-01 -2.73435712e-02 4.13904935e-01
-2.33854279e-01 -1.03264458e-01 2.41478682e-01 -2.64873296e-01
-8.58934745e-02 2.56188095e-01 -1.93073303e-01 4.10467148e-01
1.08025268e-01 1.04564476e+00 -6.98543608e-01 -2.90578544e-01
7.21581936e-01 6.99842632e-01 -1.46246731e-01 1.74488664e-01
2.51672149e-01 2.35196248e-01 -7.20850468e-01 9.64754820e-01
1.11056328e+00 -2.61499196e-01 -4.77060765e-01 -6.53966129e-01
-4.37074043e-02 -1.79501846e-01 -1.20276773e+00 1.81271732e+00
-2.53694564e-01 -1.23969227e-01 -6.81069419e-02 -7.24369228e-01
1.54195797e+00 3.52298707e-01 8.04129422e-01 -5.77538311e-01
3.49810869e-01 3.46463919e-01 -1.00935519e-01 -4.76099789e-01
6.97631985e-02 -1.35896891e-01 1.82454363e-01 1.86255351e-01
2.64212072e-01 -4.78824109e-01 -4.76248115e-01 -9.24291089e-02
5.04554451e-01 1.11035250e-01 3.16975027e-01 2.27444515e-01
8.88439655e-01 -4.12697732e-01 2.30626956e-01 1.24928035e-01
-1.54301301e-01 7.57489860e-01 5.78146912e-02 -8.91865313e-01
-8.87956560e-01 -1.03927660e+00 -1.07732847e-01 4.30129766e-01
4.81050164e-01 -2.67770797e-01 3.69548164e-02 -9.33947623e-01
3.96599740e-01 1.22811794e-01 -3.48778278e-01 -9.70922783e-02
-5.14339030e-01 -2.00926617e-01 1.62541434e-01 6.49997056e-01
8.30946505e-01 -8.06153417e-01 -3.85551214e-01 -3.89988272e-04
3.34280819e-01 -9.74836767e-01 -4.70725834e-01 -2.73132771e-01
-1.13973653e+00 -9.61688578e-01 -8.87778044e-01 -8.08470666e-01
7.03090787e-01 9.30675387e-01 9.00537252e-01 2.46803999e-01
1.06546409e-01 7.24174321e-01 -3.91775906e-01 -5.55160820e-01
1.54734731e-01 -3.62427235e-01 2.11490571e-01 1.75573260e-01
7.34772742e-01 -9.45381522e-01 -6.01538658e-01 3.52658093e-01
-7.70157337e-01 -1.24429286e-01 5.88446617e-01 7.06641674e-01
9.14983034e-01 -3.63555849e-01 2.96155274e-01 -2.24709481e-01
2.13420615e-01 -3.11637253e-01 -4.46493387e-01 2.72165358e-01
-2.63608903e-01 -3.87521386e-01 3.40938926e-01 -1.39667302e-01
-6.90242708e-01 1.48658799e-02 -2.10048780e-01 -1.33010328e+00
-3.42062145e-01 5.45998156e-01 -6.64538383e-01 -3.20297241e-01
1.91838101e-01 7.20794678e-01 1.54599622e-01 -7.35081851e-01
3.96135241e-01 7.59970188e-01 1.38107106e-01 -2.70559192e-01
8.03823352e-01 7.43896723e-01 2.32740328e-01 -7.09615529e-01
-6.25702739e-01 -6.32681370e-01 -9.70068812e-01 -2.93875784e-01
7.96198547e-01 -1.13204634e+00 -9.10209239e-01 5.63325584e-01
-1.34902513e+00 9.25706923e-01 -5.75141720e-02 6.90483093e-01
-4.80022848e-01 5.16742706e-01 -1.73040435e-01 -5.62495589e-01
-6.72113359e-01 -1.22788632e+00 1.57754242e+00 4.76257354e-01
5.61377704e-01 -6.66462123e-01 -1.98562071e-01 2.42411360e-01
1.57106385e-01 1.80692703e-01 7.52840638e-01 -8.98032606e-01
-7.78435826e-01 -4.85919178e-01 -5.43612242e-01 2.68436849e-01
3.34469587e-01 -2.05324203e-01 -7.53245234e-01 -3.28109175e-01
4.28953171e-01 4.71645333e-02 6.86658680e-01 3.60325068e-01
1.00953352e+00 1.96037874e-01 -4.84757215e-01 8.31192553e-01
1.47814798e+00 4.61315423e-01 2.58334845e-01 5.27851507e-02
9.56466377e-01 6.85687512e-02 4.46653426e-01 3.63128155e-01
6.21850491e-01 6.67930901e-01 6.45060122e-01 3.30037847e-02
-1.99133046e-02 -2.73178786e-01 -2.65794605e-01 1.41527498e+00
-3.66536647e-01 1.74211055e-01 -8.19874942e-01 4.17626441e-01
-1.67774546e+00 -9.29411054e-01 -1.66241616e-01 1.91125333e+00
-1.36435345e-01 5.48432507e-02 -1.03466295e-01 6.20290264e-02
5.47003031e-01 3.03974003e-01 -6.15580559e-01 -3.48515362e-02
-3.23653579e-01 -1.69727787e-01 2.01734379e-02 -4.80300607e-03
-1.04010546e+00 4.57153827e-01 4.69419813e+00 7.70464897e-01
-1.36101067e+00 3.92693728e-02 7.90310726e-02 8.23247433e-02
-3.83854240e-01 -1.62280515e-01 -8.65243912e-01 9.73215029e-02
-6.56632334e-03 2.63058934e-02 9.52332839e-02 8.89826357e-01
-2.24310905e-01 3.21661800e-01 -1.19013989e+00 1.63459718e+00
5.43267012e-01 -1.20842981e+00 5.42592764e-01 2.68834114e-01
4.85478908e-01 3.24853599e-01 6.74028248e-02 2.82028705e-01
-3.45827132e-01 -6.25458539e-01 3.35509926e-01 8.39487910e-01
7.41336942e-01 -7.46809542e-01 9.19069648e-01 6.83006227e-01
-1.60993302e+00 9.03606936e-02 -5.22609711e-01 1.33644581e-01
9.36893225e-02 4.73696172e-01 -4.48545158e-01 1.45655143e+00
8.27791214e-01 1.31567085e+00 -4.48918372e-01 1.13302970e+00
2.99629033e-01 -1.70142978e-01 -5.47478557e-01 -1.43139930e-02
3.78665507e-01 -3.20330858e-01 7.60993421e-01 5.54489493e-01
7.46820927e-01 5.04333436e-01 2.78078288e-01 8.10742319e-01
2.22385302e-01 6.72523901e-02 -1.17230320e+00 3.76997352e-01
4.07717377e-01 1.33029759e+00 -5.64142346e-01 -4.93314475e-01
-7.33468175e-01 4.73985553e-01 1.24114260e-01 1.12629443e-01
-3.18355471e-01 -8.98508355e-02 3.44717920e-01 -1.13513380e-01
7.28036642e-01 -3.85037184e-01 -3.52216274e-01 -1.18172801e+00
2.14498624e-01 -4.90139037e-01 2.40984231e-01 -1.18018508e+00
-1.76556873e+00 7.07681060e-01 2.01327175e-01 -1.80201197e+00
2.65076339e-01 -7.38106549e-01 -5.85905790e-01 1.32059097e+00
-1.48927963e+00 -1.60588610e+00 -3.37049097e-01 7.72932589e-01
5.08863628e-01 -7.52506256e-01 9.13368225e-01 2.35620916e-01
7.32486788e-03 2.68581182e-01 -1.40282393e-01 1.34304821e-01
3.00895154e-01 -8.46361578e-01 3.56423378e-01 1.13667011e-01
3.04480672e-01 5.81695139e-01 -2.63626486e-01 -7.09327579e-01
-1.81061089e+00 -8.48575652e-01 7.51744151e-01 -5.54133534e-01
2.97719650e-02 -4.29572277e-02 -9.15593505e-01 3.01149189e-01
9.10852253e-02 5.50371945e-01 5.00848651e-01 -1.57191142e-01
-5.51954508e-01 -3.77739519e-01 -1.18945873e+00 9.57185701e-02
1.05786490e+00 -5.56215763e-01 -8.55087399e-01 1.50065616e-01
6.37523055e-01 -8.73866141e-01 -1.20985353e+00 9.05195773e-01
5.87267280e-01 -1.09327459e+00 1.36870182e+00 -3.96354705e-01
2.73035735e-01 -5.01745820e-01 -5.83330989e-01 -1.32422376e+00
-5.86515129e-01 4.22848433e-01 -2.10737959e-01 9.70275402e-01
-9.63598564e-02 -5.62341452e-01 5.80033123e-01 6.31890818e-02
-3.13870639e-01 -1.32902730e+00 -1.20146346e+00 -6.07279837e-01
-1.48706853e-01 -1.68662310e-01 1.14310610e+00 8.33905935e-01
-4.20321047e-01 5.65393686e-01 1.36950181e-03 3.60340059e-01
4.56868112e-01 7.22772121e-01 7.74910033e-01 -1.63274896e+00
1.28836989e-01 -5.59077203e-01 -8.59465778e-01 -1.21390188e+00
-2.17751950e-01 -1.28657317e+00 -6.60012841e-01 -1.90332234e+00
3.06862175e-01 -2.52702236e-01 -3.02033216e-01 2.68413872e-01
1.76289558e-01 1.03313617e-01 6.69930220e-01 2.98818380e-01
-3.79731357e-01 9.11499262e-01 1.95260048e+00 -2.84817129e-01
-1.18389189e-01 1.27934441e-01 -4.48692203e-01 7.35887587e-01
3.80016416e-01 -1.07552707e-01 -3.38176936e-01 -7.43366838e-01
-2.24502515e-02 4.10442054e-01 5.35969794e-01 -1.00258946e+00
4.67582911e-01 1.00613907e-01 9.77193415e-01 -1.67018032e+00
8.61543179e-01 -1.31991065e+00 8.32066163e-02 2.10974380e-01
3.55277091e-01 2.77419865e-01 1.82739004e-01 6.43565893e-01
-5.40294468e-01 1.88880250e-01 3.70110095e-01 -4.20553565e-01
-4.55216825e-01 9.38699424e-01 4.26349282e-01 -4.79963958e-01
9.54483628e-01 -5.79230964e-01 -2.01419458e-01 -1.25909016e-01
-8.54371011e-01 1.57347292e-01 1.14942506e-01 8.13429832e-01
1.25161278e+00 -2.08939314e+00 -6.73619866e-01 8.15227509e-01
3.17679107e-01 2.68822104e-01 5.66570580e-01 7.34601676e-01
-1.51587993e-01 5.44546366e-01 -3.52162808e-01 -1.23043704e+00
-1.31145060e+00 5.93433022e-01 3.53112131e-01 3.61829288e-02
-7.40628600e-01 8.45858932e-01 2.84312129e-01 -8.51544261e-01
5.10415286e-02 -4.01983470e-01 -5.00582218e-01 -4.19463180e-02
2.75014430e-01 8.37019756e-02 2.76076734e-01 -1.12988949e+00
-5.91361225e-01 1.38497686e+00 -3.60345207e-02 1.96117118e-01
1.69202054e+00 8.98535624e-02 -3.18411052e-01 5.23004770e-01
1.50767410e+00 -3.31377774e-01 -7.92392790e-01 -6.50962412e-01
-6.12886250e-01 -6.90747321e-01 2.05590259e-02 -7.00584352e-01
-1.30592334e+00 1.22395778e+00 8.14796448e-01 2.33173564e-01
1.04614890e+00 8.57575759e-02 8.12325060e-01 3.41807306e-01
6.15677238e-01 -4.00653630e-01 -5.76651283e-02 8.75199080e-01
1.31223822e+00 -1.35333264e+00 1.94286719e-01 -3.22434455e-01
-4.06901032e-01 1.15849102e+00 7.72425830e-01 -4.75359261e-01
1.35661650e+00 -1.06764138e-01 -1.02953624e-03 -7.84114540e-01
-5.85273087e-01 -1.30624786e-01 9.01755333e-01 6.02286100e-01
1.10506453e-01 1.08497508e-01 -2.43463274e-03 8.76027644e-01
-2.05313973e-02 -3.38984653e-02 -2.30155572e-01 7.47436523e-01
-4.15154248e-01 -1.01532674e+00 -4.28773880e-01 6.39306545e-01
1.95315003e-01 2.14729697e-01 -5.34753203e-01 7.02650785e-01
3.84144157e-01 5.98292828e-01 1.34361714e-01 -7.74294198e-01
7.38665640e-01 6.16251491e-02 5.66035748e-01 -5.09033978e-01
-5.55969715e-01 3.72173429e-01 -6.09886348e-01 -4.22419906e-01
-6.89035535e-01 -5.66303492e-01 -1.05120575e+00 -1.40625969e-01
-3.47293109e-01 -1.81067422e-01 7.34073639e-01 9.74777222e-01
7.39035249e-01 1.46876752e-01 8.92765582e-01 -1.17495096e+00
-5.46889782e-01 -8.52055371e-01 -6.39353335e-01 3.47872823e-01
2.50895977e-01 -1.00567293e+00 -1.21083751e-01 -5.21743774e-01] | [8.144280433654785, -3.8474597930908203] |
269cd5e2-fb20-4d3f-8a79-2f5af98106d4 | spatially-dependent-u-nets-highly-accurate | 2103.11713 | null | https://arxiv.org/abs/2103.11713v1 | https://arxiv.org/pdf/2103.11713v1.pdf | Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation | In clinical practice, regions of interest in medical imaging often need to be identified through a process of precise image segmentation. The quality of this image segmentation step critically affects the subsequent clinical assessment of the patient status. To enable high accuracy, automatic image segmentation, we introduce a novel deep neural network architecture that exploits the inherent spatial coherence of anatomical structures and is well equipped to capture long-range spatial dependencies in the segmented pixel/voxel space. In contrast to the state-of-the-art solutions based on convolutional layers, our approach leverages on recently introduced spatial dependency layers that have an unbounded receptive field and explicitly model the inductive bias of spatial coherence. Our method performs favourably to commonly used U-Net and U-Net++ architectures as demonstrated by improved Dice and Jaccardscore in three different medical segmentation tasks: nuclei segmentation in microscopy images, polyp segmentation in colonoscopy videos, and liver segmentation in abdominal CT scans. | ['Joachim M. Buhmann', 'Đorđe Miladinović', 'João A. Santinha', 'João B. S. Carvalho'] | 2021-03-22 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 5.71391642e-01 1.51479125e-01 -1.36816368e-01 -4.63829726e-01
-6.49192333e-01 -6.29775763e-01 3.30816031e-01 6.65663660e-01
-8.32864761e-01 6.60400867e-01 9.41183269e-02 -4.79114175e-01
-2.97249913e-01 -6.17426217e-01 -6.85971200e-01 -7.56928325e-01
-4.23691690e-01 5.12900651e-01 7.55042285e-02 8.37937668e-02
3.55601907e-01 6.15159333e-01 -6.52156651e-01 3.25074732e-01
7.83818424e-01 9.58890378e-01 -7.34109953e-02 9.68193531e-01
6.48604035e-02 7.63222337e-01 -2.06797525e-01 1.26443610e-01
1.28271326e-01 -5.19689262e-01 -1.12229860e+00 -1.52520031e-01
2.36335486e-01 -3.41470480e-01 1.60525609e-02 9.89173651e-01
6.76331282e-01 -1.00769199e-01 7.44474649e-01 -1.18614666e-01
-5.28681695e-01 6.06073797e-01 -5.47212303e-01 8.02016556e-01
-8.20436552e-02 3.35680276e-01 8.18070769e-01 -3.32191288e-01
8.71690333e-01 6.73780322e-01 1.02493632e+00 3.05663317e-01
-1.55326831e+00 -2.08163008e-01 -2.53829330e-01 -4.54799116e-01
-9.38238800e-01 8.21303725e-02 3.10367465e-01 -6.69122338e-01
1.01278377e+00 1.94800571e-01 8.08200061e-01 5.85681438e-01
6.75215006e-01 6.83726013e-01 1.06237674e+00 -2.34096527e-01
1.26193881e-01 -4.40501958e-01 1.74977869e-01 6.39387906e-01
1.06117979e-01 8.07997957e-02 1.20397322e-01 3.43247093e-02
1.30668068e+00 2.09846973e-01 -3.82709622e-01 -4.65536803e-01
-1.57897472e+00 7.46360362e-01 1.02086973e+00 7.27433264e-01
-3.97788703e-01 4.12265360e-01 4.57200646e-01 -1.27083570e-01
2.13147536e-01 8.11645925e-01 -2.73167551e-01 1.01628631e-01
-1.37249506e+00 -1.16859665e-02 4.76259321e-01 2.60191143e-01
3.67940962e-01 -4.59235489e-01 -3.18842888e-01 4.27812219e-01
1.52623743e-01 -7.01665282e-02 6.23784721e-01 -1.10960436e+00
-1.80526197e-01 7.74398625e-01 -1.82117715e-01 -6.74954414e-01
-9.07000482e-01 -6.73886061e-01 -1.12012374e+00 3.12924922e-01
6.09097898e-01 -1.31610855e-01 -1.30321491e+00 1.38904560e+00
3.99304956e-01 6.85101599e-02 -2.04520151e-01 8.76595795e-01
8.72338235e-01 1.15059927e-01 2.45660558e-01 1.22255631e-01
1.21456730e+00 -7.02047050e-01 -4.74494189e-01 -9.48704081e-04
8.96034300e-01 -5.48889816e-01 6.48656189e-01 1.81796312e-01
-1.25562716e+00 -2.23591343e-01 -9.52179551e-01 -2.57810980e-01
-4.25863326e-01 -1.86357461e-02 7.91908503e-01 4.22144949e-01
-1.32892573e+00 7.79971540e-01 -1.01353121e+00 -3.26780260e-01
9.90060925e-01 9.10834789e-01 -3.69001806e-01 -3.33831832e-02
-9.19308066e-01 7.83590913e-01 4.57293481e-01 6.59467876e-02
-5.64884365e-01 -1.10884166e+00 -7.33642757e-01 1.58263072e-02
-1.66637301e-01 -8.73259902e-01 1.14173746e+00 -8.25259328e-01
-1.20172822e+00 1.16539299e+00 2.67691165e-01 -7.34054089e-01
7.88945019e-01 3.02598059e-01 3.34193408e-01 5.78064859e-01
-5.31179681e-02 1.19360232e+00 2.08744481e-01 -9.21432018e-01
-3.15560967e-01 -4.96316195e-01 -2.01246561e-03 1.53641388e-01
2.02844039e-01 -1.09774269e-01 -2.06435442e-01 -6.20242715e-01
1.02268107e-01 -7.95910895e-01 -9.17740881e-01 2.11293042e-01
-3.72878045e-01 4.00302529e-01 4.59453136e-01 -6.22133732e-01
9.48228061e-01 -1.97456753e+00 1.02736115e-01 3.79055470e-01
5.57240307e-01 3.29915762e-01 2.00429931e-01 -1.19022556e-01
-2.01103911e-01 3.09477270e-01 -5.95133722e-01 2.29606137e-01
-3.88612717e-01 4.21694107e-02 4.45386857e-01 7.89261341e-01
8.23412165e-02 1.37335885e+00 -1.02560771e+00 -7.60317802e-01
4.47945714e-01 7.40856349e-01 -7.47346640e-01 1.80054810e-02
-1.21371143e-01 1.05281556e+00 -3.43242228e-01 5.04362226e-01
4.43208188e-01 -8.21500242e-01 4.18337047e-01 -1.91619158e-01
-8.81586149e-02 4.50979099e-02 -4.96051759e-01 1.99985754e+00
-2.09659323e-01 4.87125188e-01 4.30870891e-01 -1.06486762e+00
2.81379908e-01 3.84956926e-01 1.11278653e+00 -6.79269433e-01
3.49855006e-01 2.87773103e-01 4.57133085e-01 -3.18521202e-01
-8.56257975e-02 -1.83479920e-01 1.57298341e-01 4.38092172e-01
1.36091664e-01 -1.83473900e-01 1.65804714e-01 -2.02048235e-02
1.09715259e+00 8.18705186e-02 3.84938627e-01 -7.95208991e-01
5.23500860e-01 1.31983384e-01 1.75923765e-01 7.95755863e-01
-5.72252572e-01 9.11246121e-01 7.39695251e-01 -7.70838678e-01
-1.10726285e+00 -8.80410790e-01 -6.50596261e-01 6.10107243e-01
-7.51921088e-02 1.77942485e-01 -9.15998399e-01 -7.12720454e-01
-5.04426323e-02 6.56243041e-02 -1.21509588e+00 3.04760247e-01
-7.91798413e-01 -1.17022574e+00 6.29079819e-01 5.77932119e-01
2.61784166e-01 -1.19036984e+00 -1.11358356e+00 4.54651892e-01
1.56243742e-01 -9.21804130e-01 -3.74814034e-01 5.58654487e-01
-1.05949318e+00 -1.31593490e+00 -1.19278646e+00 -9.61899817e-01
9.59502995e-01 -3.54437917e-01 1.39397597e+00 1.82164535e-01
-9.48932111e-01 1.69676602e-01 -6.92778314e-03 -2.43043467e-01
-2.80818850e-01 2.28442892e-01 -5.99789262e-01 -5.45381546e-01
1.95622087e-01 -5.24683416e-01 -1.35551929e+00 7.49886688e-03
-1.28161800e+00 3.61490846e-02 7.17980146e-01 1.17512596e+00
9.21417832e-01 -1.99746922e-01 1.69189349e-01 -1.16919422e+00
4.93115753e-01 -2.82191604e-01 -4.37630653e-01 3.57879885e-02
-3.94198596e-01 -6.26591742e-02 4.07970309e-01 -8.22295155e-03
-6.44414365e-01 2.48112872e-01 -3.32577974e-01 -5.21852858e-02
-2.96395957e-01 6.57442331e-01 7.77777910e-01 -5.18376887e-01
7.22324491e-01 -2.82687396e-02 4.23371434e-01 5.58910258e-02
1.83373660e-01 1.80133328e-01 5.99965334e-01 -2.25902736e-01
-4.26104739e-02 8.58604848e-01 3.57732058e-01 -6.12864614e-01
-5.71958899e-01 -5.62337101e-01 -1.14612174e+00 6.33905977e-02
1.36023295e+00 -5.68271577e-01 -8.32282424e-01 3.66017163e-01
-9.77345347e-01 -6.41950548e-01 -4.39686388e-01 4.52961594e-01
-5.25011063e-01 3.56181741e-01 -1.08794200e+00 -1.82589769e-01
-6.04757428e-01 -1.58488512e+00 1.06152046e+00 1.78191394e-01
-3.37603688e-01 -1.51556134e+00 2.10163116e-01 1.30940422e-01
5.88386774e-01 7.82659709e-01 1.15417171e+00 -5.56263685e-01
-5.88604033e-01 -1.40160426e-01 -4.46611255e-01 1.03588514e-01
5.27751073e-03 -1.04828618e-01 -6.30006790e-01 -5.93865775e-02
-2.39938065e-01 -1.71573654e-01 1.04799545e+00 1.44944620e+00
1.36350262e+00 5.14356978e-02 -3.31922591e-01 1.03791380e+00
1.46648967e+00 3.52195352e-02 5.21258593e-01 9.12610665e-02
6.38915300e-01 6.01956904e-01 -1.89046061e-03 7.10316449e-02
1.99939832e-01 1.09760784e-01 5.53726375e-01 -8.59298348e-01
-1.31194696e-01 3.95300418e-01 -4.80629712e-01 5.14197946e-01
5.35509065e-02 -3.51521783e-02 -1.21467054e+00 8.53973746e-01
-1.67767036e+00 -6.09780073e-01 -1.99040443e-01 1.91542780e+00
9.59859729e-01 3.32943536e-02 -2.16710903e-02 -1.42633781e-01
4.88289744e-01 3.45553126e-05 -6.96060836e-01 -3.60181898e-01
2.80821715e-02 4.22804475e-01 7.46329784e-01 4.43184972e-01
-1.30169904e+00 5.74187994e-01 7.65653229e+00 4.28398877e-01
-1.26095438e+00 -7.22757876e-02 1.55290425e+00 -1.17604740e-01
-1.75651178e-01 -4.51731354e-01 -1.65765718e-01 2.76776195e-01
7.63988554e-01 5.10259748e-01 9.24932957e-02 1.67046294e-01
1.39496043e-01 -3.64235371e-01 -1.21862543e+00 6.83386087e-01
-3.09538245e-01 -1.89412963e+00 -2.81375051e-01 3.27381521e-01
9.51580226e-01 5.68254352e-01 3.21962208e-01 -3.16373765e-01
4.95933712e-01 -1.76138353e+00 -1.56979829e-01 3.94880146e-01
1.07210553e+00 -5.59701502e-01 9.75185454e-01 9.32579767e-03
-6.77142501e-01 7.20960498e-02 6.35176599e-02 2.78803766e-01
-7.23183081e-02 6.41610682e-01 -1.00561178e+00 2.14625701e-01
6.66010022e-01 6.59762859e-01 -3.37463737e-01 1.19473755e+00
2.38083333e-01 4.76572454e-01 -2.87864506e-01 2.55467445e-01
8.30458343e-01 -1.95750490e-01 1.65086657e-01 1.53460646e+00
5.51462583e-02 5.16333543e-02 9.76972282e-03 9.58112836e-01
-1.07742183e-01 2.12160543e-01 -3.35378587e-01 -2.65962146e-02
-3.06234181e-01 1.39106178e+00 -1.26227856e+00 -3.30223650e-01
-2.53336221e-01 6.84398830e-01 1.42851710e-01 2.89195001e-01
-5.24591565e-01 -6.66469559e-02 2.30989769e-01 2.59336948e-01
2.87934989e-01 4.93019149e-02 -7.30711758e-01 -8.05976629e-01
-3.61134529e-01 -6.63706481e-01 5.36758184e-01 -4.30862308e-01
-1.11807752e+00 5.35471678e-01 -4.43894267e-01 -7.76488423e-01
-1.38594434e-01 -8.06892335e-01 -5.86553574e-01 9.65296447e-01
-1.82165611e+00 -1.11540401e+00 -1.24805376e-01 2.53316700e-01
-5.01828901e-02 3.93036842e-01 9.07085299e-01 2.31861651e-01
-3.10016215e-01 1.70589358e-01 3.41450691e-01 4.44632083e-01
4.98335689e-01 -1.62647712e+00 3.40828717e-01 5.23824573e-01
-2.66913086e-01 7.29833603e-01 3.61426294e-01 -4.70752418e-01
-7.56767988e-01 -9.03047979e-01 6.18516684e-01 -3.80845785e-01
5.14710486e-01 1.36015192e-01 -7.20239758e-01 7.17880726e-01
3.63947153e-01 7.20917404e-01 1.03345776e+00 -2.11266175e-01
1.34063764e-02 3.00858438e-01 -1.47547925e+00 4.34272021e-01
5.46907961e-01 -2.73620367e-01 -2.49021977e-01 4.68333423e-01
2.58064091e-01 -7.41872668e-01 -1.33231163e+00 6.07778966e-01
6.75717533e-01 -1.13642609e+00 1.20613003e+00 -6.06595695e-01
6.74814463e-01 -2.05094274e-03 2.44165078e-01 -1.04921556e+00
-4.84019130e-01 -4.36743349e-01 3.35635543e-01 1.93851843e-01
4.71832782e-01 -3.70234668e-01 1.02711487e+00 4.97317731e-01
-8.46077800e-02 -1.11948991e+00 -1.01427376e+00 4.40610573e-02
6.53049588e-01 6.59047440e-02 3.97561789e-01 9.97533798e-01
7.70249441e-02 -1.47020623e-01 3.42333198e-01 -3.68109912e-01
5.98627329e-01 9.84884873e-02 6.99370727e-02 -1.28934407e+00
-1.96637198e-01 -9.20606196e-01 -4.42382514e-01 -8.00430357e-01
-1.72571734e-01 -9.57709551e-01 -1.33044094e-01 -1.76817071e+00
3.32075983e-01 -4.67820883e-01 -7.27314293e-01 2.39494041e-01
-2.43003950e-01 7.31659830e-01 -3.34384024e-01 1.75127447e-01
-6.89867675e-01 -1.88895240e-01 1.67290902e+00 -1.57318994e-01
-1.48735687e-01 -1.78380370e-01 -6.25272870e-01 6.58906698e-01
6.20966911e-01 -1.63234204e-01 -5.12159504e-02 -2.99258173e-01
2.05460757e-01 1.45319313e-01 4.54426795e-01 -9.96240377e-01
2.00105026e-01 2.37396613e-01 9.96995032e-01 -4.79007363e-01
-1.42279088e-01 -7.64420211e-01 -1.62422165e-01 8.49389374e-01
-6.94324613e-01 -6.01630360e-02 1.98180452e-01 2.01190665e-01
-2.78021157e-01 6.48792908e-02 1.17449284e+00 -7.95110047e-01
-2.54280359e-01 4.52858746e-01 -4.57491875e-01 -2.03134734e-02
1.07773101e+00 -3.68549466e-01 -1.18318863e-01 -1.25750288e-01
-9.64980423e-01 2.59543955e-01 4.92006153e-01 -3.49749506e-01
3.53741139e-01 -9.36411023e-01 -6.26483321e-01 5.30740283e-02
-2.01776236e-01 4.76565212e-01 5.25117934e-01 1.46936786e+00
-1.26511693e+00 7.00691819e-01 -3.70875955e-01 -9.71877575e-01
-8.13275516e-01 3.19028169e-01 8.72588873e-01 -9.70402539e-01
-6.06556654e-01 1.04630733e+00 4.74471480e-01 -6.47416055e-01
-1.48050606e-01 -7.37122893e-01 -3.53630632e-01 -2.08478600e-01
2.55675286e-01 4.33025993e-02 1.43424019e-01 -7.16249704e-01
-3.37168515e-01 3.75911295e-01 -2.09729612e-01 1.47514284e-01
1.44411051e+00 -2.03281213e-02 -3.88638169e-01 8.66169780e-02
1.26222014e+00 -4.14032638e-01 -1.48218215e+00 -4.44237376e-03
-7.47708380e-02 2.07321607e-02 4.99394238e-01 -1.24844992e+00
-1.14730155e+00 1.01773763e+00 7.43853152e-01 1.89353690e-01
9.17217612e-01 -3.07464689e-01 9.02468503e-01 -5.01702018e-02
-1.21197201e-01 -9.31765318e-01 -1.23710304e-01 3.57864022e-01
2.69385099e-01 -1.45241916e+00 1.49644107e-01 -2.61750340e-01
-3.64207685e-01 1.11705017e+00 2.73184717e-01 -2.70166785e-01
6.81293547e-01 5.68779349e-01 3.83882582e-01 -5.49576283e-01
-2.17697740e-01 -8.50538611e-02 4.63593960e-01 5.41311920e-01
9.62994337e-01 -6.67359084e-02 -1.21208683e-01 2.48125084e-02
2.00806111e-01 1.70961365e-01 2.95147240e-01 9.11903024e-01
-2.31389940e-01 -8.38815510e-01 -1.12833478e-01 6.96816862e-01
-1.20133400e+00 -4.31192219e-01 -2.08199143e-01 6.27122998e-01
1.09013125e-01 2.83240110e-01 2.70903468e-01 3.12419891e-01
-2.76207089e-01 -1.73843116e-01 6.52376950e-01 -4.79766548e-01
-1.22691548e+00 2.73358226e-01 -3.98504823e-01 -6.51204407e-01
-5.80640137e-01 -5.75340986e-01 -1.57195449e+00 1.49128407e-01
9.89204943e-02 -9.51447636e-02 7.09684134e-01 8.73299718e-01
1.97222099e-01 9.88756597e-01 -1.12582725e-02 -9.40012753e-01
-1.41695235e-02 -7.40290105e-01 -5.33050776e-01 4.51855868e-01
6.62572384e-01 -9.97739751e-03 7.88204521e-02 -8.49844702e-03] | [14.526983261108398, -2.654114007949829] |
b6bdb3f0-49f8-4074-a6d2-a7aad473a763 | video-guided-curriculum-learning-for-spoken | 2209.00277 | null | https://arxiv.org/abs/2209.00277v1 | https://arxiv.org/pdf/2209.00277v1.pdf | Video-Guided Curriculum Learning for Spoken Video Grounding | In this paper, we introduce a new task, spoken video grounding (SVG), which aims to localize the desired video fragments from spoken language descriptions. Compared with using text, employing audio requires the model to directly exploit the useful phonemes and syllables related to the video from raw speech. Moreover, we randomly add environmental noises to this speech audio, further increasing the difficulty of this task and better simulating real applications. To rectify the discriminative phonemes and extract video-related information from noisy audio, we develop a novel video-guided curriculum learning (VGCL) during the audio pre-training process, which can make use of the vital visual perceptions to help understand the spoken language and suppress the external noise. Considering during inference the model can not obtain ground truth video segments, we design a curriculum strategy that gradually shifts the input video from the ground truth to the entire video content during pre-training. Finally, the model can learn how to extract critical visual information from the entire video clip to help understand the spoken language. In addition, we collect the first large-scale spoken video grounding dataset based on ActivityNet, which is named as ActivityNet Speech dataset. Extensive experiments demonstrate our proposed video-guided curriculum learning can facilitate the pre-training process to obtain a mutual audio encoder, significantly promoting the performance of spoken video grounding tasks. Moreover, we prove that in the case of noisy sound, our model outperforms the method that grounding video with ASR transcripts, further demonstrating the effectiveness of our curriculum strategy. | ['Yi Ren', 'Haoyuan Li', 'Yang Zhao', 'Shangwei Ye', 'Zhou Zhao', 'Yan Xia'] | 2022-09-01 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 2.70006418e-01 1.33299053e-01 -2.51820028e-01 -1.85484990e-01
-1.02642167e+00 -3.47096324e-01 1.49512902e-01 -3.77908856e-01
-3.25049698e-01 4.30803388e-01 4.19640183e-01 -1.29785597e-01
2.42143065e-01 -6.13798141e-01 -1.22800303e+00 -7.57068992e-01
1.56057686e-01 -7.54987076e-02 1.64788321e-01 8.61117467e-02
4.58855517e-02 -1.99203685e-01 -1.63849628e+00 5.25180578e-01
8.36185634e-01 9.48689997e-01 9.12259161e-01 8.00417960e-01
-1.65407762e-01 1.16840410e+00 -6.04867160e-01 1.93360329e-01
-2.25029841e-01 -6.31893098e-01 -4.80460286e-01 7.66848326e-01
4.05750066e-01 -8.89894366e-01 -8.67716849e-01 1.12938070e+00
3.66223276e-01 4.17954415e-01 2.62420446e-01 -1.40505564e+00
-4.63941693e-01 7.84098506e-01 -2.99910277e-01 -1.27017144e-02
7.52415419e-01 2.00685352e-01 1.00533700e+00 -1.16155922e+00
6.67288899e-01 1.17471206e+00 2.07933590e-01 6.08079076e-01
-6.74713254e-01 -8.98144066e-01 6.26356840e-01 5.65103352e-01
-1.65787387e+00 -5.33639133e-01 9.67286944e-01 -2.73181587e-01
6.30057752e-01 6.76417490e-03 8.16800952e-01 1.28414822e+00
-3.58193249e-01 1.32074296e+00 6.42304659e-01 -3.21024656e-01
1.55134708e-01 -4.68694558e-03 -1.62710473e-01 8.63815188e-01
-4.53371644e-01 5.26674427e-02 -9.85661924e-01 1.92490667e-01
5.60911536e-01 3.10887545e-02 -1.02640116e+00 -8.89113173e-02
-1.27931976e+00 5.46952963e-01 1.92724004e-01 1.48387164e-01
-2.09359109e-01 1.84285551e-01 3.12380016e-01 2.60870278e-01
1.99206129e-01 -2.75087446e-01 -1.92619056e-01 -2.10956648e-01
-1.17395699e+00 -5.21798097e-02 5.47368884e-01 1.32867968e+00
8.88470829e-01 4.03027296e-01 -8.90856311e-02 5.73850334e-01
3.93737257e-01 7.30942130e-01 4.05770391e-01 -9.50888455e-01
7.82232523e-01 2.64885604e-01 -1.45099938e-01 -8.79509807e-01
6.52133375e-02 -2.27763712e-01 -8.93634856e-01 -4.35721308e-01
1.16609812e-01 -1.54020175e-01 -8.86698782e-01 1.63038051e+00
1.73662424e-01 1.06124961e+00 2.39649549e-01 1.11351991e+00
1.03847611e+00 1.30116606e+00 -2.33299702e-01 -5.92475832e-01
1.09288931e+00 -9.94169354e-01 -9.89511728e-01 -1.95655018e-01
4.60240394e-01 -4.79327917e-01 1.29255366e+00 6.73540354e-01
-7.47100353e-01 -8.72206330e-01 -1.14642537e+00 8.86877533e-03
1.51633397e-01 1.23434201e-01 1.27221048e-01 1.23546451e-01
-8.37787926e-01 1.05116151e-01 -7.14399338e-01 1.06803283e-01
1.80631340e-01 3.96876037e-02 -3.57798457e-01 -5.16372502e-01
-1.24361789e+00 1.15888983e-01 4.90261912e-01 2.92912662e-01
-1.59488308e+00 -5.54042041e-01 -1.21810210e+00 2.26756975e-01
9.41627979e-01 -2.37470046e-01 1.20337296e+00 -1.19401217e+00
-1.50312400e+00 3.01016778e-01 -4.66247857e-01 -3.29126358e-01
3.66318703e-01 -2.43083298e-01 -3.37747008e-01 7.23434746e-01
-5.26088215e-02 9.06553805e-01 1.14124155e+00 -1.25149834e+00
-8.02159667e-01 1.06541269e-01 1.55913189e-01 3.09944093e-01
-4.09440398e-01 -3.88723820e-01 -9.67449725e-01 -7.30281651e-01
8.66428316e-02 -6.78486526e-01 1.78875834e-01 -1.89919546e-01
-2.55343884e-01 -4.68153134e-02 1.00314605e+00 -9.53265548e-01
1.23360312e+00 -2.50402403e+00 4.61258024e-01 1.47939518e-01
1.44835085e-01 -2.30186582e-02 -3.17002505e-01 2.21253663e-01
2.35093877e-01 -9.38796774e-02 -1.69425070e-01 -3.46850663e-01
-1.76665157e-01 4.87362832e-01 -6.27208352e-01 2.82484084e-01
2.52315044e-01 6.37985051e-01 -1.07539678e+00 -7.40037322e-01
2.36953333e-01 6.28473997e-01 -7.61098504e-01 5.79093575e-01
-4.41528827e-01 5.64438164e-01 -4.09142643e-01 5.23604453e-01
4.43838775e-01 -5.77562954e-03 2.17058919e-02 -1.77727446e-01
1.08707927e-01 1.95915774e-01 -1.23449743e+00 2.09038234e+00
-5.43240368e-01 8.59271467e-01 4.11280572e-01 -1.19129241e+00
7.26451278e-01 6.35002375e-01 3.23796690e-01 -6.63002253e-01
2.90371384e-02 -5.29151484e-02 -4.57577080e-01 -9.68651593e-01
2.80719221e-01 2.44865716e-01 6.56878874e-02 1.34911388e-01
4.40907598e-01 -1.86324164e-01 -8.87123346e-02 4.64495629e-01
7.47646570e-01 1.40302390e-01 -8.85086209e-02 3.32537919e-01
7.24021733e-01 -2.01201126e-01 5.20997763e-01 5.40956736e-01
-8.50744471e-02 5.71722269e-01 3.03581953e-01 1.69498339e-01
-7.21606612e-01 -9.68661368e-01 3.35561365e-01 1.08897662e+00
3.63091320e-01 -6.20225251e-01 -1.02045190e+00 -5.52944183e-01
-5.39324820e-01 4.83064532e-01 -2.34815732e-01 -2.59678066e-01
-5.58559954e-01 7.03388378e-02 3.04640621e-01 4.94903117e-01
5.54388762e-01 -1.05821419e+00 -2.12883241e-02 2.24069282e-01
-8.83816898e-01 -1.51212049e+00 -9.44004536e-01 -1.54208168e-01
-5.11037767e-01 -1.00371933e+00 -7.89660275e-01 -1.18286562e+00
6.58424854e-01 6.66531026e-01 6.87732935e-01 2.00755477e-01
-6.60292292e-03 6.67811871e-01 -5.72036922e-01 2.38944590e-02
-3.40452790e-01 -1.51408494e-01 -3.29762660e-02 3.28843921e-01
1.74739972e-01 -4.45435524e-01 -3.75699908e-01 2.82256395e-01
-9.70087171e-01 2.98144579e-01 4.15024549e-01 1.02688420e+00
7.37242639e-01 2.78648138e-01 5.35787225e-01 -2.38654435e-01
2.02409253e-01 -4.08646464e-01 -4.97678667e-01 1.12686560e-01
4.84265052e-02 -2.51410514e-01 6.77600563e-01 -7.43032813e-01
-8.08694482e-01 1.52398348e-01 -2.86597162e-01 -9.67339039e-01
-2.17419770e-02 6.48330867e-01 -5.65624654e-01 1.99482158e-01
-5.07434942e-02 8.40307593e-01 3.41544747e-02 -2.26485476e-01
3.55542094e-01 9.02140021e-01 9.21286762e-01 -5.06700873e-01
7.08503544e-01 3.58847111e-01 -3.60212862e-01 -1.32872975e+00
-8.23118091e-01 -6.15605712e-01 -2.93362975e-01 -5.96532583e-01
1.01368558e+00 -1.42070985e+00 -7.94640422e-01 2.96449810e-01
-1.15134466e+00 -3.58282536e-01 1.79865971e-01 7.04800427e-01
-6.48961484e-01 7.02641010e-01 -5.46594381e-01 -8.98138523e-01
2.89503299e-02 -1.40052664e+00 1.14616144e+00 5.19587323e-02
1.81223750e-01 -6.95616305e-01 -3.60071987e-01 4.02438134e-01
-3.00869197e-01 -2.76402116e-01 4.57097173e-01 -3.65418226e-01
-1.07355034e+00 1.63455412e-01 -4.56300424e-03 5.39764881e-01
-8.63056406e-02 -7.56838769e-02 -1.11928606e+00 -4.37058032e-01
2.85078194e-02 -4.87740606e-01 1.00495732e+00 3.34060758e-01
1.26055932e+00 -3.89911562e-01 -6.70782477e-02 7.77486086e-01
1.02480030e+00 3.27322870e-01 5.86816907e-01 4.36027441e-03
9.53538895e-01 7.43324697e-01 9.99395728e-01 3.21980208e-01
4.19025183e-01 6.00168645e-01 4.64495003e-01 -4.28195372e-02
-5.09688742e-02 -8.59887838e-01 9.46496308e-01 1.34883463e+00
2.59100109e-01 -4.06251758e-01 -5.25845408e-01 5.80270827e-01
-1.86623323e+00 -1.01071727e+00 3.94874662e-01 1.87213254e+00
6.66070402e-01 1.46388710e-02 -1.32245973e-01 3.86963755e-01
7.09929049e-01 1.64012611e-01 -3.42257828e-01 3.48001033e-01
-4.00962395e-04 -4.18423563e-02 1.39234047e-02 7.90465832e-01
-9.71402645e-01 9.27992463e-01 5.16153812e+00 1.09157288e+00
-1.17167842e+00 1.13391928e-01 3.50411505e-01 -1.01854049e-01
-4.19652402e-01 -1.93494067e-01 -7.67639220e-01 5.50429285e-01
8.14132810e-01 -1.08488716e-01 6.30394280e-01 8.22508335e-01
7.65092134e-01 1.40581012e-01 -1.10880435e+00 1.16238761e+00
2.91950673e-01 -1.17097354e+00 3.29302132e-01 -1.82727426e-01
5.41502237e-01 -3.42970014e-01 -3.31446119e-02 4.71873671e-01
-7.64901936e-02 -9.29859042e-01 9.89058137e-01 3.28747481e-01
9.82073843e-01 -9.04330969e-01 5.66188097e-01 5.08574188e-01
-1.53010893e+00 -1.33725062e-01 -3.17124814e-01 1.56381994e-01
2.63466537e-01 2.76725978e-01 -8.42531502e-01 4.82414931e-01
6.18422449e-01 9.18556869e-01 -2.90175173e-02 7.46984720e-01
-4.69084889e-01 1.00773394e+00 -1.44176051e-01 2.30601296e-01
4.26752239e-01 -8.64828452e-02 5.15686810e-01 1.08652651e+00
5.99912703e-01 1.99706554e-01 6.19152963e-01 6.22654021e-01
-7.87349269e-02 9.60053727e-02 -5.90447247e-01 -2.04403907e-01
6.74560487e-01 7.35434949e-01 -4.20497566e-01 -5.39065480e-01
-7.26409435e-01 1.13512468e+00 -9.95069295e-02 7.63224006e-01
-9.75998402e-01 -3.56789947e-01 4.71260667e-01 -1.56424493e-02
4.52075988e-01 -2.92829961e-01 6.24986827e-01 -1.38008499e+00
2.39288703e-01 -1.14097261e+00 3.13422754e-02 -1.12597418e+00
-6.46342158e-01 4.72956240e-01 -1.16524078e-01 -1.46192312e+00
-3.83222342e-01 -4.43477154e-01 -4.28600878e-01 4.03043032e-01
-1.55336106e+00 -1.07808888e+00 -6.92959964e-01 9.72267926e-01
1.14282048e+00 -1.29263297e-01 2.55127132e-01 4.84308481e-01
-4.16770697e-01 4.93500888e-01 -9.52110365e-02 3.89580160e-01
7.98378646e-01 -8.25667143e-01 -1.01038069e-01 9.07311320e-01
4.75973934e-01 2.79197693e-01 5.20732701e-01 -5.66771746e-01
-1.58231962e+00 -1.27303123e+00 4.96581823e-01 1.63975254e-01
6.04509771e-01 -6.30571663e-01 -1.07986593e+00 7.62792826e-01
2.04133719e-01 -1.86363414e-01 4.23128456e-01 -5.07653594e-01
-2.48408467e-01 -1.41947836e-01 -5.14922321e-01 3.62399042e-01
1.05144227e+00 -1.01150453e+00 -6.25605583e-01 1.55083582e-01
1.36102629e+00 -4.95184004e-01 -4.12146598e-01 1.70712799e-01
3.69229496e-01 -6.97068512e-01 9.67950761e-01 -2.01456577e-01
6.08924329e-01 -3.77872527e-01 -4.04149622e-01 -1.30852532e+00
2.93866813e-01 -8.22656393e-01 -1.96071446e-01 1.52706158e+00
3.83079574e-02 -8.90529230e-02 7.19429553e-01 -2.45963350e-01
-5.31804025e-01 -3.55492234e-01 -8.24480474e-01 -5.87427199e-01
-4.44700450e-01 -7.55976021e-01 4.93820786e-01 8.24513435e-01
1.44852564e-01 1.87781259e-01 -7.62691200e-01 5.93412042e-01
6.13776982e-01 -8.85099918e-02 7.84604669e-01 -6.04918122e-01
-4.65626150e-01 8.91404971e-02 -3.11405420e-01 -1.85798860e+00
6.40326083e-01 -5.79144537e-01 5.75281382e-01 -1.26255882e+00
1.85389742e-01 2.22819179e-01 -1.68114334e-01 1.65050104e-01
-3.16029638e-01 -3.13090719e-02 2.65873671e-01 5.06609492e-02
-6.82731032e-01 7.17024684e-01 1.58703649e+00 -5.75085759e-01
-1.04435034e-01 -1.56533480e-01 -2.36225024e-01 6.35696292e-01
1.78580895e-01 -2.95468569e-01 -8.82306993e-01 -3.12730521e-01
-6.20537810e-02 6.65872097e-01 4.69241261e-01 -9.16603625e-01
3.74551475e-01 -1.11393377e-01 -3.80517379e-03 -7.48755753e-01
4.37517554e-01 -9.47096467e-01 -2.76783079e-01 2.79618144e-01
-4.31352347e-01 -3.31222177e-01 1.33845866e-01 8.64610136e-01
-7.95793831e-01 -1.35413259e-01 3.71080488e-01 3.32893990e-02
-1.06242537e+00 3.84745181e-01 -6.30410492e-01 -4.32707276e-03
8.51581037e-01 -1.37681246e-01 -6.43385947e-02 -8.87637258e-01
-9.73202288e-01 5.40026546e-01 1.19291082e-01 4.07763690e-01
1.03031540e+00 -1.32267451e+00 -4.93571848e-01 3.07229221e-01
1.03174098e-01 2.40195602e-01 5.99679410e-01 6.58796310e-01
-2.70187914e-01 3.02823812e-01 8.69972408e-02 -9.69902515e-01
-1.39259815e+00 8.47573102e-01 6.69931620e-02 3.41485977e-01
-7.79422760e-01 7.10712135e-01 6.70923412e-01 1.15070231e-01
7.91038156e-01 -6.02710426e-01 -1.59855261e-01 7.59951398e-02
8.03582013e-01 2.85117384e-02 -2.68999338e-01 -5.45800924e-01
-1.11029483e-01 5.46978772e-01 7.85633251e-02 -3.05183262e-01
1.00342607e+00 -5.71062624e-01 2.72457510e-01 5.39802611e-01
1.29789209e+00 -1.62527245e-02 -1.68913496e+00 -3.41172576e-01
-4.89209145e-01 -3.26183408e-01 3.23733538e-01 -1.26212642e-01
-1.16772532e+00 1.29115665e+00 3.08027208e-01 -2.03619063e-01
1.34804797e+00 -8.49755853e-02 9.98476207e-01 5.99993169e-01
3.81908536e-01 -9.07312334e-01 6.68335378e-01 3.34109783e-01
7.71897733e-01 -1.01441348e+00 -4.09898520e-01 -6.10371351e-01
-7.29573369e-01 1.03405964e+00 6.25492930e-01 2.01273426e-01
4.20466691e-01 3.31927031e-01 8.58829618e-02 2.12817222e-01
-7.38693118e-01 -2.67203033e-01 1.05395027e-01 7.72957027e-01
-1.31272614e-01 -2.54488885e-01 3.82130176e-01 7.55038381e-01
-1.09660700e-01 8.92445147e-02 6.08799338e-01 6.02209628e-01
-7.50112891e-01 -6.79597914e-01 -4.44027185e-01 -9.46257114e-02
-4.63016108e-02 -2.36806825e-01 -2.54406363e-01 6.33496106e-01
1.34334236e-01 1.12215066e+00 2.10369766e-01 -5.90535343e-01
1.90089613e-01 7.60217011e-02 3.98881197e-01 -5.84415734e-01
-3.97331174e-03 6.61863983e-01 -1.09763801e-01 -5.62808752e-01
-4.82525051e-01 -2.98057348e-01 -1.42436671e+00 -1.31972641e-01
-3.72454464e-01 4.47293639e-01 3.08942020e-01 1.12904453e+00
4.13093567e-02 8.11894894e-01 7.11997330e-01 -1.02184701e+00
-2.48394191e-01 -8.80743086e-01 -5.20996392e-01 3.08785915e-01
7.47105896e-01 -6.36676013e-01 -6.08195484e-01 5.69230795e-01] | [10.131696701049805, 0.7167659997940063] |
e1e5e4a5-f671-4579-af95-af65e32d1a29 | temporal-cascade-and-structural-modelling-of | 2102.02586 | null | https://arxiv.org/abs/2102.02586v1 | https://arxiv.org/pdf/2102.02586v1.pdf | Temporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction | Predicting (1) when the next hospital admission occurs and (2) what will happen in the next admission about a patient by mining electronic health record (EHR) data can provide granular readmission predictions to assist clinical decision making. Recurrent neural network (RNN) and point process models are usually employed in modelling temporal sequential data. Simple RNN models assume that sequences of hospital visits follow strict causal dependencies between consecutive visits. However, in the real-world, a patient may have multiple co-existing chronic medical conditions, i.e., multimorbidity, which results in a cascade of visits where a non-immediate historical visit can be most influential to the next visit. Although a point process (e.g., Hawkes process) is able to model a cascade temporal relationship, it strongly relies on a prior generative process assumption. We propose a novel model, MEDCAS, to address these challenges. MEDCAS combines the strengths of RNN-based models and point processes by integrating point processes in modelling visit types and time gaps into an attention-based sequence-to-sequence learning model, which is able to capture the temporal cascade relationships. To supplement the patients with short visit sequences, a structural modelling technique with graph-based methods is used to construct the markers of the point process in MEDCAS. Extensive experiments on three real-world EHR datasets have been performed and the results demonstrate that \texttt{MEDCAS} outperforms state-of-the-art models in both tasks. | ['Wray Buntine', 'Suong Le', 'Yuan-Fang Li', 'Weiqing Wang', 'Bhagya Hettige'] | 2021-02-04 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [ 2.17625186e-01 3.48487347e-02 -1.82251796e-01 -2.70014554e-01
-2.24821091e-01 7.50926882e-02 4.41033751e-01 7.14367867e-01
-5.46396710e-02 6.10216737e-01 7.99762964e-01 -7.86092639e-01
-5.29610515e-01 -9.85816121e-01 -5.49780667e-01 -5.54939032e-01
-4.26015764e-01 9.97761786e-01 -1.94833919e-01 -1.44514292e-01
-3.23865324e-01 1.15829661e-01 -9.91604745e-01 4.31208760e-01
7.76980281e-01 6.07580066e-01 3.08475375e-01 7.90175736e-01
-3.11904848e-01 1.29375780e+00 -2.62725532e-01 -1.54072270e-01
-1.59802791e-02 -7.87379801e-01 -5.40594518e-01 -2.14562193e-01
-6.81295991e-01 -1.42963469e-01 -6.67333126e-01 4.98045146e-01
4.48820710e-01 3.82563360e-02 7.01513946e-01 -8.87489617e-01
-5.81138611e-01 9.63148057e-01 -3.49639684e-01 6.15801275e-01
3.64908934e-01 3.13455611e-01 7.97690332e-01 -3.14209044e-01
1.06086791e-01 1.27058566e+00 8.95139992e-01 4.92141157e-01
-1.04310000e+00 -4.05785829e-01 5.95071673e-01 1.37644023e-01
-1.05158770e+00 1.21411219e-01 6.19688332e-01 -3.75639588e-01
1.03328407e+00 1.63635626e-01 9.57572043e-01 1.60072660e+00
7.67992139e-01 8.04480255e-01 7.59761751e-01 1.38027184e-02
7.43615180e-02 -5.52862048e-01 3.65713865e-01 3.76940012e-01
5.69868870e-02 3.50408442e-02 -2.89143115e-01 -6.73523784e-01
1.00700557e+00 1.33575976e+00 -1.89994097e-01 3.57336342e-01
-1.28978539e+00 5.32099009e-01 5.16574681e-01 2.15301439e-01
-1.20620966e+00 6.77097067e-02 3.37124586e-01 3.38366292e-02
2.62719065e-01 -8.48588124e-02 -5.83148479e-01 -2.38884330e-01
-7.77458787e-01 -1.46625750e-02 1.01937294e+00 9.05031919e-01
3.93469855e-02 -2.23772317e-01 -7.55077660e-01 5.01126945e-01
4.29143548e-01 3.94404858e-01 6.62927926e-01 -1.28885075e-01
6.13174200e-01 9.26820040e-01 1.95403710e-01 -7.25179851e-01
-6.80174589e-01 -4.57792372e-01 -1.48601830e+00 -7.98947036e-01
1.07047372e-02 -4.01685238e-01 -1.32421768e+00 1.40328956e+00
7.21830800e-02 8.52716267e-01 5.83965816e-02 3.72310728e-01
6.83111787e-01 9.27567780e-01 4.30735737e-01 -6.42318666e-01
1.41985512e+00 -6.91762745e-01 -9.92656529e-01 1.82246193e-01
4.98599321e-01 -3.36151987e-01 5.46956956e-01 1.49988711e-01
-1.11083686e+00 -4.54541773e-01 -3.15020889e-01 5.03881693e-01
1.15073308e-01 -3.50855082e-01 4.35071260e-01 1.31667387e-02
-8.82841706e-01 5.83294868e-01 -1.53044093e+00 -4.19663072e-01
4.77309167e-01 2.17186555e-01 3.52324933e-01 -2.19865769e-01
-1.40723181e+00 4.39887881e-01 2.94159383e-01 3.92265916e-01
-1.13107479e+00 -8.90039384e-01 -5.00503540e-01 4.79527980e-01
3.98385286e-01 -1.29089427e+00 1.29064262e+00 -4.38923717e-01
-9.88078475e-01 1.65720329e-01 -6.20582879e-01 -7.66997278e-01
5.55593908e-01 -2.84681678e-01 -7.82002568e-01 -2.06375960e-02
-2.63087481e-01 -8.48045275e-02 5.73321879e-01 -9.26881731e-01
-7.20209956e-01 -3.65595490e-01 -4.12685513e-01 3.30352485e-01
-7.94846788e-02 -3.69679704e-02 -3.76086920e-01 -5.94013155e-01
-4.44076359e-02 -8.94483805e-01 -8.22649062e-01 -7.62424588e-01
-5.86206496e-01 -5.53407788e-01 4.62745935e-01 -7.02179313e-01
1.80746222e+00 -1.82187390e+00 -7.39475042e-02 1.23459265e-01
4.13743556e-01 2.49863401e-01 1.96808845e-01 1.16696870e+00
-1.43982798e-01 1.90332502e-01 -2.50159860e-01 -4.36585695e-01
-5.69105387e-01 6.29652500e-01 -2.88650781e-01 2.82790009e-02
8.63095224e-02 1.18569899e+00 -1.06237316e+00 -4.09859896e-01
2.00679842e-02 6.96040869e-01 -2.45234668e-01 4.02588457e-01
-1.74273983e-01 6.43877983e-01 -5.93271434e-01 4.69998121e-01
1.12443589e-01 -9.31110382e-01 1.51037946e-01 4.06731546e-01
1.40119940e-01 3.22233945e-01 -7.58926034e-01 1.15935814e+00
-4.61132973e-01 -1.17269576e-01 -4.72372025e-01 -7.46047139e-01
8.44420969e-01 8.45307410e-01 7.97846675e-01 -3.05906981e-01
-6.68574646e-02 -2.60606796e-01 2.67282695e-01 -6.60125434e-01
1.10991545e-01 -2.85515636e-01 2.04054952e-01 4.77543116e-01
-3.89989913e-01 7.47657061e-01 -9.36679095e-02 1.42300725e-01
1.64303434e+00 -3.84501070e-02 4.52256918e-01 9.63902250e-02
1.67997360e-01 -1.77531838e-01 9.96015072e-01 9.56441164e-01
-1.37075931e-01 4.76242632e-01 5.67242742e-01 -8.33001912e-01
-9.42200601e-01 -1.11804056e+00 2.33358473e-01 5.03830135e-01
-8.55705068e-02 -4.05574769e-01 -7.73097426e-02 -4.50980753e-01
-2.18053222e-01 7.80081093e-01 -8.17204356e-01 -1.38761178e-01
-5.98624110e-01 -1.23101115e+00 1.94677860e-01 7.91053653e-01
7.97932744e-02 -1.45759737e+00 -4.96760994e-01 9.24685657e-01
-5.10522485e-01 -7.21091211e-01 -4.39143240e-01 1.09655567e-01
-1.22838867e+00 -1.08460295e+00 -6.95795894e-01 -6.40659034e-01
5.17347038e-01 -1.21256158e-01 1.13051867e+00 -1.49905402e-03
-2.49667749e-01 3.59551281e-01 -3.18112850e-01 -8.18215489e-01
-4.65339839e-01 -7.71791711e-02 -6.34817928e-02 2.72593051e-01
7.10380614e-01 -6.88767254e-01 -1.13923717e+00 8.06544200e-02
-1.03143883e+00 5.50141819e-02 5.96404314e-01 8.83720517e-01
8.99731100e-01 2.34266013e-01 6.78436041e-01 -1.02636576e+00
9.33803320e-01 -9.89529312e-01 8.25488716e-02 2.74939030e-01
-8.02011490e-01 -7.32450783e-02 7.73356974e-01 -5.47523677e-01
-1.05112088e+00 -1.65406000e-02 -2.94737350e-02 -7.28859425e-01
-2.93231964e-01 8.86315405e-01 3.24474245e-01 1.18408740e+00
2.40692317e-01 5.92335403e-01 -2.45400798e-02 -3.48113596e-01
-1.82388201e-01 4.44986850e-01 1.43743083e-01 -5.40320687e-02
2.52942920e-01 4.93046045e-01 2.06851244e-01 -3.96325499e-01
-7.82732069e-01 -9.53200102e-01 -3.87086481e-01 2.52617262e-02
1.11185038e+00 -1.10119832e+00 -1.00052130e+00 3.00226510e-01
-1.10629046e+00 -3.72473061e-01 -2.77802944e-01 5.88866472e-01
-5.79917133e-01 3.55927721e-02 -1.22384810e+00 -1.05589879e+00
-5.67479610e-01 -7.49915659e-01 8.33224475e-01 1.91881433e-01
-3.45566005e-01 -1.59012675e+00 3.23380083e-01 -4.73322645e-02
1.20158605e-01 3.83781016e-01 1.24393272e+00 -9.36390221e-01
-6.21309996e-01 -2.17782363e-01 4.92340401e-02 -6.59530684e-02
5.85516751e-01 -3.08141679e-01 -2.18122482e-01 -5.30660897e-02
1.40261784e-01 6.87909901e-01 7.34110951e-01 8.18067431e-01
1.12866247e+00 -5.85984170e-01 -7.81187952e-01 3.59006405e-01
1.36435711e+00 7.50072002e-01 8.29013348e-01 -1.23058118e-01
9.96879518e-01 3.14524531e-01 1.95110783e-01 7.64178276e-01
7.92405546e-01 9.85983908e-02 4.25622433e-01 -3.09815019e-01
3.36449921e-01 -7.62947977e-01 6.24157041e-02 1.07366049e+00
-4.32234854e-01 -4.52585161e-01 -1.29365897e+00 6.44558549e-01
-2.35447717e+00 -1.29892790e+00 -7.12133706e-01 2.12664485e+00
6.57691061e-01 -2.55663302e-02 1.75027505e-01 -1.90019980e-01
6.50133431e-01 -9.89340246e-02 -6.17088675e-01 -2.45611578e-01
7.37012699e-02 -1.88609753e-02 4.08946425e-01 1.47366196e-01
-7.60654628e-01 4.56380546e-01 5.97895098e+00 2.71951050e-01
-6.46155238e-01 9.91831943e-02 8.82544816e-01 -1.21509783e-01
-2.60757655e-01 -1.64252624e-01 -9.97913957e-01 7.32875705e-01
1.28059530e+00 -2.31042840e-02 1.67922795e-01 2.54187852e-01
9.67486918e-01 4.25422937e-01 -1.23003364e+00 1.06072760e+00
-2.12014809e-01 -1.10417068e+00 3.76197129e-01 1.60620883e-01
7.76821077e-01 1.58997089e-01 -1.31907851e-01 4.67587292e-01
9.03826058e-01 -1.15050411e+00 -1.67959347e-01 1.43432963e+00
2.62201339e-01 -8.18158329e-01 8.35958898e-01 7.28186250e-01
-1.21851909e+00 -3.83578926e-01 5.85594364e-02 -1.41956294e-02
8.21988940e-01 6.46405816e-01 -1.15523767e+00 8.24202299e-01
7.93591499e-01 9.93257165e-01 -5.94961792e-02 1.15851617e+00
-2.89408237e-01 1.18537581e+00 -8.78003389e-02 5.46815991e-02
3.64306390e-01 -1.76916376e-01 5.29606223e-01 1.07903099e+00
6.24259472e-01 4.16435778e-01 4.38224524e-01 6.30892098e-01
1.31947592e-01 3.39448191e-02 -6.20971441e-01 -9.40255541e-03
1.58412308e-01 1.01008129e+00 -6.92364991e-01 -5.78650594e-01
-5.10424078e-01 9.81634796e-01 -1.07162170e-01 6.59549952e-01
-7.58869648e-01 1.36299789e-01 4.66517359e-01 6.29432559e-01
4.06583875e-01 4.90582921e-03 -5.31817973e-02 -9.53198195e-01
-1.73025608e-01 -6.64906502e-01 8.29967797e-01 -6.40846550e-01
-1.65765309e+00 7.17515409e-01 -3.12432051e-01 -1.14986932e+00
-5.08192301e-01 2.67870445e-02 -9.40328896e-01 1.14397347e+00
-1.51711309e+00 -1.04688239e+00 -1.23488493e-01 9.05205429e-01
6.63022697e-01 1.68984696e-01 8.23598385e-01 7.08152577e-02
-5.84308386e-01 -3.20392884e-02 -8.10995772e-02 1.48067102e-01
2.85492808e-01 -1.12152767e+00 5.95030248e-01 5.25469899e-01
-2.51756191e-01 8.83404791e-01 4.43581223e-01 -1.24487388e+00
-1.08148384e+00 -1.55609441e+00 1.22746980e+00 -6.38542295e-01
7.03083992e-01 1.10953800e-01 -1.34511113e+00 9.15251791e-01
6.40990864e-03 -3.62124085e-01 9.80382264e-01 2.49293864e-01
3.19772929e-01 1.82912350e-01 -5.55477023e-01 6.50911868e-01
1.22772539e+00 -2.76018232e-01 -7.42138743e-01 4.05147523e-01
9.97064650e-01 -1.06232867e-01 -1.01039732e+00 5.50437093e-01
6.04608208e-02 -5.27762353e-01 7.56303728e-01 -1.06503916e+00
6.39701843e-01 -5.99266142e-02 3.81625980e-01 -1.45896244e+00
-6.54238999e-01 -8.80108297e-01 -6.63072944e-01 7.77059078e-01
4.58739728e-01 -7.30558097e-01 4.95595545e-01 6.41938269e-01
-1.74551696e-01 -1.13040173e+00 -6.62969649e-01 -5.34454286e-01
-4.63937849e-01 -2.43852153e-01 9.77444291e-01 8.76042247e-01
-5.34722917e-02 4.97119278e-01 -6.30082846e-01 4.51409161e-01
2.80840933e-01 1.54566588e-02 2.50632107e-01 -1.51837492e+00
-4.82051253e-01 -3.32157224e-01 -1.67743430e-01 -1.06042802e+00
-3.80613238e-01 -6.51147902e-01 -9.48698074e-02 -2.32296205e+00
4.17563349e-01 -3.70812058e-01 -8.88481259e-01 4.22486395e-01
-7.06950545e-01 -7.53465056e-01 -1.02987647e-01 6.83853149e-01
-4.82689172e-01 5.58340490e-01 1.34446168e+00 1.85125962e-01
-9.12870169e-01 7.42017984e-01 -5.36875248e-01 4.82869565e-01
7.82339275e-01 -5.72096288e-01 -5.10523438e-01 -1.17487326e-01
4.03282493e-01 8.97698820e-01 2.62770683e-01 -6.49620712e-01
4.46267366e-01 -1.38430864e-01 2.86710203e-01 -8.70143116e-01
1.60215616e-01 -8.66558790e-01 4.07353252e-01 8.19720685e-01
-3.97302657e-01 6.51924491e-01 -5.07991575e-03 1.39711964e+00
-2.19720349e-01 3.42192531e-01 -9.97485444e-02 -4.39098567e-01
-1.47279486e-01 1.07835841e+00 -6.23481989e-01 -2.60151863e-01
9.36177909e-01 -1.82177592e-02 1.24738544e-01 -6.97325826e-01
-1.16829932e+00 6.29371285e-01 -1.71333835e-01 5.16447008e-01
7.88826644e-01 -1.09019864e+00 -8.46884668e-01 -6.36850372e-02
1.13282062e-01 2.48867452e-01 5.75301588e-01 1.15046763e+00
-9.88896489e-02 3.76744837e-01 4.30173755e-01 -7.11082280e-01
-1.29284000e+00 1.04104853e+00 1.96653292e-01 -9.06097949e-01
-9.35019970e-01 3.38137984e-01 3.00003320e-01 -8.66100118e-02
1.02495246e-01 -6.24924004e-01 -4.98609483e-01 -7.66341249e-03
6.61205590e-01 2.18723312e-01 -2.75345445e-01 -2.92736411e-01
6.52666986e-02 -4.72060405e-02 -2.49649346e-01 1.17371596e-01
1.55074370e+00 -2.14464664e-01 -1.16637446e-01 9.50213671e-01
5.53627551e-01 -7.24949837e-01 -1.21797681e+00 -5.74875832e-01
-9.14312899e-02 2.74423182e-01 -1.68956086e-01 -7.43750215e-01
-6.78579271e-01 8.32405090e-01 3.35708350e-01 4.57168221e-01
1.19195843e+00 3.72901671e-02 1.09010565e+00 -1.11849066e-02
1.93228781e-01 -5.38232148e-01 -1.52717516e-01 4.67708439e-01
5.74171126e-01 -9.27920520e-01 -5.36041260e-01 -2.11899161e-01
-7.93224871e-01 8.01127017e-01 1.90210491e-01 -2.94739157e-02
1.07431281e+00 6.47016019e-02 -3.94053720e-02 -3.19816351e-01
-1.23759115e+00 -6.07055128e-02 9.59254056e-02 2.69288093e-01
3.70148271e-01 6.20264888e-01 -7.75795504e-02 6.67408705e-01
5.46302162e-02 4.01097476e-01 3.86300385e-01 8.69520426e-01
4.84698713e-02 -8.88583541e-01 -4.07625318e-01 9.81413662e-01
-7.13102996e-01 -5.47512829e-01 8.01462531e-02 3.64908516e-01
-2.45842189e-02 9.91327822e-01 3.01383227e-01 -1.53285787e-01
4.58953917e-01 3.67628068e-01 9.65489373e-02 -7.89301038e-01
-8.42719615e-01 3.29025745e-01 -2.63507932e-01 -3.21799904e-01
-2.48633981e-01 -8.05002213e-01 -1.33537400e+00 -8.91612768e-02
1.54353663e-01 -4.14760336e-02 2.37854615e-01 8.70478034e-01
6.93588078e-01 1.26730621e+00 4.01536733e-01 -1.17442004e-01
-3.86201054e-01 -1.10982537e+00 -4.53526229e-01 5.32288015e-01
5.05815387e-01 -1.79457575e-01 -6.50542900e-02 2.72602260e-01] | [7.775449275970459, 6.037852764129639] |
1edd242d-cc72-4255-8908-239c7a6e7127 | a-contact-safe-reinforcement-learning | 2207.13438 | null | https://arxiv.org/abs/2207.13438v1 | https://arxiv.org/pdf/2207.13438v1.pdf | A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation | Reinforcement learning shows great potential to solve complex contact-rich robot manipulation tasks. However, the safety of using RL in the real world is a crucial problem, since unexpected dangerous collisions might happen when the RL policy is imperfect during training or in unseen scenarios. In this paper, we propose a contact-safe reinforcement learning framework for contact-rich robot manipulation, which maintains safety in both the task space and joint space. When the RL policy causes unexpected collisions between the robot arm and the environment, our framework is able to immediately detect the collision and ensure the contact force to be small. Furthermore, the end-effector is enforced to perform contact-rich tasks compliantly, while keeping robust to external disturbances. We train the RL policy in simulation and transfer it to the real robot. Real world experiments on robot wiping tasks show that our method is able to keep the contact force small both in task space and joint space even when the policy is under unseen scenario with unexpected collision, while rejecting the disturbances on the main task. | ['Jianyu Chen', 'Shucheng Kang', 'Xiang Zhu'] | 2022-07-27 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.86675772e-01 6.53807402e-01 -1.73867971e-01 4.06278610e-01
-1.85007766e-01 -5.84510386e-01 3.53294402e-01 -1.00652814e-01
-6.86946690e-01 1.09521997e+00 -5.69977224e-01 -2.81008810e-01
-5.46703696e-01 -4.42500800e-01 -9.84658897e-01 -8.93095016e-01
-4.75753158e-01 6.11217320e-01 4.12901223e-01 -6.02265596e-01
1.34508818e-01 6.57943487e-01 -1.38954020e+00 -4.07521516e-01
6.76110148e-01 6.60678864e-01 7.38654256e-01 5.85640430e-01
6.55832171e-01 7.42957830e-01 -5.03328562e-01 5.97447693e-01
6.33671165e-01 6.90571917e-03 -7.46679306e-01 1.38776883e-01
-3.19245666e-01 -3.65396321e-01 -2.01239839e-01 1.08423674e+00
5.04441798e-01 6.48916781e-01 3.85717571e-01 -1.66112983e+00
3.33401889e-01 3.51364613e-01 -3.77685547e-01 -2.09030628e-01
3.55292976e-01 5.48968673e-01 2.96930403e-01 -2.36827150e-01
6.52501225e-01 1.23052847e+00 1.97198838e-01 5.89041770e-01
-8.36743772e-01 -4.82348740e-01 4.81964082e-01 -1.07828923e-01
-1.17085743e+00 -1.66150689e-01 5.17691493e-01 -3.16334277e-01
8.67195487e-01 -1.51804060e-01 3.71611983e-01 1.29314327e+00
8.76743019e-01 4.86946672e-01 9.88155186e-01 -1.71257421e-01
4.62717354e-01 -2.16246262e-01 -5.52750945e-01 4.47888613e-01
2.54105121e-01 7.47011185e-01 1.22830890e-01 -1.13598056e-01
9.24545884e-01 -3.19671398e-03 -2.74092108e-01 -7.31375992e-01
-1.51010764e+00 3.95497203e-01 3.51323843e-01 1.88032284e-01
-7.59453475e-01 1.81282774e-01 5.67898691e-01 7.94978619e-01
-3.76158446e-01 8.28950465e-01 -5.28019130e-01 -3.97300541e-01
1.22842707e-01 7.56073892e-01 8.19630623e-01 1.25240195e+00
1.52770668e-01 3.48435402e-01 1.02798745e-01 3.83425474e-01
-1.53633088e-01 4.62913483e-01 1.24491356e-01 -1.08739245e+00
7.66369939e-01 3.34634691e-01 7.51490474e-01 -7.82576561e-01
-6.68660700e-01 -1.87982693e-01 -6.74673021e-01 1.07835734e+00
3.84922266e-01 -5.37108243e-01 -4.83719170e-01 1.68849981e+00
6.86293185e-01 -1.96006000e-01 2.39837721e-01 1.30684483e+00
-3.16888779e-01 5.04337132e-01 -1.11572981e-01 -5.47814548e-01
8.83826733e-01 -4.03966010e-01 -1.05479085e+00 -5.79815686e-01
4.32959557e-01 -4.05577719e-01 1.01403427e+00 7.49717355e-01
-1.15818512e+00 -6.90508187e-01 -1.23119187e+00 6.47614360e-01
7.34284222e-02 -6.73411712e-02 7.99285024e-02 -1.92646161e-01
-3.78010362e-01 9.15754080e-01 -1.02768898e+00 -1.52096912e-01
-2.41148576e-01 7.34418690e-01 -5.68805337e-01 2.57891566e-01
-1.13085449e+00 1.43651557e+00 8.80526602e-01 3.86945039e-01
-1.21580648e+00 -8.66511315e-02 -8.26975048e-01 -2.50795007e-01
1.17901695e+00 -2.29971364e-01 1.36788571e+00 -5.68253934e-01
-1.85751677e+00 1.59424931e-01 5.66089571e-01 -2.71327943e-01
9.88269329e-01 -5.57986498e-01 5.49026541e-02 -6.77968550e-04
8.58775079e-02 2.42720753e-01 1.15601254e+00 -1.43655574e+00
-7.78556406e-01 -6.02361150e-02 2.46987104e-01 4.62802082e-01
2.21148252e-01 -6.33018315e-01 2.69043386e-01 -3.35618764e-01
-5.80585264e-02 -1.49941266e+00 -5.15984476e-01 -4.61079143e-02
-2.30814174e-01 -3.17172587e-01 9.14986491e-01 3.97120565e-02
5.38395882e-01 -2.29191136e+00 4.17521089e-01 3.06376755e-01
-2.68829346e-01 6.87184483e-02 -1.06581643e-01 5.34770191e-01
-1.05254129e-01 -3.84634852e-01 -1.86891645e-01 2.74430037e-01
-4.18484919e-02 5.59660435e-01 -5.74266016e-01 6.84899271e-01
1.66202232e-01 4.33172852e-01 -1.11779511e+00 -1.72927096e-01
1.18840039e-01 -2.30799112e-02 -3.44316006e-01 8.23120654e-01
-3.88459057e-01 8.08788478e-01 -9.33145702e-01 1.86162531e-01
4.22138900e-01 6.99604809e-01 3.03906620e-01 2.61404186e-01
-4.23987061e-01 -1.76273003e-01 -1.47811651e+00 1.21537352e+00
-6.00751281e-01 1.08439624e-01 7.03661919e-01 -9.65609908e-01
9.97356892e-01 3.90410870e-01 5.48577666e-01 -7.15511203e-01
4.40907508e-01 1.99087441e-01 2.48450384e-01 -6.29150271e-01
2.74276912e-01 -1.67774886e-01 -2.09453017e-01 2.90198416e-01
-2.40826219e-01 -8.25608850e-01 -8.25859830e-02 -2.13880643e-01
1.09757519e+00 6.14538729e-01 2.92575330e-01 -2.51281589e-01
5.91584265e-01 9.18528512e-02 5.99155724e-01 6.03190899e-01
-3.30613732e-01 -1.05359837e-01 5.35367906e-01 -5.21900356e-02
-9.44525480e-01 -9.20073152e-01 3.76514792e-01 8.80081534e-01
6.77987158e-01 2.12581575e-01 -3.71344745e-01 -5.13499916e-01
1.34872183e-01 8.17657888e-01 -3.28423440e-01 -7.19634891e-01
-9.92880881e-01 9.66533497e-02 4.14695367e-02 4.16818768e-01
2.06338674e-01 -1.30098879e+00 -1.34975624e+00 5.63214183e-01
3.18474509e-02 -1.28663170e+00 -2.16181472e-01 5.85075855e-01
-6.24551952e-01 -1.43414736e+00 -2.32391849e-01 -1.00835681e+00
7.48899281e-01 6.68503493e-02 2.76363432e-01 9.59081426e-02
-2.32763663e-01 2.62032747e-01 -2.40766615e-01 -4.18666810e-01
-6.62497282e-01 -2.86201775e-01 4.15438890e-01 -5.77923775e-01
-6.16117120e-01 -8.42893869e-02 -2.47736067e-01 8.09162259e-01
-7.31085598e-01 -2.67527640e-01 3.37572485e-01 9.99119580e-01
4.74314839e-01 8.76329660e-01 6.64939761e-01 -1.99045226e-01
8.98845017e-01 -1.04334742e-01 -1.01241755e+00 -2.05563903e-01
-2.21692845e-01 -3.23582925e-02 9.52434421e-01 -9.57880676e-01
-1.07252634e+00 3.62320513e-01 5.52622341e-02 -2.76411027e-01
-1.69191390e-01 1.83909804e-01 -1.53239101e-01 1.15662016e-01
5.48810601e-01 -2.94227875e-03 4.89116073e-01 -8.89609978e-02
2.76121628e-02 4.05996472e-01 4.96724844e-01 -8.45160723e-01
1.10349011e+00 1.82803705e-01 4.63378847e-01 -5.54337502e-01
-2.17047140e-01 -2.72469759e-01 -7.74074256e-01 -4.59550619e-01
4.72005874e-01 -5.97487986e-01 -1.40962303e+00 2.33875364e-01
-1.03252745e+00 -7.45561004e-01 -2.91631192e-01 5.59869051e-01
-1.27029669e+00 2.57150590e-01 -3.72182399e-01 -1.24255133e+00
2.58822218e-02 -1.31599021e+00 9.96219933e-01 -4.75543477e-02
-2.12970570e-01 -3.84512931e-01 -1.26191109e-01 -3.33500594e-01
1.81921586e-01 6.91519976e-01 8.57093930e-01 -2.52279848e-01
-1.18044041e-01 -3.39381635e-01 4.51487660e-01 5.87086119e-02
2.23280489e-01 -4.11824733e-01 -3.60048264e-01 -9.47500527e-01
4.25586283e-01 -7.01508760e-01 1.26107320e-01 4.92446311e-02
7.55149603e-01 -3.36262345e-01 -5.07600367e-01 -1.51892856e-01
1.16249692e+00 7.16205597e-01 4.18023556e-01 6.46484077e-01
3.17023098e-01 1.03592026e+00 1.70218277e+00 4.25309360e-01
-2.47703061e-01 7.95319259e-01 1.09184968e+00 5.69995828e-02
5.66380084e-01 -2.26256654e-01 5.81840217e-01 5.77822849e-02
7.28714690e-02 -1.12093426e-01 -6.12877011e-01 3.49001497e-01
-2.30293322e+00 -6.51419163e-01 5.83997890e-02 2.50283170e+00
8.08356702e-01 6.50876641e-01 8.42349902e-02 3.39346707e-01
5.59909940e-01 -3.41552079e-01 -7.59085715e-01 -5.98057866e-01
4.68402892e-01 -3.43438327e-01 6.12079024e-01 5.99742770e-01
-8.36774051e-01 8.64057660e-01 4.99982786e+00 5.19116879e-01
-1.16397846e+00 -4.21652377e-01 -2.59354472e-01 2.67514028e-02
5.84373832e-01 -8.87391493e-02 -3.82459402e-01 2.93272227e-01
5.49243927e-01 -5.30822575e-02 3.93118978e-01 1.15593565e+00
4.22875136e-01 -4.94780749e-01 -1.30772865e+00 5.04725099e-01
-5.03258944e-01 -3.49511862e-01 -7.88820565e-01 -2.65245438e-01
2.31098399e-01 -3.95985335e-01 -3.03266719e-02 6.00008965e-01
3.78307432e-01 -8.53416681e-01 8.44147623e-01 2.47531444e-01
5.47036529e-01 -1.17366230e+00 8.85327041e-01 1.15049028e+00
-9.18093204e-01 -5.65647662e-01 -3.39871943e-01 -3.35713029e-01
3.72374535e-01 1.05295308e-01 -1.24099195e+00 4.92504478e-01
2.16981754e-01 2.76512146e-01 1.82710141e-01 8.70943964e-01
-4.06866014e-01 -1.07896633e-01 -3.58684570e-01 -2.43224695e-01
4.11082834e-01 -1.56193316e-01 9.45749640e-01 4.44875538e-01
-1.40286079e-02 2.60068360e-03 9.64078605e-01 5.95009685e-01
8.91064644e-01 -3.66047144e-01 -1.15662873e+00 2.15594411e-01
7.15498701e-02 9.58724856e-01 -4.35106725e-01 7.40842298e-02
5.32514095e-01 9.83931124e-01 3.28489512e-01 3.54478776e-01
-9.38736498e-01 -6.29765451e-01 5.75379312e-01 -6.54122233e-02
-7.52016827e-02 -7.05246329e-01 2.99346060e-01 -5.53064883e-01
2.01682895e-01 -8.00564766e-01 -9.96888895e-03 -6.91976070e-01
-1.04344046e+00 3.66583943e-01 1.88302785e-01 -1.59200561e+00
-6.20822608e-01 -6.81640089e-01 -4.71128374e-01 4.55064297e-01
-1.27054822e+00 -2.36535236e-01 3.52463387e-02 5.79556108e-01
7.82019198e-01 4.22552451e-02 6.61569297e-01 -2.21169114e-01
-4.42166984e-01 -8.25414583e-02 -1.90194488e-01 -2.99609601e-01
8.41964781e-01 -1.01660955e+00 -4.27977070e-02 2.51765609e-01
-1.02607930e+00 5.32533705e-01 1.08120489e+00 -1.00178397e+00
-1.92013252e+00 -1.01728928e+00 1.37709200e-01 -2.25262847e-02
7.45040834e-01 -5.64153910e-01 -9.08774912e-01 6.32939160e-01
-1.00089982e-01 6.32584170e-02 -4.36588466e-01 -5.61716914e-01
3.71223509e-01 -3.98509726e-02 -1.31576645e+00 9.95878816e-01
8.24066699e-01 3.43319103e-02 -8.67553353e-01 6.39102042e-01
8.62423003e-01 -7.94003963e-01 -7.43150055e-01 7.65914977e-01
5.35133481e-01 -2.79855788e-01 7.26346076e-01 -6.55510485e-01
-7.15078861e-02 -5.30075729e-01 3.45635355e-01 -1.73802614e+00
-2.94363767e-01 -9.24047768e-01 -3.66145046e-03 4.80005085e-01
9.90622863e-02 -6.94040179e-01 5.09646058e-01 9.17575285e-02
-6.22654222e-02 -6.20473802e-01 -1.46492779e+00 -1.28034270e+00
7.13574067e-02 -2.92580336e-01 1.63484097e-01 5.64622879e-01
5.27424812e-01 2.01969624e-01 -6.86735570e-01 6.13970697e-01
4.28798139e-01 -1.67854205e-01 9.97080386e-01 -8.23934376e-01
-2.53911644e-01 -1.99572593e-02 -8.53542909e-02 -7.02421486e-01
7.04124212e-01 -1.68014154e-01 1.07943404e+00 -1.42424035e+00
-4.84968364e-01 -6.12286568e-01 -2.53440719e-02 5.47454715e-01
5.92942312e-02 -6.02685213e-01 2.74522364e-01 3.68646085e-02
-4.58108693e-01 6.17106140e-01 1.77386153e+00 -1.50150284e-01
-5.38210690e-01 5.02305388e-01 2.99555510e-01 5.98910153e-01
1.06683731e+00 -3.60160768e-01 -8.17880213e-01 -1.05695561e-01
1.60334378e-01 6.83333218e-01 6.09494969e-02 -9.86097693e-01
1.27479151e-01 -7.03473806e-01 -6.65499419e-02 -3.74650419e-01
3.23257387e-01 -1.77040112e+00 1.06661700e-01 1.25202250e+00
-4.41639811e-01 9.33390185e-02 2.94520259e-01 7.50107050e-01
2.31825747e-02 -1.56716824e-01 7.63067126e-01 5.11229113e-02
-4.65946734e-01 8.22071508e-02 -6.75548732e-01 -2.88050264e-01
1.57745838e+00 -1.34162411e-01 -2.51897089e-02 -1.39436617e-01
-7.59669781e-01 8.85946453e-01 3.29283297e-01 7.07337141e-01
6.98851347e-01 -8.12487185e-01 -2.77017832e-01 3.64136785e-01
4.79211025e-02 1.00770205e-01 6.04263041e-03 8.07910562e-01
-7.82739744e-02 2.31544197e-01 -6.27134323e-01 -5.84878922e-01
-1.05992210e+00 9.20659959e-01 2.74139732e-01 1.42881600e-03
-9.35890257e-01 1.80250376e-01 1.83269739e-01 -5.13070226e-01
5.58052003e-01 -6.48461103e-01 -1.67311773e-01 -3.89743686e-01
2.69186497e-01 2.67260969e-01 3.56628522e-02 -1.04288332e-01
-3.11936527e-01 4.52180654e-01 4.29262370e-02 -1.45774394e-01
1.20963407e+00 -1.21135354e-01 1.78772911e-01 3.67132813e-01
6.16691530e-01 -3.80453199e-01 -1.78578007e+00 2.39340231e-01
6.48652315e-02 -4.37953115e-01 -2.92175502e-01 -6.73086941e-01
-5.56045175e-01 4.41721439e-01 5.19094348e-01 7.87726939e-02
7.74930954e-01 -5.48903406e-01 5.11875927e-01 8.16179872e-01
9.50070381e-01 -1.60036337e+00 5.00828683e-01 9.26707566e-01
1.38276410e+00 -9.57740724e-01 3.40943448e-02 -4.65552866e-01
-1.00760722e+00 1.06648266e+00 1.19565332e+00 -5.84349513e-01
3.37073088e-01 6.29440665e-01 -5.08894622e-02 9.78923216e-02
-7.47477829e-01 1.03733748e-01 -1.40807688e-01 8.41772795e-01
-2.18762755e-01 4.78354748e-03 -1.68688804e-01 4.86424332e-03
1.26990557e-01 -2.48186022e-01 6.83056235e-01 1.61641002e+00
-9.20673668e-01 -8.44370306e-01 -6.48415506e-01 -1.67955145e-01
-2.08103824e-02 7.46255338e-01 -1.38086498e-01 1.39761055e+00
1.13687009e-01 9.16287482e-01 -4.65220539e-03 -5.87098971e-02
1.06804979e+00 -2.67861217e-01 5.82302392e-01 -8.22309554e-01
-5.46440899e-01 2.88281858e-01 1.44221306e-01 -8.92148376e-01
2.60726660e-02 -2.04942212e-01 -1.93313050e+00 3.80000830e-01
-4.35123861e-01 1.34398684e-01 5.96746385e-01 9.45097327e-01
-6.46404773e-02 6.63763046e-01 9.22873378e-01 -1.07314861e+00
-1.34092176e+00 -8.16900849e-01 -6.22893870e-01 1.56788886e-01
9.56929386e-01 -1.29063940e+00 -3.00348818e-01 -4.80083823e-01] | [4.741327285766602, 1.5924125909805298] |
9b4ca236-71f1-4fa1-8ecb-dcdec0a8ac1b | evolutionary-multi-objective-optimization-of | 1803.10316 | null | http://arxiv.org/abs/1803.10316v1 | http://arxiv.org/pdf/1803.10316v1.pdf | Evolutionary Multi-objective Optimization of Real-Time Strategy Micro | We investigate an evolutionary multi-objective approach to good micro for
real-time strategy games. Good micro helps a player win skirmishes and is one
of the keys to developing better real-time strategy game play. In prior work,
the same multi-objective approach of maximizing damage done while minimizing
damage received was used to evolve micro for a group of ranged units versus a
group of melee units. We extend this work to consider groups composed from two
types of units. Specifically, this paper uses evolutionary multi-objective
optimization to generate micro for one group composed from both ranged and
melee units versus another group of ranged and melee units. Our micro behavior
representation uses influence maps to represent enemy spatial information and
potential fields generated from distance, health, and weapons cool down to
guide unit movement. Experimental results indicate that our multi-objective
approach leads to a Pareto front of diverse high-quality micro encapsulating
multiple possible tactics. This range of micro provided by the Pareto front
enables a human or AI player to trade-off among short term tactics that better
suit the player's longer term strategy - for example, choosing to minimize
friendly unit damage at the cost of only lightly damaging the enemy versus
maximizing damage to the enemy units at the cost of increased damage to
friendly units. We believe that our results indicate the usefulness of
potential fields as a representation, and of evolutionary multi-objective
optimization as an approach, for generating good micro. | ['Joseph Ghantous', 'Siming Liu', 'Rahul Dubey', 'Sushil Louis'] | 2018-03-27 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [ 9.75671560e-02 -2.09719408e-02 1.47982374e-01 3.22700441e-01
-2.60609668e-02 -5.85334241e-01 3.01165462e-01 2.64609635e-01
-8.03200006e-01 7.38138378e-01 3.71365137e-02 -6.42533526e-02
-9.04993296e-01 -1.38592768e+00 -3.28851670e-01 -6.06414139e-01
-2.72065818e-01 5.33035755e-01 3.06120366e-01 -8.88087213e-01
6.26383364e-01 6.55356646e-01 -1.63140798e+00 -6.05148450e-02
6.81081295e-01 5.82861125e-01 3.78759831e-01 7.40757346e-01
3.53766918e-01 7.26408958e-01 -1.32996643e+00 -3.31599474e-01
4.74683046e-01 -6.55345738e-01 -2.26466447e-01 -4.34135914e-01
-6.98734999e-01 -1.80483162e-01 2.04544604e-01 7.35143840e-01
8.37724149e-01 5.18661261e-01 7.00637579e-01 -1.33500218e+00
-2.61614829e-01 7.89766729e-01 -6.08222067e-01 3.10836285e-01
3.30482513e-01 2.32088044e-01 9.21455562e-01 1.41785368e-01
5.38380921e-01 1.29602313e+00 3.00332248e-01 4.09303218e-01
-1.17631423e+00 -4.36086863e-01 -6.99795857e-02 -1.17417723e-01
-1.28189480e+00 1.19136415e-01 6.18976116e-01 -2.06433058e-01
1.29758549e+00 8.70390058e-01 1.17600298e+00 4.85400170e-01
9.48970199e-01 3.91676694e-01 7.51401365e-01 -3.31403375e-01
5.64824581e-01 -5.53639233e-02 -6.23515189e-01 2.16635585e-01
4.71740097e-01 6.50786519e-01 -2.82607287e-01 -3.36576939e-01
5.87081671e-01 -1.66040316e-01 -9.58997980e-02 -8.72661993e-02
-7.48699725e-01 9.31047082e-01 5.00918865e-01 4.54060614e-01
-7.27693677e-01 2.51042485e-01 3.15187946e-02 2.56956756e-01
3.28328103e-01 1.65726566e+00 -7.68882409e-02 -4.87893581e-01
-6.01025522e-01 5.35369754e-01 7.72702992e-01 7.25342557e-02
5.24061918e-01 3.13059658e-01 -3.94523479e-02 4.97533441e-01
3.51627939e-04 4.91837502e-01 1.96794614e-01 -8.53047490e-01
1.86947420e-01 8.51288497e-01 1.66457266e-01 -1.43456590e+00
-4.28457052e-01 -7.12703705e-01 -1.08672462e-01 8.72279167e-01
-3.06249559e-02 -4.81930435e-01 -3.47864687e-01 1.84250152e+00
1.23089299e-01 -2.87807226e-01 -1.43326566e-01 6.43549860e-01
1.01216011e-01 7.72875130e-01 -1.70641601e-01 -2.63024807e-01
9.22597945e-01 -4.28642154e-01 -3.32384706e-01 -3.10626328e-01
4.73350585e-01 -4.69808340e-01 7.28739083e-01 3.50432098e-01
-1.24798584e+00 1.66788101e-02 -1.09258676e+00 9.10287261e-01
-4.36220586e-01 -5.01212478e-01 4.32189941e-01 8.59201550e-01
-9.05949771e-01 6.00033581e-01 -5.09008586e-01 -1.62029371e-01
-9.51519608e-02 5.00368834e-01 2.44773686e-01 5.38749933e-01
-1.14007604e+00 1.34512603e+00 3.94425184e-01 -1.02876678e-01
-9.31977272e-01 -5.58874011e-01 -6.34656668e-01 4.22648728e-01
8.22357059e-01 -6.27352118e-01 7.01624036e-01 -9.01843250e-01
-1.31000066e+00 1.95878968e-01 6.26551449e-01 -2.60978580e-01
3.92840177e-01 2.82302171e-01 -7.56089166e-02 -1.77453712e-01
1.45230412e-01 5.30429721e-01 3.91848296e-01 -1.04848635e+00
-7.40459025e-01 -2.96601027e-01 7.73987234e-01 7.14041710e-01
-5.32191396e-01 2.38093942e-01 4.23173934e-01 -7.04279959e-01
-6.44627750e-01 -8.49062264e-01 -4.87989277e-01 -5.72053790e-01
-1.96511433e-01 1.69707119e-01 5.04057288e-01 -2.42528409e-01
1.63296068e+00 -1.55733180e+00 9.74698484e-01 3.95312667e-01
8.80593434e-02 7.25652650e-02 -1.47786245e-01 7.51654923e-01
2.29986787e-01 3.15680116e-01 -1.10631444e-01 2.40548626e-01
-1.25384927e-02 2.00691134e-01 1.17340848e-01 8.28595608e-02
3.49387061e-03 7.81226516e-01 -9.79755580e-01 1.09753005e-01
4.37672921e-02 8.18571299e-02 -6.58831358e-01 1.50849000e-01
-1.38331696e-01 -8.37184489e-02 -5.19149661e-01 6.05027854e-01
1.97709143e-01 5.19134760e-01 6.36441708e-02 2.36455724e-01
-6.15916133e-01 -1.52358130e-01 -1.09500599e+00 7.59647787e-01
-4.55942452e-01 2.97413051e-01 2.97030628e-01 -6.46463752e-01
9.67661440e-01 -1.76326364e-01 7.48456180e-01 -7.74092913e-01
6.47654116e-01 2.66496867e-01 4.81103241e-01 -3.61104980e-02
9.10117209e-01 -3.58214170e-01 -4.59627151e-01 1.01782310e+00
-4.98287320e-01 -6.72463238e-01 2.66924590e-01 -3.22485827e-02
1.21483254e+00 -4.94926460e-02 3.61952782e-01 -4.27780122e-01
-4.54391614e-02 2.46352121e-01 6.06232107e-01 6.37806833e-01
-8.65653455e-02 -3.48061211e-02 7.73732066e-01 -1.15925029e-01
-8.23652148e-01 -9.02610183e-01 4.63643670e-01 1.15401423e+00
5.37797272e-01 -2.20019475e-01 -5.53869307e-01 -1.93705410e-01
-1.43316248e-02 1.13238370e+00 -8.65097940e-01 -5.81146538e-01
-7.74569929e-01 -9.99349654e-01 7.26969779e-01 2.55180538e-01
2.38639310e-01 -1.08607674e+00 -1.58648252e+00 4.41867113e-01
1.34570748e-01 8.57414603e-02 -4.85813707e-01 2.07029939e-01
-3.59674126e-01 -8.70009720e-01 -5.86755633e-01 -1.59445584e-01
5.08482516e-01 4.00337785e-01 9.54550683e-01 3.40548933e-01
-5.42137921e-01 4.27721620e-01 -3.98431510e-01 -7.07869411e-01
-2.86444813e-01 -3.13981771e-01 1.82341918e-01 -4.68314767e-01
-1.45174384e-01 -5.34763098e-01 -4.59642500e-01 6.14845216e-01
-1.10995436e+00 -1.54784381e-01 3.98167431e-01 6.78013325e-01
3.04291338e-01 7.07830608e-01 3.65661204e-01 -5.73393181e-02
1.23736632e+00 -6.35736287e-01 -6.30393744e-01 4.77485180e-01
-4.62460756e-01 1.36269093e-01 4.83647734e-01 -8.75539064e-01
-7.92662084e-01 -6.21765792e-01 1.97517291e-01 -4.08110581e-02
6.99216485e-01 6.26018941e-01 -2.11233959e-01 -1.98087543e-01
7.39478588e-01 -1.05816014e-01 1.76668558e-02 3.93821578e-03
1.18743315e-01 3.38771433e-01 8.68963972e-02 -8.25838149e-01
5.29299855e-01 1.75216645e-01 1.43825799e-01 -4.49583799e-01
2.47020751e-01 9.83847231e-02 -1.07569784e-01 -7.23999739e-01
8.45781207e-01 -1.70682177e-01 -8.33812833e-01 5.36626101e-01
-6.46457970e-01 -2.89479971e-01 -6.17305100e-01 2.85170138e-01
-5.71414053e-01 -2.06597403e-01 1.11272842e-01 -1.11372280e+00
-1.22410677e-01 -1.32486546e+00 5.74764490e-01 4.07900155e-01
-3.86666149e-01 -9.88075852e-01 3.39041620e-01 2.08219029e-02
7.09386766e-01 7.16175377e-01 7.30866849e-01 -4.07822371e-01
-3.63223255e-01 -1.94222704e-01 4.27805364e-01 -1.07313350e-01
3.59003365e-01 1.10347219e-01 -6.38414696e-02 -3.69311512e-01
7.09382817e-02 -4.13376279e-02 3.22682410e-01 4.50411975e-01
2.95964509e-01 -3.66433918e-01 -4.17263865e-01 2.81471193e-01
1.41276836e+00 9.68657792e-01 7.07798004e-01 8.35086048e-01
1.62584484e-01 9.50232327e-01 1.09569800e+00 7.00683773e-01
2.38759086e-01 1.01312625e+00 7.78762817e-01 8.62309039e-02
3.06474060e-01 -4.37956527e-02 4.77079779e-01 1.36812469e-02
-3.85123670e-01 -7.43496895e-01 -7.14338481e-01 5.67896783e-01
-1.63149261e+00 -1.45117795e+00 5.89153528e-01 2.23979235e+00
4.64067340e-01 2.28768423e-01 6.49259925e-01 -2.46964321e-02
7.86942363e-01 1.47435842e-02 -4.83144522e-01 -1.04136705e+00
-7.01113790e-02 1.27513230e-01 7.45685518e-01 6.97395205e-01
-3.85388076e-01 6.15644574e-01 5.95161390e+00 1.08435547e+00
-1.10709381e+00 -3.51162136e-01 6.47093594e-01 -9.45353746e-01
-7.96202004e-01 1.61967024e-01 -4.69493091e-01 6.06157899e-01
6.95060968e-01 -9.66935277e-01 7.08409309e-01 4.59559888e-01
5.88255048e-01 -4.92167085e-01 -4.54840392e-01 2.48093963e-01
-2.82747932e-02 -1.31581593e+00 -2.56902933e-01 5.29097736e-01
5.66470504e-01 -6.33304536e-01 1.15873352e-01 1.32845312e-01
7.76917875e-01 -1.03549469e+00 1.03283226e+00 3.81189346e-01
5.18726289e-01 -1.29168308e+00 5.97513795e-01 6.16884470e-01
-1.35556495e+00 -5.09086430e-01 -7.41224363e-02 -5.28174341e-01
4.56006378e-01 -4.68703434e-02 -7.35593915e-01 5.62364280e-01
6.23447180e-01 -1.00763686e-01 -1.75677508e-01 9.56837833e-01
5.81605583e-02 -3.94893205e-03 -6.52480245e-01 -7.24899173e-01
4.86680031e-01 -3.79465938e-01 1.20678854e+00 4.72414434e-01
6.12823904e-01 2.97946423e-01 3.82899866e-02 9.73731101e-01
6.86925530e-01 4.67741527e-02 -8.43010426e-01 -2.47205690e-01
6.36372030e-01 9.27761316e-01 -8.75390828e-01 1.97312817e-01
6.19379282e-01 5.48563123e-01 -6.37647659e-02 -9.39053372e-02
-1.04590750e+00 -7.87090540e-01 7.26188719e-01 2.29196265e-01
-1.07090309e-01 -1.07098021e-01 -4.64251667e-01 -5.79739809e-01
-4.22210574e-01 -9.02051628e-01 2.95571536e-01 -5.89519382e-01
-7.01403677e-01 8.57402444e-01 6.68890834e-01 -1.12611520e+00
-5.49087524e-01 -2.65949488e-01 -1.09958470e+00 8.76331747e-01
-6.07588172e-01 -8.28211367e-01 2.44817242e-01 -1.23298345e-02
4.32459831e-01 -5.53920686e-01 3.32639694e-01 -2.63286084e-01
-5.60827315e-01 4.71326470e-01 2.21343897e-02 -6.49329484e-01
-1.42087080e-02 -9.72588062e-01 -9.05582458e-02 9.33528423e-01
-4.32976514e-01 5.40877640e-01 9.43787873e-01 -1.08476412e+00
-1.28439915e+00 -3.92947346e-01 4.16754216e-01 -1.77842870e-01
7.05567241e-01 -2.66361702e-02 -1.23490222e-01 -1.14099763e-01
1.39051989e-01 -1.28707814e+00 6.17054820e-01 -1.24232143e-01
4.39046890e-01 -3.40514444e-02 -1.59128666e+00 1.19394648e+00
8.06120455e-01 1.90605313e-01 -3.24683607e-01 -2.00633615e-01
5.84088683e-01 -1.89954221e-01 -6.46204352e-01 4.00188327e-01
6.33065999e-01 -1.03013206e+00 9.89486158e-01 -3.17956179e-01
3.09137464e-01 -2.52670288e-01 -1.11846291e-01 -1.85733747e+00
-5.04202068e-01 -7.70886242e-01 4.83529240e-01 6.96214795e-01
3.27034682e-01 -7.25748301e-01 6.46360397e-01 6.40025198e-01
-1.20541275e-01 -9.40353513e-01 -9.70966220e-01 -8.74963343e-01
8.37384164e-02 -4.17541675e-02 7.44387925e-01 4.39598858e-01
-2.48730667e-02 -1.61004379e-01 -5.17666519e-01 -9.97688398e-02
5.95600605e-01 1.30916871e-02 5.72044432e-01 -7.89479136e-01
-8.55735898e-01 -1.02139032e+00 -4.39354837e-01 -3.10068130e-01
-2.85617262e-01 -3.18490952e-01 -4.79652919e-02 -1.40006971e+00
-1.19569793e-01 -7.08524048e-01 5.66690974e-02 4.96239275e-01
-9.00482312e-02 4.16946337e-02 5.72734058e-01 -9.22365487e-02
-1.57210082e-02 4.17212903e-01 1.20950019e+00 -1.74972445e-01
-7.01801002e-01 1.79871604e-01 -1.22594726e+00 4.82888788e-01
6.34419620e-01 -5.72463334e-01 -8.19647729e-01 -3.06946635e-01
6.52183175e-01 2.64598459e-01 3.56741101e-02 -8.73543441e-01
2.99368538e-02 -1.02189291e+00 -1.56670824e-01 -2.04162225e-01
5.90189517e-01 -7.21605897e-01 7.32465208e-01 1.06516171e+00
-8.53086729e-03 4.22763765e-01 3.95675659e-01 4.18893635e-01
8.97006541e-02 -4.18655604e-01 6.22255266e-01 -2.04150885e-01
-4.51772928e-01 -2.48158425e-01 -6.47523642e-01 -3.83596599e-01
1.58056533e+00 -7.42754698e-01 -4.04382795e-01 -6.17637157e-01
-3.48028779e-01 4.73717093e-01 6.99524164e-01 4.23319906e-01
5.76999426e-01 -9.71981823e-01 -7.56343365e-01 -2.31627792e-01
-4.25060451e-01 -6.64361477e-01 1.49423450e-01 4.67597634e-01
-8.95690203e-01 3.73759158e-02 -8.93356204e-01 6.06121123e-02
-1.42879951e+00 3.20057690e-01 5.81965566e-01 -4.96029913e-01
-4.91985902e-02 1.18779504e+00 9.46766660e-02 -1.71105891e-01
-1.32192805e-01 1.12930901e-01 -3.21239442e-01 3.95225227e-01
5.34347951e-01 9.34984922e-01 -2.36693338e-01 -6.32577360e-01
-4.73246306e-01 4.15618896e-01 3.14191312e-01 -6.26320362e-01
1.51546586e+00 1.72964498e-01 -2.30328217e-01 -4.15687636e-02
5.12680948e-01 4.79192495e-01 -9.60631073e-01 6.78955495e-01
-2.57002622e-01 -8.87950063e-01 3.45125608e-02 -1.06978464e+00
-8.17167163e-01 1.17662534e-01 2.21806705e-01 5.26339293e-01
1.60696435e+00 -4.92549181e-01 4.47479576e-01 2.21106663e-01
8.23317766e-01 -1.05416584e+00 3.95476460e-01 3.43685538e-01
9.52554405e-01 -3.65709811e-01 1.14106581e-01 1.41632110e-01
-9.45684910e-01 8.31324160e-01 7.59683490e-01 -2.54035741e-01
2.44033232e-01 6.53863311e-01 -1.85609221e-01 -2.65750349e-01
-8.74341130e-01 -2.70203173e-01 5.28859124e-02 6.82640672e-01
-1.38861716e-01 2.27457955e-01 -7.01927841e-01 3.45775217e-01
-3.27500314e-01 -5.50732136e-01 7.07012653e-01 1.34802341e+00
-8.92238021e-01 -1.44445348e+00 -7.89826393e-01 5.10559678e-01
-1.40387908e-01 1.62447151e-02 -7.04065025e-01 8.43015969e-01
4.61632997e-01 1.08796644e+00 1.51414037e-01 -7.27384508e-01
3.75661016e-01 -7.96348214e-01 4.73489523e-01 -6.19051039e-01
-1.32369161e+00 9.01930630e-02 2.44701222e-01 -2.87443221e-01
-2.71636732e-02 -4.87892210e-01 -1.03785312e+00 -7.90111363e-01
-4.00783896e-01 5.00944376e-01 5.28327882e-01 5.94654918e-01
2.19749764e-01 6.53542638e-01 8.21592212e-01 -1.02218890e+00
-4.58800644e-01 -6.15205526e-01 -6.38793111e-01 -1.58982635e-01
-2.95131445e-01 -1.14576900e+00 -2.43675441e-01 -9.27051008e-01] | [3.484910488128662, 1.5851147174835205] |
99054aae-5304-4b85-8ea2-ab5cd2e1b191 | revisiting-open-world-object-detection | 2201.00471 | null | https://arxiv.org/abs/2201.00471v2 | https://arxiv.org/pdf/2201.00471v2.pdf | Revisiting Open World Object Detection | Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones. We find that although the only previous OWOD work constructively puts forward to the OWOD definition, the experimental settings are unreasonable with the illogical benchmark, confusing metric calculation, and inappropriate method. In this paper, we rethink the OWOD experimental setting and propose five fundamental benchmark principles to guide the OWOD benchmark construction. Moreover, we design two fair evaluation protocols specific to the OWOD problem, filling the void of evaluating from the perspective of unknown classes. Furthermore, we introduce a novel and effective OWOD framework containing an auxiliary Proposal ADvisor (PAD) and a Class-specific Expelling Classifier (CEC). The non-parametric PAD could assist the RPN in identifying accurate unknown proposals without supervision, while CEC calibrates the over-confident activation boundary and filters out confusing predictions through a class-specific expelling function. Comprehensive experiments conducted on our fair benchmark demonstrate that our method outperforms other state-of-the-art object detection approaches in terms of both existing and our new metrics. Our benchmark and code are available at https://github.com/RE-OWOD/RE-OWOD. | ['Yixuan Qiao', 'Duorui Wang', 'Yuqing Ma', 'Yifan Shen', 'Xianglong Liu', 'Xiaowei Zhao'] | 2022-01-03 | null | null | null | null | ['open-world-object-detection'] | ['computer-vision'] | [-7.86072984e-02 2.21126020e-01 -2.81557381e-01 -2.32000023e-01
-6.53163671e-01 -6.20710731e-01 4.73365992e-01 -6.63590357e-02
-3.78245026e-01 8.37292910e-01 -2.47791156e-01 -1.03365131e-01
-2.81780690e-01 -7.89533436e-01 -6.45841360e-01 -6.99114859e-01
-4.85480353e-02 7.95728981e-01 8.24751914e-01 2.42635936e-01
2.76563108e-01 5.23334503e-01 -1.81187594e+00 1.23510391e-01
7.81414688e-01 1.34592962e+00 3.35108995e-01 5.87850034e-01
4.34420481e-02 4.94687468e-01 -7.62124836e-01 -3.93064648e-01
6.50978446e-01 8.85893870e-03 -7.22690582e-01 -2.32722610e-01
5.60459495e-01 -3.04894894e-01 -8.07856917e-02 9.07100856e-01
9.15701091e-01 1.22049600e-01 5.24039507e-01 -1.54480445e+00
-8.88961256e-01 5.18560708e-01 -3.05283338e-01 4.51325506e-01
9.10587236e-02 3.72459352e-01 1.30978441e+00 -1.31962693e+00
6.64228857e-01 1.21034503e+00 6.93821311e-01 5.05060673e-01
-1.14328361e+00 -7.37637758e-01 4.57635552e-01 3.61293972e-01
-1.46170747e+00 -2.85825908e-01 4.59696352e-01 -3.97620231e-01
6.40740037e-01 4.64741617e-01 3.83443624e-01 1.06582618e+00
-2.04274841e-02 9.91127074e-01 9.70693767e-01 -2.81996250e-01
4.65818942e-01 5.80743790e-01 4.04035598e-01 5.72919369e-01
6.09497726e-01 3.03710431e-01 -2.86517441e-01 -3.06417286e-01
4.20819491e-01 -7.16932118e-02 -2.94198543e-01 -8.26612294e-01
-1.05335414e+00 5.84301889e-01 4.98632252e-01 -1.26107410e-02
-1.38369322e-01 -2.59877324e-01 1.31348133e-01 1.27720535e-01
2.76868880e-01 5.17329216e-01 -5.63451469e-01 5.93616068e-02
-6.45599484e-01 2.89241761e-01 1.11697376e+00 9.01758969e-01
6.96269631e-01 -4.42041367e-01 -4.39362377e-01 8.51341665e-01
4.01544273e-01 5.31137168e-01 3.60293716e-01 -1.02043164e+00
2.40420699e-01 6.27921104e-01 2.77412087e-01 -8.94207001e-01
-3.43384296e-01 -6.31209731e-01 -3.65478814e-01 2.53993183e-01
4.46789861e-01 -5.29728159e-02 -7.28478372e-01 1.44444311e+00
7.27975488e-01 2.80500293e-01 -7.09690303e-02 1.06902325e+00
8.81840050e-01 5.64487398e-01 3.01229693e-02 -1.50507003e-01
1.35094988e+00 -1.16685271e+00 -4.29598153e-01 -3.86702210e-01
2.99244881e-01 -5.23068488e-01 1.11800015e+00 4.59788859e-01
-8.07076097e-01 -5.54555058e-01 -1.08170533e+00 1.96262017e-01
-5.53955078e-01 2.11174801e-01 4.69346613e-01 5.87368667e-01
-6.90176606e-01 2.74885267e-01 -5.39121151e-01 -5.32600582e-01
6.92111135e-01 2.16349646e-01 -9.43492278e-02 5.23451194e-02
-1.01552689e+00 8.88256013e-01 6.77003980e-01 2.39105567e-01
-1.14776564e+00 -6.06801212e-01 -3.17288995e-01 2.86061633e-02
1.04253364e+00 -6.51352704e-01 1.32994401e+00 -6.32022321e-01
-1.29887712e+00 7.03927517e-01 3.46362919e-01 -4.72894371e-01
7.26735353e-01 -2.37536699e-01 -3.06040615e-01 5.05840778e-02
1.70163497e-01 6.67836666e-01 8.09813082e-01 -1.52170312e+00
-9.19586420e-01 -3.09220493e-01 3.26777250e-01 1.68176875e-01
-2.90442169e-01 -8.60096812e-02 -5.06930172e-01 -4.42544550e-01
2.13589773e-01 -7.50874400e-01 -1.03307992e-01 6.56087458e-01
-4.42259222e-01 -4.22494352e-01 9.47191298e-01 1.49572976e-02
1.23314655e+00 -2.12623072e+00 -2.45159462e-01 4.56764065e-02
5.60907364e-01 5.15239358e-01 -9.27416682e-02 1.88221440e-01
2.26774901e-01 -5.44173643e-02 -1.20218433e-01 -7.14401305e-02
3.39530945e-01 3.99079919e-01 -5.41527331e-01 3.54951590e-01
2.05742761e-01 7.01906860e-01 -1.01738870e+00 -5.13342500e-01
9.89926532e-02 2.69163966e-01 -5.16961873e-01 3.29412639e-01
-3.35710019e-01 -1.53084034e-02 -6.29316628e-01 9.53651249e-01
7.68404722e-01 -5.11543036e-01 2.08226275e-02 -1.47454351e-01
-4.59392183e-02 6.71904534e-03 -1.83436978e+00 9.84796345e-01
-1.49004478e-02 2.17767969e-01 8.41475055e-02 -9.32355046e-01
1.11714315e+00 -1.40313148e-01 2.52316386e-01 -3.71478051e-01
2.16396555e-01 3.69439632e-01 -1.20153129e-01 -4.35063064e-01
5.31307280e-01 2.84350544e-01 2.05181479e-01 4.33185935e-01
2.88752794e-01 1.67097613e-01 2.39138678e-01 1.75614789e-01
1.12111497e+00 1.93759337e-01 4.09249425e-01 -2.02786013e-01
5.57823181e-01 -2.53790915e-01 8.97939861e-01 1.26767862e+00
-8.02507281e-01 6.11313522e-01 5.89482427e-01 -5.69875419e-01
-6.17851853e-01 -1.19301331e+00 -4.70620960e-01 1.37609112e+00
5.57618439e-01 -1.51147604e-01 -4.68471885e-01 -1.10053778e+00
3.75789225e-01 6.20544970e-01 -5.34813344e-01 5.69969462e-03
-2.55568773e-01 -1.00475919e+00 5.16118884e-01 4.12595212e-01
4.56543505e-01 -1.10110974e+00 -6.44814253e-01 1.69324651e-01
-7.25322589e-02 -1.03093278e+00 -3.10307950e-01 2.62272149e-01
-5.15269279e-01 -1.33199060e+00 -5.26453078e-01 -7.17235625e-01
5.96752524e-01 4.12963897e-01 9.92030025e-01 -4.79431003e-02
-4.69212741e-01 3.57510030e-01 -3.38625431e-01 -8.27389657e-01
-1.32446706e-01 -4.83244993e-02 1.36173874e-01 1.66261852e-01
5.19131660e-01 -2.26327434e-01 -7.22169876e-01 8.28173161e-01
-5.18476009e-01 -3.70058745e-01 5.47238290e-01 8.72993529e-01
6.47653282e-01 -3.79750580e-01 5.24878204e-01 -8.98009121e-01
3.80016536e-01 -7.08487153e-01 -9.71902370e-01 3.01711768e-01
-1.06911981e+00 -1.22939356e-01 1.81436464e-01 -6.27398551e-01
-9.65392292e-01 -1.78421602e-01 1.91772684e-01 -5.47743082e-01
-6.39005899e-02 1.20030539e-02 -1.90020129e-01 -9.64069515e-02
8.61018896e-01 -2.36072131e-02 -2.55573332e-01 -5.28083086e-01
2.37087056e-01 8.56438875e-01 5.79968572e-01 -7.36527622e-01
7.96034157e-01 5.39801359e-01 -4.35482740e-01 -3.57814878e-01
-1.23051322e+00 -5.78093350e-01 -4.86914635e-01 -2.39255920e-01
3.14414829e-01 -8.21271062e-01 -7.26626396e-01 2.74685055e-01
-1.23211980e+00 -1.42456293e-01 -6.59186661e-01 3.57594579e-01
-3.30714703e-01 2.92021811e-01 -3.54836851e-01 -9.33801711e-01
-3.23832691e-01 -1.06378281e+00 1.02467501e+00 2.89733887e-01
1.01814844e-01 -4.76467341e-01 1.69461191e-01 4.91544813e-01
2.42647350e-01 -1.01784922e-01 3.13213587e-01 -1.04467022e+00
-7.94677615e-01 -1.96582317e-01 -6.15758955e-01 3.07591826e-01
-3.47706467e-01 9.63910669e-02 -1.32465446e+00 -2.39312351e-01
-1.10837527e-01 -3.27314317e-01 7.95482039e-01 1.33499503e-01
1.37271559e+00 -2.54568547e-01 -5.54647982e-01 5.16571760e-01
1.18237984e+00 7.65210763e-02 3.99354160e-01 5.77481389e-01
1.96395993e-01 4.89187568e-01 9.25446212e-01 7.01656640e-01
4.36915904e-01 6.71440184e-01 7.41641164e-01 2.13567708e-02
-4.68840525e-02 -9.65335667e-02 3.09739977e-01 2.63399661e-01
-3.86092886e-02 -6.27447605e-01 -8.04283500e-01 4.71886575e-01
-2.02997231e+00 -9.10741031e-01 7.86965787e-02 2.17835116e+00
7.60241151e-01 4.01136518e-01 9.17977616e-02 6.74719512e-02
9.66740787e-01 2.43799798e-02 -6.23563647e-01 2.30255164e-02
-3.92886102e-02 -3.47398967e-02 3.73935431e-01 2.30183467e-01
-1.13509369e+00 7.10475564e-01 6.00133276e+00 8.37903500e-01
-8.07139099e-01 4.66004670e-01 4.04381037e-01 -1.26161814e-01
1.87626258e-01 4.87856567e-02 -1.33177125e+00 4.73361880e-01
5.66405714e-01 -2.24735484e-01 9.18520018e-02 1.30307317e+00
-1.09385841e-01 -1.60504580e-01 -1.14353192e+00 8.15498471e-01
4.61217016e-02 -1.26374817e+00 -8.87959227e-02 -1.20919786e-01
4.39385325e-01 3.14482719e-01 -1.71029299e-01 6.33076847e-01
3.59584332e-01 -4.03708607e-01 9.12182629e-01 4.11422491e-01
3.23162615e-01 -3.95544395e-02 9.02933538e-01 3.89018536e-01
-1.05037367e+00 -5.56498408e-01 -5.42554319e-01 6.16489127e-02
-1.69810042e-01 5.15649736e-01 -9.96284664e-01 4.12056088e-01
9.67378974e-01 5.07401049e-01 -6.26459956e-01 1.50010145e+00
-2.45493993e-01 6.48880899e-01 -4.30550456e-01 -7.67099857e-02
2.07848728e-01 5.87461554e-02 8.80425274e-01 1.14483058e+00
9.19213891e-02 7.19659328e-02 3.18751484e-01 1.02275550e+00
-5.33136912e-02 9.74917784e-03 -2.71134436e-01 1.98736936e-01
7.45057523e-01 1.42564285e+00 -8.05979431e-01 -3.75446737e-01
-2.62999922e-01 5.13852358e-01 3.68386060e-01 1.93834513e-01
-1.06281304e+00 -3.61249775e-01 5.14623642e-01 9.01602358e-02
4.96829897e-01 2.17029706e-01 -1.89428721e-02 -1.11606157e+00
2.57182032e-01 -5.55398107e-01 8.44112694e-01 -7.16084778e-01
-1.59665227e+00 4.34027046e-01 4.02479898e-03 -1.33524942e+00
4.23964709e-01 -8.12139571e-01 -5.49266815e-01 3.44915062e-01
-1.77504027e+00 -9.21303749e-01 -5.57299912e-01 3.18188071e-01
4.69801962e-01 -7.87112340e-02 5.47158778e-01 5.78521311e-01
-8.76102746e-01 5.74098349e-01 1.93384573e-01 2.02955082e-02
9.29639339e-01 -1.08780539e+00 -5.27012348e-02 7.23019302e-01
1.20971510e-02 4.01666760e-01 6.41924024e-01 -4.69044954e-01
-1.29866445e+00 -1.13437164e+00 5.48251808e-01 -6.74886167e-01
8.29721749e-01 -5.56055605e-01 -9.72948074e-01 5.79363525e-01
-5.81394374e-01 8.27201307e-01 3.73040050e-01 7.49069676e-02
-3.46832663e-01 -4.52211231e-01 -1.35375094e+00 2.05615714e-01
1.24472189e+00 -1.11108415e-01 -7.55533278e-01 5.14980018e-01
8.05595398e-01 -4.84735042e-01 -6.71877146e-01 4.28154498e-01
6.73696339e-01 -9.51795220e-01 1.06066287e+00 -4.67117816e-01
-1.25974134e-01 -4.83575791e-01 -1.97636217e-01 -6.67113721e-01
-2.44691133e-01 -5.35584450e-01 -5.12344241e-01 1.38302755e+00
3.16899717e-01 -1.00193226e+00 7.25294650e-01 4.83559608e-01
-8.06832239e-02 -8.59403253e-01 -1.02562964e+00 -1.12200475e+00
-3.74622464e-01 -4.88154650e-01 6.61673307e-01 7.87073970e-01
-2.98267543e-01 4.13318500e-02 -1.30487040e-01 5.30750215e-01
6.75797880e-01 2.20577791e-01 7.85294950e-01 -1.45298147e+00
-3.75726670e-01 -2.96483755e-01 -4.00420755e-01 -1.01488042e+00
-3.03751409e-01 -7.61352241e-01 2.41038203e-01 -1.15515232e+00
3.79511714e-01 -9.05712724e-01 -7.47237384e-01 6.53393805e-01
-1.66336253e-01 2.47567803e-01 3.53090733e-01 4.89604473e-01
-1.18215060e+00 6.28713548e-01 1.13289809e+00 -2.30189726e-01
-2.23349407e-01 2.21885145e-01 -7.25438237e-01 7.31783628e-01
5.60986698e-01 -7.74844110e-01 -2.31870830e-01 -2.03501493e-01
4.82448936e-02 -3.91559005e-01 7.15375781e-01 -9.66178179e-01
2.21340001e-01 -2.23472819e-01 1.34800330e-01 -6.50809765e-01
1.66520253e-01 -7.83924758e-01 -1.15177833e-01 5.36127865e-01
-1.46307632e-01 -5.77428699e-01 2.47288626e-02 7.80337214e-01
1.37343094e-01 -3.85996729e-01 8.78282547e-01 -1.92340035e-02
-9.74089682e-01 4.55468595e-01 7.16823414e-02 3.15392196e-01
1.37303019e+00 -4.13940161e-01 -6.40445709e-01 1.83931947e-01
-6.28904641e-01 5.98949671e-01 8.59856531e-02 5.35242498e-01
4.27874982e-01 -9.29154277e-01 -5.99499285e-01 2.01372951e-01
3.56522292e-01 3.24344598e-02 -1.67696133e-01 7.37523377e-01
-3.19017142e-01 2.59259462e-01 1.60561297e-02 -7.30112076e-01
-9.70961511e-01 6.34568393e-01 4.63189751e-01 -2.85283506e-01
-5.28733850e-01 8.67429197e-01 2.70819336e-01 -8.96591306e-01
6.96692109e-01 -1.70098722e-01 -1.23242907e-01 2.69452780e-01
5.76039732e-01 7.65393913e-01 1.20058075e-01 -1.59097195e-01
-3.89604390e-01 3.51776958e-01 -2.03662246e-01 4.61381465e-01
1.28998744e+00 -2.30688900e-01 8.83050263e-02 3.04760516e-01
4.75617141e-01 -3.48146826e-01 -1.35110343e+00 -5.03885508e-01
1.39659420e-01 -5.53890765e-01 -1.57085564e-02 -9.72282469e-01
-7.56314337e-01 6.50668323e-01 9.36568081e-01 2.94700086e-01
9.03525114e-01 1.67675316e-01 4.58167225e-01 7.10170329e-01
4.84771729e-01 -1.18237638e+00 8.66875798e-02 4.86211032e-01
8.55688274e-01 -1.38823056e+00 2.04110548e-01 -4.93497729e-01
-4.75340039e-01 1.01078069e+00 1.17157507e+00 1.43177852e-01
5.96581876e-01 2.71257132e-01 -1.84890434e-01 -1.46603361e-01
-9.23210680e-01 -1.31363198e-01 5.26271947e-02 5.04900992e-01
-1.95527002e-01 -5.24515323e-02 -3.11554581e-01 7.51980901e-01
1.78924769e-01 1.89910412e-01 4.02950674e-01 9.48201954e-01
-7.40407109e-01 -7.76260138e-01 -5.46431005e-01 5.03536463e-01
-1.19618423e-01 1.54193342e-01 -2.70668596e-01 7.24010587e-01
5.38984060e-01 6.32655859e-01 3.94713953e-02 -3.93276140e-02
4.22491044e-01 -8.67785290e-02 8.97256881e-02 -6.27159595e-01
-4.24639493e-01 -2.97157794e-01 -8.28627273e-02 -8.13237965e-01
-2.39465952e-01 -5.08396626e-01 -1.12515807e+00 -8.12413692e-02
-8.03031087e-01 1.57839075e-01 3.87373805e-01 5.89900434e-01
5.20964921e-01 2.18075022e-01 6.33300602e-01 -8.36216688e-01
-1.07942176e+00 -7.93381393e-01 -5.09752452e-01 1.34112043e-02
2.53562123e-01 -1.12722886e+00 -4.82715815e-01 -2.77600139e-01] | [9.259479522705078, 1.4269248247146606] |
cd649b39-7110-4a0c-8ce9-f5d4c1008cf5 | wasserstein-dictionaries-of-persistence | 2304.14852 | null | https://arxiv.org/abs/2304.14852v1 | https://arxiv.org/pdf/2304.14852v1.pdf | Wasserstein Dictionaries of Persistence Diagrams | This paper presents a computational framework for the concise encoding of an ensemble of persistence diagrams, in the form of weighted Wasserstein barycenters [99], [101] of a dictionary of atom diagrams. We introduce a multi-scale gradient descent approach for the efficient resolution of the corresponding minimization problem, which interleaves the optimization of the barycenter weights with the optimization of the atom diagrams. Our approach leverages the analytic expressions for the gradient of both sub-problems to ensure fast iterations and it additionally exploits shared-memory parallelism. Extensive experiments on public ensembles demonstrate the efficiency of our approach, with Wasserstein dictionary computations in the orders of minutes for the largest examples. We show the utility of our contributions in two applications. First, we apply Wassserstein dictionaries to data reduction and reliably compress persistence diagrams by concisely representing them with their weights in the dictionary. Second, we present a dimensionality reduction framework based on a Wasserstein dictionary defined with a small number of atoms (typically three) and encode the dictionary as a low dimensional simplex embedded in a visual space (typically in 2D). In both applications, quantitative experiments assess the relevance of our framework. Finally, we provide a C++ implementation that can be used to reproduce our results. | ['Julien Tierny', 'Julie Delon', 'Keanu Sisouk'] | 2023-04-28 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 1.22766823e-01 -4.36686724e-02 1.94253162e-01 -2.25764830e-02
-7.46983767e-01 -8.09791028e-01 7.31100798e-01 3.50139111e-01
-2.42762998e-01 3.47560644e-01 3.44267815e-01 -3.63033682e-01
-3.16093832e-01 -7.62113035e-01 -6.46510363e-01 -9.58631098e-01
-4.93884623e-01 5.49888253e-01 -5.19516766e-02 -3.36710662e-01
5.95118344e-01 6.10331774e-01 -1.75719500e+00 4.95826975e-02
5.62324345e-01 1.02412975e+00 -9.08552185e-02 1.05813420e+00
2.70207733e-01 3.94130915e-01 -1.09607063e-01 -2.35708922e-01
5.87995470e-01 -1.02382638e-01 -5.24089694e-01 4.61563654e-02
8.04506302e-01 -3.91861737e-01 -4.78936672e-01 9.60153699e-01
4.83107179e-01 2.57943332e-01 1.00767100e+00 -9.95983183e-01
-6.17746353e-01 9.30122212e-02 -5.18131077e-01 1.20996259e-01
1.78567603e-01 9.64590982e-02 1.50478053e+00 -1.16792512e+00
8.02066326e-01 1.06011891e+00 8.63145888e-01 2.14039665e-02
-1.59698141e+00 -4.19672020e-03 -1.39236912e-01 -3.28457877e-02
-1.52762067e+00 -5.73105574e-01 6.83637738e-01 -6.69174135e-01
1.31703424e+00 3.39532554e-01 8.76882732e-01 5.10558903e-01
1.68075591e-01 2.02132821e-01 9.02786911e-01 -5.86048365e-01
3.42282802e-01 -2.72932440e-01 4.28752065e-01 1.25308871e+00
5.97492814e-01 1.80841982e-01 -4.59148854e-01 -5.71590722e-01
6.68504894e-01 -4.75956611e-02 1.43012136e-01 -1.05059075e+00
-1.26573610e+00 1.03434718e+00 9.85299572e-02 3.40497270e-02
2.73742415e-02 4.69777077e-01 4.79718834e-01 2.47885033e-01
4.36188102e-01 1.91018209e-01 1.84521854e-01 -2.70077139e-01
-7.95368075e-01 3.47696006e-01 1.12336028e+00 9.57289994e-01
8.86898518e-01 -2.12722659e-01 -1.96213741e-02 6.41136527e-01
3.96335483e-01 7.06107378e-01 -7.19272718e-02 -1.12687659e+00
2.22581923e-01 3.87963653e-01 1.37740776e-01 -1.47670841e+00
-3.40526402e-01 3.70781682e-02 -9.57445085e-01 9.42284539e-02
3.73017937e-01 2.93313473e-01 -6.27150357e-01 1.56983995e+00
5.77333450e-01 3.69259007e-02 -1.45225614e-01 7.49577224e-01
3.78437817e-01 6.57709897e-01 -5.38999557e-01 -1.82469070e-01
1.32647026e+00 -7.24120975e-01 -2.69247979e-01 2.98320591e-01
7.70768583e-01 -7.04689503e-01 9.80811775e-01 4.77694005e-01
-1.20290065e+00 -5.78368232e-02 -1.27838838e+00 -5.16492307e-01
-3.00090492e-01 1.26395375e-01 5.90496898e-01 5.98353565e-01
-1.24122369e+00 9.08684969e-01 -9.20621634e-01 -3.47347200e-01
2.36161843e-01 3.20795000e-01 -2.00530678e-01 3.49733204e-01
-6.58304274e-01 7.68063426e-01 -9.94217768e-03 -1.43560916e-01
-8.35886717e-01 -6.56490147e-01 -6.62825584e-01 -1.69766136e-02
-9.11410600e-02 -6.49210095e-01 7.80840158e-01 -3.56408536e-01
-1.15341187e+00 1.10327113e+00 -1.06756605e-01 -3.96125764e-01
3.50393772e-01 1.67948544e-01 1.17869332e-01 2.95411170e-01
-1.37957945e-01 2.22449526e-01 1.02958655e+00 -1.06141973e+00
-3.97571981e-01 -2.45487139e-01 1.01713359e-01 1.32706761e-01
-6.02084100e-01 -4.76650119e-01 -3.96015316e-01 -7.72528470e-01
1.26177788e-01 -1.02315569e+00 -6.37532547e-02 1.94641441e-01
-1.64477363e-01 -1.06543712e-01 5.13714552e-01 -5.47292113e-01
1.56940925e+00 -2.23588848e+00 7.47606456e-01 7.21908152e-01
6.98344707e-01 -3.31296712e-01 -1.50562331e-01 8.15145016e-01
1.13899253e-01 -3.87060307e-02 -5.56989551e-01 -5.31782031e-01
4.95498300e-01 3.89121622e-01 -6.38719738e-01 9.91509378e-01
-2.05158219e-01 5.22712052e-01 -7.57768691e-01 -2.40889356e-01
1.29888698e-01 5.27075946e-01 -8.90392959e-01 4.04016413e-02
-4.14977744e-02 -3.09051014e-02 -5.72372861e-02 4.78868097e-01
4.99872953e-01 -3.79105240e-01 3.76085371e-01 -5.27176142e-01
-2.39397883e-01 1.97846010e-01 -1.45831215e+00 1.70934010e+00
-3.50164324e-01 6.34311557e-01 3.48417729e-01 -1.12785065e+00
7.25999415e-01 -2.89334625e-01 7.43085027e-01 -4.97448057e-01
-1.59470484e-01 3.96179378e-01 -2.96869099e-01 -1.38453305e-01
6.05578423e-01 -2.46899575e-02 -1.08703814e-01 1.06199753e+00
-1.66161746e-01 -4.18623745e-01 5.62668979e-01 6.40323997e-01
1.19913185e+00 -2.35610485e-01 2.67794877e-01 -6.98874354e-01
3.53947610e-01 -1.33087575e-01 -1.38500899e-01 8.28424752e-01
1.74073651e-02 5.72751522e-01 6.05773091e-01 -7.15448499e-01
-1.83385384e+00 -1.03739619e+00 -3.53155226e-01 1.06561875e+00
-3.49053269e-04 -1.09679675e+00 -6.45095587e-01 -3.46186727e-01
4.26818848e-01 1.67725548e-01 -7.05972910e-01 2.22574901e-02
-5.57670891e-01 -8.04414153e-01 5.16699493e-01 2.78384030e-01
-6.03421703e-02 -3.52140814e-01 -7.66907215e-01 -8.88371989e-02
2.72116840e-01 -6.47867799e-01 -6.33218050e-01 1.75509542e-01
-7.80390739e-01 -1.25945055e+00 -4.00528491e-01 -6.96238279e-01
8.25621367e-01 3.27764481e-01 1.04380858e+00 4.29133147e-01
-6.60721123e-01 9.14483666e-01 -4.08715494e-02 1.11602560e-01
-3.49660635e-01 -1.16257742e-01 4.01995659e-01 -1.50088251e-01
1.26734957e-01 -9.01004255e-01 -7.88470387e-01 -2.08651088e-02
-9.24450278e-01 4.80721444e-02 2.23034590e-01 9.71923590e-01
8.10205340e-01 -2.85962999e-01 -1.62360579e-01 -6.34983718e-01
8.59645247e-01 -3.86553138e-01 -8.30160558e-01 7.01148659e-02
-8.63498509e-01 4.88480598e-01 4.44693506e-01 -1.05681747e-01
-2.09578767e-01 -3.07568479e-02 1.16244242e-01 -3.38324100e-01
6.88144803e-01 3.45898777e-01 4.40249056e-01 -4.74695593e-01
6.53879046e-01 1.59300819e-01 3.44424933e-01 -4.38827336e-01
7.57729530e-01 5.16524136e-01 3.39106023e-01 -1.14422071e+00
7.63354719e-01 8.60035717e-01 2.79938251e-01 -7.95680225e-01
-5.24251401e-01 -4.40284401e-01 -5.60350955e-01 1.03907150e-04
2.98283726e-01 -7.90565372e-01 -9.79592025e-01 1.14251629e-01
-1.03331947e+00 -2.22991705e-01 -5.52472889e-01 -2.25263182e-02
-9.39710557e-01 7.45107412e-01 -5.52575171e-01 -4.98802960e-01
-4.12719846e-01 -1.07017457e+00 1.47574747e+00 -4.35326546e-01
-2.39611879e-01 -1.13752997e+00 7.40660250e-01 -2.45164093e-02
3.41146022e-01 3.07759345e-01 1.23396575e+00 -3.32905352e-01
-7.22004175e-01 -6.13056272e-02 -2.96621799e-01 2.54095048e-01
-1.73077434e-01 2.50433713e-01 -6.36520088e-01 -7.06373692e-01
-2.09077612e-01 -2.19858736e-01 1.00938106e+00 2.08260611e-01
1.07987523e+00 -4.67359364e-01 -2.03832671e-01 9.12054121e-01
1.56664801e+00 -5.72298050e-01 3.84120524e-01 2.27722794e-01
8.49503160e-01 4.06681865e-01 2.56703496e-01 9.65376973e-01
4.08556879e-01 6.35263681e-01 2.45568931e-01 2.93053240e-01
-9.80870724e-02 -1.21286765e-01 3.88538331e-01 1.38741887e+00
-2.38200396e-01 2.08427265e-01 -1.04210865e+00 4.95928168e-01
-1.93988180e+00 -8.69761288e-01 -5.86452074e-02 2.32023358e+00
8.25946748e-01 -1.74743950e-01 3.31218868e-01 8.81599933e-02
3.42267007e-01 4.56473351e-01 -5.73329270e-01 -5.49820840e-01
-3.16095054e-02 3.38529557e-01 6.68090463e-01 1.00130773e+00
-1.00622320e+00 4.47277546e-01 7.40186501e+00 8.01886618e-01
-5.47422409e-01 9.22052115e-02 1.72797129e-01 -3.89895558e-01
-5.38530171e-01 2.83424467e-01 -7.62035489e-01 4.37526017e-01
9.41064954e-01 -2.95126081e-01 9.78831410e-01 6.62860334e-01
-2.60069338e-03 8.27319920e-02 -1.30208743e+00 1.24538088e+00
7.02274293e-02 -1.72131240e+00 2.28371903e-01 4.23775107e-01
7.12943733e-01 8.03470518e-03 2.42593065e-01 -9.65628177e-02
3.91743243e-01 -9.73373950e-01 7.09919035e-01 6.06730580e-01
1.02271450e+00 -8.21872234e-01 -5.60793467e-02 -8.77620094e-03
-1.30104876e+00 -1.34965219e-02 -6.49838448e-01 -1.57750562e-01
-7.17719272e-02 8.90674114e-01 -4.32432026e-01 5.71109504e-02
5.78995168e-01 8.97672892e-01 -2.93777615e-01 4.89523977e-01
2.56884009e-01 2.41518587e-01 -7.04323411e-01 -6.40345812e-02
2.05192357e-01 -8.45424950e-01 7.52746940e-01 1.38682413e+00
4.43676800e-01 1.72546908e-01 6.62502870e-02 7.01862574e-01
-1.58327356e-01 1.13954477e-01 -8.96134913e-01 -1.54196188e-01
4.51801777e-01 1.25770152e+00 -4.97293413e-01 -3.52677435e-01
-2.28290543e-01 6.83920860e-01 6.92684114e-01 2.19744742e-01
-5.61038196e-01 -6.22615695e-01 8.15642476e-01 1.05935417e-01
6.12201035e-01 -6.72859907e-01 -4.59412813e-01 -1.25651717e+00
1.49607658e-01 -1.12104642e+00 2.66714811e-01 -3.70785117e-01
-1.28476274e+00 1.52178317e-01 -1.11114122e-01 -9.61469829e-01
-3.19936201e-02 -8.88102531e-01 -2.27845401e-01 5.91714978e-01
-1.02936053e+00 -7.62402177e-01 -1.35037631e-01 3.66276473e-01
-1.39343336e-01 -1.03786141e-01 8.95579338e-01 2.28618577e-01
-4.90969747e-01 3.48476678e-01 8.51789474e-01 -2.04603136e-01
5.03139913e-01 -1.49738765e+00 6.08072400e-01 7.17722833e-01
2.98245698e-01 8.60992312e-01 7.36030161e-01 -5.50500512e-01
-2.06895041e+00 -6.31286442e-01 5.70435643e-01 -6.55741215e-01
1.12895501e+00 -6.03737772e-01 -6.22032344e-01 5.75291097e-01
-4.04684581e-02 5.23071773e-02 7.69156456e-01 9.29718092e-02
-5.06775916e-01 -1.53512865e-01 -9.44362164e-01 5.59581280e-01
1.29748750e+00 -8.21893692e-01 -3.02334398e-01 7.21387565e-01
2.99571633e-01 -5.92931569e-01 -1.12929237e+00 -9.07636993e-03
7.86168158e-01 -1.02581823e+00 1.27462220e+00 -5.09649932e-01
3.62239599e-01 -2.99863786e-01 -6.56068087e-01 -1.06892598e+00
-3.39701474e-01 -8.74132991e-01 -8.26331615e-01 5.64536572e-01
-8.08679163e-02 -4.58278120e-01 4.68420088e-01 4.15066928e-01
9.92616639e-02 -9.46447670e-01 -1.01527786e+00 -5.54363787e-01
2.80653639e-03 -2.89845705e-01 3.48506868e-01 5.86872339e-01
1.18511841e-01 1.61655307e-01 -4.00597572e-01 2.33832803e-02
9.92102385e-01 2.24375457e-01 7.96643019e-01 -1.07062638e+00
-4.69107151e-01 -5.22759855e-01 -5.07025778e-01 -1.23474479e+00
1.47443846e-01 -1.20844722e+00 -2.14247957e-01 -1.19469500e+00
3.79655480e-01 -5.70279956e-01 -8.86051636e-03 1.06503516e-01
3.17220509e-01 3.57418567e-01 7.97196776e-02 5.40288627e-01
-6.94823682e-01 5.27107716e-01 9.00296390e-01 -2.39190459e-01
3.78759485e-03 -6.65493309e-01 -5.86573601e-01 5.55678964e-01
1.36416346e-01 -3.89646143e-01 -2.59740263e-01 -4.96604294e-01
7.29543686e-01 -3.89897019e-01 5.00682235e-01 -6.81635439e-01
2.89754778e-01 1.55770317e-01 -4.43219319e-02 -5.27591348e-01
2.14780673e-01 -6.33425355e-01 7.44896010e-02 4.01018947e-01
-2.13781148e-01 3.31720322e-01 9.93203819e-02 8.70666802e-01
1.93987101e-01 -2.22395644e-01 7.47392237e-01 1.69005230e-01
-2.52334625e-01 3.40923756e-01 -1.26743659e-01 1.87033474e-01
9.00974333e-01 -8.61474648e-02 -2.79560864e-01 -2.83361703e-01
-6.52734101e-01 6.79189637e-02 1.00657082e+00 -2.13759556e-01
6.29029393e-01 -1.52985036e+00 -5.94130754e-01 3.97528172e-01
-1.47297122e-02 -1.97000220e-01 -5.65863028e-02 9.66150820e-01
-9.89599884e-01 3.50371778e-01 -5.56881714e-05 -7.61980236e-01
-1.17301607e+00 4.43888128e-01 3.04333359e-01 -2.77622938e-01
-7.25038886e-01 6.83391213e-01 -8.60882476e-02 -1.49379194e-01
3.49232018e-01 -3.62472445e-01 4.86754656e-01 2.74608076e-01
5.56671977e-01 6.78027987e-01 2.06227317e-01 -4.92341459e-01
-4.77122158e-01 8.85976791e-01 -2.97766645e-02 -2.30863422e-01
1.57354593e+00 -1.39117733e-01 -5.54038882e-01 3.93756539e-01
1.53708482e+00 2.77659029e-01 -1.35351968e+00 -8.45777839e-02
-2.21070394e-01 -3.81344795e-01 7.98191503e-03 -8.46426934e-02
-6.10492110e-01 8.32590044e-01 5.15202165e-01 4.33805257e-01
8.28423440e-01 -1.76291361e-01 7.03534245e-01 7.87411451e-01
2.63034523e-01 -1.16837192e+00 1.30969048e-01 5.95714927e-01
9.03028905e-01 -8.47033381e-01 3.66466105e-01 3.55314612e-02
-1.90273240e-01 1.33818972e+00 -3.07228744e-01 -5.58108687e-01
7.33268380e-01 3.31716508e-01 -4.63670075e-01 -5.32588601e-01
-7.73454547e-01 2.32090522e-02 3.21228266e-01 2.27512792e-01
2.35686168e-01 -2.84647644e-02 -3.23145211e-01 -5.55433854e-02
-1.81099653e-01 -5.25293052e-01 4.34843093e-01 9.76189613e-01
-5.73831677e-01 -1.10513604e+00 -3.03670824e-01 5.94027460e-01
7.58789703e-02 -3.42518508e-01 -3.51168305e-01 5.02568126e-01
-2.09033370e-01 4.83088106e-01 1.94921255e-01 -3.04868490e-01
2.47609645e-01 -5.01382612e-02 8.86328697e-01 -5.09999692e-01
-1.86401069e-01 -1.34021416e-01 -3.45729180e-02 -6.54700935e-01
-4.54993039e-01 -8.40698302e-01 -1.13967812e+00 -8.93678904e-01
9.95941758e-02 1.42505378e-01 6.57662630e-01 6.43544197e-01
5.97039163e-01 -6.80508465e-02 7.89571166e-01 -1.03995752e+00
-9.80803490e-01 -4.03700829e-01 -7.55159616e-01 5.68134785e-01
5.85343421e-01 -8.04651499e-01 -6.86502993e-01 7.29322881e-02] | [7.423518180847168, 4.299807071685791] |
1ea0cc69-1552-4400-8a52-8c3a53ef5354 | medical-concept-normalization-in-user | 2006.04014 | null | https://arxiv.org/abs/2006.04014v1 | https://arxiv.org/pdf/2006.04014v1.pdf | Medical Concept Normalization in User Generated Texts by Learning Target Concept Embeddings | Medical concept normalization helps in discovering standard concepts in free-form text i.e., maps health-related mentions to standard concepts in a vocabulary. It is much beyond simple string matching and requires a deep semantic understanding of concept mentions. Recent research approach concept normalization as either text classification or text matching. The main drawback in existing a) text classification approaches is ignoring valuable target concepts information in learning input concept mention representation b) text matching approach is the need to separately generate target concept embeddings which is time and resource consuming. Our proposed model overcomes these drawbacks by jointly learning the representations of input concept mention and target concepts. First, it learns the input concept mention representation using RoBERTa. Second, it finds cosine similarity between embeddings of input concept mention and all the target concepts. Here, embeddings of target concepts are randomly initialized and then updated during training. Finally, the target concept with maximum cosine similarity is assigned to the input concept mention. Our model surpasses all the existing methods across three standard datasets by improving accuracy up to 2.31%. | ['Katikapalli Subramanyam Kalyan', 'S. Sangeetha'] | 2020-06-07 | null | null | null | null | ['medical-concept-normalization'] | ['medical'] | [ 6.04630470e-01 2.81982154e-01 -4.24473614e-01 -4.72791106e-01
-6.74967825e-01 -2.66090572e-01 5.04884720e-01 1.33933127e+00
-9.68135417e-01 4.33715880e-01 6.70881629e-01 -7.00856224e-02
-2.86877543e-01 -1.14845896e+00 -1.84041128e-01 -6.77159071e-01
1.74897775e-01 6.34765625e-01 7.77907372e-02 -4.19876307e-01
2.27642581e-01 1.36896595e-01 -1.55326962e+00 2.19027400e-01
8.36653709e-01 6.40504658e-01 -4.29746648e-03 3.61309201e-01
-8.87736499e-01 2.11071834e-01 -8.39193583e-01 -6.00619793e-01
6.40808940e-02 -1.88710064e-01 -7.52320886e-01 -6.87629879e-01
3.34436178e-01 2.54199296e-01 -1.60440683e-01 1.25192809e+00
4.82993931e-01 3.62684727e-01 9.48181272e-01 -1.14009285e+00
-1.06897259e+00 6.67073131e-01 -4.45220649e-01 2.27233499e-01
2.48795599e-01 -6.44918382e-01 1.12174535e+00 -7.08320022e-01
5.55236995e-01 1.11251187e+00 8.69890213e-01 8.51926565e-01
-8.99012744e-01 -8.58076692e-01 2.01351061e-01 -5.48751988e-02
-1.47882986e+00 -1.20736532e-01 2.67086536e-01 -4.35248286e-01
1.28335989e+00 3.02140057e-01 2.98220038e-01 7.14165926e-01
1.70885578e-01 3.40649098e-01 2.91812271e-01 -6.05739415e-01
2.10716709e-01 2.26020664e-01 5.14451861e-01 3.91052216e-01
8.37294817e-01 -9.89350900e-02 -2.17205063e-01 -4.76666421e-01
2.70694792e-01 4.94077057e-01 -4.46751676e-02 -1.23223267e-01
-1.11236548e+00 1.01938009e+00 3.40893209e-01 7.19347298e-01
-2.25492090e-01 8.19926709e-02 7.02720940e-01 1.97561249e-01
2.57379532e-01 5.76459348e-01 -6.77423060e-01 2.06934467e-01
-7.70520985e-01 1.86435729e-01 5.42625308e-01 1.12295926e+00
6.98305249e-01 -4.29463774e-01 -1.90416113e-01 7.60536790e-01
2.29954973e-01 4.18121994e-01 1.03688145e+00 -2.45585628e-02
4.64804709e-01 1.11759889e+00 -2.86249191e-01 -1.17534900e+00
-5.74540317e-01 -3.09987158e-01 -8.55526805e-01 -3.57912391e-01
1.00670144e-01 2.08098590e-02 -1.12091768e+00 1.69225156e+00
3.95132512e-01 3.31858039e-01 5.15298843e-01 3.62634301e-01
1.29580271e+00 3.80034029e-01 6.99833274e-01 -2.40766183e-02
1.75370038e+00 -6.31477058e-01 -9.45641339e-01 -2.05284119e-01
1.15621054e+00 -9.05352235e-01 8.02526116e-01 -3.65213603e-01
-3.01312864e-01 -2.98230529e-01 -1.16274381e+00 -2.46092841e-01
-9.80006039e-01 -6.83268309e-02 7.77586043e-01 9.91982877e-01
-5.71459532e-01 5.72400451e-01 -6.36408925e-01 -7.94698954e-01
4.68957096e-01 7.10659027e-01 -6.67756140e-01 -2.96029985e-01
-1.42334080e+00 8.61065030e-01 9.02486265e-01 -8.64287913e-01
-4.71870378e-02 -1.30203152e+00 -1.20628476e+00 2.14919716e-01
7.14252293e-02 -8.19126427e-01 8.60886991e-01 -6.89148903e-01
-9.23210621e-01 8.41304958e-01 -1.97460338e-01 -3.82495314e-01
-1.50402904e-01 -1.66210264e-01 -9.57600355e-01 -7.05459416e-02
2.64719248e-01 6.11934602e-01 4.88555491e-01 -7.63003349e-01
-8.28457355e-01 -3.34037721e-01 -3.91177414e-03 3.28123868e-01
-8.36533368e-01 -4.16081138e-02 -1.99423909e-01 -9.03252840e-01
4.09678370e-01 -3.62142414e-01 -2.98390120e-01 -1.55171216e-01
-1.93408236e-01 -3.95936817e-01 5.07417381e-01 -3.28975290e-01
1.40934277e+00 -1.96326482e+00 -3.61675769e-01 3.72140944e-01
3.86615187e-01 3.32296014e-01 -2.36328050e-01 5.09125113e-01
-6.39893711e-01 1.94003671e-01 -2.86371887e-01 -7.40710571e-02
-1.94789283e-02 2.54115254e-01 -1.79557949e-01 3.18775326e-01
1.88210890e-01 8.30014110e-01 -1.15804505e+00 -5.01377761e-01
1.65344983e-01 6.25654757e-01 -7.73629367e-01 1.44249825e-02
4.99428324e-02 -1.45941138e-01 -3.95510763e-01 4.87374634e-01
7.46944726e-01 8.71137753e-02 3.37383896e-01 -3.18708599e-01
2.86763966e-01 5.06935537e-01 -1.32606471e+00 1.75656819e+00
-3.27036619e-01 2.46690676e-01 -6.47984743e-01 -1.39139986e+00
1.23399842e+00 5.88872552e-01 8.35628092e-01 -5.50604463e-01
2.93332458e-01 1.40738383e-01 -1.17160618e-01 -5.09005666e-01
5.44742584e-01 -6.69547021e-01 -2.26258367e-01 4.41005826e-01
3.20207000e-01 3.36618990e-01 1.33493900e-01 2.89991111e-01
9.85038340e-01 -3.03908587e-01 8.16951394e-01 -4.35612917e-01
6.66726828e-01 -1.05394229e-01 6.52602911e-01 6.27485156e-01
1.63853556e-01 4.61587191e-01 2.30898753e-01 -4.01335388e-01
-6.96998894e-01 -1.14317954e+00 -3.19747925e-01 1.25677800e+00
1.76989213e-01 -9.05596495e-01 -5.21815062e-01 -7.17382848e-01
2.98991561e-01 7.03671217e-01 -1.06477118e+00 -5.57237148e-01
-5.40990889e-01 -9.57775295e-01 6.96080685e-01 7.46273279e-01
-1.03368908e-01 -6.74249589e-01 -2.87836134e-01 3.99056256e-01
-1.49045125e-01 -7.28096426e-01 -5.99761903e-01 1.61273479e-01
-1.07862318e+00 -1.35663474e+00 -4.93000865e-01 -9.88340199e-01
1.05381978e+00 1.08928315e-01 1.02458382e+00 3.49608302e-01
-7.39878356e-01 3.12995374e-01 -4.79268223e-01 -7.20222175e-01
-3.46069276e-01 2.29098022e-01 3.79170269e-01 -2.46552661e-01
1.34626186e+00 -3.88659596e-01 -4.30417478e-01 -4.33783932e-03
-1.23061574e+00 -3.64162028e-01 5.25131226e-01 8.38485777e-01
5.23642004e-01 1.70489941e-02 7.03084528e-01 -1.16081071e+00
7.84156561e-01 -7.79862940e-01 -1.02548502e-01 3.95826876e-01
-8.86276305e-01 2.68506587e-01 3.97322118e-01 -5.98955333e-01
-7.34351635e-01 9.69605297e-02 -3.59747797e-01 2.69089460e-01
-2.81909734e-01 5.45065939e-01 -1.79615721e-01 3.60969841e-01
8.85113120e-01 5.77397645e-03 -2.86040574e-01 -6.05385959e-01
5.85580528e-01 7.25535333e-01 5.80497324e-01 -5.83355606e-01
8.86177182e-01 3.36896211e-01 -2.52517551e-01 -6.26071572e-01
-7.50951827e-01 -1.12799799e+00 -9.71576393e-01 6.01617634e-01
9.35798764e-01 -8.14153552e-01 -6.00563467e-01 -1.26771092e-01
-1.01962507e+00 7.32164502e-01 -5.16808093e-01 7.62634695e-01
-6.81447610e-02 4.19661760e-01 -1.73253268e-01 -4.47443217e-01
-6.84828043e-01 -4.87778991e-01 8.51876259e-01 3.54486912e-01
-6.41263783e-01 -1.19682753e+00 3.10906500e-01 -1.91753674e-02
3.35688055e-01 1.72591180e-01 1.43980873e+00 -1.33392143e+00
2.69823134e-01 -8.14397037e-01 -1.77397683e-01 -1.35198116e-01
7.99374521e-01 -3.51978570e-01 -8.65501702e-01 -3.05111945e-01
-9.66590047e-02 1.67670384e-01 8.13802719e-01 1.84847981e-01
8.24879944e-01 -4.16632503e-01 -7.45456457e-01 6.10643566e-01
1.62468433e+00 1.66390345e-01 5.73940992e-01 4.76099283e-01
4.64287043e-01 6.58733845e-01 6.08681679e-01 1.68202892e-01
1.66018233e-01 4.47720319e-01 -1.60403386e-01 -1.52836218e-01
1.08599879e-01 -2.28348568e-01 -8.10628012e-02 8.42723429e-01
2.76881784e-01 -1.25330374e-01 -9.74730968e-01 8.66253614e-01
-1.52342319e+00 -8.56468499e-01 -5.58662508e-03 2.17946029e+00
1.12575352e+00 -1.86215844e-02 -2.66926616e-01 3.34194124e-01
7.60855973e-01 -4.32098031e-01 -4.18732107e-01 -3.61511379e-01
8.36888701e-02 7.11731017e-01 4.86330122e-01 3.22112739e-01
-1.19462109e+00 8.63828659e-01 5.54088736e+00 6.60664976e-01
-8.26426268e-01 3.14148158e-01 -1.05680823e-01 -8.51351097e-02
-3.13716203e-01 -2.61899382e-01 -9.16824341e-01 1.54710755e-01
9.16358888e-01 -8.19843709e-01 -4.01590854e-01 8.03774714e-01
-4.89697814e-01 2.43430108e-01 -1.25687504e+00 1.07413447e+00
3.25052261e-01 -1.35752285e+00 5.58881462e-01 -3.42704475e-01
6.62379801e-01 -2.95637995e-01 -2.09691480e-01 5.25869787e-01
1.96242213e-01 -1.32609093e+00 -1.02714263e-02 3.47298145e-01
1.04536390e+00 -8.34536791e-01 1.29770029e+00 -1.30126178e-01
-1.46901798e+00 -4.17415872e-02 -7.88793564e-01 1.74799755e-01
-1.42527997e-01 5.99398553e-01 -8.65794718e-01 7.45686650e-01
5.16785324e-01 6.00651085e-01 -4.21243519e-01 1.12277925e+00
-1.65226176e-01 2.51570702e-01 -2.75224090e-01 1.24576919e-01
6.86741471e-02 1.46933913e-01 1.93418264e-01 1.56633365e+00
2.36181200e-01 4.07184482e-01 1.43185392e-01 4.32574272e-01
-1.63699389e-01 8.37633729e-01 -4.44590390e-01 -1.61368072e-01
6.77771211e-01 1.07405424e+00 -7.91956842e-01 -6.06931567e-01
-4.31741953e-01 8.25909734e-01 -6.07985184e-02 6.39618039e-02
-5.72519064e-01 -1.10430992e+00 1.24692059e+00 5.19676842e-02
3.90511267e-02 2.46500701e-01 -3.29581648e-01 -1.04068637e+00
-1.68303877e-01 -4.28821057e-01 8.92172635e-01 2.20916346e-02
-1.41493595e+00 5.32217681e-01 -6.25830889e-02 -1.22303760e+00
4.18741331e-02 -7.29303002e-01 -5.58224738e-01 9.47197258e-01
-1.54542124e+00 -9.24690545e-01 -2.58835018e-01 7.48367369e-01
2.33143941e-01 -3.19690615e-01 1.66492045e+00 8.07421446e-01
-2.07854450e-01 1.09776795e+00 4.10972118e-01 5.72520554e-01
1.00742245e+00 -1.22446966e+00 6.53769910e-01 4.31657106e-01
-3.39820087e-02 1.33036721e+00 5.94874978e-01 -8.58314097e-01
-6.72535241e-01 -1.23179281e+00 1.56317019e+00 -5.67270935e-01
4.78742182e-01 -3.81463587e-01 -1.23157251e+00 5.53321183e-01
-1.63371041e-01 -1.04125276e-01 1.58533061e+00 1.82362631e-01
-7.44000673e-01 -5.70617355e-02 -1.39188528e+00 5.21910131e-01
7.89860666e-01 -4.56432372e-01 -1.29748535e+00 3.32740754e-01
1.02375841e+00 -2.70796269e-01 -1.07728016e+00 1.31747723e-01
4.19726819e-01 -6.18748851e-02 1.28255737e+00 -1.24568713e+00
8.45900625e-02 -3.62790316e-01 -3.05259049e-01 -1.10062277e+00
-2.36588165e-01 -1.30242631e-01 1.06652088e-01 1.28711534e+00
5.30652642e-01 -6.34293318e-01 7.94946969e-01 6.91562116e-01
7.30046928e-02 -5.61246872e-01 -9.62740839e-01 -7.13501751e-01
5.01226664e-01 -2.26257980e-01 9.94806647e-01 1.52195632e+00
3.59101593e-01 3.95753533e-01 1.31710202e-01 1.67669162e-01
5.60702026e-01 -7.19472915e-02 3.27685446e-01 -1.70433915e+00
3.50611120e-01 -3.86996448e-01 -9.33670640e-01 -5.55417418e-01
-5.55251502e-02 -1.46980703e+00 -2.27327764e-01 -1.66523135e+00
1.76611736e-01 -6.87422872e-01 -6.99970603e-01 7.59820342e-01
-3.77477437e-01 1.17412833e-02 -1.79257497e-01 -6.71325475e-02
-2.02044278e-01 4.17808354e-01 5.68902135e-01 -4.45941508e-01
-2.11776912e-01 -1.27915308e-01 -1.22645986e+00 5.47546387e-01
1.03779709e+00 -1.18240142e+00 -5.25724530e-01 -3.95738125e-01
7.59472996e-02 -5.98185956e-01 -1.43569484e-01 -8.81644666e-01
4.46598172e-01 -1.75973877e-01 3.53080779e-01 -4.37224746e-01
-5.38910215e-04 -9.45476651e-01 -1.78449169e-01 6.71602190e-01
-5.55293202e-01 1.96511820e-01 3.99996877e-01 6.31454170e-01
-1.00288853e-01 -6.59210980e-01 7.92337775e-01 -5.89230247e-02
-6.52151644e-01 1.22858368e-01 -1.97530076e-01 3.59863758e-01
9.97738004e-01 -4.05661702e-01 -2.12040856e-01 2.44071722e-01
-6.47076368e-01 8.24758559e-02 1.65301800e-01 8.16953003e-01
6.06307209e-01 -1.41599107e+00 -6.50977194e-01 2.74862021e-01
6.13944173e-01 -1.50648355e-01 2.18044911e-02 3.88205379e-01
-3.00426155e-01 6.76647484e-01 -1.90080494e-01 -1.94995239e-01
-1.50020635e+00 7.44817734e-01 2.52999544e-01 -1.44332558e-01
-8.83540213e-01 9.97197330e-01 2.49991044e-01 -9.42433119e-01
3.33167911e-01 -4.01492298e-01 -6.77908719e-01 3.05283368e-01
9.98560488e-01 9.50682536e-02 4.02209848e-01 -6.60994411e-01
-7.02553034e-01 8.65402877e-01 -3.83743495e-01 3.95290792e-01
1.50653160e+00 2.16176331e-01 -2.70996153e-01 2.41582524e-02
1.57523072e+00 -6.22324608e-02 -3.83062176e-02 -5.96384704e-01
5.84536791e-01 -4.14756238e-01 -1.69646040e-01 -6.31817162e-01
-8.84726107e-01 8.07810605e-01 8.74853551e-01 -6.13674462e-01
1.04336309e+00 -5.63323237e-02 7.56366014e-01 7.72415876e-01
-1.57806233e-01 -1.08073628e+00 -1.30218580e-01 4.44594264e-01
3.96905899e-01 -1.07602715e+00 2.53769338e-01 -4.86654520e-01
-3.12389970e-01 1.35459864e+00 5.49514771e-01 1.53678745e-01
8.10186386e-01 2.23711506e-01 3.00548136e-01 -4.26920801e-01
-4.27206725e-01 -1.43013194e-01 5.36696076e-01 9.75687921e-01
8.28686655e-01 -5.32779237e-03 -5.72056949e-01 1.01027989e+00
-3.28861564e-01 -1.53793678e-01 3.00203860e-01 8.47853661e-01
-4.64336902e-01 -1.45522630e+00 -2.81406194e-01 8.00688267e-01
-6.23352706e-01 -5.76780319e-01 -3.27001475e-02 7.21018136e-01
4.46039706e-01 7.86198676e-01 3.30850452e-01 -2.84517139e-01
6.21166110e-01 3.04837942e-01 -2.75540724e-02 -1.20887792e+00
-7.02858508e-01 -5.59707642e-01 -4.04102802e-01 -1.78949103e-01
-3.76073569e-01 -4.50892985e-01 -1.85255408e+00 -1.70095161e-01
-5.51642299e-01 6.38936102e-01 6.62814915e-01 9.32728648e-01
2.09100112e-01 5.79822600e-01 -8.28844532e-02 3.81289832e-02
-3.29404026e-01 -8.32649112e-01 -3.47362399e-01 7.96022236e-01
2.11384550e-01 -7.31055915e-01 -1.36547506e-01 1.33627594e-01] | [8.617269515991211, 8.53349781036377] |
1ad35b6b-0260-444f-b125-e1f08a9593bf | grouped-adaptive-loss-weighting-for-person | 2209.11492 | null | https://arxiv.org/abs/2209.11492v1 | https://arxiv.org/pdf/2209.11492v1.pdf | Grouped Adaptive Loss Weighting for Person Search | Person search is an integrated task of multiple sub-tasks such as foreground/background classification, bounding box regression and person re-identification. Therefore, person search is a typical multi-task learning problem, especially when solved in an end-to-end manner. Recently, some works enhance person search features by exploiting various auxiliary information, e.g. person joint keypoints, body part position, attributes, etc., which brings in more tasks and further complexifies a person search model. The inconsistent convergence rate of each task could potentially harm the model optimization. A straightforward solution is to manually assign different weights to different tasks, compensating for the diverse convergence rates. However, given the special case of person search, i.e. with a large number of tasks, it is impractical to weight the tasks manually. To this end, we propose a Grouped Adaptive Loss Weighting (GALW) method which adjusts the weight of each task automatically and dynamically. Specifically, we group tasks according to their convergence rates. Tasks within the same group share the same learnable weight, which is dynamically assigned by considering the loss uncertainty. Experimental results on two typical benchmarks, CUHK-SYSU and PRW, demonstrate the effectiveness of our method. | ['Jian Yang', 'Shanshan Zhang', 'Yunan Liu', 'Di Chen', 'Yanling Tian'] | 2022-09-23 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-4.40949015e-02 -4.14241284e-01 1.30291045e-01 -4.65273201e-01
-4.78679806e-01 -2.69611329e-01 3.61209065e-01 9.45766270e-02
-7.69014060e-01 6.92728639e-01 1.25905529e-01 2.58347720e-01
-2.87850767e-01 -4.19529438e-01 -3.35413128e-01 -8.96186173e-01
3.89533669e-01 6.52043402e-01 4.63246733e-01 2.00227067e-01
-9.50504187e-03 1.54207870e-02 -1.37705207e+00 -1.22066155e-01
1.15605831e+00 1.01253474e+00 3.96743178e-01 4.62183915e-02
-5.28730378e-02 1.47838145e-01 -7.31485248e-01 -6.75154090e-01
2.60472029e-01 -1.95769340e-01 -6.07737601e-01 2.46959060e-01
5.04637420e-01 -2.05912039e-01 -1.46362916e-01 1.12974238e+00
7.30204642e-01 5.46749473e-01 4.08699065e-01 -1.37375748e+00
-2.41399810e-01 2.49246955e-01 -1.03511333e+00 3.78157109e-01
1.74591646e-01 1.99789211e-01 7.73654461e-01 -6.85020626e-01
5.57413287e-02 1.50138879e+00 6.40145719e-01 6.25654161e-01
-1.24902320e+00 -8.81843328e-01 7.99848855e-01 3.03624481e-01
-1.64674854e+00 -2.35941142e-01 7.01613486e-01 -4.33595031e-01
1.46284029e-01 2.56801128e-01 4.66466606e-01 9.61062133e-01
-3.19987297e-01 8.50290298e-01 9.29189086e-01 -8.02825168e-02
-3.88089232e-02 3.41710031e-01 3.97417694e-01 6.43806875e-01
4.56559390e-01 -3.54644716e-01 -3.57192129e-01 -3.98983568e-01
4.37531203e-01 3.13111275e-01 -4.26216602e-01 -4.30502057e-01
-1.25614476e+00 4.72878397e-01 3.33357960e-01 -5.34588583e-02
-1.67980567e-01 -4.11415696e-02 6.52506769e-01 2.13441290e-02
4.30645227e-01 -2.42267266e-01 -5.15450835e-01 1.80174395e-01
-7.47443259e-01 6.05719507e-01 3.89211446e-01 8.42545211e-01
6.25495374e-01 -3.94916326e-01 -5.41238189e-01 1.11320114e+00
4.43600327e-01 3.51033956e-01 7.38381624e-01 -3.00845325e-01
8.57295513e-01 6.10747159e-01 3.78987819e-01 -1.16527081e+00
-4.96973336e-01 -4.68694121e-01 -9.85447168e-01 -6.72986135e-02
7.20761478e-01 -3.60520035e-01 -8.46285999e-01 1.96951556e+00
7.46205389e-01 2.57551253e-01 -3.53745788e-01 1.25425231e+00
6.67062044e-01 3.31301659e-01 3.80292952e-01 -2.07558081e-01
1.67029727e+00 -1.07801032e+00 -6.41824007e-01 -4.04080719e-01
2.24390298e-01 -6.27300560e-01 9.65346515e-01 1.69741035e-01
-8.14533174e-01 -7.06402659e-01 -7.42458999e-01 2.05378741e-01
-2.02151999e-01 3.34491223e-01 4.58054364e-01 6.86536551e-01
-4.04050291e-01 2.61380583e-01 -5.80701113e-01 -1.28070161e-01
4.19857055e-01 3.79623234e-01 -4.36542742e-02 4.07280028e-03
-1.27264237e+00 5.24171650e-01 5.37665129e-01 3.20133626e-01
-4.78781641e-01 -6.60195887e-01 -6.62395656e-01 9.33783315e-03
7.72158742e-01 -9.08066809e-01 1.06976414e+00 -6.21009171e-01
-1.16829681e+00 7.29640841e-01 -2.84110695e-01 -1.49571270e-01
1.11459458e+00 -2.71217972e-01 -3.66372555e-01 -3.67844701e-01
2.23127201e-01 2.56827384e-01 9.66478825e-01 -1.20126331e+00
-1.07416129e+00 -7.38827348e-01 -7.78045803e-02 4.10304308e-01
-4.73061413e-01 1.80378675e-01 -9.72164929e-01 -7.13761330e-01
2.95067485e-02 -1.00751591e+00 -3.24856430e-01 2.33079232e-02
-3.55465680e-01 -6.14353299e-01 6.84222102e-01 -7.70364761e-01
1.36630094e+00 -2.05741715e+00 3.64914089e-01 3.08113396e-01
2.94653237e-01 7.14148954e-02 2.28453487e-01 -3.88477862e-01
1.19922809e-01 -4.24300283e-02 -7.64094964e-02 -7.19125032e-01
1.11782409e-01 -6.29979596e-02 2.24966183e-01 5.11738300e-01
-2.00289175e-01 5.71292222e-01 -8.15937161e-01 -7.79217064e-01
1.32690817e-01 2.42297128e-01 -1.70689836e-01 7.42234290e-02
1.44418273e-02 5.83981395e-01 -8.82349491e-01 7.38175452e-01
8.66014719e-01 -3.48510593e-01 -1.40460745e-01 -2.76568919e-01
8.66406113e-02 -3.18808913e-01 -1.83232856e+00 1.46188200e+00
-3.97485316e-01 7.75374174e-02 2.04865158e-01 -8.25974405e-01
7.99194515e-01 1.78334668e-01 3.81497860e-01 -4.86573011e-01
-7.08415434e-02 1.97695136e-01 -9.67730805e-02 -3.67184758e-01
2.63774008e-01 1.63587138e-01 -1.24174409e-01 2.43258700e-01
-3.46603870e-01 6.09290779e-01 2.55865335e-01 -1.77616909e-01
5.51395178e-01 6.40588030e-02 2.00333506e-01 -1.74954250e-01
9.93702471e-01 -3.82211238e-01 9.97442901e-01 7.48685896e-01
-5.01714826e-01 5.70621550e-01 8.42258781e-02 -7.11404622e-01
-7.16367424e-01 -6.80537581e-01 -2.55965650e-01 1.32523918e+00
6.96112573e-01 -3.37858289e-01 -6.22874200e-01 -8.82361829e-01
2.90376842e-01 6.24821372e-02 -4.55729485e-01 -1.63753271e-01
-7.94523835e-01 -1.37559247e+00 3.67328614e-01 3.52048635e-01
6.94264710e-01 -9.57491577e-01 -6.02670014e-01 2.68709362e-01
-4.06827152e-01 -1.07104421e+00 -9.17412579e-01 -9.50267464e-02
-4.79375303e-01 -1.04351389e+00 -1.35587931e+00 -8.02290976e-01
7.74644256e-01 3.81799281e-01 8.90020728e-01 2.00484097e-01
-3.47690135e-01 1.66596845e-01 -2.10867614e-01 -2.76637912e-01
2.53015488e-01 1.17280588e-01 2.52106130e-01 4.65573013e-01
2.97978878e-01 -2.35295057e-01 -8.48872721e-01 7.61581779e-01
-6.51526690e-01 -3.14190760e-02 4.13963050e-01 8.68396342e-01
7.03410566e-01 3.17535698e-01 3.96358818e-01 -6.22853875e-01
7.08607256e-01 -3.92104506e-01 -6.22405589e-01 7.08330214e-01
-5.79503000e-01 -1.53425679e-01 4.85090107e-01 -8.88718963e-01
-1.24743688e+00 8.30278620e-02 1.97699979e-01 -4.31365073e-01
-8.78021568e-02 1.33340582e-01 -6.64913058e-01 -7.97411725e-02
1.74938425e-01 2.30656207e-01 -2.41936609e-01 -5.53936601e-01
-7.02379411e-03 4.87453520e-01 4.60346520e-01 -6.27888381e-01
9.63815928e-01 3.01636636e-01 -1.43561393e-01 -4.14239347e-01
-7.20552504e-01 -7.16695309e-01 -4.47838217e-01 -2.44285986e-01
7.28632033e-01 -9.01456654e-01 -9.36637044e-01 8.12111855e-01
-1.11187494e+00 -5.81703037e-02 2.51136035e-01 3.56268346e-01
-1.86262444e-01 6.56994462e-01 -2.38086194e-01 -8.94936502e-01
-4.96022791e-01 -1.21199059e+00 1.18802524e+00 7.24143863e-01
-1.81638449e-02 -7.78458834e-01 -2.82968223e-01 5.82338035e-01
8.71990174e-02 3.39722335e-01 5.92069507e-01 -5.94193697e-01
-3.49321127e-01 -1.36970371e-01 -4.48042542e-01 -4.05369848e-02
2.38813192e-01 -5.18737674e-01 -6.73196554e-01 -4.67199534e-01
-9.14469734e-02 -2.18677029e-01 9.88953233e-01 3.23687196e-01
1.47190523e+00 -1.99158788e-01 -7.50681162e-01 6.99945152e-01
1.11693501e+00 1.70304507e-01 2.40120217e-01 5.56347907e-01
9.16025162e-01 7.39938974e-01 7.84574568e-01 6.65973902e-01
4.75581229e-01 1.09700525e+00 4.78490107e-02 -1.44121563e-02
3.04578822e-02 -5.36213722e-03 -6.56404793e-02 1.99865013e-01
-2.40588814e-01 -2.29944929e-01 -8.66840720e-01 1.98097304e-01
-2.28039885e+00 -7.94065416e-01 -3.41154672e-02 2.35951662e+00
9.74402368e-01 2.24672943e-01 3.59458804e-01 -3.46296877e-02
1.15319395e+00 1.38870746e-01 -7.32931376e-01 5.37783563e-01
-3.14382054e-02 -3.88465017e-01 4.59409654e-01 1.78506568e-01
-1.30482256e+00 7.28580534e-01 4.27471638e+00 1.14818704e+00
-6.40880823e-01 2.54434258e-01 7.39076197e-01 -2.08118245e-01
1.51456922e-01 -3.18684846e-01 -1.30933690e+00 1.11996305e+00
8.90044123e-02 -3.72287929e-01 3.38635802e-01 6.98071182e-01
2.39779159e-01 -1.46157935e-01 -9.15845513e-01 1.40478766e+00
1.20114386e-01 -6.62202060e-01 -8.49564895e-02 -6.83990791e-02
4.85380173e-01 -4.79464233e-01 -1.94445485e-03 4.88763332e-01
-6.40167273e-04 -5.24420261e-01 7.29871988e-01 4.82766956e-01
3.45820606e-01 -8.29928756e-01 6.89526796e-01 5.66649556e-01
-1.52720273e+00 -2.15351805e-01 -4.89224672e-01 4.08825964e-01
4.06687081e-01 4.97554660e-01 -2.55022079e-01 5.21031439e-01
1.07288623e+00 3.31278235e-01 -7.73689210e-01 1.46088207e+00
1.42400801e-01 -1.04076304e-02 -4.52844143e-01 2.37939749e-02
-1.80655066e-02 -2.84805119e-01 5.75503230e-01 1.03049850e+00
7.46705234e-02 4.76336777e-02 8.57171476e-01 6.81813657e-01
9.55129564e-02 2.07497001e-01 9.76285785e-02 6.11387432e-01
3.82984698e-01 1.34849012e+00 -7.00811148e-01 -2.71201640e-01
-4.58321512e-01 1.12914038e+00 2.99637198e-01 4.20854449e-01
-1.10870898e+00 -2.27638483e-01 6.65512383e-01 -1.58092137e-02
2.08164975e-01 2.55347136e-02 -1.22522339e-02 -1.09981453e+00
5.43382525e-01 -6.83830857e-01 7.73951471e-01 -1.97917759e-01
-1.55260420e+00 4.28352922e-01 1.20558575e-01 -1.27756131e+00
1.21172756e-01 -1.63192242e-01 -5.76208532e-01 1.05768728e+00
-1.54575384e+00 -1.12831104e+00 -7.97745705e-01 7.56282210e-01
7.40178406e-01 -2.18468443e-01 3.33229512e-01 6.55578613e-01
-1.06663764e+00 1.00551438e+00 -1.22564621e-01 1.21769853e-01
9.97591913e-01 -1.14362073e+00 1.31083563e-01 5.75392842e-01
-3.19479525e-01 6.59819901e-01 6.40997887e-01 -8.19502056e-01
-9.06885922e-01 -1.18505812e+00 6.78901255e-01 -2.62429714e-01
4.71092582e-01 -3.40414256e-01 -1.04226875e+00 4.39030349e-01
-2.69512981e-01 2.58504331e-01 6.53512657e-01 2.57205874e-01
-1.16924763e-01 -3.79257858e-01 -1.08645892e+00 6.81217670e-01
1.23793745e+00 5.80974966e-02 -4.43064481e-01 6.92699254e-01
5.96310616e-01 -6.47198021e-01 -5.86538970e-01 5.45544326e-01
5.03358722e-01 -7.01614380e-01 1.16453850e+00 -4.88036424e-01
-2.17332587e-01 -6.27888739e-01 3.00688207e-01 -1.17692137e+00
-4.36310500e-01 -4.78345186e-01 -2.37595499e-01 1.54948616e+00
1.70991614e-01 -6.15083933e-01 7.74995506e-01 1.11169136e+00
2.80183822e-01 -7.37847924e-01 -1.08128858e+00 -8.28606427e-01
-3.73990864e-01 8.32857713e-02 6.99846387e-01 6.40584111e-01
-4.70786452e-01 1.75964206e-01 -6.97834194e-01 3.55191141e-01
9.71280873e-01 1.81259453e-01 8.11053097e-01 -1.54109716e+00
-3.47359985e-01 -5.46943784e-01 -3.18620145e-01 -1.03913987e+00
1.89222351e-01 -6.02304399e-01 2.35514734e-02 -1.16015792e+00
5.50591886e-01 -8.16602528e-01 -4.78674144e-01 4.43405867e-01
-9.50469255e-01 -1.95316970e-01 2.67669976e-01 4.86268550e-01
-9.04429674e-01 6.00060403e-01 1.03160906e+00 -3.76650900e-01
-4.12349373e-01 5.67003429e-01 -5.97225070e-01 8.02220941e-01
7.10146129e-01 -3.93031418e-01 -2.69892126e-01 -5.57102561e-01
-2.09611371e-01 -3.41156632e-01 5.25818348e-01 -1.01044774e+00
5.53303659e-01 -1.51579767e-01 7.42811918e-01 -6.15451992e-01
2.48158500e-01 -9.44632292e-01 9.28672776e-02 4.01481122e-01
-8.38481262e-02 -1.15812514e-02 8.57489109e-02 9.40446854e-01
-1.40478745e-01 -2.95380116e-01 7.10434377e-01 -2.36849219e-01
-8.45485628e-01 6.72168553e-01 2.45266914e-01 1.45384774e-01
1.15656912e+00 -2.91925341e-01 -1.21899452e-02 1.26330569e-01
-6.92551196e-01 9.68075812e-01 1.71935454e-01 6.33069873e-01
4.21836883e-01 -1.36560583e+00 -7.88753927e-01 -1.98777001e-02
2.94459939e-01 3.10527205e-01 5.74699998e-01 7.55145192e-01
1.85174555e-01 8.54598507e-02 1.31347567e-01 -6.03695631e-01
-1.68685889e+00 5.58013439e-01 4.82899666e-01 -3.74845684e-01
-6.09405041e-01 8.48064482e-01 4.34859127e-01 -1.79445475e-01
6.63779438e-01 1.42868259e-04 -4.37062263e-01 3.95929903e-01
6.94454849e-01 4.84358639e-01 -9.89441499e-02 -5.09319603e-01
-5.51756620e-01 8.10213327e-01 -3.01948637e-01 2.91418046e-01
9.19933498e-01 -3.68087351e-01 9.55965295e-02 1.95331678e-01
7.75802970e-01 -2.13375449e-01 -1.30348945e+00 -6.15835845e-01
1.19931974e-01 -7.09845006e-01 -1.85841814e-01 -4.63141561e-01
-1.17840135e+00 3.68938625e-01 7.63790786e-01 3.26714814e-02
1.02689290e+00 2.28012074e-02 9.47710872e-01 2.54755795e-01
4.99293864e-01 -1.37661195e+00 2.03475170e-02 2.09136263e-01
5.96866965e-01 -1.48832464e+00 1.60673231e-01 -4.57396775e-01
-6.18901432e-01 7.97885120e-01 1.02066803e+00 3.19710463e-01
4.46138144e-01 -1.61801025e-01 -2.42375106e-01 1.66710913e-01
-1.75770447e-01 -2.41710365e-01 4.60917205e-01 3.04469585e-01
3.11914030e-02 7.90412799e-02 -4.95282143e-01 1.02669251e+00
1.80500239e-01 -1.10854447e-01 -1.94426611e-01 7.86486864e-01
-3.33871692e-01 -1.01962304e+00 -7.28952408e-01 4.26854193e-01
-4.13586348e-01 1.69972867e-01 -8.36783424e-02 5.76861143e-01
5.39985716e-01 8.49652827e-01 -1.33715928e-01 -6.97430372e-02
5.49206495e-01 1.57746654e-02 2.28984281e-01 -3.70709777e-01
-5.96169353e-01 3.83658260e-02 -1.41245291e-01 -1.77288190e-01
-3.15428764e-01 -8.36246312e-01 -1.08632565e+00 -1.34979412e-01
-4.29169327e-01 1.32362684e-02 1.71925679e-01 9.62874055e-01
4.10042182e-02 5.02399147e-01 5.40410876e-01 -7.08841085e-01
-8.04947436e-01 -8.67969573e-01 -5.03119290e-01 7.12194383e-01
1.80732787e-01 -1.09829259e+00 -2.25756660e-01 -2.71981638e-02] | [14.746322631835938, 0.8293699026107788] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.