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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
98b331f0-8b0a-4b42-b12b-96920fd12527 | an-interactive-multi-task-learning-network | 1906.06906 | null | https://arxiv.org/abs/1906.06906v1 | https://arxiv.org/pdf/1906.06906v1.pdf | An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis | Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence. This task is usually done in a pipeline manner, with aspect term extraction performed first, followed by sentiment predictions toward the extracted aspect terms. While easier to develop, s... | ['Hwee Tou Ng', 'Daniel Dahlmeier', 'Wee Sun Lee', 'Ruidan He'] | 2019-06-17 | an-interactive-multi-task-learning-network-1 | https://aclanthology.org/P19-1048 | https://aclanthology.org/P19-1048.pdf | acl-2019-7 | ['aspect-term-extraction-and-sentiment'] | ['natural-language-processing'] | [ 2.83094198e-01 1.44092306e-01 -2.69055337e-01 -6.53097570e-01
-1.24851215e+00 -6.90387666e-01 1.00078189e+00 5.66410959e-01
-4.95726228e-01 5.85988700e-01 3.49477679e-01 -3.08206290e-01
2.24842951e-01 -6.46371782e-01 -4.79525715e-01 -6.42934561e-01
2.11275280e-01 5.74819505e-01 -1.96915623e-02 -1.43022671... | [11.393980026245117, 6.684563636779785] |
8802ae0b-d06c-45e0-9f97-1be8d6248b21 | fine-grained-angular-contrastive-learning | 2012.03515 | null | https://arxiv.org/abs/2012.03515v3 | https://arxiv.org/pdf/2012.03515v3.pdf | Fine-grained Angular Contrastive Learning with Coarse Labels | Few-shot learning methods offer pre-training techniques optimized for easier later adaptation of the model to new classes (unseen during training) using one or a few examples. This adaptivity to unseen classes is especially important for many practical applications where the pre-trained label space cannot remain fixed ... | ['Leonid Karlinsky', 'Raja Giryes', 'Rogerio Feris', 'Ori Shahar', 'Kate Saenko', 'Eli Schwartz', 'Guy Bukchin'] | 2020-12-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Bukchin_Fine-Grained_Angular_Contrastive_Learning_With_Coarse_Labels_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Bukchin_Fine-Grained_Angular_Contrastive_Learning_With_Coarse_Labels_CVPR_2021_paper.pdf | cvpr-2021-1 | ['learning-with-coarse-labels'] | ['computer-vision'] | [ 5.25423467e-01 8.01666453e-02 -4.06623870e-01 -7.13296890e-01
-4.91045684e-01 -5.23325384e-01 7.84718454e-01 4.69681531e-01
-5.70387304e-01 8.24392974e-01 -5.25996648e-02 2.73816943e-01
-1.98366940e-01 -9.26949918e-01 -6.18518531e-01 -7.73922801e-01
-1.60802707e-01 6.33292556e-01 7.12025225e-01 -4.14550483... | [9.966852188110352, 2.975789785385132] |
29dff4d1-6737-4dea-bf57-d40f45a6a9f1 | adaptive-risk-sensitive-model-predictive | 2009.01090 | null | https://arxiv.org/abs/2009.01090v2 | https://arxiv.org/pdf/2009.01090v2.pdf | Adaptive Risk Sensitive Model Predictive Control with Stochastic Search | We present a general framework for optimizing the Conditional Value-at-Risk for dynamical systems using stochastic search. The framework is capable of handling the uncertainty from the initial condition, stochastic dynamics, and uncertain parameters in the model. The algorithm is compared against a risk-sensitive distr... | ['Evangelos A. Theodorou', 'Camilo A. Duarte', 'Keuntaek Lee', 'Oswin So', 'Ziyi Wang'] | 2020-09-02 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-3.41051698e-01 5.73496930e-02 -2.72819940e-02 -5.39154410e-02
-8.31954777e-01 -4.94253099e-01 7.17010140e-01 -1.40534073e-01
-6.76362753e-01 1.36589015e+00 -1.99474931e-01 -2.40758806e-01
-7.52107978e-01 -5.88194132e-01 -6.31960809e-01 -9.26825047e-01
-3.44999611e-01 6.45298243e-01 1.29313126e-01 -4.73603874... | [4.782378673553467, 2.414876937866211] |
ede714e7-47b4-4be7-8339-4a37c73041f3 | hierarchical-transfer-learning-with | 2111.08512 | null | https://arxiv.org/abs/2111.08512v3 | https://arxiv.org/pdf/2111.08512v3.pdf | Hierarchical transfer learning with applications for electricity load forecasting | The recent abundance of data on electricity consumption at different scales opens new challenges and highlights the need for new techniques to leverage information present at finer scales in order to improve forecasts at wider scales. In this work, we take advantage of the similarity between this hierarchical predictio... | ['Solenne Gaucher', 'Anestis Antoniadis', 'Yannig Goude'] | 2021-11-16 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 1.04228653e-01 1.19357221e-02 1.26394376e-01 -3.18145156e-01
-8.10645282e-01 -5.78458846e-01 8.69502425e-01 4.09996927e-01
-1.62766233e-01 1.18919563e+00 3.88298929e-01 -4.57604378e-01
-4.45044845e-01 -1.27486265e+00 -5.28976023e-01 -9.50436890e-01
-2.52953202e-01 5.47723949e-01 2.72877067e-01 -2.79125005... | [6.254243850708008, 2.961545467376709] |
50b064d4-9a79-4372-ac1b-c934a72da0b2 | lexical-normalization-for-code-switched-data-1 | null | null | https://aclanthology.org/2021.eacl-main.200 | https://aclanthology.org/2021.eacl-main.200.pdf | Lexical Normalization for Code-switched Data and its Effect on POS Tagging | Lexical normalization, the translation of non-canonical data to standard language, has shown to improve the performance of many natural language processing tasks on social media. Yet, using multiple languages in one utterance, also called code-switching (CS), is frequently overlooked by these normalization systems, des... | ['{\\"O}zlem {\\c{C}}etino{\\u{g}}lu', 'Rob van der Goot'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['lexical-normalization'] | ['natural-language-processing'] | [ 9.02659595e-02 -3.97260115e-02 -1.83694452e-01 -4.81703460e-01
-6.69278026e-01 -7.61518300e-01 6.84785485e-01 6.15105629e-01
-8.50650907e-01 6.33366764e-01 3.64906311e-01 -5.03967464e-01
3.30801040e-01 -3.59957069e-01 -3.11882287e-01 -3.08546215e-01
3.28007638e-01 3.77369255e-01 9.86486971e-02 -5.54267406... | [10.240554809570312, 9.989635467529297] |
269e1711-fea4-454e-aeee-dcaa2e0aa58a | confidence-intervals-and-hypothesis-testing-1 | null | null | http://papers.nips.cc/paper/4931-confidence-intervals-and-hypothesis-testing-for-high-dimensional-statistical-models | http://papers.nips.cc/paper/4931-confidence-intervals-and-hypothesis-testing-for-high-dimensional-statistical-models.pdf | Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models | Fitting high-dimensional statistical models often requires the use of non-linear parameter estimation procedures. As a consequence, it is generally impossible to obtain an exact characterization of the probability distribution of the parameter estimates. This in turn implies that it is extremely challenging to quantify... | ['Andrea Montanari', 'Adel Javanmard'] | 2013-12-01 | null | null | null | neurips-2013-12 | ['diabetes-prediction'] | ['medical'] | [ 3.52089047e-01 1.87459230e-01 -3.25210452e-01 -2.90678293e-01
-9.51019108e-01 -5.22342324e-01 2.45489493e-01 5.28443754e-01
-4.08823013e-01 1.19267499e+00 -1.82379857e-01 -4.89548773e-01
-3.93375099e-01 -7.29425311e-01 -8.38109791e-01 -9.07270789e-01
-3.14342380e-01 4.32475626e-01 -1.30261462e-02 3.20285112... | [7.506474018096924, 4.493092060089111] |
e0c04221-94bd-4f11-8e2b-6c5ffcd10ec9 | evaluating-the-consistency-of-word-embeddings | null | null | https://aclanthology.org/R19-1016 | https://aclanthology.org/R19-1016.pdf | Evaluating the Consistency of Word Embeddings from Small Data | In this work, we address the evaluation of distributional semantic models trained on smaller, domain-specific texts, specifically, philosophical text. Specifically, we inspect the behaviour of models using a pre-trained background space in learning. We propose a measure of consistency which can be used as an evaluation... | ["Aur{\\'e}lie Herbelot", 'Antske Fokkens', 'Jelke Bloem'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['small-data'] | ['computer-vision'] | [ 4.84687537e-02 -3.49365128e-03 -1.69218093e-01 -7.31796980e-01
-6.10915720e-01 -6.18925512e-01 9.90170658e-01 7.04877615e-01
-9.37619984e-01 5.02314031e-01 4.57513630e-01 -1.12789981e-01
-9.95995775e-02 -7.27230549e-01 -5.14246762e-01 -7.39797890e-01
3.80469918e-01 7.42514074e-01 3.57739300e-01 -3.43610168... | [10.487313270568848, 8.975203514099121] |
ffcb3ca5-2861-454b-a3c5-096beeca1619 | isolation-distributional-kernel-a-new-tool | 2009.12196 | null | https://arxiv.org/abs/2009.12196v1 | https://arxiv.org/pdf/2009.12196v1.pdf | Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection | We introduce Isolation Distributional Kernel as a new way to measure the similarity between two distributions. Existing approaches based on kernel mean embedding, which convert a point kernel to a distributional kernel, have two key issues: the point kernel employed has a feature map with intractable dimensionality; an... | ['Zhi-Hua Zhou', 'Bi-Cun Xu', 'Takashi Washio', 'Kai Ming Ting'] | 2020-09-24 | null | null | null | null | ['group-anomaly-detection'] | ['methodology'] | [-3.89903843e-01 -2.00351492e-01 1.49670407e-01 -3.59451234e-01
-7.82475114e-01 -6.65796459e-01 5.91881454e-01 4.87210572e-01
-3.79439443e-01 5.08834198e-02 -4.72108066e-01 -8.09055030e-01
-5.76111436e-01 -7.31618822e-01 -5.27230024e-01 -9.51536238e-01
-5.36010504e-01 2.92896003e-01 4.28972334e-01 -2.12105528... | [7.6325459480285645, 2.5241799354553223] |
8545f6c9-f2b9-40f1-b95d-0d9f2e959adb | don-t-treat-the-symptom-find-the-cause | 2306.12850 | null | https://arxiv.org/abs/2306.12850v1 | https://arxiv.org/pdf/2306.12850v1.pdf | Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging | In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in e-commerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, the power grid to ensure our energy su... | ['Patrick Rodler'] | 2023-06-22 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty', 'decision-making'] | ['medical', 'reasoning', 'reasoning'] | [-2.16605067e-01 -3.48674394e-02 -2.92959273e-01 2.88215458e-01
-2.31237207e-02 -6.98308647e-01 2.24816516e-01 3.74945611e-01
1.65322334e-01 5.98743439e-01 -4.79127795e-01 -7.37628639e-01
-8.20392966e-01 -8.96148205e-01 -1.41352594e-01 -5.50037265e-01
-1.91756874e-01 5.84138572e-01 3.80916625e-01 -1.48928180... | [6.189904689788818, 2.624246597290039] |
c4038707-e35c-49f1-85f7-408a65d1208b | simple-baselines-for-human-pose-estimation | 1804.06208 | null | http://arxiv.org/abs/1804.06208v2 | http://arxiv.org/pdf/1804.06208v2.pdf | Simple Baselines for Human Pose Estimation and Tracking | There has been significant progress on pose estimation and increasing
interests on pose tracking in recent years. At the same time, the overall
algorithm and system complexity increases as well, making the algorithm
analysis and comparison more difficult. This work provides simple and effective
baseline methods. They a... | ['Haiping Wu', 'Yichen Wei', 'Bin Xiao'] | 2018-04-17 | simple-baselines-for-human-pose-estimation-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.pdf | eccv-2018-9 | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.71224907e-01 -3.27871263e-01 -4.30186003e-01 -4.29400623e-01
-9.29381430e-01 -7.23309100e-01 2.99822837e-01 -1.92590252e-01
-3.86390865e-01 5.64167857e-01 1.91102475e-01 1.15926258e-01
3.51409227e-01 -3.85446608e-01 -5.45756757e-01 -6.08611226e-01
-3.45593780e-01 6.27978861e-01 3.97420526e-01 -2.82743335... | [7.17161226272583, -0.7985823154449463] |
eb330d1f-c33d-48d6-81d2-eefb7a7ead1d | a-novel-filter-approach-for-band-selection | 2210.15477 | null | https://arxiv.org/abs/2210.15477v1 | https://arxiv.org/pdf/2210.15477v1.pdf | A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines | Band selection is a great challenging task in the classification of hyperspectral remotely sensed images HSI. This is resulting from its high spectral resolution, the many class outputs and the limited number of training samples. For this purpose, this paper introduces a new filter approach for dimension reduction and ... | ['Ahmed Hammouch', 'Elkebir Sarhrouni', 'Asma Elmaizi', 'Hasna Nhaila'] | 2022-10-27 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 8.65092933e-01 -7.09480345e-01 -1.71483180e-03 -3.18676233e-01
-3.02711964e-01 -6.18262291e-01 2.92691469e-01 1.91619009e-01
-3.00211072e-01 1.04731870e+00 -2.23718479e-01 -7.02806339e-02
-1.03690696e+00 -1.09021354e+00 1.58593193e-01 -9.36683476e-01
-4.37804401e-01 2.90680438e-01 -3.28008160e-02 -2.72095591... | [9.80390453338623, -1.8463932275772095] |
b184c74a-96ab-4a59-8c8b-2196fafbfa07 | matrix-completion-with-variational-graph | 1811.01662 | null | http://arxiv.org/abs/1811.01662v1 | http://arxiv.org/pdf/1811.01662v1.pdf | Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference | Inferring air quality from a limited number of observations is an essential
task for monitoring and controlling air pollution. Existing inference methods
typically use low spatial resolution data collected by fixed monitoring
stations and infer the concentration of air pollutants using additional types
of data, e.g., m... | ['Evaggelia Tsiligianni', 'Tien Huu Do', 'Nikos Deligiannis', 'Frank Pasveer', 'Duc Minh Nguyen', 'Valerio Panzica La Manna', 'Wilfried Philips', 'Angel Lopez Aguirre'] | 2018-11-05 | null | null | null | null | ['air-quality-inference'] | ['miscellaneous'] | [-5.89645691e-02 -3.47220600e-01 -1.67795084e-02 -2.65694350e-01
-4.59217787e-01 -4.05634165e-01 4.88269895e-01 5.24229586e-01
-3.58894348e-01 7.43240654e-01 1.58420384e-01 -4.38411534e-01
-6.96128070e-01 -1.49005747e+00 -9.43333566e-01 -6.15375578e-01
5.32072484e-02 4.02431130e-01 -1.44600004e-01 1.01270694... | [6.253604888916016, 2.524998426437378] |
24994a39-9a48-4e6b-a0b0-9e77034fc67b | mask-free-video-instance-segmentation | 2303.15904 | null | https://arxiv.org/abs/2303.15904v1 | https://arxiv.org/pdf/2303.15904v1.pdf | Mask-Free Video Instance Segmentation | The recent advancement in Video Instance Segmentation (VIS) has largely been driven by the use of deeper and increasingly data-hungry transformer-based models. However, video masks are tedious and expensive to annotate, limiting the scale and diversity of existing VIS datasets. In this work, we aim to remove the mask-a... | ['Fisher Yu', 'Chi-Keung Tang', 'Yu-Wing Tai', 'Henghui Ding', 'Martin Danelljan', 'Lei Ke'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ke_Mask-Free_Video_Instance_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ke_Mask-Free_Video_Instance_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-instance-segmentation', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [ 4.04006764e-02 -3.08247030e-01 -4.70787585e-01 -2.51492649e-01
-7.75896370e-01 -6.65779173e-01 5.13040900e-01 -2.08904997e-01
-5.88021040e-01 5.36379755e-01 6.83860481e-02 -1.64419532e-01
1.81419432e-01 -2.24950179e-01 -8.28547716e-01 -5.62734842e-01
2.88882852e-02 1.57758132e-01 7.29276538e-01 2.26588026... | [9.160717010498047, 0.0061780186370015144] |
9d27c44e-f735-42a7-8406-35598aeabc29 | towards-surgical-context-inference-and | 2302.14237 | null | https://arxiv.org/abs/2302.14237v2 | https://arxiv.org/pdf/2302.14237v2.pdf | Towards Surgical Context Inference and Translation to Gestures | Manual labeling of gestures in robot-assisted surgery is labor intensive, prone to errors, and requires expertise or training. We propose a method for automated and explainable generation of gesture transcripts that leverages the abundance of data for image segmentation. Surgical context is detected using segmentation ... | ['Homa Alemzadeh', 'Ian Reyes', 'Zongyu Li', 'Kay Hutchinson'] | 2023-02-28 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 6.49089873e-01 6.28380775e-01 -3.18533957e-01 -5.86274326e-01
-1.26391840e+00 -9.07732785e-01 2.32972294e-01 -1.41140511e-02
-3.93706828e-01 1.64239198e-01 3.92728060e-01 -3.66390586e-01
-2.57941168e-02 -5.00491858e-02 -6.55411661e-01 -5.20927310e-01
5.68012036e-02 7.36821115e-01 -1.17387727e-01 5.38499020... | [14.087203979492188, -3.3244340419769287] |
49ff3543-03ca-47ea-8d90-ca21a5162043 | vartani-spellcheck-automatic-context | 2012.07652 | null | https://arxiv.org/abs/2012.07652v1 | https://arxiv.org/pdf/2012.07652v1.pdf | Vartani Spellcheck -- Automatic Context-Sensitive Spelling Correction of OCR-generated Hindi Text Using BERT and Levenshtein Distance | Traditional Optical Character Recognition (OCR) systems that generate text of highly inflectional Indic languages like Hindi tend to suffer from poor accuracy due to a wide alphabet set, compound characters and difficulty in segmenting characters in a word. Automatic spelling error detection and context-sensitive error... | ['Abhijit Mustafi', 'Aditya Pal'] | 2020-12-14 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 5.29537618e-01 -5.97425282e-01 3.54572833e-01 -4.00697291e-01
-7.88770676e-01 -8.52706075e-01 4.39094305e-01 7.09464133e-01
-8.85063171e-01 1.07730794e+00 -1.25389010e-01 -6.29511356e-01
-4.10017893e-02 -6.83284581e-01 -3.62138391e-01 -3.00720066e-01
2.43417129e-01 8.12212110e-01 4.45341289e-01 -6.69764042... | [10.824790954589844, 10.601958274841309] |
06bea677-4dfc-4450-8d23-311bb5e05169 | reasoning-with-latent-structure-refinement | 2005.06312 | null | https://arxiv.org/abs/2005.06312v3 | https://arxiv.org/pdf/2005.06312v3.pdf | Reasoning with Latent Structure Refinement for Document-Level Relation Extraction | Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities. However, effective aggregation of relevant information in the document remains a challenging research question. Existing approaches c... | ['Ivan Sekulić', 'Zhijiang Guo', 'Wei Lu', 'Guoshun Nan'] | 2020-05-13 | reasoning-with-latent-structure-refinement-1 | https://aclanthology.org/2020.acl-main.141 | https://aclanthology.org/2020.acl-main.141.pdf | acl-2020-6 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.14527270e-01 5.95303774e-01 -4.90617752e-01 -5.86755216e-01
-1.05861425e+00 -6.40089035e-01 8.55268896e-01 8.19943666e-01
-2.67107617e-02 8.54939520e-01 6.80377543e-01 -4.50539440e-01
-4.79329169e-01 -1.06455123e+00 -5.52032709e-01 4.66563143e-02
-3.37928921e-01 6.88753247e-01 4.29471016e-01 -2.74886787... | [9.262495994567871, 8.639062881469727] |
2c33cfc7-3e97-4c27-b31b-5c6c7311081b | beating-the-worlds-best-at-super-smash-bros | 1702.06230 | null | http://arxiv.org/abs/1702.06230v3 | http://arxiv.org/pdf/1702.06230v3.pdf | Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning | There has been a recent explosion in the capabilities of game-playing
artificial intelligence. Many classes of RL tasks, from Atari games to motor
control to board games, are now solvable by fairly generic algorithms, based on
deep learning, that learn to play from experience with minimal knowledge of the
specific doma... | ['Joshua B. Tenenbaum', 'William F. Whitney', 'Vlad Firoiu'] | 2017-02-21 | null | null | null | null | ['board-games'] | ['playing-games'] | [-2.47864515e-01 5.53701743e-02 4.64730114e-02 4.61817086e-01
-4.88869429e-01 -8.48729193e-01 5.11070192e-01 -2.48534903e-01
-8.72843266e-01 1.00835419e+00 -3.44690531e-01 -4.31837469e-01
-5.06535769e-01 -6.78924203e-01 -6.18763685e-01 -5.86719930e-01
-5.85230649e-01 8.69522691e-01 5.73882103e-01 -1.06170321... | [3.5889205932617188, 1.4699798822402954] |
3b84822b-72f1-4432-902c-b7bf7d63f620 | surgical-vqla-transformer-with-gated-vision | 2305.11692 | null | https://arxiv.org/abs/2305.11692v1 | https://arxiv.org/pdf/2305.11692v1.pdf | Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery | Despite the availability of computer-aided simulators and recorded videos of surgical procedures, junior residents still heavily rely on experts to answer their queries. However, expert surgeons are often overloaded with clinical and academic workloads and limit their time in answering. For this purpose, we develop a s... | ['Hongliang Ren', 'Lalithkumar Seenivasan', 'Mobarakol Islam', 'Long Bai'] | 2023-05-19 | null | null | null | null | ['answer-generation'] | ['natural-language-processing'] | [-1.03484280e-01 3.15870017e-01 -2.67619878e-01 -1.84573438e-02
-1.08621991e+00 -6.38927996e-01 -4.38257232e-02 3.36834699e-01
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-1.32884234e-02 -5.17831504e-01 -7.54586518e-01 -5.23698688e-01
2.74487227e-01 9.93732139e-02 2.60310411e-01 -1.20572433... | [14.156536102294922, -3.2407596111297607] |
f6627c01-37e8-44c6-b188-a7c2919bf702 | an-improved-model-for-voicing-silent-speech | 2106.01933 | null | https://arxiv.org/abs/2106.01933v2 | https://arxiv.org/pdf/2106.01933v2.pdf | An Improved Model for Voicing Silent Speech | In this paper, we present an improved model for voicing silent speech, where audio is synthesized from facial electromyography (EMG) signals. To give our model greater flexibility to learn its own input features, we directly use EMG signals as input in the place of hand-designed features used by prior work. Our model u... | ['Dan Klein', 'David Gaddy'] | 2021-06-03 | null | https://aclanthology.org/2021.acl-short.23 | https://aclanthology.org/2021.acl-short.23.pdf | acl-2021-5 | ['electromyography-emg'] | ['medical'] | [ 3.52426797e-01 4.52904642e-01 1.18027739e-01 -4.66046482e-01
-1.23008132e+00 -3.48211825e-01 7.93865323e-02 -6.24453247e-01
-2.95565635e-01 4.49947566e-01 6.53320730e-01 -7.51170516e-02
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-2.37494469e-01 -5.54814860e-02 -1.32341117e-01 -1.57308459... | [15.02472972869873, 5.934204578399658] |
ca47afd9-5827-40a4-92b2-89429e984e82 | improving-transition-based-dependency-parsing | null | null | https://aclanthology.org/D12-1029 | https://aclanthology.org/D12-1029.pdf | Improving Transition-Based Dependency Parsing with Buffer Transitions | null | ["Carlos G{\\'o}mez-Rodr{\\'\\i}guez", "Daniel Fern{\\'a}ndez-Gonz{\\'a}lez"] | 2012-07-01 | null | null | null | emnlp-2012-7 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3037567138671875, 3.831505060195923] |
70ab8713-1344-4774-8230-af50157df4b7 | efficient-similarity-based-passive-filter | 2210.17416 | null | https://arxiv.org/abs/2210.17416v1 | https://arxiv.org/pdf/2210.17416v1.pdf | Efficient Similarity-based Passive Filter Pruning for Compressing CNNs | Convolution neural networks (CNNs) have shown great success in various applications. However, the computational complexity and memory storage of CNNs is a bottleneck for their deployment on resource-constrained devices. Recent efforts towards reducing the computation cost and the memory overhead of CNNs involve similar... | ['Mark D. Plumbley', 'Arshdeep Singh'] | 2022-10-27 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 2.45793208e-01 -2.17954427e-01 7.54412830e-01 -4.78668839e-01
-3.05617124e-01 -2.32871369e-01 1.75903678e-01 2.90353298e-01
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-5.37048638e-01 -1.08827913e+00 -6.64637566e-01 -6.97560668e-01
4.97373343e-02 -1.31367773e-01 7.63372838e-01 -1.20870462... | [8.537041664123535, 3.024709939956665] |
46e06b5d-daef-4ef9-8fa9-e1db9aecedcf | data-fusion-for-radio-frequency-slam-with | 2206.09746 | null | https://arxiv.org/abs/2206.09746v1 | https://arxiv.org/pdf/2206.09746v1.pdf | Data Fusion for Radio Frequency SLAM with Robust Sampling | Precise indoor localization remains a challenging problem for a variety of essential applications. A promising approach to address this problem is to exchange radio signals between mobile agents and static physical anchors (PAs) that bounce off flat surfaces in the indoor environment. Radio frequency simultaneous local... | ['Florian Meyer', 'Mingchao Liang', 'Wenyu Zhang', 'Bryan Teague', 'Erik Leitinger'] | 2022-06-20 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 1.08667873e-01 -1.14922293e-01 1.07503958e-01 -2.31638998e-01
-1.18269312e+00 -6.54941022e-01 6.74942315e-01 1.47807643e-01
-4.66853797e-01 1.40180254e+00 -4.16229397e-01 -1.41347408e-01
-3.16938519e-01 -8.57841611e-01 -9.09416378e-01 -8.11483324e-01
-5.50651550e-01 5.70096374e-01 3.46132129e-01 -1.00087270... | [6.233766555786133, 0.9882333874702454] |
d84829b7-73fb-404e-b5bb-8f07781ddfb0 | self-concordant-analysis-of-frank-wolfe | 2002.04320 | null | https://arxiv.org/abs/2002.04320v3 | https://arxiv.org/pdf/2002.04320v3.pdf | Self-Concordant Analysis of Frank-Wolfe Algorithms | Projection-free optimization via different variants of the Frank-Wolfe (FW), a.k.a. Conditional Gradient method has become one of the cornerstones in optimization for machine learning since in many cases the linear minimization oracle is much cheaper to implement than projections and some sparsity needs to be preserved... | ['Pavel Dvurechensky', 'Petr Ostroukhov', 'Mathias Staudigl', 'Shimrit Shtern', 'Kamil Safin'] | 2020-02-11 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 2.22651035e-01 2.40283936e-01 -8.36828202e-02 -3.82070810e-01
-8.93557608e-01 -5.70792139e-01 3.12948406e-01 -8.03722516e-02
-6.68238342e-01 1.07929277e+00 8.74279365e-02 -4.90443558e-01
-2.30543002e-01 -7.59725034e-01 -8.60811412e-01 -1.22586846e+00
6.05486296e-02 5.15501380e-01 3.26632150e-02 -2.83416003... | [6.491551876068115, 4.612423896789551] |
29f9fc87-7f14-45fd-aa96-5fba6a381cc6 | target-aware-spatio-temporal-reasoning-via | 2305.12397 | null | https://arxiv.org/abs/2305.12397v1 | https://arxiv.org/pdf/2305.12397v1.pdf | Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamics Audio-Visual Scenarios | Audio-visual question answering (AVQA) is a challenging task that requires multistep spatio-temporal reasoning over multimodal contexts. To achieve scene understanding ability similar to humans, the AVQA task presents specific challenges, including effectively fusing audio and visual information and capturing question-... | ['Jianqin Yin', 'Yuanyuan Jiang'] | 2023-05-21 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [-3.28514688e-02 -2.91682512e-01 3.66509240e-03 -3.23432267e-01
-1.49398553e+00 -4.76413071e-01 4.38602507e-01 3.91231596e-01
-1.82675526e-01 1.17870346e-01 4.89942789e-01 4.55903299e-02
-4.12929624e-01 -4.87063974e-01 -5.99064648e-01 -5.24839997e-01
-1.17485598e-01 1.56895131e-01 6.83470845e-01 -1.54900715... | [10.46689510345459, 1.0706003904342651] |
28686502-e962-4b9b-b4dc-f060c23d6551 | disentangling-structure-and-style-political | 2304.02247 | null | https://arxiv.org/abs/2304.02247v1 | https://arxiv.org/pdf/2304.02247v1.pdf | Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy | We address an important gap in detection of political bias in news articles. Previous works that perform supervised document classification can be biased towards the writing style of each news outlet, leading to overfitting and limited generalizability. Our approach overcomes this limitation by considering both the sen... | ['James Thorne', 'Jiyoung Han', 'Jaemin Jung', 'Yejin Cho', 'Jiwoo Hong'] | 2023-04-05 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [-1.67135838e-02 1.62387520e-01 -8.80951822e-01 -5.12148499e-01
-7.73452222e-01 -8.87752533e-01 1.17625093e+00 3.94238800e-01
-4.48575467e-01 6.98608994e-01 1.32330251e+00 -6.49191022e-01
4.29618284e-02 -6.78297460e-01 -6.34324849e-01 -3.10895681e-01
6.09384775e-01 3.52535307e-01 1.10785298e-01 -5.74054718... | [8.846879005432129, 10.099246978759766] |
18574af3-7965-4dcc-ace2-042a7ad2a4e3 | warping-of-radar-data-into-camera-image-for | 2012.12809 | null | https://arxiv.org/abs/2012.12809v2 | https://arxiv.org/pdf/2012.12809v2.pdf | Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications | We present an approach to automatically generate semantic labels for real recordings of automotive range-Doppler (RD) radar spectra. Such labels are required when training a neural network for object recognition from radar data. The automatic labeling approach rests on the simultaneous recording of camera and lidar dat... | ['Reinhold Haeb-Umbach', 'Tobias Breddermann', 'Ridha Farhoud', 'Ernst Warsitz', 'Tai Fei', 'Christopher Grimm'] | 2020-12-23 | null | null | null | null | ['direction-of-arrival-estimation', 'scene-flow-estimation'] | ['audio', 'computer-vision'] | [ 8.91952276e-01 -1.95180804e-01 9.68881100e-02 -5.46677887e-01
-6.39072299e-01 -6.18836999e-01 7.04245389e-01 -2.10598618e-01
-6.78076327e-01 4.99192536e-01 -3.26706022e-01 -2.39968121e-01
-2.06819743e-01 -8.73646617e-01 -4.96396303e-01 -7.60655582e-01
-4.35660556e-02 4.12290603e-01 1.68502867e-01 8.95302445... | [8.036446571350098, -1.3646435737609863] |
425e82d3-ec5e-4283-bfd7-a8d2a8f03a82 | learning-3d-representations-from-2d-pre | 2212.06785 | null | https://arxiv.org/abs/2212.06785v1 | https://arxiv.org/pdf/2212.06785v1.pdf | Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders | Pre-training by numerous image data has become de-facto for robust 2D representations. In contrast, due to the expensive data acquisition and annotation, a paucity of large-scale 3D datasets severely hinders the learning for high-quality 3D features. In this paper, we propose an alternative to obtain superior 3D repres... | ['Hongsheng Li', 'Peng Gao', 'Yu Qiao', 'Liuhui Wang', 'Renrui Zhang'] | 2022-12-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Learning_3D_Representations_From_2D_Pre-Trained_Models_via_Image-to-Point_Masked_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Learning_3D_Representations_From_2D_Pre-Trained_Models_via_Image-to-Point_Masked_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-point-cloud-classification', '3d-point-cloud-linear-classification', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.94692412e-02 3.54180008e-01 -4.43707943e-01 -2.95850098e-01
-1.11221099e+00 -5.23945987e-01 6.10071480e-01 -3.03569436e-01
-2.56745070e-02 1.70386672e-01 4.06619497e-02 -2.02015102e-01
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1.04909360e-01 4.07268286e-01 3.22804570e-01 -1.04631111... | [8.235313415527344, -3.3830065727233887] |
3570a465-eeae-4eaa-b9c1-11d92b4fd9de | fighting-malicious-media-data-a-survey-on | 2212.05667 | null | https://arxiv.org/abs/2212.05667v1 | https://arxiv.org/pdf/2212.05667v1.pdf | Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection | Online media data, in the forms of images and videos, are becoming mainstream communication channels. However, recent advances in deep learning, particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost, which not only poses a serious threat to the trustwo... | ['Yu-Gang Jiang', 'Larry S. Davis', 'Zuxuan Wu', 'Jingjing Chen', 'Chao Zhang', 'Zhenxin Li', 'Junke Wang'] | 2022-12-12 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 2.32217655e-01 -2.66559631e-01 -1.10395383e-02 8.62314180e-02
-3.56103450e-01 -6.22581244e-01 6.52417064e-01 -1.12891532e-01
-9.89568606e-02 4.86716211e-01 -1.46239743e-01 -3.26667666e-01
6.14900887e-01 -8.57710481e-01 -7.54778445e-01 -9.80992973e-01
1.10305727e-01 -4.58015501e-01 -3.91917452e-02 -3.15239951... | [12.653860092163086, 1.1642169952392578] |
0a647ee2-0e7a-4eda-b478-1849448055f9 | bag-of-tricks-for-effective-language-model | 2302.09268 | null | https://arxiv.org/abs/2302.09268v1 | https://arxiv.org/pdf/2302.09268v1.pdf | Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE | This technical report briefly describes our JDExplore d-team's submission Vega v1 on the General Language Understanding Evaluation (GLUE) leaderboard, where GLUE is a collection of nine natural language understanding tasks, including question answering, linguistic acceptability, sentiment analysis, text similarity, par... | ['DaCheng Tao', 'Yibing Zhan', 'Li Shen', 'Bo Du', 'Juhua Liu', 'Keqin Peng', 'Liang Ding', 'Qihuang Zhong'] | 2023-02-18 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 1.77643761e-01 1.71135310e-02 -2.42581397e-01 -5.13637483e-01
-1.30808377e+00 -7.11513281e-01 6.14893198e-01 -7.59978546e-03
-5.54141223e-01 5.72458148e-01 5.99495649e-01 -3.72053295e-01
1.63731232e-01 -5.39242148e-01 -8.56279612e-01 -4.55803305e-01
3.64442557e-01 5.47605336e-01 -2.49651730e-01 -5.89682400... | [10.966463088989258, 8.247320175170898] |
57bba24c-28d7-497c-9d8e-7938559d865b | finread-a-transfer-learning-based-tool-to | null | null | https://aclanthology.org/2021.icon-main.81 | https://aclanthology.org/2021.icon-main.81.pdf | FinRead: A Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms | Simplified definitions of complex terms help learners to understand any content better. Comprehending readability is critical for the simplification of these contents. In most cases, the standard formula based readability measures do not hold good for measuring the complexity of definitions of financial terms. Furtherm... | ['Sunny Kumar Singh', 'Sudip Naskar', 'Shovon Sengupta', 'Sohom Ghosh'] | null | null | null | null | icon-2021-12 | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-2.49251142e-01 2.35889912e-01 -1.03033073e-01 -1.49142370e-01
-3.75108212e-01 -8.79720867e-01 7.88522542e-01 8.46773088e-01
-7.37016678e-01 5.90539396e-01 4.36439425e-01 -6.31208122e-01
-3.72149378e-01 -9.26425636e-01 -4.73354459e-01 -5.71317673e-02
2.64416426e-01 3.39213103e-01 1.88329592e-01 -4.98375118... | [10.995467185974121, 10.08178424835205] |
35d9cb5a-4859-40c0-b9cb-21b8275f02b3 | gapx-generalized-autoregressive-paraphrase | 2210.01979 | null | https://arxiv.org/abs/2210.01979v1 | https://arxiv.org/pdf/2210.01979v1.pdf | GAPX: Generalized Autoregressive Paraphrase-Identification X | Paraphrase Identification is a fundamental task in Natural Language Processing. While much progress has been made in the field, the performance of many state-of-the-art models often suffer from distribution shift during inference time. We verify that a major source of this performance drop comes from biases introduced ... | ['Ser-Nam Lim', 'Hayden Housen', 'Renyu Li', 'Yifei Zhou'] | 2022-10-05 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 2.31646359e-01 4.11106944e-02 -5.31567633e-01 -5.29960573e-01
-7.35394597e-01 -7.63273180e-01 8.22452486e-01 3.85204345e-01
-7.26817548e-01 7.24376738e-01 2.28276089e-01 -6.07181191e-01
7.60710537e-02 -6.48803234e-01 -5.38524985e-01 -5.09206593e-01
3.41431737e-01 7.40315139e-01 2.50788510e-01 -1.60815254... | [11.065670013427734, 9.06217098236084] |
2df2616a-659d-4922-a4fc-16f2b18949b1 | adaptdhm-adaptive-distribution-hierarchical | 2211.12105 | null | https://arxiv.org/abs/2211.12105v1 | https://arxiv.org/pdf/2211.12105v1.pdf | AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction | Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously. Existing promising and widely-used multi-domain models discover domain relationships by ... | ['Haoyuan Hu', 'Lixia Wu', 'Minfang Lu', 'Yuanlin Liu', 'Huiwen Zheng', 'Jinyun Li'] | 2022-11-22 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-2.73991436e-01 -4.36864525e-01 -8.82917821e-01 -4.33967084e-01
-2.88700193e-01 -5.60536027e-01 3.21786582e-01 3.14896666e-02
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-5.92216969e-01 -1.04037857e+00 -4.54567492e-01 -6.51852727e-01
2.16213182e-01 9.56007540e-01 5.37328422e-01 1.46963531... | [9.968993186950684, 5.325347900390625] |
01fec7bf-ceff-45f4-a713-fcb0134956fd | safe-ds-a-domain-specific-language-to-make | 2302.14548 | null | https://arxiv.org/abs/2302.14548v2 | https://arxiv.org/pdf/2302.14548v2.pdf | Safe-DS: A Domain Specific Language to Make Data Science Safe | Due to the long runtime of Data Science (DS) pipelines, even small programming mistakes can be very costly, if they are not detected statically. However, even basic static type checking of DS pipelines is difficult because most are written in Python. Static typing is available in Python only via external linters. These... | ['Günter Kniesel-Wünsche', 'Lars Reimann'] | 2023-02-28 | null | null | null | null | ['type'] | ['speech'] | [-3.23775440e-01 9.63275433e-02 1.90135073e-02 -3.43599677e-01
-2.83678293e-01 -1.20102966e+00 4.01143849e-01 5.69999337e-01
-2.08276957e-01 2.91707307e-01 -2.76553929e-01 -7.30598390e-01
1.20080732e-01 -1.08891845e+00 -6.88265145e-01 -1.75307151e-02
-7.75381848e-02 1.18049167e-01 8.98162067e-01 -1.21237114... | [7.812972068786621, 7.727957725524902] |
b11b7f80-b952-4e27-9a07-91ea76705414 | rigidity-preserving-image-transformations-and | 2201.13065 | null | https://arxiv.org/abs/2201.13065v2 | https://arxiv.org/pdf/2201.13065v2.pdf | Rigidity Preserving Image Transformations and Equivariance in Perspective | We characterize the class of image plane transformations which realize rigid camera motions and call these transformations `rigidity preserving'. In particular, 2D translations of pinhole images are not rigidity preserving. Hence, when using CNNs for 3D inference tasks, it can be beneficial to modify the inductive bias... | ['Fredrik Kahl', 'Axel Flinth', 'Georg Bökman', 'Lucas Brynte'] | 2022-01-31 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [-1.15593500e-01 3.44774097e-01 -2.30972230e-01 -4.01913583e-01
-4.08535480e-01 -1.25358629e+00 7.79906332e-01 -6.24400020e-01
-5.30874610e-01 2.15335667e-01 5.75199246e-01 -2.86513746e-01
5.52484505e-02 -7.08005488e-01 -1.35884750e+00 -5.92343867e-01
1.42573223e-01 6.07134640e-01 7.69080222e-02 -1.04045101... | [8.888689041137695, 2.3692519664764404] |
6d118bc7-13c4-4b7b-bf85-f5bcf3aac30c | i4u-system-description-for-nist-sre-20-cts | 2211.01091 | null | https://arxiv.org/abs/2211.01091v1 | https://arxiv.org/pdf/2211.01091v1.pdf | I4U System Description for NIST SRE'20 CTS Challenge | This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge. The I4U's submission was resulted from active collaboration among researchers across eight research teams - I$^2$R (Singapore), UEF (Finland), VALPT (Italy, Spain), NEC ... | ['Luis Buera', 'Longbiao Wang', 'Alfonso Ortega Giménez', 'Haizhou Li', 'Ruijie Tao', 'Hitoshi Yamamoto', 'Koji Okabe', 'Boning Zhang', 'Sandro Cumani', 'Md Sahidullah', 'Xuechen Liu', 'Héctor Deldago', 'Meng Liu', 'Ignacio Viñals Bailo', 'Rohan Kumar Das', 'Pierre-Michel Bousquet', 'Mickael Rouvier', 'Qiongqiong Wang'... | 2022-11-02 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [-1.61674097e-02 -8.77360441e-03 2.83057570e-01 -6.58122420e-01
-1.29933226e+00 -5.61557949e-01 6.25677347e-01 -2.92906046e-01
-2.38539413e-01 8.08260441e-01 6.21797442e-01 -6.76032722e-01
-1.10446084e-02 -9.67819914e-02 -1.33764714e-01 8.19787979e-02
-1.83279455e-01 3.43293101e-01 5.09634875e-02 -2.41221502... | [14.316417694091797, 6.231239318847656] |
72b800c7-e533-44d7-a380-91ac1d03849e | sense-imagine-act-multimodal-perception | 2305.04750 | null | https://arxiv.org/abs/2305.04750v1 | https://arxiv.org/pdf/2305.04750v1.pdf | Sense, Imagine, Act: Multimodal Perception Improves Model-Based Reinforcement Learning for Head-to-Head Autonomous Racing | Model-based reinforcement learning (MBRL) techniques have recently yielded promising results for real-world autonomous racing using high-dimensional observations. MBRL agents, such as Dreamer, solve long-horizon tasks by building a world model and planning actions by latent imagination. This approach involves explicitl... | ['Ram Vasudevan', 'Yulun Zhuang', 'Hanxi Wan', 'Chetan Reddy', 'Elena Shrestha'] | 2023-05-08 | null | null | null | null | ['continuous-control', 'model-based-reinforcement-learning'] | ['playing-games', 'reasoning'] | [-1.39619142e-01 3.79688740e-01 -2.54989475e-01 -3.18101525e-01
-9.09693360e-01 -4.03250098e-01 6.19714200e-01 2.71944702e-01
-1.04573274e+00 1.10004389e+00 -8.57087038e-03 8.68057013e-02
-2.85025418e-01 -6.34625554e-01 -9.85526741e-01 -6.51277721e-01
-4.33454335e-01 1.00184369e+00 3.24195176e-01 -7.85186708... | [4.738095760345459, 1.090179204940796] |
2789e7b0-763f-45ed-9a83-0519fd09c452 | filling-in-the-details-perceiving-from-low | 1604.04125 | null | http://arxiv.org/abs/1604.04125v1 | http://arxiv.org/pdf/1604.04125v1.pdf | Filling in the details: Perceiving from low fidelity images | Humans perceive their surroundings in great detail even though most of our
visual field is reduced to low-fidelity color-deprived (e.g. dichromatic) input
by the retina. In contrast, most deep learning architectures are
computationally wasteful in that they consider every part of the input when
performing an image proc... | ['Michael L. Wick', 'Marc Pomplun', 'Farahnaz Ahmed Wick'] | 2016-04-14 | null | null | null | null | ['foveation'] | ['computer-vision'] | [ 9.74968597e-02 1.30160555e-01 5.45539618e-01 -2.41909459e-01
-3.40576395e-02 -7.07455754e-01 4.67076391e-01 -3.86174142e-01
-5.25322020e-01 5.73864281e-01 1.35462865e-01 -2.94047058e-01
-1.66983122e-03 -1.04679537e+00 -8.22254539e-01 -6.44226909e-01
4.73728836e-01 -8.26580264e-03 1.34413913e-01 -2.68339366... | [10.087371826171875, 2.2902441024780273] |
5969f3bd-28b7-47d5-959f-3b2d94dff5d3 | angular-luminance-for-material-segmentation | 2009.10825 | null | https://arxiv.org/abs/2009.10825v1 | https://arxiv.org/pdf/2009.10825v1.pdf | Angular Luminance for Material Segmentation | Moving cameras provide multiple intensity measurements per pixel, yet often semantic segmentation, material recognition, and object recognition do not utilize this information. With basic alignment over several frames of a moving camera sequence, a distribution of intensities over multiple angles is obtained. It is wel... | ['Kristin Dana', 'Jia Xue', 'Matthew Purri'] | 2020-09-22 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 9.15877521e-01 -6.54851317e-01 -2.02240497e-01 -4.97061104e-01
-8.71389925e-01 -6.92195475e-01 4.79298949e-01 -1.42981127e-01
-4.56717581e-01 4.59833443e-01 -1.65847704e-01 3.08026858e-02
1.35518655e-01 -1.06110299e+00 -9.71355855e-01 -8.91750455e-01
2.55059540e-01 1.65409654e-01 4.15374577e-01 7.73168132... | [9.663843154907227, -2.7515478134155273] |
627dfbab-76e5-4dc8-bd71-d852ff9032d2 | symmetric-optimistic-natural-policy-gradient | 2210.12812 | null | https://arxiv.org/abs/2210.12812v2 | https://arxiv.org/pdf/2210.12812v2.pdf | Symmetric (Optimistic) Natural Policy Gradient for Multi-agent Learning with Parameter Convergence | Multi-agent interactions are increasingly important in the context of reinforcement learning, and the theoretical foundations of policy gradient methods have attracted surging research interest. We investigate the global convergence of natural policy gradient (NPG) algorithms in multi-agent learning. We first show that... | ['Asuman Ozdaglar', 'Kaiqing Zhang', 'Sarath Pattathil'] | 2022-10-23 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-2.21561775e-01 1.43182620e-01 -5.07198572e-01 3.05954844e-01
-7.78234065e-01 -6.34778202e-01 1.87189817e-01 3.38234067e-01
-9.29574728e-01 1.41672361e+00 8.65549669e-02 -4.25108701e-01
-7.43854821e-01 -5.52036583e-01 -8.23217392e-01 -1.02228653e+00
-5.43719590e-01 5.03521025e-01 -2.70636708e-01 -4.60757732... | [4.220066070556641, 2.620173692703247] |
067491f6-9331-40d7-9afc-8de3290dbfff | self-supervised-learning-for-neural | 2304.01023 | null | https://arxiv.org/abs/2304.01023v1 | https://arxiv.org/pdf/2304.01023v1.pdf | Self-Supervised learning for Neural Architecture Search (NAS) | The objective of this internship is to propose an innovative method that uses unlabelled data, i.e. data that will allow the AI to automatically learn to predict the correct outcome. To reach this stage, the steps to be followed can be defined as follows: (1) consult the state of the art and position ourself against it... | ['Samuel Ducros'] | 2023-04-03 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.63369909e-01 3.66775006e-01 -2.71719158e-01 -3.79765511e-01
-3.47555339e-01 -6.60598159e-01 5.25770664e-01 1.49565861e-01
-1.26803041e-01 1.04761100e+00 1.14974245e-01 -5.23993790e-01
-5.54667175e-01 -8.04881752e-01 -4.46372092e-01 -4.61343735e-01
-9.60653648e-02 8.81811202e-01 4.81095582e-01 -3.70448828... | [8.545801162719727, 4.664829730987549] |
f42ce96b-7dcf-4757-a32f-f741d626b025 | supervising-neural-attention-models-for-video | 1707.06029 | null | http://arxiv.org/abs/1707.06029v1 | http://arxiv.org/pdf/1707.06029v1.pdf | Supervising Neural Attention Models for Video Captioning by Human Gaze Data | The attention mechanisms in deep neural networks are inspired by human's
attention that sequentially focuses on the most relevant parts of the
information over time to generate prediction output. The attention parameters
in those models are implicitly trained in an end-to-end manner, yet there have
been few trials to e... | ['Sang-Hun Lee', 'Yeonhwa Kim', 'Jongwook Choi', 'Youngjae Yu', 'Kyung Yoo', 'Gunhee Kim'] | 2017-07-19 | supervising-neural-attention-models-for-video-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Yu_Supervising_Neural_Attention_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Yu_Supervising_Neural_Attention_CVPR_2017_paper.pdf | cvpr-2017-7 | ['eye-tracking'] | ['computer-vision'] | [ 1.88618466e-01 8.04832429e-02 -1.38779692e-02 -5.50252676e-01
-4.96847212e-01 -3.50208938e-01 5.66242337e-01 -3.15307647e-01
-3.99717003e-01 5.26196241e-01 3.00013542e-01 -1.41289413e-01
1.67064294e-01 -5.09184897e-02 -7.66377568e-01 -4.69338894e-01
2.38206193e-01 5.09608053e-02 7.38952532e-02 -1.30892709... | [14.015166282653809, 0.053274061530828476] |
5f25624c-9546-4934-8aed-7525bdd9d814 | robust-pose-estimation-in-crowded-scenes-with | null | null | http://proceedings.neurips.cc/paper/2021/hash/31857b449c407203749ae32dd0e7d64a-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/31857b449c407203749ae32dd0e7d64a-Paper.pdf | Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference | Multi-person pose estimation in crowded scenes is challenging because overlapping and occlusions make it difficult to detect person bounding boxes and infer pose cues from individual keypoints. To address those issues, this paper proposes a direct pose-level inference strategy that is free of bounding box detection and... | ['Gang Hua', 'Shiliang Zhang', 'Dongkai Wang'] | 2021-12-01 | null | https://openreview.net/forum?id=AvHeCmK2fsE | https://openreview.net/pdf?id=AvHeCmK2fsE | neurips-2021-12 | ['multi-person-pose-estimation'] | ['computer-vision'] | [-4.74349000e-02 5.38085885e-02 6.56657293e-02 -2.30465427e-01
-7.23281622e-01 -4.12364691e-01 4.95365322e-01 7.18848407e-02
-3.59213352e-01 6.05865896e-01 4.05003637e-01 5.56292653e-01
1.49695531e-01 -5.45287371e-01 -6.17901623e-01 -5.03022969e-01
-1.33044243e-01 9.65402067e-01 4.97849762e-01 -1.89317837... | [7.113887310028076, -0.7899867296218872] |
472cb65b-b0ee-41a6-8f29-308e697f0b2f | learning-a-dilated-residual-network-for-sar | 1709.02898 | null | http://arxiv.org/abs/1709.02898v3 | http://arxiv.org/pdf/1709.02898v3.pdf | Learning a Dilated Residual Network for SAR Image Despeckling | In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated con... | ['Qiang Zhang', 'Xiaoshuang Ma', 'Jie Li', 'Zhen Yang', 'Qiangqiang Yuan'] | 2017-09-09 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 3.51578832e-01 -2.05342412e-01 6.81633711e-01 -5.46418071e-01
-6.06711924e-01 -3.09306741e-01 4.30173308e-01 -6.48944199e-01
-5.18495142e-01 5.31678617e-01 3.42688829e-01 -2.40832582e-01
-3.86133879e-01 -5.99777579e-01 -4.98587072e-01 -9.05676842e-01
6.62699193e-02 -3.45351815e-01 1.22938141e-01 -1.77579463... | [10.492218971252441, -2.241219997406006] |
a75ff156-ffaf-41f7-ab78-9ec94d3ac3d0 | transcending-traditional-boundaries | 2306.14374 | null | https://arxiv.org/abs/2306.14374v1 | https://arxiv.org/pdf/2306.14374v1.pdf | Transcending Traditional Boundaries: Leveraging Inter-Annotator Agreement (IAA) for Enhancing Data Management Operations (DMOps) | This paper presents a novel approach of leveraging Inter-Annotator Agreement (IAA), traditionally used for assessing labeling consistency, to optimize Data Management Operations (DMOps). We advocate for the use of IAA in predicting the labeling quality of individual annotators, leading to cost and time efficiency in da... | ['Harksoo Kim', 'Chanjun Park', 'NamHyeok Kim', 'Damrin Kim'] | 2023-06-26 | null | null | null | null | ['management'] | ['miscellaneous'] | [-2.84505755e-01 3.33542049e-01 -5.85666120e-01 -3.80061835e-01
-5.54264605e-01 -8.29662561e-01 8.15297514e-02 8.87175918e-01
-4.23486322e-01 3.92657220e-01 4.72315609e-01 -4.05437201e-01
-6.70398295e-01 -4.39340562e-01 -1.01592667e-01 -9.19692293e-02
3.98874044e-01 5.01480937e-01 -5.44689357e-01 2.67498583... | [9.525323867797852, 8.56096363067627] |
a860541c-8970-4ee9-b47b-5f7fd7e39be3 | learning-to-discover-and-detect-objects | 2210.10774 | null | https://arxiv.org/abs/2210.10774v2 | https://arxiv.org/pdf/2210.10774v2.pdf | Learning to Discover and Detect Objects | We tackle the problem of novel class discovery and localization (NCDL). In this setting, we assume a source dataset with supervision for only some object classes. Instances of other classes need to be discovered, classified, and localized automatically based on visual similarity without any human supervision. To tackle... | ['Aljoša Ošep', 'Laura Leal-Taixé', 'Deva Ramanan', 'Ismail Elezi', 'Vladimir Fomenko'] | 2022-10-19 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 4.20646280e-01 1.46469235e-01 -3.09841990e-01 -4.88663435e-01
-7.52927780e-01 -9.71753836e-01 4.94360328e-01 3.66722256e-01
-5.62018633e-01 4.23664778e-01 -2.60883868e-01 4.99112578e-03
8.58921632e-02 -7.32833743e-01 -1.04779291e+00 -7.22531259e-01
7.55662024e-02 8.62670302e-01 5.35396755e-01 4.22926575... | [9.48861312866211, 1.2621123790740967] |
ec96872f-3958-4aaf-b5c3-4161ddbf4933 | few-shot-learning-based-on-multi-stage | 2109.11806 | null | https://arxiv.org/abs/2109.11806v2 | https://arxiv.org/pdf/2109.11806v2.pdf | A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data | Diabetic retinopathy (DR) is one of the major blindness-causing diseases currently known. Automatic grading of DR using deep learning methods not only speeds up the diagnosis of the disease but also reduces the rate of misdiagnosis. However,problems such as insufficient samples and imbalanced class distribution in smal... | ['Junxing Zhang', 'Bin Wang', 'Lei Shi'] | 2021-09-24 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-3.69952142e-01 -1.18413374e-01 -1.84527546e-01 -8.15947473e-01
-8.89721453e-01 -6.70445338e-02 -7.62491524e-02 -3.07721168e-01
-3.40667278e-01 8.82821679e-01 3.24280053e-01 -7.70857483e-02
-2.51963139e-01 -1.00286520e+00 -1.74643993e-01 -8.50719929e-01
4.75218236e-01 4.44618672e-01 1.44427955e-01 4.76745367... | [15.808928489685059, -3.974264621734619] |
981a9e4d-f43e-40fc-9081-a2a97872af5b | tganet-text-guided-attention-for-improved | 2205.04280 | null | https://arxiv.org/abs/2205.04280v1 | https://arxiv.org/pdf/2205.04280v1.pdf | TGANet: Text-guided attention for improved polyp segmentation | Colonoscopy is a gold standard procedure but is highly operator-dependent. Automated polyp segmentation, a precancerous precursor, can minimize missed rates and timely treatment of colon cancer at an early stage. Even though there are deep learning methods developed for this task, variability in polyp size can impact m... | ['Sharib Ali', 'Ulas Bagci', 'Debesh Jha', 'Nikhil Kumar Tomar'] | 2022-05-09 | null | null | null | null | ['polyp-segmentation'] | ['computer-vision'] | [ 2.68845409e-01 3.87786895e-01 -4.99359131e-01 -3.95211071e-01
-6.74717844e-01 -4.89093095e-01 5.21650165e-02 7.41538644e-01
-7.06581950e-01 2.28939250e-01 4.74687994e-01 -5.43360591e-01
-5.59699237e-02 -9.09744978e-01 -7.54473269e-01 -5.39247870e-01
-3.78388613e-01 3.51340145e-01 3.63817483e-01 -6.06146678... | [14.56131649017334, -2.89798641204834] |
0cbddec5-3159-47b3-b0fd-09b0c1f59819 | the-effect-of-noise-level-on-causal | 2108.11320 | null | https://arxiv.org/abs/2108.11320v1 | https://arxiv.org/pdf/2108.11320v1.pdf | The Effect of Noise Level on Causal Identification with Additive Noise Models | In recent years a lot of research has been conducted within the area of causal inference and causal learning. Many methods have been developed to identify the cause-effect pairs in models and have been successfully applied to observational real-world data in order to determine the direction of causal relationships. Man... | ['Benjamin Kap'] | 2021-08-24 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 3.78624737e-01 -1.61167130e-01 -4.02927667e-01 -2.10829556e-01
-1.92919254e-01 -2.96495587e-01 8.44758511e-01 1.86965808e-01
-3.22988868e-01 1.03828800e+00 2.30068833e-01 -6.90659821e-01
-8.21004629e-01 -9.35110807e-01 -7.54685462e-01 -6.69745743e-01
-3.96243066e-01 1.98148072e-01 2.43766829e-01 -4.56261598... | [7.799682140350342, 5.236228942871094] |
fd5f7b9f-40d4-411e-91cf-a31e45b88e4c | merlot-reserve-neural-script-knowledge | 2201.02639 | null | https://arxiv.org/abs/2201.02639v4 | https://arxiv.org/pdf/2201.02639v4.pdf | MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound | As humans, we navigate a multimodal world, building a holistic understanding from all our senses. We introduce MERLOT Reserve, a model that represents videos jointly over time -- through a new training objective that learns from audio, subtitles, and video frames. Given a video, we replace snippets of text and audio wi... | ['Yejin Choi', 'Ali Farhadi', 'Jack Hessel', 'Aditya Kusupati', 'Mohammadreza Salehi', 'Yanpeng Zhao', 'Youngjae Yu', 'Ximing Lu', 'Jiasen Lu', 'Rowan Zellers'] | 2022-01-07 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zellers_MERLOT_Reserve_Neural_Script_Knowledge_Through_Vision_and_Language_and_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zellers_MERLOT_Reserve_Neural_Script_Knowledge_Through_Vision_and_Language_and_CVPR_2022_paper.pdf | cvpr-2022-1 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 4.35010135e-01 3.26681763e-01 -3.04171652e-01 -2.32226476e-01
-1.04489446e+00 -5.61726689e-01 6.84238613e-01 -1.08473115e-01
-5.39602995e-01 4.80566770e-01 7.86911786e-01 -1.35083914e-01
8.15567374e-02 -3.48260939e-01 -1.09580576e+00 -3.37262034e-01
-1.72059029e-01 1.91321343e-01 -8.19445774e-02 -3.30680460... | [10.455110549926758, 1.145527958869934] |
71e38a22-b28f-429c-9ac1-f180cb5213ce | efficient-entity-candidate-generation-for-low | 2206.15163 | null | https://arxiv.org/abs/2206.15163v1 | https://arxiv.org/pdf/2206.15163v1.pdf | Efficient Entity Candidate Generation for Low-Resource Languages | Candidate generation is a crucial module in entity linking. It also plays a key role in multiple NLP tasks that have been proven to beneficially leverage knowledge bases. Nevertheless, it has often been overlooked in the monolingual English entity linking literature, as naive approaches obtain very good performance. Un... | ['Robert West', 'Akhil Arora', 'Alberto García-Durán'] | 2022-06-30 | null | https://aclanthology.org/2022.lrec-1.690 | https://aclanthology.org/2022.lrec-1.690.pdf | lrec-2022-6 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-1.87032565e-01 1.59960508e-01 -6.72516704e-01 -1.11696310e-01
-1.18291628e+00 -7.24282026e-01 6.72005892e-01 3.80162776e-01
-8.24792862e-01 1.14238334e+00 2.46153012e-01 -3.81407857e-01
-3.38327974e-01 -9.31020737e-01 -9.70446348e-01 -1.48075372e-01
3.42344157e-02 9.03433859e-01 4.76899832e-01 -5.95684707... | [9.503652572631836, 8.850025177001953] |
5b5f4288-da87-4830-a314-e2f4974e2e1e | learning-audio-visual-dynamics-using-scene | 2210.16472 | null | https://arxiv.org/abs/2210.16472v1 | https://arxiv.org/pdf/2210.16472v1.pdf | Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation | There exists an unequivocal distinction between the sound produced by a static source and that produced by a moving one, especially when the source moves towards or away from the microphone. In this paper, we propose to use this connection between audio and visual dynamics for solving two challenging tasks simultaneous... | ['Anoop Cherian', 'Narendra Ahuja', 'Moitreya Chatterjee'] | 2022-10-29 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 2.06348971e-01 -3.25275719e-01 2.31428340e-01 2.46855319e-01
-1.01315689e+00 -8.55113506e-01 5.75979352e-01 -4.18518372e-02
6.71640635e-02 9.90126934e-03 5.10802805e-01 3.75491492e-02
-3.96035761e-02 -1.53909579e-01 -6.28862202e-01 -9.47899997e-01
-1.92596003e-01 1.85507610e-01 4.40179020e-01 3.94862652... | [14.85724925994873, 4.979794979095459] |
d4c57173-f9c7-46fd-8ca3-19984984f782 | recent-advances-in-transient-imaging-a | 1611.00939 | null | http://arxiv.org/abs/1611.00939v1 | http://arxiv.org/pdf/1611.00939v1.pdf | Recent Advances in Transient Imaging: A Computer Graphics and Vision Perspective | Transient imaging has recently made a huge impact in the computer graphics
and computer vision fields. By capturing, reconstructing, or simulating light
transport at extreme temporal resolutions, researchers have proposed novel
techniques to show movies of light in motion, see around corners, detect
objects in highly-s... | ['Adrian Jarabo', 'Julio Marco', 'Diego Gutierrez', 'Belen Masia'] | 2016-11-03 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 5.89695871e-01 -9.75166917e-01 6.43396437e-01 5.34851141e-02
-3.47054958e-01 -7.24834383e-01 5.35360992e-01 -9.85914320e-02
-4.55727994e-01 6.37265563e-01 -1.46709725e-01 -2.16276065e-01
1.00951791e-01 -5.09596050e-01 -1.76143900e-01 -1.08539450e+00
2.02353567e-01 1.65589288e-01 5.89170814e-01 3.32378566... | [9.747343063354492, -2.708648920059204] |
7db5758e-746c-4705-9dc1-8457eff3188f | multi-level-cnn-for-lung-nodule | 1901.00276 | null | http://arxiv.org/abs/1901.00276v1 | http://arxiv.org/pdf/1901.00276v1.pdf | Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization | This paper investigates lung nodule classification by using deep neural
networks (DNNs). Hyperparameter optimization in DNNs is a computationally
expensive problem, where evaluating a hyperparameter configuration may take
several hours or even days. Bayesian optimization has been recently introduced
for the automatical... | ['Steven Su', 'Juan Lyu', 'Sai Ho Ling', 'Miao Zhang', 'Huiqi Li'] | 2019-01-02 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [-3.16296518e-01 -9.14613754e-02 -1.45947903e-01 -1.25632361e-01
-7.36699760e-01 -3.29138488e-01 1.49587646e-01 -3.35222296e-03
-6.95140779e-01 7.22165644e-01 -8.06081221e-02 -2.62290210e-01
-8.75341654e-01 -8.38660479e-01 -5.50744116e-01 -1.46025085e+00
2.57902294e-01 7.97251165e-01 5.32041132e-01 1.83549717... | [6.856664180755615, 3.913001775741577] |
efa9ba85-517c-4f23-9950-ded744142a50 | the-computation-of-cyclic-peptide-with-prolin | 1511.01388 | null | http://arxiv.org/abs/1511.01388v1 | http://arxiv.org/pdf/1511.01388v1.pdf | The Computation of Cyclic Peptide with Prolin-Prolin Bond as Fusion Inhibitor of DENV Envelope Protein through Molecular Docking and Molecular Dynamics Simulation | A disease that caused by dengue virus (DENV) has become the major health
problem of the world. Nowadays, no effective treatment is available to overcome
the disease due to the level of dengue virus pathogeneses. A novel treatment
method such as antiviral drug is highly necessary for coping with the dengue
disease. Enve... | [] | 2015-10-27 | null | null | null | null | ['molecular-docking'] | ['medical'] | [-1.73925459e-01 -4.79106754e-01 -9.88542661e-02 -8.00170153e-02
1.81594983e-01 -7.24587381e-01 2.27505237e-01 1.20932736e-01
-4.27087873e-01 1.24886906e+00 1.49915934e-01 -3.87745231e-01
1.87750310e-01 -8.35442841e-01 -4.83232528e-01 -1.03850627e+00
-1.78212002e-01 6.08834386e-01 6.53065816e-02 -3.86278808... | [4.673398971557617, 5.103201866149902] |
b3df66d4-e573-4096-964b-adc339a394cc | reference-twice-a-simple-and-unified-baseline | 2301.01156 | null | https://arxiv.org/abs/2301.01156v2 | https://arxiv.org/pdf/2301.01156v2.pdf | Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation | Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationsh... | ['Xiangtai Li', 'Yong liu', 'Chengjie Wang', 'Yabiao Wang', 'Xintian Shen', 'Chao Xu', 'Zhucun Xue', 'Jiangning Zhang', 'Yue Han'] | 2023-01-03 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 4.66044486e-01 8.34557600e-03 -4.27597642e-01 -4.37320381e-01
-9.80392039e-01 -4.39804465e-01 5.54029942e-01 8.28505158e-02
-4.14221972e-01 3.72886807e-01 4.37742025e-02 3.18694144e-01
-2.41313249e-01 -7.65779912e-01 -7.13139296e-01 -4.59437728e-01
2.54373074e-01 4.52905804e-01 1.10468447e+00 -2.94587642... | [9.606552124023438, 1.8139228820800781] |
e908ae5a-a454-4764-999e-2e1d62a0a9e0 | simalign-high-quality-word-alignments-without | 2004.08728 | null | https://arxiv.org/abs/2004.08728v4 | https://arxiv.org/pdf/2004.08728v4.pdf | SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings | Word alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT. However, most approaches require parallel training data, and quality decreas... | ['François Yvon', 'Hinrich Schütze', 'Masoud Jalili Sabet', 'Philipp Dufter'] | 2020-04-18 | null | https://aclanthology.org/2020.findings-emnlp.147 | https://aclanthology.org/2020.findings-emnlp.147.pdf | findings-of-the-association-for-computational | ['multilingual-word-embeddings'] | ['methodology'] | [ 8.88048634e-02 9.75788981e-02 -6.32598460e-01 -2.90023446e-01
-1.38770413e+00 -8.94234359e-01 6.62362456e-01 2.73687065e-01
-1.06655908e+00 7.66331494e-01 7.30849683e-01 -5.65745652e-01
5.36840439e-01 -4.26347196e-01 -7.08245039e-01 -3.22047025e-01
4.49483186e-01 8.26843262e-01 -4.85823691e-01 -4.58641648... | [11.268411636352539, 10.215616226196289] |
2a4981a2-adde-4c73-a1c3-7210e2e41771 | mmd-fuse-learning-and-combining-kernels-for | 2306.08777 | null | https://arxiv.org/abs/2306.08777v1 | https://arxiv.org/pdf/2306.08777v1.pdf | MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting | We propose novel statistics which maximise the power of a two-sample test based on the Maximum Mean Discrepancy (MMD), by adapting over the set of kernels used in defining it. For finite sets, this reduces to combining (normalised) MMD values under each of these kernels via a weighted soft maximum. Exponential concentr... | ['Arthur Gretton', 'Antonin Schrab', 'Felix Biggs'] | 2023-06-14 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 2.83923090e-01 -1.43466219e-01 -4.46376801e-02 -4.79143053e-01
-7.97063529e-01 -7.41243660e-01 7.36352682e-01 3.18545192e-01
-8.64873171e-01 9.71377134e-01 -1.23187326e-01 -3.60174030e-01
-9.13168132e-01 -9.61544991e-01 -6.91417515e-01 -9.33301747e-01
-5.54597735e-01 5.98613799e-01 5.56053638e-01 2.19590977... | [7.467051982879639, 4.17130708694458] |
3930d8c4-9e93-4596-a095-6685272c52cd | bvi-vfi-a-video-quality-database-for-video | 2210.00823 | null | https://arxiv.org/abs/2210.00823v2 | https://arxiv.org/pdf/2210.00823v2.pdf | BVI-VFI: A Video Quality Database for Video Frame Interpolation | Video frame interpolation (VFI) is a fundamental research topic in video processing, which is currently attracting increased attention across the research community. While the development of more advanced VFI algorithms has been extensively researched, there remains little understanding of how humans perceive the quali... | ['David Bull', 'Fan Zhang', 'Duolikun Danier'] | 2022-10-03 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [ 3.44590582e-02 -7.99410760e-01 -9.44539830e-02 -4.12609398e-01
-6.56754315e-01 -2.66411394e-01 1.59961447e-01 4.85060811e-02
-3.03099751e-01 8.33207130e-01 3.67988974e-01 -1.06946968e-01
-1.34928999e-02 -6.36627257e-01 -7.17953980e-01 -3.94217819e-01
-4.46551561e-01 -3.79223615e-01 3.52414072e-01 -8.92043579... | [11.6927490234375, -1.8765909671783447] |
fbd069c9-a9a3-46da-bea2-de6932fe012a | an-attempt-towards-interpretable-audio-visual | 1812.02872 | null | http://arxiv.org/abs/1812.02872v1 | http://arxiv.org/pdf/1812.02872v1.pdf | An Attempt towards Interpretable Audio-Visual Video Captioning | Automatically generating a natural language sentence to describe the content
of an input video is a very challenging problem. It is an essential multimodal
task in which auditory and visual contents are equally important. Although
audio information has been exploited to improve video captioning in previous
works, it is... | ['Justin Goodman', 'Marc Moore', 'Yapeng Tian', 'Chenliang Xu', 'Chenxiao Guan'] | 2018-12-07 | null | null | null | null | ['audio-captioning', 'audio-visual-video-captioning'] | ['audio', 'computer-vision'] | [ 5.83184123e-01 1.24164127e-01 -1.10854916e-01 -3.59396726e-01
-9.04077947e-01 -3.24330568e-01 6.10128939e-01 6.99704839e-03
-1.72773868e-01 6.36908352e-01 7.27270067e-01 -1.48049325e-01
3.39383036e-01 -3.24315488e-01 -1.05831718e+00 -4.46436703e-01
3.71261269e-01 5.90360537e-02 -7.39208516e-03 -2.17558220... | [10.658875465393066, 0.85406494140625] |
bf83ab4b-d473-4f6c-aa4d-7d25a35d29d0 | the-search-for-agreement-on-logical-fallacy | null | null | https://aclanthology.org/2022.lrec-1.471 | https://aclanthology.org/2022.lrec-1.471.pdf | The Search for Agreement on Logical Fallacy Annotation of an Infodemic | We evaluate an annotation schema for labeling logical fallacy types, originally developed for a crowd-sourcing annotation paradigm, now using an annotation paradigm of two trained linguist annotators. We apply the schema to a variety of different genres of text relating to the COVID-19 pandemic. Our linguist (as oppose... | ['Clare Voss', 'Peter Sutor', 'Douglas Summers-Stay', 'Jeffrey Micher', 'Stephanie M. Lukin', 'Taylor Hudson', 'Austin Blodgett', 'Claire Bonial'] | null | null | null | null | lrec-2022-6 | ['logical-fallacies'] | ['miscellaneous'] | [ 4.50526804e-01 4.37265486e-01 -4.33219178e-03 -6.68636620e-01
-1.01337898e+00 -1.07166231e+00 7.62092769e-01 7.56362259e-01
-4.07975703e-01 7.45934308e-01 4.68920261e-01 -4.32444006e-01
-7.23331794e-02 -2.89337426e-01 -2.16185689e-01 -3.37866902e-01
3.29341769e-01 1.21209788e+00 3.77667695e-01 -1.69328436... | [9.71573257446289, 4.965119361877441] |
e7d4f7d5-560b-4f68-9b3b-ceb8c9d08bf0 | omni-detr-omni-supervised-object-detection | 2203.16089 | null | https://arxiv.org/abs/2203.16089v1 | https://arxiv.org/pdf/2203.16089v1.pdf | Omni-DETR: Omni-Supervised Object Detection with Transformers | We consider the problem of omni-supervised object detection, which can use unlabeled, fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for object detection. This is enabled by a unified architecture, Omni-DETR, based on the recent progress on student-teacher framework and end-to-e... | ['Stefano Soatto', 'Bernt Schiele', 'Nuno Vasconcelos', 'Gurumurthy Swaminathan', 'Hao Yang', 'Zhaowei Cai', 'Pei Wang'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Omni-DETR_Omni-Supervised_Object_Detection_With_Transformers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Omni-DETR_Omni-Supervised_Object_Detection_With_Transformers_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [-7.48497620e-02 1.46565273e-01 -4.65159148e-01 -5.99556088e-01
-1.09040427e+00 -6.43096805e-01 6.78261936e-01 1.28268480e-01
-5.58781683e-01 4.50577974e-01 7.24838376e-02 -9.34791565e-02
1.44836336e-01 -4.86514330e-01 -8.07683110e-01 -4.69701290e-01
1.00991661e-02 7.04564154e-01 4.82803762e-01 2.52465487... | [9.285476684570312, 1.2763880491256714] |
beee0d21-ce80-4754-9e58-f25c69de2ad2 | deep-learning-based-spatio-temporal-facial | 2305.00552 | null | https://arxiv.org/abs/2305.00552v1 | https://arxiv.org/pdf/2305.00552v1.pdf | Deep Learning-based Spatio Temporal Facial Feature Visual Speech Recognition | In low-resource computing contexts, such as smartphones and other tiny devices, Both deep learning and machine learning are being used in a lot of identification systems. as authentication techniques. The transparent, contactless, and non-invasive nature of these face recognition technologies driven by AI has led to th... | ['Garika Akshay', 'Pangoth Santhosh Kumar'] | 2023-04-30 | null | null | null | null | ['face-recognition', 'visual-speech-recognition'] | ['computer-vision', 'speech'] | [-7.82596916e-02 -1.36827677e-01 -4.58659589e-01 -2.22075194e-01
-2.32487723e-01 -2.49348044e-01 6.98849738e-01 -4.06961292e-01
-6.16784632e-01 6.06478512e-01 -2.92206705e-01 -3.88987571e-01
1.69129968e-01 -3.73492837e-01 -1.45672202e-01 -8.57792974e-01
1.95641845e-01 -1.71538610e-02 -6.10002838e-02 -1.55183328... | [13.261495590209961, 1.1654514074325562] |
fd15b52f-9519-4c6d-b3cf-6e707a980e03 | a-new-paradigm-for-generative-adversarial | 2306.13641 | null | https://arxiv.org/abs/2306.13641v1 | https://arxiv.org/pdf/2306.13641v1.pdf | A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules | The Generative Adversarial Network (GAN) was recently introduced in the literature as a novel machine learning method for training generative models. It has many applications in statistics such as nonparametric clustering and nonparametric conditional independence tests. However, training the GAN is notoriously difficu... | ['Faming Liang', 'Qifan Song', 'Sehwan Kim'] | 2023-06-23 | null | null | null | null | ['clustering'] | ['methodology'] | [ 1.94048807e-01 6.36379719e-02 -2.76443595e-03 3.67348753e-02
-7.03862369e-01 -5.91284156e-01 6.04412794e-01 -4.05099303e-01
-1.99498355e-01 1.12716687e+00 2.18066797e-02 -2.56266057e-01
-5.88121451e-02 -9.53489065e-01 -6.80158496e-01 -1.24755669e+00
3.05408686e-01 6.99006319e-01 -1.62311137e-01 2.45549574... | [7.023630142211914, 3.8706438541412354] |
b37e296f-c911-4cc9-89ce-869fc8eaee00 | design-in-the-dark-learning-deep-generative | null | null | https://openreview.net/forum?id=WQVouCWioh | https://openreview.net/pdf?id=WQVouCWioh | Design in the Dark: Learning Deep Generative Models for De Novo Protein Design | The design of novel protein sequences is providing paths towards the development of novel therapeutics and materials.
Generative modelling approaches to design are emerging and to date have required conditioning on 3D protein structure-derived information, and unconditional models of protein sequences have so far perf... | ['David T. Jones', 'Shaun M. Kandathil', 'Lewis Moffat'] | 2021-09-29 | null | null | null | null | ['protein-design'] | ['medical'] | [ 7.50021160e-01 1.81321427e-01 -5.43690361e-02 -2.50173002e-01
-7.60356009e-01 -7.84660697e-01 5.72966456e-01 2.40060836e-01
-3.94252270e-01 1.10022962e+00 2.09032670e-01 -5.83393812e-01
2.63773620e-01 -6.95493400e-01 -1.10896695e+00 -1.04339600e+00
9.00399014e-02 9.90581632e-01 1.68354914e-01 -1.11921698... | [4.719488143920898, 5.587850570678711] |
f402c77c-7a51-4040-be0e-394f00bf7940 | symfm6d-symmetry-aware-multi-directional | 2307.00306 | null | https://arxiv.org/abs/2307.00306v1 | https://arxiv.org/pdf/2307.00306v1.pdf | SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D Object Pose Estimation | Detecting objects and estimating their 6D poses is essential for automated systems to interact safely with the environment. Most 6D pose estimators, however, rely on a single camera frame and suffer from occlusions and ambiguities due to object symmetries. We overcome this issue by presenting a novel symmetry-aware mul... | ['Gerhard Neumann', 'Ngo Anh Vien', 'Hanna Ziesche', 'Sebastian Koch', 'Fabian Duffhauss'] | 2023-07-01 | null | null | null | null | ['camera-calibration', 'pose-estimation', 'keypoint-detection', '6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.23996949e-02 -2.48169437e-01 2.46418100e-02 -4.92243439e-01
-9.75678146e-01 -8.68817925e-01 4.31724608e-01 -5.63769676e-02
-3.54482234e-01 -5.02582416e-02 -1.53961658e-01 2.30543718e-01
-1.11766905e-01 -3.95364821e-01 -1.17823052e+00 -4.41772997e-01
4.55211967e-01 9.30657327e-01 4.86553222e-01 6.13786988... | [7.530863285064697, -2.5684778690338135] |
ccf88a33-ecb8-4bac-a3e2-9018a18ae1fc | deep-point-wise-prediction-for-action | 1909.07725 | null | https://arxiv.org/abs/1909.07725v1 | https://arxiv.org/pdf/1909.07725v1.pdf | Deep Point-wise Prediction for Action Temporal Proposal | Detecting actions in videos is an important yet challenging task. Previous works usually utilize (a) sliding window paradigms, or (b) per-frame action scoring and grouping to enumerate the possible temporal locations. Their performances are also limited to the designs of sliding windows or grouping strategies. In this ... | ['Huaping Liu', 'Luxuan Li', 'Fuchun Sun', 'Tao Kong'] | 2019-09-17 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 1.78796723e-01 -4.56109673e-01 -6.59098268e-01 -1.54350579e-01
-7.77014375e-01 -4.54520941e-01 6.53019309e-01 -3.54438871e-01
-3.24451715e-01 5.77494204e-01 2.85054892e-01 -1.41976476e-01
4.01528999e-02 -4.55829501e-01 -4.21974152e-01 -7.22893476e-01
-3.85973811e-01 -8.16520825e-02 8.75071347e-01 1.30886823... | [8.371603965759277, 0.4133458435535431] |
a5d41da8-a844-4a60-8a94-bef43eebe509 | learning-feature-matching-via-matchable | 2307.01447 | null | https://arxiv.org/abs/2307.01447v1 | https://arxiv.org/pdf/2307.01447v1.pdf | Learning Feature Matching via Matchable Keypoint-Assisted Graph Neural Network | Accurately matching local features between a pair of images is a challenging computer vision task. Previous studies typically use attention based graph neural networks (GNNs) with fully-connected graphs over keypoints within/across images for visual and geometric information reasoning. However, in the context of featur... | ['Jiayi Ma', 'Zizhuo Li'] | 2023-07-04 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [ 3.86675596e-02 -6.51284158e-02 -1.67359114e-01 5.28293326e-02
-6.62685931e-01 -4.10163969e-01 5.52859843e-01 3.52328956e-01
-3.84369045e-01 3.22304010e-01 6.83394447e-02 4.20260280e-02
-3.80435258e-01 -9.02878642e-01 -9.83024120e-01 -5.72914958e-01
-1.12888396e-01 2.77213633e-01 4.80387062e-01 -3.67617747... | [7.893206596374512, -1.9614654779434204] |
a764ea34-27ec-47c6-8514-af3514489e32 | hindsight-learning-for-mdps-with-exogenous | 2207.06272 | null | https://arxiv.org/abs/2207.06272v2 | https://arxiv.org/pdf/2207.06272v2.pdf | Hindsight Learning for MDPs with Exogenous Inputs | Many resource management problems require sequential decision-making under uncertainty, where the only uncertainty affecting the decision outcomes are exogenous variables outside the control of the decision-maker. We model these problems as Exo-MDPs (Markov Decision Processes with Exogenous Inputs) and design a class o... | ['Adith Swaminathan', 'Ishai Menache', 'Jennifer Neville', 'Jingling Li', 'Hugo Barbalho', 'Luke Marshall', 'Ching-An Cheng', 'Felipe Frujeri', 'Sean R. Sinclair'] | 2022-07-13 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 6.77565709e-02 5.11333406e-01 -7.19475567e-01 -1.98942542e-01
-6.96769893e-01 -6.26095772e-01 4.05494690e-01 -4.52010669e-02
-5.68522096e-01 1.23119414e+00 4.25753385e-01 -1.00540817e+00
-3.02282095e-01 -6.05817676e-01 -7.96085596e-01 -5.82996786e-01
-3.61071616e-01 9.35699940e-01 -5.17132342e-01 3.37095857... | [4.285417079925537, 2.787506580352783] |
4978c436-4d56-43ce-8697-9ec494f279a5 | a-survey-on-text-to-sql-parsing-concepts | 2208.13629 | null | https://arxiv.org/abs/2208.13629v1 | https://arxiv.org/pdf/2208.13629v1.pdf | A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions | Text-to-SQL parsing is an essential and challenging task. The goal of text-to-SQL parsing is to convert a natural language (NL) question to its corresponding structured query language (SQL) based on the evidences provided by relational databases. Early text-to-SQL parsing systems from the database community achieved a ... | ['Yongbin Li', 'Fei Huang', 'Luo Si', 'Jian Sun', 'Rongyu Cao', 'Ruiying Geng', 'Binhua Li', 'Jinyang Li', 'Min Yang', 'Lihan Wang', 'Binyuan Hui', 'Bowen Qin'] | 2022-08-29 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 2.22359255e-01 5.44089735e-01 -2.35238299e-01 -1.17988718e+00
-1.42008460e+00 -6.02562606e-01 4.62332904e-01 4.14157659e-01
-1.02902234e-01 4.12074327e-01 1.38046846e-01 -7.41057038e-01
3.76088202e-01 -1.42419040e+00 -1.24637115e+00 2.62681484e-01
3.26910436e-01 9.72367704e-01 2.33908936e-01 -2.82614440... | [9.98831558227539, 7.846551418304443] |
a2e0c286-a336-49a1-8137-2a47fdda1f79 | enlighten-anything-when-segment-anything | 2306.10286 | null | https://arxiv.org/abs/2306.10286v3 | https://arxiv.org/pdf/2306.10286v3.pdf | Enlighten Anything: When Segment Anything Model Meets Low-Light Image Enhancement | Image restoration is a low-level visual task, and most CNN methods are designed as black boxes, lacking transparency and intrinsic aesthetics. Many unsupervised approaches ignore the degradation of visible information in low-light scenes, which will seriously affect the aggregation of complementary information and also... | ['Shanying Zhu', 'Chaochen Gu', 'Hao Tang', 'Xiaofeng Zhang', 'Qihan Zhao'] | 2023-06-17 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement', 'image-restoration'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.24143907e-01 -3.72214347e-01 3.80304419e-02 -3.17553282e-01
-4.78151798e-01 -3.71642947e-01 5.48427254e-02 -9.63131636e-02
-2.10983261e-01 8.55970800e-01 2.47837484e-01 -1.33960485e-01
9.81861651e-02 -8.94772172e-01 -6.54926717e-01 -1.05534542e+00
6.15605950e-01 -6.27747059e-01 2.87255079e-01 -3.43333811... | [10.823568344116211, -2.4409420490264893] |
7d43666f-399e-43b7-9000-ba1c17c22b2c | detection-of-chinese-stock-market-bubbles | 1905.09640 | null | http://arxiv.org/abs/1905.09640v2 | http://arxiv.org/pdf/1905.09640v2.pdf | Detection of Chinese Stock Market Bubbles with LPPLS Confidence Indicator | We present an advance bubble detection methodology based on the Log Periodic
Power Law Singularity (LPPLS) confidence indicator for the early causal
identification of positive and negative bubbles in the Chinese stock market
using the daily data on the Shanghai Shenzhen CSI 300 stock market index from
January 2002 thro... | [] | 2019-06-13 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [-8.05538595e-01 -1.42447233e-01 -7.01035634e-02 6.06437027e-01
-4.14908767e-01 -7.87444651e-01 4.84592915e-01 1.96002260e-01
3.85166355e-03 8.46813560e-01 1.04302138e-01 -9.98209476e-01
-1.60632640e-01 -9.90909874e-01 -5.31794548e-01 -8.58253181e-01
-5.99910676e-01 2.26750240e-01 4.65837777e-01 -1.42012676... | [4.740936279296875, 4.183809280395508] |
e5cae96e-4223-499d-96df-b67380e07455 | eventea-benchmarking-entity-alignment-for | 2211.02817 | null | https://arxiv.org/abs/2211.02817v1 | https://arxiv.org/pdf/2211.02817v1.pdf | EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs | Entity alignment is to find identical entities in different knowledge graphs (KGs) that refer to the same real-world object. Embedding-based entity alignment techniques have been drawing a lot of attention recently because they can help solve the issue of symbolic heterogeneity in different KGs. However, in this paper,... | ['Wei Hu', 'Guangyao Li', 'Zequn Sun', 'Xiaobin Tian'] | 2022-11-05 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-1.04319818e-01 4.13075238e-01 -6.85774922e-01 -4.88764435e-01
-5.31978369e-01 -5.08831441e-01 3.60778123e-01 8.41797411e-01
-1.44235417e-01 7.32070804e-01 4.78385776e-01 -2.16746196e-01
-3.66443008e-01 -1.04562938e+00 -6.05467379e-01 -2.29875714e-01
-3.80996197e-01 7.64550328e-01 3.62192988e-01 -3.82240534... | [8.803617477416992, 7.9632568359375] |
c500ec56-5409-4b29-b19f-73efd0108b77 | a-richly-annotated-dataset-for-pedestrian | 1603.07054 | null | http://arxiv.org/abs/1603.07054v3 | http://arxiv.org/pdf/1603.07054v3.pdf | A Richly Annotated Dataset for Pedestrian Attribute Recognition | In this paper, we aim to improve the dataset foundation for pedestrian
attribute recognition in real surveillance scenarios. Recognition of human
attributes, such as gender, and clothes types, has great prospects in real
applications. However, the development of suitable benchmark datasets for
attribute recognition rem... | ['Haibin Ling', 'Xiaotang Chen', 'Zhang Zhang', 'Kaiqi Huang', 'Dangwei Li'] | 2016-03-23 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [-9.41889510e-02 -5.80303490e-01 -1.78241640e-01 -9.58488882e-01
-1.23109221e-01 -4.37462419e-01 7.84319520e-01 3.95548999e-01
-3.07277083e-01 8.38205218e-01 3.98774087e-01 2.73298800e-01
2.33910039e-01 -1.11146021e+00 -5.94259918e-01 -9.58465993e-01
4.61426303e-02 5.97824454e-01 1.34256288e-01 -1.43537074... | [14.49011516571045, 0.9859794974327087] |
3a1bb1b5-1f25-4dab-ae83-3ec977bb3696 | do-you-follow-me-a-survey-of-recent-1 | null | null | https://aclanthology.org/2022.sigdial-1.33 | https://aclanthology.org/2022.sigdial-1.33.pdf | “Do you follow me?”: A Survey of Recent Approaches in Dialogue State Tracking | While communicating with a user, a task-oriented dialogue system has to track the user’s needs at each turn according to the conversation history. This process called dialogue state tracking (DST) is crucial because it directly informs the downstream dialogue policy. DST has received a lot of interest in recent years w... | ['Benoit Favre', 'Lina M. Rojas Barahona', 'Léo Jacqmin'] | null | null | null | null | sigdial-acl-2022-9 | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 2.73541182e-01 5.61033607e-01 -3.51130456e-01 -6.81601524e-01
-3.08282793e-01 -7.25399911e-01 9.54662919e-01 6.17773123e-02
-4.50604022e-01 8.72779965e-01 7.95172989e-01 -3.82525265e-01
4.20918204e-02 -3.33081901e-01 2.46950909e-01 -2.04890236e-01
1.30467042e-01 5.00313878e-01 9.80608072e-03 -7.92369425... | [12.850491523742676, 7.941054821014404] |
b53ae2b9-9ce9-4be0-af75-0a1275a25d8a | program-transfer-for-answering-complex | null | null | https://openreview.net/forum?id=rn8YIulHv03 | https://openreview.net/pdf?id=rn8YIulHv03 | Program Transfer for Answering Complex Questions over Knowledge Bases | Program induction for answering complex questions over knowledge bases (KBs) aims to decompose a question into a multi-step program, whose execution against the KB produces the final answer. Learning to induce programs relies on a large number of parallel question-program pairs for the given KB. However, for most KBs,... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['program-induction'] | ['computer-code'] | [ 1.19416714e-01 3.34912151e-01 -5.58693588e-01 -4.47253793e-01
-1.02671123e+00 -7.51413286e-01 -6.55197203e-02 4.38235819e-01
-1.56865045e-01 4.12790984e-01 -9.96628478e-02 -7.48307467e-01
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2.59285629e-01 2.88349032e-01 9.00536418e-01 -2.42187595... | [9.666708946228027, 7.597813606262207] |
2cdae8ec-9526-4f54-8bac-c5c72d257be0 | pose-invariant-object-recognition-for-event | 1903.07873 | null | http://arxiv.org/abs/1903.07873v1 | http://arxiv.org/pdf/1903.07873v1.pdf | Pose-Invariant Object Recognition for Event-Based Vision with Slow-ELM | Neuromorphic image sensors produce activity-driven spiking output at every
pixel. These low-power consuming imagers which encode visual change information
in the form of spikes help reduce computational overhead and realize complex
real-time systems; object recognition and pose-estimation to name a few.
However, there ... | ['Sunil Kukreja', 'Rohan Ghosh', 'Siyi Tang', 'Nitish Thakor', 'Mahdi Rasouli'] | 2019-03-19 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 4.21747327e-01 -6.88689113e-01 5.17486274e-01 -2.88334429e-01
-9.25327465e-02 -6.36486351e-01 3.71528387e-01 -9.00750309e-02
-7.50429988e-01 6.79631650e-01 -6.51220381e-01 2.02270091e-01
-4.17049378e-02 -6.17689550e-01 -9.61924791e-01 -9.21689987e-01
1.34839505e-01 2.45360091e-01 4.60709900e-01 1.36619702... | [8.238561630249023, 2.3715646266937256] |
fe5a97ac-9c2c-45f5-a7f7-335a6e654fa1 | benchmark-dataset-for-automatic-damaged | 1812.05581 | null | http://arxiv.org/abs/1812.05581v1 | http://arxiv.org/pdf/1812.05581v1.pdf | Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery | Rapid damage assessment is of crucial importance to emergency responders
during hurricane events, however, the evaluation process is often slow,
labor-intensive, costly, and error-prone. New advances in computer vision and
remote sensing open possibilities to observe the Earth at a different scale.
However, substantial... | ['Valentina Staneva', 'Youngjun Choe', 'Tessa Schneider', 'Sean Andrew Chen', 'Christopher Haberland', 'Andrew Escay'] | 2018-12-13 | null | null | null | null | ['damaged-building-detection'] | ['computer-vision'] | [ 3.46760482e-01 -5.34716368e-01 8.04665804e-01 -1.73471823e-01
-1.10735238e+00 -4.73623097e-01 4.04423892e-01 4.80812341e-01
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-2.76444107e-01 -1.22896731e+00 -9.67544094e-02 -1.04900527e+00
-5.33621609e-01 6.15341842e-01 3.07421356e-01 -7.74411678... | [9.482156753540039, -1.2939746379852295] |
b0fe534f-19c1-4073-9b25-411330d063f6 | sketchaa-abstract-representation-for-abstract | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yang_SketchAA_Abstract_Representation_for_Abstract_Sketches_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yang_SketchAA_Abstract_Representation_for_Abstract_Sketches_ICCV_2021_paper.pdf | SketchAA: Abstract Representation for Abstract Sketches | What makes free-hand sketches appealing for humans lies with its capability as a universal tool to depict the visual world. Such flexibility at human ease, however, introduces abstract renderings that pose unique challenges to computer vision models. In this paper, we propose a purpose-made sketch representation fo... | ['Yi-Zhe Song', 'Honggang Zhang', 'Kaiyue Pang', 'Lan Yang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['sketch-based-image-retrieval', 'sketch-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.66813387e-02 -1.30964145e-01 -2.82048315e-01 -2.67724127e-01
-2.52698928e-01 -8.31538737e-01 1.19944608e+00 -1.60587877e-01
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-4.83567547e-03 -5.64788282e-01 -2.24397421e-01 -3.18945199e-01
3.40570584e-02 2.40821481e-01 5.40842898e-02 -3.45734596... | [11.723766326904297, 0.3266708552837372] |
c54b9746-bdce-4e2a-8a76-d03f99d793d3 | redwoodnlp-at-semeval-2021-task-7-ensembled | null | null | https://aclanthology.org/2021.semeval-1.171 | https://aclanthology.org/2021.semeval-1.171.pdf | RedwoodNLP at SemEval-2021 Task 7: Ensembled Pretrained and Lightweight Models for Humor Detection | An understanding of humor is an essential component of human-facing NLP systems. In this paper, we investigate several methods for detecting humor in short statements as part of Semeval-2021 Shared Task 7. For Task 1a, we apply an ensemble of fine-tuned pre-trained language models; for Tasks 1b, 1c, and 2a, we investig... | ['Ryan Chi', 'Nathan Chi'] | 2021-08-01 | null | null | null | semeval-2021 | ['humor-detection'] | ['natural-language-processing'] | [-3.09598058e-01 2.87964404e-01 -2.09894031e-01 7.44111240e-02
-6.97228611e-01 -6.42753184e-01 7.67068863e-01 3.93498063e-01
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1.24774940e-01 -4.98684645e-01 -3.45316052e-01 -4.41471264e-02
1.31702110e-01 2.35166490e-01 1.67627111e-01 -6.03884995... | [8.869324684143066, 11.076233863830566] |
b9851bb4-d1a6-414d-b310-fa5c526608c4 | vit-net-interpretable-vision-transformers | null | null | https://proceedings.mlr.press/v162/kim22g/kim22g.pdf | https://proceedings.mlr.press/v162/kim22g/kim22g.pdf | ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder | Vision transformers (ViTs), which have demonstrated a state-of-the-art performance in image classification, can also visualize global interpretations through attention-based contributions. How- ever, the complexity of the model makes it difficult to interpret the decision-making process, and the ambiguity of the attent... | ['Sangwon Kim; Jaeyeal Nam; Byoung Chul Ko'] | 2022-07-17 | null | null | null | icml-2022-7 | ['fine-grained-image-classification', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision'] | [ 1.49850219e-01 1.26887292e-01 7.83032030e-02 -4.57566559e-01
-1.88455999e-01 -4.79310185e-01 4.36024815e-01 1.13950267e-01
-4.64300811e-02 4.52319950e-01 9.55582131e-03 -5.31536102e-01
-1.47709176e-01 -6.41170025e-01 -6.23736560e-01 -7.28112519e-01
2.56135464e-01 3.30791026e-01 1.35835245e-01 8.67766961... | [10.137771606445312, 2.0016448497772217] |
c87007b0-127a-4d24-91e5-f326f7b5bd07 | fast-and-high-quality-blind-multi-spectral | 2103.09943 | null | https://arxiv.org/abs/2103.09943v4 | https://arxiv.org/pdf/2103.09943v4.pdf | Fast and High-Quality Blind Multi-Spectral Image Pansharpening | Blind pansharpening addresses the problem of generating a high spatial-resolution multi-spectral (HRMS) image given a low spatial-resolution multi-spectral (LRMS) image with the guidance of its associated spatially misaligned high spatial-resolution panchromatic (PAN) image without parametric side information. In this ... | ['Petros T. Boufounos', 'Hassan Mansour', 'Dehong Liu', 'Lantao Yu'] | 2021-03-17 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 5.93931913e-01 -5.23255765e-01 4.98584598e-01 2.31179059e-01
-9.07590270e-01 -7.51873791e-01 4.56004977e-01 -5.53454638e-01
-3.04408401e-01 5.52242994e-01 1.99678093e-01 -1.14278324e-01
-3.81642759e-01 -4.33045238e-01 -6.29732430e-01 -1.18671417e+00
2.77919620e-01 9.61951166e-02 4.95525971e-02 3.04819178... | [11.590957641601562, -2.7301619052886963] |
0c684c2d-8a78-4c06-977b-7fb3c743f0c3 | polyformer-referring-image-segmentation-as | 2302.07387 | null | https://arxiv.org/abs/2302.07387v2 | https://arxiv.org/pdf/2302.07387v2.pdf | PolyFormer: Referring Image Segmentation as Sequential Polygon Generation | In this work, instead of directly predicting the pixel-level segmentation masks, the problem of referring image segmentation is formulated as sequential polygon generation, and the predicted polygons can be later converted into segmentation masks. This is enabled by a new sequence-to-sequence framework, Polygon Transfo... | ['R. Manmatha', 'Vijay Mahadevan', 'Ravi Kumar Satzoda', 'Yuting Zhang', 'Zhaowei Cai', 'Hui Ding', 'Jiang Liu'] | 2023-02-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_PolyFormer_Referring_Image_Segmentation_As_Sequential_Polygon_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_PolyFormer_Referring_Image_Segmentation_As_Sequential_Polygon_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['referring-expression-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.30758703e-01 1.17563955e-01 -1.10212877e-01 -2.31061429e-01
-1.19231606e+00 -7.99254298e-01 3.82784843e-01 -8.88999328e-02
-4.09812331e-01 3.89869690e-01 -2.76991844e-01 -3.19353700e-01
5.90075493e-01 -8.22998524e-01 -1.30279529e+00 -4.37560797e-01
2.92221397e-01 4.31659371e-01 4.43946928e-01 1.24557987... | [9.32702350616455, -0.22387461364269257] |
1df23239-a0d0-4a85-a2ac-b01625374ea4 | composing-distributed-data-intensive-web | 1901.09894 | null | http://arxiv.org/abs/1901.09894v1 | http://arxiv.org/pdf/1901.09894v1.pdf | Composing Distributed Data-intensive Web Services Using a Flexible Memetic Algorithm | Web Service Composition (WSC) is a particularly promising application of Web
services, where multiple individual services with specific functionalities are
composed to accomplish a more complex task, which must fulfil functional
requirements and optimise Quality of Service (QoS) attributes, simultaneously.
Additionally... | ['Soheila Sadeghiram', 'Hui Ma', 'Gang Chen'] | 2019-01-26 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 2.33995512e-01 -7.13445127e-01 2.47248486e-01 -4.18247789e-01
-3.55170041e-01 -3.69235516e-01 5.38680434e-01 4.79330532e-02
-4.16975498e-01 6.06483877e-01 -2.11964902e-02 -8.42630677e-03
-7.20624447e-01 -9.58471596e-01 4.99014976e-03 -1.05700493e+00
-1.36182263e-01 7.59160399e-01 5.43438554e-01 -3.90989155... | [8.585481643676758, 6.933276653289795] |
0d06e045-331f-43c3-a8b9-e99244694ad3 | tata-a-multilingual-table-to-text-dataset-for | 2211.00142 | null | https://arxiv.org/abs/2211.00142v1 | https://arxiv.org/pdf/2211.00142v1.pdf | TaTa: A Multilingual Table-to-Text Dataset for African Languages | Existing data-to-text generation datasets are mostly limited to English. To address this lack of data, we create Table-to-Text in African languages (TaTa), the first large multilingual table-to-text dataset with a focus on African languages. We created TaTa by transcribing figures and accompanying text in bilingual rep... | ['Clara Rivera', 'Ankur Parikh', 'Michael Chavinda', 'Jan A. Botha', 'Vitaly Nikolaev', 'Sebastian Ruder', 'Sebastian Gehrmann'] | 2022-10-31 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [-6.15320802e-02 4.07469898e-01 -3.78166765e-01 -4.47985828e-01
-1.47370291e+00 -8.86678278e-01 8.43106925e-01 3.10620993e-01
-1.89058214e-01 1.17443264e+00 8.12892497e-01 -7.92964578e-01
4.25755411e-01 -7.02257037e-01 -7.89079547e-01 7.13600516e-02
3.32570970e-01 9.06419873e-01 -4.26259309e-01 -4.92450655... | [11.509474754333496, 9.6818265914917] |
9ab77d61-d045-4179-8382-b0a5d8797a7c | meta-learning-for-airflow-simulations-with | 2306.10624 | null | https://arxiv.org/abs/2306.10624v1 | https://arxiv.org/pdf/2306.10624v1.pdf | Meta-Learning for Airflow Simulations with Graph Neural Networks | The field of numerical simulation is of significant importance for the design and management of real-world systems, with partial differential equations (PDEs) being a commonly used mathematical modeling tool. However, solving PDEs remains still a challenge, as commonly used traditional numerical solvers often require h... | ['Marc Schoenauer', 'Mouadh Yagoubi', 'Wenzhuo LIU'] | 2023-06-18 | null | null | null | null | ['meta-learning', 'management'] | ['methodology', 'miscellaneous'] | [ 3.80652472e-02 -5.21059453e-01 6.00404218e-02 -1.43951967e-01
-4.64750022e-01 -5.30199647e-01 4.56830204e-01 4.92890716e-01
-2.65746504e-01 6.33829415e-01 -5.36483169e-01 -3.10018003e-01
-4.18705434e-01 -9.15097892e-01 -7.96471477e-01 -5.99997342e-01
-1.36079386e-01 5.16097665e-01 -1.38856769e-01 -2.63892710... | [6.45076322555542, 3.407475233078003] |
0ffd9d8e-0788-4ae1-b24d-b06710c76f67 | least-square-estimation-network-for-depth | 2203.03317 | null | https://arxiv.org/abs/2203.03317v2 | https://arxiv.org/pdf/2203.03317v2.pdf | Depth-Independent Depth Completion via Least Square Estimation | The depth completion task aims to complete a per-pixel dense depth map from a sparse depth map. In this paper, we propose an efficient least square based depth-independent method to complete the sparse depth map utilizing the RGB image and the sparse depth map in two independent stages. In this way can we decouple the ... | ['Yunkai Wang', 'Rong Xiong', 'Yue Wang', 'Zexi Chen', 'Xianze Fang'] | 2022-03-07 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 3.90672356e-01 2.25788400e-01 2.25277860e-02 -5.41548312e-01
-7.26959944e-01 -1.59995660e-01 1.68238550e-01 -2.19506145e-01
-3.07920963e-01 5.95929801e-01 3.34865808e-01 3.43538493e-01
1.57920301e-01 -1.11333561e+00 -8.10911775e-01 -7.00689077e-01
1.97937012e-01 3.75031322e-01 6.05269194e-01 3.18306014... | [8.844282150268555, -2.653597116470337] |
69fe7c73-2e21-4765-bb40-80df76a03e89 | ftfdnet-learning-to-detect-talking-face-video | 2307.03990 | null | https://arxiv.org/abs/2307.03990v1 | https://arxiv.org/pdf/2307.03990v1.pdf | FTFDNet: Learning to Detect Talking Face Video Manipulation with Tri-Modality Interaction | DeepFake based digital facial forgery is threatening public media security, especially when lip manipulation has been used in talking face generation, and the difficulty of fake video detection is further improved. By only changing lip shape to match the given speech, the facial features of identity are hard to be disc... | ['Yufei zha', 'Wei Huang', 'Feihan Yang', 'Junwen Xiong', 'Peng Zhang', 'Ganglai Wang'] | 2023-07-08 | null | null | null | null | ['face-detection', 'face-swapping', 'optical-flow-estimation', 'talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-6.70062825e-02 -1.85578957e-01 -1.16349302e-01 5.09073101e-02
-3.34902555e-01 -2.13336915e-01 4.11983460e-01 -7.55910456e-01
-3.59494388e-02 4.26933855e-01 1.34313241e-01 3.02433930e-02
2.92521656e-01 -4.33433414e-01 -5.14163792e-01 -8.56153727e-01
2.94957161e-01 -3.42662424e-01 8.08956474e-02 -2.78899610... | [13.02138614654541, 1.2203302383422852] |
86883909-e7be-4c83-bd18-3da24095c9f3 | graph-bert-only-attention-is-needed-for | 2001.05140 | null | https://arxiv.org/abs/2001.05140v2 | https://arxiv.org/pdf/2001.05140v2.pdf | Graph-Bert: Only Attention is Needed for Learning Graph Representations | The dominant graph neural networks (GNNs) over-rely on the graph links, several serious performance problems with which have been witnessed already, e.g., suspended animation problem and over-smoothing problem. What's more, the inherently inter-connected nature precludes parallelization within the graph, which becomes ... | ['Congying Xia', 'Haopeng Zhang', 'Li Sun', 'Jiawei Zhang'] | 2020-01-15 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-6.79161474e-02 4.85797286e-01 5.66698313e-02 -2.93228269e-01
-6.09410554e-02 -2.13161588e-01 1.91540003e-01 2.24320680e-01
-3.85288984e-01 6.13225579e-01 -3.58287275e-01 -3.44615579e-01
-1.05074823e-01 -1.18132269e+00 -7.76041090e-01 -8.33620429e-01
-5.63294888e-01 6.70612037e-01 4.03278112e-01 -1.55795351... | [7.220090389251709, 6.1733245849609375] |
f0090045-9c31-47e6-8bb3-3fd3e2d4eb9d | free-supervision-from-video-games | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Krahenbuhl_Free_Supervision_From_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Krahenbuhl_Free_Supervision_From_CVPR_2018_paper.pdf | Free Supervision From Video Games | Deep networks are extremely hungry for data. They devour hundreds of thousands of labeled images to learn robust and semantically meaningful feature representations. Current networks are so data hungry that collecting labeled data has become as important as designing the networks themselves. Unfortunately, manual data ... | ['Philipp Krähenbühl'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [-2.64999773e-02 -1.43584251e-01 -1.07984170e-01 -3.96319747e-01
-5.66969931e-01 -9.98695254e-01 3.41169894e-01 -4.40558940e-01
-6.89456284e-01 6.04629874e-01 -7.02667385e-02 -3.52283001e-01
2.97170907e-01 -8.34687889e-01 -6.64464653e-01 -3.62885207e-01
-1.25040829e-01 7.15753734e-01 5.63381255e-01 -1.71819955... | [8.724268913269043, -1.9732155799865723] |
9462838c-e33d-4a2e-be27-c847c6afd697 | large-language-models-are-human-level-prompt | 2211.01910 | null | https://arxiv.org/abs/2211.01910v2 | https://arxiv.org/pdf/2211.01910v2.pdf | Large Language Models Are Human-Level Prompt Engineers | By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers. However, task performance depends significantly on the quality of the prompt used to steer the model, and most effective prompts have been handcrafted by humans. Inspired b... | ['Jimmy Ba', 'Harris Chan', 'Silviu Pitis', 'Keiran Paster', 'Ziwen Han', 'Andrei Ioan Muresanu', 'Yongchao Zhou'] | 2022-11-03 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 4.62122351e-01 2.22110674e-01 -5.12145102e-01 -4.62167263e-01
-1.27960920e+00 -6.21743262e-01 8.17316234e-01 3.84026557e-01
-4.81850773e-01 2.99705446e-01 5.36662936e-01 -6.18432045e-01
1.82548463e-01 -4.85030025e-01 -9.09274042e-01 -1.99993789e-01
3.72835696e-01 3.21293056e-01 2.26734430e-01 -2.62133360... | [10.667472839355469, 8.177682876586914] |
02818012-af39-40ef-a386-c5fa36d6020c | opam-online-purchasing-behavior-analysis | 2102.01625 | null | https://arxiv.org/abs/2102.01625v1 | https://arxiv.org/pdf/2102.01625v1.pdf | OPAM: Online Purchasing-behavior Analysis using Machine learning | Customer purchasing behavior analysis plays a key role in developing insightful communication strategies between online vendors and their customers. To support the recent increase in online shopping trends, in this work, we present a customer purchasing behavior analysis system using supervised, unsupervised and semi-s... | ['Wenxi Li', 'Ebrahim Alareqi', 'Sohini Roychowdhury'] | 2021-02-02 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [-2.65435070e-01 -1.81128994e-01 -8.64070594e-01 -1.16274166e+00
-5.40235221e-01 -6.47727191e-01 1.89718634e-01 9.30233479e-01
-1.82017371e-01 8.83066654e-02 1.41735235e-02 -4.11024272e-01
-4.42236662e-01 -7.62388885e-01 -1.77453980e-01 -6.74292386e-01
-5.71654916e-01 8.53781521e-01 -1.85321152e-01 -3.09621960... | [9.492427825927734, 5.907097339630127] |
c9c865c4-5edf-4c4b-8a46-b6cb5ec1b653 | 3d-human-pose-and-shape-regression-with | 2103.16507 | null | https://arxiv.org/abs/2103.16507v4 | https://arxiv.org/pdf/2103.16507v4.pdf | PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop | Regression-based methods have recently shown promising results in reconstructing human meshes from monocular images. By directly mapping raw pixels to model parameters, these methods can produce parametric models in a feed-forward manner via neural networks. However, minor deviation in parameters may lead to noticeable... | ['Zhenan Sun', 'LiMin Wang', 'Yebin Liu', 'Wanli Ouyang', 'Xinchi Zhou', 'Yating Tian', 'Hongwen Zhang'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_PyMAF_3D_Human_Pose_and_Shape_Regression_With_Pyramidal_Mesh_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_PyMAF_3D_Human_Pose_and_Shape_Regression_With_Pyramidal_Mesh_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-human-pose-and-shape-estimation', '3d-human-reconstruction', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.46128803e-01 1.25228390e-01 -2.47361049e-01 -5.49744129e-01
-4.86014366e-01 4.63194102e-02 3.09523433e-01 -4.09585238e-01
-3.34465504e-02 6.51150107e-01 2.10164756e-01 2.86205351e-01
-1.94459874e-02 -7.65960395e-01 -1.21941209e+00 -3.58712852e-01
2.71197617e-01 3.48519087e-01 1.37115642e-01 -7.84392804... | [7.14317512512207, -1.2999149560928345] |
60eec990-8ca1-4cf1-9399-679af992e5c6 | b-variational-autoencoders-and-transformers | 2304.03571 | null | https://arxiv.org/abs/2304.03571v1 | https://arxiv.org/pdf/2304.03571v1.pdf | $β$-Variational autoencoders and transformers for reduced-order modelling of fluid flows | Variational autoencoder (VAE) architectures have the potential to develop reduced-order models (ROMs) for chaotic fluid flows. We propose a method for learning compact and near-orthogonal ROMs using a combination of a $\beta$-VAE and a transformer, tested on numerical data from a two-dimensional viscous flow in both pe... | ['Ricardo Vinuesa', 'Scott T. M. Dawson', 'Abdulrahman Almashjary', 'Yuning Wang', 'M. A. Gómez', 'Carlos Sanmiguel Vila', 'Alberto Solera-Rico'] | 2023-04-07 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-3.20396781e-01 -1.19872138e-01 1.19195737e-01 1.30728677e-01
2.34993458e-01 -3.97679538e-01 6.13959610e-01 -4.45947707e-01
8.34821686e-02 6.08246505e-01 4.92339969e-01 -2.40791723e-01
-4.33677942e-01 -8.51006985e-01 -4.67224061e-01 -1.10751390e+00
-5.75897753e-01 4.15842682e-01 -3.36335868e-01 -3.52478832... | [6.571417808532715, 3.4728281497955322] |
7089890c-ae2e-406d-b7d5-0d39721d2696 | efficient-online-decision-tree-learning-with | 2305.02093 | null | https://arxiv.org/abs/2305.02093v1 | https://arxiv.org/pdf/2305.02093v1.pdf | Efficient Online Decision Tree Learning with Active Feature Acquisition | Constructing decision trees online is a classical machine learning problem. Existing works often assume that features are readily available for each incoming data point. However, in many real world applications, both feature values and the labels are unknown a priori and can only be obtained at a cost. For example, in ... | ['Morteza Haghir Chehreghani', 'Yuxin Chen', 'Ziyu Ye', 'Arman Rahbar'] | 2023-05-03 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 5.37394047e-01 6.06871724e-01 -6.03211820e-01 -6.83503032e-01
-1.20575380e+00 -5.67028046e-01 -4.09270227e-02 6.91023231e-01
-4.22220111e-01 7.03818560e-01 -2.55250186e-01 -3.12476128e-01
-5.04869401e-01 -7.72856772e-01 -9.28471148e-01 -9.12386358e-01
-2.21355259e-02 9.21555638e-01 -8.07303041e-02 2.99213111... | [8.229109764099121, 4.118100166320801] |
da625c08-81c1-46ed-86dd-5998d07f73a2 | you-only-crash-once-improved-object-detection | 2303.04891 | null | https://arxiv.org/abs/2303.04891v1 | https://arxiv.org/pdf/2303.04891v1.pdf | You Only Crash Once: Improved Object Detection for Real-Time, Sim-to-Real Hazardous Terrain Detection and Classification for Autonomous Planetary Landings | The detection of hazardous terrain during the planetary landing of spacecraft plays a critical role in assuring vehicle safety and mission success. A cheap and effective way of detecting hazardous terrain is through the use of visual cameras, which ensure operational ability from atmospheric entry through touchdown. Pl... | ['Karthik Dantu', 'John Crassidis', 'Chris Gnam', 'Timothy Chase Jr'] | 2023-03-08 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 5.57865128e-02 -2.33870819e-01 1.34608507e-01 -2.49675646e-01
-8.37624192e-01 -8.53731811e-01 8.10840786e-01 1.42271638e-01
-5.70234299e-01 5.34949064e-01 -2.36318782e-01 -6.00278020e-01
-2.19193771e-01 -7.89569378e-01 -6.25427186e-01 -4.41514075e-01
-7.25119174e-01 8.58991861e-01 4.51698005e-01 -7.08561599... | [7.3794941902160645, -1.9046601057052612] |
20cdfdbf-8a30-4511-a8e5-ea699e79e52e | copula-based-deep-survival-models-for | 2306.11912 | null | https://arxiv.org/abs/2306.11912v1 | https://arxiv.org/pdf/2306.11912v1.pdf | Copula-Based Deep Survival Models for Dependent Censoring | A survival dataset describes a set of instances (e.g. patients) and provides, for each, either the time until an event (e.g. death), or the censoring time (e.g. when lost to follow-up - which is a lower bound on the time until the event). We consider the challenge of survival prediction: learning, from such data, a pre... | ['Rahul G. Krishnan', 'Russell Greiner', 'Michael Cooper', 'Ali Hossein Gharari Foomani'] | 2023-06-20 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 1.32233933e-01 1.38770603e-02 -4.47772503e-01 -6.92133307e-01
-9.13680077e-01 -6.13551378e-01 4.00779605e-01 7.63059676e-01
-3.11667919e-01 1.18378949e+00 2.20127717e-01 -6.30759776e-01
-3.56925100e-01 -9.30093169e-01 -6.09514296e-01 -9.37873662e-01
-5.52521586e-01 9.75783765e-01 -1.49361253e-01 2.89752722... | [7.7765889167785645, 5.546986103057861] |
2a10ee6e-b6c2-402b-9121-21d9c230257f | nerf-pose-a-first-reconstruct-then-regress | 2203.04802 | null | https://arxiv.org/abs/2203.04802v1 | https://arxiv.org/pdf/2203.04802v1.pdf | NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation | Pose estimation of 3D objects in monocular images is a fundamental and long-standing problem in computer vision. Existing deep learning approaches for 6D pose estimation typically rely on the assumption of availability of 3D object models and 6D pose annotations. However, precise annotation of 6D poses in real data is ... | ['Slobodan Ilic', 'Shaowu Yang', 'Benjamin Busam', 'Ivan Shugurov', 'Hao Yu', 'Fu Li'] | 2022-03-09 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.95636265e-02 6.48757592e-02 -2.78485864e-01 -6.44763947e-01
-9.53756154e-01 -7.16637552e-01 4.72293377e-01 -4.13401514e-01
-5.01890779e-01 2.76722103e-01 -2.34822094e-01 5.92341758e-02
1.40420496e-01 -3.89996260e-01 -1.18602312e+00 -4.19083983e-01
2.92262226e-01 1.18779981e+00 5.01841068e-01 1.29891098... | [7.5574846267700195, -2.5855729579925537] |
4dd68713-a73f-499c-a3e8-c03902328c8f | hybrid-deep-neural-networks-for-all-cause | 1810.08503 | null | http://arxiv.org/abs/1810.08503v1 | http://arxiv.org/pdf/1810.08503v1.pdf | Hybrid deep neural networks for all-cause Mortality Prediction from LDCT Images | Known for its high morbidity and mortality rates, lung cancer poses a
significant threat to human health and well-being. However, the same population
is also at high risk for other deadly diseases, such as cardiovascular disease.
Since Low-Dose CT (LDCT) has been shown to significantly improve the lung
cancer diagnosis... | ['Mannudeep K. Kalra', 'Hengtao Guo', 'Ge Wang', 'Ruben De Man', 'Pingkun Yan'] | 2018-10-19 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-7.24865049e-02 5.39998896e-03 -4.52160180e-01 -2.70477593e-01
-1.17336643e+00 1.49591174e-02 3.40183824e-01 2.66255647e-01
-4.81234670e-01 6.86861694e-01 3.31716210e-01 -6.33859038e-01
-1.81433350e-01 -1.14434063e+00 -3.25618847e-03 -7.97165692e-01
-6.77108616e-02 7.57920325e-01 3.63311410e-01 4.16510910... | [15.326347351074219, -2.231928586959839] |
841f8bb0-358a-423e-a78a-6ed93b838921 | semantic-parsing-in-task-oriented-dialog-with | 2109.04500 | null | https://arxiv.org/abs/2109.04500v2 | https://arxiv.org/pdf/2109.04500v2.pdf | Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder | We introduce a Recursive INsertion-based Encoder (RINE), a novel approach for semantic parsing in task-oriented dialog. Our model consists of an encoder network that incrementally builds the semantic parse tree by predicting the non-terminal label and its positions in the linearized tree. At the generation time, the mo... | ['Yi Zhang', 'Elman Mansimov'] | 2021-09-09 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [ 4.57388312e-01 9.03087556e-01 -8.88054296e-02 -7.52620399e-01
-1.16791463e+00 -9.70916867e-01 2.65782416e-01 -2.04017852e-02
-3.33965182e-01 6.39924467e-01 6.21056497e-01 -6.83221579e-01
4.16597426e-01 -7.77959526e-01 -7.81637192e-01 1.37054339e-01
-1.43282026e-01 1.04092836e+00 2.69133568e-01 -3.28499317... | [10.480875968933105, 9.303455352783203] |
6f786014-ba15-4279-9cc5-0a74d090b59e | smpconv-self-moving-point-representations-for | 2304.02330 | null | https://arxiv.org/abs/2304.02330v1 | https://arxiv.org/pdf/2304.02330v1.pdf | SMPConv: Self-moving Point Representations for Continuous Convolution | Continuous convolution has recently gained prominence due to its ability to handle irregularly sampled data and model long-term dependency. Also, the promising experimental results of using large convolutional kernels have catalyzed the development of continuous convolution since they can construct large kernels very e... | ['Eunbyung Park', 'Sanghyeon Kim'] | 2023-04-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_SMPConv_Self-Moving_Point_Representations_for_Continuous_Convolution_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_SMPConv_Self-Moving_Point_Representations_for_Continuous_Convolution_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sequential-image-classification'] | ['computer-vision'] | [-1.75111637e-01 -2.89154440e-01 1.97433028e-03 -2.10408330e-01
-2.24710733e-01 -2.28309497e-01 3.95118266e-01 -3.26426089e-01
-7.31553197e-01 6.13204002e-01 1.31364703e-01 -2.82951772e-01
-1.05993599e-01 -7.62161851e-01 -8.67864370e-01 -5.84063053e-01
-1.70246169e-01 -4.34594154e-01 2.02647448e-01 -4.41479310... | [8.95788860321045, 2.344003677368164] |
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