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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
50a1e92e-1ce7-4666-9ed1-592bc8346a5c | learning-monolingual-compositional | null | null | https://aclanthology.org/P16-2059 | https://aclanthology.org/P16-2059.pdf | Learning Monolingual Compositional Representations via Bilingual Supervision | null | ['Ahmed Elgohary', 'Marine Carpuat'] | 2016-08-01 | null | null | null | acl-2016-8 | ['cross-lingual-document-classification'] | ['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... | [-7.429886341094971, 3.7983388900756836] |
c6a6d4ea-ce15-4ea4-aad1-a18e0a5a985c | link-prediction-on-latent-heterogeneous | 2302.10432 | null | https://arxiv.org/abs/2302.10432v1 | https://arxiv.org/pdf/2302.10432v1.pdf | Link Prediction on Latent Heterogeneous Graphs | On graph data, the multitude of node or edge types gives rise to heterogeneous information networks (HINs). To preserve the heterogeneous semantics on HINs, the rich node/edge types become a cornerstone of HIN representation learning. However, in real-world scenarios, type information is often noisy, missing or inacces... | ['Yuan Fang', 'Zemin Liu', 'Trung-Kien Nguyen'] | 2023-02-21 | null | null | null | null | ['type'] | ['speech'] | [ 7.95977041e-02 2.21479133e-01 -7.06954837e-01 -2.45955110e-01
2.81813066e-03 -5.45121133e-01 5.97572565e-01 4.78069186e-01
1.00539379e-01 7.54195929e-01 3.28353077e-01 -1.91392973e-01
-3.07435006e-01 -1.36804545e+00 -5.37928581e-01 -7.00779617e-01
-2.28784829e-01 2.39265159e-01 3.46366227e-01 -8.69546682... | [7.404354095458984, 6.3651933670043945] |
f4ddcb2e-2d04-445b-bd5d-7a48e5102761 | tristereonet-a-trinocular-framework-for-multi | 2111.12502 | null | https://arxiv.org/abs/2111.12502v2 | https://arxiv.org/pdf/2111.12502v2.pdf | TriStereoNet: A Trinocular Framework for Multi-baseline Disparity Estimation | Stereo vision is an effective technique for depth estimation with broad applicability in autonomous urban and highway driving. While various deep learning-based approaches have been developed for stereo, the input data from a binocular setup with a fixed baseline are limited. Addressing such a problem, we present an en... | ['Andreas Zell', 'Faranak Shamsafar'] | 2021-11-24 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [-3.40269208e-02 4.83655743e-02 1.97937250e-01 -7.72581398e-01
-5.37019849e-01 -3.02521586e-01 5.85600853e-01 -4.60602343e-01
-5.55048704e-01 6.36500895e-01 -4.74727303e-02 -3.53168428e-01
2.95037001e-01 -6.67040825e-01 -8.58166575e-01 -7.16491163e-01
5.33414841e-01 3.04278135e-01 4.60856199e-01 -3.54728788... | [8.599235534667969, -2.3281266689300537] |
068dc885-eced-44fd-9512-00f6250328d2 | water-surface-patch-classification-using | 2207.06388 | null | https://arxiv.org/abs/2207.06388v4 | https://arxiv.org/pdf/2207.06388v4.pdf | River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index | Urban rivers provide a water environment that influences residential living. River surface monitoring has become crucial for making decisions about where to prioritize cleaning and when to automatically start the cleaning treatment. We focus on the organic mud, or "scum", that accumulates on the river's surface and con... | ['Masazumi Amakata', 'Junichiro Fujii', 'Takato Yasuno'] | 2022-07-13 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.28634888e-01 -1.18616097e-01 3.57010841e-01 3.86275053e-02
-5.05876124e-01 -6.42982543e-01 4.31965172e-01 5.46400070e-01
2.44346172e-01 4.37147558e-01 4.45941001e-01 -2.73804814e-01
-1.47168636e-02 -1.50889122e+00 -5.46945095e-01 -9.68683064e-01
-2.97984928e-01 1.03720933e-01 1.29909471e-01 -5.51442742... | [9.4097261428833, -1.4638142585754395] |
cd79c3a7-cb34-463d-8cbf-0a2de127903a | raw-image-deblurring | 2012.04264 | null | https://arxiv.org/abs/2012.04264v1 | https://arxiv.org/pdf/2012.04264v1.pdf | Raw Image Deblurring | Deep learning-based blind image deblurring plays an essential role in solving image blur since all existing kernels are limited in modeling the real world blur. Thus far, researchers focus on powerful models to handle the deblurring problem and achieve decent results. For this work, in a new aspect, we discover the gre... | ['Winston H. Hsu', 'Yueh-Cheng Liu', 'Yu-An Chen', 'Chih-Hung Liang'] | 2020-12-08 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 2.25742087e-01 -5.84644377e-01 1.11339971e-01 -1.68788821e-01
-3.40040743e-01 -3.31410289e-01 4.02099013e-01 -7.76668727e-01
-2.29729384e-01 7.89820135e-01 3.66766363e-01 -3.09330255e-01
-2.40742609e-01 -2.85351008e-01 -6.69450402e-01 -9.35552835e-01
9.28303003e-02 -4.50160474e-01 5.59919551e-02 -1.16602376... | [11.600251197814941, -2.6255111694335938] |
a43d6b10-7b29-47a2-90e7-274b11dcd77a | feature-adjacent-multi-fidelity-physics | 2303.11577 | null | https://arxiv.org/abs/2303.11577v3 | https://arxiv.org/pdf/2303.11577v3.pdf | Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations | Physics-informed neural networks have emerged as an alternative method for solving partial differential equations. However, for complex problems, the training of such networks can still require high-fidelity data which can be expensive to generate. To reduce or even eliminate the dependency on high-fidelity data, we pr... | ['Panos Stinis', 'Wenqian Chen'] | 2023-03-21 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.19006895e-01 -1.67785630e-01 3.95883918e-01 -3.36628884e-01
-5.50767720e-01 -1.09514631e-01 4.36009556e-01 -1.61138177e-01
-3.51023614e-01 1.04082620e+00 1.47954240e-01 6.36293963e-02
-5.57697773e-01 -9.95872796e-01 -9.55523133e-01 -6.20375395e-01
9.94281191e-03 2.89470196e-01 -1.64006516e-01 -1.63295910... | [6.525142669677734, 3.428812265396118] |
3fd563dc-0712-48af-930c-cc2f905a603e | booster-shot-boosting-stacked-homography | 2208.09211 | null | https://arxiv.org/abs/2208.09211v1 | https://arxiv.org/pdf/2208.09211v1.pdf | Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention | Improving multi-view aggregation is integral for multi-view pedestrian detection, which aims to obtain a bird's-eye-view pedestrian occupancy map from images captured through a set of calibrated cameras. Inspired by the success of attention modules for deep neural networks, we first propose a Homography Attention Modul... | ['Tae-hoon Kim', 'Philipp Benz', 'Jinwoo Hwang'] | 2022-08-19 | null | null | null | null | ['multiview-detection'] | ['computer-vision'] | [-2.89841831e-01 -2.17768312e-01 2.40009695e-01 -4.25517052e-01
-1.05729115e+00 -5.19808114e-01 7.93916821e-01 7.01838033e-03
-7.37852454e-01 4.71727908e-01 1.39261544e-01 5.03550470e-02
9.74766493e-01 -6.85617924e-01 -1.10766029e+00 -2.76494533e-01
2.43023515e-01 2.01293260e-01 5.64045370e-01 -1.96745604... | [7.697448253631592, -0.7285428643226624] |
8e0ebcaa-bba6-42af-8a05-0a7172980c3e | training-effective-neural-sentence-encoders | 2207.12759 | null | https://arxiv.org/abs/2207.12759v1 | https://arxiv.org/pdf/2207.12759v1.pdf | Training Effective Neural Sentence Encoders from Automatically Mined Paraphrases | Sentence embeddings are commonly used in text clustering and semantic retrieval tasks. State-of-the-art sentence representation methods are based on artificial neural networks fine-tuned on large collections of manually labeled sentence pairs. Sufficient amount of annotated data is available for high-resource languages... | ['Sławomir Dadas'] | 2022-07-26 | null | null | null | null | ['text-clustering', 'semantic-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.46253809e-01 -2.14872703e-01 -7.53206015e-02 -6.74120843e-01
-1.20586658e+00 -3.52355301e-01 5.47913432e-01 4.57377821e-01
-1.05362427e+00 7.59519339e-01 4.51805562e-01 -3.38700712e-01
3.83076370e-01 -7.24472761e-01 -5.75318396e-01 -2.24305570e-01
5.17521381e-01 5.89817047e-01 2.34144866e-01 -4.16092455... | [10.793346405029297, 8.767544746398926] |
181654a3-16f2-423c-be28-c080ceb274ef | sectioning-of-biomedical-abstracts-a-sequence | 2201.07112 | null | https://arxiv.org/abs/2201.07112v1 | https://arxiv.org/pdf/2201.07112v1.pdf | Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task | Rapid growth of the biomedical literature has led to many advances in the biomedical text mining field. Among the vast amount of information, biomedical article abstracts are the easily accessible sources. However, the number of the structured abstracts, describing the rhetorical sections with one of Background, Object... | ['K. Vijay-Shanker', 'Mehmet Efruz Karabulut'] | 2022-01-18 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 1.03088900e-01 2.39024043e-01 -2.53534913e-01 -4.14435655e-01
-5.89710057e-01 -1.94497034e-01 4.49259967e-01 5.75828493e-01
-6.69204652e-01 8.74937534e-01 4.93869811e-01 -6.19621158e-01
-2.61966437e-01 -4.95251119e-01 -4.43884999e-01 -4.98177290e-01
-1.25028029e-01 4.13094103e-01 1.40211865e-01 -2.35720292... | [8.52845573425293, 8.686948776245117] |
c1726ac2-78f8-40d7-bd73-c67d2ca9b801 | fasterrcnn-monitoring-of-road-damages | 2010.11780 | null | https://arxiv.org/abs/2010.11780v1 | https://arxiv.org/pdf/2010.11780v1.pdf | FasterRCNN Monitoring of Road Damages: Competition and Deployment | Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world. An important prerequisite for efficient infrastructure maintenance is to continuously monitor (i.e., quantify the level of safety and reliability) the state of very large structures. Meanwhile, com... | ['Yasuo Ariki', 'Tetsuya Takiguchi', 'Ryoichi Takashima', 'Persch Andreas', 'Yihao Zhang', 'Hascoet Tristan'] | 2020-10-22 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-1.52754232e-01 6.16249405e-02 -5.38323261e-02 -9.93013531e-02
-2.44739935e-01 -1.61331818e-01 3.04821521e-01 2.59635091e-01
-2.60103524e-01 7.58749127e-01 -4.91831712e-02 -5.31752527e-01
-3.75747859e-01 -1.26632810e+00 -3.08364749e-01 -6.38573945e-01
-5.09032667e-01 4.39203531e-01 1.73810631e-01 -2.91386396... | [7.416079521179199, 1.1318929195404053] |
9a6b1814-c445-46fb-bf4a-3d5ca3882ff2 | white-box-inference-attacks-against | 2301.03595 | null | https://arxiv.org/abs/2301.03595v1 | https://arxiv.org/pdf/2301.03595v1.pdf | White-box Inference Attacks against Centralized Machine Learning and Federated Learning | With the development of information science and technology, various industries have generated massive amounts of data, and machine learning is widely used in the analysis of big data. However, if the privacy of machine learning applications' customers cannot be guaranteed, it will cause security threats and losses to u... | ['Jingyi Ge'] | 2022-12-15 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [-3.79498005e-01 9.29263458e-02 -5.31535111e-02 -2.69049108e-01
-5.05851090e-01 -7.72616446e-01 1.79960608e-01 7.22786784e-02
-3.46272975e-01 5.38619280e-01 -2.16269255e-01 -6.19845510e-01
-3.57522219e-02 -8.05748820e-01 -5.84529221e-01 -1.05579138e+00
5.40265953e-03 1.27864107e-01 -6.89315870e-02 2.99119115... | [5.845576763153076, 6.832760810852051] |
3fb2f392-6d72-47d9-8ed5-30d6cf23d471 | human-perception-modeling-for-automatic | 2103.17020 | null | https://arxiv.org/abs/2103.17020v3 | https://arxiv.org/pdf/2103.17020v3.pdf | Semantic-guided Automatic Natural Image Matting with Trimap Generation Network and Light-weight Non-local Attention | Natural image matting aims to precisely separate foreground objects from background using alpha matte. Fully automatic natural image matting without external annotation is challenging. Well-performed matting methods usually require accurate labor-intensive handcrafted trimap as extra input, while the performance of aut... | ['Yangsheng Xu', 'Tin Lun Lam', 'Liguang Zhou', 'Yuhongze Zhou'] | 2021-03-31 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 4.89779085e-01 2.13964172e-02 1.00620754e-01 -2.74683654e-01
-8.17307651e-01 -3.98941100e-01 3.23626250e-01 -4.17544782e-01
-2.58878142e-01 4.86142904e-01 -1.05931632e-01 -2.54886389e-01
4.70907748e-01 -9.68395948e-01 -1.22111297e+00 -8.22829247e-01
5.42480171e-01 8.30121696e-01 4.33417708e-01 5.49426563... | [10.62704086303711, -0.9156713485717773] |
7db573e6-6e1a-4dff-8eb4-4a72f8fc1b59 | online-target-speaker-voice-activity | 2207.05920 | null | https://arxiv.org/abs/2207.05920v1 | https://arxiv.org/pdf/2207.05920v1.pdf | Online Target Speaker Voice Activity Detection for Speaker Diarization | This paper proposes an online target speaker voice activity detection system for speaker diarization tasks, which does not require a priori knowledge from the clustering-based diarization system to obtain the target speaker embeddings. First, we employ a ResNet-based front-end model to extract the frame-level speaker e... | ['Ming Li', 'Qingjian Lin', 'Weiqing Wang'] | 2022-07-13 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-7.73251131e-02 4.43258742e-03 -2.56519541e-02 -5.87849021e-01
-9.82434809e-01 -2.76738465e-01 5.14496028e-01 6.98193833e-02
-3.79567534e-01 -3.70321199e-02 5.91737390e-01 2.49948781e-02
1.68321326e-01 -2.86795408e-01 -2.19643921e-01 -8.97315085e-01
-5.30957244e-02 4.60575819e-01 2.31114939e-01 1.65310696... | [14.555879592895508, 6.1320013999938965] |
49a325bf-baa3-46b4-8d5f-a8581c3a3142 | domain-generalization-through-audio-visual | 2110.10101 | null | https://arxiv.org/abs/2110.10101v1 | https://arxiv.org/pdf/2110.10101v1.pdf | Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition | First person action recognition is becoming an increasingly researched area thanks to the rising popularity of wearable cameras. This is bringing to light cross-domain issues that are yet to be addressed in this context. Indeed, the information extracted from learned representations suffers from an intrinsic "environme... | ['Barbara Caputo', 'Emanuele Alberti', 'Chiara Plizzari', 'Mirco Planamente'] | 2021-10-19 | null | null | null | null | ['egocentric-activity-recognition'] | ['computer-vision'] | [ 5.02493382e-01 -5.04076891e-02 -2.32850209e-01 -3.89216572e-01
-5.77913225e-01 -4.73315001e-01 7.39440024e-01 -1.45371303e-01
-5.09603560e-01 8.69201362e-01 6.07193291e-01 5.89177370e-01
-2.36801624e-01 -3.35737258e-01 -5.87368548e-01 -6.72760487e-01
-2.07565837e-02 1.31222606e-01 1.38942838e-01 -1.13434315... | [8.120156288146973, 0.83561110496521] |
6a82b23f-ce78-418b-bcbd-5189bf0a83fa | eclipse-disambiguating-illumination-and | 2305.16321 | null | https://arxiv.org/abs/2305.16321v2 | https://arxiv.org/pdf/2305.16321v2.pdf | Eclipse: Disambiguating Illumination and Materials using Unintended Shadows | Decomposing an object's appearance into representations of its materials and the surrounding illumination is difficult, even when the object's 3D shape is known beforehand. This problem is ill-conditioned because diffuse materials severely blur incoming light, and is ill-posed because diffuse materials under high-frequ... | ['Pratul P. Srinivasan', 'Todd Zickler', 'Jonathan T. Barron', 'Peter Hedman', 'Ben Mildenhall', 'Dor Verbin'] | 2023-05-25 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 9.13351774e-01 -1.99738935e-01 8.95736933e-01 -1.81262374e-01
-5.95787585e-01 -8.58002722e-01 4.52551812e-01 -7.89843202e-01
-5.50026968e-02 7.19069481e-01 1.85923606e-01 -9.34336483e-02
1.28822222e-01 -7.12871671e-01 -7.46886790e-01 -1.07419562e+00
4.12479818e-01 6.48866892e-01 2.32531235e-01 1.35249853... | [9.764806747436523, -3.0415384769439697] |
6190ee23-5520-42ad-833c-4a30f18251df | building-hierarchically-disentangled-language | null | null | https://aclanthology.org/2020.coling-main.3 | https://aclanthology.org/2020.coling-main.3.pdf | Building Hierarchically Disentangled Language Models for Text Generation with Named Entities | Named entities pose a unique challenge to traditional methods of language modeling. While several domains are characterised with a high proportion of named entities, the occurrence of specific entities varies widely. Cooking recipes, for example, contain a lot of named entities {---} viz. ingredients, cooking technique... | ['Ganesh Bagler', 'Devansh Batra', 'Yash Agarwal'] | 2020-12-01 | null | null | null | coling-2020-8 | ['recipe-generation'] | ['miscellaneous'] | [ 3.96598041e-01 5.28219342e-01 -2.51495630e-01 -3.85908604e-01
-7.21001327e-01 -9.57922459e-01 7.46458411e-01 6.37896895e-01
-2.38971055e-01 7.76914775e-01 8.41631472e-01 -5.03919661e-01
1.79250628e-01 -1.06952560e+00 -8.37387979e-01 -3.29635948e-01
6.37590140e-02 5.46568990e-01 -1.39403060e-01 -2.81714946... | [10.281033515930176, 8.933767318725586] |
0ce9ffe7-3539-4e2e-8501-5230e39d8b6b | joint-discriminative-and-metric-embedding | 2212.14107 | null | https://arxiv.org/abs/2212.14107v1 | https://arxiv.org/pdf/2212.14107v1.pdf | Joint Discriminative and Metric Embedding Learning for Person Re-Identification | Person re-identification is a challenging task because of the high intra-class variance induced by the unrestricted nuisance factors of variations such as pose, illumination, viewpoint, background, and sensor noise. Recent approaches postulate that powerful architectures have the capacity to learn feature representatio... | ['Gianfranco Doretto', 'Zaigham Randhawa', 'Sinan Sabri'] | 2022-12-28 | null | null | null | null | ['person-re-identification', 'metric-learning', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.74440458e-01 -2.33027205e-01 -8.28744397e-02 -7.55541503e-01
-5.45618653e-01 -5.92054367e-01 5.60242593e-01 2.89380364e-02
-6.82206571e-01 6.66744709e-01 2.65501112e-01 1.61961630e-01
-2.09742144e-01 -3.97060543e-01 -8.44365478e-01 -6.50153995e-01
-2.06085414e-01 -7.56840184e-02 -1.93515852e-01 1.21809267... | [14.62699031829834, 0.9875750541687012] |
57bd5dd4-5d90-4e08-8749-e40901ca7599 | single-image-haze-removal-using-conditional | 1903.00395 | null | http://arxiv.org/abs/1903.00395v1 | http://arxiv.org/pdf/1903.00395v1.pdf | Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networks | We present a method to restore a clear image from a haze-affected image using
a Wasserstein generative adversarial network. As the problem is
ill-conditioned, previous methods have required a prior on natural images or
multiple images of the same scene. We train a generative adversarial network to
learn the probability... | ['Joshua Peter Ebenezer', 'Bijaylaxmi Das', 'Sudipta Mukhopadhyay'] | 2019-03-01 | null | null | null | null | ['single-image-haze-removal'] | ['computer-vision'] | [ 4.84476030e-01 1.21835239e-01 6.35285079e-01 -5.89650452e-01
-9.87099767e-01 -4.35552657e-01 5.69433510e-01 -7.02231228e-01
-4.03704345e-01 7.96566069e-01 8.66623297e-02 -2.02760145e-01
1.77182071e-02 -8.49101305e-01 -9.58032370e-01 -1.09681201e+00
8.45918804e-02 2.08655030e-01 2.72562653e-01 -1.62018225... | [10.884712219238281, -3.1514477729797363] |
c7f9531c-9c0a-4ee2-a16b-d2689448e7d1 | fashionformer-a-simple-effective-and-unified | 2204.04654 | null | https://arxiv.org/abs/2204.04654v2 | https://arxiv.org/pdf/2204.04654v2.pdf | Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition | Human fashion understanding is one crucial computer vision task since it has comprehensive information for real-world applications. This focus on joint human fashion segmentation and attribute recognition. Contrary to the previous works that separately model each task as a multi-head prediction problem, our insight is ... | ['DaCheng Tao', 'Yunhai Tong', 'Guangliang Cheng', 'Jingbo Wang', 'Xiangtai Li', 'Shilin Xu'] | 2022-04-10 | null | null | null | null | ['fashion-understanding'] | ['computer-vision'] | [ 3.60730201e-01 -3.69497202e-03 -3.54145586e-01 -6.55487716e-01
-1.07792783e+00 -4.78700280e-01 2.92869002e-01 -1.36424184e-01
-3.40906471e-01 3.28753203e-01 1.13660231e-01 5.98566001e-03
1.59260035e-01 -5.84607184e-01 -8.36927235e-01 -6.09963536e-01
4.84982729e-01 3.88459563e-01 2.12021306e-01 -1.08280264... | [9.36752700805664, 0.25357404351234436] |
f9980f0e-1843-4a26-8143-f3013ed69565 | coarse-to-fine-contrastive-learning-in-image | 2305.13812 | null | https://arxiv.org/abs/2305.13812v1 | https://arxiv.org/pdf/2305.13812v1.pdf | Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality | Contrastively trained vision-language models have achieved remarkable progress in vision and language representation learning, leading to state-of-the-art models for various downstream multimodal tasks. However, recent research has highlighted severe limitations of these models in their ability to perform compositional... | ['Yu Chen', 'Jingfei Du', 'Wenhan Xiong', 'Mengjiao Wang', 'Qifan Wang', 'Pengchuan Zhang', 'Harman Singh'] | 2023-05-23 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 7.24827468e-01 2.79080003e-01 -3.69499803e-01 -7.40817070e-01
-5.89075685e-01 -4.33962733e-01 8.40730906e-01 4.57498252e-01
-3.76616955e-01 2.89943367e-01 2.38172397e-01 -3.69659424e-01
7.59242103e-02 -7.66195059e-01 -9.11146522e-01 -5.17166018e-01
1.98613614e-01 6.36021614e-01 -1.24140911e-01 -2.43985236... | [10.56687068939209, 1.5867046117782593] |
c2e0a517-e15e-431f-a08f-8923d950070f | generative-diffeomorphic-modelling-of-large | null | null | https://doi.org/10.1016/j.neuroimage.2017.10.060 | http://discovery.ucl.ac.uk/10036377/1/Ashburner_1-s2.0-S1053811917308947-main.pdf | Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction | In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a direct application for learning average-shaped probabilistic tissue templates in a fully automated ... | ['Claudia Blaiotta', 'John Ashburner', 'Patrick Freund', 'M. Jorge Cardoso'] | 2018-02-01 | null | null | null | neuroimage-2018-2 | ['diffeomorphic-medical-image-registration'] | ['medical'] | [ 6.17211223e-01 1.94905981e-01 2.41619125e-01 -4.34997827e-01
-8.96738529e-01 -5.42334199e-01 7.13808060e-01 1.24257155e-01
-6.30909264e-01 6.83577001e-01 -1.06941797e-01 -1.84768423e-01
-3.91969770e-01 -4.36125576e-01 -4.22862202e-01 -1.03359938e+00
-1.76324382e-01 1.01229203e+00 3.33690435e-01 2.41842836... | [14.076140403747559, -2.3590550422668457] |
3fb61e4c-b66f-4e2f-8e21-1813953f56f7 | few-shot-personalized-saliency-prediction | 2307.02799 | null | https://arxiv.org/abs/2307.02799v1 | https://arxiv.org/pdf/2307.02799v1.pdf | Few-Shot Personalized Saliency Prediction Using Tensor Regression for Preserving Structural Global Information | This paper presents a few-shot personalized saliency prediction using tensor-to-matrix regression for preserving the structural global information of personalized saliency maps (PSMs). In contrast to a general saliency map, a PSM has been great potential since its map indicates the person-specific visual attention that... | ['Miki Haseyama', 'Takahiro Ogawa', 'Keisuke Maeda', 'Yuya Moroto'] | 2023-07-06 | null | null | null | null | ['saliency-prediction'] | ['computer-vision'] | [ 1.45603567e-01 -3.51524472e-01 -2.19013214e-01 -2.13302806e-01
-1.44289806e-01 3.14249486e-01 9.43023860e-02 -1.54048830e-01
-1.41720055e-02 3.26454937e-01 5.78320026e-01 2.49165967e-01
-3.34743112e-01 -2.17163578e-01 -4.30451423e-01 -9.10186231e-01
2.18270540e-01 -2.54797280e-01 7.34103680e-01 -4.79005903... | [9.797274589538574, -0.3564760386943817] |
9492d4da-3ff4-4f46-92d8-0655b6ede034 | exploring-high-quality-target-domain | 2208.06100 | null | https://arxiv.org/abs/2208.06100v1 | https://arxiv.org/pdf/2208.06100v1.pdf | Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic Segmentation | In unsupervised domain adaptive (UDA) semantic segmentation, the distillation based methods are currently dominant in performance. However, the distillation technique requires complicate multi-stage process and many training tricks. In this paper, we propose a simple yet effective method that can achieve competitive pe... | ['Xiaoming Hu', 'Yuan Gao', 'Zilei Wang', 'Junjie Li'] | 2022-08-12 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 4.55915809e-01 1.53224066e-01 -2.31949702e-01 -3.20997983e-01
-1.26290858e+00 -3.14499497e-01 3.00841212e-01 -2.57051557e-01
-3.30382794e-01 7.20616996e-01 -2.89357543e-01 -6.64716363e-02
1.69554297e-02 -8.66790771e-01 -6.52882099e-01 -9.43546951e-01
2.53226191e-01 5.36361933e-01 5.92839599e-01 -1.32385984... | [9.625194549560547, 1.3023062944412231] |
1e6e1ee8-56ba-449a-9a15-a2660a8b65ae | exploring-the-power-of-romanian-bert-for | null | null | https://aclanthology.org/2020.vardial-1.22 | https://aclanthology.org/2020.vardial-1.22.pdf | Exploring the Power of Romanian BERT for Dialect Identification | Dialect identification represents a key aspect for improving a series of tasks, for example, opinion mining, considering that the location of the speaker can greatly influence the attitude towards a subject. In this work, we describe the systems developed by our team for VarDial 2020: Romanian Dialect Identification, a... | ['Traian Rebedea', 'Dumitru-Clementin Cercel', 'Andrei-Marius Avram', 'George-Eduard Zaharia'] | null | null | null | null | vardial-coling-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-2.11987376e-01 1.10445797e-01 3.29598874e-01 -3.97466511e-01
-5.27704477e-01 -8.40649605e-01 8.78363132e-01 3.37926865e-01
-7.90347815e-01 5.37396729e-01 1.67254657e-01 -3.51752698e-01
3.35231513e-01 -8.11036229e-01 -4.99363750e-01 -4.32071120e-01
4.50963825e-02 7.64068604e-01 -1.51925916e-02 -7.03208983... | [10.197389602661133, 10.703292846679688] |
dfe6bd6e-6bd1-4d5d-a41a-1209c6bf0e8e | semantic-sentence-composition-reasoning-for | 2203.00160 | null | https://arxiv.org/abs/2203.00160v1 | https://arxiv.org/pdf/2203.00160v1.pdf | Semantic Sentence Composition Reasoning for Multi-Hop Question Answering | Due to the lack of insufficient data, existing multi-hop open domain question answering systems require to effectively find out relevant supporting facts according to each question. To alleviate the challenges of semantic factual sentences retrieval and multi-hop context expansion, we present a semantic sentence compos... | ['Qianglong Chen'] | 2022-03-01 | null | null | null | null | ['multi-hop-question-answering', 'semantic-retrieval'] | ['knowledge-base', 'natural-language-processing'] | [ 2.85163194e-01 5.53499222e-01 -1.19756617e-01 -3.23579550e-01
-1.47954440e+00 -4.09183800e-01 6.97536647e-01 5.29537916e-01
-4.47115868e-01 8.08936059e-01 6.27689958e-01 -4.10393775e-01
-3.42985898e-01 -9.50562954e-01 -5.64616382e-01 2.46139184e-01
5.30483603e-01 8.36965859e-01 9.00522113e-01 -1.07726192... | [10.806961059570312, 7.965622901916504] |
9ed81dc8-0a27-441e-a2af-7e642064da8a | unicoder-a-universal-language-encoder-by-pre | 1909.00964 | null | https://arxiv.org/abs/1909.00964v2 | https://arxiv.org/pdf/1909.00964v2.pdf | Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks | We present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data in one language and directly applied to inputs of the same task in other languages. Comparing to similar efforts such as Multilingual BERT a... | ['Haoyang Huang', 'Yaobo Liang', 'Nan Duan', 'Ming Zhou', 'Ming Gong', 'Daxin Jiang', 'Linjun Shou'] | 2019-09-03 | unicoder-a-universal-language-encoder-by-pre-1 | https://aclanthology.org/D19-1252 | https://aclanthology.org/D19-1252.pdf | ijcnlp-2019-11 | ['cross-lingual-natural-language-inference', 'cross-lingual-question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.49453653e-02 -1.21459365e-01 -3.80284935e-01 -5.34753203e-01
-1.68838036e+00 -8.78827631e-01 5.19851148e-01 -7.53191113e-03
-7.00734198e-01 9.15371239e-01 3.02512646e-01 -5.41868687e-01
5.39773643e-01 -5.84970176e-01 -9.63923872e-01 -1.49370223e-01
2.84931064e-01 6.03264868e-01 -9.42619592e-02 -1.76014692... | [11.038536071777344, 9.8592529296875] |
cc216bc0-f8f0-41f5-9a9c-7d3fc480522f | ambulance-demand-prediction-via-convolutional | 2306.04994 | null | https://arxiv.org/abs/2306.04994v1 | https://arxiv.org/pdf/2306.04994v1.pdf | Ambulance Demand Prediction via Convolutional Neural Networks | Minimizing response times is crucial for emergency medical services to reduce patients' waiting times and to increase their survival rates. Many models exist to optimize operational tasks such as ambulance allocation and dispatching. Including accurate demand forecasts in such models can improve operational decision-ma... | ['Maximilian Schiffer', 'Maximiliane Rautenstrauß'] | 2023-06-08 | null | null | null | null | ['hyperparameter-optimization', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-4.52046752e-01 -3.07699233e-01 -3.76508944e-02 -8.45058918e-01
-4.24744189e-01 -1.71536431e-01 2.93221444e-01 5.73344588e-01
-9.37753439e-01 6.40983403e-01 5.99867463e-01 -7.20583618e-01
-5.73696852e-01 -1.16554129e+00 -3.45261842e-01 -4.44095016e-01
-4.86132085e-01 7.45178759e-01 -2.81246930e-01 -4.78331804... | [6.643341064453125, 2.940605640411377] |
47da2092-8a3a-4b75-ae6c-4b04d563f847 | neural-motifs-scene-graph-parsing-with-global | 1711.06640 | null | http://arxiv.org/abs/1711.06640v2 | http://arxiv.org/pdf/1711.06640v2.pdf | Neural Motifs: Scene Graph Parsing with Global Context | We investigate the problem of producing structured graph representations of
visual scenes. Our work analyzes the role of motifs: regularly appearing
substructures in scene graphs. We present new quantitative insights on such
repeated structures in the Visual Genome dataset. Our analysis shows that
object labels are hig... | ['Mark Yatskar', 'Rowan Zellers', 'Yejin Choi', 'Sam Thomson'] | 2017-11-17 | neural-motifs-scene-graph-parsing-with-global-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zellers_Neural_Motifs_Scene_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zellers_Neural_Motifs_Scene_CVPR_2018_paper.pdf | cvpr-2018-6 | ['panoptic-scene-graph-generation'] | ['computer-vision'] | [ 6.08926117e-01 4.30068642e-01 -2.60908872e-01 -3.43096346e-01
-3.38753104e-01 -8.51247191e-01 7.42293715e-01 4.46268618e-01
2.41050497e-01 3.38420242e-01 3.58776718e-01 -1.95701420e-01
-6.54788241e-02 -7.86840320e-01 -1.19977605e+00 -5.59579432e-01
-5.50745606e-01 3.87141854e-01 4.91280943e-01 5.98266087... | [10.419750213623047, 1.6744736433029175] |
dded2683-124f-4572-8320-00d40c6f3b8c | tbpos-dataset-for-large-scale-precision | 2302.09825 | null | https://arxiv.org/abs/2302.09825v1 | https://arxiv.org/pdf/2302.09825v1.pdf | TBPos: Dataset for Large-Scale Precision Visual Localization | Image based localization is a classical computer vision challenge, with several well-known datasets. Generally, datasets consist of a visual 3D database that captures the modeled scenery, as well as query images whose 3D pose is to be discovered. Usually the query images have been acquired with a camera that differs fr... | ['Jani Boutellier', 'Juho Kannala', 'Luca Ferranti', 'Ilona Söchting', 'Masud Fahim'] | 2023-02-20 | null | null | null | null | ['image-based-localization', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [ 4.88642082e-02 -4.37956572e-01 -2.75385410e-01 -3.56524050e-01
-9.59438741e-01 -9.79121089e-01 5.91318905e-01 2.79158771e-01
-6.81492805e-01 3.30044419e-01 -4.82119054e-01 5.49693145e-02
-9.34374183e-02 -5.76912701e-01 -9.78321373e-01 -4.72968936e-01
7.82841071e-02 1.03486812e+00 4.93672967e-01 9.77775380... | [7.516819477081299, -2.1968181133270264] |
c35571d9-3e30-492f-83d9-77bb91a2fc9c | towards-better-dynamic-graph-learning-new | 2303.13047 | null | https://arxiv.org/abs/2303.13047v1 | https://arxiv.org/pdf/2303.13047v1.pdf | Towards Better Dynamic Graph Learning: New Architecture and Unified Library | We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning that solely learns from the sequences of nodes' historical first-hop interactions. DyGFormer incorporates two distinct designs: a neighbor co-occurrence encoding scheme that explores the correlations of the source node and destination... | ['Weifeng Lv', 'Bowen Du', 'Leilei Sun', 'Le Yu'] | 2023-03-23 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-1.74996093e-01 -6.22314401e-02 -1.07099533e+00 -3.21652263e-01
-4.43596452e-01 -8.13515484e-01 5.02195656e-01 2.49842152e-01
8.96282122e-02 6.67242110e-01 4.16665465e-01 -6.19853377e-01
-1.84862852e-01 -9.83509123e-01 -6.72416925e-01 -5.79290271e-01
-8.87862086e-01 6.68848515e-01 5.20341158e-01 -1.89069316... | [7.064915180206299, 6.144399642944336] |
d42ae5e3-8939-4bd4-9d35-0b11bf90b67c | blind-image-deblurring-using-row-column | 1712.01937 | null | http://arxiv.org/abs/1712.01937v1 | http://arxiv.org/pdf/1712.01937v1.pdf | Blind Image Deblurring Using Row-Column Sparse Representations | Blind image deblurring is a particularly challenging inverse problem where
the blur kernel is unknown and must be estimated en route to recover the
deblurred image. The problem is of strong practical relevance since many
imaging devices such as cellphone cameras, must rely on deblurring algorithms
to yield satisfactory... | ['Vishal Monga', 'Yuelong Li', 'Mohammad Tofighi'] | 2017-12-05 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 3.98669749e-01 -5.58216274e-01 1.02363899e-01 1.15516633e-01
-5.73989034e-01 -7.21183181e-01 2.84536153e-01 -9.72937584e-01
-1.85343832e-01 8.46989989e-01 6.09100342e-01 -2.32350484e-01
-3.92415255e-01 2.26794571e-01 -5.56132436e-01 -1.03330874e+00
7.23218620e-02 -2.35810176e-01 -1.50649592e-01 1.79129764... | [11.614982604980469, -2.7606866359710693] |
9f9c5126-d590-492a-b0e2-c1def39232c3 | controlled-sparsity-kernel-learning | 1401.0116 | null | http://arxiv.org/abs/1401.0116v1 | http://arxiv.org/pdf/1401.0116v1.pdf | Controlled Sparsity Kernel Learning | Multiple Kernel Learning(MKL) on Support Vector Machines(SVMs) has been a
popular front of research in recent times due to its success in application
problems like Object Categorization. This success is due to the fact that MKL
has the ability to choose from a variety of feature kernels to identify the
optimal kernel c... | ['Raman Sankaran', 'Sreedal Menon', 'Dinesh Govindaraj', 'Chiranjib Bhattacharyya'] | 2013-12-31 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [-9.58039984e-03 -5.14525592e-01 -2.99868733e-01 -2.18405873e-01
-4.20146286e-01 -3.39857370e-01 4.47598875e-01 3.46960068e-01
-5.68003953e-01 6.74003661e-01 -3.59711409e-01 -9.60458294e-02
-6.13319814e-01 -6.99174047e-01 -3.71260375e-01 -8.61270189e-01
-2.05502048e-01 3.29991907e-01 5.23039460e-01 -2.04729989... | [7.8892598152160645, 4.094610214233398] |
6228161c-54c9-4ada-8eaf-89b3e3376400 | ernie-20-a-continual-pre-training-framework | 1907.12412 | null | https://arxiv.org/abs/1907.12412v2 | https://arxiv.org/pdf/1907.12412v2.pdf | ERNIE 2.0: A Continual Pre-training Framework for Language Understanding | Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing. Current pre-training procedures usually focus on training the model with several simple tasks to g... | ['Shikun Feng', 'Yu Sun', 'Hua Wu', 'Hao Tian', 'Yukun Li', 'Shuohuan Wang', 'Haifeng Wang'] | 2019-07-29 | null | null | null | null | ['linguistic-acceptability', 'chinese-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-1.17020734e-01 7.64309764e-02 -2.98908919e-01 -6.52542770e-01
-7.68885016e-01 -4.57102805e-01 4.80986565e-01 3.30760449e-01
-7.63748467e-01 9.01513278e-01 5.10841250e-01 -3.16122562e-01
1.11900456e-01 -6.43629074e-01 -6.38660729e-01 -2.40350500e-01
-1.57517016e-01 6.74307942e-01 1.55339539e-01 -4.62469488... | [10.53170394897461, 9.152518272399902] |
35d873a3-ce20-462b-8ae3-f69249dd9d04 | discrete-diffusion-probabilistic-models-for | 2305.09489 | null | https://arxiv.org/abs/2305.09489v1 | https://arxiv.org/pdf/2305.09489v1.pdf | Discrete Diffusion Probabilistic Models for Symbolic Music Generation | Denoising Diffusion Probabilistic Models (DDPMs) have made great strides in generating high-quality samples in both discrete and continuous domains. However, Discrete DDPMs (D3PMs) have yet to be applied to the domain of Symbolic Music. This work presents the direct generation of Polyphonic Symbolic Music using D3PMs. ... | ['Gerhard Widmer', 'Silvan Peter', 'Matthias Plasser'] | 2023-05-16 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 4.00588721e-01 -2.82350928e-02 3.04796875e-01 2.04848610e-02
-1.27239990e+00 -8.63671064e-01 8.30173135e-01 -2.72983432e-01
-3.95740196e-02 6.02428734e-01 3.96774471e-01 1.60020858e-01
-6.81551397e-01 -7.11956680e-01 -1.58435941e-01 -7.20155299e-01
-2.45556161e-01 4.33989257e-01 1.94551378e-01 -3.23315803... | [15.727368354797363, 5.586975574493408] |
fd38e11a-b967-440b-ba76-9f388e3d1a2a | painsight-an-extendable-opinion-mining | 2306.02043 | null | https://arxiv.org/abs/2306.02043v1 | https://arxiv.org/pdf/2306.02043v1.pdf | Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews | As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers. Previous studies have endeavored to identify key aspects of customer reviews through the development of sentiment analysis mode... | ['Pilsung Kang', 'Younsun Kim', 'Yookyung Kho', 'Doyoon Kim', 'Jaehee Kim', 'Yukyung Lee'] | 2023-06-03 | null | null | null | null | ['sentiment-analysis', 'topic-models', 'opinion-mining'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.16318651e-01 -3.87985222e-02 -5.06913126e-01 -7.63954222e-01
-8.49758565e-01 -5.50839007e-01 4.65005249e-01 5.65970004e-01
-2.69310832e-01 1.21523939e-01 1.76970050e-01 -8.24067667e-02
-1.15220539e-01 -4.94885296e-01 -8.95539895e-02 -5.26198626e-01
1.46252185e-01 3.81224871e-01 -5.28811812e-01 -5.11963069... | [11.307600975036621, 6.637160301208496] |
74e91c7d-cc93-437d-87bd-aafd0906aa42 | piecewise-stationary-multi-objective-multi | 2302.05257 | null | https://arxiv.org/abs/2302.05257v2 | https://arxiv.org/pdf/2302.05257v2.pdf | Piecewise-Stationary Multi-Objective Multi-Armed Bandit with Application to Joint Communications and Sensing | We study a multi-objective multi-armed bandit problem in a dynamic environment. The problem portrays a decision-maker that sequentially selects an arm from a given set. If selected, each action produces a reward vector, where every element follows a piecewise-stationary Bernoulli distribution. The agent aims at choosin... | ['Setareh Maghsudi', 'Amir Rezaei Balef'] | 2023-02-10 | null | null | null | null | ['change-detection', 'multi-objective-reinforcement-learning', 'multi-armed-bandits'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 3.04518014e-01 3.62895355e-02 -5.05663037e-01 7.83659071e-02
-1.24080694e+00 -7.57647395e-01 -1.71793044e-01 -2.43511535e-02
-5.00289917e-01 1.04946256e+00 -1.55815750e-01 -5.24645567e-01
-8.71489823e-01 -8.75001013e-01 -7.54750371e-01 -1.15528953e+00
-4.27068055e-01 7.21465826e-01 -5.14043748e-01 1.29213020... | [4.540634632110596, 3.288489818572998] |
8576daaa-b140-4f19-b735-95c912aac6ac | exploring-visual-patterns-in-projected-human | 2001.08372 | null | https://arxiv.org/abs/2001.08372v3 | https://arxiv.org/pdf/2001.08372v3.pdf | ProjectionPathExplorer: Exploring Visual Patterns in Projected Decision-Making Paths | In problem-solving, a path towards solutions can be viewed as a sequence of decisions. The decisions, made by humans or computers, describe a trajectory through a high-dimensional representation space of the problem. By means of dimensionality reduction, these trajectories can be visualized in lower-dimensional space. ... | ['Holger Stitz', 'Moritz Schöfl', 'Christian Steinparz', 'Andreas Hinterreiter', 'Marc Streit'] | 2020-01-20 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [ 1.52968302e-01 4.91656996e-02 2.13683248e-01 -2.32767105e-01
-5.20631149e-02 -9.87725258e-01 7.93157518e-01 3.84338677e-01
-2.28763804e-01 2.80269146e-01 3.61068666e-01 -4.11802679e-01
-9.91901577e-01 -7.90759206e-01 1.06726326e-01 -9.10201907e-01
-4.19204205e-01 6.25415325e-01 -1.19153783e-01 -4.07530934... | [4.329003810882568, 1.601082682609558] |
291efab1-6f7c-44d8-9361-7af8aace5bdf | human-vs-computer-go-review-and-prospect | 1606.02032 | null | http://arxiv.org/abs/1606.02032v1 | http://arxiv.org/pdf/1606.02032v1.pdf | Human vs. Computer Go: Review and Prospect | The Google DeepMind challenge match in March 2016 was a historic achievement
for computer Go development. This article discusses the development of
computational intelligence (CI) and its relative strength in comparison with
human intelligence for the game of Go. We first summarize the milestones
achieved for computer ... | ['Tai-Hsiung Yang', 'Ming-Wan Wang', 'Chun-Hsun Chou', 'Ping-Chiang Chou', 'I-Chen Wu', 'Ting-Han Wei', 'Shi-Jim Yen', 'Mei-Hui Wang', 'Chang-Shing Lee'] | 2016-06-07 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-3.51247460e-01 3.19259197e-01 -2.45603040e-01 -7.77157024e-02
-6.85311198e-01 -4.07880366e-01 2.14185759e-01 -2.65416294e-01
-3.93301487e-01 3.25386107e-01 -1.13472745e-01 -4.36998934e-01
-2.24839374e-01 -7.57391095e-01 -6.43689036e-01 -2.74504721e-01
-3.80807310e-01 6.60665870e-01 -9.28126425e-02 -7.43042767... | [3.4743213653564453, 1.4396696090698242] |
98cd1247-970e-4e5e-a6e0-ec23e2fcf5bd | vid2curve-simultaneously-camera-motion | 2005.03372 | null | https://arxiv.org/abs/2005.03372v3 | https://arxiv.org/pdf/2005.03372v3.pdf | Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video | Thin structures, such as wire-frame sculptures, fences, cables, power lines, and tree branches, are common in the real world. It is extremely challenging to acquire their 3D digital models using traditional image-based or depth-based reconstruction methods because thin structures often lack distinct point features and ... | ['Wenping Wang', 'Hung-Kuo Chu', 'Lingjie Liu', 'Nenglun Chen', 'Peng Wang', 'Christian Theobalt'] | 2020-05-07 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 2.53967553e-01 -4.33746576e-02 2.45863497e-01 1.53175816e-01
-5.32393634e-01 -8.44522417e-01 3.87153119e-01 -1.32288501e-01
7.81911463e-02 2.43135184e-01 -3.41585577e-01 -9.61382911e-02
-7.35881105e-02 -6.40048087e-01 -7.21899688e-01 -3.79900336e-01
1.70293212e-01 9.09440458e-01 8.36540461e-01 1.02741130... | [9.288630485534668, -2.974428415298462] |
5c22c739-5fc1-4a8b-ae7c-4f3bc93aa6cb | flexible-compositional-learning-of-structured | 2105.09848 | null | https://arxiv.org/abs/2105.09848v1 | https://arxiv.org/pdf/2105.09848v1.pdf | Flexible Compositional Learning of Structured Visual Concepts | Humans are highly efficient learners, with the ability to grasp the meaning of a new concept from just a few examples. Unlike popular computer vision systems, humans can flexibly leverage the compositional structure of the visual world, understanding new concepts as combinations of existing concepts. In the current pap... | ['Brenden M. Lake', 'Yanli Zhou'] | 2021-05-20 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 1.56365022e-01 1.69821292e-01 -1.47608504e-01 -5.06135225e-01
1.08796433e-01 -7.45210230e-01 9.06571805e-01 5.53407848e-01
-4.40939218e-01 2.78133303e-01 2.09606200e-01 -4.39875424e-01
-1.58754006e-01 -9.04392779e-01 -8.23129058e-01 -1.51065990e-01
-1.62037000e-01 4.62375104e-01 4.57240641e-01 -4.40572649... | [9.540753364562988, 6.923194408416748] |
1fb8f476-ce4d-41ae-859f-232677b86e79 | heart-rate-variability-and-respiration-signal | 1605.05247 | null | http://arxiv.org/abs/1605.05247v1 | http://arxiv.org/pdf/1605.05247v1.pdf | Heart Rate Variability and Respiration Signal as Diagnostic Tools for Late Onset Sepsis in Neonatal Intensive Care Units | Apnea-bradycardia is one of the major clinical early indicators of late-onset
sepsis occurring in approximately 7% to 10% of all neonates and in more than
25% of very low birth weight infants in NICU. The objective of this paper was
to determine if HRV, respiration and their relationships help to diagnose
infection in ... | ['Yu-An Wang', 'Guy Carrault', 'Huazhong Shu', 'Nathalie Costet', 'Lotfi Senhadji', 'Alain Beuchee'] | 2016-05-12 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 2.99458094e-02 -1.21086843e-01 2.68445253e-01 -9.05617252e-02
4.59373444e-02 -5.20179331e-01 -5.91115803e-02 4.92368042e-01
-4.79976624e-01 8.55679691e-01 1.21816948e-01 -3.16662669e-01
-4.99734849e-01 -4.05398190e-01 -1.05761029e-01 -8.90129328e-01
-6.03602946e-01 -3.35744917e-02 -1.13871861e-02 -4.57125483... | [14.050130844116211, 3.065476417541504] |
1c93f14c-5647-4394-bf0a-a12d192afebb | analyzing-inexact-hypergradients-for-bilevel | 2301.04764 | null | https://arxiv.org/abs/2301.04764v1 | https://arxiv.org/pdf/2301.04764v1.pdf | Analyzing Inexact Hypergradients for Bilevel Learning | Estimating hyperparameters has been a long-standing problem in machine learning. We consider the case where the task at hand is modeled as the solution to an optimization problem. Here the exact gradient with respect to the hyperparameters cannot be feasibly computed and approximate strategies are required. We introduc... | ['Lindon Roberts', 'Matthias J. Ehrhardt'] | 2023-01-11 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-2.12613001e-01 2.05504864e-01 -2.04610169e-01 -7.66929761e-02
-1.05046439e+00 -6.63664997e-01 4.39004749e-01 -2.40592677e-02
-4.16666597e-01 1.18299663e+00 -3.00953925e-01 -3.14979166e-01
-4.78268325e-01 -4.93569046e-01 -7.39405990e-01 -9.86422360e-01
-1.68020546e-01 7.34522104e-01 -3.24376263e-02 -2.84213006... | [6.796968936920166, 4.17882776260376] |
2cdc45ba-e705-4cac-85d2-52f4cd1a0712 | weakly-and-partially-supervised-learning | null | null | https://www.researchgate.net/publication/350580836_Weakly_and_Partially_Supervised_Learning_Frameworks_for_Anomaly_Detection | https://www.di.ubi.pt/~hugomcp/doc/bruno_degardin_2019.pdf | Weakly and Partially Supervised Learning Frameworks for Anomaly Detection | The main objective is to provide several solutions to the mentioned problems, by focusing on analyzing previous state-of-the-art methods and presenting an extensive overview to clarify the concepts employed on capturing normal and abnormal patterns. Also, by exploring different strategies, we were able to develop new a... | ['Bruno Degardin'] | 2020-07-23 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video', 'anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 2.47404397e-01 6.29536733e-02 -2.17416480e-01 -4.22331631e-01
-7.74579763e-01 -1.30847901e-01 6.00183368e-01 2.41150886e-01
-5.59454322e-01 5.32002211e-01 -1.22817859e-01 1.21870384e-01
-4.14274544e-01 -4.84658867e-01 -6.95816875e-01 -8.52575719e-01
-6.67504191e-01 6.67980790e-01 5.92128456e-01 -1.72729492... | [7.973081111907959, 1.193335771560669] |
e37c5f45-3b13-4bd1-ae99-96f1055a5873 | help-the-blind-see-assistance-for-the | 2303.13536 | null | https://arxiv.org/abs/2303.13536v1 | https://arxiv.org/pdf/2303.13536v1.pdf | Help the Blind See: Assistance for the Visually Impaired through Augmented Acoustic Simulation | An estimated 253 million people have visual impairments. These visual impairments affect everyday lives, and limit their understanding of the outside world. This can pose a risk to health from falling or collisions. We propose a solution to this through quick and detailed communication of environmental spatial geometry... | ['Ritik Jalisatgi', 'Alexander Mehta'] | 2023-02-09 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-3.66615355e-02 -3.77145439e-01 7.85895169e-01 -5.99294342e-02
-3.67860019e-01 -5.64503849e-01 8.61893818e-02 4.48711403e-02
-5.14881909e-01 5.33772945e-01 -5.25587946e-02 -2.35435367e-01
-1.33223191e-01 -9.21637297e-01 -5.53305387e-01 -3.39835435e-01
-4.45352316e-01 2.49529004e-01 6.72087729e-01 -1.00801878... | [7.887882232666016, -1.887730598449707] |
8da6a2e8-088a-48eb-9f3e-6ee5f4daba0d | automatic-online-detection-of-atrial | null | null | https://doi.org/10.1186/1475-925X-13-18 | https://biomedical-engineering-online.biomedcentral.com/track/pdf/10.1186/1475-925X-13-18 | Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy | Background
Atrial fibrillation (AF) is the most common and debilitating abnormalities of the arrhythmias worldwide, with a major impact on morbidity and mortality. The detection of AF becomes crucial in preventing both acute and chronic cardiac rhythm disorders.
Objective
Our objective is to devise a method for re... | ['YuanTing Zhang', 'Emma Pickwell-MacPherson', 'Hongxia Ding', 'Benjamin Ung', 'Xiaolin Zhou'] | 2014-02-17 | null | null | null | biomedical-engineering-online-2014-2 | ['atrial-fibrillation-detection'] | ['medical'] | [ 1.93986192e-01 -3.63845825e-01 5.23379864e-03 1.01149052e-01
-3.84524912e-01 -6.45666778e-01 1.61025167e-01 3.63687873e-01
-4.46926385e-01 1.14034855e+00 -3.80332500e-01 -4.85025376e-01
-4.02675629e-01 -6.43871844e-01 7.63383806e-02 -7.73723602e-01
-5.78007340e-01 2.79966056e-01 -5.13813831e-02 3.12619746... | [14.166574478149414, 3.180955410003662] |
701fb375-b068-4150-988f-10ac66422c6e | fsnet-redesign-self-supervised-monodepth-for | 2304.10719 | null | https://arxiv.org/abs/2304.10719v1 | https://arxiv.org/pdf/2304.10719v1.pdf | FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving | Predicting accurate depth with monocular images is important for low-cost robotic applications and autonomous driving. This study proposes a comprehensive self-supervised framework for accurate scale-aware depth prediction on autonomous driving scenes utilizing inter-frame poses obtained from inertial measurements. In ... | ['Ming Liu', 'Lujia Wang', 'Huaiyang Huang', 'Zhenhua Xu', 'Yuxuan Liu'] | 2023-04-21 | null | null | null | null | ['visual-odometry'] | ['robots'] | [ 4.08487618e-02 1.68941468e-01 -1.90855786e-01 -7.90921152e-01
-2.95363784e-01 -3.64207506e-01 3.61186713e-01 -4.04502690e-01
-5.25452673e-01 6.26902640e-01 -1.99952409e-01 -2.01525614e-01
2.31335416e-01 -7.72689581e-01 -9.00850058e-01 -5.76946914e-01
-6.50800243e-02 5.39722979e-01 5.93985140e-01 -1.40041843... | [8.043575286865234, -2.188157081604004] |
4e6b85c6-15a3-4d47-87a5-1ff96a8b612e | joint-visual-and-audio-learning-for-video | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Badamdorj_Joint_Visual_and_Audio_Learning_for_Video_Highlight_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Badamdorj_Joint_Visual_and_Audio_Learning_for_Video_Highlight_Detection_ICCV_2021_paper.pdf | Joint Visual and Audio Learning for Video Highlight Detection | In video highlight detection, the goal is to identify the interesting moments within an unedited video. Although the audio component of the video provides important cues for highlight detection, the majority of existing efforts focus almost exclusively on the visual component. In this paper, we argue that both audi... | ['Li Cheng', 'Yang Wang', 'Mrigank Rochan', 'Taivanbat Badamdorj'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['highlight-detection'] | ['computer-vision'] | [ 6.21481910e-02 -3.98824394e-01 -1.97513714e-01 3.44677418e-02
-8.55413020e-01 -4.99294847e-01 3.59750956e-01 3.19847077e-01
-8.42687860e-02 2.23752052e-01 4.39477116e-01 1.02435254e-01
2.28895992e-02 -2.62496114e-01 -5.06758451e-01 -7.51534402e-01
-2.44015396e-01 -6.34509325e-01 3.70683044e-01 2.16562171... | [10.106554985046387, 0.4541068971157074] |
7ba5b56b-2155-460d-9617-4c7707e86dff | using-chatgpt-for-entity-matching | 2305.03423 | null | https://arxiv.org/abs/2305.03423v2 | https://arxiv.org/pdf/2305.03423v2.pdf | Using ChatGPT for Entity Matching | Entity Matching is the task of deciding if two entity descriptions refer to the same real-world entity. State-of-the-art entity matching methods often rely on fine-tuning Transformer models such as BERT or RoBERTa. Two major drawbacks of using these models for entity matching are that (i) the models require significant... | ['Christian Bizer', 'Ralph Peeters'] | 2023-05-05 | null | null | null | null | ['data-integration', 'entity-resolution'] | ['knowledge-base', 'natural-language-processing'] | [-1.66001201e-01 -1.60452537e-02 -9.48422477e-02 -4.34243947e-01
-1.05210471e+00 -7.36357868e-01 7.24118292e-01 4.10036147e-01
-8.13887060e-01 5.72883904e-01 -4.19119000e-02 -4.22883004e-01
-3.15031230e-01 -7.72016883e-01 -8.88123751e-01 -1.49860578e-02
-3.63500677e-02 8.20496082e-01 7.59822905e-01 -5.71642935... | [9.577474594116211, 8.39692497253418] |
03af5d42-00e7-4981-b618-344ddaf99830 | epro-pnp-generalized-end-to-end-probabilistic-1 | 2303.12787 | null | https://arxiv.org/abs/2303.12787v2 | https://arxiv.org/pdf/2303.12787v2.pdf | EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation | Locating 3D objects from a single RGB image via Perspective-n-Point (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, allowing for partial learning of 2D-3D point correspondences by backpropagating the gradients of... | ['Hao Li', 'Lu Xiong', 'Fan Wang', 'Pichao Wang', 'Wei Tian', 'Hansheng Chen'] | 2023-03-22 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [-2.12667510e-01 8.20284933e-02 -2.00018182e-01 -3.69990468e-01
-9.46410239e-01 -6.73535049e-01 3.96111041e-01 -3.64726722e-01
-4.88765985e-01 2.85772234e-01 2.95863077e-02 1.46170408e-01
-2.17271134e-01 -6.43327832e-01 -1.19644880e+00 -5.84295452e-01
7.55519122e-02 8.15413773e-01 2.48937026e-01 -1.15997948... | [7.517637252807617, -2.6741511821746826] |
56b02ade-b458-4afc-afc9-79786ef43f87 | nerf-gaze-a-head-eye-redirection-parametric | 2212.14710 | null | https://arxiv.org/abs/2212.14710v1 | https://arxiv.org/pdf/2212.14710v1.pdf | NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation | Gaze estimation is the fundamental basis for many visual tasks. Yet, the high cost of acquiring gaze datasets with 3D annotations hinders the optimization and application of gaze estimation models. In this work, we propose a novel Head-Eye redirection parametric model based on Neural Radiance Field, which allows dense ... | ['ShiLiang Pu', 'Di Xie', 'Jingjing Wang', 'Jiawu Dai', 'Pengwei Yin'] | 2022-12-30 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-3.04126684e-02 -2.04115249e-02 -2.29228929e-01 -7.78004944e-01
3.64449918e-02 -4.87443745e-01 3.94779891e-01 -6.17368102e-01
4.11974601e-02 4.06429797e-01 6.64719194e-02 -1.91145912e-02
-1.15167364e-01 -2.35024527e-01 -6.23079419e-01 -9.67184424e-01
4.85414237e-01 -1.50936134e-02 -1.55644104e-01 5.22589125... | [14.103940963745117, 0.03984552621841431] |
21fdd58e-b1ed-4552-8b5f-3d5dfdac2982 | efenet-reference-based-video-super-resolution | 2110.07797 | null | https://arxiv.org/abs/2110.07797v2 | https://arxiv.org/pdf/2110.07797v2.pdf | EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation | In this paper, we consider the problem of reference-based video super-resolution(RefVSR), i.e., how to utilize a high-resolution (HR) reference frame to super-resolve a low-resolution (LR) video sequence. The existing approaches to RefVSR essentially attempt to align the reference and the input sequence, in the presenc... | ['Shengjin Wang', 'Bin Wang', 'Ruqi Huang', 'Mengqi Ji', 'Yaping Zhao'] | 2021-10-15 | null | null | null | null | ['video-super-resolution', 'reference-based-video-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 2.50797927e-01 -4.06618118e-01 -1.29604146e-01 8.58854875e-02
-5.10451674e-01 -5.11250913e-01 2.83724248e-01 -4.41182911e-01
-9.01772976e-02 8.12178433e-01 4.98569071e-01 7.27251172e-03
-7.63063086e-03 -7.06396401e-01 -2.59079158e-01 -4.85863864e-01
-5.58389425e-02 -3.69420886e-01 7.03648865e-01 -1.76529035... | [10.942801475524902, -1.7973049879074097] |
206cc9fb-99d3-4918-a41b-7429fdc01b4a | zero-shot-detection-of-daily-objects-in-ycb | null | null | https://openreview.net/forum?id=jJWK09skiNl | https://openreview.net/pdf?id=jJWK09skiNl | Zero-shot detection of daily objects in YCB video dataset | To let robots be able to manipulate objects, they have to sense the location of objects. With the development of visual data collecting and processing technology, robots are gradually evolving to localize objects in a greater field of view rather than being limited to a small space where the object could appear. To tra... | ['Wanqing Xia'] | 2021-09-29 | null | null | null | null | ['zero-shot-object-detection'] | ['computer-vision'] | [ 2.41713554e-01 -2.07971841e-01 1.43494770e-01 -2.53810614e-01
1.71514839e-01 -3.37211400e-01 2.71432310e-01 1.62614182e-01
-2.22322911e-01 3.74917954e-01 -7.86709189e-01 9.90290567e-02
-1.22889057e-01 -7.53617167e-01 -7.39646971e-01 -7.03303695e-01
2.01137979e-02 3.99881423e-01 7.21433938e-01 -1.78364828... | [7.611684799194336, -1.250238060951233] |
22085970-e63c-45bb-89bb-9abc2b0d0d9c | unpaired-learning-for-high-dynamic-range-1 | 2111.00219 | null | https://arxiv.org/abs/2111.00219v1 | https://arxiv.org/pdf/2111.00219v1.pdf | Unpaired Learning for High Dynamic Range Image Tone Mapping | High dynamic range (HDR) photography is becoming increasingly popular and available by DSLR and mobile-phone cameras. While deep neural networks (DNN) have greatly impacted other domains of image manipulation, their use for HDR tone-mapping is limited due to the lack of a definite notion of ground-truth solution, which... | ['Raanan Fattal', 'Inbar Huberman-Spiegelglas', 'Yael Vinker'] | 2021-10-30 | unpaired-learning-for-high-dynamic-range | http://openaccess.thecvf.com//content/ICCV2021/html/Vinker_Unpaired_Learning_for_High_Dynamic_Range_Image_Tone_Mapping_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Vinker_Unpaired_Learning_for_High_Dynamic_Range_Image_Tone_Mapping_ICCV_2021_paper.pdf | iccv-2021-1 | ['tone-mapping'] | ['computer-vision'] | [ 6.92141593e-01 -2.29195610e-01 8.61299634e-02 -5.75937256e-02
-7.66981661e-01 -4.05494869e-01 6.93392336e-01 -3.03066194e-01
-4.01917607e-01 7.83904910e-01 6.62230179e-02 -3.07343096e-01
-8.44111145e-02 -8.32805216e-01 -7.93146491e-01 -7.04207659e-01
6.89413846e-02 3.29499207e-02 2.31779113e-01 -3.81010354... | [11.02550220489502, -2.173715829849243] |
c9684c86-f54e-4d89-bc83-2afc861fbb04 | can-action-be-imitated-learn-to-reconstruct | 2107.11756 | null | https://arxiv.org/abs/2107.11756v1 | https://arxiv.org/pdf/2107.11756v1.pdf | Can Action be Imitated? Learn to Reconstruct and Transfer Human Dynamics from Videos | Given a video demonstration, can we imitate the action contained in this video? In this paper, we introduce a novel task, dubbed mesh-based action imitation. The goal of this task is to enable an arbitrary target human mesh to perform the same action shown on the video demonstration. To achieve this, a novel Mesh-based... | ['Yu-Gang Jiang', 'Yanwei Fu', 'Yuqian Fu'] | 2021-07-25 | null | null | null | null | ['human-dynamics'] | ['computer-vision'] | [ 2.26810947e-01 3.44401121e-01 1.92097053e-01 7.98420161e-02
-3.79800111e-01 -2.79629469e-01 4.44347352e-01 -7.79446900e-01
6.80672526e-02 7.57073998e-01 -1.59197003e-02 4.56295311e-01
3.40356857e-01 -5.41323662e-01 -1.24961710e+00 -5.57033896e-01
9.91058126e-02 5.14020383e-01 4.37747926e-01 3.19909193... | [10.86630630493164, -0.8155102729797363] |
8d561bd9-296e-49ad-a0f0-0a19557644ca | a-unified-framework-for-multi-intent-spoken | 2210.03337 | null | https://arxiv.org/abs/2210.03337v1 | https://arxiv.org/pdf/2210.03337v1.pdf | A Unified Framework for Multi-intent Spoken Language Understanding with prompting | Multi-intent Spoken Language Understanding has great potential for widespread implementation. Jointly modeling Intent Detection and Slot Filling in it provides a channel to exploit the correlation between intents and slots. However, current approaches are apt to formulate these two sub-tasks differently, which leads to... | ['Houfeng Wang', 'Lianzhe Huang', 'Feifan Song'] | 2022-10-07 | null | null | null | null | ['spoken-language-understanding', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 4.21001494e-01 3.63881052e-01 -3.13468516e-01 -9.33357835e-01
-1.15292621e+00 -5.75638294e-01 6.19569480e-01 -1.26715273e-01
-3.12000871e-01 6.88120067e-01 9.02378917e-01 -2.98924297e-01
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1.37194574e-01 4.54186618e-01 -2.27669656e-01 -4.92838889... | [12.617301940917969, 7.357063293457031] |
bf150b3b-72d1-4a02-813a-475bb8d2f6d2 | revisiting-class-imbalance-for-end-to-end | 2306.02268 | null | https://arxiv.org/abs/2306.02268v1 | https://arxiv.org/pdf/2306.02268v1.pdf | Revisiting Class Imbalance for End-to-end Semi-Supervised Object Detection | Semi-supervised object detection (SSOD) has made significant progress with the development of pseudo-label-based end-to-end methods. However, many of these methods face challenges due to class imbalance, which hinders the effectiveness of the pseudo-label generator. Furthermore, in the literature, it has been observed ... | ['Pankaj Wasnik', 'Naoyuki Onoe', 'Vishal Chudasama', 'Purbayan Kar'] | 2023-06-04 | null | null | null | null | ['semi-supervised-object-detection', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 2.57281363e-01 1.31894529e-01 -1.61051095e-01 -6.87404752e-01
-8.97996724e-01 -4.74059284e-01 3.37380141e-01 1.42036006e-01
-5.77238441e-01 7.13525236e-01 -4.06370223e-01 9.53489318e-02
3.14996630e-01 -5.77665508e-01 -7.74194956e-01 -8.77704859e-01
3.19131613e-01 3.87153178e-01 8.05333018e-01 3.05906802... | [9.190469741821289, 1.2939280271530151] |
7b692f34-07c4-4423-be61-d6705d3140ac | improve-cross-lingual-voice-cloning-using-low | 2110.07210 | null | https://arxiv.org/abs/2110.07210v2 | https://arxiv.org/pdf/2110.07210v2.pdf | Improve Cross-lingual Voice Cloning Using Low-quality Code-switched Data | Recently, sequence-to-sequence (seq-to-seq) models have been successfully applied in text-to-speech (TTS) to synthesize speech for single-language text. To synthesize speech for multiple languages usually requires multi-lingual speech from the target speaker. However, it is both laborious and expensive to collect high-... | ['Yue Lin', 'Haitong Zhang'] | 2021-10-14 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 7.60968402e-02 -2.35745192e-01 -1.53084786e-03 -5.08449256e-01
-1.53731549e+00 -6.04669392e-01 3.09589982e-01 -5.32789171e-01
3.82368974e-02 6.70486510e-01 2.70323247e-01 -5.44736326e-01
5.32930970e-01 -1.59482494e-01 -5.25344729e-01 -5.74141681e-01
3.55285227e-01 5.68632841e-01 2.71790355e-01 -2.71851152... | [14.683709144592285, 6.875128269195557] |
8f56df27-b4f2-43db-b551-a5fde80a04bd | semi-supervised-endmember-identification-in | 1701.00804 | null | http://arxiv.org/abs/1701.00804v1 | http://arxiv.org/pdf/1701.00804v1.pdf | Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation | This paper proposes a new hyperspectral unmixing method for nonlinearly mixed
hyperspectral data using a semantic representation in a semi-supervised
fashion, assuming the availability of a spectral reference library. Existing
semi-supervised unmixing algorithms select members from an endmember library
that are present... | ['Marco F. Duarte', 'Yuki Itoh', 'Mario Parente', 'Siwei Feng'] | 2017-01-03 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 9.07538831e-01 -5.19515336e-01 -2.14461491e-01 -1.43936962e-01
-5.80498040e-01 -5.72343230e-01 5.27576029e-01 3.22216265e-02
-5.21626286e-02 7.65781224e-01 8.72851685e-02 -1.46987617e-01
-3.00339222e-01 -8.43258679e-01 -4.74426270e-01 -1.24159622e+00
4.49778289e-01 4.15458053e-01 -2.98708439e-01 -6.58254884... | [10.078018188476562, -2.046005964279175] |
47d52e64-b3a6-4020-acce-0176380cc81d | action-state-update-approach-to-dialogue | 2011.04637 | null | https://arxiv.org/abs/2011.04637v2 | https://arxiv.org/pdf/2011.04637v2.pdf | Action State Update Approach to Dialogue Management | Utterance interpretation is one of the main functions of a dialogue manager, which is the key component of a dialogue system. We propose the action state update approach (ASU) for utterance interpretation, featuring a statistically trained binary classifier used to detect dialogue state update actions in the text of a ... | ['Rama Doddipatla', 'Simon Keizer', 'Svetlana Stoyanchev'] | 2020-11-09 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [ 5.26148200e-01 8.87140155e-01 -2.59229928e-01 -8.07127833e-01
-6.92115963e-01 -6.69074833e-01 9.21477795e-01 4.22711551e-01
-4.76551563e-01 8.12230647e-01 3.38983446e-01 -5.61124146e-01
3.17806542e-01 -6.18979096e-01 -1.65300623e-01 1.02360003e-01
6.64604679e-02 9.22339082e-01 3.61122102e-01 -7.34933734... | [12.952888488769531, 7.984272480010986] |
63ba0003-4e1b-4afc-84b3-2c740bbb4191 | single-image-depth-estimation-trained-via-1 | 2001.05036 | null | https://arxiv.org/abs/2001.05036v1 | https://arxiv.org/pdf/2001.05036v1.pdf | Single Image Depth Estimation Trained via Depth from Defocus Cues | Estimating depth from a single RGB images is a fundamental task in computer vision, which is most directly solved using supervised deep learning. In the field of unsupervised learning of depth from a single RGB image, depth is not given explicitly. Existing work in the field receives either a stereo pair, a monocular v... | ['Lior Wolf', 'Shir Gur'] | 2020-01-14 | single-image-depth-estimation-trained-via | http://openaccess.thecvf.com/content_CVPR_2019/html/Gur_Single_Image_Depth_Estimation_Trained_via_Depth_From_Defocus_Cues_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Gur_Single_Image_Depth_Estimation_Trained_via_Depth_From_Defocus_Cues_CVPR_2019_paper.pdf | cvpr-2019-6 | ['lightfield'] | ['computer-vision'] | [ 4.54544872e-01 7.54446760e-02 3.94136310e-02 -6.93888664e-01
-6.54889524e-01 -5.08019388e-01 5.31350851e-01 -1.82044208e-01
-4.70380008e-01 7.46527672e-01 2.30422169e-01 6.10673167e-02
-1.00336187e-01 -7.36839592e-01 -8.72085690e-01 -9.51368451e-01
3.18721622e-01 4.36909378e-01 3.75693828e-01 3.33816290... | [8.704562187194824, -2.453115701675415] |
25f10d59-36f8-4620-8994-25be9de649e4 | standing-between-past-and-future-spatio | 2302.03802 | null | https://arxiv.org/abs/2302.03802v2 | https://arxiv.org/pdf/2302.03802v2.pdf | Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking | This work proposes an end-to-end multi-camera 3D multi-object tracking (MOT) framework. It emphasizes spatio-temporal continuity and integrates both past and future reasoning for tracked objects. Thus, we name it "Past-and-Future reasoning for Tracking" (PF-Track). Specifically, our method adapts the "tracking by atten... | ['Yu-Xiong Wang', 'Sergey Zagoruyko', 'Dian Chen', 'Pavel Tokmakov', 'Jie Li', 'Ziqi Pang'] | 2023-02-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Pang_Standing_Between_Past_and_Future_Spatio-Temporal_Modeling_for_Multi-Camera_3D_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Pang_Standing_Between_Past_and_Future_Spatio-Temporal_Modeling_for_Multi-Camera_3D_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-multi-object-tracking'] | ['computer-vision'] | [-3.32406431e-01 -3.93119514e-01 -4.20679808e-01 -3.00458491e-01
-7.83193946e-01 -6.45309210e-01 6.04789495e-01 1.45722747e-01
-3.26893479e-01 4.35257137e-01 3.27260286e-01 1.67459965e-01
-6.80789649e-02 -5.08636892e-01 -8.05981219e-01 -5.41164041e-01
-6.30791113e-02 2.86829293e-01 9.84570205e-01 1.17935479... | [6.327118396759033, -2.076695442199707] |
cdafecf5-304f-4739-b87d-5e5697b5291f | fast-classification-with-sequential-feature | 2306.14347 | null | https://arxiv.org/abs/2306.14347v1 | https://arxiv.org/pdf/2306.14347v1.pdf | Fast Classification with Sequential Feature Selection in Test Phase | This paper introduces a novel approach to active feature acquisition for classification, which is the task of sequentially selecting the most informative subset of features to achieve optimal prediction performance during testing while minimizing cost. The proposed approach involves a new lazy model that is significant... | ['Alireza Abdollahpourrostam', 'Hamid Sheikhzadeh', 'Vahid Pourahmadi', 'Ali Mirzaei'] | 2023-06-25 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 3.51544470e-01 -2.97383755e-01 -2.75488824e-01 -4.72997963e-01
-9.35501933e-01 -4.53962475e-01 3.33392024e-01 4.46641833e-01
-3.91526431e-01 7.35981941e-01 -3.16172540e-01 1.23083489e-02
-5.70281863e-01 -8.33220899e-01 -3.26921552e-01 -7.58646131e-01
-3.07685792e-01 5.41100085e-01 4.01937068e-01 1.78061530... | [8.311963081359863, 4.327530860900879] |
18bca29e-c23c-41f3-b177-d25014d25d67 | pgb-a-pubmed-graph-benchmark-for | 2305.02691 | null | https://arxiv.org/abs/2305.02691v2 | https://arxiv.org/pdf/2305.02691v2.pdf | PGB: A PubMed Graph Benchmark for Heterogeneous Network Representation Learning | There has been a rapid growth in biomedical literature, yet capturing the heterogeneity of the bibliographic information of these articles remains relatively understudied. Although graph mining research via heterogeneous graph neural networks has taken center stage, it remains unclear whether these approaches capture t... | ['Joyce C Ho', 'Eric W Lee'] | 2023-05-04 | null | null | null | null | ['graph-mining'] | ['graphs'] | [-2.60814637e-01 3.51124674e-01 -1.00381541e+00 2.08710775e-01
-4.78152394e-01 -3.35227698e-01 3.81139010e-01 9.51779127e-01
-2.76292771e-01 9.95293558e-01 5.91955602e-01 -5.24698794e-01
-1.09014757e-01 -7.36496925e-01 -4.23631459e-01 -4.95230615e-01
-2.31667072e-01 3.89207691e-01 7.35000372e-02 2.16791794... | [8.889349937438965, 7.926417827606201] |
7fab28a8-0fed-4638-8976-0d161f2068d7 | abstracting-sketches-through-simple | 2207.13543 | null | https://arxiv.org/abs/2207.13543v1 | https://arxiv.org/pdf/2207.13543v1.pdf | Abstracting Sketches through Simple Primitives | Humans show high-level of abstraction capabilities in games that require quickly communicating object information. They decompose the message content into multiple parts and communicate them in an interpretable protocol. Toward equipping machines with such capabilities, we propose the Primitive-based Sketch Abstraction... | ['Zeynep Akata', 'Diego Marcos', 'Anjan Dutta', 'Massimiliano Mancini', 'Stephan Alaniz'] | 2022-07-27 | null | null | null | null | ['sketch-based-image-retrieval', 'sketch-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.71053636e-01 1.97178349e-01 -3.30593586e-01 -4.43300933e-01
-8.50773692e-01 -8.55458558e-01 9.53785419e-01 2.34475285e-01
-9.74767953e-02 1.77916691e-01 2.91862428e-01 -6.18369207e-02
-8.75641629e-02 -9.73786056e-01 -8.33969355e-01 -3.47651035e-01
-1.43465146e-01 1.10942805e+00 -1.88383788e-01 -1.39265880... | [11.733892440795898, 0.31876230239868164] |
995b562d-bf0e-4c6e-a725-d2f9735c40d7 | molecular-graph-enhanced-transformer-for | null | null | https://openreview.net/forum?id=S1e__ANKvB | https://openreview.net/pdf?id=S1e__ANKvB | Molecular Graph Enhanced Transformer for Retrosynthesis Prediction | With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction is formulated as a Machine Translation (MT) task. Namely, since each molecule can be represented as a Simplified Molecular-Input Line-Entry System (SMILES) string, the ... | ['Junzhou Huang', 'Xi Xiao', 'Yu Rong', 'Tingyang Xu', 'Peilin Zhao', 'Kelong Mao'] | 2019-09-25 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 5.78694582e-01 4.58890535e-02 -5.77469051e-01 -6.46514967e-02
2.74262615e-02 -9.24694717e-01 7.84877181e-01 2.24470451e-01
6.74078837e-02 9.04805899e-01 3.17249417e-01 -9.36945379e-01
4.47572827e-01 -1.12443411e+00 -9.03320372e-01 -6.45810306e-01
2.22194955e-01 2.25553215e-01 -5.76863587e-02 -5.49693882... | [4.541736125946045, 6.08867883682251] |
fec2f6e2-3f9a-44f9-a880-7ad26233030d | respiratory-sound-classification-using-long | 2008.02900 | null | https://arxiv.org/abs/2008.02900v1 | https://arxiv.org/pdf/2008.02900v1.pdf | Respiratory Sound Classification Using Long-Short Term Memory | Developing a reliable sound detection and recognition system offers many benefits and has many useful applications in different industries. This paper examines the difficulties that exist when attempting to perform sound classification as it relates to respiratory disease classification. Some methods which have been em... | ['Mohammad-Parsa Hosseini', 'Chelsea Villanueva', 'Alexander Slowinski', 'Joshua Vincent'] | 2020-08-06 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 2.04008043e-01 -5.30340075e-01 2.38675609e-01 -1.68586090e-01
-7.50512660e-01 -8.55027884e-02 2.12088257e-01 -3.87129664e-01
-3.74764353e-01 4.56077874e-01 3.06081861e-01 -3.16524506e-01
-2.64780551e-01 -4.46004421e-01 6.82990439e-03 -9.31824565e-01
-2.62976348e-01 -1.73282367e-03 5.44358185e-03 1.65335506... | [14.891460418701172, 5.658397674560547] |
2cdd11ce-890c-4505-8688-241ba2d49a46 | fednoil-a-simple-two-level-sampling-method | 2205.10110 | null | https://arxiv.org/abs/2205.10110v1 | https://arxiv.org/pdf/2205.10110v1.pdf | FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels | Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Hence, the labels in practice are usually annotated by clients of varying expertise or criteria and thus contain different amounts of noises. Local training on noisy labels ... | ['Jing Jiang', 'Bo Han', 'Guodong Long', 'Tianyi Zhou', 'Zhuowei Wang'] | 2022-05-20 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 1.72616899e-01 -2.06953570e-01 -1.26570806e-01 -3.87729257e-01
-1.42398965e+00 -8.61005783e-01 3.26301783e-01 -8.47847685e-02
-1.52436689e-01 6.97371364e-01 -1.95901141e-01 -2.09360480e-01
-8.27319548e-02 -5.35377443e-01 -6.34992838e-01 -1.38985610e+00
2.12478563e-01 5.74729264e-01 1.28348768e-01 4.16401416... | [5.870107173919678, 6.334496974945068] |
26afeab2-2fb8-4bee-879d-215dccb8f3bc | that-sounds-right-auditory-self-supervision | 2210.01116 | null | https://arxiv.org/abs/2210.01116v1 | https://arxiv.org/pdf/2210.01116v1.pdf | That Sounds Right: Auditory Self-Supervision for Dynamic Robot Manipulation | Learning to produce contact-rich, dynamic behaviors from raw sensory data has been a longstanding challenge in robotics. Prominent approaches primarily focus on using visual or tactile sensing, where unfortunately one fails to capture high-frequency interaction, while the other can be too delicate for large-scale data ... | ['Lerrel Pinto', 'Abitha Thankaraj'] | 2022-10-03 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 3.27333361e-01 -9.35153291e-02 1.10656053e-01 -1.22352727e-01
-1.03733897e+00 -6.37808681e-01 2.45012358e-01 -1.56919546e-02
-2.95648098e-01 3.17484975e-01 2.77677983e-01 -3.04248221e-02
-1.47132844e-01 -2.35892341e-01 -1.09931540e+00 -5.40383399e-01
-3.18364292e-01 3.70143622e-01 3.86315197e-01 -2.74692297... | [4.623925685882568, 0.6394752264022827] |
8c70d458-e0c9-4bbd-a031-63a016cec2ea | a-marker-free-head-tracker-using-vision-based | 2103.13006 | null | https://arxiv.org/abs/2103.13006v1 | https://arxiv.org/pdf/2103.13006v1.pdf | A Marker-free Head Tracker Using Vision-based Head Pose Estimation with Adaptive Kalman Filter | The immersion and the interaction are the important features of the driving simulator. To improve these characteristics, this paper proposes a low-cost and mark-less driver head tracking framework based on the head pose estimation model, which makes the view of the simulator can automatically align with the driver's he... | ['Wenhui Huang', 'Yiran Zhang', 'Yanxin Zhou', 'Chen Lv', 'Zhongxu Hu'] | 2021-03-24 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-7.10709572e-01 -6.08467273e-02 1.07291520e-01 -3.02607298e-01
1.34862885e-01 -4.08044457e-02 1.71241183e-02 -3.54803592e-01
-7.20461965e-01 2.66520798e-01 8.36278424e-02 -2.72117257e-01
1.61128253e-01 -4.50488240e-01 -4.48847175e-01 -5.02084970e-01
5.45994878e-01 -7.68056586e-02 7.68155396e-01 -3.87400210... | [7.532632350921631, -1.9538730382919312] |
9bd8d85d-85af-440b-95cc-ae90e91772e6 | triplet-entropy-loss-improving-the | 2012.03775 | null | https://arxiv.org/abs/2012.03775v1 | https://arxiv.org/pdf/2012.03775v1.pdf | Triplet Entropy Loss: Improving The Generalisation of Short Speech Language Identification Systems | We present several methods to improve the generalisation of language identification (LID) systems to new speakers and to new domains. These methods involve Spectral augmentation, where spectrograms are masked in the frequency or time bands during training and CNN architectures that are pre-trained on the Imagenet datas... | ['Ruan van der Merwe'] | 2020-12-03 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 2.52768576e-01 3.93040068e-02 -1.00300848e-01 -3.92733008e-01
-3.29091311e-01 -5.53589582e-01 6.86566710e-01 -2.95258403e-01
-7.30893493e-01 5.78645587e-01 1.88157439e-01 -4.10293192e-01
1.27473608e-01 -3.10241610e-01 -3.60912263e-01 -5.22101641e-01
-2.22176194e-01 2.61008561e-01 -3.58963042e-01 -1.08620241... | [14.260769844055176, 6.456760883331299] |
c72a539a-39ff-4892-b8dc-8cdd6d6a5e6e | stylegene-crossover-and-mutation-of-region | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_StyleGene_Crossover_and_Mutation_of_Region-Level_Facial_Genes_for_Kinship_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_StyleGene_Crossover_and_Mutation_of_Region-Level_Facial_Genes_for_Kinship_CVPR_2023_paper.pdf | StyleGene: Crossover and Mutation of Region-Level Facial Genes for Kinship Face Synthesis | High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship da... | ['Linlin Shen', 'Zepeng Huang', 'Xianxu Hou', 'Hao Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['kinship-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 2.51556069e-01 4.46847379e-01 6.73339143e-02 -7.73335755e-01
-4.16985035e-01 -2.82991618e-01 2.46904895e-01 -6.75813079e-01
2.42316991e-01 6.10934854e-01 3.18010673e-02 2.07124770e-01
-4.56162617e-02 -1.02385175e+00 -7.24487484e-01 -9.80579674e-01
-5.06056324e-02 1.64663419e-01 -3.41139257e-01 -2.07614616... | [12.747673988342285, -0.008580612950026989] |
8223c5e9-d678-4d23-b68a-ca7d343a3b5c | spatial-temporal-concept-based-explanation-of | 2206.05275 | null | https://arxiv.org/abs/2206.05275v1 | https://arxiv.org/pdf/2206.05275v1.pdf | Spatial-temporal Concept based Explanation of 3D ConvNets | Recent studies have achieved outstanding success in explaining 2D image recognition ConvNets. On the other hand, due to the computation cost and complexity of video data, the explanation of 3D video recognition ConvNets is relatively less studied. In this paper, we present a 3D ACE (Automatic Concept-based Explanation)... | ['Jien Kato', 'Kensaku MORI', 'Yu Wang', 'Ying Ji'] | 2022-06-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ji_Spatial-Temporal_Concept_Based_Explanation_of_3D_ConvNets_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ji_Spatial-Temporal_Concept_Based_Explanation_of_3D_ConvNets_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-classification'] | ['computer-vision'] | [ 2.57537872e-01 2.32771546e-01 -3.73480976e-01 -4.48873818e-01
-1.23467244e-01 -2.36560747e-01 6.82306349e-01 1.49205346e-02
-1.66529000e-01 4.10098463e-01 3.08714092e-01 -2.93175280e-01
-1.10976316e-01 -4.23209459e-01 -6.75076783e-01 -5.73071003e-01
-3.53399105e-02 3.64543289e-01 3.05576116e-01 4.13932472... | [8.976508140563965, 1.0092955827713013] |
275d8e41-2399-4968-af53-428edf1f2a31 | msaf-multimodal-split-attention-fusion | 2012.07175 | null | https://arxiv.org/abs/2012.07175v2 | https://arxiv.org/pdf/2012.07175v2.pdf | MSAF: Multimodal Split Attention Fusion | Multimodal learning mimics the reasoning process of the human multi-sensory system, which is used to perceive the surrounding world. While making a prediction, the human brain tends to relate crucial cues from multiple sources of information. In this work, we propose a novel multimodal fusion module that learns to emph... | ['Dongpu Cao', 'Guofa Li', 'Chuqing Hu', 'Lang Su'] | 2020-12-13 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 3.33552241e-01 -2.57821918e-01 -3.65978554e-02 -4.95090276e-01
-7.51058221e-01 -3.70479047e-01 5.80704749e-01 2.54350245e-01
-5.67871690e-01 5.59239924e-01 4.65454459e-01 -2.84880418e-02
1.27958328e-01 -5.50998926e-01 -6.00473166e-01 -7.09146261e-01
5.12734115e-01 -2.25815952e-01 -3.25513743e-02 -3.84168267... | [13.161491394042969, 5.068514823913574] |
508dce17-7e8b-44af-b1ad-a7b2a1d2cd15 | twice-binnable-color-filter-arrays | 2306.17078 | null | https://arxiv.org/abs/2306.17078v1 | https://arxiv.org/pdf/2306.17078v1.pdf | Twice Binnable Color Filter Arrays | Pixel binning enables high speed, low power readout in low resolution modes, and more importantly, a reduction of read noise via floating diffusion binning. New, high resolution CMOS image sensors for mobile phones have moved beyond the once-binnable Quad Bayer and RGBW-Kodak patterns to the twice binnable Hexadeca Bay... | ['Tripurari Singh', 'Mritunjay Singh'] | 2023-06-29 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 6.18124664e-01 -5.60469747e-01 4.16918039e-01 1.13781698e-01
-1.15212631e+00 -6.87116206e-01 4.00527894e-01 -1.34245351e-01
-8.38061273e-01 6.36742473e-01 -3.65182594e-03 -6.04116738e-01
-4.60217744e-01 -8.97393167e-01 -6.87868118e-01 -1.06753027e+00
4.69180308e-02 3.09715688e-01 6.38813734e-01 -1.35841414... | [10.787907600402832, -2.4061717987060547] |
7f01c548-88c3-479b-93fb-789c8150c6e6 | diffusion-models-as-masked-autoencoders | 2304.03283 | null | https://arxiv.org/abs/2304.03283v1 | https://arxiv.org/pdf/2304.03283v1.pdf | Diffusion Models as Masked Autoencoders | There has been a longstanding belief that generation can facilitate a true understanding of visual data. In line with this, we revisit generatively pre-training visual representations in light of recent interest in denoising diffusion models. While directly pre-training with diffusion models does not produce strong rep... | ['Christoph Feichtenhofer', 'Alan Yuille', 'Cihang Xie', 'Huiyu Wang', 'Hu Xu', 'Haoqi Fan', 'Yanghao Li', 'Po-Yao Huang', 'Karttikeya Mangalam', 'Chen Wei'] | 2023-04-06 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 3.34356189e-01 2.55279362e-01 8.30450580e-02 -2.29420200e-01
-5.00840724e-01 -5.19451678e-01 1.16027510e+00 -2.71102607e-01
-1.74340308e-01 4.62075740e-01 6.04898989e-01 -5.20935357e-01
1.42333254e-01 -8.15495491e-01 -8.22667837e-01 -5.65836489e-01
8.42146277e-02 1.54437244e-01 -9.32642259e-03 -9.11125839... | [11.353228569030762, -0.302983820438385] |
5c375a4d-cb82-4ce8-98a7-e3b5d2c4bcd2 | language-anisotropic-cross-lingual-model | 2205.12677 | null | https://arxiv.org/abs/2205.12677v2 | https://arxiv.org/pdf/2205.12677v2.pdf | Language Anisotropic Cross-Lingual Model Editing | Multilingual pre-trained language models can learn task-specific abilities or memorize facts across multiple languages but inevitably make undesired predictions with specific inputs. Under similar observation, model editing aims to post-hoc calibrate a model targeted to specific inputs with keeping the model's raw beha... | ['Min Zhang', 'Wanxiang Che', 'Yutai Hou', 'Yang Xu'] | 2022-05-25 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 7.15924427e-02 2.92635951e-02 -1.74150601e-01 -5.44684231e-01
-9.23272073e-01 -8.90634656e-01 8.47800374e-01 -2.30764151e-02
-5.76096356e-01 7.78766811e-01 2.40893587e-01 -4.66711432e-01
2.13478535e-01 -5.68399310e-01 -1.10051548e+00 -2.05719158e-01
3.48182559e-01 5.72853029e-01 -1.04655251e-01 -2.46496871... | [11.112664222717285, 9.986454010009766] |
fca42905-d378-4b0a-80f2-6515f3f4d754 | bilingual-lexicon-induction-for-low-resource-1 | 2210.14378 | null | https://arxiv.org/abs/2210.14378v1 | https://arxiv.org/pdf/2210.14378v1.pdf | Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport | Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. We improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optima... | ['Philipp Koehn', 'Carey Priebe', 'Kevin Duh', 'Ali Saad-Eldin', 'Kelly Marchisio'] | 2022-10-25 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 4.12856899e-02 -5.87890483e-02 -1.00052333e+00 -3.76254290e-01
-7.40079463e-01 -9.97667551e-01 8.22370172e-01 3.62145245e-01
-4.92157936e-01 8.67320299e-01 3.13112020e-01 -8.73442769e-01
7.81878829e-02 -6.26126707e-01 -5.53171396e-01 2.89402232e-02
-7.22723454e-02 1.09114325e+00 3.10438517e-02 -7.68092692... | [11.036033630371094, 10.108062744140625] |
d707f94c-0b20-481d-9167-e26ec06b0501 | laplace-redux-effortless-bayesian-deep-1 | null | null | https://openreview.net/forum?id=gDcaUj4Myhn | https://openreview.net/pdf?id=gDcaUj4Myhn | Laplace Redux - Effortless Bayesian Deep Learning | Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection. The Laplace approximation (LA) is a classic, and arguably the simplest family of approximations for the in... | ['Philipp Hennig', 'Matthias Bauer', 'Runa Eschenhagen', 'Alexander Immer', 'Agustinus Kristiadi', 'Erik Daxberger'] | 2021-05-21 | null | null | null | neurips-2021-12 | ['misconceptions'] | ['miscellaneous'] | [-1.95319295e-01 5.36134802e-02 1.77595124e-01 -5.21418393e-01
-9.52996790e-01 -5.36663532e-01 7.06503570e-01 5.36192618e-02
-6.04771197e-01 9.54221010e-01 -1.74217373e-01 -5.78860700e-01
-4.02235925e-01 -4.77144480e-01 -8.55758429e-01 -9.59411979e-01
-1.71794802e-01 5.10463893e-01 3.02510440e-01 1.71941444... | [7.274745941162109, 3.8264806270599365] |
4879de81-b1c7-4911-8974-799f323ad35b | de-fake-detection-and-attribution-of-fake | 2210.06998 | null | https://arxiv.org/abs/2210.06998v2 | https://arxiv.org/pdf/2210.06998v2.pdf | DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models | Text-to-image generation models that generate images based on prompt descriptions have attracted an increasing amount of attention during the past few months. Despite their encouraging performance, these models raise concerns about the misuse of their generated fake images. To tackle this problem, we pioneer a systemat... | ['Yang Zhang', 'Ning Yu', 'Zheng Li', 'Zeyang Sha'] | 2022-10-13 | null | null | null | null | ['fake-image-detection'] | ['computer-vision'] | [ 3.37356508e-01 2.00893372e-01 -1.48998603e-01 -1.29192099e-01
-5.84895194e-01 -8.36912036e-01 1.03318000e+00 -6.60429373e-02
4.76533920e-02 5.45939684e-01 -8.94375741e-02 -3.56829643e-01
4.06778097e-01 -7.05893457e-01 -7.33301163e-01 -4.88530248e-01
2.64416516e-01 -1.92000400e-02 1.82254508e-01 -2.43975073... | [12.374109268188477, 1.100580096244812] |
c34c83ec-554e-4768-a287-4e3ff1030e9f | empirical-analysis-of-noising-scheme-based | null | null | https://aclanthology.org/2022.lrec-1.93 | https://aclanthology.org/2022.lrec-1.93.pdf | Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing | Automatic post-editing (APE) refers to a research field that aims to automatically correct errors included in the translation sentences derived by the machine translation system. This study has several limitations, considering the data acquisition, because there is no official dataset for most language pairs. Moreover,... | ['Heuiseok Lim', 'Sugyeong Eo', 'Jungseob Lee', 'Jaehyung Seo', 'Seolhwa Lee', 'Chanjun Park', 'Hyeonseok Moon'] | null | null | null | null | lrec-2022-6 | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 3.36168438e-01 -1.05550975e-01 1.51388466e-01 -6.25298172e-02
-1.05313706e+00 -4.72668916e-01 7.07242370e-01 6.48525804e-02
-7.47146308e-01 8.18746567e-01 -2.56346297e-02 -6.16320014e-01
-1.35151833e-01 -7.38662958e-01 -7.71795630e-01 -3.65604311e-01
4.32319552e-01 3.97202700e-01 -9.66150016e-02 -5.47370970... | [11.525708198547363, 10.212364196777344] |
b0782767-a1d9-40e7-83ab-69a6cd06d711 | deformable-kernel-expansion-model-for | 2303.15737 | null | https://arxiv.org/abs/2303.15737v1 | https://arxiv.org/pdf/2303.15737v1.pdf | Deformable Kernel Expansion Model for Efficient Arbitrary-shaped Scene Text Detection | Scene text detection is a challenging computer vision task due to the high variation in text shapes and ratios. In this work, we propose a scene text detector named Deformable Kernel Expansion (DKE), which incorporates the merits of both segmentation and contour-based detectors. DKE employs a segmentation module to seg... | ['Bo Liu', 'Wenhao Tang', 'Sheng Huang', 'Tao He'] | 2023-03-28 | null | null | null | null | ['scene-text-detection', 'graph-matching'] | ['computer-vision', 'graphs'] | [ 9.14054364e-02 -7.26545900e-02 1.05397115e-02 -1.15085803e-01
-4.91135448e-01 -3.69070232e-01 3.71144205e-01 -3.69394645e-02
-3.20799857e-01 -1.82854921e-01 -5.88758737e-02 -1.53763473e-01
3.37548584e-01 -8.80074859e-01 -3.55980873e-01 -7.14882433e-01
4.94508356e-01 3.28285038e-01 9.89178956e-01 4.85452712... | [12.11638355255127, 2.2603375911712646] |
0aafe691-c429-4f92-b402-0cef4b23906f | continual-vision-language-representaion | 2305.07437 | null | https://arxiv.org/abs/2305.07437v5 | https://arxiv.org/pdf/2305.07437v5.pdf | Continual Vision-Language Representation Learning with Off-Diagonal Information | Large-scale multi-modal contrastive learning frameworks like CLIP typically require a large amount of image-text samples for training. However, these samples are always collected continuously in real scenarios. This paper discusses the feasibility of continual CLIP training using streaming data. Unlike continual learni... | ['Qi Tian', 'Yueting Zhuang', 'Siliang Tang', 'Longhui Wei', 'Zixuan Ni'] | 2023-05-11 | null | null | null | null | ['cross-modal-retrieval'] | ['miscellaneous'] | [ 1.44280732e-01 -3.88730735e-01 -1.39673918e-01 -3.09437769e-03
-9.92043257e-01 -3.97237748e-01 5.16794205e-01 -1.29067004e-01
-1.89096138e-01 4.73362207e-01 1.92060038e-01 1.91937283e-01
-4.00286466e-01 -4.63064700e-01 -1.03695822e+00 -9.07852888e-01
-6.89616650e-02 2.10661530e-01 2.06881285e-01 -2.52380669... | [10.434452056884766, 1.1594547033309937] |
6117648a-7dbf-4685-94d7-c60dcf798bba | eliminating-mole-size-in-melanoma | 2212.05116 | null | https://arxiv.org/abs/2212.05116v1 | https://arxiv.org/pdf/2212.05116v1.pdf | Eliminating Mole Size in Melanoma Classification | While skin cancer classification has been a popular and valuable deep learning application for years, there has been little consideration of the context in which testing images are taken. Traditional melanoma classifiers rely on the assumption that their testing environments are analogous to the structured images on wh... | ['Benjamin Sanders', 'Ethan Martinez', 'Gavin Harding', 'Nick DiSanto'] | 2022-12-09 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 6.55213237e-01 -2.49076523e-02 -2.21816562e-02 -6.66092038e-01
-5.60736358e-01 -6.03848755e-01 4.17197168e-01 3.79367948e-01
-7.77518213e-01 7.31544495e-01 -1.35242924e-01 -8.21528316e-01
-9.79186594e-02 -6.69600964e-01 -4.80721265e-01 -8.27796519e-01
1.53909817e-01 1.07777104e-01 9.26827118e-02 -6.44882619... | [15.57518196105957, -2.937751531600952] |
dbeb8e1c-b21b-4417-9b89-2d3237af2420 | dynamic-graph-embedding-via-lstm-history | 1911.01551 | null | https://arxiv.org/abs/1911.01551v1 | https://arxiv.org/pdf/1911.01551v1.pdf | Dynamic Graph Embedding via LSTM History Tracking | Many real world networks are very large and constantly change over time. These dynamic networks exist in various domains such as social networks, traffic networks and biological interactions. To handle large dynamic networks in downstream applications such as link prediction and anomaly detection, it is essential for s... | ['Yonggang Hu', 'Shima Khoshraftar', 'Sedigheh Mahdavi', 'Junfeng Liu', 'Aijun An'] | 2019-11-05 | null | null | null | null | ['dynamic-graph-embedding'] | ['graphs'] | [-6.23728521e-02 2.17526659e-01 -1.15900278e-01 -1.61889642e-01
6.27896428e-01 -3.69050890e-01 5.90201974e-01 3.40766579e-01
-1.98987603e-01 6.33098125e-01 1.14502989e-01 -4.09076840e-01
-2.63232231e-01 -1.32585883e+00 -4.86287355e-01 -6.32482409e-01
-7.89758265e-01 4.02557522e-01 7.90000558e-01 -2.07923248... | [7.153805255889893, 6.092019557952881] |
f069446b-9e79-447d-b731-3e30e7d17509 | rb-dust-a-reference-based-dataset-for-vision | 2306.07244 | null | https://arxiv.org/abs/2306.07244v1 | https://arxiv.org/pdf/2306.07244v1.pdf | RB-Dust -- A Reference-based Dataset for Vision-based Dust Removal | Dust in the agricultural landscape is a significant challenge and influences, for example, the environmental perception of autonomous agricultural machines. Image enhancement algorithms can be used to reduce dust. However, these require dusty and dust-free images of the same environment for validation. In fact, to date... | ['Thomas Dietmueller', 'Timo Oksanen', 'Peter Buckel'] | 2023-06-12 | null | null | null | null | ['image-dehazing', 'image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 2.47812748e-01 -1.86410367e-01 7.75369048e-01 -3.40260565e-02
4.08815473e-01 -6.46823943e-01 2.87704080e-01 3.84882361e-01
-6.34369016e-01 5.86762547e-01 -7.53933728e-01 -5.69604158e-01
-3.28747839e-01 -1.30010760e+00 -9.53471303e-01 -9.02367055e-01
-4.80002202e-02 2.72621214e-01 3.48258853e-01 -3.67169857... | [9.563064575195312, -1.8539632558822632] |
43335516-d850-41f9-a784-08ca54337006 | faq-based-question-answering-via-word | 1507.02628 | null | http://arxiv.org/abs/1507.02628v1 | http://arxiv.org/pdf/1507.02628v1.pdf | FAQ-based Question Answering via Word Alignment | In this paper, we propose a novel word-alignment-based method to solve the
FAQ-based question answering task. First, we employ a neural network model to
calculate question similarity, where the word alignment between two questions
is used for extracting features. Second, we design a bootstrap-based feature
extraction m... | ['Abraham Ittycheriah', 'Zhiguo Wang'] | 2015-07-09 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [ 1.17643088e-01 -4.63770598e-01 -6.69834316e-02 -6.83572948e-01
-1.50176084e+00 -3.58300179e-01 2.82435238e-01 2.75124103e-01
-9.28058803e-01 6.09819472e-01 5.34388661e-01 -3.60070646e-01
-2.74247527e-01 -6.87310517e-01 -3.43002170e-01 -1.46926373e-01
2.20570311e-01 4.38407987e-01 6.83698595e-01 -7.30556965... | [11.234713554382324, 8.007743835449219] |
c5a1a948-c22a-425a-bde3-33ecd52b4ed4 | inertial-navigation-using-an-inertial-sensor | 2201.11983 | null | https://arxiv.org/abs/2201.11983v1 | https://arxiv.org/pdf/2201.11983v1.pdf | Inertial Navigation Using an Inertial Sensor Array | We present a comprehensive framework for fusing measurements from multiple and generally placed accelerometers and gyroscopes to perform inertial navigation. Using the angular acceleration provided by the accelerometer array, we show that the numerical integration of the orientation can be done with second-order accura... | ['Joakim Jaldén', 'Gustaf Hendeby', 'Isaac Skog', 'Håkan Carlsson'] | 2022-01-28 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 4.76401895e-02 -2.68744081e-02 -2.29353756e-01 -4.77219187e-02
-1.22910537e-01 -5.41771233e-01 4.42680150e-01 -1.78737879e-01
-7.00348556e-01 1.00010788e+00 6.45303056e-02 -4.53207701e-01
-7.68827796e-02 -5.94643235e-01 -8.83934855e-01 -5.40120125e-01
-2.31539980e-01 2.26605251e-01 1.55837263e-03 -2.66926706... | [7.4439263343811035, -1.9384913444519043] |
7cf56270-87e1-45b4-87ea-62ab0a00da32 | learnable-human-mesh-triangulation-for-3d | 2208.11251 | null | https://arxiv.org/abs/2208.11251v1 | https://arxiv.org/pdf/2208.11251v1.pdf | Learnable human mesh triangulation for 3D human pose and shape estimation | Compared to joint position, the accuracy of joint rotation and shape estimation has received relatively little attention in the skinned multi-person linear model (SMPL)-based human mesh reconstruction from multi-view images. The work in this field is broadly classified into two categories. The first approach performs j... | ['Ju Yong Chang', 'Sungbum Park', 'Sungho Chun'] | 2022-08-24 | null | null | null | null | ['3d-human-pose-estimation', '3d-human-pose-and-shape-estimation'] | ['computer-vision', 'computer-vision'] | [-3.47532779e-01 -5.54875992e-02 -2.97415018e-01 -2.67918017e-02
-6.43685162e-01 -5.47152832e-02 3.88882756e-01 -4.00438428e-01
-3.10201228e-01 3.91660810e-01 -3.20677981e-02 4.04740661e-01
-4.16191295e-02 -6.33854389e-01 -7.90614486e-01 -5.53445220e-01
3.44140649e-01 9.54866111e-01 1.34693354e-01 -4.73437086... | [7.021200180053711, -1.1384836435317993] |
57f5d0d3-4776-46fb-b60a-65d922dcd520 | can-discrete-information-extraction-prompts | 2302.09865 | null | https://arxiv.org/abs/2302.09865v2 | https://arxiv.org/pdf/2302.09865v2.pdf | Can discrete information extraction prompts generalize across language models? | We study whether automatically-induced prompts that effectively extract information from a language model can also be used, out-of-the-box, to probe other language models for the same information. After confirming that discrete prompts induced with the AutoPrompt algorithm outperform manual and semi-manual prompts on t... | ['Marco Baroni', 'Sebastian Riedel', 'Fabio Petroni', 'Roberto Dessì', 'Nathanaël Carraz Rakotonirina'] | 2023-02-20 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 3.58210981e-01 5.42508066e-01 -3.19621414e-01 -5.41133940e-01
-1.25143874e+00 -1.07424688e+00 8.56700718e-01 5.42838693e-01
-6.22002363e-01 7.92111158e-01 3.86319578e-01 -8.44598591e-01
-3.83151998e-03 -5.41659832e-01 -6.50163114e-01 -2.15206638e-01
1.47315472e-01 9.40174162e-01 4.88305956e-01 -1.97018489... | [10.843676567077637, 8.437564849853516] |
eff3f36f-7f6e-4299-8ff2-c15f10fe3c71 | importance-of-copying-mechanism-for-news | 1904.11475 | null | http://arxiv.org/abs/1904.11475v1 | http://arxiv.org/pdf/1904.11475v1.pdf | Importance of Copying Mechanism for News Headline Generation | News headline generation is an essential problem of text summarization
because it is constrained, well-defined, and is still hard to solve. Models
with a limited vocabulary can not solve it well, as new named entities can
appear regularly in the news and these entities often should be in the
headline. News articles in ... | ['Ilya Gusev'] | 2019-04-25 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [-3.97742763e-02 4.63198692e-01 -3.79112512e-01 6.87939227e-02
-6.39448225e-01 -5.29437065e-01 6.61717057e-01 4.58703786e-01
-7.68173754e-01 1.40906906e+00 9.37306523e-01 -1.25202581e-01
3.95057686e-02 -6.55389607e-01 -6.71616554e-01 -2.54556507e-01
3.98786962e-01 7.96716630e-01 2.45060384e-01 -5.79937816... | [12.336980819702148, 9.528461456298828] |
5d39637c-e397-4f92-be06-9d8752c8fd66 | attention-based-end-to-end-models-for-small | 1803.10916 | null | http://arxiv.org/abs/1803.10916v1 | http://arxiv.org/pdf/1803.10916v1.pdf | Attention-based End-to-End Models for Small-Footprint Keyword Spotting | In this paper, we propose an attention-based end-to-end neural approach for
small-footprint keyword spotting (KWS), which aims to simplify the pipelines of
building a production-quality KWS system. Our model consists of an encoder and
an attention mechanism. The encoder transforms the input signal into a high
level rep... | ['Yujun Wang', 'Junbo Zhang', 'Changhao Shan', 'Lei Xie'] | 2018-03-29 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 7.12205376e-03 -1.14313722e-01 -1.91136956e-01 -4.66436833e-01
-1.18677807e+00 -3.90602015e-02 3.86822790e-01 -7.23773986e-02
-5.96783042e-01 2.93516159e-01 1.93545461e-01 -5.97850263e-01
3.84184301e-01 -3.62345874e-01 -7.66652286e-01 -3.82765085e-01
-9.15933996e-02 -5.26331924e-02 1.38494849e-01 -1.64875776... | [14.261284828186035, 6.409018039703369] |
713f5f25-4e1b-4157-a301-69f229d4557d | attention-aware-face-hallucination-via-deep | 1708.03132 | null | http://arxiv.org/abs/1708.03132v1 | http://arxiv.org/pdf/1708.03132v1.pdf | Attention-Aware Face Hallucination via Deep Reinforcement Learning | Face hallucination is a domain-specific super-resolution problem with the
goal to generate high-resolution (HR) faces from low-resolution (LR) input
images. In contrast to existing methods that often learn a single
patch-to-patch mapping from LR to HR images and are regardless of the
contextual interdependency between ... | ['Qingxing Cao', 'Liang Lin', 'Xiaodan Liang', 'Yukai Shi', 'Guanbin Li'] | 2017-08-10 | attention-aware-face-hallucination-via-deep-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Cao_Attention-Aware_Face_Hallucination_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Cao_Attention-Aware_Face_Hallucination_CVPR_2017_paper.pdf | cvpr-2017-7 | ['face-hallucination'] | ['computer-vision'] | [ 2.60036051e-01 3.92319709e-01 7.64947757e-02 -3.46179217e-01
-8.35375190e-01 1.34144127e-01 3.66370469e-01 -6.36287749e-01
-3.46337282e-03 7.18184769e-01 2.25409031e-01 5.34738660e-01
-1.39091015e-01 -8.67188096e-01 -8.69768620e-01 -8.90576601e-01
5.49750887e-02 2.08206445e-01 -2.91855901e-01 -3.40320379... | [12.75593090057373, -0.11321088671684265] |
0f5dcec5-4c34-4768-a2ed-09ac05428748 | show-and-tell-a-neural-image-caption | 1411.4555 | null | http://arxiv.org/abs/1411.4555v2 | http://arxiv.org/pdf/1411.4555v2.pdf | Show and Tell: A Neural Image Caption Generator | Automatically describing the content of an image is a fundamental problem in
artificial intelligence that connects computer vision and natural language
processing. In this paper, we present a generative model based on a deep
recurrent architecture that combines recent advances in computer vision and
machine translation... | ['Samy Bengio', 'Alexander Toshev', 'Dumitru Erhan', 'Oriol Vinyals'] | 2014-11-17 | show-and-tell-a-neural-image-caption-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Vinyals_Show_and_Tell_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Vinyals_Show_and_Tell_2015_CVPR_paper.pdf | cvpr-2015-6 | ['multi-modal'] | ['miscellaneous'] | [ 3.85772765e-01 2.97358006e-01 -1.63762391e-01 -4.56343293e-01
-1.13447857e+00 -6.41232371e-01 1.09100628e+00 -2.79313952e-01
-3.61812413e-01 6.84579492e-01 4.11236227e-01 6.16047047e-02
4.33534056e-01 -4.38035131e-01 -9.43280816e-01 -5.91353238e-01
3.52890849e-01 5.22132456e-01 -3.09117407e-01 -1.53829679... | [11.055526733398438, 1.0628925561904907] |
4dcb0d7e-a0a0-4cb6-9957-fc847c0d42fc | bsn-complementary-boundary-regressor-with | 2009.07641 | null | https://arxiv.org/abs/2009.07641v5 | https://arxiv.org/pdf/2009.07641v5.pdf | BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation | Generating human action proposals in untrimmed videos is an important yet challenging task with wide applications. Current methods often suffer from the noisy boundary locations and the inferior quality of confidence scores used for proposal retrieving. In this paper, we present BSN++, a new framework which exploits co... | ['Haisheng Su', 'Yu Qiao', 'Weihao Gan', 'Wei Wu', 'Junjie Yan'] | 2020-09-15 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 3.21531177e-01 -1.92497283e-01 -6.95007980e-01 -2.35847637e-01
-8.73310864e-01 -1.28404275e-01 5.08889079e-01 -3.19803774e-01
-4.42216933e-01 7.87989676e-01 5.31676829e-01 4.70791198e-02
1.25243008e-01 -2.75790989e-01 -6.65638924e-01 -7.07163870e-01
5.77339903e-02 3.87359262e-02 7.71418869e-01 -1.77725554... | [8.393729209899902, 0.4659179151058197] |
e3cac619-c248-461b-9a38-8fc1f31de282 | diga-distil-to-generalize-and-then-adapt-for | 2304.02222 | null | https://arxiv.org/abs/2304.02222v1 | https://arxiv.org/pdf/2304.02222v1.pdf | DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic Segmentation | Domain adaptive semantic segmentation methods commonly utilize stage-wise training, consisting of a warm-up and a self-training stage. However, this popular approach still faces several challenges in each stage: for warm-up, the widely adopted adversarial training often results in limited performance gain, due to blind... | ['Alois Knoll', 'He Wang', 'Ziyuan Liu', 'Akhil Gurram', 'Fengyi Shen'] | 2023-04-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shen_DiGA_Distil_To_Generalize_and_Then_Adapt_for_Domain_Adaptive_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shen_DiGA_Distil_To_Generalize_and_Then_Adapt_for_Domain_Adaptive_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pseudo-label'] | ['miscellaneous'] | [ 2.21348137e-01 -3.76904570e-02 -4.11170304e-01 -5.19410670e-01
-9.37733889e-01 -7.66958892e-01 4.24674571e-01 1.29650682e-02
-4.34564918e-01 5.58705986e-01 -2.27800593e-01 -2.48706788e-01
3.20101798e-01 -5.77649176e-01 -7.30464220e-01 -7.52379298e-01
6.35017633e-01 6.82904184e-01 3.92636269e-01 -2.70350906... | [9.628555297851562, 1.3600581884384155] |
542f3ed8-5bf2-4bc6-bf14-4c05d701c55a | sf-dst-few-shot-self-feeding-reading | 2209.07742 | null | https://arxiv.org/abs/2209.07742v1 | https://arxiv.org/pdf/2209.07742v1.pdf | SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task | Few-shot dialogue state tracking (DST) model tracks user requests in dialogue with reliable accuracy even with a small amount of data. In this paper, we introduce an ontology-free few-shot DST with self-feeding belief state input. The self-feeding belief state input increases the accuracy in multi-turn dialogue by summ... | ['Gary Geunbae Lee', 'Jihyun Lee'] | 2022-09-16 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [-1.82253107e-01 4.76361394e-01 -3.74177814e-01 -6.64647460e-01
-7.74029553e-01 -3.99232060e-01 1.06356633e+00 1.56460688e-01
-4.35186416e-01 8.97601366e-01 6.22280300e-01 -7.22884387e-02
2.56521970e-01 -5.32103717e-01 1.99477434e-01 9.19673871e-03
3.36264491e-01 8.48466754e-01 9.09314930e-01 -1.30327165... | [12.811341285705566, 7.912482261657715] |
912dfdda-c018-4076-a30b-a6e91a11421c | learning-to-generate-reviews-and-discovering | 1704.01444 | null | http://arxiv.org/abs/1704.01444v2 | http://arxiv.org/pdf/1704.01444v2.pdf | Learning to Generate Reviews and Discovering Sentiment | We explore the properties of byte-level recurrent language models. When given
sufficient amounts of capacity, training data, and compute time, the
representations learned by these models include disentangled features
corresponding to high-level concepts. Specifically, we find a single unit which
performs sentiment anal... | ['Rafal Jozefowicz', 'Ilya Sutskever', 'Alec Radford'] | 2017-04-05 | learning-to-generate-reviews-and-discovering-1 | https://openreview.net/forum?id=SJ71VXZAZ | https://openreview.net/pdf?id=SJ71VXZAZ | iclr-2018-1 | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 7.13025257e-02 4.99874234e-01 -7.74446249e-01 -5.80890596e-01
-8.89496267e-01 -7.24983275e-01 9.03976142e-01 -2.59857532e-02
-3.64037365e-01 7.70977676e-01 7.19373643e-01 -5.81818223e-01
5.76448500e-01 -1.00246966e+00 -6.17235899e-01 -6.08425856e-01
2.60404777e-02 5.32821953e-01 -4.47044879e-01 -5.79250097... | [10.803523063659668, 8.64488410949707] |
c00e823e-0679-4b2f-8272-3164072cbb9a | reinforced-medical-report-generation-with-x | 2011.07680 | null | https://arxiv.org/abs/2011.07680v1 | https://arxiv.org/pdf/2011.07680v1.pdf | Reinforced Medical Report Generation with X-Linear Attention and Repetition Penalty | To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where attention mechanisms and reinforcement learning are integrated with the classic encoder-decoder architecture to enhance the performance of deep models. However, these state-o... | ['Thomas Lukasiewicz', 'Zhenghua Xu', 'Chang Qi', 'Wenting Xu'] | 2020-11-16 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 4.29078825e-02 3.24773788e-01 -2.52138793e-01 -5.24824083e-01
-1.14209652e+00 3.82126212e-01 4.24459457e-01 4.58302833e-02
-2.38860607e-01 7.58369505e-01 7.58716762e-01 -1.69760242e-01
-1.80049330e-01 -7.50423908e-01 -5.09854496e-01 -5.74572563e-01
1.09364307e-02 3.85462701e-01 -1.74011052e-01 -3.44727814... | [15.047916412353516, -1.3966012001037598] |
e4a49487-7d51-4c66-b3b2-997264eb9121 | cross-spatial-pixel-integration-and-cross | 2307.02974 | null | https://arxiv.org/abs/2307.02974v1 | https://arxiv.org/pdf/2307.02974v1.pdf | Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution | Remote sensing image super-resolution (RSISR) plays a vital role in enhancing spatial detials and improving the quality of satellite imagery. Recently, Transformer-based models have shown competitive performance in RSISR. To mitigate the quadratic computational complexity resulting from global self-attention, various m... | ['Teng Long', 'Yongqiang Zhao', 'Xiaoxu Wang', 'Le Zheng', 'Binglu Wang', 'Lingtong Min', 'Yuting Lu'] | 2023-07-06 | null | null | null | null | ['image-super-resolution', 'super-resolution'] | ['computer-vision', 'computer-vision'] | [ 3.64839494e-01 -4.81163740e-01 6.07249737e-02 -4.61969912e-01
-6.25362515e-01 -2.06360102e-01 5.26811182e-01 -4.95080501e-02
-4.73680586e-01 4.40334767e-01 2.99074113e-01 -8.96071717e-02
-3.94301355e-01 -1.05851364e+00 -4.81041491e-01 -6.72508299e-01
7.81356823e-03 -4.35564876e-01 4.11307096e-01 -4.63736475... | [10.069313049316406, -1.5771253108978271] |
73f0ca5b-639f-49bb-acb7-f4d5b77b8070 | unsupervised-domain-adaptation-via-1 | 1907.13590 | null | https://arxiv.org/abs/1907.13590v2 | https://arxiv.org/pdf/1907.13590v2.pdf | Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation | A deep learning model trained on some labeled data from a certain source domain generally performs poorly on data from different target domains due to domain shifts. Unsupervised domain adaptation methods address this problem by alleviating the domain shift between the labeled source data and the unlabeled target data.... | ['Nicha C. Dvornek', 'Junlin Yang', 'James S. Duncan', 'MingDe Lin', 'Julius Chapiro', 'Fan Zhang'] | 2019-07-31 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 6.18334889e-01 1.99453667e-01 -2.74568200e-01 -4.08765763e-01
-1.21927404e+00 -8.02447259e-01 5.92713416e-01 -1.02627531e-01
-4.29204136e-01 8.71199906e-01 2.64042705e-01 -2.89405808e-02
-2.54062358e-02 -6.64886475e-01 -6.45916164e-01 -1.07088614e+00
4.36486453e-02 5.70186198e-01 2.71893553e-02 -8.09527189... | [14.586112022399902, -2.033586263656616] |
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