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ddd16d49-9c69-42bd-b986-a5349a548117 | tracker-meets-night-a-transformer-enhancer | 2303.10951 | null | https://arxiv.org/abs/2303.10951v1 | https://arxiv.org/pdf/2303.10951v1.pdf | Tracker Meets Night: A Transformer Enhancer for UAV Tracking | Most previous progress in object tracking is realized in daytime scenes with favorable illumination. State-of-the-arts can hardly carry on their superiority at night so far, thereby considerably blocking the broadening of visual tracking-related unmanned aerial vehicle (UAV) applications. To realize reliable UAV tracki... | ['Bowen Li', 'Guangze Zheng', 'Shan An', 'Ziang Cao', 'Changhong Fu', 'Junjie Ye'] | 2023-03-20 | null | null | null | null | ['visual-tracking', 'blocking'] | ['computer-vision', 'natural-language-processing'] | [ 9.09742340e-02 -4.59643394e-01 1.55866325e-01 1.07039846e-02
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1.64578050e-01 -2.57125646e-01 5.17382920e-01 -3.17275912... | [6.7493414878845215, -1.986563801765442] |
33990c85-7373-4e95-8c20-62b2a013cafa | predicting-crop-yields-with-little-ground | 2106.08720 | null | https://arxiv.org/abs/2106.08720v3 | https://arxiv.org/pdf/2106.08720v3.pdf | Predicting crop yields with little ground truth: A simple statistical model for in-season forecasting | We present a fully automated model for in-season crop yield prediction, designed to work where there is a dearth of sub-national "ground truth" information. Our approach relies primarily on satellite data and is characterized by careful feature engineering combined with a simple regression model. As such, it can work a... | ['Nemo Semret'] | 2021-06-16 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [-7.79771283e-02 5.74270077e-03 -3.61307532e-01 -7.39599690e-02
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-3.42588365e-01 -3.98753248e-02 -7.91261718e-02 -7.82716930... | [9.369826316833496, -1.6022987365722656] |
cd9464ec-f045-49bd-a928-f4fd4e2d9465 | sequence-to-sequence-lexical-normalization | 2110.02869 | null | https://arxiv.org/abs/2110.02869v3 | https://arxiv.org/pdf/2110.02869v3.pdf | Sequence-to-Sequence Lexical Normalization with Multilingual Transformers | Current benchmark tasks for natural language processing contain text that is qualitatively different from the text used in informal day to day digital communication. This discrepancy has led to severe performance degradation of state-of-the-art NLP models when fine-tuned on real-world data. One way to resolve this issu... | ['Liviu P. Dinu', 'Adrian Cosma', 'Ana-Maria Bucur'] | 2021-10-06 | null | https://aclanthology.org/2021.wnut-1.53 | https://aclanthology.org/2021.wnut-1.53.pdf | wnut-acl-2021-11 | ['lexical-normalization'] | ['natural-language-processing'] | [ 6.55573308e-01 5.81636056e-02 -1.45078674e-01 -3.26702207e-01
-1.23378813e+00 -8.12221587e-01 8.32917929e-01 2.90901244e-01
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7.44196713e-01 4.39483494e-01 3.18967514e-02 -7.81699181... | [10.763033866882324, 9.88841438293457] |
b922f0ea-20da-4f77-b851-4c2d073ba1e3 | can-we-read-speech-beyond-the-lips-rethinking | 2003.03206 | null | https://arxiv.org/abs/2003.03206v2 | https://arxiv.org/pdf/2003.03206v2.pdf | Can We Read Speech Beyond the Lips? Rethinking RoI Selection for Deep Visual Speech Recognition | Recent advances in deep learning have heightened interest among researchers in the field of visual speech recognition (VSR). Currently, most existing methods equate VSR with automatic lip reading, which attempts to recognise speech by analysing lip motion. However, human experience and psychological studies suggest tha... | ['Yuan-Hang Zhang', 'Jing-Yun Xiao', 'Shiguang Shan', 'Xilin Chen', 'Shuang Yang'] | 2020-03-06 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.37151593e-01 2.81992584e-01 -4.76772666e-01 -3.81450802e-01
-9.20382261e-01 -5.12539804e-01 6.44175291e-01 -5.02368033e-01
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2.23101422e-01 -5.99454008e-02 -4.20755111e-02 -3.84798199... | [14.327417373657227, 5.017500877380371] |
6fe4caf1-15f4-4b80-9bd5-62d1cca36a5b | clustering-word-embeddings-with-self-1 | null | null | https://aclanthology.org/2021.eacl-main.81 | https://aclanthology.org/2021.eacl-main.81.pdf | Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set | Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. In this paper, we introduce LaRoSeDa, a Large Romanian Sentiment Data Set, which is composed of 15,000 positive and negative reviews collected from the large... | ['Radu Tudor Ionescu', 'Gaman Mihaela', 'Anca Tache'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['text-categorization'] | ['natural-language-processing'] | [-3.09189379e-01 4.39685099e-02 -3.15026492e-01 -5.47605813e-01
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-6.75526112e-02 6.11221731e-01 -1.01224430e-01 -5.11991799... | [10.397171974182129, 8.51895523071289] |
214f933d-daea-4372-a86e-cd8c0f70485b | deep-graph-based-spatial-consistency-for | 2303.09950 | null | https://arxiv.org/abs/2303.09950v1 | https://arxiv.org/pdf/2303.09950v1.pdf | Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration | We study the problem of outlier correspondence pruning for non-rigid point cloud registration. In rigid registration, spatial consistency has been a commonly used criterion to discriminate outliers from inliers. It measures the compatibility of two correspondences by the discrepancy between the respective distances in ... | ['Kai Xu', 'Yuxing Peng', 'Changjian Wang', 'Hao Yu', 'Zheng Qin'] | 2023-03-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qin_Deep_Graph-Based_Spatial_Consistency_for_Robust_Non-Rigid_Point_Cloud_Registration_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qin_Deep_Graph-Based_Spatial_Consistency_for_Robust_Non-Rigid_Point_Cloud_Registration_CVPR_2023_paper.pdf | cvpr-2023-1 | ['point-cloud-registration'] | ['computer-vision'] | [-2.59819597e-01 -1.35804325e-01 -5.80825657e-02 -2.78364122e-01
-6.36797428e-01 -4.69487071e-01 4.74928528e-01 2.71013379e-01
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-9.28536654e-02 7.58272827e-01 2.21647829e-01 -1.91533566... | [7.658512115478516, -3.0206754207611084] |
2f6b5078-9470-474e-b1cf-2df1b26b058d | conformal-prediction-intervals-for-markov | 2206.04860 | null | https://arxiv.org/abs/2206.04860v2 | https://arxiv.org/pdf/2206.04860v2.pdf | Conformal Prediction Intervals for Markov Decision Process Trajectories | Before delegating a task to an autonomous system, a human operator may want a guarantee about the behavior of the system. This paper extends previous work on conformal prediction for functional data and conformalized quantile regression to provide conformal prediction intervals over the future behavior of an autonomous... | ['Jesse Hostetler', 'Thomas G. Dietterich'] | 2022-06-10 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 1.81594357e-01 4.76345688e-01 -3.00649703e-01 -3.33239108e-01
-6.36664569e-01 -4.41611052e-01 4.91802037e-01 2.77736127e-01
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-6.20465755e-01 -7.75767267e-01 -6.29784524e-01 -6.32656097e-01
-8.41115355e-01 7.41303384e-01 3.08876604e-01 -6.78745136... | [4.589000701904297, 2.3097596168518066] |
defc7366-a067-4ef9-94a1-4f9e1ecd6dfc | large-scale-midi-based-composer | 2010.14805 | null | https://arxiv.org/abs/2010.14805v1 | https://arxiv.org/pdf/2010.14805v1.pdf | Large-Scale MIDI-based Composer Classification | Music classification is a task to classify a music piece into labels such as genres or composers. We propose large-scale MIDI based composer classification systems using GiantMIDI-Piano, a transcription-based dataset. We propose to use piano rolls, onset rolls, and velocity rolls as input representations and use deep n... | ['Yuxuan Wang', 'Keunwoo Choi', 'Qiuqiang Kong'] | 2020-10-28 | null | null | null | null | ['music-classification'] | ['music'] | [ 2.45580629e-01 -4.26531106e-01 -4.21935506e-02 -7.24927858e-02
-1.06578243e+00 -1.09806204e+00 4.73586410e-01 -1.20661587e-01
-5.58785126e-02 2.95919091e-01 5.04390180e-01 2.89479587e-02
-1.86658397e-01 -6.13325834e-01 -4.70276505e-01 -2.40012154e-01
-1.10485300e-01 3.04948002e-01 -2.54002869e-01 -3.98682654... | [15.8627290725708, 5.234228610992432] |
078c5680-5641-49ad-8f53-3dc96f5dbe89 | unsupervised-matching-of-data-and-text | 2112.08776 | null | https://arxiv.org/abs/2112.08776v1 | https://arxiv.org/pdf/2112.08776v1.pdf | Unsupervised Matching of Data and Text | Entity resolution is a widely studied problem with several proposals to match records across relations. Matching textual content is a widespread task in many applications, such as question answering and search. While recent methods achieve promising results for these two tasks, there is no clear solution for the more g... | ['Paolo Papotti', 'Hansjorg Sand', 'Naser Ahmadi'] | 2021-12-16 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [-1.44409360e-02 1.25884429e-01 -6.23376012e-01 -3.81614059e-01
-7.55048811e-01 -6.07785940e-01 6.06975496e-01 1.03694236e+00
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-6.27256334e-01 -1.54083884e+00 -5.12230396e-01 -5.37704304e-02
-1.78247392e-01 1.00061905e+00 5.95458865e-01 -4.70529556... | [9.274896621704102, 8.171138763427734] |
9300b032-5273-4ed2-ada9-3f6e6e93ed59 | the-typical-behavior-of-bandit-algorithms | 2210.05660 | null | https://arxiv.org/abs/2210.05660v1 | https://arxiv.org/pdf/2210.05660v1.pdf | The Typical Behavior of Bandit Algorithms | We establish strong laws of large numbers and central limit theorems for the regret of two of the most popular bandit algorithms: Thompson sampling and UCB. Here, our characterizations of the regret distribution complement the characterizations of the tail of the regret distribution recently developed by Fan and Glynn ... | ['Peter W. Glynn', 'Lin Fan'] | 2022-10-11 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-1.55701265e-01 3.47533792e-01 -5.52740097e-01 -1.63829759e-01
-9.59160745e-01 -1.08290291e+00 9.71430615e-02 1.08795196e-01
-6.28698707e-01 1.08812141e+00 1.37335137e-01 -8.65494728e-01
-7.96302795e-01 -5.50477386e-01 -1.09403503e+00 -8.62722695e-01
-1.91655457e-01 6.00351989e-01 -2.55321681e-01 2.25618362... | [4.5203094482421875, 3.296755790710449] |
57b1ba15-75b3-40d4-92b1-06a4e9f94f4c | styo-stylize-your-face-in-only-one-shot | 2303.03231 | null | https://arxiv.org/abs/2303.03231v2 | https://arxiv.org/pdf/2303.03231v2.pdf | StyO: Stylize Your Face in Only One-Shot | This paper focuses on face stylization with a single artistic target. Existing works for this task often fail to retain the source content while achieving geometry variation. Here, we present a novel StyO model, ie. Stylize the face in only One-shot, to solve the above problem. In particular, StyO exploits a disentangl... | ['Tiande Guo', 'Yinhan Hu', 'Congying Han', 'Xuecheng Nie', 'ZiCheng Zhang', 'Bonan Li'] | 2023-03-06 | null | null | null | null | ['one-shot-face-stylization'] | ['computer-vision'] | [ 3.17430019e-01 7.41672441e-02 -2.29015440e-01 -2.33936816e-01
-5.11646807e-01 -6.34890139e-01 6.92501307e-01 -7.51782775e-01
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4.99551326e-01 5.89072049e-01 -2.19688594e-01 -9.49202105... | [12.23538875579834, -0.30464065074920654] |
f84ee9ad-02ac-4c1c-b111-db7e05652ee4 | odfnet-using-orientation-distribution | 2012.04708 | null | https://arxiv.org/abs/2012.04708v2 | https://arxiv.org/pdf/2012.04708v2.pdf | ODFNet: Using orientation distribution functions to characterize 3D point clouds | Learning new representations of 3D point clouds is an active research area in 3D vision, as the order-invariant point cloud structure still presents challenges to the design of neural network architectures. Recent works explored learning either global or local features or both for point clouds, however none of the earl... | ['Gozde Unal', 'Alican Mertan', 'Yusuf H. Sahin'] | 2020-12-08 | null | null | null | null | ['3d-part-segmentation'] | ['computer-vision'] | [-1.09584488e-01 -1.39211431e-01 -1.16796896e-01 -5.55746794e-01
-3.91486019e-01 -5.83569288e-01 8.13755572e-01 5.76086402e-01
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-3.69226754e-01 -1.10125113e+00 -1.08209443e+00 -7.31964529e-01
-1.48669034e-01 9.14914191e-01 4.20817554e-01 4.17022146... | [7.970778465270996, -3.573122978210449] |
9fe9fd9a-494d-47e9-b7d1-9512243d504d | set-interdependence-transformer-set-to | 2206.03720 | null | https://arxiv.org/abs/2206.03720v1 | https://arxiv.org/pdf/2206.03720v1.pdf | Set Interdependence Transformer: Set-to-Sequence Neural Networks for Permutation Learning and Structure Prediction | The task of learning to map an input set onto a permuted sequence of its elements is challenging for neural networks. Set-to-sequence problems occur in natural language processing, computer vision and structure prediction, where interactions between elements of large sets define the optimal output. Models must exhibit ... | ['Leon Derczynski', 'Mateusz Jurewicz'] | 2022-06-08 | null | null | null | null | ['relational-reasoning', 'sentence-ordering'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.24695671e-01 1.61583070e-02 -4.39133346e-01 -5.79498649e-01
-4.50050861e-01 -8.80315006e-01 4.48577493e-01 3.09642404e-01
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-4.99245971e-01 1.06540012e+00 -5.82653433e-02 -5.33522785... | [9.402661323547363, 7.218233108520508] |
ed4081eb-3399-44f0-ac4e-e109ba274f0b | towards-content-independent-multi-reference | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4865_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700052.pdf | Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation | Recovering realistic textures from a largely down-sampled low resolution (LR) image with complicated patterns is a challenging problem in image super-resolution. This work investigates a novel multi-reference based super-resolution problem by proposing a Content Independent Multi-Reference Super-Resolution (CIMR-SR) mo... | ['Shuguang Cui', 'Kun Yuan', 'Xu Yan', 'Weibing Zhao', 'Ruimao Zhang', 'Zhen Li'] | null | null | null | null | eccv-2020-8 | ['reference-based-super-resolution'] | ['computer-vision'] | [ 7.51375377e-01 -3.87013048e-01 -5.49643673e-02 -8.60423446e-02
-1.44207537e+00 -1.90597802e-01 3.56688887e-01 -3.74420702e-01
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4.08212304e-01 1.42442107e-01 7.64835596e-01 -5.76455414... | [10.990273475646973, -2.1004343032836914] |
b65de6ea-fc55-490b-9e71-e1aa1f81824c | mtvr-multilingual-moment-retrieval-in-videos | 2108.00061 | null | https://arxiv.org/abs/2108.00061v1 | https://arxiv.org/pdf/2108.00061v1.pdf | MTVR: Multilingual Moment Retrieval in Videos | We introduce mTVR, a large-scale multilingual video moment retrieval dataset, containing 218K English and Chinese queries from 21.8K TV show video clips. The dataset is collected by extending the popular TVR dataset (in English) with paired Chinese queries and subtitles. Compared to existing moment retrieval datasets, ... | ['Mohit Bansal', 'Tamara L. Berg', 'Jie Lei'] | 2021-07-30 | null | https://aclanthology.org/2021.acl-short.92 | https://aclanthology.org/2021.acl-short.92.pdf | acl-2021-5 | ['moment-retrieval'] | ['computer-vision'] | [-4.35947984e-01 -4.86355573e-01 -9.30285037e-01 -3.61202359e-01
-1.85845673e+00 -1.04675627e+00 8.54047418e-01 -2.49958858e-01
-7.33389854e-01 5.83928764e-01 7.06427038e-01 -8.37356299e-02
-1.16699830e-01 -1.19623449e-02 -9.84657645e-01 -1.80094048e-01
-8.12919363e-02 4.57756668e-01 2.94820382e-03 -2.40956098... | [11.074193954467773, 1.4457118511199951] |
ee8b89cc-0d34-4691-ab3b-97c492dca677 | speaker-guided-encoder-decoder-framework-for | 2206.03173 | null | https://arxiv.org/abs/2206.03173v1 | https://arxiv.org/pdf/2206.03173v1.pdf | Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation | The emotion recognition in conversation (ERC) task aims to predict the emotion label of an utterance in a conversation. Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC. Although e... | ['Songlin Hu', 'Wei Zhou', 'Lingwei Wei', 'Qianwen Ma', 'Yinan Bao'] | 2022-06-07 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 6.71724379e-02 2.90901195e-02 7.31263608e-02 -9.45144653e-01
-4.61701691e-01 -2.87796289e-01 4.07802135e-01 -1.88944191e-01
-9.60494727e-02 2.83357471e-01 7.63671696e-01 -1.42985180e-01
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-1.34530574e-01 2.51275022e-02 -9.50021446e-02 -2.66278476... | [13.050408363342285, 6.067041397094727] |
7d28f2a9-9354-4c81-9ddd-1313ba9a3d0e | gan-based-domain-inference-attack | 2212.11810 | null | https://arxiv.org/abs/2212.11810v1 | https://arxiv.org/pdf/2212.11810v1.pdf | GAN-based Domain Inference Attack | Model-based attacks can infer training data information from deep neural network models. These attacks heavily depend on the attacker's knowledge of the application domain, e.g., using it to determine the auxiliary data for model-inversion attacks. However, attackers may not know what the model is used for in practice.... | ['Keke Chen', 'Yuechun Gu'] | 2022-12-22 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 4.29971457e-01 3.27287257e-01 -2.67708212e-01 -2.02974513e-01
-9.04991984e-01 -1.30930972e+00 4.95101929e-01 -5.41424572e-01
-1.24929890e-01 7.74527907e-01 -1.81727216e-01 -6.85532510e-01
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3.92946243e-01 6.09468400e-01 9.36453193e-02 -1.27846792... | [5.776897430419922, 7.695074081420898] |
23169d92-cc12-45f1-98ff-26684ccd9e8c | rethinking-image-deraining-via-rain-streaks | 2008.00823 | null | https://arxiv.org/abs/2008.00823v1 | https://arxiv.org/pdf/2008.00823v1.pdf | Rethinking Image Deraining via Rain Streaks and Vapors | Single image deraining regards an input image as a fusion of a background image, a transmission map, rain streaks, and atmosphere light. While advanced models are proposed for image restoration (i.e., background image generation), they regard rain streaks with the same properties as background rather than transmission ... | ['Chao Ma', 'Yinglong Wang', 'Bing Zeng', 'Yibing Song'] | 2020-08-03 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2761_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620358.pdf | eccv-2020-8 | ['single-image-deraining'] | ['computer-vision'] | [ 2.71399409e-01 -4.76061672e-01 5.17660916e-01 -4.70131665e-01
7.60422796e-02 -3.32342863e-01 4.54448640e-01 -4.54428792e-01
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4.98303771e-01 -1.27131021e+00 -1.10266483e+00 -1.23758805e+00
2.41878048e-01 -5.29434346e-02 2.16621131e-01 -5.47986925... | [10.919404029846191, -3.241971015930176] |
0c163e40-21b1-4910-9db0-f0cefee038d0 | smart-home-energy-management-vae-gan | 2305.08885 | null | https://arxiv.org/abs/2305.08885v1 | https://arxiv.org/pdf/2305.08885v1.pdf | Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning | Recent years have noticed an increasing interest among academia and industry towards analyzing the electrical consumption of residential buildings and employing smart home energy management systems (HEMS) to reduce household energy consumption and costs. HEMS has been developed to simulate the statistical and functiona... | ['Damla Turgut', 'Melike Erol-Kantarci', 'Hao Zhou', 'Mina Razghandi'] | 2023-05-14 | null | null | null | null | ['q-learning', 'energy-management'] | ['methodology', 'time-series'] | [-2.36158758e-01 1.02207832e-01 3.95753026e-01 -1.87451243e-01
-1.03609908e+00 -3.55312526e-01 3.48118484e-01 -2.36482069e-01
2.18585730e-01 1.14044166e+00 1.09509081e-01 -3.72044556e-02
2.46291980e-02 -1.37636888e+00 -5.26439011e-01 -1.05757749e+00
-1.12911403e-01 4.47842360e-01 -4.28259134e-01 -1.91076905... | [16.044097900390625, 7.5634446144104] |
54c83505-15aa-45fc-ad39-6c142f148b06 | relation-guided-pre-training-for-open-domain | 2109.10346 | null | https://arxiv.org/abs/2109.10346v1 | https://arxiv.org/pdf/2109.10346v1.pdf | Relation-Guided Pre-Training for Open-Domain Question Answering | Answering complex open-domain questions requires understanding the latent relations between involving entities. However, we found that the existing QA datasets are extremely imbalanced in some types of relations, which hurts the generalization performance over questions with long-tail relations. To remedy this problem,... | ['Kai-Wei Chang', 'Yizhou Sun', 'Ziniu Hu'] | 2021-09-21 | null | https://aclanthology.org/2021.findings-emnlp.292 | https://aclanthology.org/2021.findings-emnlp.292.pdf | findings-emnlp-2021-11 | ['triviaqa'] | ['miscellaneous'] | [-1.94657192e-01 5.58330715e-01 -1.04590409e-01 -3.13587368e-01
-1.51612723e+00 -7.49648273e-01 3.42341036e-01 1.86864257e-01
-1.71444416e-01 1.09382606e+00 4.72909719e-01 -5.59089243e-01
-4.68705267e-01 -1.25880039e+00 -9.51089501e-01 -1.14942767e-01
1.49183333e-01 1.13936567e+00 5.41541100e-01 -8.73514950... | [10.712722778320312, 7.96984338760376] |
84d340a6-8cf2-4329-a3c4-c21c9fe5ed31 | synscapes-a-photorealistic-synthetic-dataset | 1810.08705 | null | http://arxiv.org/abs/1810.08705v1 | http://arxiv.org/pdf/1810.08705v1.pdf | Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing | We introduce Synscapes -- a synthetic dataset for street scene parsing
created using photorealistic rendering techniques, and show state-of-the-art
results for training and validation as well as new types of analysis. We study
the behavior of networks trained on real data when performing inference on
synthetic data: a ... | ['Magnus Wrenninge', 'Jonas Unger'] | 2018-10-19 | null | null | null | null | ['street-scene-parsing'] | ['computer-vision'] | [ 4.44408685e-01 4.67891321e-02 2.45478109e-01 -6.54048681e-01
-5.73462427e-01 -8.13472092e-01 8.06656182e-01 4.26210165e-02
-4.06757951e-01 5.51734865e-01 -1.65538132e-01 -6.19248450e-01
2.04760313e-01 -7.52553880e-01 -9.99288559e-01 -4.37908620e-01
-3.69547933e-01 5.56094050e-01 7.52963841e-01 -2.51384318... | [8.655253410339355, -1.4783626794815063] |
6c271a59-1310-4de8-ba63-d6e6c107d49d | statistical-model-for-describing-heart-rate | 2207.08165 | null | https://arxiv.org/abs/2207.08165v1 | https://arxiv.org/pdf/2207.08165v1.pdf | Statistical model for describing heart rate variability in normal rhythm and atrial fibrillation | Heart rate variability (HRV) indices describe properties of interbeat intervals in electrocardiogram (ECG). Usually HRV is measured exclusively in normal sinus rhythm (NSR) excluding any form of paroxysmal rhythm. Atrial fibrillation (AF) is the most widespread cardiac arrhythmia in human population. Usually such abnor... | ['Yakov Bozhko', 'Konstantin Ushenin', 'Ilya Kotov', 'Nikita Markov'] | 2022-07-17 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 1.67383865e-01 -4.93601382e-01 8.81256908e-02 -3.17981988e-01
-1.77580453e-02 -8.08387697e-01 1.39586523e-01 4.60015923e-01
-4.00226474e-01 1.25228453e+00 1.44673921e-02 -4.15248185e-01
-6.18417382e-01 -7.02912271e-01 1.60870790e-01 -5.28233409e-01
-6.75343335e-01 6.51935339e-01 -3.81994516e-01 -3.60868908... | [14.143200874328613, 3.179441213607788] |
375687f6-db92-422f-a2ee-be5bf85a01ed | what-comprises-a-good-talking-head-video | 2005.03201 | null | https://arxiv.org/abs/2005.03201v1 | https://arxiv.org/pdf/2005.03201v1.pdf | What comprises a good talking-head video generation?: A Survey and Benchmark | Over the years, performance evaluation has become essential in computer vision, enabling tangible progress in many sub-fields. While talking-head video generation has become an emerging research topic, existing evaluations on this topic present many limitations. For example, most approaches use human subjects (e.g., vi... | ['Haitian Zheng', 'Ziyi Kou', 'Lele Chen', 'Guofeng Cui', 'Chenliang Xu'] | 2020-05-07 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 1.40441850e-01 -6.29429445e-02 -2.84753770e-01 -4.31132525e-01
-8.74049723e-01 -4.33896989e-01 8.21986318e-01 -3.10711920e-01
-1.73342526e-01 6.32553816e-01 4.94546741e-01 -5.25588766e-02
1.15699030e-01 -2.50806749e-01 -3.35065067e-01 -7.40218878e-01
6.17782697e-02 -7.23448768e-02 1.27987310e-01 -4.71621454... | [13.287247657775879, -0.30536720156669617] |
6234a4f0-0e63-4932-bbab-706dd4d8137e | augmenting-ego-vehicle-for-traffic-near-miss | 2301.02726 | null | https://arxiv.org/abs/2301.02726v1 | https://arxiv.org/pdf/2301.02726v1.pdf | Augmenting Ego-Vehicle for Traffic Near-Miss and Accident Classification Dataset using Manipulating Conditional Style Translation | To develop the advanced self-driving systems, many researchers are focusing to alert all possible traffic risk cases from closed-circuit television (CCTV) and dashboard-mounted cameras. Most of these methods focused on identifying frame-by-frame in which an anomaly has occurred, but they are unrealized, which road traf... | ['Koji Zettsu', 'Minh-Son Dao', 'Hilmil Pradana'] | 2023-01-06 | null | null | null | null | ['video-classification', 'unsupervised-image-to-image-translation'] | ['computer-vision', 'computer-vision'] | [ 1.82726696e-01 -6.09818147e-03 -2.73907602e-01 -4.14374471e-01
-7.46072590e-01 -2.90887475e-01 4.34792101e-01 -2.20914945e-01
-4.53456610e-01 5.52898526e-01 2.62306929e-01 -5.13471425e-01
5.05892672e-02 -7.05050588e-01 -7.67528832e-01 -5.45985937e-01
1.29613042e-01 2.21238241e-01 5.37245631e-01 -2.33726799... | [7.676427841186523, -0.2301357537508011] |
aa62ee54-bdf7-4506-a5d1-91291aa7be81 | auxiliary-learning-as-an-asymmetric | 2301.13501 | null | https://arxiv.org/abs/2301.13501v2 | https://arxiv.org/pdf/2301.13501v2.pdf | Auxiliary Learning as an Asymmetric Bargaining Game | Auxiliary learning is an effective method for enhancing the generalization capabilities of trained models, particularly when dealing with small datasets. However, this approach may present several difficulties: (i) optimizing multiple objectives can be more challenging, and (ii) how to balance the auxiliary tasks to be... | ['Ethan Fetaya', 'Gal Chechik', 'Kenji Kawaguchi', 'Neta Glazer', 'Aviv Navon', 'Aviv Shamsian'] | 2023-01-31 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 1.94777712e-01 1.24725580e-01 -4.60522890e-01 -3.41855660e-02
-1.19689989e+00 -5.88616610e-01 3.22840124e-01 -9.57644135e-02
-8.65312278e-01 1.19269693e+00 7.25862831e-02 -4.17037904e-01
-5.43294728e-01 -1.85418889e-01 -6.06598616e-01 -1.14528942e+00
1.88357249e-01 6.78945541e-01 -2.27218077e-01 -3.63255113... | [9.294780731201172, 3.855907917022705] |
dfcbc7a2-b42f-4070-9b02-94200f14d519 | how-does-feedback-signal-quality-impact | 2205.05888 | null | https://arxiv.org/abs/2205.05888v1 | https://arxiv.org/pdf/2205.05888v1.pdf | How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval? | Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation for a second round of retrieval. This assumption however is often not correct: some or even all of the feedback documents may be irrelevant.... | ['Guido Zuccon', 'Bevan Koopman', 'Ahmed Mourad', 'Hang Li'] | 2022-05-12 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 3.02327812e-01 -4.19165879e-01 -4.11663324e-01 -1.34896204e-01
-1.00625527e+00 -8.53769362e-01 8.03774714e-01 4.65709031e-01
-6.20160282e-01 5.97310722e-01 6.56421602e-01 -1.51494607e-01
-4.95032787e-01 -6.86575353e-01 -6.27361953e-01 -5.17587066e-01
-9.21417177e-02 3.96830618e-01 4.70862597e-01 -6.95838630... | [11.453526496887207, 7.578171253204346] |
0c42f8d6-2897-4a36-885e-4a769e0f8606 | using-openwordnet-pt-for-question-answering | null | null | https://aclanthology.org/2018.gwc-1.13 | https://aclanthology.org/2018.gwc-1.13.pdf | Using OpenWordnet-PT for Question Answering on Legal Domain | In order to practice a legal profession in Brazil, law graduates must be approved in the OAB national unified bar exam. For their topic coverage and national reach, the OAB exams provide an excellent benchmark for the performance of legal information systems, as it provides objective metrics and are challenging even fo... | ['Alexandre Rademaker', 'Gerson Zaverucha', 'Guilherme Paulino-Passos', 'Bruno Cuconato', 'Pedro Delfino'] | null | null | null | null | gwc-2018-1 | ['topic-coverage'] | ['natural-language-processing'] | [-3.04943383e-01 6.11409783e-01 -7.75911510e-01 -3.37488085e-01
-1.00404286e+00 -7.51022518e-01 4.62829739e-01 6.20821834e-01
-6.83757424e-01 1.01210558e+00 2.12170213e-01 -1.16589272e+00
-9.22293305e-01 -9.15142357e-01 -4.20058131e-01 1.88380212e-01
4.26559329e-01 5.43268502e-01 4.33741033e-01 -8.79726171... | [9.954362869262695, 9.210872650146484] |
b74799fe-7536-48af-802d-963816114de3 | a-unified-and-efficient-coordinating | 2303.05710 | null | https://arxiv.org/abs/2303.05710v1 | https://arxiv.org/pdf/2303.05710v1.pdf | A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning | Recently using machine learning (ML) based techniques to optimize modern database management systems has attracted intensive interest from both industry and academia. With an objective to tune a specific component of a DBMS (e.g., index selection, knobs tuning), the ML-based tuning agents have shown to be able to find ... | ['Bin Cui', 'Feifei Li', 'Jian Tan', 'Jia Chen', 'Yang Li', 'Hong Wu', 'Zhuo Chang', 'Xinyi Zhang'] | 2023-03-10 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-4.57467198e-01 -3.53211612e-01 -3.72881144e-01 -3.34197909e-01
-6.32157624e-01 -4.98885751e-01 6.23948202e-02 2.48814359e-01
-5.75773835e-01 8.94868255e-01 -4.14780229e-01 -3.97229195e-01
-2.65242636e-01 -1.06377721e+00 -7.19436288e-01 -8.78314495e-01
-3.14609289e-01 1.36478996e+00 6.80481970e-01 -4.86254036... | [4.566728115081787, 2.6064038276672363] |
14996b16-a7aa-4f5a-852f-2c9fde50257b | scalable-spatiotemporal-graph-neural-networks | 2209.06520 | null | https://arxiv.org/abs/2209.06520v2 | https://arxiv.org/pdf/2209.06520v2.pdf | Scalable Spatiotemporal Graph Neural Networks | Neural forecasting of spatiotemporal time series drives both research and industrial innovation in several relevant application domains. Graph neural networks (GNNs) are often the core component of the forecasting architecture. However, in most spatiotemporal GNNs, the computational complexity scales up to a quadratic ... | ['Cesare Alippi', 'Filippo Maria Bianchi', 'Ivan Marisca', 'Andrea Cini'] | 2022-09-14 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [-3.09986435e-02 5.71616702e-02 -4.37842667e-01 2.98426002e-02
-1.60869937e-02 -5.25215864e-01 7.76410818e-01 3.54793668e-01
-1.45789221e-01 2.33131886e-01 2.58987486e-01 -6.34222150e-01
-1.08776174e-01 -1.13895762e+00 -6.58872902e-01 -6.32216871e-01
-5.39415717e-01 1.18068546e-01 3.27540696e-01 -3.12393010... | [6.813604831695557, 2.8231217861175537] |
99fa4b14-c224-4447-b0f0-34891c8e24c0 | context-aware-deep-feature-compression-for | 1803.10537 | null | http://arxiv.org/abs/1803.10537v1 | http://arxiv.org/pdf/1803.10537v1.pdf | Context-aware Deep Feature Compression for High-speed Visual Tracking | We propose a new context-aware correlation filter based tracking framework to
achieve both high computational speed and state-of-the-art performance among
real-time trackers. The major contribution to the high computational speed lies
in the proposed deep feature compression that is achieved by a context-aware
scheme u... | ['Jongwon Choi', 'Sangdoo Yun', 'Jiyeoup Jeong', 'Hyung Jin Chang', 'Yiannis Demiris', 'Jin Young Choi', 'Tobias Fischer', 'Kyuewang Lee'] | 2018-03-28 | context-aware-deep-feature-compression-for-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Choi_Context-Aware_Deep_Feature_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Choi_Context-Aware_Deep_Feature_CVPR_2018_paper.pdf | cvpr-2018-6 | ['feature-compression'] | ['computer-vision'] | [-1.57988310e-01 -5.01710057e-01 -1.61080994e-02 -1.86401978e-01
-7.24382579e-01 -3.62312824e-01 4.81754065e-01 -1.34324953e-01
-6.28159702e-01 3.99560630e-01 -2.60800850e-02 1.26515165e-01
-1.37288868e-01 -4.54557657e-01 -8.05108607e-01 -6.57070577e-01
-2.09382102e-01 4.16135311e-01 4.84221101e-01 1.21184856... | [6.316038608551025, -2.1330602169036865] |
56ba78e7-c77b-4c16-9dc9-a88c037f749e | amodal-instance-segmentation | 1604.08202 | null | http://arxiv.org/abs/1604.08202v2 | http://arxiv.org/pdf/1604.08202v2.pdf | Amodal Instance Segmentation | We consider the problem of amodal instance segmentation, the objective of
which is to predict the region encompassing both visible and occluded parts of
each object. Thus far, the lack of publicly available amodal segmentation
annotations has stymied the development of amodal segmentation methods. In this
paper, we sid... | ['Ke Li', 'Jitendra Malik'] | 2016-04-27 | null | null | null | null | ['amodal-instance-segmentation'] | ['computer-vision'] | [ 3.21456909e-01 7.88672149e-01 -2.79995024e-01 -3.49562973e-01
-9.84693587e-01 -8.95457923e-01 7.24517047e-01 4.57029603e-02
-2.04671964e-01 6.84133947e-01 -2.57353753e-01 -3.74872178e-01
1.38878584e-01 -3.93458754e-01 -7.82126784e-01 -5.99534452e-01
3.83725286e-01 7.12059200e-01 2.37989441e-01 1.57383904... | [9.590301513671875, 0.4930320978164673] |
c5e55c5d-10ed-4965-8587-b970dbefd1b1 | constraint-based-sequential-pattern-mining | 1811.06086 | null | http://arxiv.org/abs/1811.06086v1 | http://arxiv.org/pdf/1811.06086v1.pdf | Constraint-based Sequential Pattern Mining with Decision Diagrams | Constrained sequential pattern mining aims at identifying frequent patterns
on a sequential database of items while observing constraints defined over the
item attributes. We introduce novel techniques for constraint-based sequential
pattern mining that rely on a multi-valued decision diagram representation of
the data... | ['Willem-Jan van Hoeve', 'Amin Hosseininasab', 'Andre A. Cire'] | 2018-11-14 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 4.03307229e-01 -2.42405951e-01 -6.51592731e-01 -4.47695851e-01
1.95230782e-01 -4.57215667e-01 3.05771500e-01 4.79161263e-01
-1.84569150e-01 6.21509492e-01 -2.62205005e-01 -6.43956065e-01
-5.10576546e-01 -1.16893566e+00 -2.03074872e-01 -1.89925402e-01
-5.36982536e-01 9.62040961e-01 7.76821852e-01 -1.86825711... | [8.30202579498291, 6.306829929351807] |
57aa0028-e395-4656-94ad-a1be4510f7b0 | co-grounding-networks-with-semantic-attention | 2103.12346 | null | https://arxiv.org/abs/2103.12346v1 | https://arxiv.org/pdf/2103.12346v1.pdf | Co-Grounding Networks with Semantic Attention for Referring Expression Comprehension in Videos | In this paper, we address the problem of referring expression comprehension in videos, which is challenging due to complex expression and scene dynamics. Unlike previous methods which solve the problem in multiple stages (i.e., tracking, proposal-based matching), we tackle the problem from a novel perspective, \textbf{... | ['Shih-Fu Chang', 'Zongming Guo', 'Jiaying Liu', 'Xudong Lin', 'Sijie Song'] | 2021-03-23 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Song_Co-Grounding_Networks_With_Semantic_Attention_for_Referring_Expression_Comprehension_in_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Song_Co-Grounding_Networks_With_Semantic_Attention_for_Referring_Expression_Comprehension_in_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-grounding'] | ['computer-vision'] | [ 2.46123478e-01 -1.95103213e-01 -2.54028648e-01 -5.11513114e-01
-7.80833781e-01 -3.22820753e-01 2.37205774e-01 -1.42756879e-01
-2.77002603e-01 4.06336457e-01 3.29559982e-01 1.03441730e-01
-1.16740458e-01 -4.62282062e-01 -8.53611052e-01 -3.46376896e-01
1.98953748e-01 -1.72172114e-02 5.75250536e-02 -4.09752548... | [9.920746803283691, 0.7465674877166748] |
d585437d-114c-4dca-b6c3-3585c8cb5717 | a-remote-sensing-image-dataset-for-cloud | 1901.00600 | null | http://arxiv.org/abs/1901.00600v1 | http://arxiv.org/pdf/1901.00600v1.pdf | A Remote Sensing Image Dataset for Cloud Removal | Cloud-based overlays are often present in optical remote sensing images, thus
limiting the application of acquired data. Removing clouds is an indispensable
pre-processing step in remote sensing image analysis. Deep learning has
achieved great success in the field of remote sensing in recent years,
including scene clas... | ['Kun fu', 'Xian Sun', 'Guangluan Xu', 'Xiaoke Wang', 'Daoyu Lin', 'Yang Wang'] | 2019-01-03 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 2.35173330e-01 -7.35032380e-01 4.76867527e-01 -1.25256002e-01
-3.28332454e-01 -4.21954751e-01 4.22472149e-01 -1.42322868e-01
-3.49499285e-01 7.07790256e-01 -2.33578622e-01 -3.80525351e-01
-4.17425446e-02 -9.92119253e-01 -3.92455101e-01 -1.16853046e+00
1.38600260e-01 7.07859322e-02 5.52781969e-02 -8.74380693... | [9.850919723510742, -1.7753591537475586] |
979a43b4-358b-4414-8678-a62c517a61fd | ad-kd-attribution-driven-knowledge | 2305.10010 | null | https://arxiv.org/abs/2305.10010v1 | https://arxiv.org/pdf/2305.10010v1.pdf | AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression | Knowledge distillation has attracted a great deal of interest recently to compress pre-trained language models. However, existing knowledge distillation methods suffer from two limitations. First, the student model simply imitates the teacher's behavior while ignoring the underlying reasoning. Second, these methods usu... | ['Rui Wang', 'Qifan Wang', 'Xiaojun Quan', 'Hongzhan Chen', 'Siyue Wu'] | 2023-05-17 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 1.85619876e-01 3.27857256e-01 -6.17206633e-01 -3.87449086e-01
-3.51367623e-01 -4.25557494e-01 6.17224157e-01 1.14587530e-01
-5.14723957e-01 7.92518854e-01 2.24538073e-01 -4.98889685e-01
-6.21286444e-02 -7.96678960e-01 -6.51636362e-01 -5.87517321e-01
5.97609878e-01 5.20155847e-01 4.36637700e-01 -1.19703732... | [9.515458106994629, 3.448256254196167] |
96e0c243-1e2f-422b-a454-8333d638b211 | sentiment-analysis-for-arabic-language-a | 1809.02782 | null | http://arxiv.org/abs/1809.02782v2 | http://arxiv.org/pdf/1809.02782v2.pdf | Sentiment analysis for Arabic language: A brief survey of approaches and techniques | With the emergence of Web 2.0 technology and the expansion of on-line social
networks, current Internet users have the ability to add their reviews, ratings
and opinions on social media and on commercial and news web sites. Sentiment
analysis aims to classify these reviews reviews in an automatic way. In the
literature... | ['Hossam Faris', "Mo'ath Alrefai", 'Ibrahim Aljarah'] | 2018-09-08 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-4.23803836e-01 -1.93633631e-01 -4.46415663e-01 -4.06098574e-01
-1.50369570e-01 -8.17364275e-01 4.83726382e-01 7.78214872e-01
-4.81231213e-01 6.53507888e-01 9.70957950e-02 -1.83286279e-01
2.06769720e-01 -8.92650843e-01 2.95196623e-01 -2.79007971e-01
2.26503313e-01 8.02796185e-02 9.63414833e-02 -1.16787612... | [11.0110502243042, 6.894454002380371] |
91bea4ed-85c1-4a51-beed-afc9a196139d | bel-a-bag-embedding-loss-for-transformer | 2303.01377 | null | https://arxiv.org/abs/2303.01377v1 | https://arxiv.org/pdf/2303.01377v1.pdf | BEL: A Bag Embedding Loss for Transformer enhances Multiple Instance Whole Slide Image Classification | Multiple Instance Learning (MIL) has become the predominant approach for classification tasks on gigapixel histopathology whole slide images (WSIs). Within the MIL framework, single WSIs (bags) are decomposed into patches (instances), with only WSI-level annotation available. Recent MIL approaches produce highly inform... | ['Carsten Marr', 'Nassir Navab', 'Francesco Paolo Casale', 'Ario Sadafi', 'Daniel Sens'] | 2023-03-02 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 4.43219304e-01 2.53041744e-01 -3.08717757e-01 -3.88588905e-01
-1.44769263e+00 -2.73967296e-01 5.43482900e-01 7.62452841e-01
-4.04410660e-01 8.19584846e-01 2.53418505e-01 -4.40587364e-02
-2.41273031e-01 -7.74369895e-01 -8.41001570e-01 -1.34678209e+00
-6.22112211e-03 5.20935595e-01 2.91015863e-01 -8.08510333... | [15.119107246398926, -2.867824077606201] |
2589bb94-6c63-47f4-863c-61d2856f700b | cap-robust-point-cloud-classification-via | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ding_CAP_Robust_Point_Cloud_Classification_via_Semantic_and_Structural_Modeling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ding_CAP_Robust_Point_Cloud_Classification_via_Semantic_and_Structural_Modeling_CVPR_2023_paper.pdf | CAP: Robust Point Cloud Classification via Semantic and Structural Modeling | Recently, deep neural networks have shown great success on 3D point cloud classification tasks, which simultaneously raises the concern of adversarial attacks that cause severe damage to real-world applications. Moreover, defending against adversarial examples in point cloud data is extremely difficult due to the e... | ['Min Yang', 'Wenxuan Li', 'Mi Zhang', 'Yuanmin Huang', 'Erling Jiang', 'Daizong Ding'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-1.24489807e-01 -1.90440118e-01 3.79386634e-01 -2.23060958e-02
-4.45423096e-01 -9.06094253e-01 5.20659626e-01 -1.25004929e-02
-2.57436037e-01 4.58344579e-01 -3.15374494e-01 -4.24265027e-01
-1.73365436e-02 -8.77974451e-01 -8.10791612e-01 -8.87262583e-01
-2.04228893e-01 6.13250351e-03 3.78694892e-01 -3.70776743... | [7.700197696685791, -4.470991134643555] |
8403935e-0805-4b95-b1f9-ff38c1c7d9d9 | polardet-a-fast-more-precise-detector-for | 2010.08720 | null | https://arxiv.org/abs/2010.08720v1 | https://arxiv.org/pdf/2010.08720v1.pdf | PolarDet: A Fast, More Precise Detector for Rotated Target in Aerial Images | Fast and precise object detection for high-resolution aerial images has been a challenging task over the years. Due to the sharp variations on object scale, rotation, and aspect ratio, most existing methods are inefficient and imprecise. In this paper, we represent the oriented objects by polar method in polar coordina... | ['ShiLiang Pu', 'Ye Ren', 'Wenming Tan', 'Yingjia Bu', 'Zhenshen Qu', 'Pengbo Zhao'] | 2020-10-17 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-8.10300335e-02 -4.71364468e-01 3.76161151e-02 -4.27600704e-02
-4.63652790e-01 -8.98957014e-01 3.68009269e-01 -1.77602351e-01
-1.96548864e-01 2.89614111e-01 -3.18943292e-01 -2.36439541e-01
-1.28808275e-01 -1.01203775e+00 -4.81859148e-01 -6.06696844e-01
-2.27632150e-01 1.89722434e-01 7.58197904e-01 -1.28892794... | [8.751679420471191, -0.8428593873977661] |
c5997fa0-74fb-464f-b87d-6c561947342f | wavelet-feature-maps-compression-for-image-to | 2205.12268 | null | https://arxiv.org/abs/2205.12268v4 | https://arxiv.org/pdf/2205.12268v4.pdf | Wavelet Feature Maps Compression for Image-to-Image CNNs | Convolutional Neural Networks (CNNs) are known for requiring extensive computational resources, and quantization is among the best and most common methods for compressing them. While aggressive quantization (i.e., less than 4-bits) performs well for classification, it may cause severe performance degradation in image-t... | ['Eran Treister', 'Maor Ashkenazi', 'Yair Zohav', 'Shahaf E. Finder'] | 2022-05-24 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 3.74092251e-01 -1.76609769e-01 -1.16760321e-01 -3.85549664e-01
-7.21618772e-01 -1.03048280e-01 2.40043804e-01 1.63287342e-01
-9.64742184e-01 4.94069517e-01 -5.55903502e-02 -4.11035389e-01
2.50229686e-01 -9.89999890e-01 -8.79680812e-01 -6.67206764e-01
-8.95747617e-02 -1.31308690e-01 4.64254230e-01 -3.36039886... | [8.572839736938477, 2.927731513977051] |
b7fc4609-7fce-4ad3-b0cd-9c03743643fe | sparse-signsgd-with-majority-vote-for | 2302.07475 | null | https://arxiv.org/abs/2302.07475v1 | https://arxiv.org/pdf/2302.07475v1.pdf | Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning | The training efficiency of complex deep learning models can be significantly improved through the use of distributed optimization. However, this process is often hindered by a large amount of communication cost between workers and a parameter server during iterations. To address this bottleneck, in this paper, we prese... | ['Namyoon Lee', 'Chanho Park'] | 2023-02-15 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-2.13629231e-01 -1.48539349e-01 -5.76376207e-02 -4.61482882e-01
-1.00460458e+00 -2.55487829e-01 -9.39389095e-02 1.56452373e-01
-9.15986836e-01 6.92114353e-01 -3.48125368e-01 -4.75534707e-01
-4.64629531e-01 -7.14687347e-01 -7.89104342e-01 -1.23971367e+00
-3.57065111e-01 2.59118676e-01 -4.37847935e-02 1.24735236... | [6.306149005889893, 4.861766338348389] |
a3a5c11c-4087-4422-bb3c-1b01155d86e9 | artifacts-mapping-multi-modal-semantic | 2307.01121 | null | https://arxiv.org/abs/2307.01121v1 | https://arxiv.org/pdf/2307.01121v1.pdf | Artifacts Mapping: Multi-Modal Semantic Mapping for Object Detection and 3D Localization | Geometric navigation is nowadays a well-established field of robotics and the research focus is shifting towards higher-level scene understanding, such as Semantic Mapping. When a robot needs to interact with its environment, it must be able to comprehend the contextual information of its surroundings. This work focuse... | ['Arash Ajoudani', 'Nikolaos Tsagarakis', 'Andrea Zunino', 'Gennaro Raiola', 'Federico Rollo'] | 2023-07-03 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 5.02899766e-01 2.11167168e-02 5.44705808e-01 -3.84890348e-01
-4.13702726e-01 -4.74785656e-01 5.45804143e-01 4.68265951e-01
-7.70161808e-01 5.51550388e-01 -4.22591239e-01 -7.00536221e-02
-3.64980340e-01 -1.15624905e+00 -6.77882731e-01 -6.31033242e-01
1.66784346e-01 6.82981730e-01 6.70761704e-01 -3.29222023... | [7.315219879150391, -2.053781270980835] |
bef79f0b-ef03-4106-a5c5-98cc066f51d6 | stochastic-nonsmooth-convex-optimization-with | 2303.12277 | null | https://arxiv.org/abs/2303.12277v3 | https://arxiv.org/pdf/2303.12277v3.pdf | Stochastic Nonsmooth Convex Optimization with Heavy-Tailed Noises: High-Probability Bound, In-Expectation Rate and Initial Distance Adaptation | Recently, several studies consider the stochastic optimization problem but in a heavy-tailed noise regime, i.e., the difference between the stochastic gradient and the true gradient is assumed to have a finite $p$-th moment (say being upper bounded by $\sigma^{p}$ for some $\sigma\geq0$) where $p\in(1,2]$, which not on... | ['Zhengyuan Zhou', 'Zijian Liu'] | 2023-03-22 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 1.55290738e-01 1.50730601e-02 1.78819105e-01 -1.40067324e-01
-1.27247787e+00 -5.52322805e-01 -2.53302783e-01 7.67114619e-03
-6.36266172e-01 9.62119401e-01 -3.43630642e-01 -2.58926421e-01
-4.42476094e-01 -6.78394616e-01 -9.99338806e-01 -1.16866827e+00
-1.92696825e-01 3.09892260e-02 -4.84196655e-02 -2.47713640... | [6.583136558532715, 4.526710033416748] |
c801981c-d745-4718-96ba-edcc7bd71c25 | mutr-multi-stage-transformer-for-hand-pose | null | null | https://www.mdpi.com/1424-8220/23/12/5509 | https://www.mdpi.com/1424-8220/23/12/5509 | MuTr: Multi-Stage Transformer for Hand Pose Estimation from Full-Scene Depth Image | This work presents a novel transformer-based method for hand pose estimation—DePOTR. We test the DePOTR method on four benchmark datasets, where DePOTR outperforms other transformer-based methods while achieving results on par with other state-of-the-art methods. To further demonstrate the strength of DePOTR, we propos... | ['Marek Hrúz', 'Jakub Straka', 'Matyáš Boháček', 'Zdeněk Krňoul', 'Ivan Gruber', 'Jakub Kanis'] | 2023-06-12 | null | null | null | sensors-2023-6 | ['pose-estimation', 'hand-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.37283549e-01 -7.13425875e-02 1.79986745e-01 1.69483751e-01
-1.08799684e+00 -6.99656248e-01 2.28260994e-01 -3.46161515e-01
-5.53591073e-01 6.07502997e-01 1.62405148e-01 1.55140366e-02
-1.23570681e-01 -3.48923922e-01 -5.95655620e-01 -5.78040004e-01
2.81564891e-01 1.01360738e+00 6.31524563e-01 -7.14122653... | [6.60228157043457, -0.7454303503036499] |
a76104d1-e2c1-42ee-934e-dd086fc743f0 | nested-named-entity-recognition-via | null | null | https://aclanthology.org/2021.acl-long.275 | https://aclanthology.org/2021.acl-long.275.pdf | Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path | This paper presents a novel method for nested named entity recognition. As a layered method, our method extends the prior second-best path recognition method by explicitly excluding the influence of the best path. Our method maintains a set of hidden states at each time step and selectively leverages them to build a di... | ['Taro Watanabe', 'Yuji Matsumoto', 'Hiroyuki Shindo', 'Yiran Wang'] | 2021-08-01 | null | null | null | acl-2021-5 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-1.39244109e-01 2.88109064e-01 -5.74150085e-01 -5.64091206e-01
-8.72886539e-01 -5.58558226e-01 4.65556860e-01 2.49013960e-01
-4.22291219e-01 7.56723762e-01 5.18747509e-01 -5.09001851e-01
-2.17550974e-02 -8.35573316e-01 -7.68793821e-01 -3.63261551e-01
-4.66493875e-01 3.69183093e-01 6.48186207e-01 1.39800712... | [9.593649864196777, 9.492574691772461] |
dc8d8d0d-7304-49e9-bb55-5d27001dea76 | mixformer-end-to-end-tracking-with-iterative-2 | 2302.02814 | null | https://arxiv.org/abs/2302.02814v2 | https://arxiv.org/pdf/2302.02814v2.pdf | MixFormer: End-to-End Tracking with Iterative Mixed Attention | Visual object tracking often employs a multi-stage pipeline of feature extraction, target information integration, and bounding box estimation. To simplify this pipeline and unify the process of feature extraction and target information integration, in this paper, we present a compact tracking framework, termed as MixF... | ['LiMin Wang', 'Gangshan Wu', 'Cheng Jiang', 'Yutao Cui'] | 2023-02-06 | mixformer-end-to-end-tracking-with-iterative | https://arxiv.org/abs/2302.02814 | https://arxiv.org/pdf/2302.02814.pdf | null | ['visual-object-tracking'] | ['computer-vision'] | [-3.25513542e-01 -3.41627061e-01 -3.34973931e-01 -1.17653444e-01
-8.11585963e-01 -8.58851790e-01 6.47693038e-01 -3.05252045e-01
-3.67795765e-01 2.99310118e-01 -1.14116184e-01 -1.11417308e-01
1.88170031e-01 -4.36904281e-01 -7.46819198e-01 -5.88727474e-01
-8.98680985e-02 3.18067640e-01 6.85887516e-01 1.55188829... | [6.2905778884887695, -2.122229814529419] |
9c359358-f448-44f2-92c6-fc3ec8655d2a | ambiguity-in-solving-imaging-inverse-problems | 2305.19774 | null | https://arxiv.org/abs/2305.19774v1 | https://arxiv.org/pdf/2305.19774v1.pdf | Ambiguity in solving imaging inverse problems with deep learning based operators | In recent years, large convolutional neural networks have been widely used as tools for image deblurring, because of their ability in restoring images very precisely. It is well known that image deblurring is mathematically modeled as an ill-posed inverse problem and its solution is difficult to approximate when noise ... | ['James Nagy', 'Elena Loli Piccolomini', 'Elena Morotti', 'Davide Evangelista'] | 2023-05-31 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 1.91706911e-01 -3.29618931e-01 2.44729251e-01 1.03946090e-01
-2.60720700e-01 -1.60757884e-01 3.60348463e-01 -2.91359276e-01
-5.51597893e-01 7.91243792e-01 6.52743205e-02 -9.00582224e-02
-3.76792431e-01 -7.12942004e-01 -7.06576586e-01 -1.12190187e+00
2.06701830e-01 1.18976288e-01 6.69300258e-02 -1.98058710... | [11.711026191711426, -2.520247220993042] |
34c8cade-9e55-4d96-92e8-9a021e151579 | followme-vehicle-behaviour-prediction-in | 2304.06121 | null | https://arxiv.org/abs/2304.06121v1 | https://arxiv.org/pdf/2304.06121v1.pdf | FollowMe: Vehicle Behaviour Prediction in Autonomous Vehicle Settings | An ego vehicle following a virtual lead vehicle planned route is an essential component when autonomous and non-autonomous vehicles interact. Yet, there is a question about the driver's ability to follow the planned lead vehicle route. Thus, predicting the trajectory of the ego vehicle route given a lead vehicle route ... | ['Christian Claudel', 'Linda Ng Boyle', 'Jundi Liu', 'Abduallah Mohamed'] | 2023-04-12 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [-2.45468885e-01 4.09770519e-01 -3.82063866e-01 -6.02030456e-01
-7.16628581e-02 -4.93569762e-01 8.29697311e-01 -4.36206520e-01
-3.15562874e-01 1.05067022e-01 4.70337480e-01 -9.14040267e-01
-2.29235440e-01 -8.27993989e-01 -9.38557148e-01 -3.39176267e-01
-6.39836341e-02 3.04582149e-01 4.55489367e-01 -5.99358261... | [5.970358371734619, 0.8524004220962524] |
243c95e5-7a34-42b2-9fc8-87e40a697d53 | reco-a-dataset-for-residential-community | 2206.04678 | null | https://arxiv.org/abs/2206.04678v2 | https://arxiv.org/pdf/2206.04678v2.pdf | ReCo: A Dataset for Residential Community Layout Planning | Layout planning is centrally important in the field of architecture and urban design. Among the various basic units carrying urban functions, residential community plays a vital part for supporting human life. Therefore, the layout planning of residential community has always been of concern, and has attracted particul... | ['Yu Ye', 'Yao Zhang', 'Tao Sheng', 'Haofen Wang', 'Siqi Wang', 'Yun Xiong', 'Xi Chen'] | 2022-06-08 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-2.10445791e-01 -3.04249078e-01 2.97572345e-01 -3.35559994e-01
-5.63574970e-01 -3.32280457e-01 4.22016889e-01 -2.06003472e-01
1.51354283e-01 7.10653245e-01 7.24265277e-01 -6.25571549e-01
-3.84743989e-01 -1.55324340e+00 -3.16650987e-01 -8.55975449e-01
6.00756407e-02 5.96841455e-01 -2.81598538e-01 -4.58878994... | [8.500718116760254, -1.6491787433624268] |
d5ed7461-542c-4e41-92eb-20fb5ca18a67 | efficienthrnet-efficient-scaling-for | 2007.08090 | null | https://arxiv.org/abs/2007.08090v2 | https://arxiv.org/pdf/2007.08090v2.pdf | EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation | There is an increasing demand for lightweight multi-person pose estimation for many emerging smart IoT applications. However, the existing algorithms tend to have large model sizes and intense computational requirements, making them ill-suited for real-time applications and deployment on resource-constrained hardware. ... | ['Aneri Sheth', 'Hamed Tabkhi', 'Steven Furgurson', 'Christopher Neff'] | 2020-07-16 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.07149504e-01 -9.02232677e-02 6.01463467e-02 -9.69753712e-02
-5.56382000e-01 2.44722161e-02 1.35674670e-01 1.94657952e-01
-6.91709638e-01 4.85035956e-01 -4.59441766e-02 1.52484506e-01
1.41795203e-01 -7.94101179e-01 -4.46899205e-01 -8.58862773e-02
-1.10402167e-01 8.01661015e-01 5.09835601e-01 -1.24432303... | [7.161841869354248, -0.758607029914856] |
2bda02fc-dd5a-4a49-9604-da153b41e8e4 | cross-view-action-recognition-via-contrastive | 2305.01733 | null | https://arxiv.org/abs/2305.01733v1 | https://arxiv.org/pdf/2305.01733v1.pdf | Cross-view Action Recognition via Contrastive View-invariant Representation | Cross view action recognition (CVAR) seeks to recognize a human action when observed from a previously unseen viewpoint. This is a challenging problem since the appearance of an action changes significantly with the viewpoint. Applications of CVAR include surveillance and monitoring of assisted living facilities where ... | ['Mario Sznaier', 'Octavia Camps', 'Balaji Sundareshan', 'Dan Luo', 'Yuexi Zhang'] | 2023-05-02 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 2.49998122e-01 -2.11285874e-01 -1.80809591e-02 -8.42368305e-02
-5.96774161e-01 -4.25081700e-01 4.38526213e-01 -2.66947955e-01
-4.22894448e-01 5.55588663e-01 3.08938950e-01 2.13258892e-01
1.62781298e-01 -3.87896657e-01 -6.24507904e-01 -8.05346668e-01
-3.67632657e-02 2.88975894e-01 4.32215661e-01 -1.15360796... | [7.816623210906982, 0.3257697820663452] |
50a62586-b701-4475-af80-01d6377583ad | effects-of-creativity-and-cluster-tightness | null | null | https://aclanthology.org/P16-1062 | https://aclanthology.org/P16-1062.pdf | Effects of Creativity and Cluster Tightness on Short Text Clustering Performance | null | ['Rui Zhang', 'Catherine Finegan-Dollak', 'Xiangyi Ye', 'Reed Coke', 'Dragomir Radev'] | 2016-08-01 | null | null | null | acl-2016-8 | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', '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... | [-7.296499729156494, 3.6718928813934326] |
65436597-db02-40f3-9858-517d93e00fc1 | no-reference-color-image-quality-assessment | 1812.10695 | null | http://arxiv.org/abs/1812.10695v1 | http://arxiv.org/pdf/1812.10695v1.pdf | No-Reference Color Image Quality Assessment: From Entropy to Perceptual Quality | This paper presents a high-performance general-purpose no-reference (NR)
image quality assessment (IQA) method based on image entropy. The image
features are extracted from two domains. In the spatial domain, the mutual
information between the color channels and the two-dimensional entropy are
calculated. In the freque... | ['Xiaoqiao Chen', 'Manhui Lin', 'Guangyi Yang', 'Qingyi Zhang', 'Chu He'] | 2018-12-27 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [-4.78308089e-02 -8.98808360e-01 1.37258455e-01 -2.28207216e-01
-8.08535159e-01 -1.84989691e-01 1.80661187e-01 -1.28796771e-01
-1.47853836e-01 3.20392370e-01 2.49078259e-01 8.52269232e-02
-4.80962396e-01 -8.18003416e-01 1.44175157e-01 -1.00783062e+00
-1.27957404e-01 -4.89870936e-01 1.61390364e-01 -7.98082873... | [11.715927124023438, -1.929663062095642] |
788b93b9-6ff0-4b63-bddc-5e5c7cbba3b4 | scene-text-retrieval-via-joint-text-detection | 2104.01552 | null | https://arxiv.org/abs/2104.01552v1 | https://arxiv.org/pdf/2104.01552v1.pdf | Scene Text Retrieval via Joint Text Detection and Similarity Learning | Scene text retrieval aims to localize and search all text instances from an image gallery, which are the same or similar to a given query text. Such a task is usually realized by matching a query text to the recognized words, outputted by an end-to-end scene text spotter. In this paper, we address this problem by direc... | ['Wenyu Liu', 'Jing Wang', 'Shenggao Zhu', 'Mingkun Yang', 'Xiang Bai', 'Hao Wang'] | 2021-04-04 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_Scene_Text_Retrieval_via_Joint_Text_Detection_and_Similarity_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_Scene_Text_Retrieval_via_Joint_Text_Detection_and_Similarity_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['text-spotting', 'scene-text-detection'] | ['computer-vision', 'computer-vision'] | [ 4.63761449e-01 -5.41883945e-01 -1.11562692e-01 -5.11667013e-01
-1.39827979e+00 -5.51419258e-01 9.39734578e-01 1.92067385e-01
-5.15190184e-01 -8.25632215e-02 1.02720514e-01 1.24920830e-01
2.29006889e-03 -4.46205497e-01 -7.20388234e-01 -5.97171009e-01
6.23579621e-01 7.01641679e-01 1.53131545e-01 2.82454163... | [11.729063034057617, 2.0629615783691406] |
4f526571-5476-494b-9586-24ff027e9a1e | variational-linearized-laplace-approximation | 2302.12565 | null | https://arxiv.org/abs/2302.12565v1 | https://arxiv.org/pdf/2302.12565v1.pdf | Variational Linearized Laplace Approximation for Bayesian Deep Learning | Pre-trained deep neural networks can be adapted to perform uncertainty estimation by transforming them into Bayesian neural networks via methods such as Laplace approximation (LA) or its linearized form (LLA), among others. To make these methods more tractable, the generalized Gauss-Newton (GGN) approximation is often ... | ['Daniel Hernández-Lobato', 'Simón Rodríguez Santana', 'Luis A. Ortega'] | 2023-02-24 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.57526278e-01 2.25781217e-01 1.17116801e-01 -2.97296375e-01
-8.58427763e-01 -2.36610487e-01 6.37071550e-01 -1.09609663e-01
-3.94176751e-01 9.42680418e-01 -6.59368262e-02 -2.47365415e-01
-2.57636577e-01 -8.56074989e-01 -9.83087420e-01 -9.20747697e-01
7.78778568e-02 7.11009741e-01 1.54247776e-01 3.67649704... | [7.115846633911133, 3.8220083713531494] |
396d59fe-c88f-44a4-9544-1f5e625df077 | depth-quality-inspired-feature-manipulation | 2107.01779 | null | https://arxiv.org/abs/2107.01779v2 | https://arxiv.org/pdf/2107.01779v2.pdf | Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection | RGB-D salient object detection (SOD) recently has attracted increasing research interest by benefiting conventional RGB SOD with extra depth information. However, existing RGB-D SOD models often fail to perform well in terms of both efficiency and accuracy, which hinders their potential applications on mobile devices a... | ['Qijun Zhao', 'Keren Fu', 'Zhuo Wang', 'Ge-Peng Ji', 'Wenbo Zhang'] | 2021-07-05 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 3.29148561e-01 -1.38318673e-01 -1.85225189e-01 -3.53613168e-01
-7.22259521e-01 -3.11905202e-02 3.14657599e-01 -2.10091360e-02
-5.27698100e-01 1.85813218e-01 1.66081369e-01 -1.70786187e-01
1.81789711e-01 -8.81608903e-01 -5.62319458e-01 -7.47775793e-01
2.54236132e-01 -1.30218804e-01 6.76167667e-01 -2.67832100... | [9.582967758178711, -0.8852068185806274] |
e1ebc651-fdb1-4e32-adf2-978a5d0c6015 | improving-language-understanding-by | null | null | https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf | https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf | Improving Language Understanding by Generative Pre-Training | Natural language understanding comprises a wide range of diverse tasks such
as textual entailment, question answering, semantic similarity assessment, and
document classification. Although large unlabeled text corpora are abundant,
labeled data for learning these specific tasks is scarce, making it challenging for
... | ['Tim Salimans', 'Ilya Sutskever', 'Alec Radford', 'Karthik Narasimhan'] | 2018-06-11 | null | null | null | preprint-2018-6 | ['cloze-test'] | ['natural-language-processing'] | [ 5.13840377e-01 1.95960701e-01 -1.43730208e-01 -8.07454705e-01
-1.37328804e+00 -8.70564997e-01 9.72830296e-01 3.50002795e-01
-5.93756616e-01 7.10492194e-01 4.31590497e-01 -5.20246625e-01
1.20440885e-01 -6.56164646e-01 -8.82490635e-01 -1.95169374e-02
5.11925340e-01 8.90799999e-01 1.08756661e-01 -4.70832080... | [11.026488304138184, 8.404294967651367] |
cad0fe39-3f50-402d-ab09-98281675973f | system-neural-diversity-measuring-behavioral | 2305.02128 | null | https://arxiv.org/abs/2305.02128v1 | https://arxiv.org/pdf/2305.02128v1.pdf | System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning | Evolutionary science provides evidence that diversity confers resilience. Yet, traditional multi-agent reinforcement learning techniques commonly enforce homogeneity to increase training sample efficiency. When a system of learning agents is not constrained to homogeneous policies, individual agents may develop diverse... | ['Amanda Prorok', 'Ajay Shankar', 'Matteo Bettini'] | 2023-05-03 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.16358884e-01 -1.48830697e-01 1.40525308e-02 5.62566578e-01
-6.86263070e-02 -8.45912337e-01 7.79334307e-01 2.52212971e-01
-5.47490180e-01 1.02766454e+00 -2.49028276e-03 -1.29640773e-01
-4.61920440e-01 -5.02621174e-01 -6.45084798e-01 -1.14280415e+00
-4.47132200e-01 2.63862997e-01 -2.80230287e-02 -5.97462535... | [3.9833285808563232, 2.1749398708343506] |
ff98c8f6-a4f8-4006-ac26-4a248a51e776 | panns-large-scale-pretrained-audio-neural-1 | 1912.10211 | null | http://arxiv.org/abs/1912.10211v5 | http://arxiv.org/pdf/1912.10211v5.pdf | PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition | Audio pattern recognition is an important research topic in the machine
learning area, and includes several tasks such as audio tagging, acoustic scene
classification, music classification, speech emotion classification and sound
event detection. Recently, neural networks have been applied to tackle audio
pattern recog... | [] | 2020-08-23 | panns-large-scale-pretrained-audio-neural | null | null | null | ['audio-tagging'] | ['audio'] | [ 2.93289185e-01 -6.63976252e-01 1.56717077e-01 -2.98211396e-01
-1.08060420e+00 -4.10482943e-01 -1.81372866e-01 2.22106948e-02
-4.90087211e-01 2.45924935e-01 9.62775499e-02 1.35705695e-01
-1.02101132e-01 -5.10117948e-01 -5.42835474e-01 -4.45332289e-01
-4.07143742e-01 2.57480815e-02 2.95764953e-01 6.90222532... | [15.19260311126709, 5.179794788360596] |
87867575-9063-49d4-9c8f-02bedea54126 | layoutlmv3-pre-training-for-document-ai-with | 2204.08387 | null | https://arxiv.org/abs/2204.08387v3 | https://arxiv.org/pdf/2204.08387v3.pdf | LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking | Self-supervised pre-training techniques have achieved remarkable progress in Document AI. Most multimodal pre-trained models use a masked language modeling objective to learn bidirectional representations on the text modality, but they differ in pre-training objectives for the image modality. This discrepancy adds diff... | ['Furu Wei', 'Yutong Lu', 'Lei Cui', 'Tengchao Lv', 'Yupan Huang'] | 2022-04-18 | null | null | null | null | ['document-image-classification', 'document-layout-analysis', 'document-ai', 'semantic-entity-labeling', 'key-information-extraction'] | ['computer-vision', 'computer-vision', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.40986836e-01 2.67550386e-02 -3.17349672e-01 -5.82522273e-01
-9.87538874e-01 -7.52077162e-01 1.02724159e+00 2.44102553e-01
-2.01687321e-01 1.24280853e-02 3.38091880e-01 -6.88941777e-01
2.51297057e-01 -4.23328102e-01 -9.49949145e-01 -6.24448717e-01
4.80812997e-01 7.56326973e-01 -3.62969488e-01 -1.71527579... | [11.214029312133789, 1.9756054878234863] |
f7d1b72c-7401-46e0-a3e7-a43f9f900198 | probabilistic-sequential-matrix-factorization | 1910.03906 | null | https://arxiv.org/abs/1910.03906v3 | https://arxiv.org/pdf/1910.03906v3.pdf | Probabilistic sequential matrix factorization | We introduce the probabilistic sequential matrix factorization (PSMF) method for factorizing time-varying and non-stationary datasets consisting of high-dimensional time-series. In particular, we consider nonlinear Gaussian state-space models where sequential approximate inference results in the factorization of a data... | ['Mark F. J. Steel', 'Ömer Deniz Akyildiz', 'Theodoros Damoulas', 'Gerrit J. J. van den Burg'] | 2019-10-09 | null | null | null | null | ['multivariate-time-series-imputation'] | ['time-series'] | [-4.19580452e-02 -3.88380975e-01 -9.84715968e-02 -2.29402632e-01
-7.13549256e-01 -6.31169140e-01 6.74204051e-01 -2.15246513e-01
-2.30624646e-01 7.16971397e-01 3.89697939e-01 -5.05926251e-01
-6.69114828e-01 -4.78595525e-01 -8.13277721e-01 -8.34305108e-01
-2.64200032e-01 3.82114708e-01 -2.26621702e-01 2.08441481... | [6.799228668212891, 3.6792781352996826] |
f130e550-9989-4aee-afc3-fb7c5126a896 | self-attention-fusion-for-audiovisual-emotion | 2201.11095 | null | https://arxiv.org/abs/2201.11095v1 | https://arxiv.org/pdf/2201.11095v1.pdf | Self-attention fusion for audiovisual emotion recognition with incomplete data | In this paper, we consider the problem of multimodal data analysis with a use case of audiovisual emotion recognition. We propose an architecture capable of learning from raw data and describe three variants of it with distinct modality fusion mechanisms. While most of the previous works consider the ideal scenario of ... | ['Moncef Gabbouj', 'Alexandros Iosifidis', 'Kateryna Chumachenko'] | 2022-01-26 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 5.88476360e-01 6.76364526e-02 2.19195075e-02 -3.11648250e-01
-1.00906134e+00 -5.65885127e-01 8.90579522e-01 4.66963416e-03
-7.44474888e-01 8.49331558e-01 3.95725995e-01 -8.89262091e-03
-2.03113854e-01 -3.02788466e-01 -7.11755633e-01 -8.50897372e-01
1.19957805e-01 2.83807844e-01 -1.46864235e-01 -2.46478394... | [13.22264575958252, 5.012643337249756] |
294e7d27-c633-4ad9-879b-9868c67d9cfe | when-do-graph-neural-networks-help-with-node | 2304.14274 | null | https://arxiv.org/abs/2304.14274v2 | https://arxiv.org/pdf/2304.14274v2.pdf | When Do Graph Neural Networks Help with Node Classification: Investigating the Homophily Principle on Node Distinguishability | Homophily principle, i.e. nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority of Graph Neural Networks (GNNs) over node-based Neural Networks on Node Classification tasks. Recent research suggests that, even in the absence of homophily, the... | ['Doina Precup', 'Jure Leskovec', 'Jie Fu', 'Xiao-Wen Chang', 'Jiaqi Zhu', 'Qincheng Lu', 'Minkai Xu', 'Chenqing Hua', 'Sitao Luan'] | 2023-04-25 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [-3.50767285e-01 9.61254165e-02 -4.04665291e-01 -3.36380243e-01
5.35652041e-01 -5.30714571e-01 7.84678996e-01 3.38374972e-01
-1.35328710e-01 7.45929539e-01 -8.02916139e-02 -4.41092163e-01
-6.34405792e-01 -1.38107145e+00 -6.88093781e-01 -8.40774536e-01
-3.60071898e-01 2.14197606e-01 2.45773017e-01 -2.15355083... | [7.0693559646606445, 6.0787577629089355] |
cb362a8f-84d4-4b5c-8f82-250159111bad | semantic-image-manipulation-using-scene | 2004.03677 | null | https://arxiv.org/abs/2004.03677v1 | https://arxiv.org/pdf/2004.03677v1.pdf | Semantic Image Manipulation Using Scene Graphs | Image manipulation can be considered a special case of image generation where the image to be produced is a modification of an existing image. Image generation and manipulation have been, for the most part, tasks that operate on raw pixels. However, the remarkable progress in learning rich image and object representati... | ['Gregory D. Hager', 'Azade Farshad', 'Federico Tombari', 'Christian Rupprecht', 'Nassir Navab', 'Helisa Dhamo', 'Iro Laina'] | 2020-04-07 | semantic-image-manipulation-using-scene-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Dhamo_Semantic_Image_Manipulation_Using_Scene_Graphs_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Dhamo_Semantic_Image_Manipulation_Using_Scene_Graphs_CVPR_2020_paper.pdf | cvpr-2020-6 | ['layout-to-image-generation'] | ['computer-vision'] | [ 1.03305149e+00 4.90684479e-01 3.73504996e-01 -6.80195749e-01
-1.22167654e-02 -5.88435531e-01 9.02779937e-01 4.59212482e-01
-3.37174326e-01 5.53558469e-01 -4.57938202e-02 -1.27742916e-01
7.44725913e-02 -1.12130237e+00 -1.19519901e+00 -4.77853507e-01
2.51867980e-01 4.58854258e-01 3.63117188e-01 -4.30910915... | [11.218180656433105, -0.24024303257465363] |
3c7e9b69-08bc-4cbb-8eaa-c68b2ee0baf7 | compressed-sensing-a-discrete-optimization | 2306.04647 | null | https://arxiv.org/abs/2306.04647v1 | https://arxiv.org/pdf/2306.04647v1.pdf | Compressed Sensing: A Discrete Optimization Approach | We study the Compressed Sensing (CS) problem, which is the problem of finding the most sparse vector that satisfies a set of linear measurements up to some numerical tolerance. CS is a central problem in Statistics, Operations Research and Machine Learning which arises in applications such as signal processing, data co... | ['Nicholas Johnson', 'Dimitris Bertsimas'] | 2023-06-05 | null | null | null | null | ['image-reconstruction', 'data-compression'] | ['computer-vision', 'time-series'] | [ 6.13124728e-01 1.88958973e-01 -1.24699540e-01 -1.69983581e-01
-1.34678292e+00 -5.47757864e-01 -4.60200369e-01 4.19986725e-01
-5.17873645e-01 6.55185938e-01 1.03120379e-01 -3.17353785e-01
-4.21969950e-01 -5.69849133e-01 -8.43679190e-01 -6.33152664e-01
-5.40507138e-01 2.57849723e-01 -3.10701847e-01 -1.08032867... | [6.867467403411865, 4.477419376373291] |
62a83586-8d82-4f07-9840-857e7a22606f | workload-forecasting-of-a-logistic-node-using | 2211.04976 | null | https://arxiv.org/abs/2211.04976v1 | https://arxiv.org/pdf/2211.04976v1.pdf | Workload Forecasting of a Logistic Node Using Bayesian Neural Networks | Purpose: Traffic volume in empty container depots has been highly volatile due to external factors. Forecasting the expected container truck traffic along with having a dynamic module to foresee the future workload plays a critical role in improving the work efficiency. This paper studies the relevant literature and de... | ['Anisa Rizvanolli und Olaf Rendel', 'Emin Nakilcioglu'] | 2022-11-09 | null | null | null | null | ['probabilistic-deep-learning', 'probabilistic-time-series-forecasting'] | ['computer-vision', 'time-series'] | [-5.71157694e-01 -1.76571608e-01 -1.84818372e-01 -8.03384364e-01
-3.69957113e-03 8.29158127e-02 2.91106075e-01 -3.24195236e-01
-1.00770377e-01 6.12358391e-01 2.10840464e-01 -4.36211973e-01
-7.89150417e-01 -1.12714720e+00 -4.08994824e-01 -1.01207101e+00
1.10851526e-01 1.32333684e+00 -2.55355597e-01 -4.46453720... | [6.295473098754883, 2.6732888221740723] |
0c1ab2ff-4447-4d7f-ac72-41c4c0d00e35 | vehicle-detection-and-classification-without | 2305.08265 | null | https://arxiv.org/abs/2305.08265v2 | https://arxiv.org/pdf/2305.08265v2.pdf | Vehicle Detection and Classification without Residual Calculation: Accelerating HEVC Image Decoding with Random Perturbation Injection | In the field of video analytics, particularly traffic surveillance, there is a growing need for efficient and effective methods for processing and understanding video data. Traditional full video decoding techniques can be computationally intensive and time-consuming, leading researchers to explore alternative approach... | ['Behçet Uğur Töreyin', 'Muhammet Sebul Beratoğlu'] | 2023-05-14 | null | null | null | null | ['image-reconstruction', 'video-understanding'] | ['computer-vision', 'computer-vision'] | [ 9.22905982e-01 -1.21762194e-01 -2.92415768e-01 -8.40281919e-02
-5.15981317e-01 -3.60348791e-01 2.23940313e-01 9.42355096e-02
-4.67560917e-01 4.79795814e-01 -1.49114773e-01 -7.08930910e-01
1.74314663e-01 -8.73811066e-01 -8.37494552e-01 -6.94575787e-01
-2.05053955e-01 -2.15902582e-01 4.89700854e-01 4.86707985... | [10.323991775512695, -0.8518844842910767] |
668fb220-01ac-478e-a1b7-d4eca502be05 | improving-human-annotation-in-single-object | 1911.02807 | null | https://arxiv.org/abs/1911.02807v1 | https://arxiv.org/pdf/1911.02807v1.pdf | Improving Human Annotation in Single Object Tracking | Human annotation is always considered as ground truth in video object tracking tasks. It is used in both training and evaluation purposes. Thus, ensuring its high quality is an important task for the success of trackers and evaluations between them. In this paper, we give a qualitative and quantitative analysis of the ... | ['Yu Pang', 'Haibin Ling', 'Lin Yuan', 'Xinyi Li'] | 2019-11-07 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [ 6.77382424e-02 -1.14592962e-01 -1.18363857e-01 -2.98638731e-01
-3.86208743e-01 -5.21577299e-01 3.88879269e-01 -2.05810275e-02
-4.05337095e-01 5.39937973e-01 -9.71046239e-02 5.60640581e-02
2.02385888e-01 -2.90324420e-01 -7.17685699e-01 -7.29706347e-01
6.89505786e-02 2.88452238e-01 9.47016597e-01 4.08469550... | [8.76412296295166, -0.5624153017997742] |
a3084d0a-d77f-4527-b899-2773c0db1e53 | multi-target-range-and-angle-detection-for | 2302.14327 | null | https://arxiv.org/abs/2302.14327v1 | https://arxiv.org/pdf/2302.14327v1.pdf | Multi-target Range and Angle detection for MIMO-FMCW radar with limited antennas | Multiple-input multiple-output (MIMO) radar has several advantages with respect to the traditional radar array systems in terms of performance and flexibility. However, in order to achieve high angular resolution, a MIMO radar requires a large number of transmit and receive antennas, which increases hardware design and... | ['Arpan Chattopadhyay', 'Himali Singh'] | 2023-02-28 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 9.85554636e-01 -6.00888252e-01 6.12830698e-01 -3.84812266e-01
-7.87985206e-01 -6.41647577e-01 4.30673599e-01 -3.05780560e-01
-2.98331797e-01 6.63525701e-01 -1.95811868e-01 -1.36445761e-01
-8.98028910e-01 -7.23395765e-01 -1.33088484e-01 -1.14955628e+00
-4.40476030e-01 2.52696455e-01 -7.57835731e-02 -1.49241285... | [6.631717205047607, 1.144851565361023] |
d1a579a5-9d49-4268-a324-7c0db8bc2bf2 | 3d-human-mesh-regression-with-dense-1 | 2006.05734 | null | https://arxiv.org/abs/2006.05734v2 | https://arxiv.org/pdf/2006.05734v2.pdf | 3D Human Mesh Regression with Dense Correspondence | Estimating 3D mesh of the human body from a single 2D image is an important task with many applications such as augmented reality and Human-Robot interaction. However, prior works reconstructed 3D mesh from global image feature extracted by using convolutional neural network (CNN), where the dense correspondences betwe... | ['Wang Zeng', 'Wentao Liu', 'Ping Luo', 'Wanli Ouyang', 'Xiaogang Wang'] | 2020-06-10 | 3d-human-mesh-regression-with-dense | http://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_3D_Human_Mesh_Regression_With_Dense_Correspondence_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zeng_3D_Human_Mesh_Regression_With_Dense_Correspondence_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 1.90817267e-01 4.64657359e-02 -1.65407181e-01 -3.54597688e-01
-5.29747844e-01 3.15944105e-02 2.29831472e-01 -2.67715245e-01
-1.62247658e-01 4.66089100e-01 -3.80299725e-02 2.84355134e-01
1.24843009e-02 -1.20019031e+00 -1.26609552e+00 -2.92555600e-01
3.06854606e-01 7.00505614e-01 2.60272920e-01 -2.52138346... | [7.124678134918213, -1.329338788986206] |
52a3eb2e-3bc6-42d1-9c7a-da2dd5bc58bf | towards-unsupervised-text-classification | null | null | https://aclanthology.org/P19-1036 | https://aclanthology.org/P19-1036.pdf | Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings | Text classification aims at mapping documents into a set of predefined categories. Supervised machine learning models have shown great success in this area but they require a large number of labeled documents to reach adequate accuracy. This is particularly true when the number of target categories is in the tens or th... | ["L{\\'e}a A. Deleris", 'Zied Haj-Yahia', 'Adrien Sieg'] | 2019-07-01 | null | null | null | acl-2019-7 | ['unsupervised-text-classification'] | ['natural-language-processing'] | [ 2.13626236e-01 2.59909838e-01 -2.47772276e-01 -5.34451962e-01
-5.03857076e-01 -7.52354145e-01 1.13984597e+00 9.89501178e-01
-6.39240921e-01 4.83213633e-01 2.81367242e-01 -4.20999855e-01
-4.39951926e-01 -7.97708333e-01 -3.39945522e-03 -4.23764825e-01
2.62857676e-01 8.68738115e-01 2.75960952e-01 -4.47759598... | [10.16830825805664, 8.667800903320312] |
3e49dbd5-c3a3-48fb-9113-2100b779e9ef | deep-learning-for-entity-matching-a-design | null | null | https://doi.org/10.1145/3183713.3196926 | http://pages.cs.wisc.edu/~anhai/papers1/deepmatcher-sigmod18.pdf | Deep Learning for Entity Matching: A Design Space Exploration | Entity matching (EM) finds data instances that refer to the same real-world entity. In this paper we examine applying deep learning (DL) to EM, to understand DL's benefits and limitations. We review many DL solutions that have been developed for related matching tasks in text processing (e.g., entity linking, textual e... | ['Vijay Raghavendra', 'Esteban Arcaute', 'Rohit Deep', 'Ganesh Krishnan', 'Youngchoon Park', 'AnHai Doan', 'Theodoros Rekatsinas', 'Han Li', 'Sidharth Mudgal'] | 2018-05-01 | null | null | null | sigmod-international-conference-on-management | ['entity-resolution'] | ['natural-language-processing'] | [-1.95183381e-01 1.83100268e-01 -1.31737694e-01 -3.46445978e-01
-7.95691729e-01 -3.77845436e-01 6.36090577e-01 3.06326717e-01
-8.09085310e-01 6.66743577e-01 3.49835455e-01 -4.82091129e-01
-2.39432901e-01 -8.73349071e-01 -7.23815858e-01 -1.59569606e-01
2.75837362e-01 1.09789789e+00 -1.71995163e-01 -3.20950329... | [9.478570938110352, 8.581327438354492] |
9d54d36c-2ff2-41cd-90b8-c7089a7aba94 | mocapdeform-monocular-3d-human-motion-capture | 2208.08439 | null | https://arxiv.org/abs/2208.08439v1 | https://arxiv.org/pdf/2208.08439v1.pdf | MoCapDeform: Monocular 3D Human Motion Capture in Deformable Scenes | 3D human motion capture from monocular RGB images respecting interactions of a subject with complex and possibly deformable environments is a very challenging, ill-posed and under-explored problem. Existing methods address it only weakly and do not model possible surface deformations often occurring when humans interac... | ['Vladislav Golyanik', 'Christian Theobalt', 'Bernt Schiele', 'Soshi Shimada', 'Zhi Li'] | 2022-08-17 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 2.41383985e-01 -4.44236435e-02 2.79382408e-01 -1.85670495e-01
-3.95662010e-01 -6.42897666e-01 5.60387969e-01 -8.19044292e-01
-6.29504025e-01 4.41169977e-01 4.20579404e-01 3.59938323e-01
2.45708764e-01 -2.47560024e-01 -7.41412461e-01 -6.28267884e-01
3.92493606e-01 9.36286747e-01 3.69281530e-01 -1.06564380... | [7.091970443725586, -1.0710183382034302] |
46d1b4fe-819c-4a51-afcd-3d39e201f6ce | dear-xai-community-we-need-to-talk | 2306.04292 | null | https://arxiv.org/abs/2306.04292v1 | https://arxiv.org/pdf/2306.04292v1.pdf | Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research | Despite progress in the field, significant parts of current XAI research are still not on solid conceptual, ethical, or methodological grounds. Unfortunately, these unfounded parts are not on the decline but continue to grow. Many explanation techniques are still proposed without clarifying their purpose. Instead, they... | ['Gunnar König', 'Timo Freiesleben'] | 2023-06-07 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-2.66316403e-02 6.42070830e-01 -8.42429936e-01 -7.67665923e-01
-1.95572317e-01 -4.46324378e-01 6.49997652e-01 2.33126804e-01
2.36518704e-03 9.41123426e-01 3.57707858e-01 -9.51799154e-01
-3.81871611e-01 -3.80128950e-01 -8.00956964e-01 -2.57230997e-01
1.14260413e-01 3.23686481e-01 -2.32499287e-01 8.27752426... | [8.86583137512207, 6.009974002838135] |
57a87d9d-514b-4510-9193-237ed82908ca | blind-image-deblurring-via-reweighted-graph | 1712.08877 | null | http://arxiv.org/abs/1712.08877v1 | http://arxiv.org/pdf/1712.08877v1.pdf | Blind Image Deblurring via Reweighted Graph Total Variation | Blind image deblurring, i.e., deblurring without knowledge of the blur
kernel, is a highly ill-posed problem. The problem can be solved in two parts:
i) estimate a blur kernel from the blurry image, and ii) given estimated blur
kernel, de-convolve blurry input to restore the target image. In this paper, by
interpreting... | ['Xian-Ming Liu', 'Yuanchao Bai', 'Wen Gao', 'Gene Cheung'] | 2017-12-24 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 3.09181184e-01 -3.40762079e-01 2.04046875e-01 -7.34170452e-02
-6.51804566e-01 -4.21219289e-01 5.76268993e-02 -9.64210689e-01
5.03489515e-03 7.30338991e-01 6.24482274e-01 4.53006700e-02
-3.74625534e-01 -1.92011431e-01 -6.96065545e-01 -9.09435213e-01
1.22503318e-01 -2.24853173e-01 -2.84553207e-02 2.33467713... | [11.610472679138184, -2.7526583671569824] |
074b9845-82ae-4047-b101-3ebd0a7358da | identitydp-differential-private | 2103.01745 | null | https://arxiv.org/abs/2103.01745v1 | https://arxiv.org/pdf/2103.01745v1.pdf | IdentityDP: Differential Private Identification Protection for Face Images | Because of the explosive growth of face photos as well as their widespread dissemination and easy accessibility in social media, the security and privacy of personal identity information becomes an unprecedented challenge. Meanwhile, the convenience brought by advanced identity-agnostic computer vision technologies is ... | ['Rong Xie', 'Ming Ding', 'Bo Liu', 'Li Song', 'Yunqian Wen'] | 2021-03-02 | null | null | null | null | ['face-anonymization'] | ['computer-vision'] | [ 2.12135896e-01 -6.31005317e-02 4.22696993e-02 -6.02950037e-01
-2.95094132e-01 -6.61074877e-01 4.32732701e-01 -3.46396148e-01
-3.38628769e-01 5.97525239e-01 1.06135145e-01 -3.15538682e-02
-1.18798241e-01 -7.78071404e-01 -4.25707191e-01 -8.94743145e-01
2.47596443e-01 6.98871836e-02 -5.12186527e-01 -9.09169670... | [12.76205062866211, 0.778588056564331] |
24def6d9-afb3-4080-972c-bc3601602010 | tensor-network-kalman-filtering-for-large | 2110.13501 | null | https://arxiv.org/abs/2110.13501v1 | https://arxiv.org/pdf/2110.13501v1.pdf | Tensor Network Kalman Filtering for Large-Scale LS-SVMs | Least squares support vector machines are a commonly used supervised learning method for nonlinear regression and classification. They can be implemented in either their primal or dual form. The latter requires solving a linear system, which can be advantageous as an explicit mapping of the data to a possibly infinite-... | ['Kim Batselier', 'Johan A. K. Suykens', 'Maximilian Lucassen'] | 2021-10-26 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 3.96731421e-02 -3.85337144e-01 -2.05807969e-01 -2.29006469e-01
-7.46535361e-01 -3.28828692e-01 5.41015387e-01 -2.02977121e-01
-4.31013703e-01 9.80234981e-01 -4.26771015e-01 -4.49929148e-01
-5.44931531e-01 -4.20635581e-01 -3.35661918e-01 -1.02412629e+00
2.69904695e-02 3.58464271e-01 1.54567346e-01 2.20107082... | [7.605876445770264, 4.155284404754639] |
41f66091-d220-4cff-b64e-a7a93d2bc77b | a-review-of-chatgpt-applications-in-education | 2305.00237 | null | https://arxiv.org/abs/2305.00237v1 | https://arxiv.org/pdf/2305.00237v1.pdf | A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directions | ChatGPT is a type of artificial intelligence language model that uses deep learning algorithms to generate human-like responses to text-based prompts. The introduction of the latest ChatGPT version in November of 2022 has caused shockwaves in the industrial and academic communities for its powerful capabilities, pletho... | ['Natheer Khasawneh', 'Mohammad Fraiwan'] | 2023-04-29 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-3.66188616e-01 3.32227111e-01 -9.47054848e-02 -1.41930670e-01
-3.24720353e-01 -5.20759225e-01 5.98345995e-01 -2.14448005e-01
-2.41380945e-01 9.04307365e-01 4.01971489e-02 -6.43241644e-01
-1.38022508e-02 -8.32542777e-01 -1.50684610e-01 -3.39195430e-01
1.40265018e-01 6.50095463e-01 1.66193187e-01 -8.20878506... | [12.692681312561035, 7.949760913848877] |
60c3e38a-de67-448f-bd51-67adad339410 | ideals-idiomatic-expressions-for-advancement | 2305.13637 | null | https://arxiv.org/abs/2305.13637v2 | https://arxiv.org/pdf/2305.13637v2.pdf | IdEALS: Idiomatic Expressions for Advancement of Language Skills | Although significant progress has been made in developing methods for Grammatical Error Correction (GEC), addressing word choice improvements has been notably lacking and enhancing sentence expressivity by replacing phrases with advanced expressions is an understudied aspect. In this paper, we focus on this area and pr... | ['Zhou Yu', 'Sam Davidson', 'Bill Sun', 'Narutatsu Ri'] | 2023-05-23 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 3.96131724e-01 3.09110045e-01 2.21560393e-02 -6.81630611e-01
-8.58633339e-01 -5.84509671e-01 3.34313035e-01 5.79642415e-01
-6.56015754e-01 1.20268607e+00 3.34001958e-01 -5.90172350e-01
8.32029954e-02 -3.96486014e-01 -3.51310045e-01 9.72842500e-02
3.27697337e-01 4.44330484e-01 -4.12492231e-02 -6.87977552... | [11.071439743041992, 10.670594215393066] |
5271dfc5-ff6d-4e5e-bc49-517a659853f6 | skin-lesion-segmentation-using-u-net-and-good | 1811.11314 | null | http://arxiv.org/abs/1811.11314v1 | http://arxiv.org/pdf/1811.11314v1.pdf | Skin lesion segmentation using U-Net and good training strategies | In this paper we approach the problem of skin lesion segmentation using a
convolutional neural network based on the U-Net architecture. We present a set
of training strategies that had a significant impact on the performance of this
model. We evaluated this method on the ISIC Challenge 2018 - Skin Lesion
Analysis Towar... | ['Teofilo E. deCampos', 'Fred Guth'] | 2018-11-27 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 7.50680923e-01 1.69413149e-01 -3.06354225e-01 -1.62752822e-01
-6.18804932e-01 -4.74614829e-01 4.43914503e-01 8.89519826e-02
-8.40542257e-01 6.14043534e-01 -1.50851265e-01 -5.91712236e-01
-2.58167088e-01 -8.27946246e-01 -3.06779772e-01 -5.72696507e-01
-3.07262331e-01 -3.14259291e-01 2.94014812e-01 -1.78415731... | [15.707708358764648, -3.003040313720703] |
d8d388bd-9bce-4309-abc0-1d5d5c3c3d1b | learning-word-representations-with-1 | null | null | https://aclanthology.org/K17-1016 | https://aclanthology.org/K17-1016.pdf | Learning Word Representations with Regularization from Prior Knowledge | Conventional word embeddings are trained with specific criteria (e.g., based on language modeling or co-occurrence) inside a single information source, disregarding the opportunity for further calibration using external knowledge. This paper presents a unified framework that leverages pre-learned or external priors, in... | ['Chia-Jung Lee', 'Yan Song', 'Fei Xia'] | 2017-08-01 | null | null | null | conll-2017-8 | ['learning-word-embeddings'] | ['methodology'] | [-3.67293842e-02 2.40984023e-01 -7.78132796e-01 -4.50052232e-01
-6.80349708e-01 -5.10497928e-01 7.71663666e-01 1.77015543e-01
-7.56148696e-01 3.85790169e-01 5.39854109e-01 -1.86330140e-01
1.97568014e-01 -8.01432610e-01 -5.67808807e-01 -6.95159256e-01
2.09550977e-01 2.13279933e-01 -1.20861128e-01 -3.84562351... | [10.521241188049316, 8.573678016662598] |
ab677083-a07e-478b-8e80-7eabd1693a8f | analyzing-the-impact-of-feature-selection-on | 2206.03239 | null | https://arxiv.org/abs/2206.03239v1 | https://arxiv.org/pdf/2206.03239v1.pdf | Analyzing the impact of feature selection on the accuracy of heart disease prediction | Heart Disease has become one of the most serious diseases that has a significant impact on human life. It has emerged as one of the leading causes of mortality among the people across the globe during the last decade. In order to prevent patients from further damage, an accurate diagnosis of heart disease on time is an... | ['Soumyabrata Dev', 'Muhammad Mohisn Pathan', 'Avishek Nag', 'Muhammad Salman Pathan'] | 2022-06-07 | null | null | null | null | ['disease-prediction'] | ['medical'] | [-5.16010858e-02 -2.47755826e-01 -1.79714903e-01 -1.96748659e-01
-3.19127738e-02 -8.89794156e-02 1.81836084e-01 6.39271855e-01
-3.38995129e-01 7.39174902e-01 -7.74354637e-02 -2.23809272e-01
-6.58502698e-01 -8.88460755e-01 2.40440175e-01 -6.86260283e-01
-2.08175167e-01 6.36726916e-01 1.26282513e-01 -1.13181926... | [8.462912559509277, 4.854751110076904] |
3538b81c-ad40-4fbd-9222-3660a268cc0c | topic-aware-neural-keyphrase-generation-for | 1906.03889 | null | https://arxiv.org/abs/1906.03889v1 | https://arxiv.org/pdf/1906.03889v1.pdf | Topic-Aware Neural Keyphrase Generation for Social Media Language | A huge volume of user-generated content is daily produced on social media. To facilitate automatic language understanding, we study keyphrase prediction, distilling salient information from massive posts. While most existing methods extract words from source posts to form keyphrases, we propose a sequence-to-sequence (... | ['Hou Pong Chan', 'Michael R. Lyu', 'Jing Li', 'Yue Wang', 'Irwin King', 'Shuming Shi'] | 2019-06-10 | topic-aware-neural-keyphrase-generation-for-1 | https://aclanthology.org/P19-1240 | https://aclanthology.org/P19-1240.pdf | acl-2019-7 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 1.66675195e-01 3.57077837e-01 -5.60063303e-01 3.49016011e-01
-9.97025073e-01 -5.99503517e-01 1.06866002e+00 4.96343523e-01
-4.53400612e-01 1.07060528e+00 1.23248982e+00 -2.88616151e-01
3.11779857e-01 -9.89590704e-01 -6.96093142e-01 -2.52080441e-01
-7.27858245e-02 6.89645335e-02 7.81306848e-02 -4.25832152... | [12.315166473388672, 8.893037796020508] |
5ec5d058-b682-462e-94c1-c988ece5d2f2 | in-n-out-generative-learning-for-dense | 2203.15312 | null | https://arxiv.org/abs/2203.15312v4 | https://arxiv.org/pdf/2203.15312v4.pdf | In-N-Out Generative Learning for Dense Unsupervised Video Segmentation | In this paper, we focus on unsupervised learning for Video Object Segmentation (VOS) which learns visual correspondence (i.e., the similarity between pixel-level features) from unlabeled videos. Previous methods are mainly based on the contrastive learning paradigm, which optimize either in image level or pixel level. ... | ['Yi Yang', 'Jingren Zhou', 'Hongxia Yang', 'Chang Zhou', 'Huiling Zhou', 'Zongxin Yang', 'Peike Li', 'Xiao Pan'] | 2022-03-29 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 6.48824275e-02 3.05003095e-02 -3.83303285e-01 -2.84475178e-01
-7.91073263e-01 -4.93294597e-01 4.74721432e-01 -4.92345482e-01
-1.84013382e-01 4.76283133e-01 2.27760419e-01 1.00618079e-01
1.28257975e-01 -8.18295598e-01 -1.27229369e+00 -9.24321771e-01
3.98493230e-01 1.12936180e-02 3.33690166e-01 -7.04186484... | [9.39154052734375, -0.1960684061050415] |
580a5cd0-46cd-4f2c-93ad-e7f22161213d | transfernet-an-effective-and-transparent | 2104.07302 | null | https://arxiv.org/abs/2104.07302v2 | https://arxiv.org/pdf/2104.07302v2.pdf | TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph | Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer. The relations can be represented in terms of labels in knowledge graph (e.g., \textit{spouse}) or text in text corpus (e.g., \textit{they have been married for 26 years})... | ['Hanwang Zhang', 'Juanzi Li', 'Lei Hou', 'Shulin Cao', 'Jiaxin Shi'] | 2021-04-15 | null | https://aclanthology.org/2021.emnlp-main.341 | https://aclanthology.org/2021.emnlp-main.341.pdf | emnlp-2021-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [-3.02106589e-02 8.75335872e-01 -5.00356734e-01 -5.74220300e-01
-9.43735600e-01 -6.87179208e-01 3.85794848e-01 5.02425253e-01
-1.09057985e-01 1.10034156e+00 4.12618279e-01 -6.29080296e-01
-5.64950585e-01 -1.20763266e+00 -7.98243821e-01 -2.07545087e-01
1.52415574e-01 1.14584196e+00 4.23157483e-01 -5.58247328... | [10.52070140838623, 7.892444610595703] |
10e56e2e-cab8-42aa-aef0-ee7b1df8cc30 | knowledge-base-question-answering-through | null | null | https://aclanthology.org/2021.eacl-main.35 | https://aclanthology.org/2021.eacl-main.35.pdf | Knowledge Base Question Answering through Recursive Hypergraphs | Knowledge Base Question Answering (KBQA) is the problem of predicting an answer for a factoid question over a given knowledge base (KB). Answering questions typically requires reasoning over multiple links in the given KB. Humans tend to answer questions by grouping different objects to perform reasoning over acquired ... | ['Srinidhi G', 'Indira K M', 'Vaishnavi S', 'Dayanidhi R S', 'Naganand Yadati'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.81637555e-01 1.01068926e+00 -8.30266923e-02 -3.80848795e-01
-4.99501288e-01 -6.38113678e-01 4.84824836e-01 5.42905986e-01
3.77263799e-02 1.08443654e+00 3.95507127e-01 -5.53681731e-01
-7.60636330e-01 -1.61635745e+00 -8.64320219e-01 -7.46150315e-02
9.25005600e-02 9.12940502e-01 1.06866419e+00 -6.82750225... | [10.433769226074219, 7.892094135284424] |
5dc10f91-8da9-4e4d-a3f4-e753061436fa | performance-evaluation-of-vanilla-residual | 2211.16570 | null | https://arxiv.org/abs/2211.16570v2 | https://arxiv.org/pdf/2211.16570v2.pdf | Performance Evaluation of Vanilla, Residual, and Dense 2D U-Net Architectures for Skull Stripping of Augmented 3D T1-weighted MRI Head Scans | Skull Stripping is a requisite preliminary step in most diagnostic neuroimaging applications. Manual Skull Stripping methods define the gold standard for the domain but are time-consuming and challenging to integrate into processing pipelines with a high number of data samples. Automated methods are an active area of r... | ['Mahesh H. Shindikar', 'Ketaki D. Kamble', 'Rashmika K. Patole', 'Anway S. Pimpalkar'] | 2022-11-29 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 3.61518785e-02 5.27681410e-01 1.14006557e-01 -5.42920768e-01
-2.37991616e-01 -2.32012570e-01 2.79469937e-01 2.27664202e-01
-5.13521314e-01 5.17191589e-01 2.01087333e-02 -2.83776134e-01
-7.40376487e-02 -8.36921751e-01 -5.68871140e-01 -4.89330560e-01
-3.37499470e-01 6.76378250e-01 6.05114460e-01 -1.23014942... | [14.272536277770996, -2.440572500228882] |
23b5d113-74c0-4490-b6a2-093884d6a6d9 | 190412602 | 1904.12602 | null | https://arxiv.org/abs/1904.12602v2 | https://arxiv.org/pdf/1904.12602v2.pdf | EV-Action: Electromyography-Vision Multi-Modal Action Dataset | Multi-modal human action analysis is a critical and attractive research topic. However, the majority of the existing datasets only provide visual modalities (i.e., RGB, depth and skeleton). To make up this, we introduce a new, large-scale EV-Action dataset in this work, which consists of RGB, depth, electromyography (E... | ['Lichen Wang', 'Yun Fu', 'Joseph Robinson', 'Taotao Jing', 'Bin Sun'] | 2019-04-20 | null | null | null | null | ['action-analysis', 'multimodal-activity-recognition', 'electromyography-emg'] | ['computer-vision', 'computer-vision', 'medical'] | [ 1.65490925e-01 -4.75833654e-01 -5.67295492e-01 1.67869702e-01
-6.03435814e-01 6.36168793e-02 2.95432538e-01 -4.55230325e-01
-4.66818690e-01 5.89773715e-01 5.86153924e-01 2.93186218e-01
-1.40074074e-01 -5.41908562e-01 -3.15151781e-01 -8.06478143e-01
5.60588278e-02 -1.18448824e-01 5.62537611e-01 -2.75772482... | [7.826160430908203, 0.38819077610969543] |
f17c42fd-f53e-4665-b5cb-993c77f15c9f | isoex-an-explainable-unsupervised-approach-to | 2306.09260 | null | https://arxiv.org/abs/2306.09260v1 | https://arxiv.org/pdf/2306.09260v1.pdf | IsoEx: an explainable unsupervised approach to process event logs cyber investigation | 39 seconds. That is the timelapse between two consecutive cyber attacks as of 2023. Meaning that by the time you are done reading this abstract, about 1 or 2 additional cyber attacks would have occurred somewhere in the world. In this context of highly increased frequency of cyber threats, Security Operation Centers (S... | ['Ismail Alaoui Hassani Atlas', 'Pierre Lavieille'] | 2023-06-07 | null | null | null | null | ['anomaly-detection', 'unsupervised-anomaly-detection'] | ['methodology', 'methodology'] | [ 1.63630679e-01 6.29946473e-04 2.15484768e-01 -1.48066714e-01
-1.85788035e-01 -7.67500818e-01 2.99888670e-01 7.62710273e-01
-4.37374003e-02 5.11078000e-01 -1.47746176e-01 -1.10442364e+00
-7.51207471e-01 -5.30636847e-01 -1.51211664e-01 -2.71016151e-01
-4.09493893e-01 2.52345085e-01 -1.84092999e-01 -1.86122939... | [5.440705299377441, 7.161055088043213] |
a66d5c80-decf-4bd6-be96-e47bc85bb892 | object-based-slam-utilizing-unambiguous-pose | 2303.07872 | null | https://arxiv.org/abs/2303.07872v1 | https://arxiv.org/pdf/2303.07872v1.pdf | Object-based SLAM utilizing unambiguous pose parameters considering general symmetry types | Existence of symmetric objects, whose observation at different viewpoints can be identical, can deteriorate the performance of simultaneous localization and mapping(SLAM). This work proposes a system for robustly optimizing the pose of cameras and objects even in the presence of symmetric objects. We classify objects i... | ['H. Jin Kim', 'Youngseok Jang', 'Taekbeom Lee'] | 2023-03-13 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-2.89814845e-02 -3.38651776e-01 1.36121854e-01 -2.75870740e-01
-4.51784551e-01 -9.22398210e-01 6.03898346e-01 8.56166631e-02
-5.68341136e-01 6.37550354e-01 -2.77728230e-01 9.43583772e-02
-4.31320816e-01 -3.22166979e-01 -6.58121645e-01 -9.77749944e-01
1.29838631e-01 8.73153448e-01 5.24618208e-01 8.62288699... | [7.38260555267334, -2.258282423019409] |
60da9243-368e-441c-8aa9-d61bbbb3db67 | select-and-attend-towards-controllable | 1909.04453 | null | https://arxiv.org/abs/1909.04453v1 | https://arxiv.org/pdf/1909.04453v1.pdf | Select and Attend: Towards Controllable Content Selection in Text Generation | Many text generation tasks naturally contain two steps: content selection and surface realization. Current neural encoder-decoder models conflate both steps into a black-box architecture. As a result, the content to be described in the text cannot be explicitly controlled. This paper tackles this problem by decoupling ... | ['Satoshi Sekine', 'Jun Suzuki', 'Xiaoyu Shen', 'Kentaro Inui', 'Dietrich Klakow', 'Hui Su'] | 2019-09-10 | select-and-attend-towards-controllable-1 | https://aclanthology.org/D19-1054 | https://aclanthology.org/D19-1054.pdf | ijcnlp-2019-11 | ['headline-generation'] | ['natural-language-processing'] | [ 4.39189643e-01 6.92464411e-01 -3.92309785e-01 -3.89636308e-01
-1.02991068e+00 -7.14558125e-01 9.24457729e-01 -2.59198789e-02
-2.95994967e-01 8.66229355e-01 5.69171369e-01 -2.72098213e-01
3.87219727e-01 -8.28220248e-01 -6.24607980e-01 -5.32043576e-01
4.78229105e-01 6.23361290e-01 -1.75991692e-02 -2.37670019... | [11.84296989440918, 9.088920593261719] |
5c2d904a-4a9a-4b68-b36b-af7b3bad8ce7 | a-general-framework-for-uncertainty-1 | 2306.01189 | null | https://arxiv.org/abs/2306.01189v1 | https://arxiv.org/pdf/2306.01189v1.pdf | A General Framework for Uncertainty Quantification via Neural SDE-RNN | Uncertainty quantification is a critical yet unsolved challenge for deep learning, especially for the time series imputation with irregularly sampled measurements. To tackle this problem, we propose a novel framework based on the principles of recurrent neural networks and neural stochastic differential equations for r... | ['Balasubramaniam Natarajan', 'Sai Munikoti', 'Shweta Dahale'] | 2023-06-01 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [-1.50730446e-01 -7.11416528e-02 2.25151047e-01 -4.96612877e-01
-1.41896594e+00 -3.49713385e-01 3.01474690e-01 6.26328774e-03
-8.14032853e-02 1.44684315e+00 3.72741520e-01 -3.78729135e-01
-8.36023331e-01 -9.73013699e-01 -9.81694818e-01 -9.74714816e-01
-2.96645105e-01 5.74642479e-01 -6.89353108e-01 1.27718642... | [6.90901517868042, 3.4161715507507324] |
321123e7-5267-4505-9fb1-776bfcace541 | differentiating-objects-by-motion-joint | 1709.04666 | null | http://arxiv.org/abs/1709.04666v3 | http://arxiv.org/pdf/1709.04666v3.pdf | Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects | While generic object detection has achieved large improvements with rich
feature hierarchies from deep nets, detecting small objects with poor visual
cues remains challenging. Motion cues from multiple frames may be more
informative for detecting such hard-to-distinguish objects in each frame.
However, how to encode di... | ['ShaoDi You', 'Rei Kawakami', 'Makoto Iida', 'Tu Tuan Trinh', 'Takeshi Naemura', 'Ryota Yoshihashi'] | 2017-09-14 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-1.08031360e-02 -4.93994415e-01 -1.83241948e-01 -3.94784182e-01
-4.54707772e-01 -7.36590743e-01 4.06126648e-01 -2.69631177e-01
-6.99965179e-01 3.38938355e-01 -5.75291403e-02 3.86861354e-01
1.48743927e-01 -2.67634004e-01 -9.08257067e-01 -7.34691858e-01
-5.15003145e-01 1.33712143e-01 7.88740098e-01 6.99312612... | [6.312832355499268, -2.055046796798706] |
3cd1a7db-265f-4388-8cf1-74f6f97a9d9e | ranking-causal-anomalies-via-temporal-and | null | null | https://www.kdd.org/kdd2016/papers/files/rfp0445-chengAemb.pdf | https://www.kdd.org/kdd2016/papers/files/rfp0445-chengAemb.pdf | Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations | Modern world has witnessed a dramatic increase in our ability to collect, transmit and distribute real-time monitoring
and surveillance data from large-scale information systems and cyber-physical systems. Detecting system anomalies
thus attracts significant amount of interest in many fields such as security, fault m... | ['Wei Wang', 'Zhengzhang Chen', 'Guofei Jiang', 'Haifeng Chen', 'Kai Zhang', 'Wei Cheng'] | 2016-07-19 | null | null | null | acm-sigkdd-international-conference-on-2 | ['root-cause-ranking'] | ['graphs'] | [ 1.76755726e-01 -3.07516664e-01 -1.80106059e-01 2.81410098e-01
8.71420428e-02 -6.63513541e-01 5.22716403e-01 4.67823893e-01
5.98561883e-01 5.87049246e-01 -2.90262312e-01 -4.85227764e-01
-9.35096502e-01 -9.28089797e-01 -4.61300045e-01 -7.93591917e-01
-8.69425952e-01 1.59749582e-01 5.92266262e-01 -1.26034498... | [7.293174743652344, 2.9400551319122314] |
eef06fec-ff8c-4548-a616-fc0a79b594ee | lidar-sensor-modeling-and-data-augmentation | 1905.07290 | null | https://arxiv.org/abs/1905.07290v1 | https://arxiv.org/pdf/1905.07290v1.pdf | LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving | In the autonomous driving domain, data collection and annotation from real vehicles are expensive and sometimes unsafe. Simulators are often used for data augmentation, which requires realistic sensor models that are hard to formulate and model in closed forms. Instead, sensors models can be learned from real data. The... | ['Mohamed Zahran', 'Nader Essam', 'Ibrahim Sobh', 'Ahmad El Sallab'] | 2019-05-17 | null | null | null | null | ['sensor-modeling', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [ 5.42105854e-01 3.05751204e-01 -2.78825670e-01 -6.49376810e-01
-5.52161396e-01 -4.63252425e-01 7.04418659e-01 4.03061276e-03
-6.55794322e-01 1.02741623e+00 -4.56841469e-01 -2.79972523e-01
8.87424946e-02 -1.09450841e+00 -1.09818637e+00 -5.26279330e-01
1.37240916e-01 7.56540835e-01 6.47580251e-02 -3.26119423... | [7.9899001121521, -2.519354820251465] |
c4ff3a68-6da1-4a46-8164-79b85ee9a213 | 3d-face-parsing-via-surface-parameterization | 2206.09221 | null | https://arxiv.org/abs/2206.09221v1 | https://arxiv.org/pdf/2206.09221v1.pdf | 3D Face Parsing via Surface Parameterization and 2D Semantic Segmentation Network | Face parsing assigns pixel-wise semantic labels as the face representation for computers, which is the fundamental part of many advanced face technologies. Compared with 2D face parsing, 3D face parsing shows more potential to achieve better performance and further application, but it is still challenging due to 3D mes... | ['Guangquan Zhou', 'Jing Jin', 'Zongpu Yu', 'Yangang Wang', 'Ping Zhou', 'Wenyuan Sun'] | 2022-06-18 | null | null | null | null | ['face-parsing', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [-4.61700782e-02 3.54819983e-01 -5.69239557e-02 -6.79643273e-01
-4.10890490e-01 -3.23296756e-01 2.46021211e-01 -5.98226368e-01
1.89404130e-01 1.21451311e-01 -1.30383193e-01 -3.12789269e-02
-8.63444954e-02 -1.10745931e+00 -4.94518787e-01 -4.91891056e-01
2.86681298e-02 9.43633556e-01 3.32491666e-01 -1.54622048... | [13.250783920288086, 0.14374059438705444] |
54076760-1627-438c-9ea7-9a78b2841f13 | mitigating-label-noise-through-data | 2305.13764 | null | https://arxiv.org/abs/2305.13764v1 | https://arxiv.org/pdf/2305.13764v1.pdf | Mitigating Label Noise through Data Ambiguation | Label noise poses an important challenge in machine learning, especially in deep learning, in which large models with high expressive power dominate the field. Models of that kind are prone to memorizing incorrect labels, thereby harming generalization performance. Many methods have been proposed to address this proble... | ['Eyke Hüllermeier', 'Julian Lienen'] | 2023-05-23 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 5.03661573e-01 2.76328057e-01 -1.63294941e-01 -5.55844665e-01
-9.38764036e-01 -5.37834167e-01 4.21392500e-01 6.86116457e-01
-6.77103937e-01 1.00444865e+00 -1.17325433e-01 -7.72006139e-02
-7.35844374e-02 -7.24392951e-01 -8.24974179e-01 -7.88150728e-01
3.05183351e-01 3.45277101e-01 1.40744433e-01 1.24979287... | [9.282781600952148, 3.885890483856201] |
b1af881a-6944-44dc-8514-4ac0de861ee0 | totto-a-controlled-table-to-text-generation | 2004.14373 | null | https://arxiv.org/abs/2004.14373v3 | https://arxiv.org/pdf/2004.14373v3.pdf | ToTTo: A Controlled Table-To-Text Generation Dataset | We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. To obtain generated targets that are natural but also faithful to the source ta... | ['Manaal Faruqui', 'Xuezhi Wang', 'Dipanjan Das', 'Ankur P. Parikh', 'Sebastian Gehrmann', 'Diyi Yang', 'Bhuwan Dhingra'] | 2020-04-29 | null | https://aclanthology.org/2020.emnlp-main.89 | https://aclanthology.org/2020.emnlp-main.89.pdf | emnlp-2020-11 | ['conditional-text-generation', 'table-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.65029365e-01 1.01218295e+00 -3.17152053e-01 -1.47380367e-01
-1.46947718e+00 -8.51069212e-01 9.62325871e-01 3.16887408e-01
-1.37608841e-01 1.61817682e+00 8.13275874e-01 -1.35105610e-01
5.84238529e-01 -1.10742271e+00 -9.89341617e-01 6.86794594e-02
3.18153530e-01 9.54462588e-01 1.74811542e-01 -6.77278459... | [11.546813011169434, 8.863469123840332] |
97b8fb35-05c7-44a1-b803-107af3294295 | reevaluating-data-partitioning-for-emotion | 2303.13364 | null | https://arxiv.org/abs/2303.13364v1 | https://arxiv.org/pdf/2303.13364v1.pdf | Reevaluating Data Partitioning for Emotion Detection in EmoWOZ | This paper focuses on the EmoWoz dataset, an extension of MultiWOZ that provides emotion labels for the dialogues. MultiWOZ was partitioned initially for another purpose, resulting in a distributional shift when considering the new purpose of emotion recognition. The emotion tags in EmoWoz are highly imbalanced and une... | ['Michael D. Porter', 'Moeen Mostafavi'] | 2023-03-15 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [-2.52457321e-01 3.27452384e-02 1.29191261e-02 -6.33402169e-01
-2.36789644e-01 -4.21864241e-01 2.19410181e-01 1.86057091e-01
-3.12378705e-01 6.85435534e-01 3.72696280e-01 -7.64223235e-03
1.11202467e-02 -8.60325754e-01 2.58351147e-01 -6.57400310e-01
8.36950168e-02 5.77440381e-01 4.81333360e-02 -3.28449041... | [12.969099044799805, 6.114431858062744] |
a6cc46b8-15a3-4f29-bd71-9d858524d655 | accurate-detection-of-mediastinal-lesions | 2303.11214 | null | https://arxiv.org/abs/2303.11214v1 | https://arxiv.org/pdf/2303.11214v1.pdf | Accurate Detection of Mediastinal Lesions with nnDetection | The accurate detection of mediastinal lesions is one of the rarely explored medical object detection problems. In this work, we applied a modified version of the self-configuring method nnDetection to the Mediastinal Lesion Analysis (MELA) Challenge 2022. By incorporating automatically generated pseudo masks, training ... | ['Klaus H. Maier-Hein', 'Peter M. Full', 'Michael Baumgartner'] | 2023-03-20 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [ 1.00092115e-02 2.46879030e-02 -2.91337296e-02 1.22812271e-01
-1.35427487e+00 -4.82134640e-01 5.78994215e-01 4.14751798e-01
-7.06964731e-01 5.62788963e-01 5.46317287e-02 -4.32486236e-01
7.54446760e-02 -2.94322312e-01 -5.57772636e-01 -6.93957448e-01
-8.76675323e-02 6.18682683e-01 8.54830265e-01 -1.74314436... | [15.181581497192383, -2.2189218997955322] |
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