paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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0eaed08f-cee3-4e69-9eac-6ee6847d478a | energy-minimization-for-active-ris-aided-uav | 2306.10233 | null | https://arxiv.org/abs/2306.10233v1 | https://arxiv.org/pdf/2306.10233v1.pdf | Energy Minimization for Active RIS-Aided UAV-Enabled SWIPT Systems | In this paper, we consider an active reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle(UAV)-enabled simultaneous wireless information and power transfer(SWIPT) system with multiple ground users. Compared with the conventional passive RIS, the active RIS deploying the internally integrated amplifier... | ['Jiangzhou Wang', 'Zhenkun Zhang', 'Cunhua Pan', 'Ruijing Liu', 'Zhangjie Peng'] | 2023-06-17 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 7.83066630e-01 7.41781235e-01 -9.33909044e-02 7.04189003e-01
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-5.99526584e-01 -8.63057911e-01 -2.88796902e-01 -1.16987455e+00
-5.33645809e-01 -4.27036196e-01 -2.21171722e-01 -4.63166267... | [5.935178756713867, 1.4852954149246216] |
6b80ba10-7f1a-4c26-8177-c96b4edd1a98 | spatio-temporal-attention-mechanism-and | 2108.03543 | null | https://arxiv.org/abs/2108.03543v1 | https://arxiv.org/pdf/2108.03543v1.pdf | Spatio-Temporal Attention Mechanism and Knowledge Distillation for Lip Reading | Despite the advancement in the domain of audio and audio-visual speech recognition, visual speech recognition systems are still quite under-explored due to the visual ambiguity of some phonemes. In this work, we propose a new lip-reading model that combines three contributions. First, the model front-end adopts a spati... | ['Nourhan Sakr', 'Omar Abugabal', 'Hadeel Mabrouk', 'Farah Eldeshnawy', 'Hesham M. Eraqi', 'Marian Ramsis', 'Shahd Elashmawy'] | 2021-08-07 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 1.58738151e-01 4.73340265e-02 -4.44223344e-01 -4.01757985e-01
-1.12889314e+00 -1.17856577e-01 6.29577160e-01 -2.17082977e-01
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2.46036425e-01 -1.13157406e-01 3.72479528e-01 2.90899128... | [14.314133644104004, 5.0015411376953125] |
4fa171ff-882c-4a7b-b1f1-b6bd9984f2e9 | learning-deep-video-stabilization-without | 2011.09697 | null | https://arxiv.org/abs/2011.09697v2 | https://arxiv.org/pdf/2011.09697v2.pdf | Deep Motion Blind Video Stabilization | Despite the advances in the field of generative models in computer vision, video stabilization still lacks a pure regressive deep-learning-based formulation. Deep video stabilization is generally formulated with the help of explicit motion estimation modules due to the lack of a dataset containing pairs of videos with ... | ['Tae Hyun Kim', 'Sangjoon Yu', 'Muhammad Kashif Ali'] | 2020-11-19 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 1.05928116e-01 -7.71943852e-02 -5.33924736e-02 1.56573385e-01
-6.45255148e-01 -3.54483366e-01 7.02533305e-01 -2.99128443e-01
-1.69564486e-01 7.41878748e-01 2.37322897e-01 -1.34694561e-01
2.33309045e-01 -3.86435211e-01 -1.08700180e+00 -1.12851131e+00
1.29188761e-01 -2.41359603e-03 2.02563211e-01 -4.58440214... | [10.665437698364258, -1.3893405199050903] |
a0f9d05c-6b2a-4a87-bd25-6c926f99374a | a-comparison-of-neuroelectrophysiology | 2306.15041 | null | https://arxiv.org/abs/2306.15041v1 | https://arxiv.org/pdf/2306.15041v1.pdf | A Comparison of Neuroelectrophysiology Databases | As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN I... | ['Dominique Duncan', 'Arthur W. Toga', 'Nader Pouratian', 'Michael Beauchamp', 'John Magnotti', 'Zhengjia Wang', 'Stephen R Arnott', 'Alana Sparks', 'Brendan Behan', 'Scott Makeig', 'Arnaud Delorme', 'Dora Hermes', 'Chris Markiewicz', 'Russell A. Poldrack', 'Benjamin Dichter', 'Yaroslav Halchenko', 'Satrajit Ghosh', 'S... | 2023-06-26 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-6.84680700e-01 -4.64410365e-01 3.45698118e-01 -5.22844017e-01
-5.12988687e-01 -3.17700058e-01 1.96954176e-01 5.30558407e-01
-6.27748609e-01 8.59014213e-01 5.55839658e-01 -2.61088848e-01
-2.83236682e-01 -4.32749242e-01 -1.09770410e-01 -5.58468580e-01
-4.55776036e-01 7.90758431e-02 8.52258280e-02 -8.19156095... | [13.210068702697754, 3.4437038898468018] |
86758bb9-b8c6-40c9-9222-584a6a05c451 | the-effects-of-input-type-and-pronunciation | 2306.00535 | null | https://arxiv.org/abs/2306.00535v1 | https://arxiv.org/pdf/2306.00535v1.pdf | The Effects of Input Type and Pronunciation Dictionary Usage in Transfer Learning for Low-Resource Text-to-Speech | We compare phone labels and articulatory features as input for cross-lingual transfer learning in text-to-speech (TTS) for low-resource languages (LRLs). Experiments with FastSpeech 2 and the LRL West Frisian show that using articulatory features outperformed using phone labels in both intelligibility and naturalness. ... | ['Esther Klabbers', 'Jelske Dijkstra', 'Matt Coler', 'Phat Do'] | 2023-06-01 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [ 9.77691934e-02 4.60297354e-02 -9.48462486e-02 -2.88959980e-01
-1.43604100e+00 -9.66592014e-01 8.24617863e-01 -1.90639302e-01
-4.84127611e-01 7.86081970e-01 4.76963282e-01 -9.05229867e-01
1.87578484e-01 -2.52172142e-01 -6.40454650e-01 -5.11512578e-01
4.70601410e-01 7.73685157e-01 -2.69255996e-01 -3.09455752... | [14.318657875061035, 6.972248554229736] |
e50618c7-78ed-40ff-8279-9c4017026825 | instance-search-via-instance-level | 1806.03576 | null | https://arxiv.org/abs/1806.03576v2 | https://arxiv.org/pdf/1806.03576v2.pdf | Instance Search via Instance Level Segmentation and Feature Representation | Instance search is an interesting task as well as a challenging issue due to the lack of effective feature representation. In this paper, an instance level feature representation built upon fully convolutional instance-aware segmentation is proposed. The feature is ROI-pooled from the segmented instance region. So that... | ['Wan-Lei Zhao', 'Yu Zhan'] | 2018-06-10 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [ 3.31251770e-01 2.92367786e-01 -2.86967605e-01 -5.97491086e-01
-6.63540721e-01 -5.33999443e-01 6.09657824e-01 3.08427334e-01
-4.76152331e-01 7.60338187e-01 -3.83968494e-04 3.69691253e-01
-5.39919257e-01 -7.93265641e-01 -6.92537248e-01 -4.69921619e-01
-1.94208827e-02 2.50593513e-01 4.87061590e-01 -8.51382315... | [9.603459358215332, 0.24133743345737457] |
ef8d4b93-584e-4bc0-894b-475e0ede3e15 | what-truly-matters-in-trajectory-prediction | 2306.15136 | null | https://arxiv.org/abs/2306.15136v1 | https://arxiv.org/pdf/2306.15136v1.pdf | What Truly Matters in Trajectory Prediction for Autonomous Driving? | In the autonomous driving system, trajectory prediction plays a vital role in ensuring safety and facilitating smooth navigation. However, we observe a substantial discrepancy between the accuracy of predictors on fixed datasets and their driving performance when used in downstream tasks. This discrepancy arises from t... | ['David Hsu', 'Sifa Zheng', 'Panpan Cai', 'Cunjun Yu', 'Tran Phong', 'Haoran Wu'] | 2023-06-27 | null | null | null | null | ['trajectory-prediction', 'autonomous-vehicles'] | ['computer-vision', 'computer-vision'] | [-1.27003014e-01 -2.03847349e-01 -2.62395352e-01 -4.81775343e-01
-3.16622108e-01 -6.99592590e-01 6.82840824e-01 2.22499445e-01
-4.64640796e-01 6.25751078e-01 2.26479247e-01 -8.08242261e-01
-3.81343096e-01 -8.63942266e-01 -5.97916186e-01 -4.90449548e-01
-1.75451458e-01 4.57469881e-01 5.93124866e-01 -5.30705869... | [5.748108863830566, 1.0744520425796509] |
d2e6e689-8eb9-4295-a85a-f684ccf3db3d | portfolio-cuts-a-graph-theoretic-framework-to | 1910.05561 | null | https://arxiv.org/abs/1910.05561v3 | https://arxiv.org/pdf/1910.05561v3.pdf | Portfolio Cuts: A Graph-Theoretic Framework to Diversification | Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate ways for domain knowledge to be conveniently incorporated into the analysis, by means of graphs. Next, to relax the assumption of the comple... | ['Danilo P. Mandic', 'Anthony G. Constantinides', 'Ljubisa Stankovic', 'Bruno Scalzo Dees'] | 2019-10-12 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [-1.39391780e-01 2.62324572e-01 -1.22815982e-01 -3.23931500e-02
-7.09267259e-02 -1.00005722e+00 3.33934516e-01 1.35713875e-01
2.97412753e-01 9.25501347e-01 -4.11484540e-01 -6.98957324e-01
-1.17716813e+00 -1.22181165e+00 -4.49070543e-01 -7.52366126e-01
-2.21742481e-01 2.67899275e-01 -2.82967597e-01 8.18778295... | [5.087348461151123, 4.058304309844971] |
20bac21c-7b08-4c0c-9076-a9ba2361a334 | xsleepnet-multi-view-sequential-model-for | 2007.05492 | null | https://arxiv.org/abs/2007.05492v4 | https://arxiv.org/pdf/2007.05492v4.pdf | XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging | Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. Learning from raw polysomnography signals and their derived time-frequency image representations has been prevalent. ... | ['Alfred Mertins', 'Minh C. Tran', 'Maarten De Vos', 'Oliver Y. Chén', 'Philipp Koch', 'Huy Phan'] | 2020-07-08 | null | null | null | null | ['sleep-stage-detection', 'sleep-staging'] | ['medical', 'medical'] | [ 4.38166223e-02 -3.07773769e-01 -2.91100919e-01 -7.17635751e-01
-4.60352749e-01 -2.17541918e-01 1.45100832e-01 -3.27145398e-01
-3.74866366e-01 5.62071800e-01 4.32125479e-01 2.70684540e-01
-2.77993698e-02 -3.50957692e-01 -1.20594099e-01 -8.70352685e-01
2.11389195e-02 2.47678161e-01 1.11092098e-01 -3.29222679... | [13.484896659851074, 3.515169143676758] |
f0960571-44e6-48ac-a997-6bc28ed54c09 | joint-aec-and-beamforming-with-double-talk | 2111.04904 | null | https://arxiv.org/abs/2111.04904v2 | https://arxiv.org/pdf/2111.04904v2.pdf | Joint Neural AEC and Beamforming with Double-Talk Detection | Acoustic echo cancellation (AEC) in full-duplex communication systems eliminates acoustic feedback. However, nonlinear distortions induced by audio devices, background noise, reverberation, and double-talk reduce the efficiency of conventional AEC systems. Several hybrid AEC models were proposed to address this, which ... | ['Dong Yu', 'Shi-Xiong Zhang', 'Meng Yu', 'Yong Xu', 'Vinay Kothapally'] | 2021-11-09 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [-1.46098733e-02 -4.69848961e-01 7.51413167e-01 -2.21837431e-01
-1.01486492e+00 -3.53117615e-01 2.19007641e-01 -5.69464684e-01
-4.94276315e-01 3.61903429e-01 1.09787452e+00 -5.01901031e-01
-1.23111248e-01 -1.84633926e-01 -4.26164895e-01 -6.99301481e-01
-2.34572724e-01 -4.57851827e-01 -3.70251499e-02 -5.62966943... | [15.002547264099121, 5.967494487762451] |
66b726cd-8ca3-4608-8419-f9da4cf9300f | machine-reading-comprehension-with-enhanced | null | null | https://openreview.net/forum?id=EVV259WQuFG | https://openreview.net/pdf?id=EVV259WQuFG | Machine Reading Comprehension with Enhanced Linguistic Verifiers | We propose two linguistic verifiers for span-extraction style machine reading comprehension to respectively tackle two challenges: how to evaluate the syntactic completeness of predicted answers and how to utilize the rich context of long documents. Our first verifier rewrites a question through replacing its interroga... | ['Xianchao Wu'] | 2021-01-01 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 5.01213133e-01 5.02975106e-01 2.72965450e-02 -5.19525826e-01
-1.79102719e+00 -9.76809978e-01 1.48996219e-01 3.49998921e-01
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5.42592525e-01 7.02214837e-01 8.45962584e-01 -3.93706739... | [11.38524055480957, 8.086621284484863] |
15d54782-6c49-4228-b270-1e2afdd6c8fe | landcover-ai-dataset-for-automatic-mapping-of | 2005.02264 | null | https://arxiv.org/abs/2005.02264v4 | https://arxiv.org/pdf/2005.02264v4.pdf | LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery | Monitoring of land cover and land use is crucial in natural resources management. Automatic visual mapping can carry enormous economic value for agriculture, forestry, or public administration. Satellite or aerial images combined with computer vision and deep learning enable precise assessment and can significantly spe... | ['Natalia Ziemba-Jankowska', 'Dominik Batorski', 'Adrian Boguszewski', 'Tomasz Dziedzic', 'Anna Zambrzycka'] | 2020-05-05 | null | null | null | null | ['object-detection-in-aerial-images', 'semantic-segmentation-of-orthoimagery'] | ['computer-vision', 'medical'] | [ 3.77584130e-01 -5.93996644e-02 -2.70605564e-01 -2.50911236e-01
-3.68712604e-01 -8.90492916e-01 4.19950187e-01 3.36887777e-01
-6.82290614e-01 1.06451058e+00 -2.00106069e-01 -7.52078176e-01
-1.36116371e-01 -1.62527382e+00 -6.59936249e-01 -8.17623675e-01
-5.80471992e-01 2.92287171e-01 9.61988121e-02 -2.51640052... | [9.396011352539062, -1.5365546941757202] |
bc9c5cff-9d97-4f05-9477-e6267bef80f7 | noisy-channel-language-model-prompting-for | 2108.04106 | null | https://arxiv.org/abs/2108.04106v3 | https://arxiv.org/pdf/2108.04106v3.pdf | Noisy Channel Language Model Prompting for Few-Shot Text Classification | We introduce a noisy channel approach for language model prompting in few-shot text classification. Instead of computing the likelihood of the label given the input (referred as direct models), channel models compute the conditional probability of the input given the label, and are thereby required to explain every wor... | ['Luke Zettlemoyer', 'Hannaneh Hajishirzi', 'Mike Lewis', 'Sewon Min'] | 2021-08-09 | null | https://aclanthology.org/2022.acl-long.365 | https://aclanthology.org/2022.acl-long.365.pdf | acl-2022-5 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 4.12224978e-01 1.56603456e-01 -5.56789100e-01 -5.16419768e-01
-1.18977106e+00 -4.93690223e-01 6.65406346e-01 4.30338025e-01
-6.98206544e-01 7.59205639e-01 3.29877108e-01 -3.30026746e-01
1.87892586e-01 -7.10021079e-01 -5.16684830e-01 -5.18926561e-01
2.65517443e-01 4.54513937e-01 1.05498560e-01 -9.87923294... | [10.862394332885742, 7.939383506774902] |
768d51d3-9416-4115-8ba0-fd279db2e024 | alfred-a-benchmark-for-interpreting-grounded | 1912.01734 | null | https://arxiv.org/abs/1912.01734v2 | https://arxiv.org/pdf/1912.01734v2.pdf | ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks | We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. ALFRED includes long, compositional tasks with non-reversible state changes to shrink the gap between r... | ['Winson Han', 'Roozbeh Mottaghi', 'Luke Zettlemoyer', 'Dieter Fox', 'Yonatan Bisk', 'Mohit Shridhar', 'Jesse Thomason', 'Daniel Gordon'] | 2019-12-03 | alfred-a-benchmark-for-interpreting-grounded-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Shridhar_ALFRED_A_Benchmark_for_Interpreting_Grounded_Instructions_for_Everyday_Tasks_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Shridhar_ALFRED_A_Benchmark_for_Interpreting_Grounded_Instructions_for_Everyday_Tasks_CVPR_2020_paper.pdf | cvpr-2020-6 | ['natural-language-visual-grounding'] | ['reasoning'] | [ 7.37450495e-02 1.05115607e-01 -6.15203707e-03 -4.73660499e-01
-4.09336418e-01 -8.04326236e-01 7.61051118e-01 -1.36359587e-01
-6.08577013e-01 6.58636808e-01 8.48707080e-01 -7.17251182e-01
3.65765333e-01 -3.57689679e-01 -9.46911454e-01 -4.71825123e-01
-1.57555804e-01 2.86828071e-01 -1.00389570e-01 -3.80580097... | [4.396026611328125, 0.7679980993270874] |
c0c45042-a295-4bdb-9ac4-b508fdf9f3f5 | back-to-optimization-diffusion-based-zero | 2307.03833 | null | https://arxiv.org/abs/2307.03833v1 | https://arxiv.org/pdf/2307.03833v1.pdf | Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation | Learning-based methods have dominated the 3D human pose estimation (HPE) tasks with significantly better performance in most benchmarks than traditional optimization-based methods. Nonetheless, 3D HPE in the wild is still the biggest challenge of learning-based models, whether with 2D-3D lifting, image-to-3D, or diffus... | ['Jenq-Neng Hwang', 'Cheng-Yen Yang', 'Wenhao Chai', 'Lei LI', 'Zhuoran Zhou', 'Zhongyu Jiang'] | 2023-07-07 | null | null | null | null | ['pose-estimation', '3d-human-pose-estimation', 'image-to-3d'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-5.20781994e-01 -8.47333223e-02 -1.53047979e-01 -5.82705699e-02
-1.17291093e+00 -7.51033872e-02 4.52999473e-02 -6.03393555e-01
-7.90478468e-01 3.67724001e-01 2.36864015e-01 3.37115705e-01
2.76886493e-01 -3.03159535e-01 -9.43821847e-01 -3.40437979e-01
-1.01170644e-01 1.17836738e+00 2.57953405e-01 -5.73378980... | [6.944225311279297, -0.9441770315170288] |
4c5f15ac-7110-411c-a116-d0256faf3922 | domain-alignment-and-temporal-aggregation-for | 2211.12036 | null | https://arxiv.org/abs/2211.12036v2 | https://arxiv.org/pdf/2211.12036v2.pdf | Dual Prototype Attention for Unsupervised Video Object Segmentation | Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos. The primary techniques used in unsupervised VOS are 1) the collaboration of appearance and motion information and 2) temporal fusion between different frames. This paper proposes two novel prototype-based attentio... | ['Dogyoon Lee', 'Sangyoun Lee', 'Seunghoon Lee', 'Minhyeok Lee', 'Suhwan Cho'] | 2022-11-22 | null | null | null | null | ['video-object-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.46397272e-01 -1.85429230e-01 -4.85020518e-01 -1.07762896e-01
-7.04600275e-01 -3.37315947e-01 5.62488675e-01 -4.58818153e-02
-5.49435377e-01 5.28664768e-01 3.48385781e-01 2.66155690e-01
1.74713552e-01 -3.12443495e-01 -8.22619021e-01 -7.12215543e-01
1.20856613e-01 -1.06233478e-01 8.33675861e-01 1.15921974... | [9.360130310058594, -0.21430765092372894] |
69c28bd9-ef8d-4bf7-8843-4fb58be5f910 | improving-sparse-representation-based | 1607.01059 | null | http://arxiv.org/abs/1607.01059v6 | http://arxiv.org/pdf/1607.01059v6.pdf | Improving Sparse Representation-Based Classification Using Local Principal Component Analysis | Sparse representation-based classification (SRC), proposed by Wright et al.,
seeks the sparsest decomposition of a test sample over the dictionary of
training samples, with classification to the most-contributing class. Because
it assumes test samples can be written as linear combinations of their
same-class training s... | ['Chelsea Weaver', 'Naoki Saito'] | 2016-07-04 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 1.63547292e-01 6.71392679e-02 -5.33031821e-01 -1.24951214e-01
-5.18061399e-01 -3.48029017e-01 4.03356463e-01 -4.63228583e-01
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-2.53384292e-01 -6.50305808e-01 -3.05818707e-01 -9.24080610e-01
1.88080668e-02 7.08346367e-01 -1.31182104e-01 1.15635921... | [12.441650390625, 0.4292293190956116] |
aab174fd-99ad-49b6-98aa-96cae0a6a485 | anomaly-detection-based-unknown-face | 2007.05856 | null | https://arxiv.org/abs/2007.05856v1 | https://arxiv.org/pdf/2007.05856v1.pdf | Anomaly Detection-Based Unknown Face Presentation Attack Detection | Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection (fPAD), where a spoof detector is learned using only non-attacked images of users. These detectors are of practical importance as they are shown to generalize well to new attack types. In this paper, we present ... | ['Pramuditha Perera', 'Vishal M. Patel', 'Poojan Oza', 'Yashasvi Baweja'] | 2020-07-11 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 2.39994869e-01 -1.73914582e-01 2.58257892e-02 -2.87137717e-01
-1.58937737e-01 -6.47319317e-01 7.61595488e-01 2.05652729e-01
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-6.24297023e-01 1.83752254e-01 1.84552878e-01 -3.47227842... | [13.065220832824707, 1.1554921865463257] |
99878aa2-8319-4b8e-a0a3-5fb3c783b1af | cooperative-trajectory-planning-in-uncertain | 2203.04452 | null | https://arxiv.org/abs/2203.04452v3 | https://arxiv.org/pdf/2203.04452v3.pdf | Cooperative Trajectory Planning in Uncertain Environments with Monte Carlo Tree Search and Risk Metrics | Automated vehicles require the ability to cooperate with humans for smooth integration into today's traffic. While the concept of cooperation is well known, developing a robust and efficient cooperative trajectory planning method is still a challenge. One aspect of this challenge is the uncertainty surrounding the stat... | ['J. Marius Zöllner', 'Karl Kurzer', 'Philipp Stegmaier'] | 2022-03-09 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-1.87089347e-04 6.24276698e-01 -2.98923522e-01 -4.67265517e-01
-1.05295169e+00 -5.54458678e-01 9.92028356e-01 2.68771440e-01
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-1.58996642e-01 -9.46816504e-01 -7.03893661e-01 -7.26813555e-01
-3.81319374e-01 7.83492208e-01 6.87114537e-01 -6.86684996... | [5.037102699279785, 1.6427311897277832] |
0c424ea9-fdd5-4e52-bf49-f1424766dea6 | dual-domain-filters-based-texture-and | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Yang_Dual_Domain_Filters_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Yang_Dual_Domain_Filters_2015_CVPR_paper.pdf | Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution | Image deconvolution continues to be an active research topic of recovering a sharp image, given a blurry one generated by a convolution. One of the most challenging problems in image deconvolution is how to preserve the fine scale texture structures while removing blur and noise. Various methods have been implemented i... | ['Yujing Guan', 'Hang Yang', 'Yan Niu', 'Ming Zhu', 'Zhongbo Zhang'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['image-deconvolution'] | ['computer-vision'] | [ 3.52582186e-01 -5.98822951e-01 5.14533341e-01 -5.11717089e-02
-2.94344127e-01 -4.03442591e-01 4.02523547e-01 -5.67448676e-01
-3.24182212e-01 9.74749565e-01 5.32519341e-01 1.78923339e-01
-5.15452266e-01 -5.11329174e-01 -4.41726834e-01 -1.07338357e+00
4.03360367e-01 -7.63404742e-02 4.18399185e-01 -2.65792936... | [11.650370597839355, -2.7054152488708496] |
72d3854b-fa7a-4917-9358-6c0688ddd9be | grammatical-error-correction-are-we-there-yet | null | null | https://aclanthology.org/2022.coling-1.246 | https://aclanthology.org/2022.coling-1.246.pdf | Grammatical Error Correction: Are We There Yet? | There has been much recent progress in natural language processing, and grammatical error correction (GEC) is no exception. We found that state-of-the-art GEC systems (T5 and GECToR) outperform humans by a wide margin on the CoNLL-2014 test set, a benchmark GEC test corpus, as measured by the standard F0.5 evaluation m... | ['Hwee Tou Ng', 'Muhammad Reza Qorib'] | null | null | null | null | coling-2022-10 | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 1.70140341e-02 1.02710582e-01 2.81975180e-01 -5.78161836e-01
-1.04072702e+00 -7.49503136e-01 6.11568987e-01 7.21052408e-01
-7.63098240e-01 6.60779178e-01 7.56158680e-02 -5.66789031e-01
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-1.90438390e-01 5.42847872e-01 2.10754260e-01 -5.00908911... | [11.06196403503418, 10.689937591552734] |
64203665-d945-4db1-aff8-355e87eb522e | end-to-end-3d-dense-captioning-with-vote2cap | 2301.02508 | null | https://arxiv.org/abs/2301.02508v1 | https://arxiv.org/pdf/2301.02508v1.pdf | End-to-End 3D Dense Captioning with Vote2Cap-DETR | 3D dense captioning aims to generate multiple captions localized with their associated object regions. Existing methods follow a sophisticated ``detect-then-describe'' pipeline equipped with numerous hand-crafted components. However, these hand-crafted components would yield suboptimal performance given cluttered objec... | ['Gang Yu', 'Tao Chen', 'Yinjie Lei', 'Xin Chen', 'Hongyuan Zhu', 'Sijin Chen'] | 2023-01-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_End-to-End_3D_Dense_Captioning_With_Vote2Cap-DETR_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_End-to-End_3D_Dense_Captioning_With_Vote2Cap-DETR_CVPR_2023_paper.pdf | cvpr-2023-1 | ['dense-captioning', '3d-dense-captioning'] | ['computer-vision', 'computer-vision'] | [ 3.47070664e-01 1.73848242e-01 3.81770246e-02 -4.16048765e-01
-1.32363367e+00 -5.62168717e-01 6.90388620e-01 -3.38991791e-01
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4.28647816e-01 7.31382608e-01 4.97685939e-01 -1.33086920... | [10.345929145812988, 0.9399077892303467] |
10328b4f-5deb-45cc-b852-677ab8e13855 | few-shot-text-classification-with-triplet | 2103.07552 | null | https://arxiv.org/abs/2103.07552v1 | https://arxiv.org/pdf/2103.07552v1.pdf | Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning | Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category. This paper explores data augmentation -- a technique particularly suitable for training with limited data -- for this few-shot, highly-multicl... | ['Shiqi Xu', 'Yu Cheng', 'Soroush Vosoughi', 'Chengyu Huang', 'Jason Wei'] | 2021-03-12 | null | https://aclanthology.org/2021.naacl-main.434 | https://aclanthology.org/2021.naacl-main.434.pdf | naacl-2021-4 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 7.16542602e-01 1.17184319e-01 -4.82340366e-01 -6.04686081e-01
-8.96030426e-01 -4.77445483e-01 9.19952035e-01 5.97141266e-01
-7.55571902e-01 6.82763815e-01 1.51806846e-01 -6.32411659e-01
1.25488207e-01 -5.36390483e-01 -3.46539944e-01 -4.80630070e-01
3.24319214e-01 8.46651673e-01 1.07758000e-01 -3.80475640... | [10.764201164245605, 7.925563335418701] |
48bf0ede-3500-4719-b258-73cf49fc8ef8 | the-rumour-mill-making-misinformation-spread | 2002.04494 | null | https://arxiv.org/abs/2002.04494v2 | https://arxiv.org/pdf/2002.04494v2.pdf | The Rumour Mill: Making the Spread of Misinformation Explicit and Tangible | Misinformation spread presents a technological and social threat to society. With the advance of AI-based language models, automatically generated texts have become difficult to identify and easy to create at scale. We present "The Rumour Mill", a playful art piece, designed as a commentary on the spread of rumours and... | ['Leon Derczynski', 'Jeanette Falk Olesen', 'Nanna Inie'] | 2020-02-11 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [ 2.41789967e-01 3.46722126e-01 -9.06852819e-03 2.88027287e-01
-1.33074448e-02 -8.52611363e-01 1.28973067e+00 6.20084479e-02
1.90908954e-01 5.71459651e-01 6.30499065e-01 -5.63556731e-01
4.63747352e-01 -7.64237404e-01 -2.50291467e-01 -7.66129866e-02
-1.01495162e-01 4.71426159e-01 2.36998037e-01 -7.96596169... | [9.298699378967285, 6.450888633728027] |
075519a6-be2f-4647-bb56-352b595ab3e9 | generating-multiple-length-summaries-via | 2212.10843 | null | https://arxiv.org/abs/2212.10843v1 | https://arxiv.org/pdf/2212.10843v1.pdf | Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization | Sentence summarization shortens given texts while maintaining core contents of the texts. Unsupervised approaches have been studied to summarize texts without human-written summaries. However, recent unsupervised models are extractive, which remove words from texts and thus they are less flexible than abstractive summa... | ['Hwanjo Yu', 'Xing Xie', 'Chanyoung Park', 'Xiting Wang', 'Dongmin Hyun'] | 2022-12-21 | null | null | null | null | ['abstractive-text-summarization', 'abstractive-sentence-summarization', 'unsupervised-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.39149052e-01 4.84894454e-01 -4.75590259e-01 -2.69477665e-01
-1.28075397e+00 -4.99101728e-01 4.76484686e-01 6.18498921e-01
-3.65741223e-01 1.42352021e+00 1.03543866e+00 3.49639505e-02
2.90162545e-02 -7.09567070e-01 -5.47168732e-01 -4.98306423e-01
4.40529674e-01 3.14890683e-01 -2.79430225e-02 2.53271870... | [12.528646469116211, 9.436277389526367] |
117dddab-9cf0-4050-8c7c-5d4fbccd9a32 | euclid-towards-efficient-unsupervised | 2210.00498 | null | https://arxiv.org/abs/2210.00498v2 | https://arxiv.org/pdf/2210.00498v2.pdf | EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model | Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks. Previous works focused on the pre-training in a model-free manner while lacking the study... | ['Changjie Fan', 'Yingfeng Chen', 'Jinyi Liu', 'Yujing Hu', 'Yan Zheng', 'Yao Mu', 'Fei Ni', 'Jianye Hao', 'Yifu Yuan'] | 2022-10-02 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [-5.24611883e-02 -2.95422614e-01 -3.59599382e-01 -8.55600759e-02
-5.86500883e-01 -3.16310316e-01 4.28926647e-01 4.38648351e-02
-6.52646661e-01 7.87018478e-01 -2.05207109e-01 -1.75375670e-01
-4.02971864e-01 -6.36156142e-01 -7.59085000e-01 -1.05491495e+00
-4.00718242e-01 5.34072936e-01 4.40410852e-01 -5.03367484... | [4.201882362365723, 2.0226352214813232] |
eb224d10-782c-4de5-b873-800dba64c0bb | document-level-time-anchoring-for-timeline | null | null | https://aclanthology.org/P15-2059 | https://aclanthology.org/P15-2059.pdf | Document Level Time-anchoring for TimeLine Extraction | null | ['German Rigau', 'Egoitz Laparra', 'Itziar Aldabe'] | 2015-07-01 | document-level-time-anchoring-for-timeline-1 | https://aclanthology.org/P15-2059 | https://aclanthology.org/P15-2059.pdf | ijcnlp-2015-7 | ['temporal-information-extraction'] | ['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... | [-7.406285762786865, 3.6772236824035645] |
4b363637-9505-4ca5-8a77-afe662a79382 | exploring-current-user-web-search-behaviours | 2104.04501 | null | https://arxiv.org/abs/2104.04501v1 | https://arxiv.org/pdf/2104.04501v1.pdf | Exploring Current User Web Search Behaviours in Analysis Tasks to be Supported in Conversational Search | Conversational search presents opportunities to support users in their search activities to improve the effectiveness and efficiency of search while reducing their cognitive load. Limitations of the potential competency of conversational agents restrict the situations for which conversational search agents can replace ... | ['Gareth J. F. Jones', 'Abhishek Kaushik'] | 2021-04-09 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.23725846e-01 4.08447355e-01 -1.20758168e-01 -2.00517908e-01
-2.19079643e-01 -8.39457214e-01 1.14389729e+00 3.99046503e-02
-6.73421264e-01 4.98376310e-01 3.39979738e-01 -9.43058491e-01
-2.81568378e-01 -4.86040652e-01 3.71334612e-01 -2.06954610e-02
2.49099955e-01 7.06681013e-01 4.78459567e-01 -4.79765475... | [12.251749992370605, 7.768195152282715] |
f730a371-4971-4386-a4f1-cdc086ef2614 | unsupervised-video-summarization-with | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Mahasseni_Unsupervised_Video_Summarization_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Mahasseni_Unsupervised_Video_Summarization_CVPR_2017_paper.pdf | Unsupervised Video Summarization With Adversarial LSTM Networks | This paper addresses the problem of unsupervised video summarization, formulated as selecting a sparse subset of video frames that optimally represent the input video. Our key idea is to learn a deep summarizer network to minimize distance between training videos and a distribution of their summarizations, in an unsupe... | ['Behrooz Mahasseni', 'Sinisa Todorovic', 'Michael Lam'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 4.82162654e-01 2.49007672e-01 -2.28499696e-01 -1.28315061e-01
-1.05538297e+00 -4.85708147e-01 5.09271562e-01 -5.01843765e-02
-1.71457469e-01 5.58831871e-01 7.18224764e-01 2.06007674e-01
4.97147173e-01 -3.70592922e-01 -1.36963165e+00 -9.20218468e-01
2.14553382e-02 4.18803215e-01 -2.07566097e-01 3.36317718... | [10.440534591674805, 0.422097384929657] |
2d463c3f-104f-4544-a8f2-789d02d6aa27 | semi-automated-extraction-of-research-topics | 2306.13075 | null | https://arxiv.org/abs/2306.13075v1 | https://arxiv.org/pdf/2306.13075v1.pdf | Semi-automated extraction of research topics and trends from NCI funding in radiological sciences from 2000-2020 | Investigators, funders, and the public desire knowledge on topics and trends in publicly funded research but current efforts in manual categorization are limited in scale and understanding. We developed a semi-automated approach to extract and name research topics, and applied this to \$1.9B of NCI funding over 21 year... | ['John Kang', 'Paul Kinahan', 'Daniel Chen', 'August Anderson', 'Joseph Tsai', 'Peter Beidler', 'Mark Nguyen'] | 2023-06-22 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [-4.08314258e-01 1.78805873e-01 -5.49797177e-01 -1.93871647e-01
-1.05461395e+00 -6.39659226e-01 4.58208382e-01 9.16093171e-01
-5.61039329e-01 7.02993989e-01 1.22563577e+00 -9.99939978e-01
-3.96246284e-01 -5.52910447e-01 -3.80589455e-01 -5.63307941e-01
-2.59654075e-01 3.98008287e-01 -1.63504750e-01 2.57542193... | [9.07591724395752, 8.274869918823242] |
421930a3-6e52-46f6-a9e4-4195781fd84a | unsupervised-temporal-video-grounding-with | 2201.05307 | null | https://arxiv.org/abs/2201.05307v1 | https://arxiv.org/pdf/2201.05307v1.pdf | Unsupervised Temporal Video Grounding with Deep Semantic Clustering | Temporal video grounding (TVG) aims to localize a target segment in a video according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on abundant video-query paired data, which is expensive and time-consuming to collect in real-world scenarios. In this ... | ['Pan Zhou', 'Zichuan Xu', 'Yu Cheng', 'Kai Zou', 'Xing Di', 'Yinzhen Wang', 'Xiaoye Qu', 'Daizong Liu'] | 2022-01-14 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 1.87083676e-01 -1.28698960e-01 -4.25268859e-01 -4.13508713e-01
-7.12551117e-01 -2.88461864e-01 4.21738803e-01 -1.03989944e-01
-3.34148049e-01 4.36897904e-01 2.38093376e-01 -9.30734426e-02
-9.40660387e-02 -5.70439219e-01 -8.47859204e-01 -5.61957955e-01
1.23749189e-01 9.70245227e-02 5.19792974e-01 -8.77969041... | [9.660659790039062, 0.6798979043960571] |
2b39cc79-7f41-4e7f-8696-03cdbc5482f8 | interpreting-pretrained-source-code-models | 2305.00875 | null | https://arxiv.org/abs/2305.00875v1 | https://arxiv.org/pdf/2305.00875v1.pdf | Interpreting Pretrained Source-code Models using Neuron Redundancy Analyses | Neural code intelligence models continue to be 'black boxes' to the human programmer. This opacity limits their application towards code intelligence tasks, particularly for applications like vulnerability detection where a model's reliance on spurious correlations can be safety-critical. We introduce a neuron-level ap... | ['Ali Jannesari', 'Christopher Quinn', 'Zefu Hu', 'Arushi Sharma'] | 2023-05-01 | null | null | null | null | ['vulnerability-detection', 'memorization'] | ['miscellaneous', 'natural-language-processing'] | [ 4.94052261e-01 2.31197730e-01 -6.87277988e-02 -2.70799458e-01
-3.83548826e-01 -9.46714520e-01 3.50178272e-01 5.64996779e-01
-2.61015236e-01 2.70063370e-01 4.06882852e-01 -7.83708930e-01
-1.47914007e-01 -7.76089966e-01 -7.23270178e-01 -4.72392112e-01
-1.22478426e-01 4.85287383e-02 1.55359402e-01 -1.47330165... | [7.566608428955078, 7.755120277404785] |
c8404ce1-7da5-4ba6-a1bf-1bc8fea0dd18 | follow-the-attention-combining-partial-pose | 1905.04430 | null | https://arxiv.org/abs/1905.04430v2 | https://arxiv.org/pdf/1905.04430v2.pdf | Follow the Attention: Combining Partial Pose and Object Motion for Fine-Grained Action Detection | Retailers have long been searching for ways to effectively understand their customers' behaviour in order to provide a smooth and pleasant shopping experience that attracts more customers everyday and maximises their revenue, consequently. Humans can flawlessly understand others' behaviour by combining different visual... | ['Mohammad Mahdi Kazemi Moghaddam', 'Javen Shi', 'Ehsan Abbasnejad'] | 2019-05-11 | null | null | null | null | ['fine-grained-action-detection'] | ['computer-vision'] | [ 4.14952964e-01 -1.35648802e-01 3.13226730e-02 -3.89274448e-01
-6.03076756e-01 -7.16194391e-01 8.43031824e-01 -1.87931836e-01
-5.17538369e-01 3.29469144e-01 1.87013343e-01 1.32946655e-01
9.60964896e-03 -4.40833896e-01 -7.92697906e-01 -8.34752560e-01
2.84926244e-03 3.07137311e-01 1.97500363e-01 -3.45685124... | [7.879979133605957, 0.14377570152282715] |
a41b6484-e749-49b7-97ce-00c9f95de380 | learning-to-represent-review-with-tensor | null | null | https://aclanthology.org/D16-1083 | https://aclanthology.org/D16-1083.pdf | Learning to Represent Review with Tensor Decomposition for Spam Detection | null | ['Jun Zhao', 'Shizhu He', 'Xuepeng Wang', 'Kang Liu'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['spam-detection'] | ['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... | [-7.331501483917236, 3.8560943603515625] |
0ae868e9-c245-4521-a7a7-05b60398c4c3 | un-solving-morphological-inflection-lemma-1 | null | null | https://aclanthology.org/2022.acl-short.96 | https://aclanthology.org/2022.acl-short.96.pdf | (Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models’ Performance | In the domain of Morphology, Inflection is a fundamental and important task that gained a lot of traction in recent years, mostly via SIGMORPHON’s shared-tasks.With average accuracy above 0.9 over the scores of all languages, the task is considered mostly solved using relatively generic neural seq2seq models, even with... | ['Reut Tsarfaty', 'David Guriel', 'Omer Goldman'] | null | null | null | null | acl-2022-5 | ['morphological-inflection'] | ['natural-language-processing'] | [-1.10380739e-01 5.58798499e-02 4.01557535e-02 -3.55953842e-01
-1.25198543e+00 -1.01978028e+00 5.94470561e-01 4.31396991e-01
-9.91315007e-01 8.79199207e-01 3.49797100e-01 -4.35986817e-01
-1.67328399e-02 -5.28343022e-01 -8.37822914e-01 -4.41044778e-01
-9.97325405e-02 5.98506749e-01 1.61120772e-01 -5.70391536... | [10.629302978515625, 9.796550750732422] |
d0d72799-fbf8-4574-8f01-0e3b76d9ac89 | learning-roi-transformer-for-detecting | 1812.00155 | null | http://arxiv.org/abs/1812.00155v1 | http://arxiv.org/pdf/1812.00155v1.pdf | Learning RoI Transformer for Detecting Oriented Objects in Aerial Images | Object detection in aerial images is an active yet challenging task in
computer vision because of the birdview perspective, the highly complex
backgrounds, and the variant appearances of objects. Especially when detecting
densely packed objects in aerial images, methods relying on horizontal
proposals for common object... | ['Gui-Song Xia', 'Yang Long', 'Qikai Lu', 'Jian Ding', 'Nan Xue'] | 2018-12-01 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.16578335e-01 -2.15226650e-01 1.25581726e-01 -2.92328179e-01
-3.58352274e-01 -6.29524171e-01 3.29725415e-01 -1.16102412e-01
-6.44809663e-01 3.16521615e-01 -2.59242237e-01 -3.15358862e-02
1.17010690e-01 -6.82795525e-01 -6.27125740e-01 -7.19386160e-01
-5.16138524e-02 -1.10722028e-01 9.83875513e-01 -1.88428238... | [8.70976448059082, -0.7398063540458679] |
0c4511ac-6ed9-4f91-926b-b40c8ba15a07 | in-the-name-of-fairness-assessing-the-bias-in | 2305.11348 | null | https://arxiv.org/abs/2305.11348v1 | https://arxiv.org/pdf/2305.11348v1.pdf | In the Name of Fairness: Assessing the Bias in Clinical Record De-identification | Data sharing is crucial for open science and reproducible research, but the legal sharing of clinical data requires the removal of protected health information from electronic health records. This process, known as de-identification, is often achieved through the use of machine learning algorithms by many commercial an... | ['Marzyeh Ghassemi', 'Tom Joseph Pollard', 'Shulammite Lim', 'Yuxin Xiao'] | 2023-05-18 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [-8.26066956e-02 -1.22431196e-01 -3.58302414e-01 -3.36106867e-01
-1.07402527e+00 -9.89789605e-01 2.78356969e-01 7.37283170e-01
-4.32028800e-01 6.80158615e-01 7.35261500e-01 -6.10166192e-01
-4.85910237e-01 -5.76025009e-01 -2.04818562e-01 -4.37574267e-01
3.53408009e-01 4.86851186e-01 -3.69306117e-01 3.75394166... | [6.992888927459717, 6.842841625213623] |
410f297a-07da-4972-a210-dac48a4144c8 | measuring-mathematical-problem-solving-with | 2103.03874 | null | https://arxiv.org/abs/2103.03874v2 | https://arxiv.org/pdf/2103.03874v2.pdf | Measuring Mathematical Problem Solving With the MATH Dataset | Many intellectual endeavors require mathematical problem solving, but this skill remains beyond the capabilities of computers. To measure this ability in machine learning models, we introduce MATH, a new dataset of 12,500 challenging competition mathematics problems. Each problem in MATH has a full step-by-step solutio... | ['Jacob Steinhardt', 'Dawn Song', 'Eric Tang', 'Steven Basart', 'Akul Arora', 'Saurav Kadavath', 'Collin Burns', 'Dan Hendrycks'] | 2021-03-05 | null | null | null | null | ['math-word-problem-solving', 'mathematical-reasoning', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'natural-language-processing', 'reasoning', 'time-series'] | [ 1.72608525e-01 3.78128678e-01 -1.29112741e-02 -5.26763201e-01
-7.24000692e-01 -9.80351985e-01 3.02220762e-01 3.23496252e-01
-7.61569366e-02 8.69447708e-01 4.29607322e-03 -1.02824438e+00
-4.04837042e-01 -1.20199180e+00 -9.12923515e-01 1.33960560e-01
1.59283608e-01 6.98458970e-01 -4.93063256e-02 -4.26056057... | [9.60161304473877, 7.330379486083984] |
b903684f-e7d7-4745-8cb9-d7357d2effe4 | grounding-dino-marrying-dino-with-grounded | 2303.05499 | null | https://arxiv.org/abs/2303.05499v4 | https://arxiv.org/pdf/2303.05499v4.pdf | Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection | In this paper, we present an open-set object detector, called Grounding DINO, by marrying Transformer-based detector DINO with grounded pre-training, which can detect arbitrary objects with human inputs such as category names or referring expressions. The key solution of open-set object detection is introducing languag... | ['Lei Zhang', 'Jun Zhu', 'Hang Su', 'Jianwei Yang', 'Chunyuan Li', 'Jie Yang', 'Hao Zhang', 'Feng Li', 'Tianhe Ren', 'Zhaoyang Zeng', 'Shilong Liu'] | 2023-03-09 | null | null | null | null | ['referring-expression', 'zero-shot-object-detection'] | ['computer-vision', 'computer-vision'] | [ 5.08483872e-02 1.55254081e-01 -1.80975288e-01 -3.25630814e-01
-1.31448686e+00 -7.30456412e-01 3.70937437e-01 1.06129192e-01
-4.65064496e-01 2.40689889e-01 -1.19105369e-01 -1.51072070e-01
8.48997086e-02 -6.81551158e-01 -8.23336363e-01 -2.21894547e-01
1.83108777e-01 6.74052298e-01 4.04105157e-01 -4.60265905... | [9.951682090759277, 1.6696674823760986] |
c8039abe-bd5b-446d-a387-804947e3074e | inductive-attention-for-video-action | 2212.08830 | null | https://arxiv.org/abs/2212.08830v2 | https://arxiv.org/pdf/2212.08830v2.pdf | Inductive Attention for Video Action Anticipation | Anticipating future actions based on spatiotemporal observations is essential in video understanding and predictive computer vision. Moreover, a model capable of anticipating the future has important applications, it can benefit precautionary systems to react before an event occurs. However, unlike in the action recogn... | ['Oswald Lanz', 'Simon See', 'Cheng-Kuang Lee', 'Giuseppe Fiameni', 'Tsung-Ming Tai'] | 2022-12-17 | null | null | null | null | ['action-anticipation', 'video-understanding'] | ['computer-vision', 'computer-vision'] | [ 2.94410855e-01 5.74651966e-03 -6.43231153e-01 -5.18973708e-01
-4.55922306e-01 -1.23627596e-01 6.32957041e-01 -2.76912570e-01
-2.70083129e-01 5.69150507e-01 8.39971364e-01 -2.07406823e-02
-1.53805122e-01 -4.98201013e-01 -8.63692164e-01 -4.80707109e-01
-1.31998256e-01 3.00181378e-03 3.81857514e-01 1.57445833... | [8.205403327941895, 0.41960087418556213] |
5895c579-b442-474d-ab87-9d8f315583dc | mam-masked-acoustic-modeling-for-end-to-end | 2010.11445 | null | https://arxiv.org/abs/2010.11445v2 | https://arxiv.org/pdf/2010.11445v2.pdf | MAM: Masked Acoustic Modeling for End-to-End Speech-to-Text Translation | End-to-end Speech-to-text Translation (E2E-ST), which directly translates source language speech to target language text, is widely useful in practice, but traditional cascaded approaches (ASR+MT) often suffer from error propagation in the pipeline. On the other hand, existing end-to-end solutions heavily depend on the... | ['Liang Huang', 'Renjie Zheng', 'Mingbo Ma', 'Junkun Chen'] | 2020-10-22 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.77410620e-01 1.28478706e-01 1.94573719e-02 -4.27735269e-01
-1.76764512e+00 -6.00957632e-01 5.73016226e-01 -4.07059431e-01
-4.90831971e-01 5.09090602e-01 4.01418000e-01 -7.62712955e-01
7.02730358e-01 -2.16117397e-01 -9.95640814e-01 -5.10891080e-01
4.78523731e-01 5.54371655e-01 8.15576315e-02 -4.00206536... | [14.524922370910645, 7.135400295257568] |
90d734cd-d40c-43a6-aa8d-63e51e814aeb | variational-interaction-information-1 | 2012.04251 | null | https://arxiv.org/abs/2012.04251v1 | https://arxiv.org/pdf/2012.04251v1.pdf | Variational Interaction Information Maximization for Cross-domain Disentanglement | Cross-domain disentanglement is the problem of learning representations partitioned into domain-invariant and domain-specific representations, which is a key to successful domain transfer or measuring semantic distance between two domains. Grounded in information theory, we cast the simultaneous learning of domain-inva... | ['Kee-Eung Kim', 'Seunghoon Hong', 'Geon-Hyeong Kim', 'HyeongJoo Hwang'] | 2020-12-08 | variational-interaction-information | http://proceedings.neurips.cc/paper/2020/hash/fe663a72b27bdc613873fbbb512f6f67-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/fe663a72b27bdc613873fbbb512f6f67-Paper.pdf | neurips-2020-12 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.79269338e-01 4.64673358e-04 -5.26884019e-01 -2.20716596e-01
-1.38305092e+00 -9.93129969e-01 1.10191143e+00 -4.17508572e-01
-8.73584449e-02 6.91966712e-01 3.26452166e-01 -1.73403442e-01
-3.54894131e-01 -5.53894460e-01 -8.72403681e-01 -5.18785417e-01
2.52856493e-01 5.94375253e-01 -2.13684991e-01 -2.71486342... | [11.536447525024414, 0.6232125163078308] |
4bc6a274-9a66-4402-9a3c-5b016a93da79 | debatesum-a-large-scale-argument-mining-and | 2011.07251 | null | https://arxiv.org/abs/2011.07251v1 | https://arxiv.org/pdf/2011.07251v1.pdf | DebateSum: A large-scale argument mining and summarization dataset | Prior work in Argument Mining frequently alludes to its potential applications in automatic debating systems. Despite this focus, almost no datasets or models exist which apply natural language processing techniques to problems found within competitive formal debate. To remedy this, we present the DebateSum dataset. De... | ['Arvind Balaji', 'Allen Roush'] | 2020-11-14 | null | https://aclanthology.org/2020.argmining-1.1 | https://aclanthology.org/2020.argmining-1.1.pdf | coling-argmining-2020-12 | ['query-based-extractive-summarization', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.31441289e-01 6.31763816e-01 -8.13601196e-01 -2.75901079e-01
-1.68595040e+00 -7.96759903e-01 1.24868119e+00 7.17397630e-01
-5.99275947e-01 1.21010220e+00 1.43869936e+00 -8.58366728e-01
1.42677091e-02 -5.34850478e-01 -5.47126710e-01 -2.25083217e-01
4.88079756e-01 7.81392992e-01 -5.86267151e-02 -5.66767156... | [12.107755661010742, 9.607613563537598] |
58f42212-505b-479a-9456-937f1d14f033 | a-deep-model-for-partial-multi-label-image | 2207.02410 | null | https://arxiv.org/abs/2207.02410v1 | https://arxiv.org/pdf/2207.02410v1.pdf | A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation | In this paper, we study the partial multi-label (PML) image classification problem, where each image is annotated with a candidate label set consists of multiple relevant labels and other noisy labels. Existing PML methods typically design a disambiguation strategy to filter out noisy labels by utilizing prior knowledg... | ['Sheng-Jun Huang', 'Ming-Kun Xie', 'Feng Sun'] | 2022-07-06 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 4.20127571e-01 -7.97115043e-02 -7.07529560e-02 -5.26730895e-01
-1.13477087e+00 -3.87096077e-01 8.28156173e-02 2.81313956e-01
-4.95930284e-01 6.73779666e-01 -2.48830631e-01 7.62444660e-02
-2.93413639e-01 -3.32649559e-01 -4.91602957e-01 -9.72949147e-01
7.19239235e-01 6.08296096e-01 1.34653717e-01 1.62830904... | [9.537548065185547, 3.9572956562042236] |
ed4c8165-babb-4547-b311-df1667fb0afa | signaltrain-profiling-audio-compressors-with | 1905.11928 | null | https://arxiv.org/abs/1905.11928v2 | https://arxiv.org/pdf/1905.11928v2.pdf | SignalTrain: Profiling Audio Compressors with Deep Neural Networks | In this work we present a data-driven approach for predicting the behavior of (i.e., profiling) a given non-linear audio signal processing effect (henceforth "audio effect"). Our objective is to learn a mapping function that maps the unprocessed audio to the processed by the audio effect to be profiled, using time-doma... | ['Stylianos I. Mimilakis', 'Benjamin Colburn', 'Scott H. Hawley'] | 2019-05-28 | null | null | null | null | ['audio-signal-processing', 'audio-effects-modeling'] | ['audio', 'audio'] | [ 4.16773349e-01 -3.47694337e-01 3.80511463e-01 -2.35719055e-01
-7.57585645e-01 -6.02362692e-01 3.51614535e-01 2.98697144e-01
1.88182238e-02 1.89862818e-01 2.47454748e-01 -3.99073437e-02
-5.07887363e-01 -2.09924772e-01 -6.89607918e-01 -5.57572305e-01
-5.77188015e-01 3.55036080e-01 2.31653601e-01 -2.19766408... | [15.516103744506836, 5.817506313323975] |
0820be32-22e0-476a-b14c-7b7e049b1499 | bone-marrow-cytomorphology-cell-detection | 2305.05430 | null | https://arxiv.org/abs/2305.05430v1 | https://arxiv.org/pdf/2305.05430v1.pdf | Bone Marrow Cytomorphology Cell Detection using InceptionResNetV2 | Critical clinical decision points in haematology are influenced by the requirement of bone marrow cytology for a haematological diagnosis. Bone marrow cytology, however, is restricted to reference facilities with expertise, and linked to inter-observer variability which requires a long time to process that could result... | ['Khandaker Tabin Hasan', 'Raisa Fairooz Meem'] | 2023-05-09 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.06415063e-01 4.88631502e-02 3.72087769e-02 -1.39029920e-01
-1.03978217e+00 -2.59583145e-01 2.54559219e-01 7.69141614e-01
-5.93473494e-01 9.14849401e-01 -2.75457382e-01 -6.77216768e-01
-3.44298601e-01 -6.22947395e-01 -4.61824238e-03 -1.14511096e+00
6.90754205e-02 9.79310930e-01 1.78313553e-01 2.90585220... | [15.044724464416504, -3.011800765991211] |
ced2bb62-70c0-4ac2-a7b8-003d17b22ce4 | multimodal-image-outpainting-with-regularized | 1910.11481 | null | https://arxiv.org/abs/1910.11481v1 | https://arxiv.org/pdf/1910.11481v1.pdf | Multimodal Image Outpainting With Regularized Normalized Diversification | In this paper, we study the problem of generating a set ofrealistic and diverse backgrounds when given only a smallforeground region. We refer to this task as image outpaint-ing. The technical challenge of this task is to synthesize notonly plausible but also diverse image outputs. Traditionalgenerative adversarial net... | ['Lingzhi Zhang', 'Jiancong Wang', 'Jianbo Shi'] | 2019-10-25 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 4.16971564e-01 1.54099971e-01 -2.86029056e-02 -2.79167980e-01
-8.93253803e-01 -5.13194919e-01 6.30283952e-01 -6.37310743e-01
-1.87756985e-01 1.17252898e+00 1.21606700e-01 1.44877851e-01
3.72149974e-01 -8.34790587e-01 -1.08060920e+00 -7.87847161e-01
4.79741454e-01 1.62796125e-01 -8.14839303e-02 -1.41905978... | [11.711695671081543, -0.5252341628074646] |
29078ec3-9060-4145-9eef-135ed876a3c5 | musical-features-for-automatic-music | 2004.07171 | null | https://arxiv.org/abs/2004.07171v1 | https://arxiv.org/pdf/2004.07171v1.pdf | Musical Features for Automatic Music Transcription Evaluation | This technical report gives a detailed, formal description of the features introduced in the paper: Adrien Ycart, Lele Liu, Emmanouil Benetos and Marcus T. Pearce. "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription", Transactions of the International Society for Music I... | ['Emmanouil Benetos', 'Lele Liu', 'Marcus T. Pearce', 'Adrien Ycart'] | 2020-04-15 | null | null | null | null | ['music-transcription'] | ['music'] | [ 5.83630092e-02 -3.17504555e-01 -2.13469535e-01 1.51964948e-01
-7.92878091e-01 -7.06861377e-01 4.43934411e-01 -1.90774113e-01
6.57640025e-02 5.63186407e-01 3.58345687e-01 1.48576245e-01
-7.03420818e-01 -4.52780388e-02 -1.59647912e-01 -3.55759293e-01
-3.38756233e-01 4.11442667e-02 -3.55992727e-02 -1.57819670... | [15.973363876342773, 5.254083633422852] |
f49d7a81-dd94-4f0b-88d6-9f08338c93ac | dualfl-a-duality-based-federated-learning | 2305.10294 | null | https://arxiv.org/abs/2305.10294v1 | https://arxiv.org/pdf/2305.10294v1.pdf | DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime | We propose a novel training algorithm called DualFL (Dualized Federated Learning), for solving a distributed optimization problem in federated learning. Our approach is based on a specific dual formulation of the federated learning problem. DualFL achieves communication acceleration under various settings on smoothness... | ['Jinchao Xu', 'Jongho Park'] | 2023-05-17 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-6.99868619e-01 2.05753390e-02 -4.97883052e-01 -6.83018267e-02
-1.42682791e+00 -6.72308326e-01 2.39119902e-01 8.36610049e-02
-1.03636689e-01 8.53712440e-01 2.50742346e-01 -5.82226157e-01
-5.61920881e-01 -7.60163724e-01 -1.06215990e+00 -7.97720611e-01
-4.92352664e-01 5.67248344e-01 -8.07959616e-01 1.62594989... | [6.093705177307129, 5.472965717315674] |
fc70414b-cd38-4f9a-b38d-304cc1d7c45e | learning-to-summarize-videos-by-contrasting | 2301.05213 | null | https://arxiv.org/abs/2301.05213v3 | https://arxiv.org/pdf/2301.05213v3.pdf | Learning to Summarize Videos by Contrasting Clips | Video summarization aims at choosing parts of a video that narrate a story as close as possible to the original one. Most of the existing video summarization approaches focus on hand-crafted labels. As the number of videos grows exponentially, there emerges an increasing need for methods that can learn meaningful summa... | ['Arnold Smeulders', 'Cees Kaandorp', 'Artem Moskalev', 'Ivan Sosnovik'] | 2023-01-12 | null | null | null | null | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 3.56165528e-01 1.03128664e-01 -4.59717065e-01 -3.84203523e-01
-9.78299558e-01 -5.30731499e-01 6.52844071e-01 4.85869527e-01
-2.63510764e-01 7.79320002e-01 8.32049429e-01 2.15613708e-01
-5.06155044e-02 -3.16230804e-01 -7.98351586e-01 -5.63096762e-01
1.27546102e-01 8.18407536e-02 1.73280567e-01 3.23942222... | [10.44701862335205, 0.45321765542030334] |
d458f153-f50d-4b47-b3ec-46a63015f4da | associating-objects-with-transformers-for | 2106.02638 | null | https://arxiv.org/abs/2106.02638v3 | https://arxiv.org/pdf/2106.02638v3.pdf | Associating Objects with Transformers for Video Object Segmentation | This paper investigates how to realize better and more efficient embedding learning to tackle the semi-supervised video object segmentation under challenging multi-object scenarios. The state-of-the-art methods learn to decode features with a single positive object and thus have to match and segment each target separat... | ['Yi Yang', 'Yunchao Wei', 'Zongxin Yang'] | 2021-06-04 | null | http://proceedings.neurips.cc/paper/2021/hash/147702db07145348245dc5a2f2fe5683-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/147702db07145348245dc5a2f2fe5683-Paper.pdf | neurips-2021-12 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 8.60407054e-02 -2.09144026e-01 -4.53816742e-01 -2.34143749e-01
-1.04400635e+00 -4.33590204e-01 -1.30185768e-01 -1.64996255e-02
-5.37175834e-01 1.05536833e-01 -1.55474976e-01 3.18368152e-02
-1.48185175e-02 -5.97379327e-01 -1.06163085e+00 -5.14012516e-01
-4.49878164e-02 6.35734975e-01 6.88216627e-01 3.22657257... | [9.323702812194824, 0.0002035892102867365] |
1a8c6068-944b-4339-b056-f69a536cd95f | giga-ssl-self-supervised-learning-for | 2212.03273 | null | https://arxiv.org/abs/2212.03273v1 | https://arxiv.org/pdf/2212.03273v1.pdf | Giga-SSL: Self-Supervised Learning for Gigapixel Images | Whole slide images (WSI) are microscopy images of stained tissue slides routinely prepared for diagnosis and treatment selection in medical practice. WSI are very large (gigapixel size) and complex (made of up to millions of cells). The current state-of-the-art (SoTA) approach to classify WSI subdivides them into tiles... | ['Thomas Walter', 'Etienne Decencière', 'Marvin Lerousseau', 'Tristan Lazard'] | 2022-12-06 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 5.76632261e-01 4.36301559e-01 -2.93639779e-01 -1.12296507e-01
-1.43456376e+00 -5.13795555e-01 6.32040381e-01 4.57564414e-01
-5.45201838e-01 9.50025439e-01 3.11621338e-01 -3.26796442e-01
-3.00668534e-02 -8.50373089e-01 -7.98368275e-01 -1.23257041e+00
-1.17111303e-01 6.88404262e-01 4.00506765e-01 -1.15002561... | [15.11540412902832, -2.963696241378784] |
c650592f-586a-4dac-bd84-d460c3c29a1f | multilingual-part-of-speech-tagging-two | 1401.5695 | null | http://arxiv.org/abs/1401.5695v1 | http://arxiv.org/pdf/1401.5695v1.pdf | Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches | We demonstrate the effectiveness of multilingual learning for unsupervised
part-of-speech tagging. The central assumption of our work is that by combining
cues from multiple languages, the structure of each becomes more apparent. We
consider two ways of applying this intuition to the problem of unsupervised
part-of-spe... | ['Regina Barzilay', 'Benjamin Snyder', 'Tahira Naseem', 'Jacob Eisenstein'] | 2014-01-15 | null | null | null | null | ['unsupervised-part-of-speech-tagging'] | ['natural-language-processing'] | [-3.26615423e-01 5.13939485e-02 -5.55352390e-01 -5.01202464e-01
-1.49064863e+00 -9.34675157e-01 8.58823299e-01 2.57523626e-01
-4.93152320e-01 7.24166453e-01 6.80146575e-01 -6.18288279e-01
1.77286789e-01 -3.73238087e-01 -5.18561721e-01 -4.53821033e-01
-2.38909394e-01 6.84509754e-01 3.55759919e-01 3.69070517... | [10.355708122253418, 9.798215866088867] |
3c63400e-ae9d-4e6c-a684-ccefe0c2d3b9 | textadain-fine-grained-adain-for-robust-text | 2105.03906 | null | https://arxiv.org/abs/2105.03906v3 | https://arxiv.org/pdf/2105.03906v3.pdf | TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers | Leveraging the characteristics of convolutional layers, neural networks are extremely effective for pattern recognition tasks. However in some cases, their decisions are based on unintended information leading to high performance on standard benchmarks but also to a lack of generalization to challenging testing conditi... | ['Ron Litman', 'Sharon Fogel', 'Oren Nuriel'] | 2021-05-09 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 2.36877054e-01 -5.53028524e-01 -7.05184415e-02 -6.73256516e-01
-6.56962216e-01 -5.43094516e-01 6.24749064e-01 -1.72660694e-01
-5.35743713e-01 3.54471058e-01 -1.98147725e-02 -3.14827472e-01
-6.69372976e-02 -6.25935256e-01 -8.39563251e-01 -8.93007755e-01
3.43186021e-01 3.52962375e-01 1.82429492e-01 -2.05883775... | [11.616665840148926, 2.456207036972046] |
04283cb1-3b71-4b6e-a30a-4d1ecb7a45bb | debiasingword-embeddings-improves-multimodal | 1905.10464 | null | https://arxiv.org/abs/1905.10464v3 | https://arxiv.org/pdf/1905.10464v3.pdf | Debiasing Word Embeddings Improves Multimodal Machine Translation | In recent years, pretrained word embeddings have proved useful for multimodal neural machine translation (NMT) models to address the shortage of available datasets. However, the integration of pretrained word embeddings has not yet been explored extensively. Further, pretrained word embeddings in high dimensional space... | ['Mamoru Komachi', 'Tosho Hirasawa'] | 2019-05-24 | debiasing-word-embeddings-improves-multimodal | https://aclanthology.org/W19-6604 | https://aclanthology.org/W19-6604.pdf | ws-2019-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 3.62000391e-02 -3.09503190e-02 -3.66493374e-01 -2.02604905e-01
-1.08583057e+00 -5.32505929e-01 8.59539807e-01 1.44816697e-01
-8.04322779e-01 8.62177849e-01 2.00792208e-01 -5.11681795e-01
2.06567332e-01 -4.89712387e-01 -5.18595636e-01 -5.68569660e-01
2.96934366e-01 6.48665667e-01 -8.18571597e-02 -3.39306086... | [11.512602806091309, 10.089245796203613] |
b09dc64b-838b-4ec5-96b3-edcbdfb46c64 | dpc-unsupervised-deep-point-correspondence | 2110.08636 | null | https://arxiv.org/abs/2110.08636v1 | https://arxiv.org/pdf/2110.08636v1.pdf | DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction | We present a new method for real-time non-rigid dense correspondence between point clouds based on structured shape construction. Our method, termed Deep Point Correspondence (DPC), requires a fraction of the training data compared to previous techniques and presents better generalization capabilities. Until now, two m... | ['Dan Raviv', 'Shai Avidan', 'Dvir Ginzburg', 'Itai Lang'] | 2021-10-16 | null | null | null | null | ['3d-dense-shape-correspondence'] | ['computer-vision'] | [ 7.51930401e-02 -2.42974255e-02 2.90428489e-01 -2.40736783e-01
-1.10144997e+00 -4.97814596e-01 8.48839998e-01 1.36393920e-01
-1.09576657e-01 4.03526694e-01 -1.66893959e-01 -9.28195491e-02
-1.94204692e-02 -9.36466813e-01 -1.12558734e+00 -4.89212275e-01
1.96792990e-01 1.09025884e+00 5.82995951e-01 -3.60186815... | [8.38163948059082, -3.3257265090942383] |
6fe450c1-ffaf-4703-9efa-4e0735d81855 | structured-occlusion-coding-for-robust-face | 1502.00478 | null | http://arxiv.org/abs/1502.00478v2 | http://arxiv.org/pdf/1502.00478v2.pdf | Structured Occlusion Coding for Robust Face Recognition | Occlusion in face recognition is a common yet challenging problem. While
sparse representation based classification (SRC) has been shown promising
performance in laboratory conditions (i.e. noiseless or random pixel
corrupted), it performs much worse in practical scenarios. In this paper, we
consider the practical face... | ['Yandong Wen', 'Youjun Xiang', 'Yuli Fu', 'Weiyang Liu', 'Rui Hu', 'Meng Yang'] | 2015-02-02 | null | null | null | null | ['robust-face-recognition', 'sparse-representation-based-classification'] | ['computer-vision', 'computer-vision'] | [ 4.45851892e-01 -1.78664744e-01 -2.89784610e-01 -4.84284967e-01
-6.58343673e-01 -8.36901069e-02 3.31699967e-01 -5.12699902e-01
2.99716681e-01 7.12795377e-01 4.02107835e-01 -2.15355791e-02
-1.76976368e-01 -5.29610276e-01 -5.10142744e-01 -1.08302009e+00
3.67928237e-01 1.47280425e-01 -3.03112596e-01 1.25327915... | [12.535969734191895, 0.4053519368171692] |
d948ded0-110d-413d-875e-ba308b794ba2 | head-and-eye-egocentric-gesture-recognition | 2201.11500 | null | https://arxiv.org/abs/2201.11500v2 | https://arxiv.org/pdf/2201.11500v2.pdf | Head and eye egocentric gesture recognition for human-robot interaction using eyewear cameras | Non-verbal communication plays a particularly important role in a wide range of scenarios in Human-Robot Interaction (HRI). Accordingly, this work addresses the problem of human gesture recognition. In particular, we focus on head and eye gestures, and adopt an egocentric (first-person) perspective using eyewear camera... | ['V. Javier Traver', 'Javier Marina-Miranda'] | 2022-01-27 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 1.76952899e-01 3.76755223e-02 -2.22782254e-01 -2.86521643e-01
-1.32916570e-01 -5.18357493e-02 8.50412667e-01 -5.48704684e-01
-5.88699460e-01 3.02751482e-01 3.39408964e-01 1.00696906e-01
-6.97721094e-02 -4.37821805e-01 -4.22030956e-01 -7.48895347e-01
-6.85744807e-02 2.83374876e-01 -1.32154405e-01 -1.74499825... | [6.904598236083984, -0.11858619004487991] |
7563776b-203b-4c68-9740-05375511c7eb | learning-multiple-gaits-of-quadruped-robot | 2112.04741 | null | https://arxiv.org/abs/2112.04741v1 | https://arxiv.org/pdf/2112.04741v1.pdf | Learning multiple gaits of quadruped robot using hierarchical reinforcement learning | There is a growing interest in learning a velocity command tracking controller of quadruped robot using reinforcement learning due to its robustness and scalability. However, a single policy, trained end-to-end, usually shows a single gait regardless of the command velocity. This could be a suboptimal solution consider... | ['Dongjun Lee', 'Bukun Son', 'Yunho Kim'] | 2021-12-09 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-2.18231142e-01 2.26623639e-01 -7.27348104e-02 2.39900887e-01
4.53954265e-02 -4.27665293e-01 1.30763352e-01 -1.26667693e-01
-3.70057076e-01 1.17493916e+00 -1.84576839e-01 -1.44499354e-02
-7.73785785e-02 -9.30541635e-01 -9.44471121e-01 -9.92001414e-01
-2.70172298e-01 3.91568661e-01 5.32479525e-01 -4.27644253... | [4.635200500488281, 1.3994934558868408] |
c7ff56b5-8414-4222-bb67-ed06af48a587 | not-end-to-end-explore-multi-stage | 2107.04810 | null | https://arxiv.org/abs/2107.04810v1 | https://arxiv.org/pdf/2107.04810v1.pdf | Not End-to-End: Explore Multi-Stage Architecture for Online Surgical Phase Recognition | Surgical phase recognition is of particular interest to computer assisted surgery systems, in which the goal is to predict what phase is occurring at each frame for a surgery video. Networks with multi-stage architecture have been widely applied in many computer vision tasks with rich patterns, where a predictor stage ... | ['Tingting Jiang', 'Fangqiu Yi'] | 2021-07-10 | null | null | null | null | ['online-surgical-phase-recognition', 'surgical-phase-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.43941414e-01 1.99024349e-01 -6.06644332e-01 -3.12688440e-01
-5.19627273e-01 -2.46281162e-01 4.35173362e-01 -5.91835119e-02
-6.20395482e-01 2.16834903e-01 3.09856713e-01 -5.51145315e-01
-5.27455807e-02 -3.80005091e-01 -5.29264987e-01 -8.13328505e-01
-1.33180976e-01 3.96896422e-01 4.04875875e-01 -1.43533140... | [14.14635944366455, -3.2945520877838135] |
7bbe9d13-e5cb-46fa-8cb4-ce68519829f0 | fusing-pre-trained-language-models-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yu_Fusing_Pre-Trained_Language_Models_With_Multimodal_Prompts_Through_Reinforcement_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_Fusing_Pre-Trained_Language_Models_With_Multimodal_Prompts_Through_Reinforcement_Learning_CVPR_2023_paper.pdf | Fusing Pre-Trained Language Models With Multimodal Prompts Through Reinforcement Learning | Language models are capable of commonsense reasoning: while domain-specific models can learn from explicit knowledge (e.g. commonsense graphs [6], ethical norms [25]), and larger models like GPT-3 manifest broad commonsense reasoning capacity. Can their knowledge be extended to multimodal inputs such as images and ... | ['Yejin Choi', 'Gunhee Kim', 'Ronan Le Bras', 'Prithviraj Ammanabrolu', 'Rowan Zellers', 'Ximing Lu', 'Jae Sung Park', 'Jack Hessel', 'Heeseung Yun', 'Jiwan Chung', 'Youngjae Yu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 6.06143475e-01 4.71006870e-01 -5.76713160e-02 -3.47549587e-01
-9.83052671e-01 -8.66484582e-01 8.96406233e-01 -2.15607509e-01
-2.37395763e-01 8.47287595e-01 5.66742718e-01 -2.37612218e-01
2.44889498e-01 -6.70412064e-01 -9.64170575e-01 -3.03881437e-01
5.01852989e-01 6.96260989e-01 -2.88764238e-01 -7.46153355... | [10.86749267578125, 1.580081582069397] |
26a85e77-e597-4b05-8bf6-5f005054a0e4 | acrobat-optimizing-auto-batching-of-dynamic | 2305.10611 | null | https://arxiv.org/abs/2305.10611v1 | https://arxiv.org/pdf/2305.10611v1.pdf | ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time | Dynamic control flow is an important technique often used to design expressive and efficient deep learning computations for applications such as text parsing, machine translation, exiting early out of deep models and so on. However, the resulting control flow divergence makes batching, an important performance optimiza... | ['Todd C. Mowry', 'Phillip B. Gibbons', 'Tianqi Chen', 'Pratik Fegade'] | 2023-05-17 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-3.35058063e-01 -4.08149093e-01 -4.18356270e-01 -5.07006228e-01
-4.61821288e-01 -7.22284555e-01 4.43171024e-01 2.61041015e-01
-4.98171747e-01 4.63805258e-01 2.10048020e-01 -1.16513574e+00
6.55651212e-01 -7.15584397e-01 -6.63772345e-01 -4.21329230e-01
-3.01497191e-01 3.78738552e-01 -1.31674707e-01 -1.58709049... | [8.46313762664795, 3.25398325920105] |
7b73fd0c-f6eb-4163-a277-fda635e93a42 | making-the-most-of-tweet-inherent-features | 1503.07405 | null | http://arxiv.org/abs/1503.07405v1 | http://arxiv.org/pdf/1503.07405v1.pdf | Making the Most of Tweet-Inherent Features for Social Spam Detection on Twitter | Social spam produces a great amount of noise on social media services such as
Twitter, which reduces the signal-to-noise ratio that both end users and data
mining applications observe. Existing techniques on social spam detection have
focused primarily on the identification of spam accounts by using extensive
historica... | ['Procter Rob', 'Liakata Maria', 'Zubiaga Arkaitz', 'Wang Bo'] | 2015-03-25 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.48052976e-01 -3.19577992e-01 -1.27417305e-02 -3.80817562e-01
-4.56903636e-01 -4.74851370e-01 1.19711065e+00 6.14011586e-01
-7.65605748e-01 4.78498399e-01 2.42184736e-02 -6.28146708e-01
-1.55854583e-01 -1.09047961e+00 1.07821807e-01 -4.50574160e-01
-2.12204278e-01 4.76511151e-01 8.14646006e-01 -6.11024797... | [7.910271644592285, 10.039311408996582] |
494c9d47-2551-4e9a-a25e-43c54af24c68 | evaluation-of-speaker-anonymization-on | 2305.01759 | null | https://arxiv.org/abs/2305.01759v1 | https://arxiv.org/pdf/2305.01759v1.pdf | Evaluation of Speaker Anonymization on Emotional Speech | Speech data carries a range of personal information, such as the speaker's identity and emotional state. These attributes can be used for malicious purposes. With the development of virtual assistants, a new generation of privacy threats has emerged. Current studies have addressed the topic of preserving speech privacy... | ['Marie Tahon', 'Anthony Larcher', 'Denis Jouvet', 'Pierre Champion', 'Hubert Nourtel'] | 2023-04-15 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [-6.76417202e-02 6.94156408e-01 2.26322562e-01 -6.23793662e-01
-7.18069196e-01 -8.80908132e-01 8.11150610e-01 1.04062051e-01
-4.49965358e-01 5.29267490e-01 7.55746901e-01 -3.86911213e-01
3.23186487e-01 -1.09300837e-01 -3.72183293e-01 -5.76998472e-01
1.86076611e-01 4.88232709e-02 -1.59551591e-01 -2.09955350... | [13.966504096984863, 5.857754230499268] |
bd8730fe-c562-4ae5-9cf6-63d62bb8ea3f | recurrent-neural-networks-with-specialized | 1706.09569 | null | http://arxiv.org/abs/1706.09569v2 | http://arxiv.org/pdf/1706.09569v2.pdf | Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition | Background. Previous state-of-the-art systems on Drug Name Recognition (DNR)
and Clinical Concept Extraction (CCE) have focused on a combination of text
"feature engineering" and conventional machine learning algorithms such as
conditional random fields and support vector machines. However, developing good
features is ... | ['Massimo Piccardi', 'Inigo Jauregi Unanue', 'Ehsan Zare Borzeshi'] | 2017-06-29 | null | null | null | null | ['clinical-concept-extraction'] | ['medical'] | [ 1.93469360e-01 6.20231628e-02 -5.63296378e-01 -3.61575782e-01
-8.46841693e-01 -1.73149839e-01 6.71626329e-01 5.97929239e-01
-9.75513220e-01 1.04221511e+00 3.51323783e-01 -6.93161309e-01
-2.21552715e-01 -6.91143453e-01 -3.40653658e-01 -5.51001906e-01
-3.54252383e-02 7.61678815e-01 -2.92401254e-01 -9.07125399... | [8.444485664367676, 8.701774597167969] |
2e08833b-9929-47f8-a1fe-8ab4faa5de20 | idiap-tiet-lt-edi-acl2022-hope-speech | null | null | https://aclanthology.org/2022.ltedi-1.49 | https://aclanthology.org/2022.ltedi-1.49.pdf | IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention Mechanism | With the increase of users on social media platforms, manipulating or provoking masses of people has become a piece of cake. This spread of hatred among people, which has become a loophole for freedom of speech, must be minimized. Hence, it is essential to have a system that automatically classifies the hatred content,... | ['Petr Motlicek', 'Muskaan Singh', 'Deepanshu Khanna'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-6.01519287e-01 2.91790932e-01 -9.41785052e-02 -5.56004196e-02
-6.87200427e-01 -5.72162211e-01 7.34144270e-01 2.59021848e-01
-4.98181909e-01 5.46938360e-01 8.45073104e-01 -3.53928864e-01
4.44725931e-01 -3.85901123e-01 -3.04212626e-02 -3.72958601e-01
2.86601990e-01 4.70660487e-03 5.28314672e-02 -5.39675593... | [8.764692306518555, 10.590055465698242] |
933bf8a7-6889-4e36-aa4e-1cd4d636721a | idiap-submission-lt-edi-acl2022-hope-speech | null | null | https://aclanthology.org/2022.ltedi-1.54 | https://aclanthology.org/2022.ltedi-1.54.pdf | IDIAP Submission@LT-EDI-ACL2022 : Hope Speech Detection for Equality, Diversity and Inclusion | Social media platforms have been provoking masses of people. The individual comments affect a prevalent way of thinking by moving away from preoccupation with discrimination, loneliness, or influence in building confidence, support, and good qualities. This paper aims to identify hope in these social media posts. Hope ... | ['Petr Motlicek', 'Muskaan Singh'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-4.87105578e-01 4.64760184e-01 -4.94783998e-01 5.66942915e-02
-5.40828824e-01 -2.55180687e-01 9.44314063e-01 6.69000864e-01
-3.12271714e-01 1.00376272e+00 9.97666836e-01 -3.97916764e-01
-1.42437488e-01 -8.24793577e-01 -1.90934435e-01 -4.80744839e-01
2.30892152e-02 2.73158215e-02 -3.16878021e-01 -5.99430978... | [8.975335121154785, 10.714468002319336] |
9f952e21-c9d7-470f-abcc-0972a9372be9 | ab-ba-analysis-a-framework-for-estimating | 2204.08474 | null | https://arxiv.org/abs/2204.08474v1 | https://arxiv.org/pdf/2204.08474v1.pdf | AB/BA analysis: A framework for estimating keyword spotting recall improvement while maintaining audio privacy | Evaluation of keyword spotting (KWS) systems that detect keywords in speech is a challenging task under realistic privacy constraints. The KWS is designed to only collect data when the keyword is present, limiting the availability of hard samples that may contain false negatives, and preventing direct estimation of mod... | ['Benjamin L. Bullough', 'Thibaud Senechal', 'Vasistakrishna Baderdinni', 'Raphael Petegrosso'] | 2022-04-18 | null | https://aclanthology.org/2022.naacl-industry.4 | https://aclanthology.org/2022.naacl-industry.4.pdf | naacl-acl-2022-7 | ['keyword-spotting'] | ['speech'] | [ 3.16233605e-01 1.80837408e-01 -1.93768442e-01 -5.10587513e-01
-1.43304574e+00 -7.11273372e-01 4.59145784e-01 3.82576913e-01
-4.75318074e-01 6.56545401e-01 -1.18610255e-01 -4.64925766e-01
2.95304712e-02 -2.77226508e-01 -8.95275295e-01 -5.33007085e-01
1.41680494e-01 2.79692978e-01 3.36835414e-01 3.41098696... | [14.241880416870117, 6.316101551055908] |
f0f453c9-f91b-4662-a8b3-2aa327d93285 | self-supervised-ppg-representation-learning | 2212.04902 | null | https://arxiv.org/abs/2212.04902v2 | https://arxiv.org/pdf/2212.04902v2.pdf | Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability | With the progress of sensor technology in wearables, the collection and analysis of PPG signals are gaining more interest. Using Machine Learning, the cardiac rhythm corresponding to PPG signals can be used to predict different tasks such as activity recognition, sleep stage detection, or more general health status. Ho... | ['Marcel J. T. Reinders', 'David M. J. Tax', 'Ramin Ghorbani'] | 2022-12-07 | null | null | null | null | ['sleep-stage-detection'] | ['medical'] | [ 3.61517400e-01 2.59002149e-01 -5.97875535e-01 -6.11246049e-01
-8.12718987e-01 -3.97927731e-01 8.42888430e-02 7.45996684e-02
3.95839773e-02 7.50839591e-01 4.85257149e-01 1.19030029e-02
-2.86195632e-02 -4.05922681e-01 -4.79048789e-01 -9.21813250e-01
-1.62967414e-01 5.35011617e-03 -3.97656441e-01 3.48752737... | [13.465763092041016, 3.435053586959839] |
2ceed2e4-db28-4e57-b47b-9eedb269f593 | topic-sensitive-neural-headline-generation | 1608.05777 | null | http://arxiv.org/abs/1608.05777v1 | http://arxiv.org/pdf/1608.05777v1.pdf | Topic Sensitive Neural Headline Generation | Neural models have recently been used in text summarization including
headline generation. The model can be trained using a set of document-headline
pairs. However, the model does not explicitly consider topical similarities and
differences of documents. We suggest to categorizing documents into various
topics so that ... | ['ZiYun Wang', 'Ayana', 'Lei Xu', 'Maosong Sun', 'Zhiyuan Liu'] | 2016-08-20 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 2.31835648e-01 2.85410166e-01 -5.46339035e-01 -4.22623336e-01
-1.23366094e+00 -4.74346936e-01 9.07226145e-01 5.91093004e-01
-4.82702814e-02 9.97254074e-01 1.37801540e+00 2.22863480e-01
6.61531463e-02 -7.94225812e-01 -5.06652176e-01 -4.42684799e-01
5.02696000e-02 5.40629625e-01 3.32218617e-01 -3.17880183... | [12.481456756591797, 9.491965293884277] |
6372c39c-ca8b-4dc4-8f3b-b90eec206e49 | local-feature-descriptor-learning-with | 1706.05358 | null | http://arxiv.org/abs/1706.05358v1 | http://arxiv.org/pdf/1706.05358v1.pdf | Local Feature Descriptor Learning with Adaptive Siamese Network | Although the recent progress in the deep neural network has led to the
development of learnable local feature descriptors, there is no explicit answer
for estimation of the necessary size of a neural network. Specifically, the
local feature is represented in a low dimensional space, so the neural network
should have mo... | ['Kwang-Ting', 'Yan-Ying Chen', 'Cheng', 'Chong Huang', 'Qiong Liu'] | 2017-06-16 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 1.58601806e-01 -4.37145770e-01 -3.73849154e-01 -4.75877553e-01
-7.18433678e-01 -1.22163162e-01 2.11992458e-01 4.81235087e-02
-4.90451634e-01 6.15514278e-01 -1.48221359e-01 2.39500701e-01
-7.13540494e-01 -9.86002028e-01 -7.12080300e-01 -8.83131027e-01
-2.51626402e-01 2.57032394e-01 4.72529888e-01 4.05566767... | [10.18256664276123, 0.013409408740699291] |
27c0c95f-90fe-4f94-9343-f4a4152f962f | hue-modification-localization-by-pair | 1903.01735 | null | http://arxiv.org/abs/1903.01735v1 | http://arxiv.org/pdf/1903.01735v1.pdf | Hue Modification Localization By Pair Matching | Hue modification is the adjustment of hue property on color images.
Conducting hue modification on an image is trivial, and it can be abused to
falsify opinions of viewers. Since shapes, edges or textural information
remains unchanged after hue modification, this type of manipulation is
relatively hard to be detected a... | ['Giulia Boato', 'Quoc-Tin Phan', 'Michele Vascotto'] | 2019-03-05 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 8.23996186e-01 1.32844709e-02 2.30939299e-01 7.83679634e-02
-2.72057876e-02 -8.02430868e-01 3.69439602e-01 3.22362095e-01
-3.53902757e-01 5.43663383e-01 -3.76449198e-01 -3.51769567e-01
9.27729160e-02 -1.14576602e+00 -8.36418152e-01 -9.72811282e-01
-2.00938240e-01 -3.17421794e-01 2.98726499e-01 -1.46437734... | [11.17641544342041, -1.417871117591858] |
fb6f5416-10d3-4598-ab49-153eefd7926c | interpretable-propaganda-detection-in-news | 2108.12802 | null | https://arxiv.org/abs/2108.12802v1 | https://arxiv.org/pdf/2108.12802v1.pdf | Interpretable Propaganda Detection in News Articles | Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis. To counter thus, a number of approaches have been designed aiming to achieve a healthier and safer online news and media consumption. Automatic systems are able to support humans in detecting such content; ye... | ['Preslav Nakov', 'James Glass', 'Mitra Mohtarami', 'Giovanni Da San Martino', 'Seunghak Yu'] | 2021-08-29 | null | https://aclanthology.org/2021.ranlp-1.179 | https://aclanthology.org/2021.ranlp-1.179.pdf | ranlp-2021-9 | ['propaganda-detection'] | ['natural-language-processing'] | [ 1.46539941e-01 3.17946881e-01 -1.79901302e-01 -3.87535930e-01
-5.08725584e-01 -7.11807847e-01 1.18825471e+00 7.23247588e-01
-3.34830225e-01 5.72387636e-01 4.28533584e-01 -4.88129079e-01
2.31031805e-01 -6.34020030e-01 -4.54065055e-01 -1.25797808e-01
1.45767167e-01 2.15382770e-01 1.16686195e-01 -4.86120939... | [8.219870567321777, 10.127910614013672] |
340d4bcb-d1f2-4a26-853e-0861263e7070 | improving-unsupervised-image-clustering-with | 2012.11150 | null | https://arxiv.org/abs/2012.11150v2 | https://arxiv.org/pdf/2012.11150v2.pdf | Improving Unsupervised Image Clustering With Robust Learning | Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC's novelty is at utilizi... | ['Meeyoung Cha', 'Seunghoon Hong', 'Sungkyu Park', 'Danu Kim', 'Sundong Kim', 'Sungwon Han', 'Sungwon Park'] | 2020-12-21 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Park_Improving_Unsupervised_Image_Clustering_With_Robust_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Park_Improving_Unsupervised_Image_Clustering_With_Robust_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['image-clustering', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision'] | [ 1.08776495e-01 2.84390569e-01 -3.55295181e-01 -7.19017982e-01
-6.15585625e-01 -2.83327609e-01 4.33636695e-01 -1.83643296e-01
-2.89198905e-01 7.48196602e-01 -1.85311794e-01 -1.10957669e-02
-2.59780943e-01 -5.10411561e-01 -6.13427997e-01 -9.68486845e-01
1.67913467e-01 2.91278422e-01 3.92495215e-01 3.03432852... | [14.90660572052002, 1.2018327713012695] |
b83921e3-2010-4147-86a2-2a9b0db9b22d | stylealign-analysis-and-applications-of-1 | 2110.11323 | null | https://arxiv.org/abs/2110.11323v2 | https://arxiv.org/pdf/2110.11323v2.pdf | StyleAlign: Analysis and Applications of Aligned StyleGAN Models | In this paper, we perform an in-depth study of the properties and applications of aligned generative models. We refer to two models as aligned if they share the same architecture, and one of them (the child) is obtained from the other (the parent) via fine-tuning to another domain, a common practice in transfer learnin... | ['Dani Lischinski', 'Eli Shechtman', 'Yotam Nitzan', 'Zongze Wu'] | 2021-10-21 | stylealign-analysis-and-applications-of | https://openreview.net/forum?id=Qg2vi4ZbHM9 | https://openreview.net/pdf?id=Qg2vi4ZbHM9 | iclr-2022-4 | ['image-morphing'] | ['computer-vision'] | [ 4.79190171e-01 3.86797965e-01 -1.16286665e-01 -3.45751256e-01
-6.69904113e-01 -7.47295320e-01 8.24065745e-01 -4.53992903e-01
-1.57242671e-01 6.82120085e-01 1.70107737e-01 -6.16705827e-02
1.80897772e-01 -7.35844970e-01 -9.87449765e-01 -7.50429690e-01
3.46410066e-01 6.36046410e-01 -6.84068948e-02 -3.66378307... | [11.579248428344727, -0.19636306166648865] |
1298ed86-7164-450a-a021-c04bfa759d5d | adcraft-an-advanced-reinforcement-learning | 2306.11971 | null | https://arxiv.org/abs/2306.11971v2 | https://arxiv.org/pdf/2306.11971v2.pdf | AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing Optimization | We introduce AdCraft, a novel benchmark environment for the Reinforcement Learning (RL) community distinguished by its stochastic and non-stationary properties. The environment simulates bidding and budgeting dynamics within Search Engine Marketing (SEM), a digital marketing technique utilizing paid advertising to enha... | ['Jonah White', 'Jeffrey Roach', 'Owen Levin', 'Maziar Gomrokchi'] | 2023-06-21 | null | null | null | null | ['marketing', 'management'] | ['miscellaneous', 'miscellaneous'] | [-1.75404489e-01 -1.66284770e-01 -5.96169889e-01 -1.33558899e-01
-9.31874514e-01 -8.07197452e-01 6.62289202e-01 1.71567231e-01
-5.75136006e-01 7.13918686e-01 3.61725599e-01 -5.93220055e-01
-5.60128570e-01 -6.24072134e-01 -6.01786852e-01 -4.06566232e-01
-5.94709158e-01 6.44051254e-01 -2.06835002e-01 -4.70849395... | [4.224656581878662, 2.6918954849243164] |
c5ba862e-0eaa-4802-8037-db889ad27bd9 | interpretable-computer-vision-models-through | 2307.02500 | null | https://arxiv.org/abs/2307.02500v1 | https://arxiv.org/pdf/2307.02500v1.pdf | Interpretable Computer Vision Models through Adversarial Training: Unveiling the Robustness-Interpretability Connection | With the perpetual increase of complexity of the state-of-the-art deep neural networks, it becomes a more and more challenging task to maintain their interpretability. Our work aims to evaluate the effects of adversarial training utilized to produce robust models - less vulnerable to adversarial attacks. It has been sh... | ['Delyan Boychev'] | 2023-07-04 | null | null | null | null | ['image-generation', 'feature-importance'] | ['computer-vision', 'methodology'] | [ 2.02681005e-01 6.00889981e-01 3.92010272e-01 -2.51437426e-01
1.90012738e-01 -7.78876185e-01 9.86598015e-01 -1.92956969e-01
-3.57047945e-01 8.31413686e-01 -3.90121453e-02 -2.62797862e-01
-1.74940869e-01 -8.29775393e-01 -8.08558166e-01 -7.25683749e-01
-3.20362598e-01 8.53056684e-02 8.06219727e-02 -5.53077102... | [5.685698509216309, 7.843221664428711] |
b4b83611-08dd-4851-93a8-e7ba30e3a765 | human-pose-estimation-in-monocular | 2304.08186 | null | https://arxiv.org/abs/2304.08186v1 | https://arxiv.org/pdf/2304.08186v1.pdf | Human Pose Estimation in Monocular Omnidirectional Top-View Images | Human pose estimation (HPE) with convolutional neural networks (CNNs) for indoor monitoring is one of the major challenges in computer vision. In contrast to HPE in perspective views, an indoor monitoring system can consist of an omnidirectional camera with a field of view of 180{\deg} to detect the pose of a person wi... | ['Gangolf Hirtz', 'Dipankar Nandi', 'Yukti Adya', 'Roman Seidel', 'Tobias Scheck', 'Jingrui Yu'] | 2023-04-17 | null | null | null | null | ['keypoint-detection', '3d-human-pose-estimation', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.65924777e-02 -1.07586280e-01 5.86151242e-01 -2.93996006e-01
-3.10558259e-01 -3.55910540e-01 4.12446290e-01 -2.84573197e-01
-8.57024670e-01 4.64943677e-01 5.24110459e-02 -4.31850180e-02
1.74499273e-01 -8.49658787e-01 -1.03451431e+00 -4.81847554e-01
-5.94934635e-03 6.46104336e-01 -5.99840358e-02 -1.05091602... | [7.2758402824401855, -0.9189152121543884] |
99ecc765-debb-44ee-ad9e-ec70fc96d12e | cgam-click-guided-attention-module-for | 2307.01015 | null | https://arxiv.org/abs/2307.01015v1 | https://arxiv.org/pdf/2307.01015v1.pdf | CGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement | Tumor region segmentation is an essential task for the quantitative analysis of digital pathology. Recently presented deep neural networks have shown state-of-the-art performance in various image-segmentation tasks. However, because of the unclear boundary between the cancerous and normal regions in pathology images, d... | ['Won-Ki Jeong', 'Seonghui Min'] | 2023-07-03 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 0.25994125 0.15417607 0.02190206 -0.4836698 -0.76641285 -0.18948425
0.14157687 0.32354987 -0.8060035 0.48931256 -0.14835371 -0.51327425
0.02662818 -0.5895083 -0.48552567 -0.6866268 0.35371348 0.2520121
0.61562425 0.08266272 0.3057379 0.2692862 -1.0754656 0.27922475
1.1848422 1.0944993 0.5... | [14.611607551574707, -2.3582446575164795] |
7755366d-9d18-48a7-bb3f-49007872ad08 | time-space-tradeoff-in-deep-learning-models | 1901.10503 | null | http://arxiv.org/abs/1901.10503v1 | http://arxiv.org/pdf/1901.10503v1.pdf | Time-Space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series | In this article, we investigate several structured deep learning models for
crop type classification on multi-spectral time series. In particular, our aim
is to assess the respective importance of spatial and temporal structures in
such data. With this objective, we consider several designs of convolutional,
recurrent,... | ['Vivien Sainte Fare Garnot', 'Sebastien Giordano', 'Nesrine Chehata', 'Loic Landrieu'] | 2019-01-29 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 1.47494022e-02 -3.81941140e-01 -1.37460589e-01 -2.82896668e-01
-2.76866198e-01 -8.52751255e-01 5.37341952e-01 3.26263130e-01
-3.58386576e-01 4.08816099e-01 8.90249573e-03 -6.53629839e-01
-5.19988537e-01 -9.83959734e-01 -5.25902510e-01 -7.73560703e-01
-6.06643438e-01 -2.08607465e-01 -2.05756381e-01 -7.16815412... | [9.451417922973633, -1.574607491493225] |
d5d85cd0-f86a-4db0-9df0-6473d4dedbb2 | automated-problem-setting-selection-in-multi | 2104.09967 | null | https://arxiv.org/abs/2104.09967v2 | https://arxiv.org/pdf/2104.09967v2.pdf | Multi-target prediction for dummies using two-branch neural networks | Multi-target prediction (MTP) serves as an umbrella term for machine learning tasks that concern the simultaneous prediction of multiple target variables. Classical instantiations are multi-label classification, multivariate regression, multi-task learning, dyadic prediction, zero-shot learning, network inference, and ... | ['Willem Waegeman', 'Bernard De Baets', 'Dimitrios Iliadis'] | 2021-04-19 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 4.78003860e-01 3.54080163e-02 -5.44300199e-01 -5.67833960e-01
-9.87700701e-01 -2.57925212e-01 4.79018688e-01 3.06022227e-01
-1.13389678e-01 8.73288512e-01 -1.87929705e-01 -3.16935517e-02
-5.68787932e-01 -6.10964060e-01 -3.15968156e-01 -7.91941643e-01
1.37053905e-02 8.44930530e-01 5.82936294e-02 -2.41832942... | [9.153657913208008, 4.204662322998047] |
7e10b990-a63b-4af7-94b8-ea6215406530 | exploring-social-influence-for-recommendation | 1109.0758 | null | https://arxiv.org/abs/1109.0758v1 | https://arxiv.org/pdf/1109.0758v1.pdf | Exploring Social Influence for Recommendation - A Probabilistic Generative Model Approach | In this paper, we propose a probabilistic generative model, called unified model, which naturally unifies the ideas of social influence, collaborative filtering and content-based methods for item recommendation. To address the issue of hidden social influence, we devise new algorithms to learn the model parameters of o... | ['Wang-Chien Lee', 'Xingjie Liu', 'Mao Ye'] | 2011-09-04 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-3.32013845e-01 1.10183179e-01 -2.03046381e-01 -4.02268857e-01
-3.21659148e-01 -1.97743341e-01 8.47738922e-01 -2.97729492e-01
-3.01881015e-01 8.02436650e-01 6.11277103e-01 -2.49414593e-01
-5.47629356e-01 -1.39066601e+00 -7.97592461e-01 -6.93208933e-01
-1.14784501e-02 6.37366354e-01 2.91344404e-01 -2.37428471... | [9.94609546661377, 5.656081676483154] |
be372652-8029-4045-b38f-5dd31ec8e87d | who-you-play-affects-how-you-play-predicting | 2303.16741 | null | https://arxiv.org/abs/2303.16741v1 | https://arxiv.org/pdf/2303.16741v1.pdf | Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution | This study presents a novel deep learning method, called GATv2-GCN, for predicting player performance in sports. To construct a dynamic player interaction graph, we leverage player statistics and their interactions during gameplay. We use a graph attention network to capture the attention that each player pays to each ... | ['Vikram Krishnamurthy', 'Rui Luo'] | 2023-03-29 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-4.47125793e-01 -2.42819384e-01 -4.78458941e-01 -8.41358379e-02
-1.99968457e-01 -3.59975487e-01 1.46924421e-01 2.10799128e-01
-2.99941123e-01 3.27017605e-01 4.63863254e-01 -2.47960538e-01
-3.76838386e-01 -1.28413999e+00 -5.40084660e-01 -2.49757722e-01
-5.11231899e-01 5.16791701e-01 3.55396807e-01 -6.36457980... | [6.698396682739258, 0.3401778042316437] |
4771fce6-7e65-417e-a707-3f33b710f968 | time-conditioned-generative-modeling-of | 2301.08951 | null | https://arxiv.org/abs/2301.08951v3 | https://arxiv.org/pdf/2301.08951v3.pdf | Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and Prediction | When perceiving the world from multiple viewpoints, humans have the ability to reason about the complete objects in a compositional manner even when an object is completely occluded from certain viewpoints. Meanwhile, humans are able to imagine novel views after observing multiple viewpoints. Recent remarkable advances... | ['Bin Li', 'Chengmin Gao'] | 2023-01-21 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 1.93070754e-01 2.14874417e-01 -3.68552841e-02 -5.55670023e-01
-4.61961061e-01 -6.40912354e-01 7.02491105e-01 -6.15047872e-01
3.46765667e-01 4.01210904e-01 4.71353173e-01 3.14213663e-01
7.82555193e-02 -4.98793721e-01 -9.05491292e-01 -7.82400191e-01
5.03364325e-01 8.40812027e-01 -2.36463454e-02 1.45467103... | [8.825374603271484, -2.9253485202789307] |
207093f2-e452-45df-918b-bc6b1f93aa0c | detecting-object-states-vs-detecting-objects | 2112.08281 | null | https://arxiv.org/abs/2112.08281v2 | https://arxiv.org/pdf/2112.08281v2.pdf | Detecting Object States vs Detecting Objects: A New Dataset and a Quantitative Experimental Study | The detection of object states in images (State Detection - SD) is a problem of both theoretical and practical importance and it is tightly interwoven with other important computer vision problems, such as action recognition and affordance detection. It is also highly relevant to any entity that needs to reason and act... | ['Theodore Patkos', 'Dimitris Plexousakis', 'Antonis Argyros', 'Filippos Gouidis'] | 2021-12-15 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [ 3.44066858e-01 1.39778838e-01 -1.88443437e-01 -2.38122255e-01
-1.91927224e-01 -5.53428531e-01 9.53631282e-01 7.37624019e-02
-6.00215375e-01 4.48933691e-01 3.72940190e-02 -1.30119264e-01
-1.07172348e-01 -2.13390812e-01 -5.37615359e-01 -6.17809892e-01
-3.55887979e-01 5.06911993e-01 9.53636944e-01 -7.35626891... | [8.24169921875, 0.09785018861293793] |
ec761a36-d054-4c68-9acc-34c4ee29bd6f | form-follows-function-a-different-approach-to | 2306.03337 | null | https://arxiv.org/abs/2306.03337v1 | https://arxiv.org/pdf/2306.03337v1.pdf | Form Follows Function: A Different Approach to Neuron Connectivity | It may be possible to discover much of the organization of synaptic connections in nervous systems by designing simple logic circuits that can perform a single, biologically advantageous function. This method has led to neuronal networks that can generate neural correlates of phenomena central to color vision, olfactio... | ['Lane Yoder'] | 2023-06-06 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 1.15483120e-01 1.98817015e-01 8.39115456e-02 6.81134313e-02
9.67594683e-01 -7.65988231e-01 8.07863533e-01 -2.32800528e-01
-3.70452732e-01 9.98420715e-01 -1.04045413e-01 -3.91793281e-01
-2.38047391e-01 -9.53205526e-01 -6.69870555e-01 -8.78669977e-01
-4.43751812e-01 -4.98779900e-02 5.37258148e-01 -7.13653803... | [8.033976554870605, 2.971726894378662] |
11c51d5f-c7dd-4938-a883-f4caca944aec | sample-based-distributional-policy-gradient | 2001.02652 | null | https://arxiv.org/abs/2001.02652v1 | https://arxiv.org/pdf/2001.02652v1.pdf | Sample-based Distributional Policy Gradient | Distributional reinforcement learning (DRL) is a recent reinforcement learning framework whose success has been supported by various empirical studies. It relies on the key idea of replacing the expected return with the return distribution, which captures the intrinsic randomness of the long term rewards. Most of the e... | ['Keuntaek Lee', 'Rahul Singh', 'Yongxin Chen'] | 2020-01-08 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.69686669e-01 4.02939767e-02 -5.13636053e-01 -4.94756289e-02
-8.05455983e-01 -4.41188216e-01 8.31283629e-01 3.43547873e-02
-8.78297210e-01 1.33797204e+00 4.17018294e-01 -2.80540973e-01
-5.53964913e-01 -8.48141253e-01 -9.22708988e-01 -9.20684636e-01
-4.46262449e-01 5.66395104e-01 5.38704805e-02 -2.62007147... | [4.035818576812744, 2.3749656677246094] |
59359406-dd05-46e1-bf08-53a650f2ec9f | sp2-a-second-order-stochastic-polyak-method | 2207.08171 | null | https://arxiv.org/abs/2207.08171v1 | https://arxiv.org/pdf/2207.08171v1.pdf | SP2: A Second Order Stochastic Polyak Method | Recently the "SP" (Stochastic Polyak step size) method has emerged as a competitive adaptive method for setting the step sizes of SGD. SP can be interpreted as a method specialized to interpolated models, since it solves the interpolation equations. SP solves these equation by using local linearizations of the model. W... | ['Robert M. Gower', 'Deanna Needell', 'Martin Takáč', 'William J. Swartworth', 'Shuang Li'] | 2022-07-17 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [-1.35245174e-01 1.90198049e-01 -5.14852218e-02 -1.52322948e-01
-1.13595796e+00 -4.69770312e-01 2.13090792e-01 -8.93272832e-02
-3.11215132e-01 8.30782652e-01 2.50369869e-02 -2.62402594e-01
-2.80534625e-01 -3.13583404e-01 -8.85147810e-01 -9.10797060e-01
-4.17630188e-02 5.99858582e-01 -8.88640657e-02 -4.64577258... | [6.929137706756592, 4.377682209014893] |
49a66483-4856-4d61-b5c6-e8fd496c19e7 | opinion-tree-parsing-for-aspect-based | 2306.08925 | null | https://arxiv.org/abs/2306.08925v1 | https://arxiv.org/pdf/2306.08925v1.pdf | Opinion Tree Parsing for Aspect-based Sentiment Analysis | Extracting sentiment elements using pre-trained generative models has recently led to large improvements in aspect-based sentiment analysis benchmarks. However, these models always need large-scale computing resources, and they also ignore explicit modeling of structure between sentiment elements. To address these chal... | ['Guodong Zhou', 'Yue Zhang', 'Zhongqing Wang', 'Xiaotong Jiang', 'Xiaoyi Bao'] | 2023-06-15 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 7.67809376e-02 1.99059680e-01 -1.96126118e-01 -6.79442227e-01
-6.61226451e-01 -7.80802727e-01 3.71449977e-01 1.49293765e-01
1.71849560e-02 2.61711150e-01 4.64933336e-01 -7.19427407e-01
4.07252282e-01 -1.22023284e+00 -2.91138858e-01 -5.51840365e-01
2.67485678e-01 3.86762828e-01 -1.02047808e-01 -5.12227476... | [11.438981056213379, 6.7012410163879395] |
e66423d9-5932-489b-8c72-a3d821b7573b | table-based-fact-verification-with-self-2 | null | null | https://aclanthology.org/2022.coling-1.120 | https://aclanthology.org/2022.coling-1.120.pdf | Table-based Fact Verification with Self-labeled Keypoint Alignment | Table-based fact verification aims to verify whether a statement sentence is trusted or fake. Most existing methods rely on graph feature or data augmentation but fail to investigate evidence correlation between the statement and table effectively. In this paper, we propose a self-Labeled Keypoint Alignment model, name... | ['Peng Yang', 'Guangzhen Zhao'] | null | null | null | null | coling-2022-10 | ['table-based-fact-verification'] | ['natural-language-processing'] | [ 2.84198951e-02 1.88507155e-01 -6.71104729e-01 -5.09083152e-01
-1.02025723e+00 -7.09518135e-01 7.56090581e-01 4.72451419e-01
1.20998964e-01 5.25791824e-01 5.58124959e-01 -4.04039562e-01
2.30542064e-01 -6.58890426e-01 -8.42947781e-01 -5.57079852e-01
4.07870471e-01 4.31150258e-01 -2.05671713e-02 -4.06063855... | [9.105487823486328, 7.915650844573975] |
e22d1fa9-1e47-4b7f-a48a-d6689d293783 | a-new-dataset-and-boundary-attention-semantic | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/6832 | https://ojs.aaai.org/index.php/AAAI/article/view/6832 | A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing | Face parsing has recently attracted increasing interest due to its numerous application potentials, such as facial make up and facial image generation. In this paper, we make contributions on face parsing task from two aspects. First, we develop a high-efficiency framework for pixel-level face parsing annotating and co... | ['Tao Mei', 'Xiaobo Wang', 'Yue Si', 'Hao Shen', 'Hailin Shi', 'Yinglu Liu'] | 2020-04-03 | null | null | null | proceedings-of-the-aaai-conference-on-1 | ['face-parsing'] | ['computer-vision'] | [ 4.09910947e-01 3.60095143e-01 -2.60871828e-01 -1.04104114e+00
-1.12042427e+00 -4.22560275e-01 2.67777920e-01 -6.64167583e-01
-1.47710860e-01 3.93062413e-01 -1.01777904e-01 4.76384833e-02
4.12703693e-01 -5.77914059e-01 -7.75429428e-01 -5.82907677e-01
1.18507698e-01 4.31096792e-01 -5.54741211e-02 1.04879007... | [13.486132621765137, 0.6509491801261902] |
bef7c26e-7d80-4069-b4d1-e50a72673ce1 | synthaspoof-developing-face-presentation | 2303.02660 | null | https://arxiv.org/abs/2303.02660v2 | https://arxiv.org/pdf/2303.02660v2.pdf | SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data | Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric dat... | ['Naser Damer', 'Marco Huber', 'Meiling Fang'] | 2023-03-05 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 1.53977633e-01 -7.24695250e-02 5.36997020e-02 -2.65161753e-01
-7.32604325e-01 -7.54064143e-01 6.30784273e-01 -3.50133598e-01
-4.00290675e-02 6.62570834e-01 -1.59951463e-01 -9.43655595e-02
-1.88918963e-01 -7.62418628e-01 -4.77480859e-01 -6.31410837e-01
-3.86295408e-01 1.82001919e-01 -3.34557593e-02 -3.37262481... | [13.039863586425781, 1.0860024690628052] |
017eb0be-5fc8-44c7-8000-da3849816cc5 | deep-word-embeddings-for-visual-speech | 1710.11201 | null | http://arxiv.org/abs/1710.11201v1 | http://arxiv.org/pdf/1710.11201v1.pdf | Deep word embeddings for visual speech recognition | In this paper we present a deep learning architecture for extracting word
embeddings for visual speech recognition. The embeddings summarize the
information of the mouth region that is relevant to the problem of word
recognition, while suppressing other types of variability such as speaker, pose
and illumination. The s... | ['Georgios Tzimiropoulos', 'Themos Stafylakis'] | 2017-10-30 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.43284963e-02 1.47000933e-02 -5.38542807e-01 -1.15342595e-01
-7.85231531e-01 -1.82232276e-01 5.06224394e-01 -4.25126463e-01
-4.97857183e-01 2.42838323e-01 2.81586230e-01 -3.79533738e-01
4.30614293e-01 -1.08419038e-01 -5.32648861e-01 -9.60291207e-01
1.06051952e-01 -1.13910072e-01 1.18555818e-02 2.12459385... | [14.276989936828613, 4.967987537384033] |
a42368c7-8201-4aea-964a-47c7e333bb9f | ebms-vs-cl-exploring-self-supervised-visual | 2206.14355 | null | https://arxiv.org/abs/2206.14355v1 | https://arxiv.org/pdf/2206.14355v1.pdf | EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering | The availability of clean and diverse labeled data is a major roadblock for training models on complex tasks such as visual question answering (VQA). The extensive work on large vision-and-language models has shown that self-supervised learning is effective for pretraining multimodal interactions. In this technical rep... | ['Damien Teney', 'Anton Van Den Hengel', 'Anthony Dick', 'Ehsan Abbasnejad', 'Violetta Shevchenko'] | 2022-06-29 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [-1.87921468e-02 2.41521806e-01 -2.60972053e-01 -5.94242573e-01
-1.08013666e+00 -7.00683475e-01 7.19160557e-01 1.73071817e-01
-5.69736838e-01 6.41893983e-01 2.17273772e-01 -2.47343928e-01
2.71162480e-01 -4.17963654e-01 -9.92368758e-01 -5.88805556e-01
1.77209124e-01 6.31912529e-01 1.74660549e-01 -3.85988206... | [10.840490341186523, 1.6965606212615967] |
bad46f28-ba64-41fd-b7f1-07dd6174654e | low-field-magnetic-resonance-image | 2304.13385 | null | https://arxiv.org/abs/2304.13385v1 | https://arxiv.org/pdf/2304.13385v1.pdf | Low-field magnetic resonance image enhancement via stochastic image quality transfer | Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher income countries e.g. for small child patients with obesity, claustrophobia, implants, or tattoos. However, low-field MR images commonly ha... | ['Daniel C. Alexander', 'Delmiro Fernandez-Reyes', 'Judith Helen Cross', 'Ikeoluwa Lagunju', 'David W. Carmichael', 'Biobele J. Brown', 'Lisa Ronan', 'Stefano B. Blumberg', 'Ryutaro Tanno', 'Godwin Ogbole', "Felice D'Arco", 'Matteo Figini', 'Hongxiang Lin'] | 2023-04-26 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 5.60815334e-01 9.93957892e-02 2.42097899e-01 -1.13267876e-01
-5.98435879e-01 -2.33670831e-01 4.15922612e-01 3.84631567e-02
-6.79874301e-01 7.89418995e-01 1.61829919e-01 -3.67942542e-01
-7.16798723e-01 -5.35655856e-01 -6.60580218e-01 -7.72021234e-01
-6.41951323e-01 7.74256051e-01 4.72198009e-01 1.06734894... | [13.58226490020752, -2.407409429550171] |
987ce365-e959-4270-bfd8-ae91ac75a12b | a-copy-augmented-generative-model-for-open | null | null | https://openreview.net/forum?id=9RHCjj-vhq3 | https://openreview.net/pdf?id=9RHCjj-vhq3 | A Copy-Augmented Generative Model for Open-Domain Question Answering | Open-domain question answering is a challenging task with a wide variety of practical applications. Existing modern approaches mostly follow a standard two-stage paradigm: retriever then reader. In this article, we focus on improving the effectiveness of the reader module and propose a novel copy-augmented generative a... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['triviaqa'] | ['miscellaneous'] | [ 5.32164350e-02 1.37070954e-01 1.79480135e-01 -1.85310394e-01
-1.20838606e+00 -6.17203951e-01 7.96926379e-01 -3.95156741e-01
-3.45869631e-01 8.71652901e-01 4.91271377e-01 -3.37803781e-01
-3.82683218e-01 -8.78051519e-01 -6.13520324e-01 -5.02472818e-01
7.21850097e-01 6.77042663e-01 5.41653991e-01 -6.14088595... | [11.275111198425293, 8.035415649414062] |
d44897ea-4f65-4f5c-849e-9354576b8ca8 | fcsr-gan-joint-face-completion-and-super | 1911.01045 | null | https://arxiv.org/abs/1911.01045v1 | https://arxiv.org/pdf/1911.01045v1.pdf | FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning | Combined variations containing low-resolution and occlusion often present in face images in the wild, e.g., under the scenario of video surveillance. While most of the existing face image recovery approaches can handle only one type of variation per model, in this work, we propose a deep generative adversarial network ... | ['Shiguang Shan', 'Xilin Chen', 'Jiancheng Cai', 'Hu Han'] | 2019-11-04 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 3.00097585e-01 -5.22923507e-02 2.24671587e-01 -5.29577732e-01
-1.08398652e+00 -1.54092178e-01 3.69284302e-01 -9.54173267e-01
-9.90165994e-02 7.60766685e-01 -1.46769598e-01 2.54449725e-01
9.62204859e-02 -9.16191339e-01 -1.00013697e+00 -9.03794825e-01
2.56432354e-01 4.11150575e-01 -2.99968004e-01 -2.85069376... | [12.824518203735352, 0.011312554590404034] |
c3450b62-d439-42d7-a392-ba8348f7111a | a-partition-filter-network-for-joint-entity | 2108.12202 | null | https://arxiv.org/abs/2108.12202v8 | https://arxiv.org/pdf/2108.12202v8.pdf | A Partition Filter Network for Joint Entity and Relation Extraction | In joint entity and relation extraction, existing work either sequentially encode task-specific features, leading to an imbalance in inter-task feature interaction where features extracted later have no direct contact with those that come first. Or they encode entity features and relation features in a parallel manner,... | ['Zhongyu Wei', 'Qi Zhang', 'Jinlan Fu', 'Chong Zhang', 'Zhiheng Yan'] | 2021-08-27 | null | https://aclanthology.org/2021.emnlp-main.17 | https://aclanthology.org/2021.emnlp-main.17.pdf | emnlp-2021-11 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 2.43431494e-01 2.17656076e-01 -3.28740031e-01 -5.80247700e-01
-6.41380906e-01 -5.95533192e-01 5.16894817e-01 2.47917458e-01
-5.20402908e-01 8.98551583e-01 3.07328850e-01 -3.29718441e-02
-1.86831176e-01 -7.62556672e-01 -7.94401467e-01 -4.59764779e-01
-2.11299658e-01 3.79772455e-01 3.10861826e-01 -1.02156125... | [9.264056205749512, 8.726468086242676] |
71fd6398-d3bd-4497-a1f0-fc0045238b22 | heavy-tailed-features-and-empirical-analysis | 1210.7215 | null | http://arxiv.org/abs/1210.7215v2 | http://arxiv.org/pdf/1210.7215v2.pdf | Heavy-Tailed Features and Empirical Analysis of the Limit Order Book Volume Profiles in Futures Markets | This paper poses a few fundamental questions regarding the attributes of the
volume profile of a Limit Order Books stochastic structure by taking into
consideration aspects of intraday and interday statistical features, the impact
of different exchange features and the impact of market participants in
different asset s... | [] | 2015-04-22 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-6.19043469e-01 -4.07195091e-01 4.68944833e-02 -3.57734740e-01
-5.42389274e-01 -1.21143794e+00 1.01458466e+00 5.06584942e-01
-2.70886242e-01 6.67167068e-01 3.33619535e-01 -7.35820949e-01
-7.82022953e-01 -9.85545278e-01 -4.20007378e-01 -5.26089549e-01
-5.28263986e-01 8.79612148e-01 5.03710270e-01 -4.48282063... | [4.737300395965576, 4.0869460105896] |
9c76f119-a604-4b5a-adbd-6cb95112d4c8 | social-media-analysis-for-organizations-us | 1803.09133 | null | http://arxiv.org/abs/1803.09133v1 | http://arxiv.org/pdf/1803.09133v1.pdf | Social Media Analysis For Organizations: Us Northeastern Public And State Libraries Case Study | Social networking sites such as Twitter have provided a great opportunity for
organizations such as public libraries to disseminate information for public
relations purposes. However, there is a need to analyze vast amounts of social
media data. This study presents a computational approach to explore the content
of twe... | ['Matthew Collins', 'Amir Karami'] | 2018-03-24 | null | null | null | null | ['public-relations'] | ['miscellaneous'] | [-4.87726331e-01 2.17327908e-01 -4.06435311e-01 -2.18786672e-02
-9.00171220e-01 -6.38248026e-01 9.16317165e-01 1.02668345e+00
-4.77088541e-01 8.72151554e-01 8.96891415e-01 -4.11816597e-01
2.89843440e-01 -1.10755241e+00 -3.14868763e-02 -3.09422910e-01
-6.37151077e-02 3.80827896e-02 5.96827976e-02 -5.10353684... | [10.531303405761719, 7.093046188354492] |
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