paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2b4fe9f6-0040-4f28-b7ab-aa7c39d413ec | u-det-a-modified-u-net-architecture-with | 2003.09293 | null | https://arxiv.org/abs/2003.09293v1 | https://arxiv.org/pdf/2003.09293v1.pdf | U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation | Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodule in the CT image pose a challenging problem to the robust segmentation of the lung nodules. This article ... | ['Chandra Sekhara Rao Annavarapu', 'Samson Anosh Babu P', 'Nikhil Varma Keetha'] | 2020-03-20 | null | null | null | null | ['lung-nodule-segmentation'] | ['medical'] | [-1.13632372e-02 2.27669358e-01 -4.26320165e-01 -2.61858076e-01
-7.99456358e-01 -3.84188592e-01 3.03973526e-01 -2.00744763e-01
-4.49531347e-01 2.46802583e-01 1.57417312e-01 -5.09279728e-01
2.01865345e-01 -5.46624184e-01 -5.70737302e-01 -6.45750999e-01
-3.53352390e-02 8.31069529e-01 6.43001854e-01 2.65782118... | [15.384090423583984, -2.0899295806884766] |
87007eda-28db-4b58-8519-8b20654336a1 | online-clustering-by-penalized-weighted-gmm | 1902.02544 | null | http://arxiv.org/abs/1902.02544v1 | http://arxiv.org/pdf/1902.02544v1.pdf | Online Clustering by Penalized Weighted GMM | With the dawn of the Big Data era, data sets are growing rapidly. Data is
streaming from everywhere - from cameras, mobile phones, cars, and other
electronic devices. Clustering streaming data is a very challenging problem.
Unlike the traditional clustering algorithms where the dataset can be stored
and scanned multipl... | ['Shlomo Bugdary', 'Shay Maymon'] | 2019-02-07 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-6.83903396e-02 -3.98866832e-01 -1.64155718e-02 -4.61058259e-01
-2.91740865e-01 -6.04489028e-01 5.17425872e-02 7.48809636e-01
-5.74498177e-01 3.61727357e-01 -1.38386637e-01 1.11609548e-01
-4.07173961e-01 -1.02609253e+00 -3.81252378e-01 -6.77852273e-01
-5.90096533e-01 9.63662505e-01 9.78287458e-01 -5.67727163... | [7.197559833526611, 4.523019790649414] |
6ca608ba-6491-45fc-8450-64739196b631 | scene-graph-generation-with-external | 1904.00560 | null | http://arxiv.org/abs/1904.00560v1 | http://arxiv.org/pdf/1904.00560v1.pdf | Scene Graph Generation with External Knowledge and Image Reconstruction | Scene graph generation has received growing attention with the advancements
in image understanding tasks such as object detection, attributes and
relationship prediction,~\etc. However, existing datasets are biased in terms
of object and relationship labels, or often come with noisy and missing
annotations, which makes... | ['Handong Zhao', 'Jiuxiang Gu', 'Jianfei Cai', 'Zhe Lin', 'Sheng Li', 'Mingyang Ling'] | 2019-04-01 | scene-graph-generation-with-external-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Gu_Scene_Graph_Generation_With_External_Knowledge_and_Image_Reconstruction_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Gu_Scene_Graph_Generation_With_External_Knowledge_and_Image_Reconstruction_CVPR_2019_paper.pdf | cvpr-2019-6 | ['visual-relationship-detection'] | ['computer-vision'] | [ 5.76029122e-01 2.54818738e-01 -3.07255477e-01 -6.16835237e-01
-8.79150927e-02 -3.25319231e-01 5.03817081e-01 2.19455734e-01
-8.49423036e-02 5.58668613e-01 2.78013259e-01 -1.52413502e-01
4.70197164e-02 -1.03973389e+00 -9.29538786e-01 -3.93147051e-01
4.31692719e-01 3.29699367e-01 3.76080960e-01 -8.63184780... | [10.327539443969727, 1.6361089944839478] |
f3480a0f-2157-4097-b17b-9bcdc6f57000 | mult-an-end-to-end-multitask-learning | 2205.08303 | null | https://arxiv.org/abs/2205.08303v1 | https://arxiv.org/pdf/2205.08303v1.pdf | MulT: An End-to-End Multitask Learning Transformer | We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edge detection. Based on the Swin transformer model, our framework ... | ['Mathieu Salzmann', 'Sabine Süsstrunk', 'Tong Zhang', 'Deblina Bhattacharjee'] | 2022-05-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Bhattacharjee_MulT_An_End-to-End_Multitask_Learning_Transformer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Bhattacharjee_MulT_An_End-to-End_Multitask_Learning_Transformer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['edge-detection'] | ['computer-vision'] | [ 1.76103771e-01 1.07873611e-01 -7.40359500e-02 -4.23940897e-01
-1.06135809e+00 -3.49811882e-01 5.80061376e-01 -2.35819682e-01
-4.00401264e-01 2.51647353e-01 2.57380366e-01 -1.32400051e-01
2.72635847e-01 -4.55249578e-01 -1.09311318e+00 -4.13911432e-01
2.61665910e-01 5.43380558e-01 6.14534736e-01 1.98452666... | [9.689898490905762, 1.2588812112808228] |
2abb756d-dc21-45d5-8a3b-0dac741aa5cd | face-attention-network-an-effective-face | 1711.07246 | null | http://arxiv.org/abs/1711.07246v2 | http://arxiv.org/pdf/1711.07246v2.pdf | Face Attention Network: An Effective Face Detector for the Occluded Faces | The performance of face detection has been largely improved with the
development of convolutional neural network. However, the occlusion issue due
to mask and sunglasses, is still a challenging problem. The improvement on the
recall of these occluded cases usually brings the risk of high false positives.
In this paper,... | ['Jianfeng Wang', 'Ye Yuan', 'Gang Yu'] | 2017-11-20 | null | null | null | null | ['occluded-face-detection'] | ['computer-vision'] | [ 6.84486628e-02 -6.81150258e-02 1.54296011e-02 -3.22803795e-01
-4.25530821e-01 -2.84148246e-01 3.25307995e-01 -5.36086440e-01
-2.30219558e-01 4.47363228e-01 -2.59280726e-02 -1.61614474e-02
1.90887243e-01 -6.91159546e-01 -5.50524890e-01 -5.90698957e-01
8.87701735e-02 -1.70377329e-01 1.89007327e-01 -8.60693678... | [13.283285140991211, 0.6246783137321472] |
4442e1c7-4cf2-4d79-b821-17c3a8722f98 | examining-european-press-coverage-of-the | 2305.00182 | null | https://arxiv.org/abs/2305.00182v1 | https://arxiv.org/pdf/2305.00182v1.pdf | Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework | This paper examines how the European press dealt with the no-vax reactions against the Covid-19 vaccine and the dis- and misinformation associated with this movement. Using a curated dataset of 1786 articles from 19 European newspapers on the anti-vaccine movement over a period of 22 months in 2020-2021, we used Natura... | ['Daniel Gatica-Perez', 'David Alonso del Barrio'] | 2023-04-29 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-3.62018883e-01 2.79992789e-01 -7.25298643e-01 3.41356277e-01
-1.47452548e-01 -9.21401560e-01 1.38972354e+00 1.14275205e+00
-8.17467749e-01 4.03486013e-01 1.24623251e+00 -6.71824098e-01
-1.43468186e-01 -1.00155199e+00 -6.83205903e-01 -4.14896816e-01
-2.60646373e-01 5.56288123e-01 2.17503645e-02 -6.51649535... | [8.47394847869873, 9.785730361938477] |
eab11ef5-3414-46b2-8ba5-c7ac84b97476 | agilegan-stylizing-portraits-by-inversion | null | null | https://dl.acm.org/doi/10.1145/3450626.3459771 | https://guoxiansong.github.io/homepage/paper/AgileGAN.pdf | AgileGAN: stylizing portraits by inversion-consistent transfer learning | Portraiture as an art form has evolved from realistic depiction into a plethora of creative styles. While substantial progress has been made in automated stylization, generating high quality stylistic portraits is still a challenge, and even the recent popular Toonify suffers from several artifacts when used on real in... | ['Tat-Jen Cham', 'Chuanxia Zheng', 'Chun-Pong Lai', 'Wan-Chun Ma', 'Jing Liu', 'Linjie Luo', 'Guoxian Song'] | 2021-07-19 | null | null | null | acm-transactions-on-graphics-2021-7 | ['motion-retargeting'] | ['computer-vision'] | [ 4.20668572e-01 -1.00877382e-01 -4.07690741e-02 -1.50881216e-01
-6.11232638e-01 -7.90067315e-01 7.41547585e-01 -6.10516965e-01
4.54357266e-02 9.63270843e-01 1.11012064e-01 5.54804243e-02
1.45448878e-01 -9.23640728e-01 -6.96768343e-01 -5.70472956e-01
3.83786857e-01 5.21578968e-01 -2.12955251e-01 -3.54731381... | [11.888160705566406, -0.4252932667732239] |
e67ca740-c443-420f-8ca9-519d3e15151d | evaluating-the-performance-of-ann-prediction | 1612.02666 | null | http://arxiv.org/abs/1612.02666v1 | http://arxiv.org/pdf/1612.02666v1.pdf | Evaluating the Performance of ANN Prediction System at Shanghai Stock Market in the Period 21-Sep-2016 to 11-Oct-2016 | This research evaluates the performance of an Artificial Neural Network based
prediction system that was employed on the Shanghai Stock Exchange for the
period 21-Sep-2016 to 11-Oct-2016. It is a follow-up to a previous paper in
which the prices were predicted and published before September 21. Stock market
price predi... | ['Barack Wamkaya Wanjawa'] | 2016-12-05 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-7.69942701e-01 -8.02169219e-02 -2.89374709e-01 -5.99826537e-02
2.41418526e-01 -3.17367494e-01 4.62560385e-01 -6.58016056e-02
-4.66652989e-01 1.08821285e+00 1.46986237e-02 -8.14580977e-01
-2.99599737e-01 -1.03429842e+00 -2.98645258e-01 -4.39570516e-01
-2.10351184e-01 3.35013360e-01 1.30368859e-01 -6.54579103... | [4.494966506958008, 4.243381977081299] |
bbee4978-3e3e-4865-b496-1a838b6507ba | dcf-asn-coarse-to-fine-real-time-visual | 2103.10607 | null | https://arxiv.org/abs/2103.10607v1 | https://arxiv.org/pdf/2103.10607v1.pdf | DCF-ASN: Coarse-to-fine Real-time Visual Tracking via Discriminative Correlation Filter and Attentional Siamese Network | Discriminative correlation filters (DCF) and siamese networks have achieved promising performance on visual tracking tasks thanks to their superior computational efficiency and reliable similarity metric learning, respectively. However, how to effectively take advantages of powerful deep networks, while maintaining the... | ['Qiang Shen', 'Xiaoyue Yin', 'Ying Li', 'Xizhe Xue'] | 2021-03-19 | null | null | null | null | ['real-time-visual-tracking'] | ['computer-vision'] | [-3.24042380e-01 -5.56452155e-01 -2.05830008e-01 -1.33387923e-01
-6.09136164e-01 -7.17597663e-01 4.89647210e-01 -2.48030469e-01
-4.70464796e-01 6.29045665e-01 -1.93063542e-01 2.26904079e-01
-2.90908575e-01 -3.59366864e-01 -6.43720269e-01 -1.00402439e+00
-1.91351324e-01 2.62711346e-01 4.69550490e-01 1.26167610... | [6.2835516929626465, -2.1301283836364746] |
5c68091b-ffc0-47e6-b10b-185f6cf7205a | explainable-data-driven-optimization-from | 2301.10074 | null | https://arxiv.org/abs/2301.10074v1 | https://arxiv.org/pdf/2301.10074v1.pdf | Explainable Data-Driven Optimization: From Context to Decision and Back Again | Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is dedicated to interpreting machine learning models in the classification setting, explaining decision pipelines involving learning algorithms... | ['Thibaut Vidal', 'Axel Parmentier', 'Alexandre Forel'] | 2023-01-24 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 5.85436344e-01 1.03776503e+00 -1.02804351e+00 -1.10085654e+00
-4.92957413e-01 -4.34254616e-01 4.06630278e-01 4.25309449e-01
1.16494395e-01 7.85164416e-01 6.49804831e-01 -1.38125575e+00
-1.02805519e+00 -5.35883248e-01 -7.25449204e-01 -2.17006460e-01
-6.31795600e-02 7.38997400e-01 -7.00216234e-01 -1.96054708... | [8.732169151306152, 5.7935471534729] |
b786ae7a-0d0d-4138-930c-b1063b3bb018 | multilingual-neural-rst-discourse-parsing | 2012.01704 | null | https://arxiv.org/abs/2012.01704v1 | https://arxiv.org/pdf/2012.01704v1.pdf | Multilingual Neural RST Discourse Parsing | Text discourse parsing plays an important role in understanding information flow and argumentative structure in natural language. Previous research under the Rhetorical Structure Theory (RST) has mostly focused on inducing and evaluating models from the English treebank. However, the parsing tasks for other languages s... | ['Nancy F. Chen', 'Ke Shi', 'Zhengyuan Liu'] | 2020-12-03 | null | https://aclanthology.org/2020.coling-main.591 | https://aclanthology.org/2020.coling-main.591.pdf | coling-2020-8 | ['discourse-parsing'] | ['natural-language-processing'] | [ 1.42538041e-01 4.58455235e-01 -5.08791208e-01 -3.67492288e-01
-1.13287818e+00 -8.47927809e-01 8.70773137e-01 5.41453600e-01
-4.68515068e-01 9.40956235e-01 8.27590585e-01 -1.02719176e+00
4.83196199e-01 -5.88340700e-01 -6.92216456e-01 -2.22670808e-01
3.39158699e-02 3.51159453e-01 2.92505145e-01 -4.35552269... | [10.775768280029297, 9.364920616149902] |
f578cead-8673-42b4-8369-04438679774c | ucm-2-a-rule-based-approach-to-infer-the | null | null | https://aclanthology.org/S12-1038 | https://aclanthology.org/S12-1038.pdf | UCM-2: a Rule-Based Approach to Infer the Scope of Negation via Dependency Parsing | null | ["Pablo Gerv{\\'a}s", "Alberto D{\\'\\i}az", 'Miguel Ballesteros', 'Laura Plaza', 'Virginia Francisco', 'Jorge Carrillo de Albornoz'] | 2012-07-01 | null | null | null | semeval-2012-7 | ['negation-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... | [-7.355528831481934, 3.5328574180603027] |
e56a8905-eb04-490b-8f68-6be86b7deffa | using-object-information-for-spotting-text | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Shitala_Prasad_Using_Object_Information_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Shitala_Prasad_Using_Object_Information_ECCV_2018_paper.pdf | Using Object Information for Spotting Text | Text spotting, also called text detection, is a challenging computer vision task because of cluttered backgrounds, diverse imaging environments, various text sizes and similarity between some objects and characters, e.g., tyre and âoâ. However, text spotting is a vital step in numerous AI and computer vision system... | ['Shitala Prasad', 'Adams Wai Kin Kong'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['text-spotting'] | ['computer-vision'] | [ 6.04720533e-01 -6.48046315e-01 2.31136605e-01 -3.01816314e-01
2.43969820e-02 -1.38248891e-01 7.06682384e-01 -3.06005388e-01
-4.88700747e-01 5.29507518e-01 -1.04986958e-01 -3.42838794e-01
5.11156470e-02 -5.22203863e-01 -6.07210219e-01 -8.02228749e-01
6.40532494e-01 5.43061674e-01 6.46794260e-01 -2.52455086... | [12.037247657775879, 2.2684683799743652] |
f730e5f6-f302-49ad-a49a-8c8e87d10e0d | automated-optical-multi-layer-design-via-deep | 2006.11940 | null | https://arxiv.org/abs/2006.11940v1 | https://arxiv.org/pdf/2006.11940v1.pdf | Automated Optical Multi-layer Design via Deep Reinforcement Learning | Optical multi-layer thin films are widely used in optical and energy applications requiring photonic designs. Engineers often design such structures based on their physical intuition. However, solely relying on human experts can be time-consuming and may lead to sub-optimal designs, especially when the design space is ... | ['L. Jay Guo', 'Haozhu Wang', 'Zeyu Zheng', 'Chengang Ji'] | 2020-06-21 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [ 4.04768944e-01 -8.72428194e-02 -5.92530295e-02 1.87913835e-01
-5.51087737e-01 -4.49149430e-01 1.88564993e-02 -4.68815953e-01
-2.82794714e-01 1.41007578e+00 -9.12772417e-02 -4.97485250e-01
1.52598113e-01 -7.99902201e-01 -9.14926350e-01 -9.85822976e-01
4.20468897e-01 4.42947298e-01 -1.65465400e-01 -6.30044332... | [5.168126106262207, 5.335023403167725] |
ab22f59a-d40a-484d-9c51-ea9af3d64abe | scoop-self-supervised-correspondence-and | 2211.14020 | null | https://arxiv.org/abs/2211.14020v2 | https://arxiv.org/pdf/2211.14020v2.pdf | SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow | Scene flow estimation is a long-standing problem in computer vision, where the goal is to find the 3D motion of a scene from its consecutive observations. Recently, there have been efforts to compute the scene flow from 3D point clouds. A common approach is to train a regression model that consumes source and target po... | ['Michael Rubinstein', 'Shai Avidan', 'Forrester Cole', 'Dror Aiger', 'Itai Lang'] | 2022-11-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lang_SCOOP_Self-Supervised_Correspondence_and_Optimization-Based_Scene_Flow_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lang_SCOOP_Self-Supervised_Correspondence_and_Optimization-Based_Scene_Flow_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.26698622e-02 -3.52867275e-01 1.21756755e-02 -2.21437722e-01
-5.64474642e-01 -6.08923554e-01 5.95572233e-01 1.08935818e-01
-4.76994514e-01 3.64550918e-01 -1.90749377e-01 -2.48848274e-01
2.72075623e-01 -7.55866349e-01 -8.05666089e-01 -5.60664058e-01
9.29948986e-02 6.61120534e-01 5.29599965e-01 7.03140348... | [8.526656150817871, -2.0467145442962646] |
9b217d3e-fb59-4162-9c36-672ccaffbd02 | thinking-the-fusion-strategy-of-multi | 2202.10758 | null | https://arxiv.org/abs/2202.10758v1 | https://arxiv.org/pdf/2202.10758v1.pdf | Thinking the Fusion Strategy of Multi-reference Face Reenactment | In recent advances of deep generative models, face reenactment -manipulating and controlling human face, including their head movement-has drawn much attention for its wide range of applicability. Despite its strong expressiveness, it is inevitable that the models fail to reconstruct or accurately generate unseen side ... | ['Tamaki Kojima', 'Takuya Narihira', 'Takuya Yashima'] | 2022-02-22 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 2.09673807e-01 3.15343827e-01 3.37806433e-01 -5.23080230e-01
-5.56836843e-01 -4.99232531e-01 8.00629497e-01 -1.17026997e+00
-8.94573890e-03 8.62043202e-01 4.50011522e-01 3.28463465e-01
2.82373577e-01 -6.07783794e-01 -9.36240017e-01 -6.22021735e-01
1.24116160e-01 5.38138032e-01 -1.19262971e-01 -6.40595108... | [12.822565078735352, -0.24185024201869965] |
6a6dae82-36f7-4f7b-9232-132d296c5f2c | factorizable-graph-convolutional-networks | 2010.05421 | null | https://arxiv.org/abs/2010.05421v1 | https://arxiv.org/pdf/2010.05421v1.pdf | Factorizable Graph Convolutional Networks | Graphs have been widely adopted to denote structural connections between entities. The relations are in many cases heterogeneous, but entangled together and denoted merely as a single edge between a pair of nodes. For example, in a social network graph, users in different latent relationships like friends and colleague... | ['Xinchao Wang', 'Mingli Song', 'Zunlei Feng', 'Yiding Yang'] | 2020-10-12 | null | http://proceedings.neurips.cc/paper/2020/hash/ea3502c3594588f0e9d5142f99c66627-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/ea3502c3594588f0e9d5142f99c66627-Paper.pdf | neurips-2020-12 | ['graph-regression'] | ['graphs'] | [-1.37944311e-01 3.85318726e-01 -2.89919317e-01 -1.73287198e-01
1.37717798e-01 -8.83893788e-01 9.03198719e-01 1.05930679e-02
2.84020036e-01 6.86287344e-01 4.47174370e-01 -3.42734665e-01
-4.71567482e-01 -1.05772531e+00 -4.47922766e-01 -6.08607233e-01
-4.03947622e-01 3.41590047e-01 -2.32728705e-01 -1.15539886... | [7.244682788848877, 6.239664554595947] |
5e699986-3422-4f1b-9e4d-91ea97e3d28c | service-choreography-sbvr-and-time | 1512.07685 | null | http://arxiv.org/abs/1512.07685v1 | http://arxiv.org/pdf/1512.07685v1.pdf | Service Choreography, SBVR, and Time | We propose the use of structured natural language (English) in specifying
service choreographies, focusing on the what rather than the how of the
required coordination of participant services in realising a business
application scenario. The declarative approach we propose uses the OMG standard
Semantics of Business Vo... | ['Sotiris Moschoyiannis', 'Paul Krause', 'Nurulhuda A. Manaf'] | 2015-12-24 | null | null | null | null | ['service-composition', 'formal-logic'] | ['miscellaneous', 'reasoning'] | [ 2.36748129e-01 5.46402693e-01 1.41260505e-01 -6.64905131e-01
1.69579402e-01 -6.76756203e-01 1.29893577e+00 -5.24009392e-02
-1.67631969e-01 1.63193464e-01 2.05307335e-01 -8.46828580e-01
-3.45557451e-01 -1.15198576e+00 -1.21046960e-01 -2.81757206e-01
-1.63943291e-01 6.96554303e-01 9.66240048e-01 -5.99008024... | [8.634760856628418, 6.893792152404785] |
06934474-f5d0-457e-922f-14a1495d1ed6 | real-time-audio-visual-end-to-end-speech | 2303.07005 | null | https://arxiv.org/abs/2303.07005v1 | https://arxiv.org/pdf/2303.07005v1.pdf | Real-Time Audio-Visual End-to-End Speech Enhancement | Audio-visual speech enhancement (AV-SE) methods utilize auxiliary visual cues to enhance speakers' voices. Therefore, technically they should be able to outperform the audio-only speech enhancement (SE) methods. However, there are few works in the literature on an AV-SE system that can work in real time on a CPU. In th... | ['Huaming Wang', 'Sefik Emre Eskimez', 'ZiYi Yang', 'Min Tang', 'Hemin Yang', 'Zirun Zhu'] | 2023-03-13 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [-6.97906613e-02 -1.81731552e-01 4.44328815e-01 -2.19164059e-01
-9.41423953e-01 2.09357813e-02 3.23099703e-01 -5.89024164e-02
-5.65188289e-01 3.65283281e-01 5.37127495e-01 -3.50089729e-01
2.42211401e-01 -4.53014165e-01 -3.47929209e-01 -5.90217948e-01
1.45979553e-01 -6.81351364e-01 5.33544660e-01 -4.82101232... | [14.808537483215332, 5.828755855560303] |
15256d7f-ca7a-4541-a1ad-8b81c675bcd7 | learning-invariance-from-generated-variance | 2301.00725 | null | https://arxiv.org/abs/2301.00725v1 | https://arxiv.org/pdf/2301.00725v1.pdf | Learning Invariance from Generated Variance for Unsupervised Person Re-identification | This work focuses on unsupervised representation learning in person re-identification (ReID). Recent self-supervised contrastive learning methods learn invariance by maximizing the representation similarity between two augmented views of a same image. However, traditional data augmentation may bring to the fore undesir... | ['Francois Bremond', 'Antitza Dantcheva', 'Benoit Lagadec', 'Yaohui Wang', 'Hao Chen'] | 2023-01-02 | null | null | null | null | ['person-re-identification', 'unsupervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 3.55421305e-01 1.27538383e-01 1.67221546e-01 -6.56173289e-01
-5.98005593e-01 -6.62114799e-01 1.07962692e+00 -4.70507026e-01
-4.12898183e-01 7.08958685e-01 5.60306728e-01 3.53002071e-01
1.27576470e-01 -7.14039207e-01 -7.77062058e-01 -6.85347617e-01
2.41978988e-01 6.81145668e-01 -8.41881156e-01 -2.19965264... | [14.671342849731445, 0.9199724793434143] |
b88c7638-537a-403f-99ab-9a63c30977aa | a-simple-approach-to-improve-single-model | 2205.00403 | null | https://arxiv.org/abs/2205.00403v2 | https://arxiv.org/pdf/2205.00403v2.pdf | A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness | Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular approaches to estimate predictive uncertainty in deep learning are methods that combine predictions fro... | ['Balaji Lakshminarayanan', 'Dustin Tran', 'Jasper Snoek', 'Zack Nado', 'Ghassen Jerfel', 'Yeming Wen', 'Zi Lin', 'Jie Ren', 'Shreyas Padhy', 'Jeremiah Zhe Liu'] | 2022-05-01 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-1.44945472e-01 1.34763181e-01 -1.85318999e-02 -6.44452810e-01
-9.86437082e-01 -5.51774204e-01 5.95102489e-01 -3.81614864e-02
-3.17148924e-01 9.26194549e-01 -8.12768191e-02 -3.17026764e-01
-3.42152625e-01 -8.81516039e-01 -1.07228863e+00 -8.78107011e-01
1.84065327e-01 6.07563734e-01 1.56229213e-01 3.56015563... | [7.463440895080566, 3.756512403488159] |
4213709b-cefd-4a11-8bca-a6406e7291e8 | gene-communities-in-co-expression-networks | 2305.12963 | null | https://arxiv.org/abs/2305.12963v1 | https://arxiv.org/pdf/2305.12963v1.pdf | Gene communities in co-expression networks across different tissues | With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Com... | ['Naoki Masuda', 'Omer Gokcumen', 'Marie Saitou', 'Madison Russell'] | 2023-05-22 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 1.56604171e-01 -2.12898731e-01 2.71147430e-01 -1.49678811e-02
1.18571490e-01 -9.13180828e-01 3.87344629e-01 5.34380436e-01
2.92164832e-02 5.25539994e-01 2.12645024e-01 -3.26274008e-01
-3.91097277e-01 -8.32869053e-01 -3.82829368e-01 -1.10063267e+00
-7.29780674e-01 2.96119422e-01 1.98556155e-01 -5.93378022... | [6.663071632385254, 5.364526748657227] |
4d295134-6f76-4f2a-a431-628190475b3c | analytical-modelling-of-exoplanet-transit | 2112.11600 | null | https://arxiv.org/abs/2112.11600v1 | https://arxiv.org/pdf/2112.11600v1.pdf | Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression | The physical characteristics and atmospheric chemical composition of newly discovered exoplanets are often inferred from their transit spectra which are obtained from complex numerical models of radiative transfer. Alternatively, simple analytical expressions provide insightful physical intuition into the relevant atmo... | ['Alexander Roman', 'Katia Matcheva', 'Konstantin T. Matchev'] | 2021-12-22 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [-1.19603068e-01 -2.44387478e-01 2.50922620e-01 -2.15450615e-01
-6.93533421e-02 -8.12482595e-01 9.03970361e-01 9.12340507e-02
-2.99022108e-01 7.14207947e-01 -5.60845315e-01 -7.73155928e-01
-2.52121389e-01 -9.17676508e-01 -4.19418246e-01 -1.02685726e+00
-2.04275116e-01 7.27574408e-01 -1.07984208e-01 -4.84296322... | [6.697889804840088, 3.356170892715454] |
07055ba3-1104-495d-9e10-c6c8b7bfdd46 | deep-mixture-of-linear-inverse-regressions | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Lathuiliere_Deep_Mixture_of_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Lathuiliere_Deep_Mixture_of_CVPR_2017_paper.pdf | Deep Mixture of Linear Inverse Regressions Applied to Head-Pose Estimation | Convolutional Neural Networks (ConvNets) have become the state-of-the-art for many classification and regression problems in computer vision. When it comes to regression, approaches such as measuring the Euclidean distance of target and predictions are often employed as output layer. In this paper, we propose the coupl... | ['Rafael Munoz-Salinas', 'Pablo Mesejo', 'Remi Juge', 'Radu Horaud', 'Stephane Lathuiliere'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['head-pose-estimation'] | ['computer-vision'] | [-1.46763295e-01 2.32624739e-01 9.32430550e-02 -5.29308081e-01
-5.20742536e-01 -6.31958544e-02 6.99791372e-01 1.66028142e-02
-1.03506696e+00 6.01133287e-01 -1.33084767e-02 -5.28260879e-02
-1.23627409e-02 -3.82903844e-01 -8.54805410e-01 -6.35093987e-01
9.63663608e-02 7.28947043e-01 7.65952766e-02 -3.26008648... | [13.670577049255371, 0.2902841567993164] |
ee6a46ae-0a97-487c-8620-d1f8d56bf550 | modet-learning-deformable-image-registration | 2306.05688 | null | https://arxiv.org/abs/2306.05688v1 | https://arxiv.org/pdf/2306.05688v1.pdf | ModeT: Learning Deformable Image Registration via Motion Decomposition Transformer | The Transformer structures have been widely used in computer vision and have recently made an impact in the area of medical image registration. However, the use of Transformer in most registration networks is straightforward. These networks often merely use the attention mechanism to boost the feature learning as the s... | ['Yi Wang', 'Dong Ni', 'Haiqiao Wang'] | 2023-06-09 | null | null | null | null | ['image-registration', 'medical-image-registration'] | ['computer-vision', 'medical'] | [ 5.02584875e-02 9.10203606e-02 -2.25116447e-01 -5.65185666e-01
-7.86762476e-01 -2.30783254e-01 5.30019283e-01 -4.74702924e-01
-5.18813312e-01 1.91487372e-01 4.98040348e-01 4.52446844e-03
-7.02329054e-02 -4.82150257e-01 -5.85026324e-01 -9.15617645e-01
2.09641326e-02 5.80422342e-01 2.95409977e-01 -3.00929636... | [14.006367683410645, -2.5897305011749268] |
9ec2e938-c024-4a5f-ba42-37e1814d28b9 | on-the-confidence-of-neural-network | null | null | https://openreview.net/forum?id=HJf2ds2ssm | https://openreview.net/pdf?id=HJf2ds2ssm | On the Confidence of Neural Network Predictions for some NLP Tasks | Neural networks are known to produce unexpected results on inputs that are far from the training distribution. One approach to tackle this problem is to detect the samples on which the trained network can not answer reliably. ODIN is a recently proposed method for out-of-distribution detection that does not modify the ... | ['Anonymous'] | 2018-10-22 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 6.51389807e-02 6.64519891e-02 -3.21161598e-01 -9.32542086e-01
-7.63371468e-01 -6.28086388e-01 4.76520896e-01 4.20657098e-01
-4.76092547e-01 1.10164726e+00 -3.12298745e-01 -2.49990955e-01
6.14158250e-02 -7.27060139e-01 -8.46496820e-01 -7.67890394e-01
1.04915805e-01 6.44976556e-01 7.72946298e-01 3.39460820... | [9.315056800842285, 3.04284930229187] |
fdf7e52d-ac8c-4eab-8b45-622e92a3bead | neural-sequential-phrase-grounding-seqground | 1903.07669 | null | http://arxiv.org/abs/1903.07669v1 | http://arxiv.org/pdf/1903.07669v1.pdf | Neural Sequential Phrase Grounding (SeqGROUND) | We propose an end-to-end approach for phrase grounding in images. Unlike
prior methods that typically attempt to ground each phrase independently by
building an image-text embedding, our architecture formulates grounding of
multiple phrases as a sequential and contextual process. Specifically, we
encode region proposal... | ['Leonid Sigal', 'Pelin Dogan', 'Markus Gross'] | 2019-03-18 | neural-sequential-phrase-grounding-seqground-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Dogan_Neural_Sequential_Phrase_Grounding_SeqGROUND_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Dogan_Neural_Sequential_Phrase_Grounding_SeqGROUND_CVPR_2019_paper.pdf | cvpr-2019-6 | ['phrase-grounding'] | ['natural-language-processing'] | [ 4.58602011e-01 3.42539757e-01 -3.50316316e-01 -5.47584832e-01
-1.16974640e+00 -7.10478246e-01 7.85128534e-01 2.78892875e-01
-4.04578894e-01 3.20490390e-01 6.85073197e-01 -3.96144867e-01
2.90105492e-01 -7.76550770e-01 -1.10992634e+00 -4.91267562e-01
9.26198065e-02 2.12931678e-01 1.43417194e-01 -2.99517941... | [10.552774429321289, 1.4528958797454834] |
9b045687-fd82-471b-b182-143563135f21 | mixing-specific-data-augmentation-techniques | 2008.02480 | null | https://arxiv.org/abs/2008.02480v1 | https://arxiv.org/pdf/2008.02480v1.pdf | Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separation | Blind music source separation has been a popular and active subject of research in both the music information retrieval and signal processing communities. To counter the lack of available multi-track data for supervised model training, a data augmentation method that creates artificial mixtures by combining tracks from... | ['Alvin Wen-Yu Su', 'Yi-Hsuan Yang', 'Yin-Cheng Yeh', 'Wen-Yi Hsiao', 'Ching-Yu Chiu'] | 2020-08-06 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 4.50354874e-01 -2.88802981e-01 -5.47641851e-02 6.67783767e-02
-8.75662506e-01 -7.55199194e-01 7.20487416e-01 1.63975060e-01
-2.95515865e-01 5.44035852e-01 4.72419083e-01 2.49237847e-02
-5.31472862e-01 -1.92759439e-01 -4.45437402e-01 -8.73212278e-01
7.31661096e-02 2.26930141e-01 -4.68821451e-02 -1.27369672... | [15.541414260864258, 5.5316362380981445] |
18a59629-9940-44f6-b3d7-dd1f225204c4 | mutual-clustering-on-comparative-texts-via | 1903.03762 | null | http://arxiv.org/abs/1903.03762v1 | http://arxiv.org/pdf/1903.03762v1.pdf | Mutual Clustering on Comparative Texts via Heterogeneous Information Networks | Currently, many intelligence systems contain the texts from multi-sources,
e.g., bulletin board system (BBS) posts, tweets and news. These texts can be
``comparative'' since they may be semantically correlated and thus provide us
with different perspectives toward the same topics or events. To better
organize the multi... | ['Fei-Yue Wang', 'Philip S. Yu', 'Danyan Wen', 'Zhaohui Peng', 'Senzhang Wang', 'Jianping Cao'] | 2019-03-09 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-3.72761518e-01 -2.28859439e-01 -1.83197215e-01 -1.63414642e-01
-7.21487761e-01 -4.48338121e-01 7.19028413e-01 5.02226830e-01
-3.65854204e-01 4.03439045e-01 5.49432099e-01 -4.14264165e-02
-3.72659624e-01 -8.07417691e-01 -1.80186257e-01 -7.45730340e-01
1.04942113e-01 6.26067400e-01 4.89933610e-01 -3.35616589... | [10.280964851379395, 6.783207893371582] |
840ed9d4-aad6-4568-8c44-5f4a0c39e0b0 | botshape-a-novel-social-bots-detection | 2303.10214 | null | https://arxiv.org/abs/2303.10214v2 | https://arxiv.org/pdf/2303.10214v2.pdf | BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns | An essential topic in online social network security is how to accurately detect bot accounts and relieve their harmful impacts (e.g., misinformation, rumor, and spam) on genuine users. Based on a real-world data set, we construct behavioral sequences from raw event logs. After extracting critical characteristics from ... | ['Chengjie Mou', 'Xuesong Ye', 'Jun Wu'] | 2023-03-17 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-1.43759742e-01 -2.85526097e-01 -5.04568696e-01 -3.59215625e-02
-4.64346521e-02 -7.99843669e-01 6.51376963e-01 2.27861777e-01
-3.55652004e-01 6.03501081e-01 -2.26577744e-01 -4.56305683e-01
3.30645323e-01 -7.84525275e-01 1.18863195e-01 -3.49217981e-01
-1.76147029e-01 3.28084886e-01 7.65320063e-01 -8.16252530... | [8.135527610778809, 10.143219947814941] |
801c234f-fa4f-4a25-b410-98de33d1a972 | geospark-sparking-up-point-cloud-segmentation | 2303.08274 | null | https://arxiv.org/abs/2303.08274v1 | https://arxiv.org/pdf/2303.08274v1.pdf | GeoSpark: Sparking up Point Cloud Segmentation with Geometry Clue | Current point cloud segmentation architectures suffer from limited long-range feature modeling, as they mostly rely on aggregating information with local neighborhoods. Furthermore, in order to learn point features at multiple scales, most methods utilize a data-agnostic sampling approach to decrease the number of poin... | ['Ioannis Brilakis', 'Georgios Hadjidemetriou', 'Shujun Wang', 'Lei Zhu', 'Hengshuang Zhao', 'Xiaoyang Wu', 'Zhening Huang'] | 2023-03-14 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-3.12664032e-01 -2.88252592e-01 -2.16178194e-01 -4.05704767e-01
-6.88692212e-01 -7.65167594e-01 5.41158199e-01 5.18113315e-01
-3.00065309e-01 2.87359208e-01 3.26836072e-02 -5.52330501e-02
-3.91469784e-02 -1.14873624e+00 -8.53321791e-01 -4.44544494e-01
-4.90094982e-02 4.47463602e-01 5.97539723e-01 -3.06293219... | [7.88925313949585, -3.469700574874878] |
e6da7e66-c66d-4bb1-be55-d80944320030 | egocentric-video-description-based-on | 1704.02163 | null | http://arxiv.org/abs/1704.02163v3 | http://arxiv.org/pdf/1704.02163v3.pdf | Egocentric Video Description based on Temporally-Linked Sequences | Egocentric vision consists in acquiring images along the day from a first
person point-of-view using wearable cameras. The automatic analysis of this
information allows to discover daily patterns for improving the quality of life
of the user. A natural topic that arises in egocentric vision is storytelling,
that is, ho... | ['Francisco Casacuberta', 'Álvaro Peris', 'Marc Bolaños', 'Petia Radeva', 'Sergi Soler'] | 2017-04-07 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 2.81903774e-01 -1.18091032e-01 9.17144418e-02 -3.63151193e-01
-6.42506063e-01 -3.54665786e-01 8.32585871e-01 1.27113223e-01
-2.91361481e-01 7.15071738e-01 9.43267405e-01 7.34788597e-01
-2.06663758e-01 -3.91042262e-01 -8.80698144e-01 -7.11385131e-01
2.77273148e-01 2.57603824e-01 -1.61511570e-01 -1.67401150... | [10.28252124786377, 0.6605015993118286] |
0ac07271-39a4-484c-a991-6cb0e3219554 | multilingual-sense-intersection-in-a-parallel | null | null | https://aclanthology.org/2016.gwc-1.8 | https://aclanthology.org/2016.gwc-1.8.pdf | Multilingual Sense Intersection in a Parallel Corpus with Diverse Language Families | Supervised methods for Word Sense Disambiguation (WSD) benefit from high-quality sense-annotated resources, which are lacking for many languages less common than English. There are, however, several multilingual parallel corpora that can be inexpensively annotated with senses through cross-lingual methods. We test the ... | ['Francis Bond', 'Giulia Bonansinga'] | null | null | null | null | gwc-2016-1 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-7.68276751e-02 -3.07483166e-01 -4.27025050e-01 -1.86818223e-02
-8.23934972e-01 -9.92565930e-01 6.57354653e-01 6.38990045e-01
-1.04200423e+00 1.25274515e+00 3.59342903e-01 -3.29077035e-01
-2.08773836e-01 -7.02086866e-01 8.47411230e-02 -3.59884322e-01
3.11745286e-01 7.72818804e-01 3.35303783e-01 -7.63536096... | [10.363567352294922, 9.383463859558105] |
27f59828-0d97-490e-9de6-420a4ff5d430 | multi-output-gaussian-processes-for | 1812.08739 | null | https://arxiv.org/abs/1812.08739v2 | https://arxiv.org/pdf/1812.08739v2.pdf | Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation | Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such user-generated data for m... | ['Filipe Rodrigues', 'Francisco C. Pereira', 'Kristian Henrickson'] | 2018-12-20 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [-2.16228604e-01 -1.44843593e-01 -1.16635062e-01 -5.76273024e-01
-1.02097714e+00 -3.04533213e-01 6.95962548e-01 5.08484066e-01
-6.64817870e-01 8.88366580e-01 4.62425649e-01 -4.79856014e-01
-3.03551584e-01 -1.10128391e+00 -8.37327242e-01 -6.29266202e-01
2.42509827e-01 7.65104532e-01 2.01321319e-01 -2.54784733... | [6.649166584014893, 2.0679574012756348] |
f0ae0cc4-7737-42d7-8ba0-938ef33d6c56 | siamese-network-for-rgb-d-salient-object | 2008.12134 | null | https://arxiv.org/abs/2008.12134v2 | https://arxiv.org/pdf/2008.12134v2.pdf | Siamese Network for RGB-D Salient Object Detection and Beyond | Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited amount of training data or over-reliance on an elaborately designed training process. Inspired by... | ['Qijun Zhao', 'Ge-Peng Ji', 'Deng-Ping Fan', 'Ce Zhu', 'Keren Fu', 'Jianbing Shen'] | 2020-08-26 | null | null | null | null | ['rgb-d-salient-object-detection', 'salient-object-detection'] | ['computer-vision', 'computer-vision'] | [ 3.21793109e-01 3.86989140e-03 -4.89256442e-01 -2.34770522e-01
-9.98695731e-01 -4.75989163e-01 5.48338234e-01 9.94884670e-02
-5.32498777e-01 2.63552457e-01 -2.46119639e-03 8.43914151e-02
-1.30830705e-01 -5.56912422e-01 -6.82743192e-01 -8.96888554e-01
2.17529118e-01 -1.97675079e-02 7.91789532e-01 -3.74492556... | [9.662993431091309, -0.7715610265731812] |
c006d2a2-bcc7-4adb-9957-a3624bbb4f8c | collaborative-regression-of-expressive-bodies | 2105.05301 | null | https://arxiv.org/abs/2105.05301v2 | https://arxiv.org/pdf/2105.05301v2.pdf | Collaborative Regression of Expressive Bodies using Moderation | Recovering expressive humans from images is essential for understanding human behavior. Methods that estimate 3D bodies, faces, or hands have progressed significantly, yet separately. Face methods recover accurate 3D shape and geometric details, but need a tight crop and struggle with extreme views and low resolution. ... | ['Michael J. Black', 'Dimitrios Tzionas', 'Timo Bolkart', 'Vasileios Choutas', 'Yao Feng'] | 2021-05-11 | null | null | null | null | ['3d-human-reconstruction', '3d-multi-person-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [-3.20855290e-01 2.99270302e-01 -7.62153342e-02 -5.61362684e-01
-4.34969276e-01 -6.45610869e-01 2.88298309e-01 -6.79144740e-01
1.85234696e-01 3.56782019e-01 4.25533682e-01 5.34766138e-01
3.58891189e-01 -4.97323364e-01 -5.44407904e-01 -5.46408772e-01
1.66666925e-01 8.67313623e-01 -1.79349542e-01 -1.65635452... | [7.160050392150879, -1.1093348264694214] |
4de95214-4be5-4590-939f-90b6c95c5573 | towards-effective-image-manipulation | 2210.08529 | null | https://arxiv.org/abs/2210.08529v2 | https://arxiv.org/pdf/2210.08529v2.pdf | Towards Effective Image Manipulation Detection with Proposal Contrastive Learning | Deep models have been widely and successfully used in image manipulation detection, which aims to classify tampered images and localize tampered regions. Most existing methods mainly focus on extracting global features from tampered images, while neglecting the relationships of local features between tampered and authe... | ['Shu-Tao Xia', 'Tao Dai', 'Shanzhao Qiu', 'Bowen Zhao', 'Yuyuan Zeng'] | 2022-10-16 | null | null | null | null | ['image-manipulation-detection', 'image-manipulation'] | ['computer-vision', 'computer-vision'] | [ 1.92713112e-01 -6.55084252e-01 -4.03174430e-01 -1.05769280e-02
-9.20760274e-01 -6.45792425e-01 6.26422346e-01 -1.65037170e-01
-3.00621092e-01 3.21406603e-01 -5.50240129e-02 -1.37241080e-01
4.11245406e-01 -6.55512393e-01 -6.11480832e-01 -1.12157071e+00
-8.90906714e-03 -3.59853119e-01 3.65996480e-01 -8.38837847... | [12.292232513427734, 0.9306294918060303] |
2251e2d1-09c2-4b7d-9279-9bca8f7416fd | event-based-structured-light-for-depth | 1811.10771 | null | http://arxiv.org/abs/1811.10771v1 | http://arxiv.org/pdf/1811.10771v1.pdf | Event-Based Structured Light for Depth Reconstruction using Frequency Tagged Light Patterns | This paper presents a new method for 3D depth estimation using the output of
an asynchronous time driven image sensor. In association with a high speed
Digital Light Processing projection system, our method achieves real-time
reconstruction of 3D points cloud, up to several hundreds of hertz. Unlike
state of the art me... | ['S. -H. Ieng', 'R. Benosman', 'T. Leroux'] | 2018-11-27 | null | null | null | null | ['3d-depth-estimation'] | ['computer-vision'] | [ 4.70342100e-01 -3.31012607e-01 7.33237922e-01 -2.85873294e-01
-3.22644651e-01 -3.73559475e-01 7.71845281e-01 3.78823094e-02
-6.26621425e-01 5.41935384e-01 -2.95388371e-01 -3.45905200e-02
-1.80078335e-02 -1.13268697e+00 -5.70748568e-01 -5.13052762e-01
1.59167379e-01 5.77645779e-01 9.54880118e-01 2.08074916... | [9.07041072845459, -2.3610386848449707] |
7a91b71e-18c0-496e-9c23-7594539be16e | artificial-cognitively-inspired-generation-of | 2112.02457 | null | https://arxiv.org/abs/2112.02457v1 | https://arxiv.org/pdf/2112.02457v1.pdf | Artificial Cognitively-inspired Generation of the Notion of Topological Group in the Context of Artificial Mathematical Intelligence | The new computational paradigm of conceptual computation has been introduced in the research program of Artificial Mathematical Intelligence. We provide the explicit artificial generation (or conceptual computation) for the fundamental mathematical notion of topological groups. Specifically, we start with two basic not... | ['Florian Geismann', 'Yoe A. Herrera-Jaramillo', 'Danny A. J. Gomez-Ramirez'] | 2021-12-05 | null | null | null | null | ['abstract-algebra'] | ['reasoning'] | [-7.36453235e-02 6.48545682e-01 2.08937332e-01 -1.17268145e-01
6.66799664e-01 -9.67307985e-01 1.46081352e+00 9.93070751e-02
2.34051362e-01 1.64622381e-01 8.76636710e-03 -6.83458924e-01
-1.05684769e+00 -1.31940091e+00 -1.81602374e-01 -2.08280772e-01
-6.13472879e-01 7.49146760e-01 -1.99904572e-02 -8.24565887... | [9.023005485534668, 6.94024133682251] |
4109adb1-a92c-433e-83be-9685c069f661 | interactive-learning-from-natural-language | 2207.00627 | null | https://arxiv.org/abs/2207.00627v1 | https://arxiv.org/pdf/2207.00627v1.pdf | Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logic | Natural language is an intuitive way for humans to communicate tasks to a robot. While natural language (NL) is ambiguous, real world tasks and their safety requirements need to be communicated unambiguously. Signal Temporal Logic (STL) is a formal logic that can serve as a versatile, expressive, and unambiguous formal... | ['Jyotirmoy V. Deshmukh', 'Jesse Thomason', 'Sara Mohammadinejad'] | 2022-07-01 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 2.35423610e-01 5.18747389e-01 -2.47465014e-01 -5.54117680e-01
-8.11510921e-01 -8.57264400e-01 6.49882734e-01 -1.67071611e-01
-1.73002005e-01 9.76762891e-01 -7.58793727e-02 -6.23050630e-01
-1.72423646e-01 -3.53582233e-01 -9.92662430e-01 -1.70307495e-02
4.70287129e-02 7.71462142e-01 5.43378592e-01 -4.70517218... | [4.438179969787598, 0.9375279545783997] |
a00277ed-c1f7-4e4e-be09-56b0b0a35e09 | degree-quant-quantization-aware-training-for | 2008.05000 | null | https://arxiv.org/abs/2008.05000v3 | https://arxiv.org/pdf/2008.05000v3.pdf | Degree-Quant: Quantization-Aware Training for Graph Neural Networks | Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data. Despite their promise, there exists little research exploring methods to make them more efficient at inference time. In this work, we explore the viability of training ... | ['Nicholas D. Lane', 'Javier Fernandez-Marques', 'Shyam A. Tailor'] | 2020-08-11 | null | https://openreview.net/forum?id=NSBrFgJAHg | https://openreview.net/pdf?id=NSBrFgJAHg | iclr-2021-1 | ['graph-regression'] | ['graphs'] | [ 1.26876608e-01 2.66599059e-01 -3.28097224e-01 -5.13066053e-01
-5.47258139e-01 -4.62812364e-01 6.64617360e-01 2.97081262e-01
-5.09237409e-01 7.21581399e-01 1.29801527e-01 -8.49251509e-01
8.97102877e-02 -1.33423853e+00 -9.33350503e-01 -3.20885688e-01
-2.68154174e-01 6.09491885e-01 2.98978865e-01 -9.72817987... | [6.995605945587158, 5.9654693603515625] |
c83548ba-3d88-48bb-bb1b-b45adbf85099 | machine-learning-for-early-prediction-of | 1904.07990 | null | http://arxiv.org/abs/1904.07990v2 | http://arxiv.org/pdf/1904.07990v2.pdf | Machine learning for early prediction of circulatory failure in the intensive care unit | Intensive care clinicians are presented with large quantities of patient
information and measurements from a multitude of monitoring systems. The
limited ability of humans to process such complex information hinders
physicians to readily recognize and act on early signs of patient
deterioration. We used machine learnin... | ['Gunnar Rätsch', 'Karsten Borgwardt', 'Cristóbal Esteban', 'Matthias Hüser', 'Martin Faltys', 'Tobias M. Merz', 'Stephanie L. Hyland', 'Max Horn', 'Xinrui Lyu', 'Marc Zimmermann', 'Bastian Rieck', 'Michael Moor', 'Dean Bodenham', 'Christian Bock', 'Thomas Gumbsch'] | 2019-04-16 | null | null | null | null | ['circulatory-failure'] | ['medical'] | [ 9.53442007e-02 3.91848981e-02 -1.13522328e-01 -3.68819773e-01
-5.72484195e-01 -3.25474501e-01 -1.21486127e-01 1.16799843e+00
-7.04598606e-01 8.33867550e-01 1.18633471e-01 -7.02394307e-01
-4.21231091e-01 -5.78082383e-01 1.32013485e-01 -1.52952954e-01
-5.61928868e-01 8.90769422e-01 -3.34347002e-02 4.31656808... | [7.978334426879883, 6.159065246582031] |
a69765fd-e7a2-43b5-a6fa-4e47218a2619 | bayesian-sparsification-of-deep-c-valued | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6728-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6728-Paper.pdf | Bayesian Sparsification of Deep C-valued Networks | With continual miniaturization ever more applications of deep learning can be found in embedded systems, where it is common to encounter data with natural representation in the complex domain. To this end we extend Sparse Variational Dropout to complex-valued neural networks and verify the proposed Bayesian technique b... | ['Evgeny Burnaev', 'Ivan Nazarov'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6728-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6728-Paper.pdf | icml-2020-1 | ['music-transcription'] | ['music'] | [ 1.54032245e-01 3.39079946e-01 -1.46664366e-01 -4.91652995e-01
-7.19895840e-01 -3.62360895e-01 4.31307524e-01 -3.23505223e-01
-7.40011215e-01 8.33673954e-01 -1.32187352e-01 -9.78957936e-02
-6.48094416e-01 -3.56124520e-01 -9.97886300e-01 -7.26740897e-01
-3.26583862e-01 6.08105958e-01 -1.98980033e-01 3.34150344... | [8.548210144042969, 3.21889066696167] |
b340a49e-916f-47db-866d-1a763af11146 | distributed-optimization-in-distribution | 2205.09981 | null | https://arxiv.org/abs/2205.09981v1 | https://arxiv.org/pdf/2205.09981v1.pdf | Distributed Optimization in Distribution Systems with Grid-Forming and Grid-Supporting Inverters | With massive penetrations of active grid-edge technologies, distributed computing and optimization paradigm has gained significant attention to solve distribution-level optimal power flow (OPF) problems. However, the application of generic distributed optimization techniques to OPF problems leads to a very large number... | ['Anamika Dubey', 'Rabayet Sadnan'] | 2022-05-20 | null | null | null | null | ['distributed-optimization', 'problem-decomposition'] | ['methodology', 'miscellaneous'] | [-6.95427358e-01 -2.39049241e-01 2.88849056e-01 2.54735295e-02
-3.06923300e-01 -1.12804139e+00 2.59298861e-01 4.95340139e-01
3.80335480e-01 1.28838086e+00 -2.69566089e-01 -2.78692663e-01
-7.95097947e-01 -1.09224761e+00 -1.30044669e-01 -9.40591753e-01
-6.73677862e-01 9.40504491e-01 -2.28150666e-01 -3.80777210... | [5.663600921630859, 2.5455076694488525] |
fcabab28-193b-4436-a096-9c50d0661b98 | mining-social-science-publications-for-survey | null | null | https://aclanthology.org/W17-2907 | https://aclanthology.org/W17-2907.pdf | Mining Social Science Publications for Survey Variables | Research in Social Science is usually based on survey data where individual research questions relate to observable concepts (variables). However, due to a lack of standards for data citations a reliable identification of the variables used is often difficult. In this paper, we present a work-in-progress study that see... | ['Peter Mutschke', 'Andrea Zielinski'] | 2017-08-01 | null | null | null | ws-2017-8 | ['variable-detection'] | ['natural-language-processing'] | [ 9.85265598e-02 3.54311280e-02 -5.90798736e-01 -4.67471033e-01
-9.11457360e-01 -6.19364679e-01 1.03737617e+00 6.96818292e-01
-4.93211627e-01 7.97947645e-01 4.85935658e-01 -5.72532117e-01
-3.39294910e-01 -4.22571361e-01 -6.41346648e-02 -1.50914699e-01
6.84059337e-02 4.81459796e-01 -3.77313524e-01 8.25356971... | [9.981464385986328, 8.963281631469727] |
7c9e0c29-b940-46f8-abbf-f9ed54ed40da | invariant-representations-in-deep-learning | 2305.00335 | null | https://arxiv.org/abs/2305.00335v1 | https://arxiv.org/pdf/2305.00335v1.pdf | Invariant Representations in Deep Learning for Optoacoustic Imaging | Image reconstruction in optoacoustic tomography (OAT) is a trending learning task highly dependent on measured physical magnitudes present at sensing time. The large number of different settings, and also the presence of uncertainties or partial knowledge of parameters, can lead to reconstructions algorithms that are s... | ['Leonardo Rey Vega', 'Martin G. Gonzalez', 'Matias Vera'] | 2023-04-29 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 3.75920773e-01 -3.33748966e-01 3.31652254e-01 -2.03593537e-01
-5.33707142e-01 -3.89637589e-01 5.09532809e-01 -4.42646816e-02
-5.14258206e-01 9.06288266e-01 -1.04553588e-01 -1.41231596e-01
-8.88609529e-01 -5.71990669e-01 -8.05923581e-01 -1.03354478e+00
-4.59469622e-03 6.78423345e-01 2.23491773e-01 -1.72615543... | [11.455086708068848, -2.3258068561553955] |
34d65a77-0c1e-4efb-b776-e03249f25d3f | development-and-evaluation-of-conformal | 2304.00970 | null | https://arxiv.org/abs/2304.00970v1 | https://arxiv.org/pdf/2304.00970v1.pdf | Development and Evaluation of Conformal Prediction Methods for QSAR | The quantitative structure-activity relationship (QSAR) regression model is a commonly used technique for predicting biological activities of compounds using their molecular descriptors. Predictions from QSAR models can help, for example, to optimize molecular structure; prioritize compounds for further experimental te... | ['Vladimir Svetnik', 'Robert P. Sheridan', 'Andy Liaw', 'Yuting Xu'] | 2023-04-03 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 2.35054150e-01 -3.85347188e-01 -6.13242805e-01 -5.03226459e-01
-8.93888891e-01 -6.81070685e-01 4.05130476e-01 8.76563907e-01
-1.25079289e-01 1.37038112e+00 -2.36535758e-01 -5.83711863e-01
-3.28321427e-01 -8.57663631e-01 -8.59324276e-01 -8.73326480e-01
-4.66178596e-01 6.18147731e-01 1.35885686e-01 3.98574919... | [5.072172164916992, 5.553449630737305] |
bcfde3c1-3724-40db-a972-a30c3f20af39 | online-multi-object-tracking-with-instance | 1902.08231 | null | http://arxiv.org/abs/1902.08231v1 | http://arxiv.org/pdf/1902.08231v1.pdf | Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment | Recent progresses in model-free single object tracking (SOT) algorithms have
largely inspired applying SOT to \emph{multi-object tracking} (MOT) to improve
the robustness as well as relieving dependency on external detector. However,
SOT algorithms are generally designed for distinguishing a target from its
environment... | ['Chiu C. Tan', 'Heng Fan', 'Haibin Ling', 'Peng Chu'] | 2019-02-21 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [ 1.99461401e-01 -3.79191786e-01 -7.56670907e-02 -5.60842417e-02
-4.59994286e-01 -5.98060071e-01 6.26228929e-01 1.96600467e-01
-4.10800636e-01 4.20911849e-01 -3.71752262e-01 1.87783912e-01
5.67557253e-02 -6.09840333e-01 -9.07954991e-01 -8.41068685e-01
-1.97841033e-01 6.88458383e-01 1.15653133e+00 1.07407734... | [6.304345607757568, -2.074509620666504] |
ea0d32f3-a9b8-4f40-a8ab-d667e7e8d2b7 | analysing-diffusion-based-generative | 2211.02397 | null | https://arxiv.org/abs/2211.02397v2 | https://arxiv.org/pdf/2211.02397v2.pdf | Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech Restoration | Diffusion-based generative models have had a high impact on the computer vision and speech processing communities these past years. Besides data generation tasks, they have also been employed for data restoration tasks like speech enhancement and dereverberation. While discriminative models have traditionally been argu... | ['Timo Gerkmann', 'Simon Welker', 'Julius Richter', 'Jean-Marie Lemercier'] | 2022-11-04 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension', 'speech-denoising', 'speech-dereverberation'] | ['audio', 'speech', 'speech', 'speech'] | [ 2.1260795e-01 8.9674205e-02 2.2906917e-01 -2.3485799e-01
-9.5092815e-01 -2.5753736e-01 8.4782857e-01 -1.3655451e-01
-4.0893605e-01 4.9706522e-01 6.6843814e-01 -2.4814512e-01
-3.9915726e-01 -4.7570491e-01 -2.3909862e-01 -1.2174311e+00
9.2308842e-02 5.6803610e-02 9.2931338e-02 -3.9724740e-01
9.3054362e-03... | [15.165258407592773, 5.984804630279541] |
0223373b-d432-45d2-b35e-0b396249f3da | frugalml-how-to-use-ml-prediction-apis-more | 2006.07512 | null | https://arxiv.org/abs/2006.07512v1 | https://arxiv.org/pdf/2006.07512v1.pdf | FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply | Prediction APIs offered for a fee are a fast-growing industry and an important part of machine learning as a service. While many such services are available, the heterogeneity in their price and performance makes it challenging for users to decide which API or combination of APIs to use for their own data and budget. W... | ['James Zou', 'Matei Zaharia', 'Lingjiao Chen'] | 2020-06-12 | null | http://proceedings.neurips.cc/paper/2020/hash/789ba2ae4d335e8a2ad283a3f7effced-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/789ba2ae4d335e8a2ad283a3f7effced-Paper.pdf | neurips-2020-12 | ['facial-emotion-recognition'] | ['computer-vision'] | [-7.08003156e-03 -1.30927294e-01 -7.07677186e-01 -9.04549420e-01
-8.97819281e-01 -6.37290239e-01 2.54897922e-01 -1.15210600e-01
-2.13784561e-01 1.53041542e-01 -9.19077471e-02 -2.27503598e-01
-2.02031627e-01 -2.55949765e-01 -5.42139292e-01 -3.70543480e-01
-1.40891135e-01 4.06643242e-01 -1.17738374e-01 1.52221784... | [9.075872421264648, 4.396977424621582] |
51e06212-d8d5-4238-8fa7-0390ae5ab90d | on-expert-behaviors-and-question-types-for | 2001.05952 | null | https://arxiv.org/abs/2001.05952v2 | https://arxiv.org/pdf/2001.05952v2.pdf | On Expert Behaviors and Question Types for Efficient Query-Based Ontology Fault Localization | We challenge existing query-based ontology fault localization methods wrt. assumptions they make, criteria they optimize, and interaction means they use. We find that their efficiency depends largely on the behavior of the interacting expert, that performed calculations can be inefficient or imprecise, and that used op... | ['Patrick Rodler'] | 2020-01-16 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-2.16556102e-01 3.19438368e-01 8.06946084e-02 -3.18178982e-01
-4.16381687e-01 -6.17629528e-01 1.62416250e-01 5.11764228e-01
-4.31129515e-01 8.45055103e-01 -2.28367120e-01 -3.04070741e-01
-9.32768822e-01 -7.08860397e-01 -4.15347457e-01 3.31641436e-02
-1.58596337e-01 9.94102120e-01 8.11412990e-01 -4.49358165... | [5.49169921875, 2.808230400085449] |
cfea139a-4165-4f65-8495-e3b1678fd2cc | learning-to-execute-efficient-learning-of | 2111.07908 | null | https://arxiv.org/abs/2111.07908v1 | https://arxiv.org/pdf/2111.07908v1.pdf | Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics | Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand. On the other hand, approximate models are readily available in many robotics scenarios, making model-based approaches like planning a data-efficient alternative. Still, the performance of these methods suffers if the model is... | ['Marc Toussaint', 'Ozgur S. Oguz', 'Danny Driess', 'Ingmar Schubert'] | 2021-11-15 | null | http://proceedings.neurips.cc/paper/2021/hash/0e9b734aa25ca8096cb7b56dc0dd8929-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/0e9b734aa25ca8096cb7b56dc0dd8929-Paper.pdf | neurips-2021-12 | ['learning-to-execute'] | ['computer-code'] | [-8.60256106e-02 5.24991274e-01 -6.45806372e-01 -7.91110620e-02
-7.09220529e-01 -4.95523959e-01 8.54784727e-01 2.12949499e-01
-5.47893703e-01 1.02771616e+00 2.86246628e-01 -1.54893205e-01
-3.30834538e-01 -9.19659734e-01 -7.97668099e-01 -2.94639587e-01
-2.85906792e-02 8.24342728e-01 3.23588043e-01 -3.42706382... | [4.319046497344971, 1.3514196872711182] |
2f5d1ae3-65f3-4001-81bd-d37967a781a1 | experiments-on-hybrid-corpus-based-sentiment | null | null | https://aclanthology.org/W12-0501 | https://aclanthology.org/W12-0501.pdf | Experiments on Hybrid Corpus-Based Sentiment Lexicon Acquisition | null | ["Bojana Dalbelo Ba{\\v{s}}i{\\'c}", 'Jan {\\v{S}}najder', 'Goran Glava{\\v{s}}'] | 2012-04-01 | null | null | null | ws-2012-4 | ['subjectivity-analysis'] | ['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... | [-7.391997814178467, 3.721400499343872] |
c0d6d828-55e5-470a-a6f1-f61f8f9e85a3 | score-and-lyrics-free-singing-voice-1 | 1912.11747 | null | https://arxiv.org/abs/1912.11747v2 | https://arxiv.org/pdf/1912.11747v2.pdf | Score and Lyrics-Free Singing Voice Generation | Generative models for singing voice have been mostly concerned with the task of ``singing voice synthesis,'' i.e., to produce singing voice waveforms given musical scores and text lyrics. In this work, we explore a novel yet challenging alternative: singing voice generation without pre-assigned scores and lyrics, in bo... | ['Yi-Hsuan Yang', 'Yin-Cheng Yeh', 'Yu-Hua Chen', 'Jen-Yu Liu'] | 2019-12-26 | null | https://openreview.net/forum?id=HygcdeBFvr | https://openreview.net/pdf?id=HygcdeBFvr | null | ['audio-generation', 'singing-voice-synthesis'] | ['audio', 'speech'] | [ 1.25684112e-01 2.76662529e-01 2.99201697e-01 1.78603679e-02
-1.14837289e+00 -1.03397775e+00 3.73343557e-01 -7.48396277e-01
1.17680840e-01 7.35691309e-01 3.13133657e-01 5.71669973e-02
1.19126335e-01 -6.89793468e-01 -4.46562678e-01 -6.40240014e-01
3.49950254e-01 5.30707002e-01 -2.04568624e-01 -2.80031234... | [15.58244800567627, 6.0288801193237305] |
8567c628-aabc-45c5-ac02-84fcf9411375 | dependency-parsing-in-a-morphological-rich | null | null | https://aclanthology.org/2021.pail-1.3 | https://aclanthology.org/2021.pail-1.3.pdf | Dependency Parsing in a Morphological rich language, Tamil | Dependency parsing is the process of analysing the grammatical structure of a sentence based on the dependencies between the words in a sentence. The annotation of dependency parsing is done using different formalisms at word-level namely Universal Dependencies and chunk-level namely AnnaCorra. Though dependency parsin... | ['Sobha Lalitha Devi', 'Vijay Sundar Ram'] | null | null | null | null | pail-icon-2021-12 | ['dependency-parsing'] | ['natural-language-processing'] | [-5.48424959e-01 2.08942562e-01 2.73964614e-01 -5.70476830e-01
-3.75654429e-01 -8.28776181e-01 2.13227138e-01 7.02869356e-01
-6.66075408e-01 1.09233713e+00 4.51376379e-01 -5.75134516e-01
9.16968882e-02 -6.82067215e-01 4.39605862e-02 -4.69265878e-01
-1.09195188e-01 7.91029930e-01 4.46723819e-01 -5.98839879... | [10.344305992126465, 10.068018913269043] |
46790817-e70d-4d7b-8d8f-7705664837f3 | end-to-end-annotator-bias-approximation-on | 2111.02326 | null | https://arxiv.org/abs/2111.02326v1 | https://arxiv.org/pdf/2111.02326v1.pdf | End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis | Sentiment analysis is often a crowdsourcing task prone to subjective labels given by many annotators. It is not yet fully understood how the annotation bias of each annotator can be modeled correctly with state-of-the-art methods. However, resolving annotator bias precisely and reliably is the key to understand annotat... | ['Georg Groh', 'Hannah Danner', 'Maximilian Wich', 'Christian Widmer', 'Maria Luisa Ripoll Dominguez', 'Andreas Koch', 'David Szabo', 'Gerhard Hagerer'] | 2021-11-03 | null | https://aclanthology.org/2021.icnlsp-1.1 | https://aclanthology.org/2021.icnlsp-1.1.pdf | icnlsp-2021-11 | ['misconceptions'] | ['miscellaneous'] | [ 1.95470124e-01 7.00132430e-01 -2.46390328e-01 -9.50146794e-01
-8.23128462e-01 -1.07543468e+00 1.15487099e-01 7.12945700e-01
-4.76424128e-01 8.58478785e-01 2.89084613e-01 -5.88384718e-02
7.14679122e-01 -2.67866999e-01 -9.94515300e-01 -5.76940536e-01
6.50592446e-01 7.62641072e-01 8.06967244e-02 -3.99153054... | [9.6477632522583, 4.687412261962891] |
428b8136-b0e7-4aee-b578-447e36fe1710 | accurate-and-robust-scale-recovery-for | 2101.05995 | null | https://arxiv.org/abs/2101.05995v2 | https://arxiv.org/pdf/2101.05995v2.pdf | Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry | Scale ambiguity is a fundamental problem in monocular visual odometry. Typical solutions include loop closure detection and environment information mining. For applications like self-driving cars, loop closure is not always available, hence mining prior knowledge from the environment becomes a more promising approach. ... | ['Dermot Kerr', 'Sonya Coleman', 'Shiwen Liang', 'Delong Zhu', 'Yunzhou Zhang', 'Rui Tian'] | 2021-01-15 | null | null | null | null | ['loop-closure-detection', 'monocular-visual-odometry'] | ['computer-vision', 'robots'] | [-1.33632779e-01 -2.11337402e-01 -3.57306510e-01 -4.41680163e-01
-6.22980893e-01 -2.48896718e-01 2.02539474e-01 1.42837852e-01
-5.09123623e-01 3.77644211e-01 -3.59511971e-01 -1.68972909e-01
-1.02137029e-01 -7.30418026e-01 -8.64682376e-01 -5.68571448e-01
1.47216335e-01 5.77346146e-01 3.83849770e-01 -3.09635758... | [7.494079113006592, -2.129279375076294] |
f468c8aa-181d-4b53-bf3c-2362a0e40680 | context-driven-satirical-news-generation | null | null | https://aclanthology.org/2020.figlang-1.5 | https://aclanthology.org/2020.figlang-1.5.pdf | Context-Driven Satirical News Generation | While mysterious, humor likely hinges on an interplay of entities, their relationships, and cultural connotations. Motivated by the importance of context in humor, we consider methods for constructing and leveraging contextual representations in generating humorous text. Specifically, we study the capacity of transform... | ['Nam Do', 'Zachary Horvitz', 'Michael L. Littman'] | 2020-07-01 | null | null | null | ws-2020-7 | ['news-generation'] | ['natural-language-processing'] | [-4.98025306e-02 4.35435206e-01 5.79964295e-02 -6.92561790e-02
-7.32958198e-01 -7.69392610e-01 1.13728416e+00 3.26308131e-01
-1.59139737e-01 9.14656699e-01 1.43819427e+00 -5.20344079e-02
2.48856664e-01 -8.31432045e-01 -5.15839875e-01 1.01429403e-01
3.44389647e-01 4.51611489e-01 -1.21720940e-01 -1.14459836... | [8.93290901184082, 10.967108726501465] |
ab57ff6a-e5dd-4f40-badf-c5c0c970a2f9 | online-multi-object-tracking-with-historical | 1805.10916 | null | http://arxiv.org/abs/1805.10916v4 | http://arxiv.org/pdf/1805.10916v4.pdf | Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering | In this paper, we propose the methods to handle temporal errors during
multi-object tracking. Temporal error occurs when objects are occluded or noisy
detections appear near the object. In those situations, tracking may fail and
various errors like drift or ID-switching occur. It is hard to overcome
temporal errors onl... | ['Young-min Song', 'Young-chul Yoon', 'Kwangjin Yoon', 'Moongu Jeon', 'Abhijeet Boragule'] | 2018-05-28 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-1.17520444e-01 -8.39854240e-01 3.85249592e-03 5.09013794e-02
-2.54013330e-01 -6.25607133e-01 3.49501818e-01 -2.69214004e-01
-4.88876551e-01 8.46259713e-01 -3.83634567e-01 2.78294116e-01
1.28462380e-02 -2.64363348e-01 -6.54387236e-01 -7.09891796e-01
1.19563388e-02 4.60573554e-01 8.59809160e-01 1.02927007... | [6.5064005851745605, -2.0052082538604736] |
c4d08fc7-9224-4721-abd6-69b12e4aaa88 | accelerated-parallel-mri-using-memory | 2304.01351 | null | https://arxiv.org/abs/2304.01351v1 | https://arxiv.org/pdf/2304.01351v1.pdf | Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL) | Model-based deep learning methods that combine imaging physics with learned regularization priors have been emerging as powerful tools for parallel MRI acceleration. The main focus of this paper is to determine the utility of the monotone operator learning (MOL) framework in the parallel MRI setting. The MOL algorithm ... | ['Mathews Jacob', 'Aniket Pramanik'] | 2023-04-03 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 5.31027019e-01 1.02572694e-01 -1.19703086e-02 -3.50759029e-01
-7.35327184e-01 6.28883541e-02 3.79449278e-01 1.00329787e-01
-8.41799200e-01 6.59772277e-01 4.30955201e-01 -3.83274764e-01
-4.99013364e-01 -2.03790754e-01 -8.31401348e-01 -8.76185775e-01
-6.07213914e-01 2.95959264e-01 1.01751097e-01 3.84408352... | [13.42078971862793, -2.392184019088745] |
071463ae-639f-4b3e-b4a9-f52da0135ec6 | self-supervised-visual-place-recognition-by | 2208.09315 | null | https://arxiv.org/abs/2208.09315v1 | https://arxiv.org/pdf/2208.09315v1.pdf | Self-Supervised Visual Place Recognition by Mining Temporal and Feature Neighborhoods | Visual place recognition (VPR) using deep networks has achieved state-of-the-art performance. However, most of them require a training set with ground truth sensor poses to obtain positive and negative samples of each observation's spatial neighborhood for supervised learning. When such information is unavailable, temp... | ['Chen Feng', 'Ruoyu Wang', 'Li Ding', 'Yiming Li', 'Xuchu Xu', 'Xinhao Liu', 'Chao Chen'] | 2022-08-19 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 8.86517018e-02 -2.21834555e-01 -4.72669899e-01 -5.32182813e-01
-7.24223077e-01 -7.08278656e-01 5.68738163e-01 1.71021059e-01
-5.90950429e-01 6.95905685e-01 3.21468152e-02 -1.86821520e-01
-7.19946623e-02 -9.37718809e-01 -1.02482140e+00 -7.17394769e-01
-2.75993407e-01 3.03230494e-01 1.63017422e-01 -6.95405453... | [7.532192230224609, -2.016571044921875] |
06683ca9-94cb-4a1f-97ee-e63fe6f46379 | sequence-level-knowledge-distillation-for | 1811.04531 | null | http://arxiv.org/abs/1811.04531v1 | http://arxiv.org/pdf/1811.04531v1.pdf | Sequence-Level Knowledge Distillation for Model Compression of Attention-based Sequence-to-Sequence Speech Recognition | We investigate the feasibility of sequence-level knowledge distillation of
Sequence-to-Sequence (Seq2Seq) models for Large Vocabulary Continuous Speech
Recognition (LVSCR). We first use a pre-trained larger teacher model to
generate multiple hypotheses per utterance with beam search. With the same
input, we then train ... | ["Raden Mu'az Mun'im", 'Nakamasa Inoue', 'Koichi Shinoda'] | 2018-11-12 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 6.98009133e-01 7.30535030e-01 8.45754445e-02 -4.73113924e-01
-1.49255240e+00 -4.93456364e-01 3.88442427e-01 5.36768995e-02
-8.25730741e-01 1.36642981e+00 2.77436852e-01 -7.58532047e-01
3.85956168e-01 -4.05821383e-01 -8.59696209e-01 -5.13453066e-01
2.38214374e-01 5.50181687e-01 3.01061362e-01 -3.29063416... | [14.431419372558594, 6.870495319366455] |
aaeaddd5-6b9a-4a76-b967-ed62aca04ee4 | 1st-place-solution-for-youtubevos-challenge | 2106.06649 | null | https://arxiv.org/abs/2106.06649v2 | https://arxiv.org/pdf/2106.06649v2.pdf | 1st Place Solution for YouTubeVOS Challenge 2021:Video Instance Segmentation | Video Instance Segmentation (VIS) is a multi-task problem performing detection, segmentation, and tracking simultaneously. Extended from image set applications, video data additionally induces the temporal information, which, if handled appropriately, is very useful to identify and predict object motions. In this work,... | ['Masao Yamanaka', 'Masayuki Yamazaki', 'Chuong H. Nguyen', 'Nam LH. Phan', 'Tuan N. Tang', 'Thuy C. Nguyen'] | 2021-06-12 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 6.12079725e-02 -3.87744576e-01 -4.61505413e-01 -2.59174943e-01
-7.27368414e-01 -3.45831126e-01 4.23815906e-01 -2.74507701e-01
-4.70463663e-01 6.53911173e-01 4.25227322e-02 4.65457030e-02
7.33021870e-02 -2.75976121e-01 -8.62082899e-01 -8.58129561e-01
7.54550248e-02 -3.10255978e-02 7.24856973e-01 1.57075584... | [9.23218822479248, 0.02550787292420864] |
556f86ac-8bbe-4422-b1ac-9442d7e564cf | a-system-for-real-time-twitter-sentiment | null | null | https://aclanthology.org/P12-3020 | https://aclanthology.org/P12-3020.pdf | A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle | null | ['Fran{\\c{c}}ois Bar', 'Abe Kazemzadeh', 'Hao Wang', 'Shrikanth Narayanan', 'Dogan Can'] | 2012-07-01 | null | null | null | acl-2012-7 | ['twitter-sentiment-analysis'] | ['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... | [-7.312845706939697, 3.64819598197937] |
f87d9a12-74f9-461c-acd5-d353dc177192 | group-collaborative-learning-for-co-salient | 2104.01108 | null | https://arxiv.org/abs/2104.01108v2 | https://arxiv.org/pdf/2104.01108v2.pdf | Group Collaborative Learning for Co-Salient Object Detection | We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects b... | ['Yu-Wing Tai', 'Ling Shao', 'Chi Keung Tang', 'Huazhu Fu', 'Deng-Ping Fan', 'Qi Fan'] | 2021-03-15 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Fan_Group_Collaborative_Learning_for_Co-Salient_Object_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Fan_Group_Collaborative_Learning_for_Co-Salient_Object_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['co-saliency-detection'] | ['computer-vision'] | [-1.75756812e-02 6.59924326e-03 -3.14302742e-01 -1.88551083e-01
-9.01815236e-01 -2.90337890e-01 5.44067144e-01 4.76192653e-01
-2.67415375e-01 1.49761587e-01 1.62373975e-01 6.27878085e-02
-4.46174473e-01 -2.96453059e-01 -6.40121877e-01 -8.42164755e-01
-1.82954043e-01 3.02882075e-01 5.26211619e-01 1.70626506... | [9.730093955993652, -0.05714463070034981] |
d4cbb30c-e91d-40ab-925b-16bc8160ed38 | penalizing-the-hard-example-but-not-too-much | null | null | https://ieeexplore.ieee.org/abstract/document/9956020 | https://ieeexplore.ieee.org/abstract/document/9956020 | Penalizing the Hard Example But Not Too Much: A Strong Baseline for Fine-Grained Visual Classification | Though significant progress has been achieved on fine-grained visual classification (FGVC), severe overfitting still hinders model generalization. A recent study shows that hard samples in the training set can be easily fit, but most existing FGVC methods fail to classify some hard examples in the test set. The reason ... | ['Yi Yang', 'Xiaohan Wang', 'Linchao Zhu', 'Yuanzhi Liang'] | 2022-11-21 | null | null | null | ieee-transactions-on-neural-networks-and-8 | ['fine-grained-visual-recognition', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [ 1.75930440e-01 -1.36549711e-01 -4.64335173e-01 -5.51677167e-01
-7.24164784e-01 -7.53131032e-01 6.03594184e-01 -4.78824049e-01
-2.39068553e-01 7.92615294e-01 -4.35164720e-02 -1.21111661e-01
7.43816197e-02 -6.08659804e-01 -7.21240401e-01 -6.24480844e-01
3.69250208e-01 4.40534204e-01 3.64075214e-01 -9.03781727... | [9.643061637878418, 2.1889684200286865] |
468e8e5a-2b7a-4981-bae9-997bf5e6dc24 | magnushammer-a-transformer-based-approach-to | 2303.04488 | null | https://arxiv.org/abs/2303.04488v1 | https://arxiv.org/pdf/2303.04488v1.pdf | Magnushammer: A Transformer-based Approach to Premise Selection | Premise selection is a fundamental problem of automated theorem proving. Previous works often use intricate symbolic methods, rely on domain knowledge, and require significant engineering effort to solve this task. In this work, we show that Magnushammer, a neural transformer-based approach, can outperform traditional ... | ['Yuhuai Wu', 'Piotr Miłoś', 'Łukasz Kuciński', 'Christian Szegedy', 'Jin Peng Zhou', 'Albert Qiaochu Jiang', 'Szymon Tworkowski', 'Szymon Antoniak', 'Maciej Mikuła'] | 2023-03-08 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 7.75638297e-02 2.94943571e-01 -3.38359326e-01 1.58057854e-01
-9.73105371e-01 -6.87058508e-01 2.31568530e-01 1.67198882e-01
-9.88588929e-02 1.25159824e+00 -8.55316341e-01 -1.16613615e+00
-2.44905636e-01 -1.15994060e+00 -1.21634960e+00 1.15369130e-02
-3.53679568e-01 4.27210569e-01 4.17597920e-01 -4.08295423... | [8.89887523651123, 7.021091938018799] |
5a4ea988-1f12-4df1-8a3e-c691fa31b84f | learning-interpretable-deep-state-space-model | 2102.00397 | null | https://arxiv.org/abs/2102.00397v1 | https://arxiv.org/pdf/2102.00397v1.pdf | Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting | Probabilistic time series forecasting involves estimating the distribution of future based on its history, which is essential for risk management in downstream decision-making. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are pa... | ['Yaohui Jin', 'Xiaokang Yang', 'Junchi Yan', 'Longyuan Li'] | 2021-01-31 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-1.72991708e-01 2.91503102e-01 -9.45818126e-02 -4.22318310e-01
-8.32075596e-01 -7.02451766e-01 9.57040310e-01 -7.11233392e-02
-5.62122092e-02 8.99609506e-01 9.09624577e-01 -7.33674765e-01
-3.84965450e-01 -9.14305270e-01 -4.25034940e-01 -7.73135960e-01
-4.05453861e-01 3.98870707e-01 1.70427531e-01 -1.16360344... | [6.750422477722168, 3.120046377182007] |
cc2674f5-0452-4203-9339-dd4d59d519cd | semantic-segmentation-using-super-resolution | 2306.15218 | null | https://arxiv.org/abs/2306.15218v1 | https://arxiv.org/pdf/2306.15218v1.pdf | Semantic Segmentation Using Super Resolution Technique as Pre-Processing | Combining high-level and low-level visual tasks is a common technique in the field of computer vision. This work integrates the technique of image super resolution to semantic segmentation for document image binarization. It demonstrates that using image super-resolution as a preprocessing step can effectively enhance ... | ['Tingkai Chang', 'Chun-Tse Chien', 'Jen-Shiun Chiang', 'Wei-Han Chen', 'Chih-Chia Chen'] | 2023-06-27 | null | null | null | null | ['image-super-resolution', 'super-resolution'] | ['computer-vision', 'computer-vision'] | [ 8.88135374e-01 -1.10226177e-01 -3.60004932e-01 -2.93068916e-01
-6.59598112e-01 -4.34802026e-01 4.22625840e-01 1.67391613e-01
-4.02571052e-01 3.64660174e-01 6.03293851e-02 -1.37571454e-01
7.97542557e-02 -8.68757665e-01 -2.78495193e-01 -5.33763647e-01
8.47483039e-01 2.38090515e-01 8.71862769e-01 -3.93867612... | [9.571313858032227, 0.19211427867412567] |
55d5c81c-62d3-4dc3-8c0f-6d3227635812 | streaming-multi-talker-speech-recognition | 2104.02109 | null | https://arxiv.org/abs/2104.02109v1 | https://arxiv.org/pdf/2104.02109v1.pdf | Streaming Multi-talker Speech Recognition with Joint Speaker Identification | In multi-talker scenarios such as meetings and conversations, speech processing systems are usually required to transcribe the audio as well as identify the speakers for downstream applications. Since overlapped speech is common in this case, conventional approaches usually address this problem in a cascaded fashion th... | ['Yifan Gong', 'Jinyu Li', 'Naoyuki Kanda', 'Liang Lu'] | 2021-04-05 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 4.52456564e-01 -1.74721070e-02 1.48311064e-01 -6.53822005e-01
-1.46572852e+00 -6.89069211e-01 4.46475804e-01 -1.37947917e-01
-1.66873038e-01 2.71051645e-01 5.28874516e-01 -5.65409899e-01
1.13864392e-01 6.75110668e-02 -3.29500347e-01 -8.50229442e-01
4.34161909e-02 6.19883120e-01 -9.28015187e-02 -1.80003896... | [14.617280960083008, 6.333874225616455] |
6865a6a0-1b44-4c83-86ff-d88cc86be734 | tensorize-factorize-and-regularize-robust | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Hwang_Tensorize_Factorize_and_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Hwang_Tensorize_Factorize_and_CVPR_2018_paper.pdf | Tensorize, Factorize and Regularize: Robust Visual Relationship Learning | Visual relationships provide higher-level information of objects and their relations in an image â this enables a semantic understanding of the scene and helps downstream applications. Given a set of localized objects in some training data, visual relationship detection seeks to detect the most likely ârelationship... | ['Sathya N. Ravi', 'Vikas Singh', 'Seong Jae Hwang', 'Maxwell D. Collins', 'Hyunwoo J. Kim', 'Zirui Tao'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.71117276e-01 1.36348903e-01 -3.70237917e-01 -3.61869603e-01
-7.07730353e-01 -7.00448871e-01 6.60157859e-01 4.51878786e-01
-9.98770520e-02 3.99976432e-01 8.69862363e-03 -1.88267261e-01
-4.48420137e-01 -6.61087632e-01 -1.01551199e+00 -7.37771630e-01
-2.27116212e-01 6.08951032e-01 2.75570422e-01 1.21672057... | [10.217864036560059, 1.6609879732131958] |
1223fc86-6165-494c-a817-982083c269dd | posegpt-quantization-based-3d-human-motion | 2210.10542 | null | https://arxiv.org/abs/2210.10542v1 | https://arxiv.org/pdf/2210.10542v1.pdf | PoseGPT: Quantization-based 3D Human Motion Generation and Forecasting | We address the problem of action-conditioned generation of human motion sequences. Existing work falls into two categories: forecast models conditioned on observed past motions, or generative models conditioned on action labels and duration only. In contrast, we generate motion conditioned on observations of arbitrary ... | ['Grégory Rogez', 'Philippe Weinzaepfel', 'Fabien Baradel', 'Thomas Lucas'] | 2022-10-19 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 3.34624499e-01 8.27401280e-02 -1.65510714e-01 -3.01575452e-01
-9.32781756e-01 -3.21394235e-01 7.53076375e-01 -7.36286521e-01
-3.57027769e-01 6.80703461e-01 7.61153102e-01 2.41265863e-01
1.67810738e-01 -8.45427513e-01 -8.27378094e-01 -9.17856514e-01
-2.34728858e-01 7.52027452e-01 2.79975176e-01 -4.52447720... | [7.322096347808838, -0.12120560556650162] |
a6628893-5355-4dbf-a8e5-a2ac34130246 | rms-flownet-efficient-and-robust-multi-scale | 2204.00354 | null | https://arxiv.org/abs/2204.00354v1 | https://arxiv.org/pdf/2204.00354v1.pdf | RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds | The proposed RMS-FlowNet is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation which can operate on point clouds of high density. For hierarchical scene flow estimation, the existing methods depend on either expensive Farthest-Point-Sampling (FPS) or structure-based scaling ... | ['Didier Stricker', 'Mohammad-Ali Nikouei Mahani', 'René Schuster', 'Ramy Battrawy'] | 2022-04-01 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-4.20189679e-01 -3.92858177e-01 -1.13361321e-01 -2.99641162e-01
-4.28934008e-01 -4.00581300e-01 4.67961371e-01 6.47943690e-02
-4.08295989e-01 5.18561780e-01 1.74867719e-01 -1.76388863e-02
-1.89037785e-01 -1.01488543e+00 -6.48038805e-01 -2.63571024e-01
-5.56763828e-01 5.09225726e-01 6.73024178e-01 -3.19170326... | [8.538954734802246, -2.041053295135498] |
b8e140d5-d81a-4b03-a79e-92e8f0918007 | relvit-concept-guided-vision-transformer-for-1 | 2204.11167 | null | https://arxiv.org/abs/2204.11167v2 | https://arxiv.org/pdf/2204.11167v2.pdf | RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning | Reasoning about visual relationships is central to how humans interpret the visual world. This task remains challenging for current deep learning algorithms since it requires addressing three key technical problems jointly: 1) identifying object entities and their properties, 2) inferring semantic relations between pai... | ['Anima Anandkumar', 'Song-Chun Zhu', 'Yuke Zhu', 'Chaowei Xiao', 'Huaizu Jiang', 'Zhiding Yu', 'Weili Nie', 'Xiaojian Ma'] | 2022-04-24 | relvit-concept-guided-vision-transformer-for | https://openreview.net/forum?id=afoV8W3-IYp | https://openreview.net/pdf?id=afoV8W3-IYp | iclr-2022-4 | ['systematic-generalization'] | ['reasoning'] | [-1.56862102e-02 1.48886561e-01 -1.10801160e-01 -1.99196339e-01
-2.74325788e-01 -7.16501951e-01 9.13568258e-01 1.80771768e-01
-3.40638161e-01 2.14816809e-01 3.25783253e-01 -4.22087997e-01
-2.43891016e-01 -8.57941568e-01 -8.31336796e-01 -3.52684498e-01
9.20664072e-02 6.28234088e-01 3.57321918e-01 -2.84555942... | [10.564416885375977, 1.6552205085754395] |
30387e97-6144-44a7-be64-06c62d8c23d9 | contextual-explanation-networks | 1705.10301 | null | https://arxiv.org/abs/1705.10301v4 | https://arxiv.org/pdf/1705.10301v4.pdf | Contextual Explanation Networks | Modern learning algorithms excel at producing accurate but complex models of the data. However, deploying such models in the real-world requires extra care: we must ensure their reliability, robustness, and absence of undesired biases. This motivates the development of models that are equally accurate but can be also e... | ['Maruan Al-Shedivat', 'Avinava Dubey', 'Eric P. Xing'] | 2017-05-29 | contextual-explanation-networks-1 | https://openreview.net/forum?id=HJUOHGWRb | https://openreview.net/pdf?id=HJUOHGWRb | iclr-2018-1 | ['interpretability-techniques-for-deep-learning'] | ['miscellaneous'] | [ 5.20040512e-01 7.46035695e-01 -4.88813519e-01 -6.41951680e-01
-6.68028831e-01 -2.39061996e-01 9.00337219e-01 4.18210894e-01
7.40863457e-02 8.11968267e-01 1.19137645e-01 -6.75729334e-01
-6.55915201e-01 -5.28499722e-01 -7.03729808e-01 -6.98720694e-01
-2.36102179e-01 7.71001816e-01 7.88219646e-02 2.11210355... | [8.774094581604004, 5.557348728179932] |
5bb8b974-bd04-4ea8-9dad-d454c2a0ccfa | iplan-interactive-and-procedural-layout | 2203.14412 | null | https://arxiv.org/abs/2203.14412v2 | https://arxiv.org/pdf/2203.14412v2.pdf | iPLAN: Interactive and Procedural Layout Planning | Layout design is ubiquitous in many applications, e.g. architecture/urban planning, etc, which involves a lengthy iterative design process. Recently, deep learning has been leveraged to automatically generate layouts via image generation, showing a huge potential to free designers from laborious routines. While automat... | ['He Wang', 'Yanlong Huang', 'Feixiang He'] | 2022-03-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/He_iPLAN_Interactive_and_Procedural_Layout_Planning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/He_iPLAN_Interactive_and_Procedural_Layout_Planning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['layout-design'] | ['computer-vision'] | [ 1.12116322e-01 2.63345316e-02 1.09766237e-01 -2.04403296e-01
-1.91355318e-01 -7.15033948e-01 8.17983270e-01 -3.83070916e-01
6.61765039e-02 5.32491684e-01 2.81701624e-01 -3.55128527e-01
-1.63480029e-01 -9.15745318e-01 -4.98556763e-01 -4.66302693e-01
4.61490840e-01 6.15633368e-01 -2.17986450e-01 -3.67183357... | [11.345625877380371, -0.23379634320735931] |
780f90e6-e555-4ace-bd29-6fc2d7244d84 | escaping-saddle-points-with-bias-variance | 2202.06083 | null | https://arxiv.org/abs/2202.06083v3 | https://arxiv.org/pdf/2202.06083v3.pdf | Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning | In recent centralized nonconvex distributed learning and federated learning, local methods are one of the promising approaches to reduce communication time. However, existing work has mainly focused on studying first-order optimality guarantees. On the other side, second-order optimality guaranteed algorithms, i.e., al... | ['Taiji Suzuki', 'Tomoya Murata'] | 2022-02-12 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-7.46697187e-01 -1.05236694e-02 -2.41274208e-01 -1.37640357e-01
-1.51574218e+00 -2.94577390e-01 -2.50922382e-01 2.33248964e-01
-3.77471030e-01 1.17922843e+00 1.22109577e-01 -2.44819194e-01
-5.54151773e-01 -5.66399693e-01 -1.01872420e+00 -1.24845839e+00
-2.50302166e-01 1.56692654e-01 -7.89785534e-02 -6.98290439... | [6.30286169052124, 4.901219367980957] |
0a21e90d-3551-4ae8-a2c9-23472f553e1f | fine-grained-session-recommendations-in-e | 2210.15451 | null | https://arxiv.org/abs/2210.15451v1 | https://arxiv.org/pdf/2210.15451v1.pdf | Fine-Grained Session Recommendations in E-commerce using Deep Reinforcement Learning | Sustaining users' interest and keeping them engaged in the platform is very important for the success of an e-commerce business. A session encompasses different activities of a user between logging into the platform and logging out or making a purchase. User activities in a session can be classified into two groups: Kn... | ['Sreekanth Vempati', 'Saif Jawaid', 'Lakshya Kumar', 'Diddigi Raghu Ram Bharadwaj'] | 2022-10-20 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 1.02392465e-01 -1.19291529e-01 -7.58281887e-01 -4.55321878e-01
-2.82699853e-01 -6.52261555e-01 3.48454446e-01 4.00158197e-01
-2.92267799e-01 3.30191851e-01 -3.04581691e-02 -5.68700314e-01
-3.81931633e-01 -8.05549502e-01 -6.70025408e-01 -8.43141913e-01
-3.90918255e-01 4.92072970e-01 -3.01405728e-01 -2.45352671... | [4.849436283111572, 3.0987398624420166] |
e5f504f8-cd32-4e8c-85e8-b8a8f9ea87e4 | connecting-a-french-dictionary-from-the | 2206.11022 | null | https://arxiv.org/abs/2206.11022v3 | https://arxiv.org/pdf/2206.11022v3.pdf | Connecting a French Dictionary from the Beginning of the 20th Century to Wikidata | The \textit{Petit Larousse illustr\'e} is a French dictionary first published in 1905. Its division in two main parts on language and on history and geography corresponds to a major milestone in French lexicography as well as a repository of general knowledge from this period. Although the value of many entries from 19... | ['Pierre Nugues'] | 2022-06-22 | null | https://aclanthology.org/2022.lrec-1.272 | https://aclanthology.org/2022.lrec-1.272.pdf | lrec-2022-6 | ['general-knowledge'] | ['miscellaneous'] | [-3.54687750e-01 -2.06968077e-02 -3.98617506e-01 -1.40744090e-01
-6.13521814e-01 -9.93564188e-01 1.01358545e+00 5.88755965e-01
-5.56694329e-01 1.00587547e+00 7.44638920e-01 -4.58055049e-01
-3.33010525e-01 -8.17393124e-01 -2.38569990e-01 -2.42619932e-01
8.81510302e-02 5.42171419e-01 8.96304399e-02 -6.38592839... | [10.196975708007812, 10.089967727661133] |
9bf99b6e-3587-4b5f-bbca-35fb4e9125f6 | retinex-based-image-denoising-contrast | 2307.02625 | null | https://arxiv.org/abs/2307.02625v1 | https://arxiv.org/pdf/2307.02625v1.pdf | Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer | Images captured in poorly lit conditions are often corrupted by acquisition noise. Leveraging recent advances in graph-based regularization, we propose a fast Retinex-based restoration scheme that denoises and contrast-enhances an image. Specifically, by Retinex theory we first assume that each image pixel is a multipl... | ['Xianming Liu', 'Gene Cheung', 'Yeganeh Gharedaghi'] | 2023-07-05 | null | null | null | null | ['image-denoising'] | ['computer-vision'] | [ 7.06344783e-01 -1.43529415e-01 2.09041998e-01 -1.12005793e-01
-6.81844175e-01 -3.74454886e-01 2.31719404e-01 -2.38155156e-01
-2.42998660e-01 7.43346691e-01 1.91996112e-01 -5.87575287e-02
2.04236843e-02 -4.31653172e-01 -9.07858431e-01 -9.35241878e-01
-1.22948643e-02 -3.64852011e-01 -3.89365166e-01 1.32997409... | [10.450117111206055, -2.751959800720215] |
148aa2bd-d813-48c1-9462-324f4e7b3b92 | learning-surface-parameterization-for | null | null | https://openreview.net/forum?id=PGGjnBiQ84G | https://openreview.net/pdf?id=PGGjnBiQ84G | Learning Surface Parameterization for Document Image Unwarping | In this paper, we present a novel approach to learn texture mapping for a 3D surface and apply it to document image unwarping. We propose an efficient method to learn surface parameterization by learning a continuous bijective mapping between 3D surface positions and 2D texture-space coordinates. Our surface parameteri... | ['Dimitris Samaras', 'Zhixin Shu', 'Ke Ma', 'Sagnik Das'] | 2021-09-29 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 7.35431075e-01 7.47028440e-02 4.06961679e-01 -3.59143704e-01
-1.03665876e+00 -8.47068012e-01 8.86756182e-01 -4.08904821e-01
2.41224453e-01 1.82697326e-01 -1.22201197e-01 -1.84974670e-01
1.38932660e-01 -9.44839060e-01 -1.14071023e+00 -4.76735175e-01
1.99098647e-01 7.69490123e-01 1.86491594e-01 -3.87684286... | [9.270092010498047, -3.254979133605957] |
a6327c21-7958-44d8-aac0-90ec45b9af80 | unsupervised-learning-of-accurate-siamese | 2204.01475 | null | https://arxiv.org/abs/2204.01475v1 | https://arxiv.org/pdf/2204.01475v1.pdf | Unsupervised Learning of Accurate Siamese Tracking | Unsupervised learning has been popular in various computer vision tasks, including visual object tracking. However, prior unsupervised tracking approaches rely heavily on spatial supervision from template-search pairs and are still unable to track objects with strong variation over a long time span. As unlimited self-s... | ['Wanli Ouyang', 'Wei Wu', 'Weihao Gan', 'Weitao Feng', 'Bo Li', 'Xin Li', 'Peixia Li', 'Jinyang Guo', 'Lei Qiao', 'Qiuhong Shen'] | 2022-04-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Shen_Unsupervised_Learning_of_Accurate_Siamese_Tracking_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Shen_Unsupervised_Learning_of_Accurate_Siamese_Tracking_CVPR_2022_paper.pdf | cvpr-2022-1 | ['visual-object-tracking'] | ['computer-vision'] | [ 6.15603896e-03 -3.09072077e-01 -5.33728898e-01 -2.59539187e-01
-5.55658400e-01 -6.45535648e-01 4.85132456e-01 -3.22504222e-01
-5.89578211e-01 7.96701670e-01 -1.01820841e-01 2.08461061e-01
-6.25605360e-02 -2.12933481e-01 -7.75298476e-01 -9.29974198e-01
-1.59879643e-02 2.73503691e-01 5.55902123e-01 3.44098002... | [6.379184722900391, -2.113546133041382] |
0cf90b6b-1f38-408f-b9c1-2b695cf56a9a | im-loss-information-maximization-loss-for | null | null | https://openreview.net/forum?id=Jw34v_84m2b | https://openreview.net/pdf?id=Jw34v_84m2b | IM-Loss: Information Maximization Loss for Spiking Neural Networks | Spiking Neural Network (SNN), recognized as a type of biologically plausible architecture, has recently drawn much research attention. It transmits information by 0/1 spikes. This bio-mimetic mechanism of SNN demonstrates extreme energy efficiency since it avoids any multiplications on neuromorphic hardware. However, t... | ['Zhe Ma', 'Xuhui Huang', 'YingLei Wang', 'Xiaode Liu', 'Liwen Zhang', 'Yuanpei Chen', 'Yufei Guo'] | 2022-10-31 | null | null | null | neurips-2022-10 | ['event-data-classification'] | ['computer-vision'] | [ 5.04310846e-01 -3.28247666e-01 3.97681236e-01 -2.45263934e-01
-6.60477765e-03 -8.35346654e-02 4.85622197e-01 -4.60100845e-02
-8.27182412e-01 9.96722281e-01 -2.83275157e-01 1.59196138e-01
-8.19137990e-02 -7.82570899e-01 -9.99172926e-01 -1.05470228e+00
4.77019325e-02 -9.01071280e-02 4.94254500e-01 -1.53147712... | [8.233723640441895, 2.4896819591522217] |
5cf46312-815f-4e87-926f-770d82c14ee3 | calibrated-and-partially-calibrated-semi | 2103.06535 | null | https://arxiv.org/abs/2103.06535v3 | https://arxiv.org/pdf/2103.06535v3.pdf | Calibrated and Partially Calibrated Semi-Generalized Homographies | In this paper, we propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera. The proposed solvers use five 2D-2D image point correspondences induced by a scene plane. One of them assumes the perspective camera to be fully calibrated, while the other ... | ['Zuzana Kukelova', 'Janne Heikkila', 'Patrik Beliansky', 'Daniel Barath', 'Torsten Sattler', 'Snehal Bhayani'] | 2021-03-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Bhayani_Calibrated_and_Partially_Calibrated_Semi-Generalized_Homographies_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Bhayani_Calibrated_and_Partially_Calibrated_Semi-Generalized_Homographies_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-based-localization'] | ['computer-vision'] | [ 4.78535481e-02 4.89618815e-02 6.06303252e-02 -1.24998279e-01
-6.10609353e-01 -9.66476679e-01 4.56263244e-01 -7.23063648e-02
-3.86334598e-01 5.02398789e-01 -4.73135442e-01 1.34669140e-01
1.43581787e-02 -3.53139788e-01 -8.03052247e-01 -4.59697604e-01
2.29338899e-01 9.00300741e-01 1.92748234e-01 1.02989659... | [7.872624397277832, -2.325680732727051] |
baac7915-21d8-4e91-bcfc-3e6d30342cfb | rescue-conversations-from-dead-ends-efficient | 2305.03262 | null | https://arxiv.org/abs/2305.03262v1 | https://arxiv.org/pdf/2305.03262v1.pdf | Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization | Training a dialogue policy using deep reinforcement learning requires a lot of exploration of the environment. The amount of wasted invalid exploration makes their learning inefficient. In this paper, we find and define an important reason for the invalid exploration: dead-ends. When a conversation enters a dead-end st... | ['Shihan Wang', 'Mehdi Dastani', 'Zhenyu Wang', 'Yangyang Zhao'] | 2023-05-05 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [ 3.12889507e-03 6.05429709e-01 -2.22043931e-01 -4.11483914e-01
-3.76834542e-01 -7.62516856e-01 7.00877607e-01 1.03829745e-02
-4.97754395e-01 1.28151572e+00 5.02412677e-01 -4.93915439e-01
1.05361760e-01 -7.26679027e-01 -3.69314283e-01 -6.10372663e-01
-7.20396563e-02 6.59392536e-01 8.09285417e-03 -5.60212314... | [13.050880432128906, 8.04122543334961] |
a534004a-7c78-4699-b802-049f231bd0c9 | learn-from-each-other-to-classify-better | null | null | https://www.sciencedirect.com/science/article/pii/S0031320323002509 | https://reader.elsevier.com/reader/sd/pii/S0031320323002509?token=58FF76D12E1144ADB66AD959AB1396F4468B3FE8F47CB2AB90846908B89FA214B4EC69F4E1AD384E9CF4F40D51740518&originRegion=us-east-1&originCreation=20230416125734 | Learn from Each Other to Classify Better: Cross-layer Mutual Attention Learning for Fine-grained Visual Classification | Fine-grained visual classification (FGVC) is valuable yet challenging. The difficulty of FGVC mainly lies in its intrinsic inter-class similarity, intra-class variation, and limited training data. Moreover, with the popularity of deep convolutional neural networks, researchers have mainly used deep, abstract, semantic ... | ['Jien Kato', 'Yu Wang', 'Longjiao Zhao', 'Dichao Liu'] | 2023-03-22 | null | null | null | pattern-recognition-2023-3 | ['fine-grained-image-classification'] | ['computer-vision'] | [-3.94702643e-01 -1.25888884e-01 -3.60805064e-01 -4.37907308e-01
-3.45325530e-01 -4.42368120e-01 2.31470257e-01 9.53573287e-02
-2.81263232e-01 3.80918056e-01 3.48685145e-01 -6.57332316e-02
1.27444208e-01 -7.33694673e-01 -6.55989408e-01 -4.63480830e-01
2.72322029e-01 1.27303138e-01 5.26398003e-01 6.66265562... | [9.537590980529785, 1.8556393384933472] |
dbd8e2f3-b710-4ace-99c1-c24e031d5a9f | toward-super-resolution-for-appearance-based | 2303.10151 | null | https://arxiv.org/abs/2303.10151v1 | https://arxiv.org/pdf/2303.10151v1.pdf | Toward Super-Resolution for Appearance-Based Gaze Estimation | Gaze tracking is a valuable tool with a broad range of applications in various fields, including medicine, psychology, virtual reality, marketing, and safety. Therefore, it is essential to have gaze tracking software that is cost-efficient and high-performing. Accurately predicting gaze remains a difficult task, partic... | ['Majid Komeili', "Galen O'Shea"] | 2023-03-17 | null | null | null | null | ['gaze-estimation', 'eye-tracking', 'marketing'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 6.50318146e-01 1.69040576e-01 -3.52197707e-01 -4.59489763e-01
-3.25707585e-01 -6.22076206e-02 2.33327389e-01 -2.91212082e-01
-5.50043166e-01 8.23038936e-01 1.47578955e-01 -1.85913831e-01
-9.90791097e-02 -2.94167191e-01 -8.37571859e-01 -6.08663976e-01
2.47471884e-01 -2.41742685e-01 4.56989735e-01 -3.00961018... | [14.097604751586914, 0.07312560081481934] |
cd8dc0c3-4a38-4a8b-a395-209e983f95dc | point-m2ae-multi-scale-masked-autoencoders | 2205.14401 | null | https://arxiv.org/abs/2205.14401v2 | https://arxiv.org/pdf/2205.14401v2.pdf | Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training | Masked Autoencoders (MAE) have shown great potentials in self-supervised pre-training for language and 2D image transformers. However, it still remains an open question on how to exploit masked autoencoding for learning 3D representations of irregular point clouds. In this paper, we propose Point-M2AE, a strong Multi-s... | ['Rongyao Fang', 'Hongsheng Li', 'Yu Qiao', 'Dong Wang', 'Bin Zhao', 'Peng Gao', 'Ziyu Guo', 'Renrui Zhang'] | 2022-05-28 | null | null | null | null | ['3d-point-cloud-classification', '3d-point-cloud-linear-classification', 'point-cloud-pre-training', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-6.99189678e-03 2.55651236e-01 -1.01773053e-01 -3.95078778e-01
-1.00886667e+00 -3.99433076e-01 4.63868678e-01 -1.76794186e-01
1.26324249e-02 1.91521347e-02 3.70536447e-02 -2.48488829e-01
3.05715263e-01 -1.07789075e+00 -1.33410275e+00 -5.93627334e-01
-8.09880793e-02 4.80780363e-01 3.40748847e-01 -1.38103679... | [8.0751314163208, -3.427947759628296] |
0f9a58cf-b8f4-42ab-8c4b-37aaa77f7c79 | chatgpt-a-blessing-or-a-curse-for | 2304.14993 | null | https://arxiv.org/abs/2304.14993v2 | https://arxiv.org/pdf/2304.14993v2.pdf | ChatGPT -- a Blessing or a Curse for Undergraduate Computer Science Students and Instructors? | ChatGPT is an AI language model developed by OpenAI that can understand and generate human-like text. It can be used for a variety of use cases such as language generation, question answering, text summarization, chatbot development, language translation, sentiment analysis, content creation, personalization, text comp... | ['Harshal D. Akolekar', 'Dhruv Kumar', 'Sayan Mitra', 'M. Osama Ataullah', 'Jahnvi Kadia', 'Harshal Dev', 'Ritvik Budhiraja', 'Ishika Joshi'] | 2023-04-28 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 6.55089393e-02 8.75699043e-01 8.13467577e-02 -2.23097041e-01
-9.86734748e-01 -9.21749175e-01 6.02903187e-01 7.06761003e-01
1.42092124e-01 9.07825112e-01 2.91950494e-01 -9.46965218e-01
-1.04765862e-01 -6.34253502e-01 -4.22613919e-01 -9.25073326e-02
6.54956460e-01 2.37632275e-01 -2.68850736e-02 -7.35119760... | [10.307145118713379, 7.4398908615112305] |
72ad05f4-18f2-45fc-a86c-b7acf8ea2947 | material-recognition-cnns-and-hierarchical | 1706.08685 | null | http://arxiv.org/abs/1706.08685v1 | http://arxiv.org/pdf/1706.08685v1.pdf | Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain | In this paper we tackle the problem of visually predicting surface friction
for environments with diverse surfaces, and integrating this knowledge into
biped robot locomotion planning. The problem is essential for autonomous robot
locomotion since diverse surfaces with varying friction abound in the real
world, from wo... | ['Yukitoshi Minami Shiguematsu', 'Martim Brandao', 'Kenji Hashimoto', 'Atsuo Takanishi'] | 2017-06-27 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 1.59641609e-01 1.79252788e-01 1.34014159e-01 -7.93041289e-02
-2.70373195e-01 -2.81506300e-01 2.74897993e-01 1.13505997e-01
-3.21891606e-01 1.02887762e+00 -3.03240716e-01 1.90500945e-01
-1.85275644e-01 -1.14762497e+00 -9.72862005e-01 -6.03213251e-01
-7.10226178e-01 1.05732834e+00 6.42545700e-01 -6.91424072... | [4.827092170715332, 1.0360214710235596] |
9e66ab64-7e16-4091-b51c-d6ca2af6cc1f | generative-adversarial-exploration-for | 2201.11685 | null | https://arxiv.org/abs/2201.11685v1 | https://arxiv.org/pdf/2201.11685v1.pdf | Generative Adversarial Exploration for Reinforcement Learning | Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel. Most previous work focuses on designing heuristic rules or distance metrics to check whether a state is novel without considering such a discrimination process that can... | ['Peng Sun', 'Yong Yu', 'Ming Zhou', 'Weinan Zhang', 'Minghuan Liu', 'Menghui Zhu', 'Weijun Hong'] | 2022-01-27 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [ 1.06208794e-01 4.47648942e-01 -4.20590043e-01 7.85056651e-02
-8.12753379e-01 -7.60011852e-01 6.68295860e-01 -1.84040830e-01
-4.85103101e-01 1.21342635e+00 -9.86783653e-02 -6.20512843e-01
1.08374804e-01 -9.73963439e-01 -8.26838493e-01 -9.02143419e-01
-2.70353675e-01 6.63193226e-01 -1.08340189e-01 -3.65390062... | [3.971839666366577, 1.8104054927825928] |
f3558275-c040-46d5-92d7-69c76d84833a | autoremover-automatic-object-removal-for | 1911.12588 | null | https://arxiv.org/abs/1911.12588v1 | https://arxiv.org/pdf/1911.12588v1.pdf | AutoRemover: Automatic Object Removal for Autonomous Driving Videos | Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm \emph{AutoRemover}, designed specifically for generating street-view videos without any moving objects. In our setup we have two challenges: the first is the shadow, shadows are usually unla... | ['Baoquan Chen', 'Ruigang Yang', 'Rong Zhang', 'Weiwei Xu', 'Peng Wang', 'Chenye Guan', 'Wei Li', 'Jinhui Yu', 'Jin Fang', 'Yuhang Song'] | 2019-11-28 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 3.01297903e-01 1.36502430e-01 4.91218090e-01 -3.35427016e-01
-4.42504466e-01 -3.74817878e-01 1.70782521e-01 -8.22807252e-01
-1.65996492e-01 7.61559188e-01 4.83422652e-02 -8.67367815e-03
4.82766479e-01 -6.43938720e-01 -1.25768530e+00 -7.20899701e-01
1.66581407e-01 2.59760559e-01 6.79937541e-01 -3.18192273... | [8.990234375, -2.4878571033477783] |
4f4833bf-7d49-4a65-835f-cd68c2fd37f0 | implicitly-normalized-forecaster-with | 2305.06743 | null | https://arxiv.org/abs/2305.06743v2 | https://arxiv.org/pdf/2305.06743v2.pdf | Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits | The Implicitly Normalized Forecaster (INF) algorithm is considered to be an optimal solution for adversarial multi-armed bandit (MAB) problems. However, most of the existing complexity results for INF rely on restrictive assumptions, such as bounded rewards. Recently, a related algorithm was proposed that works for bot... | ['Nikita Kornilov', 'Alexander Gasnikov', 'Eduard Gorbunov', 'Alexander Nazin', 'Nikolay Kutuzov', 'Yuriy Dorn'] | 2023-05-11 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-8.16404745e-02 3.05243246e-02 -6.53720379e-01 -3.09442729e-01
-1.02549720e+00 -1.07090557e+00 2.90482819e-01 -7.78333396e-02
-4.37158495e-01 1.29606545e+00 -1.23963188e-02 -6.38866305e-01
-4.47686553e-01 -7.30728805e-01 -1.14219272e+00 -9.09515560e-01
1.29798427e-01 8.75702858e-01 -1.16932988e-01 -3.30144674... | [4.5424394607543945, 3.3402926921844482] |
22b549ea-892d-4ab8-a2a2-54633c7f3629 | classification-specific-parts-for-improving | 1909.07075 | null | https://arxiv.org/abs/1909.07075v1 | https://arxiv.org/pdf/1909.07075v1.pdf | Classification-Specific Parts for Improving Fine-Grained Visual Categorization | Fine-grained visual categorization is a classification task for distinguishing categories with high intra-class and small inter-class variance. While global approaches aim at using the whole image for performing the classification, part-based solutions gather additional local information in terms of attentions or parts... | ['Dimitri Korsch', 'Paul Bodesheim', 'Joachim Denzler'] | 2019-09-16 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 2.48690784e-01 1.36072144e-01 -1.25591174e-01 -3.69836122e-01
-8.33091438e-01 -7.84817576e-01 8.45521867e-01 8.22147012e-01
-5.56274891e-01 6.34350359e-01 5.20925149e-02 4.06674385e-01
-3.34768891e-01 -7.35520184e-01 -5.61640620e-01 -7.62009323e-01
2.16122605e-02 7.36468911e-01 8.09964001e-01 -7.66685512... | [9.535089492797852, 1.1396801471710205] |
cbb30a20-70a4-4f95-99b2-7a429fc98f79 | depth-attentional-features-for-single-image | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Hu_Depth-Attentional_Features_for_Single-Image_Rain_Removal_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Hu_Depth-Attentional_Features_for_Single-Image_Rain_Removal_CVPR_2019_paper.pdf | Depth-Attentional Features for Single-Image Rain Removal | Rain is a common weather phenomenon, where object visibility varies with depth from the camera and objects faraway are visually blocked more by fog than by rain streaks. Existing methods and datasets for rain removal, however, ignore these physical properties, thereby limiting the rain removal efficiency on real photos... | [' Pheng-Ann Heng', ' Lei Zhu', ' Chi-Wing Fu', 'Xiaowei Hu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['single-image-deraining'] | ['computer-vision'] | [-4.26822379e-02 -5.32082021e-01 5.36813676e-01 -6.52328074e-01
-3.33203338e-02 -4.60848421e-01 -3.73636633e-02 -4.68390375e-01
-2.03049079e-01 9.81093049e-01 1.80111397e-02 -3.68246526e-01
4.57332194e-01 -8.86269093e-01 -6.89465284e-01 -9.97948587e-01
-8.05662200e-02 -1.92695856e-01 3.63883734e-01 -1.56588271... | [10.923750877380371, -3.236196756362915] |
46143e7e-493e-46f6-bc83-0dd8b7c6c20b | facial-soft-biometrics-for-recognition-in-the | 2210.13129 | null | https://arxiv.org/abs/2210.13129v1 | https://arxiv.org/pdf/2210.13129v1.pdf | Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation | The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied. Here, we explore the utility of the following modalities: gender, ethnicity, age, glasses, beard, and moustache. We consider two assumptions: 1) manual estimation of soft biometrics and 2) auto... | ['Fernando Alonso-Fernandez', 'Ruben Vera-Rodriguez', 'Julian Fierrez', 'Ester Gonzalez-Sosa'] | 2022-10-24 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 3.83040607e-02 8.59363824e-02 6.66799620e-02 -8.30571949e-01
-4.14915472e-01 -4.22965646e-01 6.95514560e-01 -5.99628806e-01
-4.15950269e-01 6.98910415e-01 -6.98931813e-02 -9.48395357e-02
1.39170647e-01 -3.48981440e-01 -4.47906286e-01 -9.25350845e-01
1.81000471e-01 3.17796528e-01 -2.80160367e-01 -2.29392171... | [13.338809967041016, 1.032435417175293] |
46d39929-07d3-4de3-ba07-aab6ade54779 | uncertainty-quantification-via-spatial | 2306.09882 | null | https://arxiv.org/abs/2306.09882v1 | https://arxiv.org/pdf/2306.09882v1.pdf | Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction | crucial for transportation management. However, traditional spatial-temporal deep learning models grapple with addressing the sparse and long-tail characteristics in high-resolution O-D matrices and quantifying prediction uncertainty. This dilemma arises from the numerous zeros and over-dispersed demand patterns within... | ['Xiaowei Gao', 'Jiayuan Luo', 'Hao Chen', 'Xianghui Zhang', 'Dingyi Zhuang', 'Xinke Jiang'] | 2023-06-16 | null | null | null | null | ['management'] | ['miscellaneous'] | [-4.64480847e-01 -2.69483387e-01 -5.59586048e-01 -3.06394815e-01
-5.74705422e-01 -4.97735411e-01 7.63595462e-01 3.86591434e-01
-5.00435494e-02 7.41209924e-01 7.36280560e-01 -9.01516914e-01
-8.31386089e-01 -1.01606476e+00 -6.98740005e-01 -5.01712978e-01
-7.31691062e-01 4.90857899e-01 -8.21348950e-02 -2.68832535... | [6.540089130401611, 2.278674602508545] |
6ece5270-5411-4ee4-bf33-d46ed0cf004e | wild-face-anti-spoofing-challenge-2023 | 2304.05753 | null | https://arxiv.org/abs/2304.05753v3 | https://arxiv.org/pdf/2304.05753v3.pdf | Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results | Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in ... | ['Zhen Lei', 'Jiankang Deng', 'Jun Wan', 'Hugo Jair Escalante', 'Sergio Escalera', 'Ajian Liu', 'Chuanbao Xiao', 'Zhian Chen', 'Haochi He', 'Qiqi Shao', 'Jia Guo', 'Dong Wang'] | 2023-04-12 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 6.73804402e-01 -4.12585646e-01 -2.10961521e-01 -1.64286882e-01
-3.12379390e-01 -8.25652063e-01 6.02400780e-01 -3.56278092e-01
-4.39380929e-02 5.10421515e-01 1.68342084e-01 -3.15471202e-01
-8.02851394e-02 -5.80426455e-01 -6.06706560e-01 -6.74986541e-01
-4.03566599e-01 -1.95115840e-03 9.83897597e-02 -1.62113607... | [13.017666816711426, 1.133664846420288] |
5be4780f-c9c3-4d36-aaa7-65ee0676c659 | spatiotemporal-augmentation-on-selective | 2204.03865 | null | https://arxiv.org/abs/2204.03865v2 | https://arxiv.org/pdf/2204.03865v2.pdf | Frequency Selective Augmentation for Video Representation Learning | Recent self-supervised video representation learning methods focus on maximizing the similarity between multiple augmented views from the same video and largely rely on the quality of generated views. However, most existing methods lack a mechanism to prevent representation learning from bias towards static information... | ['Junmo Kim', 'Dongyoon Wee', 'Dongyoon Han', 'Minho Shim', 'Taeoh Kim', 'Jinhyung Kim'] | 2022-04-08 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 4.39548254e-01 -5.15458314e-03 -5.78506768e-01 -3.33591074e-01
-7.87757576e-01 -4.71558005e-01 5.86880744e-01 -2.31602564e-01
-1.50680274e-01 5.62813342e-01 9.99352992e-01 3.06824833e-01
2.67499387e-02 -4.10401255e-01 -8.39079380e-01 -7.07100093e-01
-3.36419076e-01 -1.46913290e-01 1.48226693e-01 -1.34224370... | [8.677473068237305, 0.7462456822395325] |
3f3c75be-21de-4e7d-9a60-e9c547b0d8c1 | an-extractive-abstractive-approach-for-multi | null | null | https://aclanthology.org/2022.sdp-1.25 | https://aclanthology.org/2022.sdp-1.25.pdf | An Extractive-Abstractive Approach for Multi-document Summarization of Scientific Articles for Literature Review | Research in the biomedical domain is con- stantly challenged by its large amount of ever- evolving textual information. Biomedical re- searchers are usually required to conduct a lit- erature review before any medical interven- tion to assess the effectiveness of the con- cerned research. However, the process is time- ... | ['Tirthankar Ghosal', 'Trinita Roy', 'Kartik Shinde'] | null | null | null | null | sdp-coling-2022-10 | ['document-summarization'] | ['natural-language-processing'] | [ 0.33524302 0.16554677 -0.3953433 -0.05593806 -1.6207607 -0.7711609
0.62020934 0.9571796 -0.45398402 1.0779637 0.74065673 -0.6726878
-0.29708368 -0.29835343 -0.6783738 -0.3054492 0.39717847 0.43665144
-0.16740069 0.11379215 1.044635 0.4944595 -1.1034858 0.4625229
1.3576516 0.19559538 0.358... | [12.289118766784668, 9.601730346679688] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.