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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
90984327-93d5-439f-b795-bcc4a94c0034 | stability-of-q-learning-through-design-and | 2307.02632 | null | https://arxiv.org/abs/2307.02632v1 | https://arxiv.org/pdf/2307.02632v1.pdf | Stability of Q-Learning Through Design and Optimism | Q-learning has become an important part of the reinforcement learning toolkit since its introduction in the dissertation of Chris Watkins in the 1980s. The purpose of this paper is in part a tutorial on stochastic approximation and Q-learning, providing details regarding the INFORMS APS inaugural Applied Probability Tr... | ['Sean Meyn'] | 2023-07-05 | null | null | null | null | ['q-learning'] | ['methodology'] | [-3.51761311e-01 2.56477714e-01 -5.46907604e-01 9.94907469e-02
-1.08540785e+00 -3.98984015e-01 4.06815678e-01 -1.65912822e-01
-6.35644555e-01 1.36865687e+00 -3.31159048e-02 -6.67099774e-01
-4.13904935e-01 -4.71799701e-01 -8.71727407e-01 -1.07169962e+00
-2.22992793e-01 5.19094825e-01 -1.11981757e-01 -3.05990487... | [4.196168422698975, 2.536020040512085] |
4e5d46f0-a539-46e9-808e-7c0233a79998 | handling-imbalanced-classification-problems | 2204.10231 | null | https://arxiv.org/abs/2204.10231v1 | https://arxiv.org/pdf/2204.10231v1.pdf | Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization | Support vector machines (SVMs) are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class; however, real-world problems may have an uneven class distribution. This article introduces EBCS-SVM: evolutionary bilevel cost-sensitive SV... | ['Francisco Herrera', 'Salvador García', 'Alejandro Rosales-Pérez'] | 2022-04-21 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 1.70314535e-01 -3.17063212e-01 -4.48172450e-01 -4.26015705e-01
-1.14325128e-01 -1.32057905e-01 1.05658680e-01 2.87518859e-01
-4.10411716e-01 1.03158998e+00 -4.98852283e-01 -2.42762595e-01
-5.13768554e-01 -6.59724891e-01 -2.61881202e-01 -1.10923374e+00
9.18038934e-02 7.94389963e-01 4.99729395e-01 -3.53005767... | [8.487807273864746, 4.160153388977051] |
016b3cd5-1fb2-4e13-bc22-a57ebb4cb925 | an-integrated-inverse-space-sparse | 1803.03562 | null | http://arxiv.org/abs/1803.03562v4 | http://arxiv.org/pdf/1803.03562v4.pdf | An Integrated Inverse Space Sparse Representation Framework for Tumor Classification | Microarray gene expression data-based tumor classification is an active and
challenging issue. In this paper, an integrated tumor classification framework
is presented, which aims to exploit information in existing available samples,
and focuses on the small sample problem and unbalanced classification problem.
Firstly... | ['Wen-Ming Wu', 'Li-Jun Yang', 'Yun-Mei Chen', 'Xiaohui Yang', 'Xianqi Li', 'Dan Long', 'Juan Zhang'] | 2018-03-09 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 2.85654873e-01 -4.38206166e-01 -4.93451864e-01 -2.11013556e-01
-5.00297666e-01 1.94133639e-01 3.20872702e-02 -1.40975013e-01
-2.17209592e-01 7.62196898e-01 3.86506200e-01 -9.20503289e-02
-6.37726963e-01 -6.77601755e-01 -1.69645697e-01 -1.43082368e+00
1.09756522e-01 1.17461279e-01 -3.40639412e-01 -2.85234541... | [12.455596923828125, 0.4333871006965637] |
aa873092-6fbb-4b45-8fa5-1a8fabf82166 | dataset2vec-learning-dataset-meta-features | 1905.11063 | null | https://arxiv.org/abs/1905.11063v4 | https://arxiv.org/pdf/1905.11063v4.pdf | Dataset2Vec: Learning Dataset Meta-Features | Meta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven approach, meta-learning requires meta-features that represent the primary learning tasks or datasets, and are estimated traditonally as engineer... | ['Josif Grabocka', 'Lars Schmidt-Thieme', 'Hadi S. Jomaa'] | 2019-05-27 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 1.69065639e-01 3.76944020e-02 -2.43341878e-01 -6.52366877e-01
-9.06431437e-01 -4.22706157e-01 7.15418935e-01 2.73464233e-01
-3.61834913e-01 6.97564662e-01 1.01177976e-01 9.77314487e-02
-4.07799095e-01 -7.28959620e-01 -9.41659272e-01 -7.09273279e-01
2.28047311e-01 4.06724781e-01 -2.39827141e-01 -4.67774123... | [9.905158996582031, 3.1631836891174316] |
c92feec0-150c-46a8-9b9d-b3f315398b1f | florunito-trac-2-retrofitting-word-embeddings | null | null | https://aclanthology.org/2020.trac-1.17 | https://aclanthology.org/2020.trac-1.17.pdf | FlorUniTo@TRAC-2: Retrofitting Word Embeddings on an Abusive Lexicon for Aggressive Language Detection | This paper describes our participation to the TRAC-2 Shared Tasks on Aggression Identification. Our team, FlorUniTo, investigated the applicability of using an abusive lexicon to enhance word embeddings towards improving detection of aggressive language. The embeddings used in our paper are word-aligned pre-trained vec... | ['Anna Koufakou', 'Viviana Patti', 'Valerio Basile'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['aggression-identification'] | ['natural-language-processing'] | [-5.97745121e-01 -2.99721748e-01 8.10202733e-02 -3.91202211e-01
-7.81873345e-01 -4.39858466e-01 5.85139513e-01 3.73497419e-02
-1.29683256e+00 4.72286344e-01 6.58830225e-01 3.80198620e-02
-2.64784694e-01 -2.95815498e-01 1.88932478e-01 -3.87538403e-01
-7.52418414e-02 7.86052287e-01 -1.00865424e-01 -7.83552527... | [8.852959632873535, 10.728266716003418] |
72e8bef3-bf76-4ecc-86af-d89d27379820 | state-regularized-policy-optimization-on-data | 2306.03552 | null | https://arxiv.org/abs/2306.03552v1 | https://arxiv.org/pdf/2306.03552v1.pdf | State Regularized Policy Optimization on Data with Dynamics Shift | In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of current methods address such issue by training context encoders to identify environment parameters. Data with dynamics shift are separated acc... | ['Bo An', 'Kun Gai', 'Peng Jiang', 'Dong Zheng', 'Shuchang Liu', 'Qingpeng Cai', 'Zhenghai Xue'] | 2023-06-06 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 7.18517900e-02 -3.38812232e-01 -6.18979692e-01 -1.06361300e-01
-3.17834139e-01 -8.89807880e-01 5.30429542e-01 1.18061505e-01
-8.53850186e-01 9.40288484e-01 2.91804910e-01 -3.98508519e-01
-1.95498496e-01 -7.31287062e-01 -1.10925841e+00 -1.10546386e+00
-4.56802815e-01 5.30376732e-01 1.25927135e-01 -3.53442401... | [4.105900764465332, 2.1710898876190186] |
ad27c03d-166d-4479-b3dc-1a53a08947de | racial-bias-trends-in-the-text-of-us-legal | 2307.01693 | null | https://arxiv.org/abs/2307.01693v1 | https://arxiv.org/pdf/2307.01693v1.pdf | Racial Bias Trends in the Text of US Legal Opinions | Although there is widespread recognition of racial bias in US law, it is unclear how such bias appears in the language of law, namely judicial opinions, and whether it varies across time period or region. Building upon approaches for measuring implicit racial bias in large-scale corpora, we approximate GloVe word embed... | ['Rohan Jinturkar'] | 2023-07-04 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [-3.43350470e-01 -2.91845560e-01 -8.34090531e-01 -5.82928717e-01
-4.13411111e-01 -1.04390359e+00 9.44130361e-01 5.74407279e-01
-1.06632411e+00 8.18412542e-01 1.11603045e+00 -1.15464938e+00
-2.11722367e-02 -9.14511323e-01 -2.70804405e-01 -4.12757665e-01
6.05035782e-01 -1.20626859e-01 -8.33566308e-01 -3.40373427... | [9.284314155578613, 10.175551414489746] |
3bc245c4-f879-4fca-9af8-55f4c9873bef | from-hypergraph-energy-functions-to | 2306.09623 | null | https://arxiv.org/abs/2306.09623v2 | https://arxiv.org/pdf/2306.09623v2.pdf | From Hypergraph Energy Functions to Hypergraph Neural Networks | Hypergraphs are a powerful abstraction for representing higher-order interactions between entities of interest. To exploit these relationships in making downstream predictions, a variety of hypergraph neural network architectures have recently been proposed, in large part building upon precursors from the more traditio... | ['David Wipf', 'Xuanjing Huang', 'Xipeng Qiu', 'Quan Gan', 'Yuxin Wang'] | 2023-06-16 | null | null | null | null | ['node-classification', 'bilevel-optimization'] | ['graphs', 'methodology'] | [ 1.54153496e-01 7.63326943e-01 -5.95496416e-01 -4.12624180e-01
-1.95658103e-01 -4.76036042e-01 7.26404309e-01 4.40830231e-01
-4.15971912e-02 7.57616162e-01 3.06680381e-01 -5.92445374e-01
-5.70742428e-01 -1.02323472e+00 -6.75984263e-01 -6.12574279e-01
-5.37568271e-01 7.30346620e-01 -1.56469300e-01 -2.98782438... | [6.958078384399414, 6.272940635681152] |
f80b9957-331b-496f-9864-1143d0c391d2 | depth-map-estimation-and-colorization-of | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Williem_Depth_Map_Estimation_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Williem_Depth_Map_Estimation_ICCV_2015_paper.pdf | Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution | In this paper, we present a joint iterative anaglyph stereo matching and colorization framework for obtaining a set of disparity maps and colorized images. Conventional stereo matching algorithms fail when addressing anaglyph images that do not have similar intensities on their two respective view images. To resolve th... | ['Ramesh Raskar', 'W. Williem', 'In Kyu Park'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['stereo-matching'] | ['computer-vision'] | [ 5.57954192e-01 -3.70344728e-01 -2.29746662e-02 -3.91067445e-01
-2.71129251e-01 -5.73799312e-01 1.72692806e-01 -8.68174434e-02
-4.18872595e-01 9.39724028e-01 -8.03180933e-02 1.50036933e-02
1.33691579e-01 -9.93268073e-01 -4.90402073e-01 -6.91389322e-01
6.35807157e-01 1.31929651e-01 4.41639155e-01 2.66914144... | [9.24197006225586, -2.4735517501831055] |
d3889b88-46cf-45bc-9a35-737ad914fbc7 | prototypical-residual-networks-for-anomaly | 2212.02031 | null | https://arxiv.org/abs/2212.02031v2 | https://arxiv.org/pdf/2212.02031v2.pdf | Prototypical Residual Networks for Anomaly Detection and Localization | Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models easily over-fit to these seen anomalies with a handful of abnormal samples, producing unsatisfactory performance. On the other hand, anomalies... | ['Yu-Gang Jiang', 'Zhineng Chen', 'Zheng Wang', 'Zuxuan Wu', 'HUI ZHANG'] | 2022-12-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Prototypical_Residual_Networks_for_Anomaly_Detection_and_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Prototypical_Residual_Networks_for_Anomaly_Detection_and_Localization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 3.59954655e-01 -2.67858148e-01 9.61731896e-02 -2.76129693e-01
-6.56871557e-01 -2.78216362e-01 3.57240677e-01 9.47978646e-02
5.51276565e-01 1.89515099e-01 -3.53251129e-01 -1.89728573e-01
-2.15347484e-01 -5.88594019e-01 -7.15820968e-01 -9.17688668e-01
-1.25826642e-01 3.71818334e-01 3.11984837e-01 -2.16808125... | [7.562793254852295, 2.1530961990356445] |
e39e4b08-5d65-414f-b8c7-9f8415a6e268 | statistical-performance-of-radio | 1902.10448 | null | http://arxiv.org/abs/1902.10448v2 | http://arxiv.org/pdf/1902.10448v2.pdf | Statistical Performance of Radio Interferometric Calibration | Calibration is an essential step in radio interferometric data processing
that corrects the data for systematic errors and in addition, subtracts bright
foreground interference to reveal weak signals hidden in the residual. These
weak and unknown signals are much sought after to reach many science goals but
the effect ... | ['Sarod Yatawatta'] | 2019-02-27 | null | null | null | null | ['radio-interferometry'] | ['miscellaneous'] | [ 4.28624868e-01 -3.53882492e-01 4.07266438e-01 -3.59654397e-01
-7.16655672e-01 -4.22359973e-01 3.46577644e-01 -3.81443977e-01
-3.68719459e-01 8.99175346e-01 4.20329235e-02 -2.49811888e-01
-5.20135343e-01 -7.11244285e-01 -6.16800904e-01 -1.25063694e+00
2.84650475e-01 6.70801938e-01 2.70122796e-01 5.67028672... | [10.27605152130127, -2.1931521892547607] |
6c2fcf28-dfab-48c4-ad12-2a534eee8418 | image-correction-via-deep-reciprocating-hdr | 1804.04371 | null | http://arxiv.org/abs/1804.04371v1 | http://arxiv.org/pdf/1804.04371v1.pdf | Image Correction via Deep Reciprocating HDR Transformation | Image correction aims to adjust an input image into a visually pleasing one.
Existing approaches are proposed mainly from the perspective of image pixel
manipulation. They are not effective to recover the details in the under/over
exposed regions. In this paper, we revisit the image formation procedure and
notice that ... | ['Qiang Zhang', 'Xin Yang', 'Xiaopeng Wei', 'Rynson Lau', 'Ke Xu', 'Yibing Song'] | 2018-04-12 | image-correction-via-deep-reciprocating-hdr-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_Image_Correction_via_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_Image_Correction_via_CVPR_2018_paper.pdf | cvpr-2018-6 | ['hdr-reconstruction', 'tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 7.54188299e-01 -4.95778769e-02 1.31415054e-02 -2.32396051e-01
-4.89495486e-01 -1.04236096e-01 4.34383482e-01 -4.33408797e-01
-1.42040551e-01 6.38787627e-01 3.11396986e-01 -7.91308135e-02
3.44790369e-01 -8.06355178e-01 -9.84056592e-01 -7.06158757e-01
6.09986365e-01 -1.85794324e-01 3.17593962e-01 -3.72275829... | [10.925179481506348, -2.159020185470581] |
5dbb4e78-8477-4cac-998f-bb9ffccdf421 | deepdpm-dynamic-population-mapping-via-deep | 1811.02644 | null | http://arxiv.org/abs/1811.02644v2 | http://arxiv.org/pdf/1811.02644v2.pdf | DeepDPM: Dynamic Population Mapping via Deep Neural Network | Dynamic high resolution data on human population distribution is of great
importance for a wide spectrum of activities and real-life applications, but is
too difficult and expensive to obtain directly. Therefore, generating
fine-scaled population distributions from coarse population data is of great
significance. Howev... | ['Hongzhi Shi', 'Kechun Liu', 'Zefang Zong', 'Jie Feng', 'Yong Li'] | 2018-10-25 | null | null | null | null | ['population-mapping'] | ['computer-vision'] | [-1.72636673e-01 -5.80867231e-01 -3.52656007e-01 -8.73011649e-02
-6.04189694e-01 -2.36800890e-02 7.07586586e-01 8.31634849e-02
-3.31377774e-01 1.27712405e+00 6.36554718e-01 -2.15580896e-01
-4.78973269e-01 -1.64655256e+00 -7.47928441e-01 -6.11838639e-01
-5.65444827e-01 7.39455283e-01 1.49827152e-01 -6.52231276... | [6.4984869956970215, 2.041382074356079] |
81e3529e-a507-4d5d-ad52-e82d1f97aed3 | autonomy-2-0-the-quest-for-economies-of-scale | 2307.03973 | null | https://arxiv.org/abs/2307.03973v1 | https://arxiv.org/pdf/2307.03973v1.pdf | Autonomy 2.0: The Quest for Economies of Scale | With the advancement of robotics and AI technologies in the past decade, we have now entered the age of autonomous machines. In this new age of information technology, autonomous machines, such as service robots, autonomous drones, delivery robots, and autonomous vehicles, rather than humans, will provide services. In ... | ['Yuhao Zhu', 'Shaoshan Liu', 'Bo Yu', 'Shuang Wu'] | 2023-07-08 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [-2.90013880e-01 2.87155211e-01 -1.47308111e-01 9.70028862e-02
2.18348891e-01 -6.23419464e-01 5.71640372e-01 4.96314242e-02
-5.10272324e-01 6.27948105e-01 -1.87125877e-01 -3.56685042e-01
-7.78234079e-02 -8.38712037e-01 -3.13128382e-01 -3.50835919e-01
-2.59030432e-01 6.15726769e-01 2.05318689e-01 -6.46504104... | [4.937410831451416, 1.2834296226501465] |
11d590cc-5177-44e5-a1d8-f4df4550b108 | ld-sds-towards-an-expressive-spoken-dialogue | 1710.02973 | null | http://arxiv.org/abs/1710.02973v1 | http://arxiv.org/pdf/1710.02973v1.pdf | LD-SDS: Towards an Expressive Spoken Dialogue System based on Linked-Data | In this work we discuss the related challenges and describe an approach
towards the fusion of state-of-the-art technologies from the Spoken Dialogue
Systems (SDS) and the Semantic Web and Information Retrieval domains. We
envision a dialogue system named LD-SDS that will support advanced, expressive,
and engaging user ... | ['Margarita Kotti', 'Alexandros Papangelis', 'Yannis Tzitzikas', 'Panagiotis Papadakos', 'Yannis Stylianou', 'Dimitris Plexousakis'] | 2017-10-09 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-2.64464408e-01 8.31247985e-01 2.22353190e-02 -3.80340278e-01
-8.45662832e-01 -8.80856514e-01 1.22651780e+00 7.61174023e-01
-3.71166766e-01 9.45835054e-01 1.05181944e+00 -1.15203224e-02
-5.37101388e-01 -6.25357211e-01 4.38789517e-01 2.71029383e-01
-3.62755209e-02 1.23147357e+00 6.61973894e-01 -1.18840194... | [12.526223182678223, 7.86883020401001] |
44b65562-e483-4e27-9cb5-58eed892378a | a-comparative-study-of-transformers-on-word | 2111.15417 | null | https://arxiv.org/abs/2111.15417v1 | https://arxiv.org/pdf/2111.15417v1.pdf | A Comparative Study of Transformers on Word Sense Disambiguation | Recent years of research in Natural Language Processing (NLP) have witnessed dramatic growth in training large models for generating context-aware language representations. In this regard, numerous NLP systems have leveraged the power of neural network-based architectures to incorporate sense information in embeddings,... | ['Dr. Anil Kumar Singh', 'Gaurav Dhama', 'Vikas Bishnoi', 'Nidhi Mulay', 'Avi Chawla'] | 2021-11-30 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-7.13444054e-02 1.80588719e-02 -2.46542796e-01 -4.48018312e-01
-7.13357449e-01 -5.43066025e-01 1.02302623e+00 7.22367227e-01
-9.60483074e-01 7.90835798e-01 6.76108241e-01 -6.02941751e-01
-1.59746841e-01 -9.39806342e-01 -3.58796977e-02 -2.81067431e-01
-9.95898098e-02 6.10196233e-01 7.70806968e-02 -8.39168668... | [10.373838424682617, 8.963250160217285] |
e52f0111-0310-4735-aa37-ea0dd30b6c8b | sygma-system-for-generalizable-modular | 2109.13430 | null | https://arxiv.org/abs/2109.13430v1 | https://arxiv.org/pdf/2109.13430v1.pdf | SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases | Knowledge Base Question Answering (KBQA) tasks that in-volve complex reasoning are emerging as an important re-search direction. However, most KBQA systems struggle withgeneralizability, particularly on two dimensions: (a) acrossmultiple reasoning types where both datasets and systems haveprimarily focused on multi-hop... | ['L Venkata Subramaniam', 'Francois Luus', 'Guilherme LimaRyan Riegel', 'Alexander Gray', 'Salim Roukos', 'Rosario Uceda-Sosa', 'Maria Chang', 'Sairam Gurajada', 'Srinivas Ravishankar', 'Dinesh Khandelwal', 'G P Shrivatsa Bhargav', 'Achille Fokoue', 'Dinesh Garg', 'Saswati Dana', 'Cezar Pendus', 'Santosh Srivastava', '... | 2021-09-28 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-7.96065032e-01 4.30192381e-01 -3.28094721e-01 -2.86900252e-01
-1.25743222e+00 -8.59801710e-01 4.49257493e-01 3.65544349e-01
-4.27831352e-01 1.23736513e+00 2.84821570e-01 -6.12496376e-01
-7.27928162e-01 -1.30724347e+00 -7.76845217e-01 -4.05914895e-02
1.41065344e-01 1.11053038e+00 9.61072624e-01 -1.01914144... | [10.304666519165039, 7.925469875335693] |
2e745b7f-563e-49fb-8f1e-0acfa52a02b3 | some-voices-are-too-common-building-fair | 2306.03773 | null | https://arxiv.org/abs/2306.03773v1 | https://arxiv.org/pdf/2306.03773v1.pdf | Some voices are too common: Building fair speech recognition systems using the Common Voice dataset | Automatic speech recognition (ASR) systems become increasingly efficient thanks to new advances in neural network training like self-supervised learning. However, they are known to be unfair toward certain groups, for instance, people speaking with an accent. In this work, we use the French Common Voice dataset to quan... | ['Yannick Estève', 'Lucas Maison'] | 2023-06-01 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [-1.32406503e-02 1.87330440e-01 -1.91588059e-01 -8.66596460e-01
-6.19165957e-01 -4.53173757e-01 7.08226740e-01 6.92042112e-02
-7.77183950e-01 5.85721254e-01 9.15355265e-01 -6.85353696e-01
2.11599767e-01 -3.98517191e-01 -2.45228022e-01 -3.79850954e-01
-2.15660315e-02 3.66213471e-01 -3.89820307e-01 -4.87468243... | [14.269275665283203, 6.547680854797363] |
ad0659d1-8b92-4769-b1ef-516e98bc2495 | 3d-scanning-system-for-automatic-high | 1702.08112 | null | http://arxiv.org/abs/1702.08112v1 | http://arxiv.org/pdf/1702.08112v1.pdf | 3D Scanning System for Automatic High-Resolution Plant Phenotyping | Thin leaves, fine stems, self-occlusion, non-rigid and slowly changing
structures make plants difficult for three-dimensional (3D) scanning and
reconstruction -- two critical steps in automated visual phenotyping. Many
current solutions such as laser scanning, structured light, and multiview
stereo can struggle to acqu... | ['Chuong V. Nguyen', 'David R. Lovell', 'Robert Furbank', 'Peter Kuffner', 'Xavier Sirault', 'Helen Daily', 'Jurgen Fripp'] | 2017-02-26 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 4.35330302e-01 -2.72105366e-01 1.06382996e-01 -1.96187064e-01
-7.61746021e-04 -1.17375994e+00 -1.38382837e-01 1.15146279e-01
2.07000673e-01 2.38406196e-01 -4.06887293e-01 -8.26675832e-01
-7.44422972e-02 -5.31566024e-01 -1.32155448e-01 -1.44485727e-01
2.11534590e-01 8.37508082e-01 7.44447351e-01 -9.34841707... | [9.0714750289917, -1.687591314315796] |
3fb27faa-4f6a-4299-893a-bcf19b0a1ec7 | weakly-supervised-gaze-estimation-from | 2212.02997 | null | https://arxiv.org/abs/2212.02997v2 | https://arxiv.org/pdf/2212.02997v2.pdf | Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views | Developing gaze estimation models that generalize well to unseen domains and in-the-wild conditions remains a challenge with no known best solution. This is mostly due to the difficulty of acquiring ground truth data that cover the distribution of possible faces, head poses and environmental conditions that exist in th... | ['Michail Christos Doukas', 'Stefanos Zafeiriou', 'Jia Guo', 'Jiankang Deng', 'Polydefkis Gkagkos', 'Evangelos Ververas'] | 2022-12-06 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 4.42095473e-02 3.24471802e-01 4.57527675e-02 -6.40976906e-01
-4.23855156e-01 -4.19489443e-01 2.37738073e-01 -5.51371694e-01
-2.97783375e-01 6.73364639e-01 9.83408839e-02 9.73530486e-02
7.99654275e-02 -1.44478098e-01 -9.88353014e-01 -4.03267503e-01
1.39373079e-01 4.64360267e-01 -1.31601855e-01 -1.46030843... | [14.129305839538574, 0.015147840604186058] |
5b518e05-eaeb-4d40-a5d7-ab6974f7034c | dynamic-graph-learning-with-content-guided | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Dynamic_Graph_Learning_With_Content-Guided_Spatial-Frequency_Relation_Reasoning_for_Deepfake_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Dynamic_Graph_Learning_With_Content-Guided_Spatial-Frequency_Relation_Reasoning_for_Deepfake_CVPR_2023_paper.pdf | Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection | With the springing up of face synthesis techniques, it is prominent in need to develop powerful face forgery detection methods due to security concerns. Some existing methods attempt to employ auxiliary frequency-aware information combined with CNN backbones to discover the forged clues. Due to the inadequate infor... | ['Silong Peng', 'Xiyuan Hu', 'Chen Chen', 'Kun Yu', 'YuAn Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deepfake-detection', 'face-swapping', 'face-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.06252611e-01 -2.35374019e-01 -3.33525509e-01 -7.14890808e-02
-6.58265829e-01 -2.27705002e-01 5.04851162e-01 -9.03926138e-03
2.14239627e-01 3.99686098e-01 4.70392317e-01 -9.95384157e-03
-2.84924328e-01 -7.40280986e-01 -4.61786836e-01 -6.78449690e-01
-3.98861021e-01 -4.54038322e-01 2.40576029e-01 -5.40772796... | [12.788732528686523, 0.9908974766731262] |
1da86258-e1f4-4f36-b6bf-3b68ea924d48 | qadi-arabic-dialect-identification-in-the | null | null | https://aclanthology.org/2021.wanlp-1.1 | https://aclanthology.org/2021.wanlp-1.1.pdf | QADI: Arabic Dialect Identification in the Wild | Proper dialect identification is important for a variety of Arabic NLP applications. In this paper, we present a method for rapidly constructing a tweet dataset containing a wide range of country-level Arabic dialects —covering 18 different countries in the Middle East and North Africa region. Our method relies on appl... | ['Kareem Darwish', 'Sabit Hassan', 'Younes Samih', 'Hamdy Mubarak', 'Ahmed Abdelali'] | null | null | null | null | eacl-wanlp-2021-4 | ['dialect-identification'] | ['natural-language-processing'] | [-3.02802444e-01 -1.79559365e-01 -1.13365337e-01 -4.91778404e-01
-1.03942120e+00 -1.12160468e+00 8.26084435e-01 2.76992261e-01
-4.83877599e-01 6.57971859e-01 2.12013841e-01 -3.28641862e-01
2.92873949e-01 -1.08067536e+00 -2.11437047e-01 -4.50832158e-01
8.64024237e-02 8.56762052e-01 -1.92932650e-01 -7.30868042... | [10.167427062988281, 10.736719131469727] |
5b078860-4166-4879-b4b0-68a1257fa93a | semi-supervised-segmentation-of-multi-vendor | null | null | https://ieeexplore.ieee.org/abstract/document/9477818 | https://ieeexplore.ieee.org/abstract/document/9477818 | Semi-Supervised Segmentation of Multi-vendor and Multi-center Cardiac MRI | Automatic segmentation of the heart cavity is an essential task for the diagnosis of cardiac diseases. In this paper, we propose a semi-supervised segmentation setup for leveraging unlabeled data to segment Left-ventricle, Right-ventricle, and Myocardium. We utilize an enhanced version of residual U-Net architecture on... | ['Mahyar Bolhassani; Ilkay Oksuz'] | 2021-05-09 | null | null | null | ieee-2021-5 | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 1.06397964e-01 2.03487262e-01 1.75124668e-02 -5.06175518e-01
-7.70314813e-01 -5.50135791e-01 2.13687159e-02 1.86898112e-01
-4.91874039e-01 7.65948296e-01 1.97277740e-02 -1.43280491e-01
1.91577211e-01 -4.47539985e-01 -3.41264546e-01 -6.11263573e-01
-3.18783745e-02 4.50405687e-01 3.89526099e-01 4.12640899... | [14.331894874572754, -2.2603020668029785] |
d6e8c2db-6fa1-4281-8ce9-b3005f25e181 | convolutional-neural-network-based-image | 1504.05241 | null | http://arxiv.org/abs/1504.05241v1 | http://arxiv.org/pdf/1504.05241v1.pdf | Convolutional Neural Network-Based Image Representation for Visual Loop Closure Detection | Deep convolutional neural networks (CNN) have recently been shown in many
computer vision and pattern recog- nition applications to outperform by a
significant margin state- of-the-art solutions that use traditional
hand-crafted features. However, this impressive performance is yet to be fully
exploited in robotics. In... | ['Yi Hou', 'Shilin Zhou', 'Hong Zhang'] | 2015-04-20 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 1.23305120e-01 -1.95162833e-01 2.93355603e-02 -3.03078353e-01
-4.48308617e-01 -3.31269324e-01 9.06503260e-01 4.38032061e-01
-8.09776068e-01 3.09187770e-01 -9.96282324e-02 -2.67327935e-01
-9.33612958e-02 -7.85615504e-01 -9.82440770e-01 -3.31534684e-01
-3.26911211e-01 3.90840977e-01 4.56376940e-01 -5.55970967... | [7.758461952209473, -1.8449761867523193] |
7bc5bdf7-2269-48e1-8979-7bbf5e042e61 | interpretable-knowledge-tracing-simple-and | 2112.11209 | null | https://arxiv.org/abs/2112.11209v1 | https://arxiv.org/pdf/2112.11209v1.pdf | Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations | Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning-based KT models have shown sign... | ['Feida Zhu', 'Hisashi Kashima', 'Koh Takeuchi', 'Jill-Jenn Vie', 'Sein Minn'] | 2021-12-15 | null | null | null | null | ['skill-mastery'] | ['robots'] | [-4.29827869e-02 3.80909175e-01 -4.49272752e-01 -5.48522770e-01
-9.01407972e-02 -4.11435604e-01 6.37775138e-02 6.18840277e-01
-6.56930916e-03 7.58904040e-01 2.04022124e-01 -8.94411445e-01
-9.57992375e-01 -9.88170981e-01 -5.08010268e-01 -2.26244003e-01
1.55407071e-01 4.28612560e-01 3.56839806e-01 -4.60559994... | [10.117523193359375, 7.247601509094238] |
6ec53047-b2f2-4144-a964-1b1acb2cdbed | deep-optimal-transport-a-practical-algorithm | 2306.02342 | null | https://arxiv.org/abs/2306.02342v1 | https://arxiv.org/pdf/2306.02342v1.pdf | Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration | We propose an image restoration algorithm that can control the perceptual quality and/or the mean square error (MSE) of any pre-trained model, trading one over the other at test time. Our algorithm is few-shot: Given about a dozen images restored by the model, it can significantly improve the perceptual quality and/or ... | ['Michael Elad', 'Tomer Michaeli', 'Guy Ohayon', 'Theo Adrai'] | 2023-06-04 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 5.98106742e-01 2.64268845e-01 1.72841072e-01 -1.07303232e-01
-1.09613740e+00 -2.45850161e-01 3.75698000e-01 -2.03560606e-01
-3.32087874e-01 6.04242802e-01 1.81735098e-01 4.81391372e-03
-2.72271395e-01 -4.88113731e-01 -9.34807301e-01 -1.11443186e+00
6.57265261e-02 6.22055167e-03 -4.28264663e-02 1.74178407... | [11.581391334533691, -2.1860554218292236] |
eccc6163-f93e-402a-8e6c-710d022ab112 | topical-coherence-in-lda-based-models-through | null | null | https://aclanthology.org/P17-1165 | https://aclanthology.org/P17-1165.pdf | Topical Coherence in LDA-based Models through Induced Segmentation | This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words. The coherence between topics is ensured through a copula, binding the topics associated to the words of a segment. In addition, this model relies on bot... | ['Hesam Amoualian', 'Massih R. Amini', 'Wei Lu', 'Marianne Clausel', 'Georgios Balikas', 'Eric Gaussier'] | 2017-07-01 | null | null | null | acl-2017-7 | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-1.90745980e-01 1.95151582e-01 -5.99449813e-01 -4.42260534e-01
-1.17029345e+00 -7.14401186e-01 9.92751062e-01 3.48609298e-01
1.79615721e-01 7.32866585e-01 6.89690053e-01 -3.12511586e-02
-7.38720372e-02 -6.86449707e-01 -4.35892373e-01 -8.54682207e-01
-1.46318689e-01 6.85636640e-01 1.99347556e-01 1.27732813... | [10.392345428466797, 6.990381240844727] |
4c6de37b-7ca1-4597-a16a-fc16552e4606 | a-generalized-alternating-method-for-bilevel | 2306.02422 | null | https://arxiv.org/abs/2306.02422v2 | https://arxiv.org/pdf/2306.02422v2.pdf | A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition | Bilevel optimization has recently regained interest owing to its applications in emerging machine learning fields such as hyperparameter optimization, meta-learning, and reinforcement learning. Recent results have shown that simple alternating (implicit) gradient-based algorithms can achieve the same convergence rate o... | ['Tianyi Chen', 'Songtao Lu', 'Quan Xiao'] | 2023-06-04 | null | null | null | null | ['bilevel-optimization', 'hyperparameter-optimization'] | ['methodology', 'methodology'] | [-2.75824815e-01 1.55825123e-01 -2.18550757e-01 -2.59891003e-01
-8.96388054e-01 -3.23438346e-01 5.80206960e-02 2.47696206e-01
-6.43445194e-01 1.14283967e+00 -1.77606612e-01 -3.89596075e-01
-6.34281814e-01 -4.32013422e-01 -7.78525174e-01 -9.69262660e-01
-1.37049913e-01 3.74746889e-01 -1.65225402e-01 -2.90155321... | [6.733695983886719, 4.3987932205200195] |
455f2513-cece-45b3-9a09-4a01d3a15757 | shearlet-based-detection-of-flame-fronts | 1511.03753 | null | http://arxiv.org/abs/1511.03753v2 | http://arxiv.org/pdf/1511.03753v2.pdf | Shearlet-Based Detection of Flame Fronts | Identifying and characterizing flame fronts is the most common task in the
computer-assisted analysis of data obtained from imaging techniques such as
planar laser-induced fluorescence (PLIF), laser Rayleigh scattering (LRS), or
particle imaging velocimetry (PIV). We present a novel edge and ridge (line)
detection algo... | ['Rafael Reisenhofer', 'Johannes Kiefer', 'Emily J. King'] | 2015-11-12 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 2.84498423e-01 -7.40891278e-01 4.62369472e-01 2.38313004e-01
-5.72039545e-01 -6.21294439e-01 5.60332894e-01 5.19272387e-01
-6.10344589e-01 3.27752680e-01 -1.06890537e-01 -3.48327130e-01
-3.34921718e-01 -7.26987004e-01 -1.13148600e-01 -8.64378631e-01
-2.76827455e-01 1.43276319e-01 3.86730373e-01 -4.60490137... | [11.628507614135742, -2.4454238414764404] |
88b4bd6a-2d2c-406f-9327-1389c0a28954 | sequence-to-segment-networks-for-segment | null | null | http://papers.nips.cc/paper/7610-sequence-to-segment-networks-for-segment-detection | http://papers.nips.cc/paper/7610-sequence-to-segment-networks-for-segment-detection.pdf | Sequence-to-Segment Networks for Segment Detection | Detecting segments of interest from an input sequence is a challenging problem which often requires not only good knowledge of individual target segments, but also contextual understanding of the entire input sequence and the relationships between the target segments. To address this problem, we propose the Sequence-t... | ['Minh Hoai Nguyen', 'Boyu Wang', 'Xiaohui Shen', 'Zijun Wei', 'Zhe Lin', 'Radomir Mech', 'Jianming Zhang', 'Dimitris Samaras'] | 2018-12-01 | null | null | null | neurips-2018-12 | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 9.71956551e-01 4.49844718e-01 -5.34653962e-01 -2.83123463e-01
-1.29477823e+00 -4.01851922e-01 3.06013525e-01 5.84615103e-04
-2.19805226e-01 5.85760534e-01 5.46971560e-01 4.31914255e-02
3.97069544e-01 -4.70979124e-01 -9.67502654e-01 -4.52613205e-01
-1.65531710e-01 2.99903363e-01 7.57134020e-01 -5.33947237... | [10.009760856628418, 0.626686155796051] |
0c1ea6bb-fb56-4520-bb7a-97a383ffd2ca | weather-event-severity-prediction-using-buoy | 1911.09001 | null | https://arxiv.org/abs/1911.09001v1 | https://arxiv.org/pdf/1911.09001v1.pdf | Weather event severity prediction using buoy data and machine learning | In this paper, we predict severity of extreme weather events (tropical storms, hurricanes, etc.) using buoy data time series variables such as wind speed and air temperature. The prediction/forecasting method is based on various forecasting and machine learning models. The following steps are used. Data sources for the... | ['Vikas Ramachandra'] | 2019-11-17 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-3.90748382e-01 -4.91622686e-01 -4.43352684e-02 -6.08487487e-01
-4.95944262e-01 -4.55460727e-01 5.46575665e-01 3.12283576e-01
-2.37178832e-01 1.29696953e+00 6.89720333e-01 -5.16356468e-01
-2.31274650e-01 -1.00227571e+00 -1.50278270e-01 -7.99545765e-01
-7.18208373e-01 1.84241518e-01 -2.84713507e-01 -2.78886676... | [6.544656753540039, 2.959895133972168] |
a4ab005e-e22e-4855-a92b-8f41eaae62db | continual-density-ratio-estimation-cdre-a-new | null | null | https://openreview.net/forum?id=HJemQJBKDr | https://openreview.net/pdf?id=HJemQJBKDr | Continual Density Ratio Estimation (CDRE): A new method for evaluating generative models in continual learning | We propose a new method Continual Density Ratio Estimation (CDRE), which can estimate density ratios between a target distribution of real samples and a distribution of samples generated by a model while the model is changing over time and the data of the target distribution is not available after a certain time point.... | ['Peter Flach', 'Tom Diethe', 'Song Liu', 'Yu Chen'] | 2019-09-25 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-1.70645118e-01 -1.88125297e-01 -1.85533971e-01 -3.00566792e-01
-7.06687808e-01 -5.30461967e-01 9.40687239e-01 5.34602441e-02
-6.33671224e-01 1.09456372e+00 -5.07579565e-01 -2.23843127e-01
-2.08683476e-01 -9.78824496e-01 -1.15269637e+00 -6.58182204e-01
1.94101725e-02 1.11624956e+00 2.54555434e-01 2.68913448... | [7.811567306518555, 3.2803850173950195] |
38decfb6-0fa3-46e0-83b9-98137321e918 | best-k-search-algorithm-for-neural-text | 2211.11924 | null | https://arxiv.org/abs/2211.11924v1 | https://arxiv.org/pdf/2211.11924v1.pdf | Best-$k$ Search Algorithm for Neural Text Generation | Modern natural language generation paradigms require a good decoding strategy to obtain quality sequences out of the model. Beam search yields high-quality but low diversity outputs; stochastic approaches suffer from high variance and sometimes low quality, but the outputs tend to be more natural and creative. In this ... | ['Yingbo Zhou', 'Silvio Savarese', 'Caiming Xiong', 'Jiacheng Xu'] | 2022-11-22 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 3.72633636e-01 2.35784277e-01 -2.64063269e-01 -2.11131409e-01
-1.14239192e+00 -6.34169400e-01 6.85586870e-01 1.90351367e-01
-3.92912716e-01 1.16708624e+00 5.95829666e-01 -2.30286658e-01
-4.19049561e-02 -9.58829999e-01 -7.06967354e-01 -6.46179557e-01
3.90612274e-01 5.76570392e-01 1.67137831e-01 -2.85897911... | [11.993850708007812, 9.069579124450684] |
82d45837-303e-42ed-ace6-b3c463c1f78c | summarizing-source-code-using-a-neural | null | null | https://aclanthology.org/P16-1195 | https://aclanthology.org/P16-1195.pdf | Summarizing Source Code using a Neural Attention Model | null | ['Ioannis Konstas', 'Luke Zettlemoyer', 'Alvin Cheung', 'Srinivasan Iyer'] | 2016-08-01 | null | null | null | acl-2016-8 | ['code-summarization'] | ['computer-code'] | [-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... | [-7.4027099609375, 3.6039180755615234] |
0a6c94fc-69f4-4c70-8a43-d2900024d27f | explaining-link-predictions-in-knowledge | 2212.02651 | null | https://arxiv.org/abs/2212.02651v1 | https://arxiv.org/pdf/2212.02651v1.pdf | Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples | We study the problem of explaining link predictions in the Knowledge Graph Embedding (KGE) models. We propose an example-based approach that exploits the latent space representation of nodes and edges in a knowledge graph to explain predictions. We evaluated the importance of identified triples by observing progressing... | ['Luca Costabello', 'Adrianna Janik'] | 2022-12-05 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [ 1.37918845e-01 1.41231298e+00 -1.03752625e+00 -2.83707410e-01
-2.15753645e-01 -1.07409045e-01 9.82200980e-01 3.47502202e-01
5.05531669e-01 1.10235703e+00 8.86894405e-01 -6.27608418e-01
-5.14180124e-01 -1.21553266e+00 -1.18550181e+00 1.27821192e-01
-3.99516612e-01 7.74813414e-01 2.31798083e-01 -3.33472043... | [8.856038093566895, 7.7843732833862305] |
dabd969a-289d-45cc-9329-55792106fb1a | local-information-assisted-attention-free | 2201.03217 | null | https://arxiv.org/abs/2201.03217v2 | https://arxiv.org/pdf/2201.03217v2.pdf | Local Information Assisted Attention-free Decoder for Audio Captioning | Automated audio captioning aims to describe audio data with captions using natural language. Existing methods often employ an encoder-decoder structure, where the attention-based decoder (e.g., Transformer decoder) is widely used and achieves state-of-the-art performance. Although this method effectively captures globa... | ['Haiyan Lan', 'Wenwu Wang', 'Qiaoxi Zhu', 'Jian Guan', 'Feiyang Xiao'] | 2022-01-10 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 3.68471682e-01 1.48715362e-01 1.09453902e-01 -1.69391394e-01
-1.46089482e+00 -3.62511456e-01 4.52301472e-01 -6.55500814e-02
1.21989595e-02 8.95759463e-01 8.54873657e-01 1.74991652e-01
2.63656616e-01 -5.80135167e-01 -1.02233148e+00 -4.91904050e-01
2.03296110e-01 4.45391536e-01 2.79416829e-01 -1.85614586... | [15.25768756866455, 4.888449192047119] |
9b37069e-cd83-4869-ace3-f0149f7edc11 | transfer-learning-based-road-damage-detection | 2008.13101 | null | https://arxiv.org/abs/2008.13101v1 | https://arxiv.org/pdf/2008.13101v1.pdf | Transfer Learning-based Road Damage Detection for Multiple Countries | Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. Although some countries, like Japan, have developed less expensive and readily a... | ['Sanjay Kumar Ghosh', 'Yoshihide Sekimoto', 'Takehiro Kashiyama', 'Alexander Mraz', 'Hiroya Maeda', 'Deeksha Arya', 'Durga Toshniwal'] | 2020-08-30 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-2.23314300e-01 -1.35749783e-02 -3.84640902e-01 -3.20522673e-02
-1.10066879e+00 -3.03132236e-01 2.55513996e-01 1.84850633e-01
-3.64419669e-01 7.54881442e-01 4.52077359e-01 -7.08967030e-01
-1.04498766e-01 -1.30701041e+00 -2.57261604e-01 -5.40273130e-01
3.80199283e-01 -3.65781784e-02 3.15994054e-01 -1.01740927... | [7.416479587554932, 1.0938782691955566] |
bcc43c3c-fceb-4371-b0ba-848e2a9b33de | mixspeech-cross-modality-self-learning-with | 2303.05309 | null | https://arxiv.org/abs/2303.05309v1 | https://arxiv.org/pdf/2303.05309v1.pdf | MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and Recognition | Multi-media communications facilitate global interaction among people. However, despite researchers exploring cross-lingual translation techniques such as machine translation and audio speech translation to overcome language barriers, there is still a shortage of cross-lingual studies on visual speech. This lack of res... | ['Zhou Zhao', 'Aoxiong Yin', 'Ye Wang', 'Huangdai Liu', 'Zehan Wang', 'Wang Lin', 'Rongjie Huang', 'Tao Jin', 'Linjun Li', 'Xize Cheng'] | 2023-03-09 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 4.97092456e-02 -7.90845137e-03 -4.34368283e-01 -1.26358019e-02
-1.46607244e+00 -6.33095801e-01 5.69485366e-01 -1.31080106e-01
-2.94919193e-01 7.19731212e-01 5.16251326e-01 -6.11179352e-01
4.45817143e-01 -3.80689800e-01 -9.15568769e-01 -4.07971233e-01
6.78459644e-01 3.81610066e-01 -9.71977934e-02 -2.29167029... | [14.349783897399902, 5.319440841674805] |
1273cc2e-bd5f-49e4-8f79-9cce5c84a96a | local-supports-global-deep-camera | 1908.04391 | null | https://arxiv.org/abs/1908.04391v1 | https://arxiv.org/pdf/1908.04391v1.pdf | Local Supports Global: Deep Camera Relocalization with Sequence Enhancement | We propose to leverage the local information in image sequences to support global camera relocalization. In contrast to previous methods that regress global poses from single images, we exploit the spatial-temporal consistency in sequential images to alleviate uncertainty due to visual ambiguities by incorporating a vi... | ['Zike Yan', 'Junqiu Wang', 'Hongbin Zha', 'Fei Xue', 'Qiuyuan Wang', 'Xin Wang'] | 2019-08-06 | local-supports-global-deep-camera-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Xue_Local_Supports_Global_Deep_Camera_Relocalization_With_Sequence_Enhancement_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Xue_Local_Supports_Global_Deep_Camera_Relocalization_With_Sequence_Enhancement_ICCV_2019_paper.pdf | iccv-2019-10 | ['camera-relocalization'] | ['computer-vision'] | [ 2.20775321e-01 2.14404240e-02 -8.42771605e-02 -2.96786249e-01
-5.92572212e-01 -5.63941538e-01 5.44308662e-01 9.81979351e-03
-4.94330317e-01 6.93381727e-01 1.32647470e-01 1.94520026e-01
-7.23828897e-02 -6.36509240e-01 -9.72157240e-01 -7.12498665e-01
3.65479231e-01 2.99847692e-01 4.41686571e-01 -2.71055400... | [7.871405124664307, -2.2374179363250732] |
77746e9a-2887-4a88-9170-1fef6ce4a2e0 | ladder-variational-autoencoders | 1602.02282 | null | http://arxiv.org/abs/1602.02282v3 | http://arxiv.org/pdf/1602.02282v3.pdf | Ladder Variational Autoencoders | Variational Autoencoders are powerful models for unsupervised learning.
However deep models with several layers of dependent stochastic variables are
difficult to train which limits the improvements obtained using these highly
expressive models. We propose a new inference model, the Ladder Variational
Autoencoder, that... | ['Søren Kaae Sønderby', 'Lars Maaløe', 'Casper Kaae Sønderby', 'Tapani Raiko', 'Ole Winther'] | 2016-02-06 | ladder-variational-autoencoders-1 | http://papers.nips.cc/paper/6275-ladder-variational-autoencoders | http://papers.nips.cc/paper/6275-ladder-variational-autoencoders.pdf | neurips-2016-12 | ['unsupervised-mnist'] | ['methodology'] | [-2.76226997e-01 4.72262233e-01 -6.63290322e-02 -4.44496363e-01
-8.06257188e-01 -4.50775415e-01 1.00143397e+00 -3.46288979e-01
-7.39252567e-02 8.87271345e-01 4.79301035e-01 -1.74013808e-01
-2.38123491e-01 -8.98915291e-01 -1.02370286e+00 -1.01099229e+00
1.64120063e-01 1.11470783e+00 9.96262878e-02 7.57393390... | [6.952874660491943, 3.9809532165527344] |
7b9c7352-e23f-438f-8e25-85d215bd6af2 | model-agnostic-explainability-for-visual | 2103.00370 | null | https://arxiv.org/abs/2103.00370v3 | https://arxiv.org/pdf/2103.00370v3.pdf | Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning | Visual search, recommendation, and contrastive similarity learning power technologies that impact billions of users worldwide. Modern model architectures can be complex and difficult to interpret, and there are several competing techniques one can use to explain a search engine's behavior. We show that the theory of fa... | ['William T. Freeman', 'Stephanie Fu', 'Lei Zhang', 'Scott Lundberg', 'Mark Hamilton'] | 2021-02-28 | axiomatic-explanations-for-visual-search | https://openreview.net/forum?id=TqNsv1TuCX9 | https://openreview.net/pdf?id=TqNsv1TuCX9 | iclr-2022-4 | ['image-similarity-search'] | ['computer-vision'] | [-6.86913356e-02 1.72756478e-01 -5.10793805e-01 -4.62187052e-01
-5.86070418e-01 -8.32366645e-01 7.30551243e-01 -1.80399269e-01
-3.38338315e-01 4.61572737e-01 7.14100385e-03 -8.64540279e-01
-6.30503178e-01 -3.32169384e-01 -6.14459872e-01 -1.79865211e-01
6.79693148e-02 5.52505016e-01 -8.97838473e-02 -2.17780992... | [8.834311485290527, 5.41861629486084] |
62a2d27b-1538-4e11-86a2-038492de4116 | so-different-yet-so-alike-constrained-1 | 2205.04093 | null | https://arxiv.org/abs/2205.04093v1 | https://arxiv.org/pdf/2205.04093v1.pdf | So Different Yet So Alike! Constrained Unsupervised Text Style Transfer | Automatic transfer of text between domains has become popular in recent times. One of its aims is to preserve the semantic content of text being translated from source to target domain. However, it does not explicitly maintain other attributes between the source and translated text, for e.g., text length and descriptiv... | ['Soujanya Poria', 'Roger Zimmermann', 'Min-Yen Kan', 'Devamanyu Hazarika', 'Abhinav Ramesh Kashyap'] | 2022-05-09 | null | https://aclanthology.org/2022.acl-long.32 | https://aclanthology.org/2022.acl-long.32.pdf | acl-2022-5 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 6.00776494e-01 3.35024893e-01 -2.09652483e-01 -5.66115737e-01
-8.75284374e-01 -9.51888502e-01 7.83743799e-01 1.93804353e-01
-4.49491501e-01 1.17343807e+00 3.15793782e-01 2.81796139e-03
2.98989773e-01 -8.33538353e-01 -8.27539742e-01 -7.48307824e-01
5.87605298e-01 6.69314206e-01 2.23075002e-02 -3.64488691... | [11.702509880065918, 9.569007873535156] |
220a74de-adf9-4f4f-87ee-e63620fae665 | semantic-slam-with-autonomous-object-level | 2011.10625 | null | https://arxiv.org/abs/2011.10625v1 | https://arxiv.org/pdf/2011.10625v1.pdf | Semantic SLAM with Autonomous Object-Level Data Association | It is often desirable to capture and map semantic information of an environment during simultaneous localization and mapping (SLAM). Such semantic information can enable a robot to better distinguish places with similar low-level geometric and visual features and perform high-level tasks that use semantic information a... | ['Jing Xiao', 'Jie Fu', 'Kartik Patath', 'Zhentian Qian'] | 2020-11-20 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [ 8.27027038e-02 -2.37714916e-01 -2.61342019e-01 -7.45575666e-01
-6.92419469e-01 -3.33618522e-01 3.98137867e-01 5.68039834e-01
-5.95162868e-01 3.80080462e-01 -1.68485522e-01 2.86604203e-02
-5.97456634e-01 -9.10273373e-01 -7.31006980e-01 -3.15088421e-01
-1.24032222e-01 1.11557579e+00 4.21087056e-01 -2.80403733... | [7.287022113800049, -2.2199277877807617] |
e7955d27-b830-4d5d-9018-1a7447305db9 | pointcontrast-unsupervised-pre-training-for | 2007.10985 | null | https://arxiv.org/abs/2007.10985v3 | https://arxiv.org/pdf/2007.10985v3.pdf | PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding | Arguably one of the top success stories of deep learning is transfer learning. The finding that pre-training a network on a rich source set (eg., ImageNet) can help boost performance once fine-tuned on a usually much smaller target set, has been instrumental to many applications in language and vision. Yet, very little... | ['Or Litany', 'Jiatao Gu', 'Saining Xie', 'Leonidas J. Guibas', 'Demi Guo', 'Charles R. Qi'] | 2020-07-21 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/893_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480579.pdf | eccv-2020-8 | ['point-cloud-pre-training'] | ['computer-vision'] | [ 2.33393565e-01 6.57057613e-02 -1.38835341e-01 -7.84800351e-01
-9.18534696e-01 -8.17023575e-01 7.88486958e-01 7.01613501e-02
-5.82409859e-01 3.57294679e-01 1.80626094e-01 -5.59609771e-01
1.55113742e-01 -6.50237143e-01 -9.91433501e-01 -2.97664285e-01
-1.31518513e-01 6.24075055e-01 2.08491698e-01 -1.84802815... | [8.235452651977539, -3.309448480606079] |
dc83cadf-61bd-4a85-acb6-21eced201820 | bbam-bounding-box-attribution-map-for-weakly | 2103.08907 | null | https://arxiv.org/abs/2103.08907v1 | https://arxiv.org/pdf/2103.08907v1.pdf | BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation | Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object. Existing methods typically depend on a class-agnostic mask generator, which operates on the low-level information intrinsic to an image. In this work, we utilize higher-level i... | ['Sungroh Yoon', 'Chaehun Shin', 'Jihun Yi', 'Jungbeom Lee'] | 2021-03-16 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lee_BBAM_Bounding_Box_Attribution_Map_for_Weakly_Supervised_Semantic_and_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lee_BBAM_Bounding_Box_Attribution_Map_for_Weakly_Supervised_Semantic_and_CVPR_2021_paper.pdf | cvpr-2021-1 | ['box-supervised-instance-segmentation', 'weakly-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.85278034e-01 5.47005177e-01 -4.05313998e-01 -5.19614398e-01
-1.00109649e+00 -8.53614330e-01 5.05694449e-01 3.01847547e-01
-3.77906293e-01 4.82580364e-01 -2.29322925e-01 5.78238852e-02
5.02697647e-01 -6.53826416e-01 -1.11888194e+00 -7.67111301e-01
3.26723844e-01 5.49741328e-01 8.08145761e-01 1.40831873... | [9.516204833984375, 0.5791480541229248] |
cbe1d97e-8104-45a0-b5f0-d3a27b8a9fe3 | uniter-learning-universal-image-text | null | null | https://openreview.net/forum?id=S1eL4kBYwr | https://openreview.net/pdf?id=S1eL4kBYwr | UNITER: Learning UNiversal Image-TExt Representations | Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are jointly processed for visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (CO... | ['Jingjing Liu', 'Yu Cheng', 'Zhe Gan', 'Faisal Ahmed', 'Ahmed El Kholy', 'Licheng Yu', 'Linjie Li', 'Yen-Chun Chen'] | 2019-09-25 | null | null | null | null | ['visual-commonsense-reasoning', 'visual-entailment'] | ['reasoning', 'reasoning'] | [ 5.52576423e-01 2.58062214e-01 -4.25845057e-01 -4.13401812e-01
-9.45715725e-01 -7.40973234e-01 9.96084452e-01 5.54591119e-02
-5.40591180e-01 2.11190701e-01 5.16140759e-01 -8.40698421e-01
5.17758548e-01 -3.19557756e-01 -1.23389435e+00 -2.10523278e-01
4.89603490e-01 3.06737453e-01 -3.01390022e-01 -1.14283323... | [10.855721473693848, 1.6742076873779297] |
c3897179-6727-49dd-a0fe-925af8153d94 | wind-park-power-prediction-attention-based | 2201.03229 | null | https://arxiv.org/abs/2201.03229v2 | https://arxiv.org/pdf/2201.03229v2.pdf | Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses | With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn complex patterns in the data and have found wide success in predicting wake losses and expected power pro... | ['Paal Engelstad', 'Roy Stenbro', 'Narada Dilp Warakagoda', 'Lars Ødegaard Bentsen'] | 2022-01-10 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [-2.73078769e-01 3.31900418e-01 1.61155432e-01 -1.13688715e-01
5.44644237e-01 -3.87560129e-01 4.14121389e-01 2.87413180e-01
1.48380876e-01 5.04738390e-01 1.08657748e-01 -8.83754313e-01
-5.07769108e-01 -1.10103953e+00 -3.77204686e-01 -7.64420271e-01
-6.77579403e-01 1.42602697e-01 -1.99357718e-01 -5.95848382... | [6.444276332855225, 2.851170301437378] |
6c5106df-2f11-4761-a8c7-78ae4ac47bb4 | adaptively-topological-tensor-network-for | 2305.00716 | null | https://arxiv.org/abs/2305.00716v1 | https://arxiv.org/pdf/2305.00716v1.pdf | Adaptively Topological Tensor Network for Multi-view Subspace Clustering | Multi-view subspace clustering methods have employed learned self-representation tensors from different tensor decompositions to exploit low rank information. However, the data structures embedded with self-representation tensors may vary in different multi-view datasets. Therefore, a pre-defined tensor decomposition m... | ['Ce Zhu', 'Zhen Long', 'Weiting Ou', 'Yingcong Lu', 'Yipeng Liu'] | 2023-05-01 | null | null | null | null | ['multi-view-subspace-clustering', 'tensor-networks'] | ['computer-vision', 'methodology'] | [-5.20890355e-01 -5.59178889e-01 -2.70960748e-01 1.42929226e-01
-2.17532471e-01 -8.29903722e-01 1.05339564e-01 -4.93392229e-01
8.98996890e-02 -2.61351140e-03 6.70144081e-01 5.60538657e-03
-8.57168555e-01 -5.28988063e-01 -1.51565701e-01 -1.09841573e+00
-2.69502610e-01 4.81075227e-01 2.30034105e-02 -2.30833367... | [8.251001358032227, 4.638099670410156] |
c5cd2480-ef56-4b76-81cb-f2afd5997c92 | parallel-corpus-for-japanese-spoken-to | null | null | https://aclanthology.org/2020.lrec-1.779 | https://aclanthology.org/2020.lrec-1.779.pdf | Parallel Corpus for Japanese Spoken-to-Written Style Conversion | With the increase of automatic speech recognition (ASR) applications, spoken-to-written style conversion that transforms spoken-style text into written-style text is becoming an important technology to increase the readability of ASR transcriptions. To establish such conversion technology, a parallel corpus of spoken-s... | ['Mana Ihori', 'Ryo Masumura', 'Akihiko Takashima'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 0.35037115 -0.14209086 0.27439785 -0.61243296 -0.8230863 -0.620573
0.48582184 -0.45979816 -0.57148737 0.8594566 0.38605642 -0.49991798
0.60948217 -0.49567205 -0.31802934 -0.49733898 0.93876255 0.76273054
0.22720502 -0.74847424 -0.03188023 0.21850362 -0.9767748 0.40201765
1.0864766 0.40611702 0.74... | [14.430892944335938, 7.161182403564453] |
62cced47-b6de-4ad6-a60d-ec8e7f4b23ce | hauser-towards-holistic-and-automatic | 2306.07554 | null | https://arxiv.org/abs/2306.07554v1 | https://arxiv.org/pdf/2306.07554v1.pdf | HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation | Similes play an imperative role in creative writing such as story and dialogue generation. Proper evaluation metrics are like a beacon guiding the research of simile generation (SG). However, it remains under-explored as to what criteria should be considered, how to quantify each criterion into metrics, and whether the... | ['Yunwen Chen', 'Yanghua Xiao', 'Yuncheng Huang', 'Jiaqing Liang', 'Yikai Zhang', 'Qianyu He'] | 2023-06-13 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [-2.99898237e-02 -4.36754785e-02 -1.89834028e-01 -1.64896578e-01
-5.92200696e-01 -6.59409881e-01 1.00021255e+00 -1.17284417e-01
-1.12483919e-01 7.32624114e-01 7.99807131e-01 1.81449771e-01
-1.49553329e-01 -5.95443189e-01 5.54720759e-02 -2.38751188e-01
5.26710868e-01 3.61313879e-01 3.77670266e-02 -6.16051972... | [11.638143539428711, 9.020976066589355] |
05e29030-ef2d-482f-b47d-a9a26028c563 | semantic-instance-segmentation-via-deep | 1703.10277 | null | http://arxiv.org/abs/1703.10277v1 | http://arxiv.org/pdf/1703.10277v1.pdf | Semantic Instance Segmentation via Deep Metric Learning | We propose a new method for semantic instance segmentation, by first
computing how likely two pixels are to belong to the same object, and then by
grouping similar pixels together. Our similarity metric is based on a deep,
fully convolutional embedding model. Our grouping method is based on selecting
all points that ar... | ['Sergio Guadarrama', 'Vivek Rathod', 'Peng Wang', 'Kevin P. Murphy', 'Hyun Oh Song', 'Zbigniew Wojna', 'Alireza Fathi'] | 2017-03-30 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 3.42112511e-01 2.19340488e-01 -1.01091936e-01 -7.85340190e-01
-1.01340437e+00 -5.68788946e-01 3.83131057e-01 4.39681113e-01
-7.28875816e-01 2.57052869e-01 -7.98290689e-03 3.06670368e-01
5.97326383e-02 -9.07025576e-01 -8.88161659e-01 -4.47627276e-01
5.71905822e-03 7.88163185e-01 7.20645487e-01 1.34933352... | [9.51784896850586, 0.4683340787887573] |
b4e17473-3934-4668-b962-2ec03df0d188 | empirical-evaluation-of-gated-recurrent | 1412.3555 | null | http://arxiv.org/abs/1412.3555v1 | http://arxiv.org/pdf/1412.3555v1.pdf | Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling | In this paper we compare different types of recurrent units in recurrent
neural networks (RNNs). Especially, we focus on more sophisticated units that
implement a gating mechanism, such as a long short-term memory (LSTM) unit and
a recently proposed gated recurrent unit (GRU). We evaluate these recurrent
units on the t... | ['Kyunghyun Cho', 'Junyoung Chung', 'Yoshua Bengio', 'Caglar Gulcehre'] | 2014-12-11 | null | null | null | null | ['music-modeling'] | ['music'] | [ 1.39781728e-01 1.24629416e-01 -1.27552211e-01 -7.47998990e-03
-3.14624697e-01 -7.26069212e-02 5.57376444e-01 -4.90649909e-01
-4.31992710e-01 7.48238325e-01 6.53254330e-01 -5.64819932e-01
4.66264009e-01 -7.32667208e-01 -6.36413813e-01 -6.54380083e-01
-1.23882554e-01 -3.25061560e-01 1.47084817e-01 -3.38693053... | [10.896929740905762, 6.351787567138672] |
0e84a979-969a-4b31-aa20-67ffefd6f94e | search-in-the-chain-towards-the-accurate | 2304.14732 | null | https://arxiv.org/abs/2304.14732v5 | https://arxiv.org/pdf/2304.14732v5.pdf | Search-in-the-Chain: Towards Accurate, Credible and Traceable Large Language Models for Knowledge-intensive Tasks | Making the contents generated by Large Language Model (LLM) such as ChatGPT, accurate, credible and traceable is crucial, especially in complex knowledge-intensive tasks that require multi-step reasoning and each of which needs knowledge to solve. Introducing Information Retrieval (IR) to provide LLM with external know... | ['Tat-Seng Chua', 'Xueqi Cheng', 'HuaWei Shen', 'Liang Pang', 'Shicheng Xu'] | 2023-04-28 | null | null | null | null | ['multi-hop-question-answering', 'long-form-question-answering', 'slot-filling'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-1.41343370e-01 6.61519706e-01 -4.07221258e-01 -9.58232209e-02
-1.25957990e+00 -8.23749006e-01 3.62437069e-01 3.71209472e-01
-4.77085352e-01 7.66464412e-01 2.19410405e-01 -7.02180207e-01
-5.23380458e-01 -1.11186683e+00 -8.79522085e-01 1.24772064e-01
4.41292733e-01 1.04764533e+00 1.04536688e+00 -5.38389325... | [10.80337142944336, 7.918675422668457] |
f72c1970-8352-4784-b3cb-f67c1522efb4 | automatic-microscopic-cell-counting-by-use-of-1 | 1903.01084 | null | http://arxiv.org/abs/1903.01084v3 | http://arxiv.org/pdf/1903.01084v3.pdf | Automatic microscopic cell counting by use of deeply-supervised density regression model | Accurately counting cells in microscopic images is important for medical
diagnoses and biological studies, but manual cell counting is very tedious,
time-consuming, and prone to subjective errors, and automatic counting can be
less accurate than desired. To improve the accuracy of automatic cell counting,
we propose he... | ['Lilianna Solnica-Krezel', 'Kyaw Thu Minn', 'Shenghua He', 'Mark Anastasio', 'Hua Li'] | 2019-03-04 | null | null | null | null | ['automatic-cell-counting'] | ['miscellaneous'] | [-1.06864884e-01 -1.41122013e-01 -9.18132663e-02 -3.58144581e-01
-2.82013476e-01 -1.56212062e-01 3.30669820e-01 3.17592472e-01
-8.11273932e-01 9.48686898e-01 -1.80088460e-01 -2.53956199e-01
3.50156188e-01 -1.14530671e+00 -3.92989486e-01 -8.95856738e-01
2.97698170e-01 3.09611529e-01 3.85023624e-01 2.13492572... | [14.795684814453125, -3.046536684036255] |
5cfa24af-0054-4367-93ef-dd0a0d1439db | cparr-category-based-proposal-analysis-for | 2004.08028 | null | https://arxiv.org/abs/2004.08028v1 | https://arxiv.org/pdf/2004.08028v1.pdf | CPARR: Category-based Proposal Analysis for Referring Relationships | The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of \texttt{<subject, predicate, object>}. This requires simultaneous localization of the subject and object entities in a specified relationship. We introduce a simple y... | ['Jiyang Gao', 'Chuanzi He', 'Ram Nevatia', 'Haidong Zhu', 'Kan Chen'] | 2020-04-17 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.88346654e-01 2.03532517e-01 -1.00821197e-01 -6.58236444e-01
-8.00547004e-01 -6.58601284e-01 5.88829339e-01 2.77717590e-01
-1.23132259e-01 3.69621187e-01 8.69780034e-02 -1.47717610e-01
-4.03695889e-02 -9.26709950e-01 -6.58370793e-01 -1.52273551e-01
2.09262982e-01 6.44797623e-01 1.03253686e+00 -1.21345297... | [10.289451599121094, 1.6493656635284424] |
fb716c4c-b7a8-45c7-b124-890a754c23a5 | learning-from-suspected-target-bootstrapping | 2003.01109 | null | https://arxiv.org/abs/2003.01109v1 | https://arxiv.org/pdf/2003.01109v1.pdf | Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography | Deep learning object detection algorithm has been widely used in medical image analysis. Currently all the object detection tasks are based on the data annotated with object classes and their bounding boxes. On the other hand, medical images such as mammography usually contain normal regions or objects that are similar... | ['Cheng Zhu', 'Yi Zhao', 'Peifang Liu', 'Chunlong Luo', 'Li Xiao', 'Junjun Liu'] | 2020-03-01 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 4.80171770e-01 4.80892003e-01 -3.79868269e-01 -5.75172663e-01
-8.31411123e-01 6.92366734e-02 2.76291668e-01 6.72076166e-01
-6.59324288e-01 4.51424897e-01 -1.79132700e-01 -3.02203059e-01
-2.40435123e-01 -1.00285006e+00 -8.37109089e-01 -9.02302504e-01
-2.70612091e-01 6.69256568e-01 7.84738541e-01 3.50492805... | [15.136717796325684, -2.490812301635742] |
f9fb1100-b66a-4a7f-aa24-637c6cc88c37 | efficient-and-differentiable-conformal-1 | 2202.11091 | null | https://arxiv.org/abs/2202.11091v2 | https://arxiv.org/pdf/2202.11091v2.pdf | Efficient and Differentiable Conformal Prediction with General Function Classes | Quantifying the data uncertainty in learning tasks is often done by learning a prediction interval or prediction set of the label given the input. Two commonly desired properties for learned prediction sets are \emph{valid coverage} and \emph{good efficiency} (such as low length or low cardinality). Conformal predictio... | ['Caiming Xiong', 'Yingbo Zhou', 'Huan Wang', 'Song Mei', 'Yu Bai'] | 2022-02-22 | efficient-and-differentiable-conformal | https://openreview.net/forum?id=Ht85_jyihxp | https://openreview.net/pdf?id=Ht85_jyihxp | iclr-2022-4 | ['prediction-intervals'] | ['miscellaneous'] | [ 6.34558380e-01 7.59256721e-01 -8.08084726e-01 -7.17247605e-01
-1.60529602e+00 -4.79086339e-01 2.89124418e-02 5.38981855e-01
-1.39817297e-01 1.02307296e+00 -4.83786762e-02 -1.38809592e-01
-8.64973128e-01 -9.44544077e-01 -1.12031722e+00 -8.08778286e-01
-1.87320709e-01 7.86128044e-01 4.17194441e-02 3.83793831... | [8.007633209228516, 4.282559871673584] |
f150835d-3f23-403a-9649-89359ceb7e02 | bilingual-lexicon-induction-by-learning-to | null | null | https://aclanthology.org/E17-1102 | https://aclanthology.org/E17-1102.pdf | Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations | We study the problem of bilingual lexicon induction (BLI) in a setting where some translation resources are available, but unknown translations are sought for certain, possibly domain-specific terminology. We frame BLI as a classification problem for which we design a neural network based classification architecture co... | ['Marie-Francine Moens', "Ivan Vuli{\\'c}", 'Geert Heyman'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [ 1.91907465e-01 -2.56661683e-01 -1.06391871e+00 -3.30433488e-01
-1.04530191e+00 -6.04356349e-01 7.21712708e-01 -5.78860417e-02
-5.49491167e-01 9.11907792e-01 4.35913980e-01 -8.05121481e-01
1.72061875e-01 -6.11476302e-01 -7.93856382e-01 -2.20271885e-01
3.60540077e-02 1.05738223e+00 -4.97023404e-01 -5.38724124... | [11.27636432647705, 10.03506851196289] |
3f51a365-91d3-4d8a-8a71-127b76a95f46 | grad2task-improved-few-shot-text-1 | 2201.11576 | null | https://arxiv.org/abs/2201.11576v1 | https://arxiv.org/pdf/2201.11576v1.pdf | Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation | Large pretrained language models (LMs) like BERT have improved performance in many disparate natural language processing (NLP) tasks. However, fine tuning such models requires a large number of training examples for each target task. Simultaneously, many realistic NLP problems are "few shot", without a sufficiently lar... | ['Michael Brudno', 'Frank Rudzicz', 'Kuan-Chieh Wang', 'Jixuan Wang'] | 2022-01-27 | grad2task-improved-few-shot-text | http://proceedings.neurips.cc/paper/2021/hash/33a854e247155d590883b93bca53848a-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/33a854e247155d590883b93bca53848a-Paper.pdf | neurips-2021-12 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 5.59416890e-01 -4.18613814e-02 -2.42211387e-01 -5.21797121e-01
-9.78650272e-01 -1.67195156e-01 9.56149578e-01 2.96562940e-01
-7.82769322e-01 6.68867290e-01 5.25411248e-01 1.28869906e-01
-8.09633434e-02 -6.56173587e-01 -5.34423530e-01 -5.31510174e-01
3.80870640e-01 8.26504946e-01 2.77082175e-01 -3.08739364... | [10.83221435546875, 7.896386623382568] |
4472b8fd-f2fe-4731-a957-b08eea4d94a7 | surgical-phase-recognition-in-laparoscopic | 2206.07198 | null | https://arxiv.org/abs/2206.07198v1 | https://arxiv.org/pdf/2206.07198v1.pdf | Surgical Phase Recognition in Laparoscopic Cholecystectomy | Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis. In this report, we propose a Transformer-based method that utilizes calibrated confidence scores for a 2-stage inference pipeline, which dynamically switches between a baseline model and a separately trained... | ['Himanshu Gupta', 'Haibin Ling', 'I. V. Ramakrishnan', 'Prateek Prasanna', 'Vinayak Shenoy', 'Yunfan Li'] | 2022-06-14 | null | null | null | null | ['surgical-phase-recognition', 'action-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.55201054e-01 2.53681570e-01 -7.85300434e-01 -4.07797486e-01
-1.18945682e+00 -6.33876741e-01 3.76474619e-01 1.99241817e-01
-6.36663139e-01 3.68649364e-01 2.83388346e-01 -6.88139975e-01
-1.18983828e-01 -2.19120249e-01 -4.42419022e-01 -7.06481159e-01
1.66570231e-01 6.59558117e-01 5.85870802e-01 2.65679926... | [14.131370544433594, -3.327810287475586] |
06e9ed4c-f1d3-4242-87f1-7cd9b69ed034 | r2d2-reliable-and-repeatable-detectors-and | 1906.06195 | null | https://arxiv.org/abs/1906.06195v2 | https://arxiv.org/pdf/1906.06195v2.pdf | R2D2: Repeatable and Reliable Detector and Descriptor | Interest point detection and local feature description are fundamental steps in many computer vision applications. Classical methods for these tasks are based on a detect-then-describe paradigm where separate handcrafted methods are used to first identify repeatable keypoints and then represent them with a local descri... | ['Gabriela Csurka', 'César De Souza', 'Philippe Weinzaepfel', 'Jerome Revaud', 'Yohann Cabon', 'Noe Pion', 'Martin Humenberger'] | 2019-06-14 | null | null | null | null | ['interest-point-detection'] | ['computer-vision'] | [-4.76415046e-02 -8.75697881e-02 -4.45463806e-01 -2.11075246e-01
-1.14175200e+00 -5.78591466e-01 9.29558158e-01 6.88277960e-01
-5.98240912e-01 5.03402114e-01 -9.96749103e-02 3.38844448e-01
-3.10419410e-01 -4.58978176e-01 -8.61568689e-01 -6.98559344e-01
-2.84188122e-01 3.74910682e-01 6.09006047e-01 -8.20354074... | [7.874083042144775, -2.004042387008667] |
85050ecd-cf5e-460e-8009-32c3766308a0 | cmrnet-camera-to-lidar-map-registration | 1906.10109 | null | https://arxiv.org/abs/1906.10109v3 | https://arxiv.org/pdf/1906.10109v3.pdf | CMRNet: Camera to LiDAR-Map Registration | In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network to localize an RGB image of a scene in a map built from LiDAR data. Our network is not trained in the working area, i.e. CMRNet does not learn the map. Instead it learns to match an image to the map. We validate our approach on... | ['Augusto Luis Ballardini', 'Daniele Cattaneo', 'Domenico Giorgio Sorrenti', 'Wolfram Burgard', 'Simone Fontana', 'Matteo Vaghi'] | 2019-06-24 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-7.99922347e-02 1.28911227e-01 2.74852157e-01 -5.73023796e-01
-4.76793975e-01 -6.86716139e-01 3.39760512e-01 -9.11781341e-02
-9.10552859e-01 5.88225663e-01 -4.76976812e-01 -6.23740852e-02
8.74688253e-02 -8.10085416e-01 -1.31298339e+00 -1.07400499e-01
-1.41418278e-01 9.21198010e-01 4.18772727e-01 -1.30830884... | [7.6785359382629395, -2.15964412689209] |
9941406a-40c3-46fa-810b-0c9842ca6523 | adaptive-edge-attention-for-graph-matching | null | null | https://www.ijcai.org/proceedings/2021/134 | https://www.ijcai.org/proceedings/2021/0134.pdf | Adaptive Edge Attention for Graph Matching with Outliers | Graph matching aims at establishing correspondence between node sets of given graphs while keeping the consistency between their edge sets. However, outliers in practical scenarios and equivalent learning of edge representations in deep learning methods are still challenging. To address these issues, we present an Edge... | ['Zhi Tang', 'Xiaoqing Lyu', 'Chenrui Zhang', 'Haibin Ling', 'Jingwei Qu'] | 2021-08-19 | null | null | null | international-joint-conference-on-artificial-4 | ['graph-matching'] | ['graphs'] | [-2.49427572e-01 3.89517725e-01 -7.69850612e-02 -3.17383379e-01
-5.05912840e-01 -3.08136791e-01 2.68255293e-01 3.61061662e-01
1.20939814e-01 1.57292798e-01 3.29379737e-01 8.55539814e-02
-2.24327415e-01 -8.14533532e-01 -8.24982882e-01 -4.68090087e-01
-2.47540712e-01 4.38544720e-01 -1.69040769e-01 -4.23675543... | [7.194049835205078, 6.401156425476074] |
6ec96fe9-ebb3-4c35-a4a4-8f48694dd735 | one-model-is-all-you-need-multi-task-learning | 2203.00077 | null | https://arxiv.org/abs/2203.00077v2 | https://arxiv.org/pdf/2203.00077v2.pdf | One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification | The recent surge in performance for image analysis of digitised pathology slides can largely be attributed to the advances in deep learning. Deep models can be used to initially localise various structures in the tissue and hence facilitate the extraction of interpretable features for biomarker discovery. However, thes... | ['Nasir Rajpoot', 'David Snead', 'Fayyaz Minhas', 'Shan E Ahmed Raza', 'Mostafa Jahanifar', 'Quoc Dang Vu', 'Simon Graham'] | 2022-02-28 | null | null | null | null | ['explainable-models', 'cell-detection'] | ['computer-vision', 'computer-vision'] | [ 4.58704680e-01 2.25580901e-01 -2.96331018e-01 -3.43314409e-01
-1.45263386e+00 -8.23185623e-01 4.61906374e-01 5.48444033e-01
-4.84714687e-01 6.37708187e-01 1.45932436e-01 -4.29828256e-01
-9.09294188e-02 -5.57334423e-01 -7.26770163e-01 -1.08261025e+00
-2.85851583e-02 5.72480023e-01 3.29982847e-01 1.39721977... | [15.060638427734375, -2.9686596393585205] |
7f291d51-e556-4848-aa84-3e0e70d56993 | a-point-cloud-generative-model-based-on | null | null | https://openreview.net/forum?id=O1GEH9X8848 | https://openreview.net/pdf?id=O1GEH9X8848 | A Point Cloud Generative Model Based on Nonequilibrium Thermodynamics | We present a probabilistic model for point cloud generation, which is critical for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation. Inspired by the diffusion process in non-equilibrium thermodynamics, we view points in point clouds as particles in a thermodynamic system in ... | ['Wei Hu', 'Shitong Luo'] | 2021-01-01 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 3.41444492e-01 1.46766871e-01 2.17793390e-01 -9.43043455e-02
-7.58251429e-01 -5.52887857e-01 9.77940083e-01 -1.52593762e-01
-2.31322553e-02 4.08717930e-01 -1.41389549e-01 -1.77291900e-01
2.74358004e-01 -1.05502760e+00 -1.02560389e+00 -9.48493421e-01
3.42905551e-01 1.09321070e+00 -1.24604702e-01 2.29254976... | [8.866205215454102, -3.6440517902374268] |
642e00d3-7699-479d-ac87-863af0e24fb5 | current-status-and-performance-analysis-of | 2104.14272 | null | https://arxiv.org/abs/2104.14272v2 | https://arxiv.org/pdf/2104.14272v2.pdf | Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks | The first phase of table recognition is to detect the tabular area in a document. Subsequently, the tabular structures are recognized in the second phase in order to extract information from the respective cells. Table detection and structural recognition are pivotal problems in the domain of table understanding. Howev... | ['Muhammad Zeshan Afzal', 'Muhammad Ahtsham Afzal', 'Muhammad Adnan Afzal', 'Didier Stricker', 'Marcus Liwicki', 'Khurram Azeem Hashmi'] | 2021-04-29 | null | null | null | null | ['table-recognition', 'table-detection'] | ['computer-vision', 'miscellaneous'] | [ 3.41291130e-01 -3.52381051e-01 -3.76887858e-01 -2.50075787e-01
-5.81268132e-01 -7.37917006e-01 4.66582894e-01 5.99373937e-01
1.79901272e-01 5.86935043e-01 1.24886334e-02 -2.16791347e-01
-1.83629580e-02 -1.06957459e+00 -6.16397262e-01 -6.53723359e-01
-4.55728266e-03 5.21616161e-01 -3.32010567e-01 -4.26932544... | [11.68690299987793, 2.9848380088806152] |
b6aaf7b2-4260-4456-8aea-a0a073d19789 | collaborative-noisy-label-cleaner-learning | 2303.14768 | null | https://arxiv.org/abs/2303.14768v1 | https://arxiv.org/pdf/2303.14768v1.pdf | Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies | Movie highlights stand out of the screenplay for efficient browsing and play a crucial role on social media platforms. Based on existing efforts, this work has two observations: (1) For different annotators, labeling highlight has uncertainty, which leads to inaccurate and time-consuming annotations. (2) Besides previo... | ['Bo Ren', 'Hanjun Li', 'Keyu Chen', 'Haoqian Wu', 'Ruizhi Qiao', 'Xiujun Shu', 'Bei Gan'] | 2023-03-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gan_Collaborative_Noisy_Label_Cleaner_Learning_Scene-Aware_Trailers_for_Multi-Modal_Highlight_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gan_Collaborative_Noisy_Label_Cleaner_Learning_Scene-Aware_Trailers_for_Multi-Modal_Highlight_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-segmentation', 'highlight-detection', 'learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [ 1.20201394e-01 -3.96012753e-01 -2.50001401e-01 -2.62883127e-01
-1.15779686e+00 -6.66958749e-01 3.08544338e-01 6.90246224e-02
-3.25214535e-01 4.21128869e-01 2.70455867e-01 1.49051309e-01
8.90587941e-02 -3.12665910e-01 -7.42987692e-01 -7.47699440e-01
2.66256124e-01 -2.76450098e-01 2.54975170e-01 4.80569899... | [9.952313423156738, 0.49473437666893005] |
74e9517d-e5b7-401d-be8e-a098b97f500e | what-do-questions-exactly-ask-mfae-duplicate | null | null | https://epubs.siam.org/doi/10.1137/1.9781611976236.26 | https://epubs.siam.org/doi/pdf/10.1137/1.9781611976236.26 | What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking Emphasis | Duplicate Question Identification (DQI) improves the processing efficiency and accuracy of large-scale community question answering and automatic QA system. The purpose of DQI task is to identify whether the paired questions are semantically equivalent. However, how to distinguish the synonyms or homonyms in paired que... | ['Tong Mo', 'Weiping Li', 'Bo Wu', 'Qifei Zhou', 'Rong Zhang'] | 2020-05-07 | null | null | null | siam-international-conference-on-data-mining | ['community-question-answering', 'community-question-answering', 'paraphrase-identification'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 5.71442917e-02 -2.74757117e-01 1.87322512e-01 -5.27115464e-01
-1.05920517e+00 -6.29450321e-01 4.93938029e-01 1.54072136e-01
-6.73978686e-01 4.74830389e-01 5.89345634e-01 -3.25464398e-01
-1.87332481e-01 -9.11632240e-01 -4.35492247e-01 -3.65960807e-01
7.23950148e-01 5.09693325e-01 2.34276026e-01 -5.49530566... | [11.074350357055664, 8.084122657775879] |
c44896a4-f195-4c6c-ad76-b89c42f049ba | energy-inspired-self-supervised-pretraining | 2302.01384 | null | https://arxiv.org/abs/2302.01384v1 | https://arxiv.org/pdf/2302.01384v1.pdf | Energy-Inspired Self-Supervised Pretraining for Vision Models | Motivated by the fact that forward and backward passes of a deep network naturally form symmetric mappings between input and output representations, we introduce a simple yet effective self-supervised vision model pretraining framework inspired by energy-based models (EBMs). In the proposed framework, we model energy e... | ['Qiang Qiu', 'Zicheng Liu', 'Jiang Wang', 'Ze Wang'] | 2023-02-02 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 5.14204741e-01 2.42299020e-01 2.04750020e-02 -5.30950665e-01
-6.25791371e-01 -1.08467191e-01 6.17854357e-01 -2.15873063e-01
-4.77271497e-01 4.74871099e-01 2.52213687e-01 -3.98283377e-02
2.75080651e-01 -8.40037286e-01 -1.20558953e+00 -8.15266728e-01
5.35043061e-01 1.16954140e-01 8.90370086e-02 3.66538018... | [11.257096290588379, -2.2426648139953613] |
ac9a5b3f-8f24-4aaf-a0c2-97b0e9418957 | progressive-sampling-based-bayesian | 1812.02855 | null | http://arxiv.org/abs/1812.02855v1 | http://arxiv.org/pdf/1812.02855v1.pdf | Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection | Purpose: Machine learning is broadly used for clinical data analysis. Before
training a model, a machine learning algorithm must be selected. Also, the
values of one or more model parameters termed hyper-parameters must be set.
Selecting algorithms and hyper-parameter values requires advanced machine
learning knowledge... | ['Gang Luo', 'Xueqiang Zeng'] | 2018-12-06 | null | null | null | null | ['automatic-machine-learning-model-selection', 'miscellaneous'] | ['methodology', 'miscellaneous'] | [ 3.73418331e-01 -2.90605873e-01 -4.72158998e-01 -4.27816838e-01
-1.31693101e+00 -4.16917115e-01 1.11920044e-01 5.59584498e-01
-5.85715115e-01 8.16042364e-01 1.13128042e-02 -5.09219229e-01
-4.30524707e-01 -6.63941622e-01 -2.01996759e-01 -9.03907895e-01
8.61590728e-02 1.25221801e+00 -5.30623384e-02 2.72385955... | [6.859979152679443, 4.345553874969482] |
992d92d5-addd-4f99-a29a-793cade80dd5 | selective-manipulation-of-disentangled | 2208.12632 | null | https://arxiv.org/abs/2208.12632v1 | https://arxiv.org/pdf/2208.12632v1.pdf | Selective manipulation of disentangled representations for privacy-aware facial image processing | Camera sensors are increasingly being combined with machine learning to perform various tasks such as intelligent surveillance. Due to its computational complexity, most of these machine learning algorithms are offloaded to the cloud for processing. However, users are increasingly concerned about privacy issues such as... | ['Pieter Simoens', 'Sam Leroux', 'Wei-Cheng Wang', 'Sander De Coninck'] | 2022-08-26 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 5.91730952e-01 9.45996940e-02 -2.73818374e-01 -6.16767049e-01
-4.68467325e-01 -9.29594636e-01 3.04894716e-01 2.21342400e-01
-5.22883892e-01 3.07182044e-01 1.93567097e-01 -3.60969961e-01
-6.31243503e-03 -6.75117373e-01 -6.19653821e-01 -6.66311741e-01
-9.07636881e-02 -2.18260422e-01 -3.06280196e-01 2.70044357... | [12.676725387573242, 0.7690796256065369] |
b5f32005-a69a-44aa-bf37-30bbcbef8a44 | sim-to-real-via-sim-to-seg-end-to-end-off | 2210.14721 | null | https://arxiv.org/abs/2210.14721v1 | https://arxiv.org/pdf/2210.14721v1.pdf | Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data | Autonomous driving is complex, requiring sophisticated 3D scene understanding, localization, mapping, and control. Rather than explicitly modelling and fusing each of these components, we instead consider an end-to-end approach via reinforcement learning (RL). However, collecting exploration driving data in the real wo... | ['Stephen James', 'Pieter Abbeel', 'Ali Agha-mohammadi', 'Rohan Thakker', 'Jeffrey Edlund', 'Sunggoo Jung', 'Amber Xie', 'John So'] | 2022-10-25 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.00369751e-01 3.91557813e-01 3.93190771e-01 -4.02852863e-01
-6.85424089e-01 -8.83444548e-01 6.92623377e-01 -6.58041909e-02
-7.04904735e-01 6.58223867e-01 -4.84405071e-01 -9.07764375e-01
3.02417099e-01 -8.52678776e-01 -1.18054724e+00 -3.43060195e-01
-3.71681899e-01 9.06442761e-01 5.07940590e-01 -6.17002726... | [4.800814628601074, 0.8287252187728882] |
c21e2d5e-d304-464e-9906-e1c6305f44c7 | clipscore-a-reference-free-evaluation-metric | 2104.08718 | null | https://arxiv.org/abs/2104.08718v3 | https://arxiv.org/pdf/2104.08718v3.pdf | CLIPScore: A Reference-free Evaluation Metric for Image Captioning | Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans. This is in contrast to the reference-free manner in which humans assess caption quality. In this paper, we report the surprising empirical finding that CLIP (Radfo... | ['Yejin Choi', 'Ronan Le Bras', 'Maxwell Forbes', 'Ari Holtzman', 'Jack Hessel'] | 2021-04-18 | null | https://aclanthology.org/2021.emnlp-main.595 | https://aclanthology.org/2021.emnlp-main.595.pdf | emnlp-2021-11 | ['human-judgment-correlation', 'human-judgment-classification'] | ['reasoning', 'reasoning'] | [ 5.80591977e-01 -9.10231471e-02 -3.12521130e-01 -4.27884489e-01
-1.61893356e+00 -9.75919545e-01 1.13492882e+00 3.30483049e-01
-5.84789991e-01 6.77356839e-01 6.82481945e-01 -1.23370215e-01
2.98780389e-02 -1.18504718e-01 -7.42439926e-01 -2.22299397e-01
2.72973627e-01 4.06764418e-01 4.93058227e-02 -3.59977514... | [11.149700164794922, 1.0483036041259766] |
3958e9f4-e9f2-4c27-b367-cffce310192b | an-analysis-of-the-effect-of-emotional-speech | null | null | https://aclanthology.org/W18-5044 | https://aclanthology.org/W18-5044.pdf | An Analysis of the Effect of Emotional Speech Synthesis on Non-Task-Oriented Dialogue System | This paper explores the effect of emotional speech synthesis on a spoken dialogue system when the dialogue is non-task-oriented. Although the use of emotional speech responses have been shown to be effective in a limited domain, e.g., scenario-based and counseling dialogue, the effect is still not clear in the non-task... | ['Mai Yamanaka', 'Akinori Ito', 'Taketo Kase', 'Takashi Nose', 'Yuya Chiba'] | 2018-07-01 | null | null | null | ws-2018-7 | ['emotional-speech-synthesis'] | ['speech'] | [-4.45809066e-01 6.29866540e-01 3.89860809e-01 -8.34713697e-01
-1.99780524e-01 -4.75419313e-01 6.66710615e-01 -1.41455829e-01
-3.07291240e-01 9.51145649e-01 5.11273146e-01 -7.52109587e-02
2.05537234e-03 -3.87124449e-01 2.57519543e-01 -3.97113651e-01
2.83446819e-01 4.41253185e-01 1.01887479e-01 -7.52901793... | [13.123150825500488, 7.702223777770996] |
e68e2d3b-a2e4-4404-b122-bf10101b3b71 | gollic-learning-global-context-beyond-patches | 2210.03301 | null | https://arxiv.org/abs/2210.03301v1 | https://arxiv.org/pdf/2210.03301v1.pdf | GOLLIC: Learning Global Context beyond Patches for Lossless High-Resolution Image Compression | Neural-network-based approaches recently emerged in the field of data compression and have already led to significant progress in image compression, especially in achieving a higher compression ratio. In the lossless image compression scenario, however, existing methods often struggle to learn a probability model of fu... | ['Jie Sun', 'Yang Xiang', 'Zhaoyi Sun', 'Liang Qin', 'Yuan Lan'] | 2022-10-07 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 4.66891706e-01 -1.44629255e-01 -4.32319522e-01 -3.08255404e-01
-7.72864997e-01 2.19392315e-01 7.06834868e-02 1.40317142e-01
-2.05390602e-01 6.07315361e-01 2.71670550e-01 2.14350641e-01
-3.14902216e-01 -1.12942207e+00 -6.79059625e-01 -8.96079659e-01
-5.62262125e-02 2.72991985e-01 7.72778364e-03 4.98588920... | [11.271964073181152, -1.626848578453064] |
18cde0ec-3c33-4e28-b21e-e691c99c2043 | multimodal-dataset-from-harsh-sub-terranean | 2304.14520 | null | https://arxiv.org/abs/2304.14520v2 | https://arxiv.org/pdf/2304.14520v2.pdf | Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration | Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and Light Detection and Rangings (LiDARs), the performance of which may degrade in the presence of aerosol pa... | ['George Nikolakopoulos', 'Anton Koval', 'Vignesh Kottayam Viswanathan', 'Nikolaos Stathoulopoulos', 'Niklas Dahlquist', 'Alexander Kyuroson'] | 2023-04-27 | null | null | null | null | ['autonomous-navigation'] | ['computer-vision'] | [ 2.77830541e-01 -6.36450768e-01 4.71307635e-01 -3.77988726e-01
-2.21300855e-01 -8.32316339e-01 4.29133117e-01 2.80831367e-01
-8.44920218e-01 1.04049551e+00 -5.34717739e-01 -2.00550139e-01
-4.43769753e-01 -1.26750350e+00 -5.07262707e-01 -8.21142495e-01
-2.16279969e-01 6.01162672e-01 5.53261399e-01 -6.00366354... | [7.3116278648376465, -2.031522512435913] |
9684b5b9-bad1-457d-bd27-c73288a73c6f | learning-the-precise-feature-for-cluster | 2106.06159 | null | https://arxiv.org/abs/2106.06159v1 | https://arxiv.org/pdf/2106.06159v1.pdf | Learning the Precise Feature for Cluster Assignment | Clustering is one of the fundamental tasks in computer vision and pattern recognition. Recently, deep clustering methods (algorithms based on deep learning) have attracted wide attention with their impressive performance. Most of these algorithms combine deep unsupervised representation learning and standard clustering... | ['Junyu Dong', 'Feng Gao', 'Huiyu Zhou', 'Xinghui Dong', 'Yanhai Gan'] | 2021-06-11 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.34662271e-01 -3.64881605e-01 -2.11361088e-02 -4.39783901e-01
-6.82416916e-01 -3.73493463e-01 8.11947823e-01 3.74103487e-02
-3.38490367e-01 1.45460770e-01 -4.91381884e-02 2.40767553e-01
-4.97306257e-01 -5.03147721e-01 -3.67401719e-01 -1.26044297e+00
2.03538269e-01 8.24465990e-01 -1.30735844e-01 2.45842427... | [9.130873680114746, 3.2315659523010254] |
00cf00e2-4548-44d6-9199-b674e6324429 | decipherment-complexity-in-11-substitution | null | null | https://aclanthology.org/P13-1060 | https://aclanthology.org/P13-1060.pdf | Decipherment Complexity in 1:1 Substitution Ciphers | null | ['Hermann Ney', 'Malte Nuhn'] | 2013-08-01 | null | null | null | acl-2013-8 | ['decipherment'] | ['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... | [-7.2946648597717285, 3.653299331665039] |
30430e4e-52a4-4a9f-88aa-8d56af44dc5f | representation-compression-and-generalization | null | null | https://openreview.net/forum?id=SkeL6sCqK7 | https://openreview.net/pdf?id=SkeL6sCqK7 | REPRESENTATION COMPRESSION AND GENERALIZATION IN DEEP NEURAL NETWORKS | Understanding the groundbreaking performance of Deep Neural Networks is one
of the greatest challenges to the scientific community today. In this work, we
introduce an information theoretic viewpoint on the behavior of deep networks
optimization processes and their generalization abilities. By studying the Information
... | ['Ravid Shwartz-Ziv', 'Naftali Tishby', 'Amichai Painsky'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['information-plane'] | ['methodology'] | [ 2.34979644e-01 4.07235861e-01 1.13222815e-01 -2.36076593e-01
-1.10053360e-01 -4.34296638e-01 3.64764303e-01 2.93148249e-01
-6.59425557e-01 5.06065726e-01 -3.19686644e-02 -1.04924865e-01
-5.83536744e-01 -7.02661455e-01 -7.21206248e-01 -1.15542722e+00
-3.16262841e-01 3.76380205e-01 -1.66534573e-01 -6.03798814... | [7.932992458343506, 3.576878547668457] |
f1ca3986-ec3b-44a4-8058-7f6cbcc2a0d6 | accelerating-diffusion-models-via-pre | 2210.17408 | null | https://arxiv.org/abs/2210.17408v1 | https://arxiv.org/pdf/2210.17408v1.pdf | Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation | Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise uncertainty maps of the segmentation and allows an implicit ensemble of segmentations to boost the segmentation performance. However, DDPM r... | ['Ting Ma', 'Yang Xiang', 'Shang Lu', 'Chenfei Ye', 'Yanwu Yang', 'Xutao Guo'] | 2022-10-27 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 5.67296267e-01 4.81301278e-01 1.66616306e-01 -2.74877906e-01
-9.20478702e-01 -2.41449729e-01 5.02212703e-01 -8.19336772e-02
-4.83448565e-01 4.26670820e-01 -3.22457217e-02 -3.74953538e-01
1.82713866e-01 -1.05743384e+00 -5.48570752e-01 -1.03339112e+00
4.02947396e-01 5.15773118e-01 7.30444014e-01 2.46774957... | [14.445724487304688, -2.0794434547424316] |
7138dd64-5405-41b5-9fe2-c508f5b39a4f | learning-self-modulating-attention-in | 2204.06517 | null | https://arxiv.org/abs/2204.06517v1 | https://arxiv.org/pdf/2204.06517v1.pdf | Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation | User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data. Among existing user behavior modeling solutions, attention networks are widely adopted for its effectiveness and relative simplicity. Despite being exten... | ['Xiaokang Yang', 'Jianping Yu', 'Daiyue Xue', 'Junchi Yan', 'Nianzu Yang', 'Haoyu Geng', 'Chao Chen'] | 2022-03-30 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-6.71436265e-02 -7.23646998e-01 -5.84631145e-01 -5.29082179e-01
-1.93440720e-01 -1.85061350e-01 3.27887982e-01 -3.86041552e-02
-3.39164317e-01 4.61262107e-01 7.18419313e-01 -1.70922577e-01
-4.44902927e-01 -5.94016731e-01 -4.49786395e-01 -5.59392869e-01
-2.99970418e-01 5.41317403e-01 -1.73446182e-02 -3.31812412... | [10.085731506347656, 5.539830684661865] |
8e892ff2-e26c-42de-bc79-5056c3682944 | a-multi-objective-deep-reinforcement-learning | 1803.02965 | null | https://arxiv.org/abs/1803.02965v3 | https://arxiv.org/pdf/1803.02965v3.pdf | A Multi-Objective Deep Reinforcement Learning Framework | This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental ... | ['Saeid Nahavandi', 'Peter Vamplew', 'Ngoc Duy Nguyen', 'Chee Peng Lim', 'Thanh Thi Nguyen', 'Richard Dazeley'] | 2018-03-08 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-5.83463132e-01 -4.45407689e-01 -2.60904521e-01 -1.51643530e-01
-7.07133353e-01 -3.94876122e-01 3.33350599e-01 1.61500230e-01
-9.61698413e-01 1.47804236e+00 -2.51835525e-01 -1.60541072e-01
-7.06488669e-01 -1.19695652e+00 -5.03908336e-01 -9.89161015e-01
-4.75324064e-01 7.73784637e-01 1.33303180e-01 -8.36825788... | [4.003037929534912, 2.1129939556121826] |
351db756-c3f0-4d9e-a30d-b2e57173401d | large-scale-multi-view-subspace-clustering-in | 1911.09290 | null | https://arxiv.org/abs/1911.09290v1 | https://arxiv.org/pdf/1911.09290v1.pdf | Large-scale Multi-view Subspace Clustering in Linear Time | A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently diffi... | ['Zhitong Zhao', 'Zenglin Xu', 'Zhao Kang', 'Meng Han', 'Wangtao Zhou', 'Junming Shao'] | 2019-11-21 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-1.48633882e-01 -2.00208843e-01 -8.93761218e-02 -9.85134244e-02
-7.64680505e-01 -7.34263957e-01 3.66443396e-01 9.33774635e-02
8.88537392e-02 2.83830553e-01 1.22378543e-01 -8.68847035e-03
-3.92878592e-01 -5.92960775e-01 -5.37339687e-01 -8.83853078e-01
-3.82690951e-02 4.68496978e-01 5.06261170e-01 3.44013236... | [8.128499984741211, 4.630303859710693] |
fb463d3e-7641-4462-ba21-6710513f9e1f | feature-informed-latent-space-regularization | 2203.09132 | null | https://arxiv.org/abs/2203.09132v2 | https://arxiv.org/pdf/2203.09132v2.pdf | Feature-informed Latent Space Regularization for Music Source Separation | The integration of additional side information to improve music source separation has been investigated numerous times, e.g., by adding features to the input or by adding learning targets in a multi-task learning scenario. These approaches, however, require additional annotations such as musical scores, instrument labe... | ['Alexander Lerch', 'Yun-Ning Hung'] | 2022-03-17 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 5.75417519e-01 -2.32268289e-01 -2.33387664e-01 -2.61714756e-01
-1.45959973e+00 -7.20895171e-01 4.92734939e-01 1.60815328e-01
-2.35135615e-01 6.87567055e-01 4.49502021e-01 2.81669736e-01
-5.57935655e-01 -3.53214890e-01 -5.88996410e-01 -9.21597421e-01
7.60631487e-02 2.08248630e-01 4.29536738e-02 -4.70022261... | [15.622161865234375, 5.32828950881958] |
9671c80c-b47f-43b7-8928-f97f659ee546 | cigar-cross-modality-graph-reasoning-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_CIGAR_Cross-Modality_Graph_Reasoning_for_Domain_Adaptive_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_CIGAR_Cross-Modality_Graph_Reasoning_for_Domain_Adaptive_Object_Detection_CVPR_2023_paper.pdf | CIGAR: Cross-Modality Graph Reasoning for Domain Adaptive Object Detection | Unsupervised domain adaptive object detection (UDA-OD) aims to learn a detector by generalizing knowledge from a labeled source domain to an unlabeled target domain. Though the existing graph-based methods for UDA-OD perform well in some cases, they cannot learn a proper node set for the graph. In addition, these m... | ['Yong Xu', 'YaoWei Wang', 'Chao Huang', 'Jinghua Wang', 'Yabo Liu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['graph-matching'] | ['graphs'] | [-8.50614682e-02 7.59535329e-03 -4.31287348e-01 -5.12071073e-01
-3.21407765e-01 -4.69617546e-01 5.25306165e-01 2.79197961e-01
-1.91025019e-01 1.94559127e-01 7.02298656e-02 -1.04042431e-02
-1.89928681e-01 -9.38102365e-01 -5.18245101e-01 -6.56836808e-01
4.56755966e-01 3.67753506e-01 5.87195039e-01 -5.86426482... | [9.954493522644043, 2.325629234313965] |
f5361a28-27a2-472b-85d3-9149c0ff3eac | arkittrack-a-new-diverse-dataset-for-tracking | 2303.13885 | null | https://arxiv.org/abs/2303.13885v1 | https://arxiv.org/pdf/2303.13885v1.pdf | ARKitTrack: A New Diverse Dataset for Tracking Using Mobile RGB-D Data | Compared with traditional RGB-only visual tracking, few datasets have been constructed for RGB-D tracking. In this paper, we propose ARKitTrack, a new RGB-D tracking dataset for both static and dynamic scenes captured by consumer-grade LiDAR scanners equipped on Apple's iPhone and iPad. ARKitTrack contains 300 RGB-D se... | ['Huchuan Lu', 'Lijun Wang', 'Junsong Chen', 'Haojie Zhao'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_ARKitTrack_A_New_Diverse_Dataset_for_Tracking_Using_Mobile_RGB-D_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_ARKitTrack_A_New_Diverse_Dataset_for_Tracking_Using_Mobile_RGB-D_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-tracking'] | ['computer-vision'] | [-1.11431651e-01 -4.74375337e-01 -2.74948686e-01 -2.78169394e-01
-7.85912752e-01 -9.25997734e-01 4.72004592e-01 -4.15140778e-01
-3.06973130e-01 2.55425751e-01 4.57447506e-02 -2.27843359e-01
2.96302348e-01 -3.88724506e-01 -6.27702236e-01 -4.25589770e-01
1.55957624e-01 -1.60640702e-01 4.79536831e-01 9.60115790... | [6.631370544433594, -2.146113872528076] |
5669cba8-62d2-4636-a9cf-e77325ac9fdf | wspalign-word-alignment-pre-training-via | 2306.05644 | null | https://arxiv.org/abs/2306.05644v1 | https://arxiv.org/pdf/2306.05644v1.pdf | WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction | Most existing word alignment methods rely on manual alignment datasets or parallel corpora, which limits their usefulness. Here, to mitigate the dependence on manual data, we broaden the source of supervision by relaxing the requirement for correct, fully-aligned, and parallel sentences. Specifically, we make noisy, pa... | ['Yoshimasa Tsuruoka', 'Masaaki Nagata', 'Qiyu Wu'] | 2023-06-09 | null | null | null | null | ['word-alignment'] | ['natural-language-processing'] | [ 1.72930375e-01 -2.77692080e-01 -5.35746932e-01 -4.83188808e-01
-1.48265553e+00 -6.71453595e-01 4.42648083e-01 -4.61989045e-02
-6.87545240e-01 8.73561025e-01 5.18815935e-01 -4.31460351e-01
3.23604524e-01 -4.99316752e-01 -6.05232596e-01 -5.40169418e-01
3.93695354e-01 6.97998822e-01 2.27424085e-01 -7.73225188... | [11.332830429077148, 10.199739456176758] |
7a44f954-6dc0-41ae-a0df-04384cb2eef2 | learning-granularity-unified-representations | 2207.07802 | null | https://arxiv.org/abs/2207.07802v1 | https://arxiv.org/pdf/2207.07802v1.pdf | Learning Granularity-Unified Representations for Text-to-Image Person Re-identification | Text-to-image person re-identification (ReID) aims to search for pedestrian images of an interested identity via textual descriptions. It is challenging due to both rich intra-modal variations and significant inter-modal gaps. Existing works usually ignore the difference in feature granularity between the two modalitie... | ['Changxing Ding', 'Jian Wang', 'Zhifeng Lin', 'Meng Fang', 'Xinyu Zhang', 'Zhiyin Shao'] | 2022-07-16 | null | null | null | null | ['nlp-based-person-retrival'] | ['computer-vision'] | [-2.96109617e-01 -5.66399872e-01 -2.61647731e-01 -4.91202980e-01
-9.39484298e-01 -4.15612847e-01 7.86539435e-01 -1.23112641e-01
-2.96173602e-01 4.75551575e-01 7.00215697e-01 2.55115598e-01
8.66449103e-02 -7.26695359e-01 -5.96462488e-01 -7.55864918e-01
5.60322702e-01 2.86611229e-01 1.34636417e-01 -9.39276665... | [14.669608116149902, 0.894597589969635] |
db5f4a7d-3a55-4be8-ae5c-fc62c71371a7 | wave-san-wavelet-based-style-augmentation | 2203.07656 | null | https://arxiv.org/abs/2203.07656v1 | https://arxiv.org/pdf/2203.07656v1.pdf | Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning | Previous few-shot learning (FSL) works mostly are limited to natural images of general concepts and categories. These works assume very high visual similarity between the source and target classes. In contrast, the recently proposed cross-domain few-shot learning (CD-FSL) aims at transferring knowledge from general nat... | ['Yu-Gang Jiang', 'Jingjing Chen', 'Yanwei Fu', 'Yu Xie', 'Yuqian Fu'] | 2022-03-15 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 4.35517102e-01 -1.71941787e-01 -1.36859044e-01 -5.42250097e-01
-5.20983040e-01 -6.34754062e-01 7.34421551e-01 -5.78775033e-02
-5.67805693e-02 6.05581284e-01 -2.56677326e-02 2.54864126e-01
1.36925876e-01 -8.58512461e-01 -8.74449670e-01 -6.67928398e-01
3.20169091e-01 1.42168686e-01 4.71784920e-01 -3.52984011... | [10.101237297058105, 2.708676815032959] |
c1a7eb0a-d776-4af1-9a7d-1422128b9d37 | flavr-flow-agnostic-video-representations-for | 2012.08512 | null | https://arxiv.org/abs/2012.08512v3 | https://arxiv.org/pdf/2012.08512v3.pdf | FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation | A majority of methods for video frame interpolation compute bidirectional optical flow between adjacent frames of a video, followed by a suitable warping algorithm to generate the output frames. However, approaches relying on optical flow often fail to model occlusions and complex non-linear motions directly from the v... | ['Du Tran', 'Manmohan Chandraker', 'Deepak Pathak', 'Tarun Kalluri'] | 2020-12-15 | null | null | null | null | ['motion-magnification'] | ['computer-vision'] | [ 6.61124513e-02 -6.04218841e-01 -5.30910552e-01 -1.63727090e-01
-5.99583924e-01 -4.09997553e-01 4.99100655e-01 -3.58808488e-01
-3.02167356e-01 9.22210455e-01 4.03281271e-01 -3.68228197e-01
2.94520617e-01 -4.16170120e-01 -9.08037841e-01 -2.72104681e-01
-1.25710249e-01 -3.90110724e-02 2.00389564e-01 1.27014622... | [10.65634536743164, -1.385749101638794] |
62b59f57-f89b-4ac0-ac36-59dafa1df3f1 | tofa-transfer-once-for-all | 2303.15485 | null | https://arxiv.org/abs/2303.15485v2 | https://arxiv.org/pdf/2303.15485v2.pdf | Transfer-Once-For-All: AI Model Optimization for Edge | Weight-sharing neural architecture search aims to optimize a configurable neural network model (supernet) for a variety of deployment scenarios across many devices with different resource constraints. Existing approaches use evolutionary search to extract models of different sizes from a supernet trained on a very larg... | ['Luis Angel Bathen', 'Rhui Dih Lee', 'Laura Wynter', 'Achintya Kundu'] | 2023-03-27 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 5.89049980e-02 -1.39780506e-01 -2.62812883e-01 -2.86394715e-01
-2.50221282e-01 -6.65115833e-01 -8.46831873e-02 -1.14723623e-01
-4.84790206e-01 5.71640015e-01 -7.48562038e-01 -5.27539551e-01
-5.70313036e-01 -8.18637609e-01 -6.91978276e-01 -5.71646452e-01
-1.43135097e-02 1.15699959e+00 1.39650971e-01 -6.12704232... | [8.453652381896973, 3.26548433303833] |
540589e6-de85-4f4a-9aee-d661f49f7101 | boosting-human-object-interaction-detection | 2305.12252 | null | https://arxiv.org/abs/2305.12252v1 | https://arxiv.org/pdf/2305.12252v1.pdf | Boosting Human-Object Interaction Detection with Text-to-Image Diffusion Model | This paper investigates the problem of the current HOI detection methods and introduces DiffHOI, a novel HOI detection scheme grounded on a pre-trained text-image diffusion model, which enhances the detector's performance via improved data diversity and HOI representation. We demonstrate that the internal representatio... | ['Ruimao Zhang', 'Lei Zhang', 'Ailing Zeng', 'Fengyu Yang', 'Bingliang Li', 'Jie Yang'] | 2023-05-20 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 5.59440196e-01 3.74904312e-02 -1.53489754e-01 -1.07011989e-01
-8.78461897e-01 -1.27437234e-01 3.29500616e-01 -3.14629883e-01
9.42379143e-03 3.16427499e-01 4.63742226e-01 3.92572016e-01
-4.81592156e-02 -6.51617527e-01 -7.20181882e-01 -6.45322204e-01
3.80830765e-01 6.52172685e-01 5.55368185e-01 -1.82234794... | [9.61227798461914, 1.4157007932662964] |
4798370b-e3f1-47c6-a38a-75250c2413bf | a-comparative-study-of-source-finding | 2211.12809 | null | https://arxiv.org/abs/2211.12809v1 | https://arxiv.org/pdf/2211.12809v1.pdf | A comparative study of source-finding techniques in HI emission line cubes using SoFiA, MTObjects, and supervised deep learning | The 21 cm spectral line emission of atomic neutral hydrogen (HI) is one of the primary wavelengths observed in radio astronomy. However, the signal is intrinsically faint and the HI content of galaxies depends on the cosmic environment, requiring large survey volumes and survey depth to investigate the HI Universe. As ... | ['M. H. F. Wilkinson', 'E. T. Martínez', 'M. A. W. Verheijen', 'J. A. Barkai'] | 2022-11-23 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 1.61305308e-01 4.67583314e-02 2.45136142e-01 2.87746023e-02
-4.52763796e-01 -4.31516677e-01 8.38842928e-01 -1.87346280e-01
-4.99423891e-01 6.45968974e-01 3.49501930e-02 -2.67201722e-01
-1.50691450e-01 -8.94047439e-01 -3.56272459e-01 -9.80725110e-01
-1.03406410e-03 8.50871623e-01 8.76561046e-01 -3.69232260... | [7.630101680755615, 3.057694673538208] |
2764ba43-9ff0-4ef3-ae9b-d05f88bdc939 | multiearth-2022-the-champion-solution-for-the | 2206.08970 | null | https://arxiv.org/abs/2206.08970v1 | https://arxiv.org/pdf/2206.08970v1.pdf | MultiEarth 2022 -- The Champion Solution for the Matrix Completion Challenge via Multimodal Regression and Generation | Earth observation satellites have been continuously monitoring the earth environment for years at different locations and spectral bands with different modalities. Due to complex satellite sensing conditions (e.g., weather, cloud, atmosphere, orbit), some observations for certain modalities, bands, locations, and times... | ['Jui-Hsin Lai', 'Yuchuan Gou', 'Hang Zhou', 'Hongchen Liu', 'Bo Peng'] | 2022-06-17 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 2.23051339e-01 -7.93968379e-01 -2.50875175e-01 -2.38427326e-01
-1.19422519e+00 -6.51314735e-01 3.71973187e-01 -1.54140115e-01
-3.88709545e-01 7.85941839e-01 8.52128193e-02 -1.55528292e-01
-1.24727763e-01 -4.52401996e-01 -6.74381435e-01 -8.47122014e-01
-3.34076852e-01 1.51993170e-01 -3.07246983e-01 -1.88783422... | [9.940826416015625, -1.7392947673797607] |
65fff80d-ea9d-4336-89f0-03b0c3f99d00 | ganonymization-a-gan-based-face-anonymization | 2305.02143 | null | https://arxiv.org/abs/2305.02143v1 | https://arxiv.org/pdf/2305.02143v1.pdf | GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions | In recent years, the increasing availability of personal data has raised concerns regarding privacy and security. One of the critical processes to address these concerns is data anonymization, which aims to protect individual privacy and prevent the release of sensitive information. This research focuses on the importa... | ['Elisabeth André', 'Peter Krawitz', 'Cristina Conati', 'Tzung-Chien Hsieh', 'Alexander Hustinx', 'Mohamed Benouis', 'Silvan Mertes', 'Fabio Hellmann'] | 2023-05-03 | null | null | null | null | ['face-anonymization'] | ['computer-vision'] | [ 2.28964433e-01 3.46007168e-01 2.51547635e-01 -5.80416203e-01
-3.24016601e-01 -6.85689688e-01 4.56731051e-01 -8.10279101e-02
-2.83060700e-01 7.62196183e-01 3.13821971e-01 3.82299244e-01
1.19641796e-01 -8.78582239e-01 -5.21943271e-01 -7.18421876e-01
1.29775614e-01 -7.72893280e-02 -6.54684722e-01 -1.98363662... | [12.756244659423828, 0.7158843874931335] |
517c6f74-04de-4f53-a0b3-79b08437cc16 | epileptic-seizure-risk-assessment-by-multi | 2204.07034 | null | https://arxiv.org/abs/2204.07034v1 | https://arxiv.org/pdf/2204.07034v1.pdf | Epileptic Seizure Risk Assessment by Multi-Channel Imaging of the EEG | Refractory epileptic patients can suffer a seizure at any moment. Seizure prediction would substantially improve their lives. In this work, based on scalp EEG and its transformation into images, the likelihood of an epileptic seizure occurring at any moment is computed using an average of the softmax layer output (the ... | ['Antonio Dourado', 'Cesar Teixeira', 'Fabio Lopes', 'Tiago Leal'] | 2022-04-12 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.28088057e-01 5.00332594e-01 1.23412587e-01 -4.44547802e-01
-4.52556640e-01 -1.45595878e-01 3.87986779e-01 2.76626348e-01
-5.36478579e-01 9.30788517e-01 1.30099328e-02 -1.74211442e-01
-1.07013389e-01 -7.29369938e-01 -3.56148064e-01 -8.53344858e-01
-5.55183589e-01 2.16156408e-01 2.42933214e-01 1.80861354... | [13.230745315551758, 3.5238869190216064] |
07e2e36f-dc2d-4de3-8d7f-9a7a4e41734b | histopathological-image-classification-based | 2210.09021 | null | https://arxiv.org/abs/2210.09021v2 | https://arxiv.org/pdf/2210.09021v2.pdf | Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels | Whole Slide Image (WSI) analysis is a powerful method to facilitate the diagnosis of cancer in tissue samples. Automating this diagnosis poses various issues, most notably caused by the immense image resolution and limited annotations. WSIs commonly exhibit resolutions of 100Kx100K pixels. Annotating cancerous areas in... | ['Heinz Koeppl', 'Nadine Flinner', 'Tim Prangemeier', 'Christoph Reich', 'Oezdemir Cetin', 'Ahmet Gokberk Gul'] | 2022-10-17 | null | null | null | null | ['histopathological-image-classification', 'multiple-instance-learning'] | ['medical', 'methodology'] | [ 5.98547876e-01 2.13026181e-01 -2.90230185e-01 -2.19139293e-01
-1.59147465e+00 -5.10248303e-01 4.62931544e-01 4.56519067e-01
-5.62375665e-01 5.05119920e-01 -1.34827271e-01 -3.13210905e-01
1.94744885e-01 -6.67986751e-01 -7.34220147e-01 -1.11420906e+00
3.04438651e-01 3.42927188e-01 4.09634531e-01 2.52484918... | [15.105401039123535, -2.9384305477142334] |
864aeb1c-7d03-4a37-8101-003707b7dc4c | multi-classification-of-brain-tumor-images | 2206.08543 | null | https://arxiv.org/abs/2206.08543v1 | https://arxiv.org/pdf/2206.08543v1.pdf | Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network | In recent advancement towards computer based diagnostics system, the classification of brain tumor images is a challenging task. This paper mainly focuses on elevating the classification accuracy of brain tumor images with transfer learning based deep neural network. The classification approach is started with the imag... | ['Md. Saiful Islam', 'Khaleda Akhter Sathi', 'Pramit Dutta'] | 2022-06-17 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 1.87059104e-01 3.29677276e-02 9.33400244e-02 -2.71377712e-01
-3.49828809e-01 -6.38697669e-02 5.34663737e-01 4.08562599e-03
-6.11902654e-01 7.05016613e-01 -1.64393596e-02 -6.41274154e-01
-2.20727205e-01 -7.38541007e-01 -2.20237702e-01 -9.95195806e-01
9.92662236e-02 4.99736577e-01 1.02420911e-01 -2.26605624... | [14.90623950958252, -2.569298267364502] |
1c4f21f8-f279-45d5-bb57-8ccad70d9a66 | statistical-analysis-of-time-frequency | 2209.13350 | null | https://arxiv.org/abs/2209.13350v1 | https://arxiv.org/pdf/2209.13350v1.pdf | Statistical Analysis of Time-Frequency Features Based On Multivariate Synchrosqueezing Transform for Hand Gesture Classification | In this study, the four joint time-frequency (TF) moments; mean, variance, skewness, and kurtosis of TF matrix obtained from Multivariate Synchrosqueezing Transform (MSST) are proposed as features for hand gesture recognition. A publicly available dataset containing surface EMG (sEMG) signals of 40 subjects performing ... | ['Onan Guren', 'Mehmet Akif Ozdemir', 'Deniz Hande Kisa', 'Lutfiye Saripinar'] | 2022-09-24 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.85111661e-02 -5.97938538e-01 -5.55395782e-01 -5.60708679e-02
-3.25452805e-01 -3.41655493e-01 3.44470561e-01 -2.56104022e-01
-5.58614314e-01 6.53388917e-01 2.92107254e-01 6.37915656e-02
-8.02719593e-01 -3.11143463e-04 4.70397156e-03 -9.23061609e-01
-9.32265103e-01 -6.15799055e-02 -2.42844075e-02 7.45723918... | [6.848217487335205, 0.1853857934474945] |
190bf0e9-e5f4-41a5-a988-3acef021ac8d | neural-compression-based-feature-learning-for | 2203.09208 | null | https://arxiv.org/abs/2203.09208v2 | https://arxiv.org/pdf/2203.09208v2.pdf | Neural Compression-Based Feature Learning for Video Restoration | How to efficiently utilize the temporal features is crucial, yet challenging, for video restoration. The temporal features usually contain various noisy and uncorrelated information, and they may interfere with the restoration of the current frame. This paper proposes learning noise-robust feature representations to he... | ['Yan Lu', 'Dong Liu', 'Bin Li', 'Jiahao Li', 'Cong Huang'] | 2022-03-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Huang_Neural_Compression-Based_Feature_Learning_for_Video_Restoration_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Huang_Neural_Compression-Based_Feature_Learning_for_Video_Restoration_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-denoising', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 2.37457067e-01 -7.44569480e-01 -4.73932065e-02 -3.03364158e-01
-4.52301949e-01 -9.52648371e-02 -2.40088962e-02 -2.96476126e-01
-3.25988024e-01 6.00114405e-01 6.62699401e-01 -9.89942700e-02
-1.19197428e-01 -7.50058174e-01 -6.20545506e-01 -1.17435396e+00
-2.36382067e-01 -8.07942688e-01 3.44628036e-01 -3.31255704... | [11.252801895141602, -2.02382493019104] |
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