paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1edb8be9-9f77-40b5-8909-3638403e0e69 | pcace-a-statistical-approach-to-ranking | 2112.15571 | null | https://arxiv.org/abs/2112.15571v1 | https://arxiv.org/pdf/2112.15571v1.pdf | PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability | In this paper we introduce a new problem within the growing literature of interpretability for convolution neural networks (CNNs). While previous work has focused on the question of how to visually interpret CNNs, we ask what it is that we care to interpret, that is, which layers and neurons are worth our attention? Du... | ['Seth Flaxman', 'Esra Suel', 'Sílvia Casacuberta'] | 2021-12-31 | null | null | null | null | ['air-pollution-prediction'] | ['miscellaneous'] | [ 3.85114878e-01 2.81988263e-01 2.88292140e-01 -6.28077030e-01
1.29927501e-01 -5.86421967e-01 4.76447880e-01 3.88522834e-01
-5.86027205e-01 5.32994449e-01 2.94688016e-01 -6.88234925e-01
-3.82832795e-01 -7.23946810e-01 -4.87776756e-01 -5.59844851e-01
-1.30907163e-01 2.22964864e-02 1.01033784e-01 -5.64840920... | [8.882684707641602, 5.486849784851074] |
5f3fd0a0-00a7-4642-adf0-318fb91ad03b | invisible-backdoor-attack-with-dynamic | 2211.10933 | null | https://arxiv.org/abs/2211.10933v2 | https://arxiv.org/pdf/2211.10933v2.pdf | Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification | In recent years, person Re-identification (ReID) has rapidly progressed with wide real-world applications, but also poses significant risks of adversarial attacks. In this paper, we focus on the backdoor attack on deep ReID models. Existing backdoor attack methods follow an all-to-one or all-to-all attack scenario, whe... | ['Cairong Zhao', 'Cheng Deng', 'Duoqian Miao', 'Dongsheng Li', 'Shuguang Dou', 'Xinyang Jiang', 'Wenli Sun'] | 2022-11-20 | null | null | null | null | ['image-steganography', 'open-set-learning'] | ['computer-vision', 'miscellaneous'] | [ 2.22792551e-01 -3.06846648e-01 4.28701192e-02 -2.80219197e-01
-3.58668298e-01 -1.05202460e+00 6.51440024e-01 -1.88160494e-01
-3.74112815e-01 7.33972847e-01 -8.65594074e-02 -7.39485398e-02
5.54741807e-02 -1.18499899e+00 -8.24213207e-01 -9.11301136e-01
-1.22962177e-01 3.88231933e-01 4.98317853e-02 -3.69477063... | [13.213784217834473, 1.0628199577331543] |
8c7fcc4f-8983-46eb-9c2b-243957a9123c | experimental-assessment-of-polynomial | 2011.08520 | null | https://arxiv.org/abs/2011.08520v1 | https://arxiv.org/pdf/2011.08520v1.pdf | Experimental assessment of polynomial nonlinear state-space and nonlinear-mode models for near-resonant vibrations | In the present paper, two existing nonlinear system identification methodologies are used to identify data-driven models. The first methodology focuses on identifying the system using steady-state excitations. To accomplish this, a phase-locked loop controller is implemented to acquire periodic oscillations near resona... | ['Malte Krack', 'Matthew S. Allen', 'Jean-Philippe Noël', 'Simon Peter', 'Matthew R. W. Brake', 'Ali Tatar', 'Gleb Kleyman', 'Maren Scheel'] | 2020-11-17 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [ 4.24716324e-01 -1.25663340e-01 -7.95674324e-02 4.22061205e-01
-5.48005402e-01 -5.96380949e-01 3.45696002e-01 -3.49133551e-01
1.44040018e-01 6.58815622e-01 -4.63665336e-01 -1.48934603e-01
-8.76665890e-01 -3.42643440e-01 -3.27858388e-01 -9.57315326e-01
-2.80749388e-02 2.29690507e-01 1.30260155e-01 -4.29178029... | [5.853240489959717, 3.0029609203338623] |
76f6873b-6374-49aa-9b90-e1f84fb1aeff | deep-pixel-wise-binary-supervision-for-face | 1907.04047 | null | https://arxiv.org/abs/1907.04047v1 | https://arxiv.org/pdf/1907.04047v1.pdf | Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection | Face recognition has evolved as a prominent biometric authentication modality. However, vulnerability to presentation attacks curtails its reliable deployment. Automatic detection of presentation attacks is essential for secure use of face recognition technology in unattended scenarios. In this work, we introduce a Con... | ['Anjith George', 'Sebastien Marcel'] | 2019-07-09 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 4.84987199e-01 -3.20694059e-01 -3.59340869e-02 -1.47046655e-01
-6.03996098e-01 -8.84541392e-01 6.24351025e-01 2.43577920e-03
-5.48414230e-01 3.80488366e-01 -1.64583862e-01 -7.18057096e-01
1.59499258e-01 -6.74201369e-01 -5.60874045e-01 -7.62318194e-01
-1.52252927e-01 -5.26062787e-01 -1.15690291e-01 3.26111093... | [13.070781707763672, 1.1432968378067017] |
475409d4-4376-4b56-b267-2903f935878d | tad-transfer-learning-based-multi-adversarial | 2210.15700 | null | https://arxiv.org/abs/2210.15700v1 | https://arxiv.org/pdf/2210.15700v1.pdf | TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems | Nowadays, intrusion detection systems based on deep learning deliver state-of-the-art performance. However, recent research has shown that specially crafted perturbations, called adversarial examples, are capable of significantly reducing the performance of these intrusion detection systems. The objective of this paper... | ['Wim Mees', 'Tayeb Kenaza', 'Jean-Michel Dricot', 'Thibault Debatty', 'Richard Bauwens', 'Islam Debicha'] | 2022-10-27 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 1.95237041e-01 -9.53049958e-03 1.51577219e-01 8.84868857e-03
-3.90443951e-01 -6.89220488e-01 9.65646505e-01 -8.47778320e-02
-5.12982130e-01 4.31062996e-01 -3.59871626e-01 -8.20634007e-01
1.59832241e-04 -1.06707966e+00 -6.95340037e-01 -4.52574760e-01
-4.68203515e-01 7.24967957e-01 5.83106816e-01 -7.66345620... | [5.51564884185791, 7.566676616668701] |
2dea7051-53c3-4f1d-9097-debdf20442fe | a-generative-model-for-user-simulation-in-a | null | null | https://aclanthology.org/E14-1066 | https://aclanthology.org/E14-1066.pdf | A Generative Model for User Simulation in a Spatial Navigation Domain | null | ['Mark Steedman', 'Aciel Eshky', 'Ben Allison', 'Subramanian Ramamoorthy'] | 2014-04-01 | null | null | null | eacl-2014-4 | ['user-simulation'] | ['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... | [-7.413116931915283, 3.5917794704437256] |
5b4dd31d-6ebb-4793-9b66-995275c84656 | generalizable-lightweight-proxy-for-robust | 2306.05031 | null | https://arxiv.org/abs/2306.05031v1 | https://arxiv.org/pdf/2306.05031v1.pdf | Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations | Recent neural architecture search (NAS) frameworks have been successful in finding optimal architectures for given conditions (e.g., performance or latency). However, they search for optimal architectures in terms of their performance on clean images only, while robustness against various types of perturbations or corr... | ['Sung Ju Hwang', 'Minseon Kim', 'Hyeonjeong Ha'] | 2023-06-08 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.67712048e-02 -7.44920790e-01 1.72184333e-01 -1.30903870e-01
-1.08595026e+00 -9.53036070e-01 4.96654421e-01 -2.73524016e-01
-4.58056301e-01 4.58860070e-01 -1.36632055e-01 -2.65833944e-01
-1.57002702e-01 -4.87641186e-01 -1.05138206e+00 -9.32915688e-01
9.66981500e-02 2.08954047e-02 1.89292923e-01 -3.72643083... | [5.560215950012207, 7.951155185699463] |
0b41c858-f852-4f60-87a5-2f7b8272f050 | habicrowd-a-high-performance-simulator-for | 2306.11377 | null | https://arxiv.org/abs/2306.11377v1 | https://arxiv.org/pdf/2306.11377v1.pdf | HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation | Visual navigation, a foundational aspect of Embodied AI (E-AI), has been significantly studied in the past few years. While many 3D simulators have been introduced to support visual navigation tasks, scarcely works have been directed towards combining human dynamics, creating the gap between simulation and real-world a... | ['Anh Nguyen', 'Thieu Vo', 'Huynh Thi Thanh Binh', 'Dzung Nguyen', 'Baoru Huang', 'Minh Nhat Vu', 'Toan Tien Nguyen', 'An Dinh Vuong'] | 2023-06-20 | null | null | null | null | ['human-dynamics', 'visual-navigation'] | ['computer-vision', 'robots'] | [-5.55465281e-01 -2.13353172e-01 2.60890692e-01 1.23332553e-01
8.42460468e-02 -3.69142324e-01 7.36664712e-01 -1.51279539e-01
-7.25889444e-01 6.98392749e-01 1.70155659e-01 -3.80320340e-01
2.16110468e-01 -5.55786133e-01 -4.22095060e-01 -5.66040814e-01
-4.35132354e-01 4.10279125e-01 6.33650720e-01 -9.48531985... | [4.63317346572876, 0.68758624792099] |
3ded8005-8ea5-4e95-bc0b-0bf1820cf6b9 | evaluating-prompt-based-question-answering | 2305.12900 | null | https://arxiv.org/abs/2305.12900v2 | https://arxiv.org/pdf/2305.12900v2.pdf | Evaluating Prompt-based Question Answering for Object Prediction in the Open Research Knowledge Graph | There have been many recent investigations into prompt-based training of transformer language models for new text genres in low-resource settings. The prompt-based training approach has been found to be effective in generalizing pre-trained or fine-tuned models for transfer to resource-scarce settings. This work, for t... | ['Sören Auer', 'Moussab Hrou', "Jennifer D'Souza"] | 2023-05-22 | null | null | null | null | ['general-knowledge', 'relation-extraction'] | ['miscellaneous', 'natural-language-processing'] | [ 1.45315722e-01 5.56524038e-01 -6.20342731e-01 -1.63350180e-01
-8.46594691e-01 -7.47404993e-01 9.54331279e-01 1.38544515e-01
-3.02715123e-01 6.83163941e-01 4.01692450e-01 -6.51177526e-01
-5.96104920e-01 -8.53460014e-01 -7.16295600e-01 5.93023258e-04
1.09660529e-01 9.29427564e-01 3.88187736e-01 -2.38240927... | [9.948092460632324, 8.512968063354492] |
395fc019-46a4-4dea-b1ba-cda4a9506e45 | solving-single-objective-tasks-by-preference | null | null | https://openreview.net/forum?id=HJxV5yHYwB | https://openreview.net/pdf?id=HJxV5yHYwB | Solving single-objective tasks by preference multi-objective reinforcement learning | There ubiquitously exist many single-objective tasks in the real world that are inevitably related to some other objectives and influenced by them. We call such task as the objective-constrained task, which is inherently a multi-objective problem. Due to the conflict among different objectives, a trade-off is needed. A... | ['Feng Chen', 'Shangqi Guo', 'Jinsheng Ren'] | 2019-09-25 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-1.52840093e-02 -2.22967952e-01 -2.66243815e-01 -2.21122548e-01
-4.20565426e-01 -6.23578548e-01 -2.09404305e-02 -5.14949076e-02
-7.46594191e-01 1.10589933e+00 1.14723057e-01 -6.44949451e-02
-7.96150446e-01 -4.67309356e-01 -3.60061884e-01 -9.17958140e-01
-1.12405054e-01 6.36352599e-01 1.53436614e-02 -3.10681313... | [4.394063472747803, 2.485564708709717] |
5c503cab-7030-458d-a17d-cefc0de2f08b | scalable-deletion-robust-submodular | null | null | https://icml.cc/Conferences/2018/Schedule?showEvent=1927 | http://proceedings.mlr.press/v80/kazemi18a/kazemi18a.pdf | Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints |
Can we efficiently extract useful information from a large user-generated dataset while protecting the privacy of the users and/or ensuring fairness in representation? We cast this problem as an instance of a deletion-robust submodular maximization where part of the data may be deleted or masked due to privacy con... | ['Morteza Zadimoghaddam', 'Amin Karbasi', 'Ehsan Kazemi'] | 2018-07-01 | null | null | null | icml-2018-7 | ['data-summarization'] | ['miscellaneous'] | [ 2.92559117e-01 2.08101407e-01 -3.31580788e-01 -4.99198228e-01
-1.05095911e+00 -9.14806962e-01 -3.72225642e-02 6.14832938e-01
-7.31269538e-01 9.76772189e-01 4.56550896e-01 -2.82990307e-01
-2.89640248e-01 -8.31386805e-01 -7.47645199e-01 -7.13125646e-01
-5.95366895e-01 4.15928334e-01 -3.60022277e-01 -3.27940024... | [6.480630874633789, 5.207437038421631] |
6d20cc38-32b1-4b62-9eb0-92d7f8eb07b9 | unit-based-speech-to-speech-translation | 2305.15405 | null | https://arxiv.org/abs/2305.15405v1 | https://arxiv.org/pdf/2305.15405v1.pdf | Unit-based Speech-to-Speech Translation Without Parallel Data | We propose an unsupervised speech-to-speech translation (S2ST) system that does not rely on parallel data between the source and target languages. Our approach maps source and target language speech signals into automatically discovered, discrete units and reformulates the problem as unsupervised unit-to-unit machine t... | ['Eunsol Choi', 'David Harwath', 'Anirudh Srinivasan', 'Anuj Diwan'] | 2023-05-24 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 6.97574556e-01 5.12286067e-01 -7.40410462e-02 -6.88668072e-01
-1.68265259e+00 -6.43307269e-01 8.50054741e-01 -1.65565088e-01
-4.72912401e-01 7.12097824e-01 4.70731676e-01 -8.01355064e-01
6.46273196e-01 -3.46688062e-01 -9.07004297e-01 -4.23951209e-01
3.76963288e-01 8.63990247e-01 -1.52857140e-01 -2.73852468... | [14.535393714904785, 7.145258903503418] |
4deb0894-1045-40e9-b99a-b84ae900638c | revisiting-shadow-detection-a-new-benchmark | 1911.06998 | null | https://arxiv.org/abs/1911.06998v3 | https://arxiv.org/pdf/1911.06998v3.pdf | Revisiting Shadow Detection: A New Benchmark Dataset for Complex World | Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world. Though recent shadow detectors have already achieved remarkable performance on various benchmark data, their performance is still limited for general real-world situations. In this work, we collected shadow images for m... | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Xiaowei Hu', 'Tianyu Wang', 'Qiong Wang', 'Yitong Jiang'] | 2019-11-16 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 6.46842360e-01 -3.62799048e-01 2.44624034e-01 -5.66014290e-01
-4.63049896e-02 -5.61156929e-01 2.74439991e-01 -5.19851029e-01
1.01677917e-01 1.00754154e+00 2.11009800e-01 -5.76752365e-01
4.99020070e-01 -5.86546361e-01 -3.01585436e-01 -9.94713962e-01
-1.94710582e-01 3.34608644e-01 1.15597689e+00 -2.53421247... | [10.83560848236084, -4.096983432769775] |
ac25ddb5-6092-4ffd-9a2f-00246c699245 | deepfakeart-challenge-a-benchmark-dataset-for | 2306.01272 | null | https://arxiv.org/abs/2306.01272v2 | https://arxiv.org/pdf/2306.01272v2.pdf | DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection | The tremendous recent advances in generative artificial intelligence techniques have led to significant successes and promise in a wide range of different applications ranging from conversational agents and textual content generation to voice and visual synthesis. Amid the rise in generative AI and its increasing wides... | ['Dayou Mao', 'Alexander Wong', 'Carol Xu', 'Hossein Aboutalebi'] | 2023-06-02 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 5.0488478e-01 9.7303346e-02 4.9850482e-01 9.5219128e-02
-1.0225378e+00 -9.2484432e-01 1.1398598e+00 -3.7586734e-01
6.6898842e-03 6.1221170e-01 4.8318598e-01 -2.2392406e-01
3.2491454e-01 -8.1724501e-01 -8.9983076e-01 -8.8344026e-01
1.8993768e-01 5.3038502e-01 -2.2056603e-01 -1.6357957e-01
1.8379526e-01... | [12.443496704101562, 1.0989665985107422] |
184705b0-ed15-49c7-a9e0-3a0c043e1320 | hierarchical-multi-scale-attention-networks | 1708.07590 | null | http://arxiv.org/abs/1708.07590v2 | http://arxiv.org/pdf/1708.07590v2.pdf | Hierarchical Multi-scale Attention Networks for Action Recognition | Recurrent Neural Networks (RNNs) have been widely used in natural language
processing and computer vision. Among them, the Hierarchical Multi-scale RNN
(HM-RNN), a kind of multi-scale hierarchical RNN proposed recently, can learn
the hierarchical temporal structure from data automatically. In this paper, we
extend the ... | ['Bai-Ling Zhang', 'Shi-Yang Yan', 'Wenjin Lu', 'Jeremy S. Smith'] | 2017-08-25 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 3.89392883e-01 -1.72918320e-01 4.11935635e-02 -4.80648503e-03
-2.70534873e-01 9.03092604e-03 3.80128860e-01 -4.88377094e-01
-4.92584318e-01 6.69368386e-01 2.43164271e-01 -6.29443908e-03
1.30749550e-02 -4.73317355e-01 -5.65295756e-01 -1.09557164e+00
1.83966547e-01 -1.32386684e-01 5.38689017e-01 -1.12732932... | [8.040557861328125, 0.6490355730056763] |
518b47db-3132-4fc9-a013-627134e051b0 | attention-guided-generative-models-for | 2110.06393 | null | https://arxiv.org/abs/2110.06393v1 | https://arxiv.org/pdf/2110.06393v1.pdf | Attention-guided Generative Models for Extractive Question Answering | We propose a novel method for applying Transformer models to extractive question answering (QA) tasks. Recently, pretrained generative sequence-to-sequence (seq2seq) models have achieved great success in question answering. Contributing to the success of these models are internal attention mechanisms such as cross-atte... | ['Bing Xiang', 'Zhiheng Huang', 'Davis Liang', 'Peng Xu'] | 2021-10-12 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 3.53332758e-01 5.77090561e-01 3.70247304e-01 -3.40671688e-01
-1.69848764e+00 -7.13784993e-01 7.98201442e-01 -8.71142372e-02
-3.50034148e-01 7.72186637e-01 6.71210229e-01 -5.81733525e-01
1.37768745e-01 -8.75647068e-01 -9.63326871e-01 -1.16458602e-01
4.48551536e-01 8.21803868e-01 2.04314053e-01 -6.53659165... | [11.283794403076172, 8.085051536560059] |
06462998-0d84-4599-9a68-4977e955e313 | using-meta-knowledge-mined-from-identifiers-1 | null | null | https://aclanthology.org/2021.acl-long.545 | https://aclanthology.org/2021.acl-long.545.pdf | Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems | In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers. Developers often include such knowledge, structure as taxonomies, in the documentation of chatbots. By using neuro-symbolic algorithms to incorporate those taxonomies int... | ['Henrique Ferreira', 'Gabriel Malfatti', 'Maira de Bayser', 'Melina Guerra', 'Mauro Pichiliani', 'Julio Nogima', 'Heloisa Candello', 'Ana Appel', 'Victor Henrique Alves Ribeiro', 'Paulo Cavalin', 'Claudio Pinhanez'] | 2021-08-01 | null | null | null | acl-2021-5 | ['intent-recognition'] | ['natural-language-processing'] | [ 4.91992868e-02 7.12018728e-01 3.80584180e-01 -3.47240269e-01
-3.78900379e-01 -5.64690113e-01 6.38815463e-01 7.31222238e-03
-5.53389609e-01 6.98485196e-01 2.52717167e-01 -2.82395124e-01
-2.60515362e-01 -4.28824514e-01 -5.01591086e-01 -3.85283053e-01
1.08012214e-01 4.60795492e-01 2.28706643e-01 -4.78934735... | [12.52093505859375, 7.787801265716553] |
1baaffdc-34ce-4445-8bbe-fc36d07ebf12 | a-survey-of-software-defined-smart-grid | 2306.14697 | null | https://arxiv.org/abs/2306.14697v1 | https://arxiv.org/pdf/2306.14697v1.pdf | A Survey of Software-Defined Smart Grid Networks: Security Threats and Defense Techniques | Smart grids are replacing conventional power grids due to rising electricity use, failing infrastructure, and reliability problems. Two-way communication, demand-side administration, and real-time pricing make smart grids (SGs) dependent on its communication system. Manual network administration slows down SG communica... | ['Janise McNair', 'Sharon Boamah', 'Dennis Agnew'] | 2023-06-26 | null | null | null | null | ['security-studies'] | ['miscellaneous'] | [-3.20077628e-01 -3.95708345e-02 -3.97710800e-01 -1.19302273e-01
3.64233375e-01 -1.18188989e+00 3.53648782e-01 -1.21430166e-01
3.34872395e-01 9.29935098e-01 -1.59206763e-01 -7.15021193e-01
-5.81143685e-02 -1.30018711e+00 3.05037111e-01 -9.64356244e-01
-5.06577909e-01 2.61230767e-02 2.95836210e-01 -3.31714824... | [5.913158893585205, 2.607565402984619] |
5671a576-a98c-4b3f-b7ac-45306510b5db | a-novel-approach-for-generating-customizable | 2212.06701 | null | https://arxiv.org/abs/2212.06701v1 | https://arxiv.org/pdf/2212.06701v1.pdf | A Novel Approach For Generating Customizable Light Field Datasets for Machine Learning | To train deep learning models, which often outperform traditional approaches, large datasets of a specified medium, e.g., images, are used in numerous areas. However, for light field-specific machine learning tasks, there is a lack of such available datasets. Therefore, we create our own light field datasets, which hav... | ['Vidhi Chhabra', 'Aloukika Patro', 'Toure Smith', 'Julia Huang'] | 2022-12-13 | null | null | null | null | ['unity'] | ['computer-vision'] | [-8.80985856e-02 -8.77862155e-01 1.66029513e-01 -5.69753587e-01
-3.18024099e-01 -3.77484828e-01 4.17417288e-01 -3.30396116e-01
-1.88019469e-01 9.04878318e-01 -2.63699889e-01 -2.65146285e-01
-1.62923876e-02 -1.01861191e+00 -7.38565266e-01 -8.46654773e-01
5.07770240e-01 2.26019830e-01 5.01568258e-01 -7.29764923... | [9.604804039001465, -2.6068248748779297] |
4a81901b-5ded-4c32-b27c-f751cc1165af | transferable-deep-learning-power-system-short | 2303.07138 | null | https://arxiv.org/abs/2303.07138v1 | https://arxiv.org/pdf/2303.07138v1.pdf | Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering | Deep learning (DL) algorithms have been widely applied to short-term voltage stability (STVS) assessment in power systems. However, transferring the knowledge learned in one power grid to other power grids with topology changes is still a challenging task. This paper proposed a transferable DL-based model for STVS asse... | ['Kai Wu', 'Peiyuan Sun', 'Zijian Lv', 'Xin Chen', 'Zijian Feng'] | 2023-03-13 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-8.41622293e-01 -8.37218106e-01 9.03222710e-03 -1.88103035e-01
-7.37898171e-01 -7.94001102e-01 4.69166547e-01 3.02383780e-01
4.10699099e-01 1.21617270e+00 -2.09031656e-01 -2.82929659e-01
-5.62708735e-01 -1.06147492e+00 -3.64846706e-01 -1.07426190e+00
-9.23538327e-01 4.59761411e-01 -7.96608329e-02 -4.13022637... | [5.9634504318237305, 2.6055026054382324] |
05fd7bd6-8a19-4a2c-a843-950c9ac1c6cb | self-supervised-learning-of-event-based | 2106.01862 | null | https://arxiv.org/abs/2106.01862v2 | https://arxiv.org/pdf/2106.01862v2.pdf | Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks | The field of neuromorphic computing promises extremely low-power and low-latency sensing and processing. Challenges in transferring learning algorithms from traditional artificial neural networks (ANNs) to spiking neural networks (SNNs) have so far prevented their application to large-scale, complex regression tasks. F... | ['Federico Paredes-Vallés', 'Jesse Hagenaars', 'Guido de Croon'] | 2021-06-03 | null | http://proceedings.neurips.cc/paper/2021/hash/39d4b545fb02556829aab1db805021c3-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/39d4b545fb02556829aab1db805021c3-Paper.pdf | neurips-2021-12 | ['event-based-optical-flow'] | ['computer-vision'] | [ 5.24189949e-01 -2.02893302e-01 4.18066859e-01 -2.65698522e-01
5.92722669e-02 -4.99593645e-01 6.12309813e-01 -4.67749760e-02
-1.10767674e+00 8.19853067e-01 -2.10996211e-01 -2.60306580e-04
-1.36803493e-01 -6.62787497e-01 -9.68110263e-01 -8.99631381e-01
-1.87768489e-01 3.63962315e-02 5.35315156e-01 3.64581198... | [8.232086181640625, 2.3732922077178955] |
e5673ae5-5c2c-4922-98b6-3199728913be | stprivacy-spatio-temporal-tubelet | 2301.03046 | null | https://arxiv.org/abs/2301.03046v2 | https://arxiv.org/pdf/2301.03046v2.pdf | STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition | Existing methods of privacy-preserving action recognition (PPAR) mainly focus on frame-level (spatial) privacy removal through 2D CNNs. Unfortunately, they have two major drawbacks. First, they may compromise temporal dynamics in input videos, which are critical for accurate action recognition. Second, they are vulnera... | ['Shuicheng Yan', 'Mike Zheng Shou', 'Jussi Keppo', 'Pan Zhou', 'Xiangyu Xu', 'Jiahe Li', 'Jia-Wei Liu', 'Hehe Fan', 'Jun Liu', 'Ming Li'] | 2023-01-08 | null | null | null | null | ['facial-expression-recognition', 'video-understanding'] | ['computer-vision', 'computer-vision'] | [ 3.77474606e-01 2.57498417e-02 -3.34889233e-01 -1.43907323e-01
-6.36415064e-01 -9.36533034e-01 3.00492167e-01 -1.49785981e-01
-5.72555065e-01 5.18370628e-01 3.69074583e-01 -3.83966476e-01
1.09163150e-01 -6.11278355e-01 -8.38119924e-01 -8.73924375e-01
-1.90297917e-01 -3.70120376e-01 1.78939566e-01 9.37267169... | [5.83485221862793, 6.745745658874512] |
c32ffd4f-67b1-40d0-b557-f5f7c86c5427 | conrpg-paraphrase-generation-using-contexts | 2109.00363 | null | https://arxiv.org/abs/2109.00363v1 | https://arxiv.org/pdf/2109.00363v1.pdf | ConRPG: Paraphrase Generation using Contexts as Regularizer | A long-standing issue with paraphrase generation is how to obtain reliable supervision signals. In this paper, we propose an unsupervised paradigm for paraphrase generation based on the assumption that the probabilities of generating two sentences with the same meaning given the same context should be the same. Inspire... | ['Jiwei Li', 'Chun Fan', 'Fei Wu', 'Qinghong Han', 'Xiaofei Sun', 'Qing He', 'Xiang Ao', 'Yuxian Meng'] | 2021-09-01 | null | https://aclanthology.org/2021.emnlp-main.199 | https://aclanthology.org/2021.emnlp-main.199.pdf | emnlp-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 5.75306118e-01 1.61792368e-01 -1.65918663e-01 -5.82999170e-01
-7.52448380e-01 -5.12213767e-01 6.98813796e-01 2.54941225e-01
-5.36889210e-02 7.30187416e-01 4.00505245e-01 -1.22838564e-01
-1.51568636e-01 -8.86017263e-01 -8.90723526e-01 -2.95872688e-01
6.11925900e-01 3.19682479e-01 1.01764016e-01 -2.62408733... | [11.657265663146973, 9.288808822631836] |
84858811-11ad-4069-a2c4-4201eb6be177 | identifying-water-stress-in-chickpea-plant-by | 2104.07911 | null | https://arxiv.org/abs/2104.07911v3 | https://arxiv.org/pdf/2104.07911v3.pdf | Intelligent Monitoring of Stress Induced by Water Deficiency in Plants using Deep Learning | In the recent decade, high-throughput plant phenotyping techniques, which combine non-invasive image analysis and machine learning, have been successfully applied to identify and quantify plant health and diseases. However, these techniques usually do not consider the progressive nature of plant stress and often requir... | ['Tapan K. Gandhi', 'Rohan Wadhawan', 'Shiva Azimi'] | 2021-04-16 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 4.33856875e-01 -3.26628745e-01 4.29783091e-02 1.76348209e-01
-8.51609334e-02 -8.39877605e-01 1.03871904e-01 6.51927650e-01
-1.36857644e-01 3.60853821e-01 -6.33666396e-01 -5.63759804e-01
-2.68046290e-01 -9.34184611e-01 -4.25610662e-01 -8.52709293e-01
-5.18800616e-01 -3.58407609e-02 3.06095421e-01 -1.99352056... | [9.159419059753418, -1.570779800415039] |
1b2c8393-09b0-487c-bb5f-40d50e1d7997 | anticipatory-music-transformer | 2306.08620 | null | https://arxiv.org/abs/2306.08620v1 | https://arxiv.org/pdf/2306.08620v1.pdf | Anticipatory Music Transformer | We introduce anticipation: a method for constructing a controllable generative model of a temporal point process (the event process) conditioned asynchronously on realizations of a second, correlated process (the control process). We achieve this by interleaving sequences of events and controls, such that controls appe... | ['Percy Liang', 'Chris Donahue', 'David Hall', 'John Thickstun'] | 2023-06-14 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 6.62223577e-01 1.62617788e-01 1.72154844e-01 -7.31369555e-02
-8.43868077e-01 -9.21631098e-01 1.11047518e+00 -1.05080627e-01
-3.59069481e-02 6.12346113e-01 8.73458683e-01 1.67425573e-01
-1.80367202e-01 -5.84834158e-01 -7.34978139e-01 -6.11778080e-01
-3.48727137e-01 8.23617816e-01 -2.44229689e-01 -1.36056721... | [15.750670433044434, 5.753371715545654] |
c0756db4-8393-4af2-befa-8a48e7fac7c9 | ada-vad-unpaired-adversarial-domain | null | null | https://ieeexplore.ieee.org/document/9746755 | https://sigport.org/sites/default/files/docs/ADA-VAD_ICASSP2022_Poster_v2.pdf.pdf | ADA-VAD: Unpaired Adversarial Domain Adaptation for Noise-Robust Voice Activity Detection | Voice Activity Detection (VAD) is becoming an essential front-end component in various speech processing systems. As those systems are commonly deployed in environments with diverse noise types and low signal-to-noise ratios (SNRs), an effective VAD method should perform robust detection of speech region out of noisy b... | ['Jong Hwan Ko', 'Jiho Chang', 'Taesoo Kim'] | 2022-04-22 | null | null | null | icassp-2022-4 | ['action-detection', 'activity-detection'] | ['computer-vision', 'computer-vision'] | [ 1.06653765e-01 -4.21190500e-01 3.90702605e-01 -1.28268853e-01
-1.22431636e+00 -5.77392340e-01 5.19427001e-01 -3.16013455e-01
-2.19718024e-01 6.11680388e-01 4.51076835e-01 -3.69637281e-01
2.32009083e-01 -4.66876447e-01 -3.46456915e-01 -8.75910103e-01
1.41802937e-01 -1.00208297e-01 1.54575318e-01 -9.81308073... | [14.903179168701172, 6.103391647338867] |
b91c77e4-6267-44ff-a3f1-912116173966 | simultaneous-fidelity-and-regularization | 1804.04522 | null | https://arxiv.org/abs/1804.04522v4 | https://arxiv.org/pdf/1804.04522v4.pdf | Simultaneous Fidelity and Regularization Learning for Image Restoration | Most existing non-blind restoration methods are based on the assumption that a precise degradation model is known. As the degradation process can only be partially known or inaccurately modeled, images may not be well restored. Rain streak removal and image deconvolution with inaccurate blur kernels are two representat... | ['Ming-Hsuan Yang', 'WangMeng Zuo', 'David Zhang', 'Lei Zhang', 'Dongwei Ren'] | 2018-04-12 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 3.10284048e-01 -5.99390209e-01 3.44277084e-01 -3.02946597e-01
-5.04639566e-01 -3.62681836e-01 3.00807506e-01 -4.79178429e-01
6.18201564e-04 1.03044808e+00 1.61477178e-01 7.31119439e-02
-2.05199346e-01 -4.11116093e-01 -6.65621579e-01 -1.09074426e+00
4.76564199e-01 4.64741439e-02 8.64949822e-03 4.04546745... | [11.535736083984375, -2.679246425628662] |
a8459d1d-3449-4278-aff2-82b4473e4908 | incremental-learning-on-food-instance | 2306.15910 | null | https://arxiv.org/abs/2306.15910v1 | https://arxiv.org/pdf/2306.15910v1.pdf | Incremental Learning on Food Instance Segmentation | Food instance segmentation is essential to estimate the serving size of dishes in a food image. The recent cutting-edge techniques for instance segmentation are deep learning networks with impressive segmentation quality and fast computation. Nonetheless, they are hungry for data and expensive for annotation. This pape... | ['Wing-Kwong Chan', 'Chong-Wah Ngo', 'Yu Cao', 'Huu-Thanh Nguyen'] | 2023-06-28 | null | null | null | null | ['instance-segmentation', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 4.38379019e-01 3.04814965e-01 -6.46754205e-01 -8.37145030e-01
-8.43248785e-01 -5.75315833e-01 -2.44668514e-01 8.43323469e-01
-4.13429260e-01 4.02875215e-01 -1.96520224e-01 -8.60424191e-02
2.17974290e-01 -9.29789722e-01 -9.27920878e-01 -7.48719156e-01
1.26204178e-01 8.97234261e-01 2.40767188e-02 1.26168609... | [9.713847160339355, 0.5356376767158508] |
311ad86d-5e10-414d-ae3d-cc9093c31599 | isar-imaging-analysis-of-a-hypersonic-vehicle | null | null | https://ieeexplore.ieee.org/document/9552517 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9552517 | ISAR Imaging Analysis of a Hypersonic Vehicle Covered With Plasma Sheath | In this article, a hypersonic target electromagnetic (EM) scattering echo model combined with the inhomogeneous zonal medium model (IZMM) and the classical scattering
center model (SCM) is proposed with a distributed satelliteborne
array radar as the detection platform. A parallel physical
optics (PO) method is us... | ['Bian Zheng'] | 2021-10-09 | null | null | null | journal-2021-10 | ['motion-compensation'] | ['computer-vision'] | [ 0.55951536 -0.45245016 0.6713532 0.08018668 -0.34091377 -0.6057434
0.42850733 -0.96770823 -0.13593327 0.59817755 -0.27061206 -0.2914349
-0.5346201 -0.64174074 0.10941912 -1.2759154 -0.27300957 0.46700305
-0.04275592 -0.37128034 0.11698871 1.0885347 -1.482499 0.09945097
0.94781005 1.1970807 0.32... | [6.81529426574707, 1.0786495208740234] |
a1edc43b-3059-4d30-bcf4-3898b47c6a8a | combining-recurrent-convolutional-and-1 | null | null | https://openreview.net/forum?id=yWd42CWN3c | https://openreview.net/pdf?id=yWd42CWN3c | Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers | Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency. We introduce a simple sequence model inspired by control systems ... | ['Christopher Re', 'Atri Rudra', 'Tri Dao', 'Khaled Kamal Saab', 'Karan Goel', 'Isys Johnson', 'Albert Gu'] | 2021-05-21 | null | null | null | neurips-2021-12 | ['sequential-image-classification'] | ['computer-vision'] | [ 2.60353208e-01 -3.89547199e-01 -2.61033595e-01 -2.56067425e-01
-3.28732044e-01 -3.80906552e-01 6.82340026e-01 -1.51662037e-01
-6.22079492e-01 6.41236544e-01 3.52501608e-02 -8.84536922e-01
-4.05537814e-01 -5.67086577e-01 -1.01356578e+00 -7.52119005e-01
-6.30942941e-01 2.21527386e-02 -3.36285025e-01 -4.76461738... | [7.430160045623779, 3.3743021488189697] |
56807d9d-4ffa-438c-b818-19f58a69f31f | image-forensics-detecting-duplication-of | 1802.06515 | null | https://arxiv.org/abs/1802.06515v3 | https://arxiv.org/pdf/1802.06515v3.pdf | Image Forensics: Detecting duplication of scientific images with manipulation-invariant image similarity | Manipulation and re-use of images in scientific publications is a concerning problem that currently lacks a scalable solution. Current tools for detecting image duplication are mostly manual or semi-automated, despite the availability of an overwhelming target dataset for a learning-based approach. This paper addresses... | ['M. Cicconet', 'H. Elliott', 'D. Wainstock', 'M. Walsh', 'D. L. Richmond'] | 2018-02-19 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 5.37642241e-01 -3.53250772e-01 -2.71893889e-01 -3.80499661e-01
-5.27816892e-01 -7.65933454e-01 3.71840209e-01 1.83059946e-01
-5.46413779e-01 5.76305509e-01 -3.59257847e-01 -4.74622756e-01
-2.39886716e-01 -3.90428066e-01 -1.01421022e+00 -5.19536734e-01
-2.92008482e-02 2.93018103e-01 -2.49229390e-02 2.15219140... | [12.035745620727539, 0.8549147844314575] |
79c7c612-48f0-4fc9-bb36-6695735e55cd | unsupervised-image-matching-and-object | 1904.03148 | null | http://arxiv.org/abs/1904.03148v1 | http://arxiv.org/pdf/1904.03148v1.pdf | Unsupervised Image Matching and Object Discovery as Optimization | Learning with complete or partial supervision is powerful but relies on
ever-growing human annotation efforts. As a way to mitigate this serious
problem, as well as to serve specific applications, unsupervised learning has
emerged as an important field of research. In computer vision, unsupervised
learning comes in var... | ['Patrick Perez', 'Yann Lecun', 'Kai Han', 'Francis Bach', 'Jean Ponce', 'Minsu Cho', 'Huy V. Vo'] | 2019-04-05 | unsupervised-image-matching-and-object-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Vo_Unsupervised_Image_Matching_and_Object_Discovery_as_Optimization_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Vo_Unsupervised_Image_Matching_and_Object_Discovery_as_Optimization_CVPR_2019_paper.pdf | cvpr-2019-6 | ['single-object-discovery'] | ['computer-vision'] | [ 0.38673636 0.06548534 -0.465888 -0.5092344 -0.4405604 -0.49993882
0.7754321 0.18152449 -0.535016 0.32062954 -0.0683106 -0.03137114
-0.21099372 -0.4310087 -0.41867235 -0.8268449 -0.06307613 0.26528516
0.2827017 0.14087428 0.28643408 0.43445745 -1.6852611 0.09887974
0.6566105 0.9721534 0.... | [9.465348243713379, 2.3677542209625244] |
106770b0-0a19-4af6-9050-c5afb5db1817 | graph-neural-network-aided-exploratory | 2304.04497 | null | https://arxiv.org/abs/2304.04497v1 | https://arxiv.org/pdf/2304.04497v1.pdf | Graph Neural Network-Aided Exploratory Learning for Community Detection with Unknown Topology | In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often unknown, thereby rendering established community detection approaches ineffect... | ['Won-Yong Shin', 'Ming Li', 'Cong Tran', 'Yu Hou'] | 2023-04-10 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 1.27579376e-01 2.17379570e-01 -2.04673246e-01 3.78295705e-02
-2.83514678e-01 -7.41512299e-01 4.55246866e-01 6.88576043e-01
-1.76136643e-01 5.79686880e-01 8.31668545e-03 -4.11708206e-01
-4.68686193e-01 -9.15807426e-01 -5.29258370e-01 -5.16790807e-01
-9.21831369e-01 8.23140740e-01 1.16833396e-01 1.80494800... | [7.155932426452637, 5.977251052856445] |
8821419c-f5b9-4dd3-95ae-f8f3cca2796f | interpretable-machine-learning-for-science | 2305.01582 | null | https://arxiv.org/abs/2305.01582v3 | https://arxiv.org/pdf/2305.01582v3.pdf | Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl | PySR is an open-source library for practical symbolic regression, a type of machine learning which aims to discover human-interpretable symbolic models. PySR was developed to democratize and popularize symbolic regression for the sciences, and is built on a high-performance distributed back-end, a flexible search algor... | ['Miles Cranmer'] | 2023-05-02 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-1.38760209e-01 -3.24874103e-01 -2.35585675e-01 -3.17750961e-01
-8.21490228e-01 -3.97449106e-01 4.97369081e-01 -1.26610905e-01
-1.62618726e-01 8.78855407e-01 -7.46908784e-01 -5.19606113e-01
-1.15353160e-01 -6.26189351e-01 -9.99658883e-01 -8.59624922e-01
-4.09790814e-01 1.00029564e+00 -1.17279189e-02 -4.05892789... | [8.486225128173828, 6.810619354248047] |
15173198-2239-419f-afa5-b954371cff31 | 2nd-place-solution-for-visda-2021-challenge | 2110.14240 | null | https://arxiv.org/abs/2110.14240v1 | https://arxiv.org/pdf/2110.14240v1.pdf | 2nd Place Solution for VisDA 2021 Challenge -- Universally Domain Adaptive Image Recognition | The Visual Domain Adaptation (VisDA) 2021 Challenge calls for unsupervised domain adaptation (UDA) methods that can deal with both input distribution shift and label set variance between the source and target domains. In this report, we introduce a universal domain adaptation (UniDA) method by aggregating several popul... | ['Qiang Wang', 'Pengfei Xu', 'Tengfei Xing', 'Yueming Zhang', 'Xingxu Yao', 'Xiangyu Yue', 'Shanghang Zhang', 'Sicheng Zhao', 'Xiaolin Song', 'Haojin Liao'] | 2021-10-27 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [-4.53758948e-02 -2.67586678e-01 -2.18123689e-01 -2.21819788e-01
-7.64701307e-01 -9.78779197e-01 8.86296034e-01 -1.25005543e-01
-2.83470571e-01 7.87065804e-01 -9.07045156e-02 -1.22381374e-01
2.44361743e-01 -3.11900020e-01 -6.02233052e-01 -6.34586334e-01
1.55286491e-01 4.78884995e-01 5.04933000e-01 -1.34211570... | [10.153226852416992, 2.7321789264678955] |
3ac10931-05fe-4302-8fea-097fa32a1c30 | 3d-high-resolution-cardiac-segmentation | 1902.11000 | null | http://arxiv.org/abs/1902.11000v1 | http://arxiv.org/pdf/1902.11000v1.pdf | 3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders | Accurate segmentation of heart structures imaged by cardiac MR is key for the
quantitative analysis of pathology. High-resolution 3D MR sequences enable
whole-heart structural imaging but are time-consuming, expensive to acquire and
they often require long breath holds that are not suitable for patients.
Consequently, ... | ['Daniel Rueckert', "Declan P. O'Regan", 'Stuart A. Cook', 'Giacomo Tarroni', 'Juan J. Cerrolaza', 'Carlo Biffi', 'Antonio de Marvao'] | 2019-02-28 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [-7.70384967e-02 1.02196865e-01 1.88174337e-01 -4.76339608e-01
-8.40475559e-01 -5.55918276e-01 6.17304407e-02 5.32702822e-03
-4.61935431e-01 7.05489635e-01 -1.13810390e-01 -2.73171484e-01
-8.27464238e-02 -5.88775814e-01 -4.00753856e-01 -7.18175054e-01
-2.67225385e-01 1.09476840e+00 1.27589926e-01 4.33659047... | [14.002737998962402, -2.4329380989074707] |
1e8ae94e-a3e0-486d-81a1-82086b1395a5 | hierarchical-discriminative-learning-improves | 2303.01605 | null | https://arxiv.org/abs/2303.01605v1 | https://arxiv.org/pdf/2303.01605v1.pdf | Hierarchical discriminative learning improves visual representations of biomedical microscopy | Learning high-quality, self-supervised, visual representations is essential to advance the role of computer vision in biomedical microscopy and clinical medicine. Previous work has focused on self-supervised representation learning (SSL) methods developed for instance discrimination and applied them directly to image p... | ['Todd C. Hollon', 'Honglak Lee', 'Daniel A. Orringer', 'Christian W. Freudiger', 'Asadur Chowdury', 'Akhil Kondepudi', 'Xinhai Hou', 'Cheng Jiang'] | 2023-03-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_Hierarchical_Discriminative_Learning_Improves_Visual_Representations_of_Biomedical_Microscopy_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_Hierarchical_Discriminative_Learning_Improves_Visual_Representations_of_Biomedical_Microscopy_CVPR_2023_paper.pdf | cvpr-2023-1 | ['whole-slide-images'] | ['computer-vision'] | [ 7.2912419e-01 1.5383075e-01 -2.7130568e-01 -1.8346517e-01
-1.0724519e+00 -5.1778543e-01 5.6537837e-01 5.6194985e-01
-2.1623354e-01 6.3096392e-01 2.7543467e-01 -3.1786376e-01
-6.3180290e-02 -5.4646128e-01 -8.1059420e-01 -1.1987801e+00
7.2512731e-02 6.0983390e-01 -1.6645376e-02 1.4293177e-01
-3.6462851e-02... | [14.963303565979004, -2.88017201423645] |
e339cc3e-5fd9-4314-b458-7980a0791d62 | correction-of-cloud-removal-by-fusing-multi | 1707.09959 | null | http://arxiv.org/abs/1707.09959v1 | http://arxiv.org/pdf/1707.09959v1.pdf | Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images" | Remote sensing images often suffer from cloud cover. Cloud removal is
required in many applications of remote sensing images. Multitemporal-based
methods are popular and effective to cope with thick clouds. This paper
contributes to a summarization and experimental comparation of the existing
multitemporal-based method... | ['Qing Cheng', 'Zhiwei Li', 'Xinghua Li', 'Chengyue Zhang', 'Huanfeng Shen'] | 2017-07-25 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 3.69602203e-01 -1.27618325e+00 4.04017031e-01 -1.64627716e-01
-7.79774785e-01 -6.81999266e-01 3.53539139e-01 -8.82595330e-02
-3.68202299e-01 7.76425421e-01 -5.23737967e-01 -2.74559826e-01
-3.12981844e-01 -9.82962251e-01 2.82663144e-02 -1.11052012e+00
-1.02155589e-01 1.30471066e-01 4.52389956e-01 -3.10363799... | [9.766403198242188, -1.7598479986190796] |
4a57a840-9e4f-40e7-ba6d-3a8d033b5622 | arrhythmia-classifier-using-convolutional | 2202.12943 | null | https://arxiv.org/abs/2202.12943v1 | https://arxiv.org/pdf/2202.12943v1.pdf | Arrhythmia Classifier Using Convolutional Neural Network with Adaptive Loss-aware Multi-bit Networks Quantization | Cardiovascular disease (CVDs) is one of the universal deadly diseases, and the detection of it in the early stage is a challenging task to tackle. Recently, deep learning and convolutional neural networks have been employed widely for the classification of objects. Moreover, it is promising that lots of networks can be... | ['Zhi Qi', 'Hao liu', 'Junguang Huang', 'Zhiqing Li', 'Ninghao Pu', 'Ao Wang', 'Hanshi Sun'] | 2022-02-27 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 6.84813708e-02 -1.48473725e-01 -1.86291456e-01 -3.11650097e-01
-2.92603076e-01 1.20624721e-01 -3.45245123e-01 2.88884908e-01
-6.01538301e-01 7.96077788e-01 -2.79319465e-01 -2.88791806e-01
-2.53689915e-01 -9.15476024e-01 -2.42304668e-01 -7.97998726e-01
-2.74703115e-01 7.33786263e-03 1.12118889e-02 4.22691181... | [14.05824089050293, 3.232600450515747] |
66a06533-6bb0-4a47-b795-d2ce8dbcac40 | mandarin-singing-voice-synthesis-with | 2209.10446 | null | https://arxiv.org/abs/2209.10446v1 | https://arxiv.org/pdf/2209.10446v1.pdf | Mandarin Singing Voice Synthesis with Denoising Diffusion Probabilistic Wasserstein GAN | Singing voice synthesis (SVS) is the computer production of a human-like singing voice from given musical scores. To accomplish end-to-end SVS effectively and efficiently, this work adopts the acoustic model-neural vocoder architecture established for high-quality speech and singing voice synthesis. Specifically, this ... | ['Yi-Wen Liu', 'Hsin-Min Wang', 'Yu Tsao', 'Yin-Ping Cho'] | 2022-09-21 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 1.18229806e-01 3.84348482e-01 5.40673375e-01 -2.84685474e-02
-1.36004841e+00 -4.81858939e-01 3.69966984e-01 -1.02056754e+00
1.22539565e-01 6.11672521e-01 4.46245342e-01 4.35025059e-02
-3.70898545e-02 -4.77137476e-01 -6.41858339e-01 -9.55562532e-01
3.30864757e-01 2.43961826e-01 -2.20485538e-01 -2.27026701... | [15.515559196472168, 6.167105197906494] |
42b84c4e-d2f0-46f8-9b56-ce97949c57df | decomposing-normal-and-abnormal-features-of | 2011.06224 | null | https://arxiv.org/abs/2011.06224v1 | https://arxiv.org/pdf/2011.06224v1.pdf | Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval | Medical images can be decomposed into normal and abnormal features, which is considered as the compositionality. Based on this idea, we propose an encoder-decoder network to decompose a medical image into two discrete latent codes: a normal anatomy code and an abnormal anatomy code. Using these latent codes, we demonst... | ['Ryuji Hamamoto', 'Tatsuya Harada', 'Yusuke Kurose', 'Ryuichiro Hataya', 'Kazuma Kobayashi'] | 2020-11-12 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 5.62546551e-01 2.47152030e-01 -4.91149843e-01 -4.74274337e-01
-6.01885200e-01 -3.58128458e-01 5.48588336e-01 3.64126116e-01
-2.00859625e-02 1.64797723e-01 7.05029607e-01 -3.22198682e-02
-1.35793179e-01 -6.10769570e-01 -2.66446471e-01 -7.81955719e-01
-1.96998313e-01 4.30358797e-01 -5.55704534e-02 3.66047561... | [14.600654602050781, -1.742917776107788] |
05b59ae1-32e6-49cd-add8-db12073afac3 | inferring-player-location-in-sports-matches | 2302.06569 | null | https://arxiv.org/abs/2302.06569v1 | https://arxiv.org/pdf/2302.06569v1.pdf | Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations | Understanding agent behaviour in Multi-Agent Systems (MAS) is an important problem in domains such as autonomous driving, disaster response, and sports analytics. Existing MAS problems typically use uniform timesteps with observations for all agents. In this work, we analyse the problem of agent location imputation, sp... | ['Sarvapali D. Ramchurn', 'Timothy J. Norman', 'Joseph Early', 'Tim Matthews', 'Ryan J. Beal', 'Gregory Everett'] | 2023-02-13 | null | null | null | null | ['pitch-control', 'sports-analytics'] | ['audio', 'computer-vision'] | [-1.63389713e-01 -5.32250963e-02 -1.85233042e-01 1.22694537e-01
-6.96825087e-01 -6.47892833e-01 5.87293983e-01 4.50827926e-01
-7.77706206e-01 9.37318087e-01 3.09843779e-01 4.38166112e-02
-4.87363160e-01 -8.83023739e-01 -9.82386649e-01 -4.22493219e-01
-4.22430009e-01 1.09511101e+00 5.02863705e-01 -5.47606647... | [5.788835525512695, 0.6786837577819824] |
286a280b-4f71-4921-94c3-a0cc5e22768b | adversarial-continual-learning-for-multi | 2107.08751 | null | https://arxiv.org/abs/2107.08751v4 | https://arxiv.org/pdf/2107.08751v4.pdf | Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation | Deep learning for medical imaging suffers from temporal and privacy-related restrictions on data availability. To still obtain viable models, continual learning aims to train in sequential order, as and when data is available. The main challenge that continual learning methods face is to prevent catastrophic forgetting... | ['Anirban Mukhopadhyay', 'Camila Gonzalez', 'Marius Memmel'] | 2021-07-19 | null | null | null | null | ['continual-semantic-segmentation'] | ['computer-vision'] | [ 5.37909269e-01 1.96137324e-01 -3.03060144e-01 -4.62965548e-01
-8.43101025e-01 -6.09797299e-01 3.97712201e-01 4.98889416e-01
-9.64709699e-01 9.51606929e-01 8.16895738e-02 -2.98568666e-01
-1.31342128e-01 -6.19649470e-01 -9.95534301e-01 -7.25589693e-01
-4.97122929e-02 5.95089674e-01 3.44178736e-01 -4.04526219... | [14.607062339782715, -1.8672319650650024] |
eaefbb05-adc0-408c-a59b-53093432bfbe | insights-from-insurance-for-fair-machine | 2306.14624 | null | https://arxiv.org/abs/2306.14624v1 | https://arxiv.org/pdf/2306.14624v1.pdf | Insights From Insurance for Fair Machine Learning: Responsibility, Performativity and Aggregates | We argue that insurance can act as an analogon for the social situatedness of machine learning systems, hence allowing machine learning scholars to take insights from the rich and interdisciplinary insurance literature. Tracing the interaction of uncertainty, fairness and responsibility in insurance provides a fresh pe... | ['Robert C. Williamson', 'Christian Fröhlich'] | 2023-06-26 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 1.08007513e-01 7.73506820e-01 -1.02658403e+00 -6.18117392e-01
-4.41367328e-01 -4.92556542e-01 4.75907266e-01 5.52914202e-01
-4.40640360e-01 6.03707016e-01 9.49068785e-01 -8.96583736e-01
-4.31418240e-01 -5.73126078e-01 -2.72671878e-01 -2.66927660e-01
4.10830528e-01 6.19538017e-02 -7.52504349e-01 -2.74081618... | [8.806891441345215, 5.53641414642334] |
57a999b0-356c-440c-b082-785002bf4922 | umduluth-cs8761-at-semeval-2018-task-9 | 1805.10271 | null | http://arxiv.org/abs/1805.10271v1 | http://arxiv.org/pdf/1805.10271v1.pdf | UMDuluth-CS8761 at SemEval-2018 Task 9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings | Hypernym Discovery is the task of identifying potential hypernyms for a given
term. A hypernym is a more generalized word that is super-ordinate to more
specific words. This paper explores several approaches that rely on
co-occurrence frequencies of word pairs, Hearst Patterns based on regular
expressions, and word emb... | ['Ted Pedersen', 'Arshia Z. Hassan', 'Manikya S. Vallabhajosyula'] | 2018-05-25 | null | null | null | null | ['hypernym-discovery'] | ['natural-language-processing'] | [-6.52952641e-02 3.65305215e-01 -4.55725253e-01 -1.13494933e-01
4.89864312e-02 -4.32259083e-01 9.19533610e-01 7.87574887e-01
-1.03593242e+00 8.95022571e-01 3.63203824e-01 -5.46095908e-01
-5.66769302e-01 -1.14274478e+00 6.68920204e-02 -3.70488852e-01
-4.23495710e-01 9.94023442e-01 5.08015566e-02 -7.93223023... | [9.864885330200195, 8.77247428894043] |
5fa7208e-385f-43c2-ba40-7ec84d2c73cc | geometric-models-for-temporally-attributed | 2108.12239 | null | https://arxiv.org/abs/2108.12239v1 | https://arxiv.org/pdf/2108.12239v1.pdf | Geometric Models for (Temporally) Attributed Description Logics | In the search for knowledge graph embeddings that could capture ontological knowledge, geometric models of existential rules have been recently introduced. It has been shown that convex geometric regions capture the so-called quasi-chained rules. Attributed description logics (DL) have been defined to bridge the gap be... | ['Jeff Z. Pan', 'Ana Ozaki', 'Camille Bourgaux'] | 2021-08-27 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-1.27751842e-01 8.62685740e-01 -2.75313914e-01 -4.94351208e-01
1.15599565e-01 -6.30463183e-01 8.60362530e-01 5.80658019e-01
-8.98841619e-02 4.87760931e-01 2.29845688e-01 -1.78824738e-01
-9.59146321e-01 -1.39903700e+00 -6.97519839e-01 -4.24796164e-01
-5.66658974e-01 7.76705027e-01 6.63188577e-01 -5.77485085... | [8.724778175354004, 6.905439376831055] |
a0c78263-bda1-47f6-8cd6-2f99b7c3bc52 | cross-modal-learning-for-audio-visual-video | 2104.04598 | null | https://arxiv.org/abs/2104.04598v2 | https://arxiv.org/pdf/2104.04598v2.pdf | Cross-Modal learning for Audio-Visual Video Parsing | In this paper, we present a novel approach to the audio-visual video parsing (AVVP) task that demarcates events from a video separately for audio and visual modalities. The proposed parsing approach simultaneously detects the temporal boundaries in terms of start and end times of such events. We show how AVVP can benef... | ['Ganesh Ramakrishnan', 'Preethi Jyothi', 'Rishabh Dabral', 'Jayaprakash Akula', 'abhishek', 'Jatin Lamba'] | 2021-04-03 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 4.99076396e-01 2.46026561e-01 1.19003460e-01 -4.68751818e-01
-1.52857327e+00 -6.54291153e-01 7.07325935e-01 2.81775333e-02
-2.45436162e-01 3.37529987e-01 4.93034840e-01 -1.43908873e-01
9.51734185e-02 -4.25076842e-01 -1.03876400e+00 -4.30701792e-01
-5.60009897e-01 2.22177580e-01 3.19027483e-01 3.03932074... | [10.076506614685059, 1.0087207555770874] |
fd1368b4-7971-41f7-b3ff-e7df548867cf | improving-mutual-information-estimation-with-1 | 2303.06992 | null | https://arxiv.org/abs/2303.06992v1 | https://arxiv.org/pdf/2303.06992v1.pdf | Improving Mutual Information Estimation with Annealed and Energy-Based Bounds | Mutual information (MI) is a fundamental quantity in information theory and machine learning. However, direct estimation of MI is intractable, even if the true joint probability density for the variables of interest is known, as it involves estimating a potentially high-dimensional log partition function. In this work,... | ['Alireza Makhzani', 'Roger Grosse', 'Greg Ver Steeg', 'Marzyeh Ghassemi', 'Sicong Huang', 'Rob Brekelmans'] | 2023-03-13 | improving-mutual-information-estimation-with | https://openreview.net/forum?id=T0B9AoM_bFg | https://openreview.net/pdf?id=T0B9AoM_bFg | iclr-2022-4 | ['mutual-information-estimation'] | ['methodology'] | [ 3.05244207e-01 8.56346339e-02 -3.90626162e-01 -2.09212765e-01
-1.41828251e+00 -5.73676109e-01 5.08842289e-01 -1.89283371e-01
-4.52855289e-01 1.11087251e+00 -9.92791951e-02 -3.54892462e-01
-2.75476754e-01 -7.75342405e-01 -1.13445985e+00 -1.00819242e+00
-8.49024057e-02 8.66799414e-01 3.59772108e-02 1.89879745... | [7.127038955688477, 3.9796037673950195] |
c496fdd8-16ae-45a3-a28b-9ead4f19d41b | sparsely-constrained-neural-networks-for | 2011.04336 | null | https://arxiv.org/abs/2011.04336v2 | https://arxiv.org/pdf/2011.04336v2.pdf | Sparsely constrained neural networks for model discovery of PDEs | Sparse regression on a library of candidate features has developed as the prime method to discover the partial differential equation underlying a spatio-temporal data-set. These features consist of higher order derivatives, limiting model discovery to densely sampled data-sets with low noise. Neural network-based appro... | ['Remy Kusters', 'Gijs Vermarien', 'Gert-Jan Both'] | 2020-11-09 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-4.43859175e-02 -4.57042605e-02 -3.89713168e-01 -2.99657822e-01
-7.88730383e-01 -3.30109566e-01 5.68887353e-01 -2.78379053e-01
-1.38414726e-01 8.79403234e-01 2.16321334e-01 -1.11000217e-01
-4.65189040e-01 -4.84349310e-01 -6.80257261e-01 -7.20073700e-01
-3.66970211e-01 2.82700330e-01 -1.09303802e-01 -1.08635955... | [6.63385534286499, 3.5245778560638428] |
1c38895a-8083-459d-9bdd-33ff62b270e1 | allo-centric-occupancy-grid-prediction-for | 2301.04454 | null | https://arxiv.org/abs/2301.04454v1 | https://arxiv.org/pdf/2301.04454v1.pdf | Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks | Prediction of dynamic environment is crucial to safe navigation of an autonomous vehicle. Urban traffic scenes are particularly challenging to forecast due to complex interactions between various dynamic agents, such as vehicles and vulnerable road users. Previous approaches have used egocentric occupancy grid maps to ... | ['Christian Laugier', 'Anne Spalanzani', 'Lukas Rummelhard', 'Rabbia Asghar'] | 2023-01-11 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [-2.22995758e-01 1.24417461e-01 1.43808335e-01 -2.86867827e-01
1.11759022e-01 -2.66104043e-01 9.12756681e-01 -1.28670752e-01
-2.22625777e-01 9.72386658e-01 2.45510787e-01 -2.03956679e-01
1.97288021e-02 -1.12621582e+00 -6.45501375e-01 -6.47138298e-01
-1.45832241e-01 6.20060205e-01 1.03491974e+00 -3.15364987... | [5.9248223304748535, 0.8181715607643127] |
62efb6ee-8efd-41a8-9b0b-8cb2a3653793 | precise-stock-price-prediction-for-optimized | 2203.01326 | null | https://arxiv.org/abs/2203.01326v1 | https://arxiv.org/pdf/2203.01326v1.pdf | Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model | Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio of stocks with the identification of proper weights of allocation to achieve the optimized values of return and risk. We present optimized portfolios based on the seven sectors of the... | ['Saikat Mondal', 'Abhishek Dutta', 'Sidra Mehtab', 'Jaydip Sen'] | 2022-03-02 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-5.88321924e-01 -6.78011850e-02 -2.93945849e-01 -1.35643795e-01
-4.97955233e-01 -7.40144253e-01 7.02035666e-01 -3.08830440e-01
-3.30553025e-01 8.09370518e-01 5.09418070e-01 -5.94021857e-01
-4.24130797e-01 -1.12198281e+00 -5.31012356e-01 -4.41123039e-01
-3.18002015e-01 1.92317888e-01 -1.05991244e-01 -2.29243953... | [4.5837016105651855, 4.109222888946533] |
92cd9d70-46f3-4093-a4d8-502c940a874a | deep-stable-multi-interest-learning-for-out | 2304.05615 | null | https://arxiv.org/abs/2304.05615v1 | https://arxiv.org/pdf/2304.05615v1.pdf | Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation | Recently, multi-interest models, which extract interests of a user as multiple representation vectors, have shown promising performances for sequential recommendation. However, none of existing multi-interest recommendation models consider the Out-Of-Distribution (OOD) generalization problem, in which interest distribu... | ['Liang Wang', 'Shu Wu', 'Zhenxi Zhu', 'Zhaocheng Liu', 'Qiang Liu'] | 2023-04-12 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [-7.18188137e-02 -2.48349145e-01 -2.66875803e-01 -4.71779346e-01
-7.17547536e-01 -2.33094409e-01 3.74660641e-01 -3.75228599e-02
-8.34764466e-02 8.37172389e-01 5.11946917e-01 2.50915527e-01
-2.54890382e-01 -8.32507968e-01 -6.16832495e-01 -7.29587615e-01
-2.36515269e-01 3.46429974e-01 1.00370750e-01 -1.53831556... | [10.168803215026855, 5.557633876800537] |
f4f0f26b-c43f-44f0-9e12-daaca0cf5374 | slim-u-net-efficient-anatomical-feature | 2302.11524 | null | https://arxiv.org/abs/2302.11524v1 | https://arxiv.org/pdf/2302.11524v1.pdf | Slim U-Net: Efficient Anatomical Feature Preserving U-net Architecture for Ultrasound Image Segmentation | We investigate the applicability of U-Net based models for segmenting Urinary Bladder (UB) in male pelvic view UltraSound (US) images. The segmentation of UB in the US image aids radiologists in diagnosing the UB. However, UB in US images has arbitrary shapes, indistinct boundaries and considerably large inter- and int... | ['Subir Kumar Saha', 'SH Chandrashekhara', 'Kashish Verma', 'Deepak Raina'] | 2023-02-22 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 3.14227045e-01 5.56321740e-01 -1.45151585e-01 -2.49858737e-01
-4.05459493e-01 -5.54767728e-01 -1.17775232e-01 -7.60555565e-02
-4.63453114e-01 5.36925495e-01 -2.09250942e-01 -5.92844188e-01
-7.47298077e-02 -6.82209373e-01 -8.31648648e-01 -6.20976210e-01
-2.25184828e-01 2.17696741e-01 3.94001245e-01 -5.40470593... | [14.541326522827148, -2.6660821437835693] |
4147e864-d338-4b2e-8419-3a439835db6a | coda-an-end-to-end-neural-program-decompiler | null | null | http://papers.nips.cc/paper/8628-coda-an-end-to-end-neural-program-decompiler | http://papers.nips.cc/paper/8628-coda-an-end-to-end-neural-program-decompiler.pdf | Coda: An End-to-End Neural Program Decompiler | Reverse engineering of binary executables is a critical problem in the computer security domain. On the one hand, malicious parties may recover interpretable source codes from the software products to gain commercial advantages. On the other hand, binary decompilation can be leveraged for code vulnerability analysis an... | ['Haolan Liu', 'Yuandong Tian', 'Huili Chen', 'Farinaz Koushanfar', 'Xinyun Chen', 'Jishen Zhao', 'Cheng Fu'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['computer-security'] | ['miscellaneous'] | [ 4.95869339e-01 -1.23047881e-01 -7.27289855e-01 5.17877042e-02
-6.93628132e-01 -8.33368361e-01 2.08942682e-01 7.00982660e-02
1.15021087e-01 2.32351556e-01 -1.84918106e-01 -1.31277800e+00
4.57441509e-01 -7.52487004e-01 -1.06413591e+00 -1.58228859e-01
1.66462943e-01 -2.03174483e-02 3.00987512e-01 -1.91891909... | [7.058852195739746, 7.820750713348389] |
9707fede-911e-448e-b0e4-0447f1f50ebe | steadyflow-spatially-smooth-optical-flow-for | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Liu_SteadyFlow_Spatially_Smooth_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Liu_SteadyFlow_Spatially_Smooth_2014_CVPR_paper.pdf | SteadyFlow: Spatially Smooth Optical Flow for Video Stabilization | We propose a novel motion model, SteadyFlow, to represent the motion between neighboring video frames for stabilization. A SteadyFlow is a specific optical flow by enforcing strong spatial coherence, such that smoothing feature trajectories can be replaced by smoothing pixel profiles, which are motion vectors collected... | ['Shuaicheng Liu', 'Ping Tan', 'Jian Sun', 'Lu Yuan'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['video-stabilization'] | ['computer-vision'] | [-1.52351066e-01 -4.32128757e-01 -2.88046986e-01 3.32852788e-02
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-6.16512299e-02 -6.68277204e-01 7.16897130e-01 1.81455025... | [10.650837898254395, -1.4319148063659668] |
1729cb9d-9291-4086-8828-e2bbc275146c | spcl-a-new-framework-for-domain-adaptive | 2111.12358 | null | https://arxiv.org/abs/2111.12358v2 | https://arxiv.org/pdf/2111.12358v2.pdf | SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive Learning | Although there is significant progress in supervised semantic segmentation, it remains challenging to deploy the segmentation models to unseen domains due to domain biases. Domain adaptation can help in this regard by transferring knowledge from a labeled source domain to an unlabeled target domain. Previous methods ty... | ['Mingjia Li', 'Shuang Li', 'Binhui Xie'] | 2021-11-24 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 4.69526738e-01 2.96220090e-03 -5.07779717e-01 -6.71835601e-01
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5.10737598e-01 5.31050503e-01 4.42318290e-01 7.97441825... | [9.676398277282715, 1.306711196899414] |
5251eeaf-ff48-49ce-9ec2-4103c79b9bba | graph-boosted-active-learning-for-multi | null | null | https://link.springer.com/chapter/10.1007%2F978-3-030-88361-4_11 | https://link.springer.com/content/pdf/10.1007%2F978-3-030-88361-4_11.pdf | Graph-boosted Active Learning for Multi-Source Entity Resolution | Supervised entity resolution methods rely on labeled record pairs for learning matching patterns between two or more data sources. Active learning minimizes the labeling effort by selecting informative pairs for labeling. The existing active learning methods for entity resolution all target two-source matching scenario... | ['Christian Bizer', 'Anna Primpeli'] | 2021-09-30 | null | null | null | international-semantic-web-conference-2021-9 | ['entity-resolution'] | ['natural-language-processing'] | [ 2.02749759e-01 6.67397499e-01 -1.27816129e+00 -5.03972590e-01
-1.69985485e+00 -4.74112362e-01 6.02639139e-01 9.76734042e-01
-3.79807204e-01 8.70121419e-01 3.30525666e-01 1.27089605e-01
-5.19812286e-01 -8.24465275e-01 -8.78242671e-01 -9.06962156e-02
-2.48852074e-01 8.15737307e-01 4.45930004e-01 -1.56939045... | [9.364476203918457, 8.618194580078125] |
222c6d5d-a661-4132-b3da-57e70890e4a0 | tyolov5-a-temporal-yolov5-detector-based-on | 2111.08867 | null | https://arxiv.org/abs/2111.08867v2 | https://arxiv.org/pdf/2111.08867v2.pdf | TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video | Timely handgun detection is a crucial problem to improve public safety; nevertheless, the effectiveness of many surveillance systems still depends of finite human attention. Much of the previous research on handgun detection is based on static image detectors, leaving aside valuable temporal information that could be u... | ['Leonardo Chang', 'Cuauhtemoc Daniel Suarez-Ramirez', 'Miguel Gonzalez-Mendoza', 'Mario Alberto Duran-Vega'] | 2021-11-17 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 1.51328044e-02 -1.40252665e-01 -1.85311139e-01 1.12077389e-02
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-6.14628255e-01 2.75183648e-01 -1.03427604e-01 -2.72825330e-01
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-1.59819156e-01 -1.26896063e-02 7.60235846e-01 -3.35913062... | [8.057464599609375, 0.6200469732284546] |
29129db4-3a4c-4b18-8617-12076f40f7bb | a-normalized-gaussian-wasserstein-distance | 2110.13389 | null | https://arxiv.org/abs/2110.13389v2 | https://arxiv.org/pdf/2110.13389v2.pdf | A Normalized Gaussian Wasserstein Distance for Tiny Object Detection | Detecting tiny objects is a very challenging problem since a tiny object only contains a few pixels in size. We demonstrate that state-of-the-art detectors do not produce satisfactory results on tiny objects due to the lack of appearance information. Our key observation is that Intersection over Union (IoU) based metri... | ['Lei Yu', 'Wen Yang', 'Chang Xu', 'Jinwang Wang'] | 2021-10-26 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-2.31781334e-01 -1.61539257e-01 5.44746742e-02 -2.33741790e-01
-9.07542527e-01 -5.36816537e-01 3.45600128e-01 3.19144189e-01
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3.16238075e-01 -6.94263995e-01 -8.04076552e-01 -6.75207913e-01
8.02786276e-02 2.46136904e-01 9.40843761e-01 -2.19353363... | [8.688347816467285, -0.44020959734916687] |
477c287f-f9fe-4053-9f65-5744d46ca2ef | cross-task-attention-mechanism-for-dense | 2206.08927 | null | https://arxiv.org/abs/2206.08927v1 | https://arxiv.org/pdf/2206.08927v1.pdf | Cross-task Attention Mechanism for Dense Multi-task Learning | Multi-task learning has recently become a promising solution for a comprehensive understanding of complex scenes. Not only being memory-efficient, multi-task models with an appropriate design can favor exchange of complementary signals across tasks. In this work, we jointly address 2D semantic segmentation, and two geo... | ['Raoul de Charette', 'Tuan-Hung Vu', 'Ivan Lopes'] | 2022-06-17 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 1.85046792e-01 -2.31315896e-01 1.02171630e-01 -6.30676448e-01
-1.22274649e+00 -3.40712219e-01 6.41809762e-01 1.35805467e-02
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-6.82400391e-02 -5.15011847e-01 -9.32360470e-01 -5.08377254e-01
-3.49028893e-02 4.33484763e-01 2.98478603e-01 4.12053987... | [9.626075744628906, 1.2132996320724487] |
6d59123a-2d3e-4e8e-b4eb-790844f53f3b | bigcolor-colorization-using-a-generative | 2207.09685 | null | https://arxiv.org/abs/2207.09685v1 | https://arxiv.org/pdf/2207.09685v1.pdf | BigColor: Colorization using a Generative Color Prior for Natural Images | For realistic and vivid colorization, generative priors have recently been exploited. However, such generative priors often fail for in-the-wild complex images due to their limited representation space. In this paper, we propose BigColor, a novel colorization approach that provides vivid colorization for diverse in-the... | ['Sunghyun Cho', 'Seung-Hwan Baek', 'Jonghyun Kim', 'Sehoon Kim', 'Hwayoon Lee', 'Seongtae Kim', 'Kyoungkook Kang', 'Geonung Kim'] | 2022-07-20 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 5.27521372e-01 9.30314660e-02 2.45098367e-01 -1.92121446e-01
-6.04630530e-01 -7.74853885e-01 5.94000518e-01 -6.35367990e-01
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5.17589450e-01 -8.86789057e-03 -8.11651051e-02 -2.08145767... | [11.507645606994629, -0.8668014407157898] |
2e499fa8-631a-496f-be37-b77f4585ca17 | extrapolative-controlled-sequence-generation | 2303.04562 | null | https://arxiv.org/abs/2303.04562v3 | https://arxiv.org/pdf/2303.04562v3.pdf | Extrapolative Controlled Sequence Generation via Iterative Refinement | We study the problem of extrapolative controlled generation, i.e., generating sequences with attribute values beyond the range seen in training. This task is of significant importance in automated design, especially drug discovery, where the goal is to design novel proteins that are \textit{better} (e.g., more stable) ... | ['Ankur P. Parikh', 'He He', 'Richard Yuanzhe Pang', 'Vishakh Padmakumar'] | 2023-03-08 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 7.34824955e-01 1.58609927e-01 -1.26300976e-01 -3.72983366e-01
-7.20730186e-01 -8.78490150e-01 3.54639649e-01 3.73626590e-01
-2.56290495e-01 1.49552798e+00 -1.23182155e-01 -4.85796720e-01
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1.24612093e-01 6.90874219e-01 -5.68303801e-02 -5.25470674... | [4.726588249206543, 5.601823806762695] |
41cc94c4-2ff6-4881-ba81-fdc807562956 | humans-in-humans-out-on-gpt-converging-toward | 2303.17276 | null | https://arxiv.org/abs/2303.17276v1 | https://arxiv.org/pdf/2303.17276v1.pdf | Humans in Humans Out: On GPT Converging Toward Common Sense in both Success and Failure | Increase in computational scale and fine-tuning has seen a dramatic improvement in the quality of outputs of large language models (LLMs) like GPT. Given that both GPT-3 and GPT-4 were trained on large quantities of human-generated text, we might ask to what extent their outputs reflect patterns of human thinking, both... | ['Vincent Wang-Maścianica', 'Philipp Koralus'] | 2023-03-30 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-5.72639257e-02 7.32121348e-01 2.85435438e-01 -3.70321542e-01
-4.94179875e-01 -5.88932097e-01 9.17457461e-01 1.10370211e-01
-2.77534723e-01 6.79394901e-01 5.40769219e-01 -8.32867563e-01
-4.31106687e-01 -9.97981191e-01 -6.94001496e-01 -2.91310847e-01
2.22304195e-01 8.45054686e-01 4.12595719e-02 -5.30683041... | [9.742290496826172, 7.506002902984619] |
7a8bc600-3b76-401e-b00b-a85a82f319c3 | towards-clustering-friendly-representations | 2106.09874 | null | https://arxiv.org/abs/2106.09874v1 | https://arxiv.org/pdf/2106.09874v1.pdf | Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering | Finding a suitable data representation for a specific task has been shown to be crucial in many applications. The success of subspace clustering depends on the assumption that the data can be separated into different subspaces. However, this simple assumption does not always hold since the raw data might not be separab... | ['Ling Tian', 'Guangchun Luo', 'Zhao Kang', 'Zhengrui Ma'] | 2021-06-18 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-7.12807663e-03 -2.50250399e-01 -1.56057671e-01 -2.75388986e-01
-4.26633894e-01 -7.78698504e-01 4.69686061e-01 1.83540300e-01
-1.06404208e-01 4.62948419e-02 4.48940098e-01 -2.45393328e-02
-3.24498773e-01 -5.73833346e-01 -3.89678091e-01 -1.19081044e+00
-7.63191581e-02 2.71266490e-01 -2.90046372e-02 1.76010564... | [7.968088626861572, 4.10377311706543] |
ad23964e-263c-4fff-941e-26e3df0821e1 | probabilistic-robust-linear-quadratic | 2105.07668 | null | https://arxiv.org/abs/2105.07668v2 | https://arxiv.org/pdf/2105.07668v2.pdf | Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes | Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. While learning-based control has the potential to yield superior performance in demanding applications, robustness to uncertainty remains an important challenge. Si... | ['Sebastian Trimpe', 'Matthias Neumann-Brosig', 'Alexander von Rohr'] | 2021-05-17 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.00433946e-01 2.28903458e-01 -1.96783125e-01 1.48794994e-01
-1.10981536e+00 -7.58109987e-01 5.95969379e-01 3.10854226e-01
-1.03222296e-01 1.08197260e+00 -2.08972827e-01 -4.32736993e-01
-8.57847691e-01 -6.96281195e-01 -8.06994140e-01 -1.06043017e+00
-3.38481106e-02 2.24521637e-01 2.19635502e-01 6.34889528... | [5.076535224914551, 2.4790050983428955] |
bcb4a796-130e-46c3-92d5-a2b4994a63c5 | sun-exploring-intrinsic-uncertainties-in-text | 2209.06442 | null | https://arxiv.org/abs/2209.06442v2 | https://arxiv.org/pdf/2209.06442v2.pdf | SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers | This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncertainty perspective, it is indisputable that a single SQL can be learned from multiple semantically-equivalent questions.Different from previo... | ['Yongbin Li', 'Min Yang', 'Luo Si', 'Fei Huang', 'Binhua Li', 'Xiangpeng Wei', 'Bowen Li', 'Binyuan Hui', 'Lihan Wang', 'Bowen Qin'] | 2022-09-14 | null | https://aclanthology.org/2022.coling-1.471 | https://aclanthology.org/2022.coling-1.471.pdf | coling-2022-10 | ['text-to-sql'] | ['computer-code'] | [ 1.27830684e-01 4.58907962e-01 -4.11451273e-02 -9.40153658e-01
-1.22663569e+00 -8.33610833e-01 1.00049399e-01 2.15390697e-01
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4.09748346e-01 5.47168076e-01 2.22125039e-01 -7.93541074... | [10.02431869506836, 7.916052341461182] |
96fe82cc-4620-4d3f-9eca-1066889ff1a5 | negation-detection-in-dutch-clinical-texts-an | 2209.00470 | null | https://arxiv.org/abs/2209.00470v1 | https://arxiv.org/pdf/2209.00470v1.pdf | Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods | As structured data are often insufficient, labels need to be extracted from free text in electronic health records when developing models for clinical information retrieval and decision support systems. One of the most important contextual properties in clinical text is negation, which indicates the absence of findings... | ['Saskia Haitjema', 'Miguel A. R. Rios', 'Sebastiaan R. S. Arends', 'Myrthe M. Hemker', 'Marijn Schraagen', 'Sander C. Tan', 'Leon C. Reteig', 'Bram van Es'] | 2022-09-01 | null | null | null | null | ['negation-detection'] | ['natural-language-processing'] | [ 4.00249213e-01 2.13039353e-01 -3.38018984e-01 -4.19911802e-01
-1.16071749e+00 -6.35991454e-01 3.26227307e-01 1.12574637e+00
-8.35923791e-01 8.63917112e-01 3.71321678e-01 -8.55824172e-01
-4.64861631e-01 -5.58081508e-01 -2.07576826e-01 -3.99683177e-01
9.78685245e-02 6.56676412e-01 3.42876285e-01 -8.35702196... | [8.44442367553711, 8.759385108947754] |
2f053290-1cb1-4b58-9ec3-d2d685107e4e | a-survey-of-deep-visual-cross-domain-few-shot | 2303.09253 | null | https://arxiv.org/abs/2303.09253v1 | https://arxiv.org/pdf/2303.09253v1.pdf | A Survey of Deep Visual Cross-Domain Few-Shot Learning | Few-Shot transfer learning has become a major focus of research as it allows recognition of new classes with limited labeled data. While it is assumed that train and test data have the same data distribution, this is often not the case in real-world applications. This leads to decreased model transfer effects when the ... | ['Zhaoxiang Zhang', 'Zhi Gong', 'Junsong Fan', 'Yuxi Wang', 'Lijuan Duan', 'Wenjian Wang'] | 2023-03-16 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 1.42334074e-01 -1.21804193e-01 -6.16226673e-01 -9.35186148e-01
-1.04118013e+00 -5.97563267e-01 3.40286314e-01 -8.17271024e-02
-1.39172539e-01 7.88414419e-01 -2.17433885e-01 -4.12040532e-01
-1.89993940e-02 -8.56028676e-01 -6.18748248e-01 -6.07485056e-01
6.03940003e-02 6.95229828e-01 6.68538928e-01 3.91251221... | [9.95096206665039, 2.8570895195007324] |
39d4ab56-bbc7-49ca-8cb6-aa69ca7fecc7 | samsung-r-d-institute-poland-submission-to | null | null | https://aclanthology.org/2021.wat-1.27 | https://aclanthology.org/2021.wat-1.27.pdf | Samsung R&D Institute Poland submission to WAT 2021 Indic Language Multilingual Task | This paper describes the submission to the WAT 2021 Indic Language Multilingual Task by Samsung R&D Institute Poland. The task covered translation between 10 Indic Languages (Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil and Telugu) and English. We combined a variety of techniques: transl... | ['Paweł Przybysz', 'Marcin Chochowski', 'Marcin Szymański', 'Adam Dobrowolski'] | null | null | null | null | acl-wat-2021-8 | ['transliteration'] | ['natural-language-processing'] | [-3.93300727e-02 -1.57812595e-01 -2.86688730e-02 -2.79113322e-01
-1.41127467e+00 -1.09878409e+00 1.01858890e+00 -1.22313976e-01
-5.54148853e-01 1.30573189e+00 2.76555240e-01 -9.47234392e-01
1.55271128e-01 -3.45941335e-01 -6.56526327e-01 -2.80562162e-01
1.91481590e-01 1.25440955e+00 -4.15254757e-02 -7.38993406... | [11.443184852600098, 10.46853256225586] |
f0114910-f1e4-4755-b07f-04444cb6e987 | polyu-cbs-comp-at-semeval-2021-task-1-lexical | null | null | https://aclanthology.org/2021.semeval-1.70 | https://aclanthology.org/2021.semeval-1.70.pdf | PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP) | In this contribution, we describe the system presented by the PolyU CBS-Comp Team at the Task 1 of SemEval 2021, where the goal was the estimation of the complexity of words in a given sentence context. Our top system, based on a combination of lexical, syntactic, word embeddings and Transformers-derived features and o... | ['Chu-Ren Huang', 'Qin Lu', 'Wenjie Li', 'Emmanuele Chersoni', 'Jinghang Gu', 'Rong Xiang'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-2.94366956e-01 -4.10916768e-02 -5.82193397e-02 -5.12488008e-01
-7.07317770e-01 -4.17633325e-01 7.37680316e-01 6.43794179e-01
-1.04072917e+00 5.46846449e-01 4.86713797e-01 -4.62998927e-01
1.52106553e-01 -3.06671649e-01 -3.38523626e-01 -1.46952942e-01
-1.37743264e-01 2.30821207e-01 1.52649134e-01 -5.66985071... | [10.56201171875, 10.34835147857666] |
41f0b08e-5d03-4591-8467-aeffecf677d9 | exploiting-the-intrinsic-neighborhood | 2110.04202 | null | https://arxiv.org/abs/2110.04202v3 | https://arxiv.org/pdf/2110.04202v3.pdf | Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation | Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem... | ['Shangling Jui', 'Luis Herranz', 'Joost Van de Weijer', 'Yaxing Wang', 'Shiqi Yang'] | 2021-10-08 | null | http://proceedings.neurips.cc/paper/2021/hash/f5deaeeae1538fb6c45901d524ee2f98-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/f5deaeeae1538fb6c45901d524ee2f98-Paper.pdf | neurips-2021-12 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 4.67398949e-02 -1.74541265e-01 -4.63314831e-01 -5.14913738e-01
-6.88831925e-01 -7.42250264e-01 4.03845131e-01 1.24871977e-01
-2.75161296e-01 7.80384660e-01 2.77912647e-01 1.83784127e-01
-1.09964453e-01 -7.87235677e-01 -8.25291038e-01 -9.22669411e-01
2.80291140e-01 3.91515255e-01 1.98886558e-01 -3.94317973... | [10.335724830627441, 3.0685224533081055] |
302caf28-1b2d-4916-9330-a68621bdb587 | iterative-proposal-refinement-for-weakly | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cao_Iterative_Proposal_Refinement_for_Weakly-Supervised_Video_Grounding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cao_Iterative_Proposal_Refinement_for_Weakly-Supervised_Video_Grounding_CVPR_2023_paper.pdf | Iterative Proposal Refinement for Weakly-Supervised Video Grounding | Weakly-Supervised Video Grounding (WSVG) aims to localize events of interest in untrimmed videos with only video-level annotations. To date, most of the state-of-the-art WSVG methods follow a two-stage pipeline, i.e., firstly generating potential temporal proposals and then grounding with these proposal candidates.... | ['Daxin Jiang', 'Tao Shen', 'Yuexian Zou', 'Can Zhang', 'Long Chen', 'Xiubo Geng', 'Can Xu', 'Fangyun Wei', 'Meng Cao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-grounding'] | ['computer-vision'] | [ 3.84436250e-01 1.44044846e-01 -3.20501238e-01 -4.39389467e-01
-6.81937993e-01 -2.39328623e-01 5.87513149e-01 3.61091733e-01
-4.75189984e-01 5.71000934e-01 2.93033242e-01 7.07507282e-02
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1.74318731e-01 2.13157862e-01 8.04125071e-01 2.19783410... | [9.423538208007812, 0.729713499546051] |
61564f6c-74ca-4e32-a711-044086bab52e | spatial-separated-curve-rendering-network-for | 2109.05750 | null | https://arxiv.org/abs/2109.05750v4 | https://arxiv.org/pdf/2109.05750v4.pdf | Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization | Image harmonization aims to modify the color of the composited region with respect to the specific background. Previous works model this task as a pixel-wise image-to-image translation using UNet family structures. However, the model size and computational cost limit the ability of their models on edge devices and high... | ['Jue Wang', 'Chi-Man Pun', 'Xiaodong Cun', 'Jingtang Liang'] | 2021-09-13 | null | null | null | null | ['image-harmonization', '2048'] | ['computer-vision', 'playing-games'] | [ 3.25159132e-01 -5.09079061e-02 2.07912534e-01 -2.11045191e-01
-8.02155614e-01 -4.42548901e-01 2.55359709e-01 -2.72501200e-01
-2.92988539e-01 4.50420916e-01 -2.55715132e-01 -2.67236292e-01
9.16059613e-02 -1.03256464e+00 -8.32802713e-01 -6.69554651e-01
3.66763830e-01 3.64557430e-02 6.55956566e-01 -3.69335949... | [11.072680473327637, -1.5495492219924927] |
7537d350-fa95-4ff3-ac12-41fd944eecca | towards-deep-attention-in-graph-neural | 2306.02376 | null | https://arxiv.org/abs/2306.02376v1 | https://arxiv.org/pdf/2306.02376v1.pdf | Towards Deep Attention in Graph Neural Networks: Problems and Remedies | Graph neural networks (GNNs) learn the representation of graph-structured data, and their expressiveness can be further enhanced by inferring node relations for propagation. Attention-based GNNs infer neighbor importance to manipulate the weight of its propagation. Despite their popularity, the discussion on deep graph... | ['Kijung Shin', 'Jaemin Yoo', 'Fanchen Bu', 'Soo Yong Lee'] | 2023-06-04 | null | null | null | null | ['deep-attention', 'graph-attention', 'deep-attention'] | ['computer-vision', 'graphs', 'natural-language-processing'] | [-2.09837437e-01 5.30619204e-01 -2.10862398e-01 2.39265244e-02
-7.22317845e-02 -2.95125246e-01 5.41936696e-01 3.50213021e-01
-1.37837842e-01 5.86873949e-01 2.70889640e-01 -6.78086281e-01
-2.57174999e-01 -1.06673014e+00 -7.25486755e-01 -5.46362460e-01
-5.29335976e-01 3.17408413e-01 1.80678591e-01 -5.02425194... | [6.980536460876465, 6.231955051422119] |
82798c3b-8c70-43a5-a2e6-a39a6f57d322 | an-integrated-platform-for-live-3d-human | 1712.03084 | null | http://arxiv.org/abs/1712.03084v1 | http://arxiv.org/pdf/1712.03084v1.pdf | An Integrated Platform for Live 3D Human Reconstruction and Motion Capturing | The latest developments in 3D capturing, processing, and rendering provide
means to unlock novel 3D application pathways. The main elements of an
integrated platform, which target tele-immersion and future 3D applications,
are described in this paper, addressing the tasks of real-time capturing,
robust 3D human shape/a... | ['IEEE', 'Senior Member', 'Georgios Louizis', 'Dimitrios Zarpalas', 'Petros Daras', 'Olga Zoidi', 'Dimitrios S. Alexiadis', 'Nikolaos Zioulis', 'Anargyros Chatzitofis'] | 2017-12-08 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [ 3.34071279e-01 -2.01055676e-01 2.23549664e-01 -1.92394704e-01
-5.17172039e-01 -1.01414233e-01 2.11796433e-01 8.57990459e-02
-5.44433832e-01 3.33758831e-01 -2.13634241e-02 1.92678332e-01
-1.77375555e-01 -6.48015320e-01 -2.75313526e-01 -4.69802886e-01
1.86280627e-02 6.58674598e-01 5.13722360e-01 -3.58713120... | [7.2633490562438965, -1.042111873626709] |
b06c38bd-b4a0-49c9-b5b8-97a39e19b6db | dialoguernn-an-attentive-rnn-for-emotion | 1811.00405 | null | https://arxiv.org/abs/1811.00405v4 | https://arxiv.org/pdf/1811.00405v4.pdf | DialogueRNN: An Attentive RNN for Emotion Detection in Conversations | Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc. Currently, systems do not treat the parties in the conversation individually b... | ['Soujanya Poria', 'Navonil Majumder', 'Erik Cambria', 'Devamanyu Hazarika', 'Rada Mihalcea', 'Alexander Gelbukh'] | 2018-11-01 | null | null | null | null | ['multimodal-emotion-recognition', 'emotion-recognition-in-conversation', 'multimodal-emotion-recognition'] | ['computer-vision', 'natural-language-processing', 'speech'] | [ 4.71084751e-02 1.48062631e-01 -3.00459713e-01 -6.49563670e-01
-4.53309745e-01 -5.51209092e-01 7.39044785e-01 4.45477426e-01
-2.06547156e-01 6.44340098e-01 6.44051611e-01 -3.14495414e-01
5.49897730e-01 -5.83882511e-01 -1.59573015e-02 -6.30403936e-01
2.83219386e-02 3.75080317e-01 8.47032145e-02 -7.34058499... | [12.982868194580078, 6.226597309112549] |
ddf3e7c2-e020-4dcb-8223-94c1247dac13 | fast-vid2vid-spatial-temporal-compression-for | 2207.05049 | null | https://arxiv.org/abs/2207.05049v1 | https://arxiv.org/pdf/2207.05049v1.pdf | Fast-Vid2Vid: Spatial-Temporal Compression for Video-to-Video Synthesis | Video-to-Video synthesis (Vid2Vid) has achieved remarkable results in generating a photo-realistic video from a sequence of semantic maps. However, this pipeline suffers from high computational cost and long inference latency, which largely depends on two essential factors: 1) network architecture parameters, 2) sequen... | ['Ziwei Liu', 'Wayne Wu', 'Shikai Li', 'Guangcong Wang', 'Long Zhuo'] | 2022-07-11 | null | null | null | null | ['video-to-video-synthesis', 'motion-compensation'] | ['computer-vision', 'computer-vision'] | [ 2.39041865e-01 -1.02955863e-01 -9.10629928e-02 -2.08447918e-01
-5.37001371e-01 -1.44202322e-01 6.97253704e-01 -4.03315604e-01
-3.78200799e-01 7.27961838e-01 1.76381052e-01 -3.19961727e-01
7.34462216e-02 -1.04490995e+00 -9.07239199e-01 -5.76206863e-01
1.98978364e-01 2.95964450e-01 4.84852105e-01 1.52623415... | [10.768903732299805, -0.9170828461647034] |
772f851a-9ef5-4931-84e2-0e073b465f07 | 190600050 | 1906.00050 | null | https://arxiv.org/abs/1906.00050v1 | https://arxiv.org/pdf/1906.00050v1.pdf | DISCO: Depth Inference from Stereo using Context | Recent deep learning based approaches have outperformed classical stereo matching methods. However, current deep learning based end-to-end stereo matching methods adopt a generic encoder-decoder style network with skip connections. To limit computational requirement, many networks perform excessive down sampling, which... | ['Kaushik Raghavan', 'Kunal Swami', 'Rituparna Sarkar', 'Pankaj Bajpai', 'Nikhilanj Pelluri'] | 2019-05-31 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 3.33727211e-01 -3.28330547e-01 -8.60619992e-02 -4.40505624e-01
-4.89675820e-01 -8.13881606e-02 5.38904250e-01 -1.67732254e-01
-5.69646001e-01 6.91767633e-01 4.67372209e-01 -4.88079600e-02
1.77718937e-01 -9.49555457e-01 -6.71947658e-01 -3.73869866e-01
1.80956602e-01 -6.52593225e-02 4.98819381e-01 -3.46810132... | [8.856545448303223, -2.309432029724121] |
8e5ad6da-8ff4-4577-8222-8311113d90d4 | graph-learning-with-1d-convolutions-on-random | 2102.08786 | null | https://arxiv.org/abs/2102.08786v2 | https://arxiv.org/pdf/2102.08786v2.pdf | Graph Learning with 1D Convolutions on Random Walks | We propose CRaWl (CNNs for Random Walks), a novel neural network architecture for graph learning. It is based on processing sequences of small subgraphs induced by random walks with standard 1D CNNs. Thus, CRaWl is fundamentally different from typical message passing graph neural network architectures. It is inspired b... | ['Martin Grohe', 'Hinrikus Wolf', 'Martin Ritzert', 'Jan Toenshoff'] | 2021-02-17 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 2.41915341e-02 1.95757732e-01 -3.27194124e-01 -1.75221592e-01
-1.28952498e-02 -5.66236496e-01 8.40547621e-01 4.26886767e-01
-3.68002623e-01 4.63628232e-01 6.70662522e-02 -9.56394374e-01
-1.20550610e-01 -1.46280169e+00 -1.08119774e+00 -3.38195741e-01
-9.38525438e-01 7.01043069e-01 5.83627999e-01 -1.16848223... | [6.895805358886719, 6.264211654663086] |
867c018a-300e-44a6-a326-af31a2a2a444 | fine-grained-software-vulnerability-detection | null | null | https://openreview.net/forum?id=sKiAuHhc3w | https://openreview.net/pdf?id=sKiAuHhc3w | Fine-grained Software Vulnerability Detection via Information Theory and Contrastive Learning | Software vulnerabilities existing in a program or function of computer systems have been becoming a serious and crucial concern. In a program or function consisting of hundreds or thousands of source code statements, there are only few statements causing the corresponding vulnerabilities. Vulnerability labeling on a fu... | ['Dinh Phung', 'John C. Grundy', 'Trung Le', 'Van Nguyen'] | 2021-09-29 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-3.89578901e-02 -4.01286930e-01 -3.00500039e-02 -4.85515773e-01
-1.01842749e+00 -7.91369855e-01 7.01647922e-02 6.48966730e-01
8.98473933e-02 1.60274774e-01 9.91708040e-02 -7.45673060e-01
-1.14370540e-01 -9.58719492e-01 -6.00046515e-01 -5.06600142e-01
-1.65037826e-01 -2.30155423e-01 5.00378072e-01 1.75479834... | [7.117496967315674, 7.763134956359863] |
85f684ce-102e-4add-a1b1-c021a6717839 | a-new-expert-questioning-approach-to-more | 1904.00317 | null | https://arxiv.org/abs/1904.00317v2 | https://arxiv.org/pdf/1904.00317v2.pdf | A New Expert Questioning Approach to More Efficient Fault Localization in Ontologies | When ontologies reach a certain size and complexity, faults such as inconsistencies, unsatisfiable classes or wrong entailments are hardly avoidable. Locating the incorrect axioms that cause these faults is a hard and time-consuming task. Addressing this issue, several techniques for semi-automatic fault localization i... | ['Patrick Rodler', 'Michael Eichholzer'] | 2019-03-31 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [ 8.32901802e-03 5.59200108e-01 1.12937354e-01 -4.60215300e-01
-4.60213929e-01 -5.82704842e-01 1.76928908e-01 5.91880083e-01
2.49912962e-02 6.36863291e-01 -4.62826878e-01 -3.65058601e-01
-7.88511515e-01 -9.27418172e-01 -4.53202605e-01 -8.06294233e-02
2.73990870e-01 7.97586620e-01 7.80288100e-01 -3.70539874... | [5.496842861175537, 2.83546781539917] |
6f1b7762-a586-494b-9ed5-642d3ad22a93 | pedestrian-trajectory-forecasting-using-deep | 2305.16620 | null | https://arxiv.org/abs/2305.16620v1 | https://arxiv.org/pdf/2305.16620v1.pdf | Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing Uncertainty | One of the fundamental challenges in the prediction of dynamic agents is robustness. Usually, most predictions are deterministic estimates of future states which are over-confident and prone to error. Recently, few works have addressed capturing uncertainty during forecasting of future states. However, these probabilis... | ['Prasenjit Ghorai', 'Zachary Doerzaph', 'Azim Eskandarian', 'Anshul Nayak'] | 2023-05-26 | null | null | null | null | ['trajectory-forecasting', 'bayesian-inference'] | ['computer-vision', 'methodology'] | [-9.20189247e-02 -6.78239986e-02 -5.20522930e-02 -6.94289029e-01
-6.42478585e-01 -3.57151806e-01 6.77952766e-01 1.52281135e-01
-3.00538301e-01 1.06606507e+00 3.73626083e-01 1.31020367e-01
-2.86386311e-01 -8.05546463e-01 -7.92790949e-01 -5.87919116e-01
-1.72609240e-01 2.49952823e-01 3.32340986e-01 1.48098215... | [6.859510898590088, 3.447544813156128] |
c110b62a-7954-4bf8-97a4-41975e3a4aed | representing-and-reasoning-with-qualitative | 1401.3899 | null | http://arxiv.org/abs/1401.3899v1 | http://arxiv.org/pdf/1401.3899v1.pdf | Representing and Reasoning with Qualitative Preferences for Compositional Systems | Many applications, e.g., Web service composition, complex system design, team
formation, etc., rely on methods for identifying collections of objects or
entities satisfying some functional requirement. Among the collections that
satisfy the functional requirement, it is often necessary to identify one or
more collectio... | ['Vasant Honavar', 'Samik Basu', 'Ganesh Ram Santhanam'] | 2014-01-16 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 4.73752571e-03 -1.78771988e-01 -2.33974651e-01 -4.13507760e-01
-2.00868964e-01 -9.16479647e-01 2.87441283e-01 5.75668395e-01
-3.30874711e-01 6.93391383e-01 2.85640866e-01 -5.52201457e-02
-9.37959254e-01 -9.85024989e-01 -4.19070870e-01 -7.62388170e-01
-3.63794327e-01 8.49046350e-01 3.27509463e-01 -3.70963186... | [7.962146282196045, 5.124330997467041] |
f8d7fa68-0c12-4326-b149-d68fb1a27efe | neural-representations-of-cryo-em-maps-and-a | 2104.01468 | null | https://arxiv.org/abs/2104.01468v1 | https://arxiv.org/pdf/2104.01468v1.pdf | Neural Representations of Cryo-EM Maps and a Graph-Based Interpretation | Advances in imagery at atomic and near-atomic resolution, such as cryogenic electron microscopy (cryo-EM), have led to an influx of high resolution images of proteins and other macromolecular structures to data banks worldwide. Producing a protein structure from the discrete voxel grid data of cryo-EM maps involves int... | ['Dong Si', 'Nathan Ranno'] | 2021-04-03 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [ 2.36412540e-01 3.48057359e-01 2.40867466e-01 -5.31708658e-01
-8.80725920e-01 -2.52235681e-01 1.78146109e-01 4.62009817e-01
-6.79915428e-01 1.38258970e+00 -1.64683059e-01 -5.90524614e-01
7.22376630e-02 -7.69123137e-01 -1.12855649e+00 -9.66063857e-01
-4.51532006e-01 1.01536882e+00 1.91743094e-02 -9.41745192... | [13.325600624084473, -3.086984157562256] |
802d391e-8a9a-4d21-8218-5ac11c3b5d9a | discrete-contrastive-diffusion-for-cross | 2206.07771 | null | https://arxiv.org/abs/2206.07771v2 | https://arxiv.org/pdf/2206.07771v2.pdf | Discrete Contrastive Diffusion for Cross-Modal Music and Image Generation | Diffusion probabilistic models (DPMs) have become a popular approach to conditional generation, due to their promising results and support for cross-modal synthesis. A key desideratum in conditional synthesis is to achieve high correspondence between the conditioning input and generated output. Most existing methods le... | ['Yan Yan', 'Sergey Tulyakov', 'Jian Ren', 'Kyle Olszewski', 'Yu Wu', 'Ye Zhu'] | 2022-06-15 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.07171792e-01 -1.07796639e-01 -4.99342605e-02 -2.74644256e-01
-1.09289730e+00 -4.69501406e-01 9.24915135e-01 -1.36446252e-01
-3.08254510e-01 6.46363318e-01 3.46337646e-01 3.09940688e-02
-9.68218148e-02 -8.42790365e-01 -7.61055350e-01 -9.64538276e-01
4.22827214e-01 3.31172556e-01 2.27429777e-01 -4.62022908... | [11.44488525390625, -0.3229948580265045] |
115ee166-5ac6-4bce-b83a-ac6f97e091b0 | layer-wise-regularized-adversarial-training | 2202.02626 | null | https://arxiv.org/abs/2202.02626v3 | https://arxiv.org/pdf/2202.02626v3.pdf | Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) framework | Deep neural network models are used today in various applications of artificial intelligence, the strengthening of which, in the face of adversarial attacks is of particular importance. An appropriate solution to adversarial attacks is adversarial training, which reaches a trade-off between robustness and generalizatio... | ['Maryam Amirmazlaghani', 'Mohammad Mehdi Homayounpour', 'Mohammad Khalooei'] | 2022-02-05 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.71041363e-01 2.31858119e-01 4.03001875e-01 -3.19103152e-02
-3.14759225e-01 -9.23055589e-01 5.62760115e-01 6.17601499e-02
-4.63577867e-01 5.87628424e-01 -8.95872712e-02 -7.05368876e-01
-2.46521562e-01 -9.88615394e-01 -1.08464825e+00 -9.04201090e-01
-1.61708698e-01 -1.38606839e-02 2.78724551e-01 -4.58749712... | [5.519748687744141, 7.931460857391357] |
5aba3512-e0cd-4e92-b6c0-c6bf9a1afa6b | valor-vision-audio-language-omni-perception | 2304.08345 | null | https://arxiv.org/abs/2304.08345v1 | https://arxiv.org/pdf/2304.08345v1.pdf | VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset | In this paper, we propose a Vision-Audio-Language Omni-peRception pretraining model (VALOR) for multi-modal understanding and generation. Different from widely-studied vision-language pretraining models, VALOR jointly models relationships of vision, audio and language in an end-to-end manner. It contains three separate... | ['Jing Liu', 'Jinhui Tang', 'Weining Wang', 'Xinxin Zhu', 'Longteng Guo', 'Xingjian He', 'Sihan Chen'] | 2023-04-17 | null | null | null | null | ['audio-captioning', 'video-captioning', 'video-question-answering', 'video-retrieval', 'conditional-text-generation'] | ['audio', 'computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [ 2.22480491e-01 -2.46950760e-02 -1.17968880e-01 -3.12063247e-01
-1.48901784e+00 -5.87002754e-01 7.38671303e-01 -1.81453675e-01
-3.47648978e-01 2.96057910e-01 8.40602100e-01 -3.45667183e-01
4.44567591e-01 -3.27091366e-01 -1.25974882e+00 -4.39419091e-01
3.75227660e-01 4.11931455e-01 -2.17453718e-01 -8.02145526... | [10.836864471435547, 1.245600938796997] |
fedd5913-f39f-4e6b-9390-392c16a53666 | mol-instructions-a-large-scale-biomolecular | 2306.08018 | null | https://arxiv.org/abs/2306.08018v1 | https://arxiv.org/pdf/2306.08018v1.pdf | Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models | Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their proficiency within specialized domains such as biomolecular studies remains limited. To address this challenge, we introduce Mol-Inst... | ['Huajun Chen', 'Xiaohui Fan', 'Zhuo Chen', 'Rui Huang', 'Kangwei Liu', 'Ningyu Zhang', 'Xiaozhuan Liang', 'Yin Fang'] | 2023-06-13 | null | null | null | null | ['domain-motif-prediction', 'protein-design', 'chemical-entity-recognition', 'forward-reaction-prediction', 'chemical-protein-interaction-extraction', 'catalytic-activity-prediction', 'retrosynthesis', 'functional-description-generation', 'property-prediction', 'reagent-prediction', 'protein-function-prediction', 'chem... | ['medical', 'medical', 'medical', 'medical', 'medical', 'medical', 'medical', 'medical', 'medical', 'medical', 'medical', 'medical', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.06632757e-01 -1.81545988e-01 -3.38774413e-01 -4.82307434e-01
-6.31213844e-01 -4.93448853e-01 4.31118190e-01 5.98782957e-01
-3.66309613e-01 9.80245590e-01 9.06550884e-02 -7.95909584e-01
-6.27101064e-02 -4.29853022e-01 -9.94460762e-01 -4.60411757e-01
-2.75346518e-01 2.86886781e-01 -3.92965786e-02 -1.82092220... | [4.757711887359619, 5.735409736633301] |
cf9e2dd5-eaf6-4e09-a322-6d6c90fe0729 | residue-based-natural-language-adversarial | null | null | https://openreview.net/forum?id=eFGgjI4Wk-V | https://openreview.net/pdf?id=eFGgjI4Wk-V | Residue-Based Natural Language Adversarial Attack Detection | Deep learning based systems are susceptible to adversarial attacks, where a small, imperceptible change at the input alters the model prediction. However, to date the majority of the approaches to detect these attacks have been designed for image processing systems. Many popular image adversarial detection approaches a... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 6.75067484e-01 1.26455277e-01 2.28491679e-01 -4.30107936e-02
-6.06089890e-01 -1.09874845e+00 1.17434466e+00 1.02922134e-01
-4.05682445e-01 1.89230144e-01 -3.92568037e-02 -5.14867067e-01
4.04846132e-01 -8.13461304e-01 -8.22892964e-01 -4.86767650e-01
4.95560430e-02 1.06608838e-01 4.49853212e-01 -4.82138008... | [5.880184650421143, 8.002958297729492] |
b2968aa3-65ad-4cda-86b0-024be437044e | time-to-embrace-natural-language-processing | 2302.10406 | null | https://arxiv.org/abs/2302.10406v1 | https://arxiv.org/pdf/2302.10406v1.pdf | Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines | NLP-based computer vision models, particularly vision transformers, have been shown to outperform CNN models in many imaging tasks. However, most digital pathology artificial-intelligence models are based on CNN architectures, probably owing to a lack of data regarding NLP models for pathology images. In this study, we... | ['Xu Steven Xu', 'Hong Zhang', 'Jitendra Jonnagaddala', 'Bangwei Guo', 'Xingyu Li', 'Min Cen'] | 2023-02-21 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 1.22582115e-01 1.85470609e-03 -4.86001998e-01 1.36523366e-01
-7.72758126e-01 -3.78859520e-01 3.72906029e-01 4.97989178e-01
-6.42748356e-01 6.11831069e-01 2.48665288e-01 -6.26672566e-01
-1.26421958e-01 -6.98533475e-01 -4.19770569e-01 -6.97570324e-01
1.83278173e-02 4.81085539e-01 2.92472273e-01 7.84259960... | [15.139322280883789, -2.9261550903320312] |
20982686-39ee-46df-bd93-e5dccc522c4e | attention-aware-deep-reinforcement-learning | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Rao_Attention-Aware_Deep_Reinforcement_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Rao_Attention-Aware_Deep_Reinforcement_ICCV_2017_paper.pdf | Attention-Aware Deep Reinforcement Learning for Video Face Recognition | In this paper, we propose an attention-aware deep reinforcement learning (ADRL) method for video face recognition, which aims to discard the misleading and confounding frames and find the focuses of attention in face videos for person recognition. We formulate the process of finding the attentions of videos as a Markov... | ['Jie zhou', 'Jiwen Lu', 'Yongming Rao'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['person-recognition'] | ['computer-vision'] | [ 4.85219061e-02 -2.53236949e-01 -1.38840646e-01 -4.12649810e-01
-5.89292288e-01 -1.35304317e-01 3.24436843e-01 -7.21118391e-01
-3.25080395e-01 4.80525523e-01 9.63590145e-02 -1.31870145e-02
-1.02260962e-01 -5.05193174e-01 -6.85193241e-01 -7.30723023e-01
1.41751051e-01 1.81808442e-01 -3.48669112e-01 2.83036768... | [13.395435333251953, 1.2484471797943115] |
5adf713f-71a5-4fe4-a9ac-c51d1a7cb1f1 | improving-extreme-weather-events-detection | 2304.00176 | null | https://arxiv.org/abs/2304.00176v1 | https://arxiv.org/pdf/2304.00176v1.pdf | Improving extreme weather events detection with light-weight neural networks | To advance automated detection of extreme weather events, which are increasing in frequency and intensity with climate change, we explore modifications to a novel light-weight Context Guided convolutional neural network architecture trained for semantic segmentation of tropical cyclones and atmospheric rivers in climat... | ['David Lüdeke', 'Lucas Hendren', 'Hannah Grossman', 'Romain Lacombe'] | 2023-03-31 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 2.21266076e-01 -3.50389302e-01 -3.16938758e-02 -8.42774808e-01
-3.72095525e-01 -7.45530784e-01 6.57960236e-01 5.55698931e-01
-7.71530151e-01 6.78890467e-01 4.64534551e-01 -8.72550189e-01
-2.57057697e-02 -1.04473162e+00 -3.58334273e-01 -3.13341886e-01
-3.73481482e-01 3.01023155e-01 -2.40920931e-01 -2.92768002... | [6.719411373138428, 2.9784047603607178] |
10f7d8ae-4b47-40cc-8327-669e2c1369d0 | can-a-frozen-pretrained-language-model-be | 2303.05153 | null | https://arxiv.org/abs/2303.05153v1 | https://arxiv.org/pdf/2303.05153v1.pdf | Can a Frozen Pretrained Language Model be used for Zero-shot Neural Retrieval on Entity-centric Questions? | Neural document retrievers, including dense passage retrieval (DPR), have outperformed classical lexical-matching retrievers, such as BM25, when fine-tuned and tested on specific question-answering datasets. However, it has been shown that the existing dense retrievers do not generalize well not only out of domain but ... | ['Jun Deguchi', 'Osamu Torii', 'Youyang Ng', 'Yasuhiro Morioka', 'Daisuke Miyashita', 'Yasuto Hoshi'] | 2023-03-09 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-4.59112793e-01 -6.82379827e-02 -2.23156229e-01 1.96804553e-01
-1.29506886e+00 -6.54537320e-01 7.93225586e-01 6.24632061e-01
-9.85559821e-01 8.64044547e-01 6.23374104e-01 -6.58220872e-02
-6.30169034e-01 -1.17431366e+00 -8.33664894e-01 -1.00133494e-01
1.17059136e-02 8.34350288e-01 7.18481362e-01 -9.23219204... | [11.459321975708008, 7.76085090637207] |
301258fe-3f7f-4f30-a8fe-5f7446159686 | low-rank-tensor-function-representation-for | 2212.00262 | null | https://arxiv.org/abs/2212.00262v1 | https://arxiv.org/pdf/2212.00262v1.pdf | Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery | Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the emerging areas in machine learning and computer vision. However, classical low-rank tensor representations can only represent data ... | ['Deyu Meng', 'Michael K. Ng', 'Zhemin Li', 'XiLe Zhao', 'YiSi Luo'] | 2022-12-01 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [-1.18173234e-01 -4.12226945e-01 -8.29285011e-02 2.20733538e-01
-4.37778294e-01 -3.53128433e-01 3.88136029e-01 -8.68531242e-02
-3.77242006e-02 5.47757745e-01 1.07442565e-01 -6.23689033e-04
-5.22297740e-01 -7.14338899e-01 -5.72018623e-01 -9.39270437e-01
-8.75409842e-02 2.29776934e-01 -4.34506796e-02 -4.65837002... | [7.426629066467285, 4.456050872802734] |
2ef9ce88-67ca-4c2e-9211-4dc9651da3d9 | unbiased-teacher-for-semi-supervised-object-1 | 2102.09480 | null | https://arxiv.org/abs/2102.09480v1 | https://arxiv.org/pdf/2102.09480v1.pdf | Unbiased Teacher for Semi-Supervised Object Detection | Semi-supervised learning, i.e., training networks with both labeled and unlabeled data, has made significant progress recently. However, existing works have primarily focused on image classification tasks and neglected object detection which requires more annotation effort. In this work, we revisit the Semi-Supervised ... | ['Peter Vajda', 'Zsolt Kira', 'Bichen Wu', 'Peizhao Zhang', 'Kan Chen', 'Chia-Wen Kuo', 'Zijian He', 'Chih-Yao Ma', 'Yen-Cheng Liu'] | 2021-02-18 | unbiased-teacher-for-semi-supervised-object | https://openreview.net/forum?id=MJIve1zgR_ | https://openreview.net/pdf?id=MJIve1zgR_ | iclr-2021-1 | ['semi-supervised-object-detection', 'semi-supervised-person-bounding-box-detection'] | ['computer-vision', 'computer-vision'] | [ 2.98492134e-01 3.58439595e-01 -4.22252387e-01 -6.03089154e-01
-1.06077766e+00 -5.74075758e-01 6.38267636e-01 5.98442741e-02
-7.52162576e-01 7.90976763e-01 -3.44228476e-01 -1.92819893e-01
4.30287808e-01 -3.62230599e-01 -9.48176742e-01 -6.98826551e-01
1.89146951e-01 3.94648671e-01 5.77955782e-01 3.51780862... | [9.169909477233887, 1.2545608282089233] |
5dd94511-fa36-42e2-81eb-7c89e654cf6f | spectral-variability-augmented-sparse | 2110.09744 | null | https://arxiv.org/abs/2110.09744v2 | https://arxiv.org/pdf/2110.09744v2.pdf | Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images | Spectral unmixing (SU) expresses the mixed pixels existed in hyperspectral images as the product of endmember and abundance, which has been widely used in hyperspectral imagery analysis. However, the influence of light, acquisition conditions and the inherent properties of materials, results in that the identified endm... | ['Qian Du', 'Yan Feng', 'Mingyang Ma', 'Shaohui Mei', 'Ge Zhang'] | 2021-10-19 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 7.60542512e-01 -8.04897547e-01 -1.39620751e-01 1.56020239e-01
-1.88644648e-01 -6.74547315e-01 5.10887742e-01 -4.08310920e-01
9.84293222e-02 9.82198775e-01 1.32019460e-01 8.85593891e-02
-1.88396186e-01 -8.06243718e-01 -4.26872164e-01 -1.47853494e+00
2.43763775e-01 -2.69882791e-02 -3.05322170e-01 -1.12359367... | [10.077312469482422, -2.045492172241211] |
d09ec51c-e417-4bd3-9e23-359c4b732ee6 | the-dirha-english-corpus-and-related-tasks | 1710.02560 | null | http://arxiv.org/abs/1710.02560v1 | http://arxiv.org/pdf/1710.02560v1.pdf | The DIRHA-English corpus and related tasks for distant-speech recognition in domestic environments | This paper introduces the contents and the possible usage of the
DIRHA-ENGLISH multi-microphone corpus, recently realized under the EC DIRHA
project. The reference scenario is a domestic environment equipped with a large
number of microphones and microphone arrays distributed in space.
The corpus is composed of both ... | ['Mirco Ravanelli', 'Maurizio Omologo'] | 2017-10-06 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 1.90394018e-02 -3.71126145e-01 6.13011181e-01 -6.43283606e-01
-1.10016823e+00 -5.70180655e-01 6.39085114e-01 -2.87778050e-01
-6.69963181e-01 6.65206432e-01 5.08743465e-01 -3.85730922e-01
1.34032533e-01 -3.51014078e-01 -5.36748707e-01 -8.11115563e-01
-1.05209455e-01 4.28290486e-01 -1.25565737e-01 -2.19577238... | [14.879437446594238, 6.086802959442139] |
24e352d9-7d64-45ee-83fb-8ded43833acb | eranns-efficient-residual-audio-neural | 2106.01621 | null | https://arxiv.org/abs/2106.01621v7 | https://arxiv.org/pdf/2106.01621v7.pdf | ERANNs: Efficient Residual Audio Neural Networks for Audio Pattern Recognition | Audio pattern recognition (APR) is an important research topic and can be applied to several fields related to our lives. Therefore, accurate and efficient APR systems need to be developed as they are useful in real applications. In this paper, we propose a new convolutional neural network (CNN) architecture and a meth... | [] | 2021-06-03 | eranns-efficient-residual-audio-neural-1 | https://arxiv.org/abs/2106.01621 | https://arxiv.org/abs/2106.01621 | null | ['audio-tagging'] | ['audio'] | [-9.43420753e-02 -3.97245288e-01 1.05681933e-01 -2.69170761e-01
-8.55606437e-01 -1.34125218e-01 -1.38847485e-01 2.13713013e-02
-8.04867744e-01 5.68187475e-01 -2.06094146e-01 -2.74873465e-01
1.04814358e-01 -7.68125534e-01 -8.05081964e-01 -3.65657836e-01
-1.62281901e-01 -9.02793035e-02 4.43322808e-01 -1.68466151... | [15.021461486816406, 5.2648396492004395] |
867f86e9-1cdd-4c54-b13b-31bcebef40e1 | self-supervised-multi-modal-sequential | 2304.13277 | null | https://arxiv.org/abs/2304.13277v1 | https://arxiv.org/pdf/2304.13277v1.pdf | Self-Supervised Multi-Modal Sequential Recommendation | With the increasing development of e-commerce and online services, personalized recommendation systems have become crucial for enhancing user satisfaction and driving business revenue. Traditional sequential recommendation methods that rely on explicit item IDs encounter challenges in handling item cold start and domai... | ['Yaming Yang', 'Kai Zheng', 'Can Xu', 'Qingfeng Sun', 'Kunzhe Song'] | 2023-04-26 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [-9.79622640e-03 -5.68148017e-01 -4.22797203e-01 -4.91750896e-01
-5.48251808e-01 -5.70393980e-01 4.04285491e-01 3.06183100e-02
-6.11867964e-01 3.81173909e-01 4.17241752e-01 -1.92781910e-02
-2.80747235e-01 -6.73854709e-01 -5.05202353e-01 -5.75018942e-01
2.68437952e-01 2.49027327e-01 1.76339615e-02 -2.64631301... | [10.168173789978027, 5.531567096710205] |
fe17f9da-ae85-4b00-a9e1-5931c66143b1 | react-temporal-action-detection-with | 2207.07097 | null | https://arxiv.org/abs/2207.07097v1 | https://arxiv.org/pdf/2207.07097v1.pdf | ReAct: Temporal Action Detection with Relational Queries | This work aims at advancing temporal action detection (TAD) using an encoder-decoder framework with action queries, similar to DETR, which has shown great success in object detection. However, the framework suffers from several problems if directly applied to TAD: the insufficient exploration of inter-query relation in... | ['DaCheng Tao', 'Jia Li', 'Lin Ma', 'Jing Zhang', 'Qiong Cao', 'Yujie Zhong', 'Dingfeng Shi'] | 2022-07-14 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 2.37521589e-01 -4.44369055e-02 -5.83487093e-01 -1.71295315e-01
-1.13877988e+00 -7.73194656e-02 5.22845089e-01 -1.78852484e-01
-4.70761031e-01 4.13234740e-01 2.31426120e-01 -2.25195717e-02
2.61649974e-02 -5.75931251e-01 -5.53784847e-01 -4.54312205e-01
1.01671167e-01 2.69243747e-01 6.69586599e-01 1.12074785... | [8.44940185546875, 0.5115834474563599] |
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