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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5919a1cb-c1e7-4c04-be08-bc937cb533e7 | lipschitz-continuity-of-signal-temporal-logic | 2304.03849 | null | https://arxiv.org/abs/2304.03849v1 | https://arxiv.org/pdf/2304.03849v1.pdf | Lipschitz Continuity of Signal Temporal Logic Robustness Measures: Synthesizing Control Barrier Functions from One Expert Demonstration | Control Barrier Functions (CBFs) allow for efficient synthesis of controllers to maintain desired invariant properties of safety-critical systems. However, the problem of identifying a CBF remains an open question. As such, this paper provides a constructive method for control barrier function synthesis around one expe... | ['Aaron D. Ames', 'Richard M. Murray', 'Apurva Badithela', 'Prithvi Akella'] | 2023-04-07 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 5.29326618e-01 7.22368002e-01 -3.48550320e-01 3.21221828e-01
-6.85563862e-01 -1.08446491e+00 3.84309262e-01 1.94249168e-01
2.21797779e-01 7.78051615e-01 -4.37268913e-01 -6.00710213e-01
-4.89891380e-01 -5.67860544e-01 -1.11688793e+00 -6.01920247e-01
-2.09089473e-01 -5.13275564e-01 4.53765661e-01 -2.35169813... | [4.809057712554932, 2.2785587310791016] |
0df338bd-0b2b-435a-9600-b88909514daa | on-the-optimization-landscape-of-dynamic | 2209.05042 | null | https://arxiv.org/abs/2209.05042v2 | https://arxiv.org/pdf/2209.05042v2.pdf | On the Optimization Landscape of Dynamic Output Feedback: A Case Study for Linear Quadratic Regulator | The convergence of policy gradient algorithms hinges on the optimization landscape of the underlying optimal control problem. Theoretical insights into these algorithms can often be acquired from analyzing those of linear quadratic control. However, most of the existing literature only considers the optimization landsc... | ['Lin Zhao', 'Yang Zheng', 'Wenhan Cao', 'Jingliang Duan'] | 2022-09-12 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 3.41478512e-02 2.98528552e-01 -7.26198792e-01 4.23620969e-01
-5.99729300e-01 -8.66881490e-01 2.71083593e-01 3.38784531e-02
-3.30006987e-01 8.63327086e-01 2.49227546e-02 -7.90901065e-01
-4.37608868e-01 -3.31306845e-01 -8.21990967e-01 -1.10558784e+00
1.69641316e-01 -2.68157832e-02 -1.72517642e-01 -4.05801117... | [4.761800765991211, 2.560778856277466] |
048ff087-806b-4e79-8f76-c89d5b1bcf1e | semi-mae-masked-autoencoders-for-semi | 2301.01431 | null | https://arxiv.org/abs/2301.01431v1 | https://arxiv.org/pdf/2301.01431v1.pdf | Semi-MAE: Masked Autoencoders for Semi-supervised Vision Transformers | Vision Transformer (ViT) suffers from data scarcity in semi-supervised learning (SSL). To alleviate this issue, inspired by masked autoencoder (MAE), which is a data-efficient self-supervised learner, we propose Semi-MAE, a pure ViT-based SSL framework consisting of a parallel MAE branch to assist the visual representa... | ['Xiaoming Xu', 'Kang Zhao', 'Haojie Yu'] | 2023-01-04 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 4.19971198e-01 4.82687086e-01 -3.71789664e-01 -5.41333973e-01
-6.65899038e-01 -9.65113491e-02 6.62602663e-01 -5.27699709e-01
-2.24001691e-01 6.20158613e-01 1.33898467e-01 -3.24220121e-01
7.10638642e-01 -4.74527806e-01 -1.05481219e+00 -7.75869787e-01
5.57039082e-01 4.13819462e-01 1.63720116e-01 9.62594599... | [9.570018768310547, 1.5485973358154297] |
63f86b7d-dda4-43bd-a375-e6e7e8d4a29a | original-loop-closure-detection-algorithm-for | 1707.04771 | null | http://arxiv.org/abs/1707.04771v1 | http://arxiv.org/pdf/1707.04771v1.pdf | Original Loop-closure Detection Algorithm for Monocular vSLAM | Vision-based simultaneous localization and mapping (vSLAM) is a
well-established problem in mobile robotics and monocular vSLAM is one of the
most challenging variations of that problem nowadays. In this work we study one
of the core post-processing optimization mechanisms in vSLAM, e.g. loop-closure
detection. We anal... | ['Konstantin Yakovlev', 'Andrey Bokovoy'] | 2017-07-15 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-1.78801179e-01 -3.49648565e-01 1.52221814e-01 -7.57981464e-02
-1.60266295e-01 -7.39045382e-01 7.99697220e-01 8.13623592e-02
-7.44722545e-01 6.69562995e-01 -3.55905890e-01 -3.74444604e-01
-9.37561318e-02 -5.36506116e-01 -7.73201942e-01 -1.88211173e-01
6.71339333e-02 7.82943666e-01 6.52944982e-01 -4.53359216... | [7.398834705352783, -2.0896337032318115] |
bf22dafb-9603-4063-843a-cc6b45430d63 | pure-transformer-with-integrated-experts-for | 2211.04963 | null | https://arxiv.org/abs/2211.04963v1 | https://arxiv.org/pdf/2211.04963v1.pdf | Pure Transformer with Integrated Experts for Scene Text Recognition | Scene text recognition (STR) involves the task of reading text in cropped images of natural scenes. Conventional models in STR employ convolutional neural network (CNN) followed by recurrent neural network in an encoder-decoder framework. In recent times, the transformer architecture is being widely adopted in STR as i... | ['Jung-jae Kim', 'Adams Wai-Kin Kong', 'Yew Lee Tan'] | 2022-11-09 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.95512962e-01 -2.43319318e-01 2.30908599e-02 -6.66243583e-02
-4.56564218e-01 -2.79796898e-01 7.39543617e-01 -3.10057282e-01
-2.47851178e-01 3.20324421e-01 2.02332243e-01 -2.88526624e-01
1.75197005e-01 -7.66004205e-01 -7.88018584e-01 -6.64422452e-01
5.85341930e-01 2.01844841e-01 6.39613986e-01 -4.62691456... | [11.857671737670898, 2.1943657398223877] |
5c0afaae-4bdb-49ab-9ea0-320f992c9672 | odn-opening-the-deep-network-for-open-set | 1901.07757 | null | http://arxiv.org/abs/1901.07757v1 | http://arxiv.org/pdf/1901.07757v1.pdf | ODN: Opening the Deep Network for Open-set Action Recognition | In recent years, the performance of action recognition has been significantly
improved with the help of deep neural networks. Most of the existing action
recognition works hold the \textit{closed-set} assumption that all action
categories are known beforehand while deep networks can be well trained for
these categories... | ['Yao-Wei Wang', 'Yu Shu', 'Yixiong Zou', 'Yemin Shi', 'Qingsheng Yuan', 'Yonghong Tian'] | 2019-01-23 | null | null | null | null | ['open-set-action-recognition'] | ['computer-vision'] | [ 7.58340240e-01 -9.46593750e-03 -2.81049848e-01 -3.27596426e-01
-1.70116603e-01 -4.77461129e-01 3.85043412e-01 -2.39654854e-01
-3.96474898e-01 7.38996267e-01 -1.95873603e-01 -2.20640544e-02
-1.51327968e-01 -9.50873494e-01 -5.26459157e-01 -9.53798175e-01
3.28574449e-01 6.23846412e-01 4.18348342e-01 -1.64744759... | [8.438355445861816, 0.8800064325332642] |
446c3ed4-17d0-444a-a60c-d186b086c474 | optimizing-protein-fitness-using-gibbs | 2307.00494 | null | https://arxiv.org/abs/2307.00494v1 | https://arxiv.org/pdf/2307.00494v1.pdf | Optimizing protein fitness using Gibbs sampling with Graph-based Smoothing | The ability to design novel proteins with higher fitness on a given task would be revolutionary for many fields of medicine. However, brute-force search through the combinatorially large space of sequences is infeasible. Prior methods constrain search to a small mutational radius from a reference sequence, but such heu... | ['Ila Fiete', 'Regina Barzilay', 'Tommi Jaakkola', 'Raman Samusevich', 'Jason Yim', 'Andrew Kirjner'] | 2023-07-02 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [ 7.57111013e-01 2.37997901e-02 2.30483748e-02 -3.14913355e-02
-9.58811104e-01 -8.50240052e-01 1.62081599e-01 -3.16760577e-02
-3.55266422e-01 1.35448503e+00 -1.56721964e-01 -4.91526544e-01
-1.90765366e-01 -5.12089968e-01 -1.04606175e+00 -9.32994723e-01
-1.41338989e-01 3.41166317e-01 8.75101015e-02 -9.16048512... | [4.754087448120117, 5.545462608337402] |
ee204be6-d12b-4f7c-b7c2-eda0cecdabc4 | paravs-a-simple-fast-efficient-and-flexible | 2102.06086 | null | https://arxiv.org/abs/2102.06086v1 | https://arxiv.org/pdf/2102.06086v1.pdf | ParaVS: A Simple, Fast, Efficient and Flexible Graph Neural Network Framework for Structure-Based Virtual Screening | Structure-based virtual screening (SBVS) is a promising in silico technique that integrates computational methods into drug design. An extensively used method in SBVS is molecular docking. However, the docking process can hardly be computationally efficient and accurate simultaneously because classic mechanics scoring ... | ['Lurong Pan', 'Dawei Leng', 'Junfeng Wu'] | 2021-02-08 | null | null | null | null | ['molecular-docking'] | ['medical'] | [-4.01650637e-01 -3.09576690e-01 -1.26891077e-01 -5.44265434e-02
-7.42085993e-01 -5.73689580e-01 2.08764419e-01 2.68739074e-01
-5.75816274e-01 1.44678211e+00 -5.04257023e-01 -6.45328224e-01
3.51975411e-02 -9.43446994e-01 -1.11324251e+00 -7.99474359e-01
-9.78330076e-02 7.84918845e-01 3.71116668e-01 -4.76592213... | [4.8986077308654785, 5.592639923095703] |
fd0f7542-0a66-4c4f-b963-ba24060e15d8 | is-model-attention-aligned-with-human | 2306.01220 | null | https://arxiv.org/abs/2306.01220v1 | https://arxiv.org/pdf/2306.01220v1.pdf | Is Model Attention Aligned with Human Attention? An Empirical Study on Large Language Models for Code Generation | Large Language Models (LLMs) have been demonstrated effective for code generation. Due to the complexity and opacity of LLMs, little is known about how these models generate code. To deepen our understanding, we investigate whether LLMs attend to the same parts of a natural language description as human programmers dur... | ['Tianyi Zhang', 'Lei Ma', 'Zhijie Wang', 'Shengmai Chen', 'Bonan Kou'] | 2023-06-02 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-8.25405568e-02 3.83882582e-01 2.42377687e-02 -1.20027393e-01
-3.73247325e-01 -5.84387600e-01 5.71553051e-01 4.98533666e-01
-1.45264611e-01 2.16745585e-01 4.05834079e-01 -4.17534590e-01
4.11446184e-01 -4.34140027e-01 -7.16696262e-01 -3.68464813e-02
2.60748714e-01 1.55791566e-01 -6.50295392e-02 -2.99973696... | [7.957571029663086, 7.737530708312988] |
53b88b21-3f76-4226-9e77-06e89bb294f8 | simultaneous-job-interview-system-using | null | null | https://aclanthology.org/2022.sigdial-1.12 | https://aclanthology.org/2022.sigdial-1.12.pdf | Simultaneous Job Interview System Using Multiple Semi-autonomous Agents | In recent years, spoken dialogue systems have been applied to job interviews where an applicant talks to a system that asks pre-defined questions, called on-demand and self-paced job interviews. We propose a simultaneous job interview system, where one interviewer can conduct one-on-one interviews with multiple applica... | ['Tatsuya Kawahara', 'Koji Inoue', 'Divesh Lala', 'Kenta Yamamoto', 'Yusuke Muraki', 'Haruki Kawai'] | null | null | null | null | sigdial-acl-2022-9 | ['keyword-extraction', 'dialogue-understanding', 'spoken-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 3.52907568e-01 7.67648458e-01 2.55555157e-02 -8.12578440e-01
-9.52911556e-01 -1.09551775e+00 6.64658368e-01 2.68593311e-01
-4.84733105e-01 7.98775613e-01 1.92995936e-01 -6.66042507e-01
1.61973849e-01 -5.67209065e-01 4.13267195e-01 -1.53460249e-01
4.63273793e-01 1.37988341e+00 1.68820634e-01 -6.15816116... | [12.932321548461914, 7.986958026885986] |
9f935eca-ea70-45d5-9121-60103a47477b | deep-machine-learning-based-egyptian-vehicle | 2107.11640 | null | https://arxiv.org/abs/2107.11640v1 | https://arxiv.org/pdf/2107.11640v1.pdf | Deep Machine Learning Based Egyptian Vehicle License Plate Recognition Systems | Automated Vehicle License Plate (VLP) detection and recognition have ended up being a significant research issue as of late. VLP localization and recognition are some of the most essential techniques for managing traffic using digital techniques. In this paper, four smart systems are developed to recognize Egyptian veh... | ['Hany Elnashar', 'Mohamed Taha Abou-Kreisha', 'Mohamed Shehata'] | 2021-07-24 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [-4.09330904e-01 -7.43523419e-01 3.30449119e-02 8.87031779e-02
-5.14709711e-01 -5.36125660e-01 6.63618267e-01 -4.78356928e-01
-4.49175090e-01 4.50390875e-01 -4.62939709e-01 -2.61993468e-01
3.55265588e-01 -9.43119228e-01 -5.34833014e-01 -8.73193324e-01
2.78312922e-01 6.74404025e-01 8.60577345e-01 -2.96507478... | [9.823433876037598, -4.968441963195801] |
26bf426c-731b-4462-bac3-3b1d824f7a43 | demystifying-misconceptions-in-social-bots | 2303.17251 | null | https://arxiv.org/abs/2303.17251v1 | https://arxiv.org/pdf/2303.17251v1.pdf | Demystifying Misconceptions in Social Bots Research | The science of social bots seeks knowledge and solutions to one of the most debated forms of online misinformation. Yet, social bots research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming... | ['Marinella Petrocchi', 'Maurizio Tesconi', 'Angelo Spognardi', 'Roberto Di Pietro', 'Stefano Cresci'] | 2023-03-30 | null | null | null | null | ['misconceptions', 'misinformation'] | ['miscellaneous', 'miscellaneous'] | [ 7.50797018e-02 2.30189309e-01 -4.10068184e-01 1.18105732e-01
3.17901820e-02 -9.37090397e-01 7.82291353e-01 5.29820085e-01
-4.54296917e-01 6.15528584e-01 4.27864939e-01 -1.23090672e+00
-7.26477951e-02 -4.75067586e-01 -4.51123238e-01 -4.38505709e-01
3.93420637e-01 -7.09097972e-03 2.55152285e-01 -3.65681052... | [8.455018997192383, 9.975789070129395] |
69adad40-269d-4a78-8e38-717c517230a4 | contranet-a-single-end-to-end-hybrid-network | 2206.10677 | null | https://arxiv.org/abs/2206.10677v1 | https://arxiv.org/pdf/2206.10677v1.pdf | ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces | Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-invasive bio-signals, which are widely used in human machine interface (HMI) technologies (EEG-HMI and EMG-HMI paradigm) for the rehabilitation of physically disabled people. Successful decoding of EEG and EMG signals into respective control... | ['Christian Klaes', 'Ioannis Iossifidis', 'Tobias Glasmachers', 'Muhammad Saif-ur-Rehman', 'Omair Ali'] | 2022-06-21 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 4.59633768e-01 -7.26591125e-02 1.42699987e-01 -2.36169219e-01
-4.23581630e-01 -4.44105305e-02 3.64903688e-01 -4.48368907e-01
-5.67438126e-01 1.07501471e+00 1.85003489e-01 -7.36520961e-02
-7.70516455e-01 -4.32429463e-01 -7.75454760e-01 -6.63354993e-01
-5.42453170e-01 3.40076208e-01 -7.84434006e-02 -4.24008876... | [13.05759334564209, 3.4565787315368652] |
4997e621-b132-4a51-9cf6-ecfe7bff6f93 | t2ranking-a-large-scale-chinese-benchmark-for | 2304.03679 | null | https://arxiv.org/abs/2304.03679v1 | https://arxiv.org/pdf/2304.03679v1.pdf | T2Ranking: A large-scale Chinese Benchmark for Passage Ranking | Passage ranking involves two stages: passage retrieval and passage re-ranking, which are important and challenging topics for both academics and industries in the area of Information Retrieval (IR). However, the commonly-used datasets for passage ranking usually focus on the English language. For non-English scenarios,... | ['Jin Ma', 'Yiqun Liu', 'Haitao Li', 'Xiangsheng Li', 'Zhijing Wu', 'Weinan Gan', 'Ting Yao', 'Feiyang Lv', 'Bingning Wang', 'Qian Dong', 'Xiaohui Xie'] | 2023-04-07 | null | null | null | null | ['passage-ranking', 'passage-re-ranking', 'passage-retrieval'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.08798632e-01 -6.37131512e-01 -3.54348332e-01 -6.87999725e-02
-1.50782847e+00 -8.75572264e-01 6.11218452e-01 5.49403548e-01
-7.67015815e-01 9.04300570e-01 6.90798163e-01 -1.46653101e-01
-2.99718618e-01 -6.31524682e-01 -3.00125211e-01 -2.27692142e-01
1.27387214e-02 4.11345929e-01 6.59061491e-01 -6.64499938... | [11.47692584991455, 7.733522415161133] |
31bff8f5-836f-4b05-a99b-76e0b7ae6461 | 2-bit-conformer-quantization-for-automatic | 2305.16619 | null | https://arxiv.org/abs/2305.16619v1 | https://arxiv.org/pdf/2305.16619v1.pdf | 2-bit Conformer quantization for automatic speech recognition | Large speech models are rapidly gaining traction in research community. As a result, model compression has become an important topic, so that these models can fit in memory and be served with reduced cost. Practical approaches for compressing automatic speech recognition (ASR) model use int8 or int4 weight quantization... | ['Yanzhang He', 'David Rim', 'Jian Li', 'David Qiu', 'Shaojin Ding', 'Phoenix Meadowlark', 'Oleg Rybakov'] | 2023-05-26 | null | null | null | null | ['model-compression', 'automatic-speech-recognition'] | ['methodology', 'speech'] | [ 4.40718114e-01 -1.13442661e-02 -3.83188307e-01 -3.62952352e-01
-1.13143384e+00 -7.36052841e-02 2.39392698e-01 2.31742859e-01
-6.50887907e-01 4.26591367e-01 3.29903096e-01 -5.11807621e-01
2.62728661e-01 -5.04737079e-01 -5.05535901e-01 -4.19361681e-01
4.30982420e-03 3.54310304e-01 2.85909384e-01 -2.00347289... | [14.277769088745117, 6.270174980163574] |
ff8ab487-dcc6-47b2-a4e2-ac50a8395026 | neural-view-synthesis-and-matching-for-semi | 2110.14213 | null | https://arxiv.org/abs/2110.14213v1 | https://arxiv.org/pdf/2110.14213v1.pdf | Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose | We study the problem of learning to estimate the 3D object pose from a few labelled examples and a collection of unlabelled data. Our main contribution is a learning framework, neural view synthesis and matching, that can transfer the 3D pose annotation from the labelled to unlabelled images reliably, despite unseen 3D... | ['Adam Kortylewski', 'Alan Yuille', 'Shenxiao Mei', 'Angtian Wang'] | 2021-10-27 | null | http://proceedings.neurips.cc/paper/2021/hash/3a61ed715ee66c48bacf237fa7bb5289-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/3a61ed715ee66c48bacf237fa7bb5289-Paper.pdf | neurips-2021-12 | ['3d-pose-estimation'] | ['computer-vision'] | [ 3.07077080e-01 3.34848315e-01 -8.18925202e-02 -5.43977380e-01
-1.06425953e+00 -9.21115100e-01 8.27417612e-01 -3.86718243e-01
-2.31552467e-01 2.61924148e-01 6.01136610e-02 2.52501577e-01
3.16061288e-01 -3.24523985e-01 -1.28883600e+00 -7.69666910e-01
2.51947552e-01 1.04495502e+00 2.62383252e-01 1.51913360... | [8.072380065917969, -2.870124101638794] |
40d3becc-2d56-4c9f-a8b8-994c092a5b82 | sign-language-production-a-review | 2103.15910 | null | https://arxiv.org/abs/2103.15910v1 | https://arxiv.org/pdf/2103.15910v1.pdf | Sign Language Production: A Review | Sign Language is the dominant yet non-primary form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice... | ['Mohammad Sabokrou', 'Sergio Escalera', 'Kourosh Kiani', 'Razieh Rastgoo'] | 2021-03-29 | null | null | null | null | ['sign-language-production'] | ['natural-language-processing'] | [-9.82526466e-02 -9.08412486e-02 -1.08401880e-01 -3.23268026e-01
-6.20652497e-01 -4.17202920e-01 4.15657848e-01 -7.97689676e-01
-4.23282295e-01 7.04266191e-01 6.01467848e-01 -2.75520325e-01
8.46611559e-02 -6.97049499e-01 -2.76852876e-01 -8.50499451e-01
-6.48494661e-02 3.47035602e-02 1.25193298e-01 -4.52655762... | [9.100822448730469, -6.411412715911865] |
ae7a7753-6b6a-467c-a056-80d6141f6eee | cm3-a-causal-masked-multimodal-model-of-the | 2201.07520 | null | https://arxiv.org/abs/2201.07520v1 | https://arxiv.org/pdf/2201.07520v1.pdf | CM3: A Causal Masked Multimodal Model of the Internet | We introduce CM3, a family of causally masked generative models trained over a large corpus of structured multi-modal documents that can contain both text and image tokens. Our new causally masked approach generates tokens left to right while also masking out a small number of long token spans that are generated at the... | ['Luke Zettlemoyer', 'Mike Lewis', 'Gargi Ghosh', 'Mandar Joshi', 'Dmytro Okhonko', 'Naman Goyal', 'Hu Xu', 'Vladimir Karpukhin', 'Candace Ross', 'Bernie Huang', 'Armen Aghajanyan'] | 2022-01-19 | null | null | null | null | ['entity-disambiguation'] | ['natural-language-processing'] | [ 6.77669704e-01 7.11996853e-01 -3.35838675e-01 -8.57481435e-02
-1.33138943e+00 -9.46843505e-01 1.40792644e+00 -4.94441539e-02
-1.11369535e-01 8.38324666e-01 9.40198123e-01 -2.26506457e-01
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8.45967010e-02 6.84952855e-01 3.28018889e-02 -7.91761428... | [11.152073860168457, 0.7152987122535706] |
3b49e948-5e2c-4fef-a0f6-31298085474b | combining-attention-module-and-pixel-shuffle | 2210.16836 | null | https://arxiv.org/abs/2210.16836v1 | https://arxiv.org/pdf/2210.16836v1.pdf | Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution | The License Plate Recognition (LPR) field has made impressive advances in the last decade due to novel deep learning approaches combined with the increased availability of training data. However, it still has some open issues, especially when the data come from low-resolution (LR) and low-quality images/videos, as in s... | ['David Menotti', 'William Robson Schwartz', 'Jorge de A. Lambert', 'Rayson Laroca', 'Valfride Nascimento'] | 2022-10-30 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 2.31859952e-01 -4.98942435e-01 8.42330419e-03 -1.39143273e-01
-1.36703908e+00 -4.61998224e-01 6.00866377e-01 -6.50046766e-01
-1.76954314e-01 6.72432184e-01 2.64185339e-01 1.60706118e-01
4.91849445e-02 -7.14878798e-01 -9.62357998e-01 -6.76049709e-01
3.18108618e-01 1.44102126e-01 4.19349641e-01 -2.37713158... | [11.108196258544922, -2.1879682540893555] |
3e13ff0e-62f6-40d7-ba8f-4a265fc21114 | sequence-to-backward-and-forward-sequences-a | 1607.00970 | null | http://arxiv.org/abs/1607.00970v2 | http://arxiv.org/pdf/1607.00970v2.pdf | Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation | Using neural networks to generate replies in human-computer dialogue systems
is attracting increasing attention over the past few years. However, the
performance is not satisfactory: the neural network tends to generate safe,
universally relevant replies which carry little meaning. In this paper, we
propose a content-i... | ['Zhi Jin', 'Lili Mou', 'Ge Li', 'Yiping Song', 'Rui Yan', 'Lu Zhang'] | 2016-07-04 | sequence-to-backward-and-forward-sequences-a-2 | https://aclanthology.org/C16-1316 | https://aclanthology.org/C16-1316.pdf | coling-2016-12 | ['short-text-conversation'] | ['natural-language-processing'] | [ 1.73573241e-01 6.15075409e-01 -1.80025715e-02 -5.07646203e-01
-5.13601661e-01 -5.01843810e-01 9.06071246e-01 -3.27904485e-02
-5.44043899e-01 1.16385102e+00 8.92982781e-01 -3.32091957e-01
8.94466490e-02 -8.37404191e-01 -2.17021137e-01 -4.00300831e-01
3.89878243e-01 6.28729999e-01 2.81618349e-02 -9.05731916... | [12.671626091003418, 8.323009490966797] |
fff41ae3-f5b7-46e8-af34-b23f3eaa2e9d | trigger-gnn-a-trigger-based-graph-neural | 2204.05518 | null | https://arxiv.org/abs/2204.05518v2 | https://arxiv.org/pdf/2204.05518v2.pdf | Trigger-GNN: A Trigger-Based Graph Neural Network for Nested Named Entity Recognition | Nested named entity recognition (NER) aims to identify the entity boundaries and recognize categories of the named entities in a complex hierarchical sentence. Some works have been done using character-level, word-level, or lexicon-level based models. However, such researches ignore the role of the complementary annota... | ['Liang Zhang', 'Wei Yan', 'Yingting Hu', 'Fanyang Bu', 'Yuan Sui'] | 2022-04-12 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-2.22548321e-01 3.45140755e-01 -1.37611836e-01 -4.49814230e-01
-4.22774762e-01 -6.35493696e-01 1.82832330e-01 5.41472852e-01
-7.36181915e-01 4.73721057e-01 8.18714321e-01 -1.61425203e-01
1.04964405e-01 -9.99491692e-01 -4.78749603e-01 -1.42970428e-01
-1.88207433e-01 2.23093748e-01 4.23375875e-01 -2.17618346... | [9.54372787475586, 9.416807174682617] |
4302ae02-6edc-411a-ae0c-d4ef8475d177 | blind-omnidirectional-image-quality-1 | 2302.12393 | null | https://arxiv.org/abs/2302.12393v1 | https://arxiv.org/pdf/2302.12393v1.pdf | Blind Omnidirectional Image Quality Assessment: Integrating Local Statistics and Global Semantics | Omnidirectional image quality assessment (OIQA) aims to predict the perceptual quality of omnidirectional images that cover the whole 180$\times$360$^{\circ}$ viewing range of the visual environment. Here we propose a blind/no-reference OIQA method named S$^2$ that bridges the gap between low-level statistics and high-... | ['Zhou Wang', 'Wei Zhou'] | 2023-02-24 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [-3.22008617e-02 -5.02548873e-01 1.64921612e-01 -6.49499893e-01
-9.70424533e-01 -1.38930872e-01 3.83258909e-01 -2.87331045e-01
-3.25883567e-01 4.77262437e-01 6.15499735e-01 -2.18168095e-01
-5.70159018e-01 -8.52068543e-01 -2.26599887e-01 -5.15756428e-01
-1.52833775e-01 -4.64460790e-01 -1.50452092e-01 -2.84899652... | [11.80144214630127, -1.9180090427398682] |
6e4469c9-07b8-4ea4-a2ea-8a5f4df868c3 | e-2tad-an-energy-efficient-tracking-based | 2204.04416 | null | https://arxiv.org/abs/2204.04416v4 | https://arxiv.org/pdf/2204.04416v4.pdf | E^2TAD: An Energy-Efficient Tracking-based Action Detector | Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays. It has high practical impacts for many applications across robotics, security, healthcare, etc. The two-stage paradigm of Faster R-CNN inspires a standard paradigm of vid... | ['Gang Hua', 'Zhangyang Wang', 'Zhou Ren', 'Yi Wu', 'Pengcheng Pi', 'Zhenyu Hu', 'Taiyu Long', 'Siqi Fan', 'Hao-Yu Miao', 'Zhenyu Wu', 'Xin Hu'] | 2022-04-09 | null | null | null | null | ['fine-grained-action-detection', 'action-localization', 'spatio-temporal-action-localization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.00193453e-01 -3.28375340e-01 -3.24127227e-01 7.30910227e-02
-4.32372630e-01 -4.97361273e-01 7.13369071e-01 -1.43635496e-01
-6.14105940e-01 5.33397496e-01 3.20504844e-01 -4.78931889e-02
-1.65608555e-01 -6.55161917e-01 -4.63525593e-01 -7.57283449e-01
-2.65984178e-01 9.83796865e-02 5.79750419e-01 -1.00993320... | [8.346537590026855, 0.4420221745967865] |
9a5f2956-a38b-4201-a622-a179a4c84c74 | point-set-voting-for-partial-point-cloud | 2007.04537 | null | https://arxiv.org/abs/2007.04537v2 | https://arxiv.org/pdf/2007.04537v2.pdf | Point Set Voting for Partial Point Cloud Analysis | The continual improvement of 3D sensors has driven the development of algorithms to perform point cloud analysis. In fact, techniques for point cloud classification and segmentation have in recent years achieved incredible performance driven in part by leveraging large synthetic datasets. Unfortunately these same state... | ['Jun-ming Zhang', 'Yu-Ping Wang', 'Matthew Johnson-Roberson', 'Ram Vasudevan', 'Weijia Chen'] | 2020-07-09 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 2.84595013e-01 4.77121584e-03 -4.31529731e-02 -3.92606914e-01
-9.11812603e-01 -6.50486052e-01 6.48795009e-01 3.79067242e-01
5.89920208e-03 3.16198051e-01 -5.85643470e-01 -3.45755517e-02
-1.20237850e-01 -1.05122209e+00 -7.84373522e-01 -7.20553517e-01
3.43981311e-02 1.18310189e+00 4.84436631e-01 1.43025309... | [8.231078147888184, -3.103618860244751] |
d5ca28d4-c101-420a-ab88-508c3ede4663 | wave-u-net-discriminator-fast-and-lightweight | 2303.13909 | null | https://arxiv.org/abs/2303.13909v1 | https://arxiv.org/pdf/2303.13909v1.pdf | Wave-U-Net Discriminator: Fast and Lightweight Discriminator for Generative Adversarial Network-Based Speech Synthesis | In speech synthesis, a generative adversarial network (GAN), training a generator (speech synthesizer) and a discriminator in a min-max game, is widely used to improve speech quality. An ensemble of discriminators is commonly used in recent neural vocoders (e.g., HiFi-GAN) and end-to-end text-to-speech (TTS) systems (e... | ['Shogo Seki', 'Kou Tanaka', 'Hirokazu Kameoka', 'Takuhiro Kaneko'] | 2023-03-24 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 1.90680072e-01 2.24931791e-01 2.88086325e-01 -5.08813784e-02
-1.14180231e+00 -6.37432992e-01 4.75883752e-01 -6.17483020e-01
-9.71711148e-03 7.46105194e-01 2.47081682e-01 -4.30949479e-01
4.14639354e-01 -8.59692514e-01 -6.23208284e-01 -8.18147719e-01
2.57015705e-01 6.43520802e-02 -1.13556378e-01 -3.48859757... | [15.372417449951172, 6.191558361053467] |
9914e777-debc-4c41-ab0f-b35fcb7a334a | 10000-times-accelerated-robust-subset | 1409.3660 | null | http://arxiv.org/abs/1409.3660v4 | http://arxiv.org/pdf/1409.3660v4.pdf | 10,000+ Times Accelerated Robust Subset Selection (ARSS) | Subset selection from massive data with noised information is increasingly
popular for various applications. This problem is still highly challenging as
current methods are generally slow in speed and sensitive to outliers. To
address the above two issues, we propose an accelerated robust subset selection
(ARSS) method... | ['Feiyun Zhu', 'Xinliang Zhu', 'Ying Wang', 'Shiming Xiang', 'Chunhong Pan', 'Bin Fan'] | 2014-09-12 | null | null | null | null | ['temporal-action-proposal-generation', 'music-modeling', 'nested-named-entity-recognition'] | ['computer-vision', 'music', 'natural-language-processing'] | [ 1.60365283e-01 -4.48900670e-01 -1.51268477e-02 -1.95744321e-01
-8.82427812e-01 -3.08123082e-01 -8.93339664e-02 5.49515665e-01
-5.25725245e-01 8.72369707e-01 -3.30220908e-01 -9.95468870e-02
-3.23275596e-01 -8.89365017e-01 -5.44125617e-01 -8.15534413e-01
-1.82496831e-01 2.50213593e-01 3.53258431e-01 -2.03090400... | [6.900694370269775, 4.596887588500977] |
44e16135-e83f-45d7-a183-e5f5a6a76ce7 | understanding-unfairness-via-training-concept | 2306.17828 | null | https://arxiv.org/abs/2306.17828v1 | https://arxiv.org/pdf/2306.17828v1.pdf | Understanding Unfairness via Training Concept Influence | Knowing the causes of a model's unfairness helps practitioners better understand their data and algorithms. This is an important yet relatively unexplored task. We look into this problem through the lens of the training data - one of the major sources of unfairness. We ask the following questions: how would a model's f... | ['Yang Liu', 'Yuanshun Yao'] | 2023-06-30 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 3.60324055e-01 2.85381675e-01 -4.77242500e-01 -5.97122848e-01
-2.89879918e-01 -6.15954161e-01 5.47774434e-01 3.11390191e-01
-6.53481781e-01 1.16260850e+00 5.19530118e-01 -4.83059168e-01
-1.59612596e-01 -8.26275349e-01 -8.78123939e-01 -8.17840159e-01
2.23394230e-01 3.10129702e-01 -4.50406432e-01 8.39882791... | [8.907381057739258, 5.37070369720459] |
6534b4c5-a6dc-47ee-86f6-606d9178e8bd | mixture-of-graphs-zero-shot-relational | null | null | https://openreview.net/forum?id=RdQXgI1pXt1 | https://openreview.net/pdf?id=RdQXgI1pXt1 | Mixture-of-Graphs: Zero-shot Relational Learning for Knowledge Graph by Fusing Ontology and Textual Experts | Knowledge Graph Embedding (KGE) have been proposed and succeed utilized to knowledge Graph Completion (KGC). But dominant KGE models often fail in zero-shot relational learning because they cannot learn effective representations for unseen relations. Previous studies mainly separately utilize the textual description of... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['relational-reasoning'] | ['natural-language-processing'] | [-3.97191107e-01 5.60240686e-01 -5.01522303e-01 -1.89503849e-01
-3.13326985e-01 -8.99230912e-02 6.16123915e-01 4.29908007e-01
-7.11189508e-02 6.26734257e-01 5.14728844e-01 -5.93783893e-02
-6.23781800e-01 -1.44744217e+00 -5.53866684e-01 -3.82333606e-01
-7.39983469e-03 8.19437742e-01 6.99549496e-01 -5.60069323... | [8.813823699951172, 7.969298839569092] |
35688835-c1b9-4e50-9db5-5febe096b430 | native-language-identification-with-attention | null | null | https://aclanthology.org/2020.icon-main.35 | https://aclanthology.org/2020.icon-main.35.pdf | Native-Language Identification with Attention | The paper explores how an attention-based approach can increase performance on the task of native-language identification (NLI), i.e., to identify an author’s first language given information expressed in a second language. Previously, Support Vector Machines have consistently outperformed deep learning-based methods o... | ['Björn Gambäck', 'Stian Steinbakken'] | null | null | null | null | icon-2020-12 | ['native-language-identification'] | ['natural-language-processing'] | [-6.73528090e-02 8.21059048e-02 -3.04251820e-01 -1.80645838e-01
-1.12965059e+00 -9.36147451e-01 9.17515755e-01 1.29051089e-01
-7.08630323e-01 7.74079800e-01 2.54350811e-01 -8.26028347e-01
6.79452792e-02 -2.10087985e-01 -4.59252447e-01 -1.79436043e-01
2.86716312e-01 9.87323046e-01 -3.52448016e-01 -1.17454417... | [10.374920845031738, 10.54114818572998] |
a7e7b315-df76-4cb6-ab2b-f13a31546e0e | bridging-the-gap-between-training-and-2 | 2005.09343 | null | https://arxiv.org/abs/2005.09343v1 | https://arxiv.org/pdf/2005.09343v1.pdf | Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting | Spatio-temporal sequence forecasting is one of the fundamental tasks in spatio-temporal data mining. It facilitates many real world applications such as precipitation nowcasting, citywide crowd flow prediction and air pollution forecasting. Recently, a few Seq2Seq based approaches have been proposed, but one of the dra... | ['Hong-Bin Liu', 'Ickjai Lee'] | 2020-05-19 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [ 5.34284592e-01 -1.54413328e-01 -2.81595200e-01 -5.17970502e-01
-5.36779046e-01 -4.14290577e-01 6.72232032e-01 1.11388721e-01
-5.63464820e-01 1.22032809e+00 3.63083124e-01 -4.87946242e-01
-1.49916625e-02 -9.18259025e-01 -7.49475241e-01 -8.40925038e-01
-1.81611568e-01 4.87400770e-01 5.55627942e-01 -3.44621927... | [6.853796482086182, 2.490302324295044] |
85ca518a-195c-4fd5-9c07-b528a4919af1 | towards-less-generic-responses-in-neural | null | null | https://aclanthology.org/D18-1297 | https://aclanthology.org/D18-1297.pdf | Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method | Sequence-to-sequence neural generation models have achieved promising performance on short text conversation tasks. However, they tend to generate generic/dull responses, leading to unsatisfying dialogue experience. We observe that in the conversation tasks, each query could have multiple responses, which forms a 1-to-... | ['Yahui Liu', 'Xiaojiang Liu', 'Jian Yao', 'Jun Gao', 'Shuming Shi', 'Wei Bi'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['short-text-conversation'] | ['natural-language-processing'] | [ 3.47302914e-01 2.44362324e-01 2.44155200e-03 -7.11813331e-01
-9.89782870e-01 -4.27641213e-01 7.44023561e-01 -3.58200938e-01
-3.64889681e-01 1.10750663e+00 7.62661219e-01 -1.69874191e-01
3.48045021e-01 -7.67206728e-01 -4.85920906e-02 -5.60176969e-01
5.41504622e-01 7.26237953e-01 8.84836689e-02 -8.87766242... | [12.587491035461426, 8.29146957397461] |
b2ea3727-5f02-4728-96b3-71fa0e46cbf7 | denseattentionseg-segment-hands-from | 1903.12368 | null | http://arxiv.org/abs/1903.12368v2 | http://arxiv.org/pdf/1903.12368v2.pdf | DenseAttentionSeg: Segment Hands from Interacted Objects Using Depth Input | We propose a real-time DNN-based technique to segment hand and object of
interacting motions from depth inputs. Our model is called DenseAttentionSeg,
which contains a dense attention mechanism to fuse information in different
scales and improves the results quality with skip-connections. Besides, we
introduce a contou... | ['Hao Zhang', 'Zihao Bo', 'Junhai Yong', 'Feng Xu'] | 2019-03-29 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [-9.28481966e-02 -3.13286595e-02 -2.10970327e-01 -3.56653929e-01
-6.11430466e-01 -4.27480817e-01 9.16893110e-02 -7.86804557e-01
-4.39900041e-01 5.15116692e-01 4.53027874e-01 7.60730579e-02
1.61920682e-01 -5.27378440e-01 -7.16598392e-01 -6.11014426e-01
2.60967463e-01 7.55258024e-01 8.72483373e-01 -1.81464791... | [6.705166339874268, -0.5973857045173645] |
01d2dd34-019b-470c-a390-72650fe29910 | real-time-end-to-end-video-text-spotter-with | 2207.08417 | null | https://arxiv.org/abs/2207.08417v3 | https://arxiv.org/pdf/2207.08417v3.pdf | Real-time End-to-End Video Text Spotter with Contrastive Representation Learning | Video text spotting(VTS) is the task that requires simultaneously detecting, tracking and recognizing text in the video. Existing video text spotting methods typically develop sophisticated pipelines and multiple models, which is not friend for real-time applications. Here we propose a real-time end-to-end video text s... | ['Ping Luo', 'Zhongyuan Wang', 'Size Li', 'Hong Zhou', 'Chunhua Shen', 'Jiahong Li', 'Zhuang Li', 'Wejia Wu'] | 2022-07-18 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 2.26697668e-01 -8.38872194e-01 -1.35478795e-01 1.77911390e-02
-9.71342921e-01 -4.41111982e-01 3.72491211e-01 -1.59341604e-01
-4.16108906e-01 -9.37451199e-02 8.64812210e-02 -3.68251830e-01
5.78817368e-01 -1.86906219e-01 -6.32291853e-01 -5.84662199e-01
4.58508968e-01 2.46148601e-01 7.04526365e-01 2.15301856... | [11.982329368591309, 2.2038843631744385] |
f1db9ec6-d237-486b-88ec-8b08feb7c638 | dct-dual-channel-training-of-action | 2306.15913 | null | https://arxiv.org/abs/2306.15913v1 | https://arxiv.org/pdf/2306.15913v1.pdf | DCT: Dual Channel Training of Action Embeddings for Reinforcement Learning with Large Discrete Action Spaces | The ability to learn robust policies while generalizing over large discrete action spaces is an open challenge for intelligent systems, especially in noisy environments that face the curse of dimensionality. In this paper, we present a novel framework to efficiently learn action embeddings that simultaneously allow us ... | ['Harshad Khadilkar', 'Hardik Meisheri', 'Pranavi Pathakota'] | 2023-06-28 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 1.62967399e-01 1.60888478e-01 -3.65787297e-01 -2.98880577e-01
-9.02262866e-01 -5.02054393e-01 7.61027694e-01 -2.03561574e-01
-7.19630837e-01 7.82893896e-01 7.84725845e-01 -3.03867310e-01
-7.69769354e-03 -6.50902212e-01 -7.66746759e-01 -6.84153795e-01
-2.55204916e-01 4.78165716e-01 -1.51600257e-01 -3.27173591... | [4.14881706237793, 1.6179198026657104] |
6178682e-29d4-4852-a687-70d76e7a1153 | easytransfer-a-simple-and-scalable-deep | 2011.09463 | null | https://arxiv.org/abs/2011.09463v3 | https://arxiv.org/pdf/2011.09463v3.pdf | EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications | The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose. To bridge this gap, the EasyTransfer pla... | ['Wei Lin', 'Deng Cai', 'Yaliang Li', 'Xianyan Jia', 'Hanjie Pan', 'Chengyu Wang', 'Ang Wang', 'Jun Huang', 'Cen Chen', 'Peng Li', 'Minghui Qiu'] | 2020-11-18 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-4.61083233e-01 -2.09992141e-01 -4.02074635e-01 -4.74244148e-01
-1.15577257e+00 -7.47231662e-01 3.73808503e-01 -1.29386485e-01
-2.03194946e-01 5.26455283e-01 4.96482924e-02 -6.83529496e-01
1.21523537e-01 -7.34853148e-01 -6.44822299e-01 -2.83759356e-01
8.72298703e-02 1.01014912e+00 -9.21136886e-02 -4.03466254... | [10.922798156738281, 8.569759368896484] |
d83c7fc7-a33b-4946-85f3-4e9cd356aad9 | understanding-bias-in-anomaly-detection-a | null | null | https://openreview.net/forum?id=sAzh_FTFDxz | https://openreview.net/pdf?id=sAzh_FTFDxz | Understanding Bias in Anomaly Detection: A Semi-Supervised View with PAC Guarantees | Anomaly detection presents a unique challenge in machine learning, due to the scarcity of labeled anomaly data. Existing work attempts to mitigate such problems via semi-supervised learning, i.e., augmenting unsupervised anomaly detection models with additional labeled anomaly samples. However, the labeled data often d... | ['Haitao Zheng', 'Yuxin Chen', 'Ziyu Ye'] | 2021-01-01 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 2.83895522e-01 1.60373822e-02 -8.40967819e-02 -6.46671891e-01
-7.99260318e-01 -4.71707314e-01 5.15038252e-01 4.27169085e-01
-3.01023275e-01 3.06661308e-01 -2.25329533e-01 -3.62441540e-01
-1.20694109e-04 -5.46710074e-01 -6.06916606e-01 -5.52806377e-01
-3.80511791e-01 4.29033637e-01 2.15967372e-01 2.61431485... | [7.605988502502441, 2.5022377967834473] |
41776d66-c6a7-4710-a512-583e933ced9a | imaginarynet-learning-object-detectors | 2210.06886 | null | https://arxiv.org/abs/2210.06886v1 | https://arxiv.org/pdf/2210.06886v1.pdf | ImaginaryNet: Learning Object Detectors without Real Images and Annotations | Without the demand of training in reality, humans can easily detect a known concept simply based on its language description. Empowering deep learning with this ability undoubtedly enables the neural network to handle complex vision tasks, e.g., object detection, without collecting and annotating real images. To this e... | ['WangMeng Zuo', 'Kailai Feng', 'Zitong Huang', 'Minheng Ni'] | 2022-10-13 | null | null | null | null | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 4.69525933e-01 4.67755169e-01 -7.68000931e-02 -2.97588527e-01
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3.31598550e-01 5.52870095e-01 4.17115539e-01 2.24986132... | [9.744929313659668, 1.4526995420455933] |
dafa9a3a-9f42-45e7-b9fd-7fd326f63c76 | handsformer-keypoint-transformer-for | 2104.14639 | null | https://arxiv.org/abs/2104.14639v2 | https://arxiv.org/pdf/2104.14639v2.pdf | Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation | We propose a robust and accurate method for estimating the 3D poses of two hands in close interaction from a single color image. This is a very challenging problem, as large occlusions and many confusions between the joints may happen. State-of-the-art methods solve this problem by regressing a heatmap for each joint, ... | ['Vincent Lepetit', 'Mahdi Rad', 'Sayan Deb Sarkar', 'Shreyas Hampali'] | 2021-04-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hampali_Keypoint_Transformer_Solving_Joint_Identification_in_Challenging_Hands_and_Object_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hampali_Keypoint_Transformer_Solving_Joint_Identification_in_Challenging_Hands_and_Object_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-pose-estimation'] | ['computer-vision'] | [-2.54058987e-01 -6.40183091e-02 -1.57195795e-02 -1.51038155e-01
-7.57078111e-01 -6.05803668e-01 4.16493118e-01 -3.00807148e-01
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3.81219499e-02 1.03935528e+00 4.69568461e-01 -6.87504606... | [6.629334449768066, -0.833489716053009] |
97165113-7b27-488e-829f-d3d94adcf7e3 | learning-to-accelerate-partial-differential | 2206.07681 | null | https://arxiv.org/abs/2206.07681v2 | https://arxiv.org/pdf/2206.07681v2.pdf | Learning to Accelerate Partial Differential Equations via Latent Global Evolution | Simulating the time evolution of Partial Differential Equations (PDEs) of large-scale systems is crucial in many scientific and engineering domains such as fluid dynamics, weather forecasting and their inverse optimization problems. However, both classical solvers and recent deep learning-based surrogate models are typ... | ['Jure Leskovec', 'Takashi Maruyama', 'Tailin Wu'] | 2022-06-15 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-2.10677743e-01 -4.74366128e-01 4.24883842e-01 1.78301811e-01
-6.73749804e-01 -5.46774268e-01 4.78431523e-01 -1.05315529e-01
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-4.89125907e-01 8.46729636e-01 -2.05061048e-01 -1.17473386... | [6.529872894287109, 3.4285295009613037] |
c79a1607-4d58-4c45-a38c-feace353db5a | aiir-mix-multi-agent-reinforcement-learning | 2302.09531 | null | https://arxiv.org/abs/2302.09531v1 | https://arxiv.org/pdf/2302.09531v1.pdf | AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network | Deducing the contribution of each agent and assigning the corresponding reward to them is a crucial problem in cooperative Multi-Agent Reinforcement Learning (MARL). Previous studies try to resolve the issue through designing an intrinsic reward function, but the intrinsic reward is simply combined with the environment... | ['Shiyi Huang', 'Shitong Shao', 'Weiyan Liu', 'Wei Li'] | 2023-02-19 | null | null | null | null | ['starcraft-ii', 'starcraft'] | ['playing-games', 'playing-games'] | [-3.15712154e-01 3.67646031e-02 -1.13745943e-01 1.91797227e-01
-3.66671830e-01 -3.11042279e-01 4.74019766e-01 2.37864424e-02
-9.64299321e-01 1.07347333e+00 6.62656575e-02 7.66427591e-02
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-3.03546578e-01 6.10877216e-01 5.30581355e-01 -1.19587111... | [3.737269163131714, 1.9153109788894653] |
eef1cb44-e92b-4538-b41e-0ecf73371be9 | analyzing-an-imitation-learning-network-for | 1912.10837 | null | https://arxiv.org/abs/1912.10837v1 | https://arxiv.org/pdf/1912.10837v1.pdf | Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach | Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions. The differences between intra-patient images can be assessed quantitatively by registering serial acquisitions. Due to the variability of ... | ['Nishant Ravikumar', 'Siming Bayer', 'Weilin Fu', 'Xia Zhong', 'Andreas Maier'] | 2019-12-19 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 3.02106626e-02 -1.02491021e-01 -4.19991463e-02 -3.57484967e-01
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-1.60651475e-01 -4.18037593e-01 -4.50929403e-01 -8.28546822e-01
-4.05215919e-02 -4.24069352e-02 1.69703782e-01 1.03880249... | [15.742307662963867, -3.926579236984253] |
3986ed8b-3e91-4fce-80b1-76c44f795e49 | knowledge-distillation-for-multi-target | 2205.06237 | null | https://arxiv.org/abs/2205.06237v2 | https://arxiv.org/pdf/2205.06237v2.pdf | Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification | Despite the recent success of deep learning architectures, person re-identification (ReID) remains a challenging problem in real-word applications. Several unsupervised single-target domain adaptation (STDA) methods have recently been proposed to limit the decline in ReID accuracy caused by the domain shift that typica... | ['Eric Granger', 'Rafael M. O. Cruz', 'Le Thanh Nguyen-Meidine', 'Sajjad Abdoli', 'Djebril Mekhazni', 'Félix Remigereau'] | 2022-05-12 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 4.06328700e-02 -2.78942913e-01 2.61445786e-03 -5.17267466e-01
-5.20237625e-01 -7.14751244e-01 7.34759927e-01 -1.88118573e-02
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-9.22620296e-02 -5.85498214e-01 -7.65260875e-01 -5.79676926e-01
6.43808320e-02 7.93094933e-01 3.03702712e-01 -2.00884163... | [14.69869613647461, 1.0414929389953613] |
e809de63-c891-42bc-bb47-3b920d7d9370 | gadformer-an-attention-based-model-for-group | 2303.09841 | null | https://arxiv.org/abs/2303.09841v1 | https://arxiv.org/pdf/2303.09841v1.pdf | GADFormer: An Attention-based Model for Group Anomaly Detection on Trajectories | Group Anomaly Detection (GAD) reveals anomalous behavior among groups consisting of multiple member instances, which are, individually considered, not necessarily anomalous. This task is of major importance across multiple disciplines, in which also sequences like trajectories can be considered as a group. However, wit... | ['Peer Kröger', 'Darpan Malik', 'Andreas Lohrer'] | 2023-03-17 | null | null | null | null | ['group-anomaly-detection'] | ['methodology'] | [ 4.17461395e-02 -6.18763044e-02 2.01689288e-01 -2.58144021e-01
-5.17759860e-01 -5.96662760e-01 7.23843396e-01 5.67865610e-01
-3.08945268e-01 4.80667114e-01 2.08114281e-01 -4.74760890e-01
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-5.25698960e-01 5.04675448e-01 1.91862896e-01 -2.09319666... | [7.496887683868408, 2.4433155059814453] |
158e92f0-6123-4963-afed-b57bbee0567b | spiking-fer-spiking-neural-network-for-facial | 2304.10211 | null | https://arxiv.org/abs/2304.10211v1 | https://arxiv.org/pdf/2304.10211v1.pdf | Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Cameras | Facial Expression Recognition (FER) is an active research domain that has shown great progress recently, notably thanks to the use of large deep learning models. However, such approaches are particularly energy intensive, which makes their deployment difficult for edge devices. To address this issue, Spiking Neural Net... | ['Chaabane Djéraba', 'José Mennesson', 'Amel Aissaoui', 'Benjamin Allaert', 'Sami Barchid'] | 2023-04-20 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 4.92759913e-01 -2.69495577e-01 6.11150935e-02 -3.44495714e-01
-2.06993267e-01 -5.72512411e-02 5.60249388e-01 8.79141465e-02
-6.73973739e-01 7.62662828e-01 -2.65032589e-01 1.45896778e-01
8.76265690e-02 -8.47448230e-01 -9.38832343e-01 -7.98804700e-01
-2.14889999e-02 -1.84856519e-01 2.14541912e-01 -6.03874736... | [8.253815650939941, 2.425004720687866] |
99f26656-2d98-47d8-b99f-dbc052083bf3 | discriminatively-trained-sparse-code | null | null | http://papers.nips.cc/paper/4787-discriminatively-trained-sparse-code-gradients-for-contour-detection | http://papers.nips.cc/paper/4787-discriminatively-trained-sparse-code-gradients-for-contour-detection.pdf | Discriminatively Trained Sparse Code Gradients for Contour Detection | Finding contours in natural images is a fundamental problem that serves as the basis of many tasks such as image segmentation and object recognition. At the core of contour detection technologies are a set of hand-designed gradient features, used by most existing approaches including the state-of-the-art Global Pb (gPb... | ['Ren Xiaofeng', 'Liefeng Bo'] | 2012-12-01 | null | null | null | neurips-2012-12 | ['contour-detection'] | ['computer-vision'] | [ 3.41064811e-01 -2.65151083e-01 -4.04753983e-01 -2.38565743e-01
-7.01761603e-01 -6.30233347e-01 3.08740973e-01 5.25983512e-01
-5.23715198e-01 2.71777868e-01 -1.16870932e-01 -4.33321223e-02
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-3.76477391e-01 2.67747253e-01 6.62553549e-01 -2.15149760... | [9.499054908752441, 0.09624890238046646] |
5c9cd15f-f74f-46ac-893c-5b11a8c80c86 | learning-discriminative-and-robust-time | 1912.06808 | null | https://arxiv.org/abs/1912.06808v3 | https://arxiv.org/pdf/1912.06808v3.pdf | Environmental Sound Classification with Parallel Temporal-spectral Attention | Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC). Recently, temporal attention mechanisms have been used in CNN to capture the useful information from the relevant time frames for audio classification, especially for weakly labe... | ['Dading Chong', 'Wenwu Wang', 'Yuexian Zou', 'Helin Wang'] | 2019-12-14 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 3.19663316e-01 -6.31951213e-01 3.84456456e-01 -4.57421660e-01
-5.43413281e-01 -3.49927992e-01 2.98041612e-01 2.10559249e-01
-5.50095201e-01 3.52588803e-01 3.40411514e-01 1.34299740e-01
-3.31166625e-01 -5.33825457e-01 -4.20318872e-01 -8.88458550e-01
-2.86663741e-01 -4.65169758e-01 4.86529827e-01 -1.13909684... | [15.185385704040527, 5.23579740524292] |
edf86f71-1b48-4e6e-8ba4-6313f87c0943 | real-time-polyp-detection-localisation-and | 2011.07631 | null | https://arxiv.org/abs/2011.07631v2 | https://arxiv.org/pdf/2011.07631v2.pdf | Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning | Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of res... | ['Håvard D. Johansen', 'Nikhil Kumar Tomar', 'Pål Halvorsen', 'Michael A. Riegler', 'Jens Rittscher', 'Dag D. Johansen', 'Sharib Ali', 'Debesh Jha'] | 2020-11-15 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [ 1.79710060e-01 5.58420718e-02 -2.52822861e-02 -5.95568540e-03
-9.88091826e-01 -5.68804801e-01 2.64060885e-01 8.12538743e-01
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-2.17583831e-02 -6.43172681e-01 -3.84778678e-01 -8.50340247e-01
-4.56250340e-01 2.87136436e-01 6.31819129e-01 4.14032489... | [14.259086608886719, -3.0056605339050293] |
edacbac9-a5d8-4d88-8cf6-881fe9965bba | multilingual-sentence-transformer-as-a | 2301.12140 | null | https://arxiv.org/abs/2301.12140v1 | https://arxiv.org/pdf/2301.12140v1.pdf | Multilingual Sentence Transformer as A Multilingual Word Aligner | Multilingual pretrained language models (mPLMs) have shown their effectiveness in multilingual word alignment induction. However, these methods usually start from mBERT or XLM-R. In this paper, we investigate whether multilingual sentence Transformer LaBSE is a strong multilingual word aligner. This idea is non-trivial... | ['Yun Chen', 'Yue Han', 'Hanqing Wang', 'Guanhua Chen', 'Weikang Wang'] | 2023-01-28 | null | null | null | null | ['word-alignment', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [-2.44323581e-01 -2.80696660e-01 -4.81552660e-01 -3.71293575e-01
-1.18012071e+00 -7.23076403e-01 6.22340798e-01 1.70081079e-01
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1.31769672e-01 9.97418821e-01 -1.87662855e-01 -8.34089816... | [11.129159927368164, 10.123950958251953] |
8bbb079a-d199-41b1-ad17-0d0279d0eb4a | irnlp-daiict-lt-edi-eacl2021-hope-speech | null | null | https://aclanthology.org/2021.ltedi-1.15 | https://aclanthology.org/2021.ltedi-1.15.pdf | IRNLP_DAIICT@LT-EDI-EACL2021: Hope Speech detection in Code Mixed text using TF-IDF Char N-grams and MuRIL | This paper presents the participation of the IRNLP_DAIICT team from Information Retrieval and Natural Language Processing lab at DA-IICT, India in LT-EDI@EACL2021 Hope Speech Detection task. The aim of this shared task is to identify hope speech from a code-mixed data-set of YouTube comments. The task is to classify co... | ['Prasenjit Majumder', 'Shripad Bhat', 'Bhargav Dave'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-4.90908533e-01 1.61005482e-01 -3.87103021e-01 -6.63555190e-02
-1.08126783e+00 -8.10289800e-01 7.80072391e-01 5.68060517e-01
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9.52489600e-02 -2.92694181e-01 -6.99924529e-02 -1.14502341e-01
8.57926682e-02 4.07571137e-01 -1.68989137e-01 -1.82553485... | [9.367328643798828, 10.686226844787598] |
b929e4da-dde7-40e1-9799-25181f8118d0 | finding-strength-in-weakness-learning-to | 1911.02182 | null | https://arxiv.org/abs/1911.02182v2 | https://arxiv.org/pdf/1911.02182v2.pdf | Finding Strength in Weakness: Learning to Separate Sounds with Weak Supervision | While there has been much recent progress using deep learning techniques to separate speech and music audio signals, these systems typically require large collections of isolated sources during the training process. When extending audio source separation algorithms to more general domains such as environmental monitori... | ['Fatemeh Pishdadian', 'Jonathan Le Roux', 'Gordon Wichern'] | 2019-11-06 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 5.92010617e-01 -1.94351777e-01 1.27366811e-01 -1.14016376e-01
-1.31962633e+00 -6.97574615e-01 3.58146131e-01 3.49789768e-01
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-2.98456281e-01 2.72068024e-01 3.12243074e-01 1.61688030... | [15.216172218322754, 5.475631237030029] |
2781f26f-476a-4a0b-85cd-3359d71c6205 | deep-learning-systems-for-advanced-driving | 2304.06041 | null | https://arxiv.org/abs/2304.06041v1 | https://arxiv.org/pdf/2304.06041v1.pdf | Deep Learning Systems for Advanced Driving Assistance | Next generation cars embed intelligent assessment of car driving safety through innovative solutions often based on usage of artificial intelligence. The safety driving monitoring can be carried out using several methodologies widely treated in scientific literature. In this context, the author proposes an innovative a... | ['Francesco Rundo'] | 2023-04-05 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 1.91539135e-02 3.43364179e-01 1.15946926e-01 -2.32657567e-01
2.97029167e-01 -2.62294292e-01 1.85121551e-01 -1.49301067e-01
-3.13286722e-01 9.11795914e-01 2.64304150e-02 2.49009044e-03
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5.69796443e-01 -1.74351439e-01 -1.63207635e-01 -3.54549289... | [13.594582557678223, 2.9362919330596924] |
c24ccfbb-c823-43d0-bd17-4e0a6c92d01b | generalized-few-shot-semantic-segmentation | 2010.05210 | null | https://arxiv.org/abs/2010.05210v4 | https://arxiv.org/pdf/2010.05210v4.pdf | Generalized Few-shot Semantic Segmentation | Training semantic segmentation models requires a large amount of finely annotated data, making it hard to quickly adapt to novel classes not satisfying this condition. Few-Shot Segmentation (FS-Seg) tackles this problem with many constraints. In this paper, we introduce a new benchmark, called Generalized Few-Shot Sema... | ['Hengshuang Zhao', 'Michelle Shu', 'Shu Liu', 'Jiaya Jia', 'Li Jiang', 'Xin Lai', 'Zhuotao Tian'] | 2020-10-11 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tian_Generalized_Few-Shot_Semantic_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tian_Generalized_Few-Shot_Semantic_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['generalized-few-shot-semantic-segmentation'] | ['computer-vision'] | [ 3.95427078e-01 1.44880027e-01 -2.83973724e-01 -5.34239352e-01
-1.01611805e+00 -5.73991895e-01 3.36423367e-01 3.81046012e-02
-4.36020613e-01 4.82429534e-01 -1.95677832e-01 -1.26087949e-01
2.59146422e-01 -5.65680981e-01 -9.76503134e-01 -5.65612376e-01
1.75099462e-01 4.93614107e-01 9.70412314e-01 -9.76892412... | [9.56368350982666, 1.231203317642212] |
0fec5321-cfe2-4023-8714-be8bb191a485 | robust-full-fov-depth-estimation-in-tele-wide | 1909.03375 | null | https://arxiv.org/abs/1909.03375v2 | https://arxiv.org/pdf/1909.03375v2.pdf | Robust Full-FoV Depth Estimation in Tele-wide Camera System | Tele-wide camera system with different Field of View (FoV) lenses becomes very popular in recent mobile devices. Usually it is difficult to obtain full-FoV depth based on traditional stereo-matching methods. Pure Deep Neural Network (DNN) based depth estimation methods can obtain full-FoV depth, but have low robustness... | ['Tae-ui Kim', 'Seungmin Han', 'Irina Kim', 'Soonkeun Chang', 'Seongwook Song', 'Kai Guo'] | 2019-09-08 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 1.14621907e-01 -2.34776095e-01 3.30659486e-02 -4.04371619e-01
-4.93533820e-01 -1.37054265e-01 2.19595060e-01 -6.19171560e-01
-4.33959454e-01 7.28772223e-01 1.84616849e-01 -7.55551755e-02
2.12766320e-01 -1.11078918e+00 -5.97896516e-01 -7.64500201e-01
5.72442293e-01 -1.32041156e-01 5.77802181e-01 6.16918551... | [8.93398666381836, -2.4525365829467773] |
f5d67f8c-f133-47a7-91b9-552cfb92b315 | ssegcn-syntactic-and-semantic-enhanced-graph | null | null | https://aclanthology.org/2022.naacl-main.362 | https://aclanthology.org/2022.naacl-main.362.pdf | SSEGCN: Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis | Aspect-based Sentiment Analysis (ABSA) aims to predict the sentiment polarity towards a particular aspect in a sentence. Recently, graph neural networks based on dependency tree convey rich structural information which is proven to be utility for ABSA. However, how to effectively harness the semantic and syntactic stru... | ['Yanna Wang', 'Zili Zhou', 'Zheng Zhang'] | null | null | null | null | naacl-2022-7 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 5.74755110e-02 1.12637289e-01 3.65230814e-02 -7.04113901e-01
-2.54206836e-01 -3.06177646e-01 2.91598797e-01 3.53083104e-01
-1.03413165e-01 2.14776322e-01 7.00057268e-01 -2.39730418e-01
-1.34473532e-01 -1.12612998e+00 -5.09832442e-01 -5.33940971e-01
2.55066544e-01 1.23865813e-01 -1.28546834e-01 -7.21813381... | [11.497591972351074, 6.605571746826172] |
6e7c799e-7808-47ec-bf82-1ecb3b6da057 | collaborative-training-of-medical-artificial | 2211.13606 | null | https://arxiv.org/abs/2211.13606v2 | https://arxiv.org/pdf/2211.13606v2.pdf | Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels | Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While multiple stakeholders have provided publicly available datasets, the ways in which these data are lab... | ['Daniel Truhn', 'Sven Nebelung', 'Christiane Kuhl', 'Jakob Nikolas Kather', 'Firas Khader', 'Gustav Mueller-Franzes', 'Marwin Saehn', 'Peter Isfort', 'Soroosh Tayebi Arasteh'] | 2022-11-24 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 1.07131399e-01 5.67964315e-02 -3.64717156e-01 -5.46762168e-01
-1.07357347e+00 -5.12009442e-01 2.10701182e-01 1.98452279e-01
-2.84460515e-01 7.69015193e-01 2.78660268e-01 -6.21329486e-01
-3.22513431e-01 -8.68440688e-01 -5.96787333e-01 -7.90000439e-01
1.42971026e-02 6.01751208e-01 -2.04779655e-01 3.97775739... | [6.099012851715088, 6.463272571563721] |
36c78981-5f5c-4d28-a56c-a69e61cacf94 | multimodal-pre-training-framework-for | 2303.11879 | null | https://arxiv.org/abs/2303.11879v1 | https://arxiv.org/pdf/2303.11879v1.pdf | Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning | Sequential recommendation systems utilize the sequential interactions of users with items as their main supervision signals in learning users' preferences. However, existing methods usually generate unsatisfactory results due to the sparsity of user behavior data. To address this issue, we propose a novel pre-training ... | ['Zhiqi Shen', 'Xin Zhou', 'Lingzi Zhang'] | 2023-03-21 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 2.75474578e-01 -8.52718532e-01 -6.98009014e-01 -4.59383041e-01
-6.69747472e-01 -6.35828614e-01 3.92084718e-01 -3.49349707e-01
-4.05919611e-01 2.85639226e-01 5.29495299e-01 -1.77081779e-01
8.54885057e-02 -3.82902920e-01 -8.38493049e-01 -6.55452371e-01
2.67850488e-01 1.32244870e-01 -1.70542896e-01 -3.09298724... | [10.177943229675293, 5.593347549438477] |
c4592ada-776a-454e-b0b0-36b306664570 | graph-convolution-for-semi-supervised | 2102.06966 | null | https://arxiv.org/abs/2102.06966v4 | https://arxiv.org/pdf/2102.06966v4.pdf | Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization | Recently there has been increased interest in semi-supervised classification in the presence of graphical information. A new class of learning models has emerged that relies, at its most basic level, on classifying the data after first applying a graph convolution. To understand the merits of this approach, we study th... | ['Aukosh Jagannath', 'Kimon Fountoulakis', 'Aseem Baranwal'] | 2021-02-13 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.80713171e-01 4.94832873e-01 -4.26699594e-02 -5.06386995e-01
-3.88431787e-01 -5.39766967e-01 6.74440324e-01 4.89538491e-01
-3.17654669e-01 4.93452579e-01 -3.56789947e-01 -5.63821077e-01
-2.89743632e-01 -1.01078534e+00 -6.72796369e-01 -1.16196799e+00
-4.95937288e-01 7.04092503e-01 1.17159434e-01 3.31855536... | [6.948781967163086, 5.6425089836120605] |
5aa463cf-5668-4594-8860-58bebc7f4139 | active-fine-tuning-from-gmad-examples | 2003.03849 | null | https://arxiv.org/abs/2003.03849v2 | https://arxiv.org/pdf/2003.03849v2.pdf | Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment | The research in image quality assessment (IQA) has a long history, and significant progress has been made by leveraging recent advances in deep neural networks (DNNs). Despite high correlation numbers on existing IQA datasets, DNN-based models may be easily falsified in the group maximum differentiation (gMAD) competit... | ['Kede Ma', 'Zhihua Wang'] | 2020-03-08 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 2.70546854e-01 2.67350413e-02 -1.33251818e-02 -4.88624185e-01
-1.70954096e+00 -6.87525809e-01 3.32910091e-01 -1.18705772e-01
-5.45111299e-01 6.80331349e-01 5.61064184e-01 -1.74564108e-01
-3.80614191e-01 -4.00797904e-01 -5.27330637e-01 -7.28909016e-01
-2.14809760e-01 6.19328082e-01 1.99186485e-02 -3.04681640... | [11.887497901916504, -1.8003774881362915] |
b3137b8b-2e73-417d-a638-b65f125ebc42 | towards-better-evaluation-for-dynamic-link | 2207.10128 | null | https://arxiv.org/abs/2207.10128v2 | https://arxiv.org/pdf/2207.10128v2.pdf | Towards Better Evaluation for Dynamic Link Prediction | Despite the prevalence of recent success in learning from static graphs, learning from time-evolving graphs remains an open challenge. In this work, we design new, more stringent evaluation procedures for link prediction specific to dynamic graphs, which reflect real-world considerations, to better compare the strength... | ['Reihaneh Rabbany', 'Kellin Pelrine', 'Shenyang Huang', 'Farimah Poursafaei'] | 2022-07-20 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [ 7.31337145e-02 1.67941898e-01 -5.39990664e-01 -1.46757767e-01
-2.42890999e-01 -8.05429339e-01 7.04308271e-01 3.92812163e-01
1.08026387e-03 8.00185680e-01 1.29694179e-01 -6.32740617e-01
-2.08880082e-01 -8.66943836e-01 -5.31106472e-01 -2.51131505e-01
-9.36636984e-01 3.53271544e-01 4.78148669e-01 -2.84972519... | [7.030940532684326, 6.122877597808838] |
3d03e6bd-4778-469b-9bd4-207cbf99778b | what-makes-convolutional-models-great-on-long | 2210.09298 | null | https://arxiv.org/abs/2210.09298v1 | https://arxiv.org/pdf/2210.09298v1.pdf | What Makes Convolutional Models Great on Long Sequence Modeling? | Convolutional models have been widely used in multiple domains. However, most existing models only use local convolution, making the model unable to handle long-range dependency efficiently. Attention overcomes this problem by aggregating global information but also makes the computational complexity quadratic to the s... | ['Debadeepta Dey', 'Deming Chen', 'Yi Zhang', 'Tianle Cai', 'Yuhong Li'] | 2022-10-17 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [-8.26044828e-02 -4.82791632e-01 -1.13853946e-01 -3.41696650e-01
-1.50064141e-01 -7.35576093e-01 5.10304987e-01 -1.41617775e-01
-6.79767609e-01 3.12789947e-01 1.17943071e-01 -6.67363107e-01
-9.84608531e-02 -6.72300041e-01 -6.50647342e-01 -5.15689492e-01
-2.00181514e-01 -2.11695686e-01 5.68249047e-01 -3.64841968... | [10.80197525024414, 6.547518253326416] |
a6c584d5-d50f-46e2-b095-942a86d75a20 | mivolo-multi-input-transformer-for-age-and | 2307.04616 | null | https://arxiv.org/abs/2307.04616v1 | https://arxiv.org/pdf/2307.04616v1.pdf | MiVOLO: Multi-input Transformer for Age and Gender Estimation | Age and gender recognition in the wild is a highly challenging task: apart from the variability of conditions, pose complexities, and varying image quality, there are cases where the face is partially or completely occluded. We present MiVOLO (Multi Input VOLO), a straightforward approach for age and gender estimation ... | ['Irina Tolstykh', 'Maksim Kuprashevich'] | 2023-07-10 | null | null | null | null | ['age-and-gender-estimation', 'facial-attribute-classification', 'age-and-gender-classification', 'age-estimation', 'gender-prediction', 'age-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [-5.35673602e-03 1.53653115e-01 8.28952566e-02 -7.18702972e-01
-4.44687873e-01 -4.24009234e-01 7.69716144e-01 1.13595210e-01
-7.15718687e-01 5.74137628e-01 1.15643412e-01 3.16893458e-01
1.04000099e-01 -5.71008623e-01 -4.54939127e-01 -5.89502454e-01
-5.73859662e-02 6.59867346e-01 -1.58264190e-01 1.20960660... | [13.53357982635498, 1.0014959573745728] |
f26e9eb3-fb5b-4013-8ae3-236c0251831a | latent-space-semi-supervised-time-series-data | null | null | https://openreview.net/forum?id=0qbEq5UBfGD | https://openreview.net/pdf?id=0qbEq5UBfGD | Latent Space Semi-Supervised Time Series Data Clustering | Time series data is abundantly available in the real world, but there is a distinct lack of large, labeled datasets available for many types of learning tasks. Semi-supervised models, which can leverage small amounts of expert-labeled data along with a larger unlabeled dataset, have been shown to improve performance ov... | ['Farnoush Kashani', 'Russell Bowler', 'Katerina Kechris', 'Andrew Hill'] | 2021-01-01 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-1.43172428e-01 -2.24580362e-01 -3.97127062e-01 -5.81767440e-01
-9.54581738e-01 -6.71248794e-01 6.61927342e-01 5.77173531e-02
-3.02935869e-01 3.31636637e-01 2.73122013e-01 -1.27402797e-01
-2.14650199e-01 -5.20722687e-01 -5.35926700e-01 -7.66128719e-01
-2.65307069e-01 6.19971812e-01 -7.65980035e-02 1.45461336... | [7.4112653732299805, 2.883622884750366] |
ed528c1c-0921-48dd-8b2c-ee21eb6cd4ad | security-consideration-for-deep-learning | 1803.11157 | null | http://arxiv.org/abs/1803.11157v2 | http://arxiv.org/pdf/1803.11157v2.pdf | Security Consideration For Deep Learning-Based Image Forensics | Recently, image forensics community has paied attention to the research on
the design of effective algorithms based on deep learning technology and facts
proved that combining the domain knowledge of image forensics and deep learning
would achieve more robust and better performance than the traditional schemes.
Instead... | ['Rongrong Ni', 'Pengpeng Yang', 'Yao Zhao', 'Wei Zhao', 'Haorui Wu'] | 2018-03-29 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [-2.92144027e-02 -9.75949913e-02 1.95706934e-01 -1.23911098e-01
-3.17901224e-01 -1.98745847e-01 4.35744703e-01 -8.05215985e-02
-6.19191110e-01 4.27543849e-01 -1.81701198e-01 -4.93555456e-01
1.96369155e-03 -9.19541657e-01 -7.91503489e-01 -8.51426542e-01
7.58019760e-02 -1.84293464e-01 1.48360774e-01 -1.80582460... | [12.331591606140137, 0.943594753742218] |
c1ee7760-8476-4888-a586-818c8d782d5f | speaker-aware-bert-for-multi-turn-response | 2004.03588 | null | https://arxiv.org/abs/2004.03588v2 | https://arxiv.org/pdf/2004.03588v2.pdf | Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots | In this paper, we study the problem of employing pre-trained language models for multi-turn response selection in retrieval-based chatbots. A new model, named Speaker-Aware BERT (SA-BERT), is proposed in order to make the model aware of the speaker change information, which is an important and intrinsic property of mul... | ['Zhen-Hua Ling', 'Jia-Chen Gu', 'Xiaodan Zhu', 'Quan Liu', 'Tianda Li', 'Zhiming Su', 'Si Wei'] | 2020-04-07 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [ 3.76609759e-03 1.02382727e-01 -2.79615968e-01 -6.60051048e-01
-1.41880476e+00 -6.44638896e-01 8.17309201e-01 -4.11559083e-03
-5.42290747e-01 8.22745740e-01 5.12288451e-01 -2.11979359e-01
-1.08190246e-01 -2.73067057e-01 -1.76982507e-02 -6.98281109e-01
2.76821941e-01 8.67268443e-01 4.05332327e-01 -8.69094670... | [12.601560592651367, 7.827559947967529] |
d41c9f1a-be07-45a0-805b-fa114ee5a7d3 | cem500k-a-large-scale-heterogeneous-unlabeled | null | null | https://www.biorxiv.org/content/10.1101/2020.12.11.421792v1 | https://www.biorxiv.org/content/10.1101/2020.12.11.421792v1.full.pdf | CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning | Automated segmentation of cellular electron microscopy (EM) datasets remains a challenge. Supervised deep learning (DL) methods that rely on region-of-interest (ROI) annotations yield models that fail to generalize to unrelated datasets. Newer unsupervised DL algorithms require relevant pre-training images, however, pr... | ['Kedar Narayan', 'Ryan W Conrad'] | 2020-12-11 | null | null | null | null | ['electron-microscopy-image-segmentation'] | ['computer-vision'] | [ 3.84130895e-01 -2.34202873e-02 5.66315770e-01 -4.03942853e-01
-1.14615667e+00 -4.53365058e-01 6.12756073e-01 1.48248509e-01
-1.02026725e+00 1.16178501e+00 -3.20696145e-01 -2.03489035e-01
2.25295037e-01 -2.82125592e-01 -8.52120757e-01 -9.39816296e-01
3.42393145e-02 1.03471208e+00 3.86559308e-01 3.11326385... | [14.28128719329834, -3.1044530868530273] |
c5ff7451-8493-4738-8f37-8b0b469a0198 | multi-tasking-dialogue-comprehension-with | 2110.03269 | null | https://arxiv.org/abs/2110.03269v1 | https://arxiv.org/pdf/2110.03269v1.pdf | Multi-tasking Dialogue Comprehension with Discourse Parsing | Multi-party dialogue machine reading comprehension (MRC) raises an even more challenging understanding goal on dialogue with more than two involved speakers, compared with the traditional plain passage style MRC. To accurately perform the question-answering (QA) task according to such multi-party dialogue, models have ... | ['Hai Zhao', 'Zhuosheng Zhang', 'Yuchen He'] | 2021-10-07 | null | https://aclanthology.org/2021.paclic-1.64 | https://aclanthology.org/2021.paclic-1.64.pdf | paclic-2021-11 | ['discourse-parsing'] | ['natural-language-processing'] | [ 4.89538640e-01 1.07588053e+00 4.68246222e-01 -3.98567498e-01
-1.31746209e+00 -7.54799724e-01 1.14499617e+00 3.39446336e-01
-3.30080837e-01 1.05684698e+00 9.37023580e-01 -7.15299487e-01
-1.18084468e-01 -5.05319059e-01 -5.04607379e-01 -5.15351593e-01
-5.84145226e-02 9.35201705e-01 4.15780604e-01 -1.12702084... | [12.18153190612793, 8.13184928894043] |
9ae156ac-de1a-4eaf-a236-7c96bf133641 | topnet-structural-point-cloud-decoder | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Tchapmi_TopNet_Structural_Point_Cloud_Decoder_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Tchapmi_TopNet_Structural_Point_Cloud_Decoder_CVPR_2019_paper.pdf | TopNet: Structural Point Cloud Decoder | 3D point cloud generation is of great use for 3D scene modeling and understanding. Real-world 3D object point clouds can be properly described by a collection of low-level and high-level structures such as surfaces, geometric primitives, semantic parts,etc. In fact, there exist many different representations ... | [' Silvio Savarese', ' Ian Reid', ' Hamid Rezatofighi', ' Vineet Kosaraju', 'Lyne P. Tchapmi'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['point-cloud-completion'] | ['computer-vision'] | [ 6.37236461e-02 2.46354535e-01 1.64792374e-01 -2.82831877e-01
-6.24749482e-01 -8.60856533e-01 7.46715665e-01 2.81824559e-01
3.22209716e-01 1.24819949e-02 -1.90580770e-01 -3.78190964e-01
1.74525052e-01 -1.17253733e+00 -1.37759626e+00 -3.68384391e-01
-7.25544989e-02 1.15410793e+00 2.72934198e-01 -3.12778056... | [8.46279525756836, -3.501328945159912] |
21271248-2aaa-45b5-93a5-0ca226043d65 | infotec-centrogeo-at-semeval-2020-task-8-deep | null | null | https://aclanthology.org/2020.semeval-1.151 | https://aclanthology.org/2020.semeval-1.151.pdf | Infotec + CentroGEO at SemEval-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification | The information shared on social media is increasingly important; both images and text, and maybe the most popular combination of these two kinds of data are the memes. This manuscript describes our participation in Memotion task at SemEval 2020. This task is about to classify the memes in several categories related to... | ['Mario Graff', "Tania Ram{\\'\\i}rez-delReal", "Sabino Miranda-Jim{\\'e}nez", 'Daniela Moctezuma', 'Eric S. Tellez', 'Guillermo Ruiz'] | 2020-12-01 | null | null | null | semeval-2020 | ['text-categorization'] | ['natural-language-processing'] | [-2.14756504e-01 2.92207003e-02 -2.62856446e-02 -2.40501717e-01
-1.17858753e-01 -5.52623272e-01 9.21424448e-01 7.27489591e-01
-6.44639492e-01 9.33624327e-01 2.69616038e-01 2.99418867e-01
1.67525575e-01 -8.79498422e-01 -2.51059443e-01 -5.17640650e-01
2.44427308e-01 3.83152425e-01 2.15954348e-01 -4.34774458... | [8.513882637023926, 10.712678909301758] |
2b629cfb-479f-4baf-9a21-f82abc0351d0 | unifying-the-discrete-and-continuous-emotion | 2210.16642 | null | https://arxiv.org/abs/2210.16642v1 | https://arxiv.org/pdf/2210.16642v1.pdf | Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition | Traditionally, in paralinguistic analysis for emotion detection from speech, emotions have been identified with discrete or dimensional (continuous-valued) labels. Accordingly, models that have been proposed for emotion detection use one or the other of these label types. However, psychologists like Russell and Plutchi... | ['Rita Singh', 'Bhiksha Raj', 'Hira Dhamyal', 'Roshan Sharma'] | 2022-10-29 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [ 1.00630261e-01 -9.18231830e-02 -2.03957066e-01 -9.59565163e-01
-8.21106911e-01 -3.23218554e-01 6.35498226e-01 7.00095221e-02
-4.62053210e-01 2.66257137e-01 2.56565809e-01 1.88521463e-02
2.32047561e-04 -2.53924400e-01 -9.19509530e-02 -5.78420520e-01
9.37465578e-02 2.71506429e-01 -4.23971266e-01 -2.75722712... | [13.403241157531738, 5.747459411621094] |
ad19bbb1-5e1c-4087-9882-7a6fb4969ce7 | danna-sep-unite-to-separate-them-all | 2112.03752 | null | https://arxiv.org/abs/2112.03752v1 | https://arxiv.org/pdf/2112.03752v1.pdf | Danna-Sep: Unite to separate them all | Deep learning-based music source separation has gained a lot of interest in the last decades. Most of the existing methods operate with either spectrograms or waveforms. Spectrogram based models learn suitable masks for separating magnitude spectrogram into different sources, and waveform-based models directly generate... | ['Kin-Wai Cheuk', 'Chin-Yun Yu'] | 2021-12-07 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 9.54955444e-02 -5.21031916e-01 2.62342453e-01 3.65679823e-02
-1.07637775e+00 -6.57005072e-01 4.85343188e-01 -2.97589242e-01
3.51202935e-02 5.63147366e-01 3.08891237e-01 -4.50545922e-02
-4.30041790e-01 -5.31545281e-01 -5.19975722e-01 -8.38391125e-01
-2.20249236e-01 -1.43789008e-01 1.85799718e-01 -2.97494113... | [15.435449600219727, 5.60034704208374] |
71776043-4833-47a4-a2c3-25e729ce58de | video-inpainting-of-complex-scenes | 1503.05528 | null | http://arxiv.org/abs/1503.05528v2 | http://arxiv.org/pdf/1503.05528v2.pdf | Video Inpainting of Complex Scenes | We propose an automatic video inpainting algorithm which relies on the
optimisation of a global, patch-based functional. Our algorithm is able to deal
with a variety of challenging situations which naturally arise in video
inpainting, such as the correct reconstruction of dynamic textures, multiple
moving objects and m... | ['Patrick Pérez', 'Matthieu Fradet', 'Andrés Almansa', 'Yann Gousseau', 'Alasdair Newson'] | 2015-03-18 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 6.41637325e-01 -1.58837855e-01 1.75279066e-01 -1.37414619e-01
-4.92964923e-01 -3.94876570e-01 3.74686331e-01 7.93465450e-02
-5.80470264e-01 8.42873633e-01 -2.06292018e-01 -7.56156817e-02
2.86831427e-02 -6.62306607e-01 -7.28991985e-01 -7.22738206e-01
9.43747833e-02 2.71650851e-01 4.73436028e-01 -2.80346990... | [11.345494270324707, -2.217207670211792] |
71f60a5e-6ee3-48bf-bfad-0aea90144138 | deepadversaries-examining-the-robustness-of | 2112.14299 | null | https://arxiv.org/abs/2112.14299v3 | https://arxiv.org/pdf/2112.14299v3.pdf | DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification | With increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly importa... | ['Stefan M. Wild', 'Sandeep Madireddy', 'Brian Nord', 'Kevin Pedro', 'Gabriel Nathan Perdue', 'F. Javier Sánchez', 'Gregory Snyder', 'Diana Kafkes', 'Aleksandra Ćiprijanović'] | 2021-12-28 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [ 6.32171929e-01 -7.23709911e-02 7.22103715e-01 -1.82740673e-01
-6.69513226e-01 -9.03421640e-01 7.78468549e-01 2.80881464e-01
-6.47418261e-01 2.73957372e-01 4.16838348e-01 -4.89572674e-01
-4.51030917e-02 -9.07437205e-01 -9.26015675e-01 -9.27097499e-01
-5.69421798e-02 2.96363860e-01 3.70998263e-01 9.98991281... | [5.584811210632324, 7.879670143127441] |
a3fa97ba-0719-4332-aa18-3c468bc104b8 | software-vulnerability-prediction-knowledge | 2303.06177 | null | https://arxiv.org/abs/2303.06177v1 | https://arxiv.org/pdf/2303.06177v1.pdf | Software Vulnerability Prediction Knowledge Transferring Between Programming Languages | Developing automated and smart software vulnerability detection models has been receiving great attention from both research and development communities. One of the biggest challenges in this area is the lack of code samples for all different programming languages. In this study, we address this issue by proposing a tr... | ['Goksu Karadag', 'Basak Gencer Unsalver', 'Ramin F Fouladi', 'Khadija Hanifi'] | 2023-03-10 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-8.16964284e-02 -1.66061278e-02 -3.69738415e-02 -3.92094761e-01
-6.06866419e-01 -8.40759754e-01 3.79117936e-01 2.95742661e-01
1.25065774e-01 2.18385622e-01 1.19735315e-01 -6.91911697e-01
2.34656092e-02 -9.75006342e-01 -6.66785657e-01 8.34795609e-02
5.98820113e-02 -3.65310460e-01 3.43686402e-01 -3.01975995... | [7.08120584487915, 7.773388862609863] |
9061b2e9-5e9a-4a2d-992d-9150411bdb5d | bayesian-modeling-of-lexical-resources-for | null | null | https://aclanthology.org/P17-1095 | https://aclanthology.org/P17-1095.pdf | Bayesian Modeling of Lexical Resources for Low-Resource Settings | Lexical resources such as dictionaries and gazetteers are often used as auxiliary data for tasks such as part-of-speech induction and named-entity recognition. However, discriminative training with lexical features requires annotated data to reliably estimate the lexical feature weights and may result in overfitting th... | ['Mark Dredze', 'Nicholas Andrews', 'Jason Eisner', 'Benjamin Van Durme'] | 2017-07-01 | null | null | null | acl-2017-7 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [ 1.03193866e-02 4.03929949e-01 -3.71528059e-01 -7.38510311e-01
-6.83477044e-01 -5.88450313e-01 7.38112569e-01 1.71854064e-01
-8.27262878e-01 8.13993216e-01 2.93528557e-01 -2.68476367e-01
2.12238163e-01 -7.15525925e-01 -6.94607615e-01 -6.53125823e-01
1.53111234e-01 5.40876925e-01 -5.39578050e-02 6.30578306... | [10.320990562438965, 9.621506690979004] |
12fa1e2c-78dd-4592-b23a-52d443e70b59 | deep-steganalysis-end-to-end-learning-with | 1806.10443 | null | http://arxiv.org/abs/1806.10443v1 | http://arxiv.org/pdf/1806.10443v1.pdf | Deep Steganalysis: End-to-End Learning with Supervisory Information beyond Class Labels | Recently, deep learning has shown its power in steganalysis. However, the
proposed deep models have been often learned from pre-calculated noise
residuals with fixed high-pass filters rather than from raw images. In this
paper, we propose a new end-to-end learning framework that can learn
steganalytic features directly... | ['Yinlong Qian', 'Jing Dong', 'Tieniu Tan', 'Wei Wang'] | 2018-06-27 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 6.01948917e-01 9.48381126e-02 1.76785529e-01 -2.61577547e-01
-5.57227135e-01 9.39417332e-02 6.38934135e-01 -7.03805566e-01
-3.73948336e-01 5.69542468e-01 -2.41683573e-01 -4.31435287e-01
4.45570856e-01 -8.85407686e-01 -7.53863454e-01 -9.95059490e-01
8.56791586e-02 -2.69287348e-01 2.19841123e-01 -1.88377738... | [4.294366836547852, 8.057324409484863] |
42f9f148-f980-4890-901e-77684b674242 | making-better-use-of-edges-via-perceptual | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Qi_Making_Better_Use_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Qi_Making_Better_Use_2015_CVPR_paper.pdf | Making Better Use of Edges via Perceptual Grouping | We propose a perceptual grouping framework that organizes image edges into meaningful structures and demonstrate its usefulness on various computer vision tasks. Our grouper formulates edge grouping as a graph partition problem, where a learning to rank method is developed to encode probabilities of candidate edge pair... | ['Timothy Hospedales', 'Tao Xiang', 'Yi-Zhe Song', 'Yi Li', 'Yonggang Qi', 'Honggang Zhang', 'Jun Guo'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.29008251e-01 7.55351186e-02 -2.74681836e-01 -2.83208072e-01
-6.02211893e-01 -6.49341345e-01 8.85040462e-01 3.87271434e-01
-2.73201764e-01 9.90740508e-02 -1.42688453e-01 -1.58213466e-01
-4.66681927e-01 -8.29383731e-01 -3.45309943e-01 -4.06128794e-01
-3.59694473e-02 5.81141829e-01 4.47698206e-01 1.52151454... | [11.702627182006836, 0.436005562543869] |
baa70a46-c872-4771-a902-6e214180ab97 | answering-questions-over-knowledge-graphs | 2303.02206 | null | https://arxiv.org/abs/2303.02206v1 | https://arxiv.org/pdf/2303.02206v1.pdf | Answering Questions Over Knowledge Graphs Using Logic Programming Along with Language Models | Question Answering over Knowledge Graphs (KGQA) is the task of answering natural language questions over a knowledge graph (KG). This task requires a model to reason over multiple edges of the KG to reach the right answer. In this work, we present a method to equip large language models (LLMs) with classic logical prog... | ['Kenneth Joseph', 'Navid Madani'] | 2023-03-03 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-7.56997690e-02 1.14709818e+00 7.10578337e-02 -3.95819753e-01
-7.17583477e-01 -6.78256810e-01 2.74415016e-01 3.50923985e-01
2.37538457e-01 7.18960822e-01 -1.40294820e-01 -8.91368151e-01
-3.28888535e-01 -1.62625337e+00 -1.04013574e+00 2.41873294e-01
-1.00869024e-02 6.53020322e-01 7.43709207e-01 -3.66957664... | [10.112194061279297, 7.727481365203857] |
6211f56d-abb3-4b24-898a-7f10a6075295 | cppf-towards-robust-category-level-9d-pose | 2203.03089 | null | https://arxiv.org/abs/2203.03089v2 | https://arxiv.org/pdf/2203.03089v2.pdf | CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild | In this paper, we tackle the problem of category-level 9D pose estimation in the wild, given a single RGB-D frame. Using supervised data of real-world 9D poses is tedious and erroneous, and also fails to generalize to unseen scenarios. Besides, category-level pose estimation requires a method to be able to generalize t... | ['Cewu Lu', 'Weiming Wang', 'Ruoxi Shi', 'Yang You'] | 2022-03-07 | null | http://openaccess.thecvf.com//content/CVPR2022/html/You_CPPF_Towards_Robust_Category-Level_9D_Pose_Estimation_in_the_Wild_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/You_CPPF_Towards_Robust_Category-Level_9D_Pose_Estimation_in_the_Wild_CVPR_2022_paper.pdf | cvpr-2022-1 | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [ 1.39391527e-01 -1.38412297e-01 1.08224697e-01 -3.03496718e-01
-1.08267021e+00 -8.17354441e-01 5.66456258e-01 -4.56509031e-02
-1.89458296e-01 3.51211905e-01 -3.43263030e-01 -1.11248530e-02
8.24047625e-02 -6.71618164e-01 -9.49019611e-01 -5.81846952e-01
7.80514777e-02 8.58645260e-01 5.06896198e-01 -1.36823818... | [7.576259613037109, -2.679096221923828] |
a8b34e58-7baf-48c6-b2ea-2a0e4e65e347 | tiling-and-stitching-segmentation-output-for | 1805.12219 | null | http://arxiv.org/abs/1805.12219v3 | http://arxiv.org/pdf/1805.12219v3.pdf | Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations | In this work we consider the application of convolutional neural networks
(CNNs) for pixel-wise labeling (a.k.a., semantic segmentation) of remote
sensing imagery (e.g., aerial color or hyperspectral imagery). Remote sensing
imagery is usually stored in the form of very large images, referred to as
"tiles", which are t... | ['Kyle Bradbury', 'Leslie M. Collins', 'Bohao Huang', 'Jordan M. Malof', 'Daniel Reichman'] | 2018-05-30 | null | null | null | null | ['segmentation-of-remote-sensing-imagery'] | ['miscellaneous'] | [ 9.92736101e-01 1.09474942e-01 -1.92019101e-02 -3.93132031e-01
-6.14265680e-01 -9.90201533e-01 3.65557492e-01 2.27050290e-01
-5.36679804e-01 6.89338326e-01 -5.81733704e-01 -6.57798946e-01
7.51260743e-02 -1.07423007e+00 -7.31781244e-01 -9.15175319e-01
1.12825088e-01 1.61963359e-01 -8.57742911e-04 1.93867177... | [9.402003288269043, -1.21368408203125] |
865853e3-486c-4bb5-9568-edf1209a3873 | sydog-a-synthetic-dog-dataset-for-improved-2d | 2108.00249 | null | https://arxiv.org/abs/2108.00249v1 | https://arxiv.org/pdf/2108.00249v1.pdf | SyDog: A Synthetic Dog Dataset for Improved 2D Pose Estimation | Estimating the pose of animals can facilitate the understanding of animal motion which is fundamental in disciplines such as biomechanics, neuroscience, ethology, robotics and the entertainment industry. Human pose estimation models have achieved high performance due to the huge amount of training data available. Achie... | ['Adrian Hilton', 'Charles Malleson', 'Moira Shooter'] | 2021-07-31 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [-2.09404439e-01 8.05190429e-02 7.64931962e-02 -3.43171835e-01
-1.55452222e-01 -4.88454670e-01 2.09402189e-01 -1.72551591e-02
-7.80744016e-01 6.90368056e-01 -8.05439204e-02 5.36814518e-02
3.11598182e-01 -3.95193160e-01 -8.91101718e-01 -2.31523901e-01
-2.69111335e-01 5.66930354e-01 6.40464365e-01 -4.04103607... | [7.561185836791992, -0.9850667715072632] |
9bb2a102-6d9c-41ee-a260-9e04e542253a | partially-personalized-federated-learning | 2305.18285 | null | https://arxiv.org/abs/2305.18285v1 | https://arxiv.org/pdf/2305.18285v1.pdf | Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity | We present a partially personalized formulation of Federated Learning (FL) that strikes a balance between the flexibility of personalization and cooperativeness of global training. In our framework, we split the variables into global parameters, which are shared across all clients, and individual local parameters, whic... | ['Samuel Horváth', 'Eduard Gorbunov', 'Rustem Islamov', 'Konstantin Mishchenko'] | 2023-05-29 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-5.39925396e-01 3.49871874e-01 -4.33619589e-01 -4.83210117e-01
-7.49745607e-01 -8.20283175e-01 1.97559923e-01 -3.68206799e-02
-3.18107367e-01 6.74926281e-01 2.87639052e-01 -1.15365386e-01
-6.20691955e-01 -7.85270512e-01 -8.24941516e-01 -1.11733079e+00
-1.44237980e-01 7.15605438e-01 -2.07798854e-01 6.53616488... | [5.827064037322998, 6.2793450355529785] |
2e324bc2-262a-46c5-80f7-371862caa3ed | empirical-interpretation-of-speech-emotion | null | null | http://www.interspeech2020.org/index.php?m=content&c=index&a=show&catid=350&id=1146 | http://www.interspeech2020.org/uploadfile/pdf/Thu-2-2-8.pdf | Empirical Interpretation of Speech Emotion Perception with Attention Based Model for Speech Emotion Recognition | Speech emotion recognition is essential for obtaining emotional intelligence which affects the understanding of context and meaning of speech. Harmonically structured vowel and consonant sounds add indexical and linguistic cues in spoken in- formation. Previous research argued whether vowel sound cues were more importa... | ['Thomas Hain Speech', 'Rosanna Milner', 'Md AsifJalal'] | 2020-10-28 | null | null | null | interspeech-2020-10 | ['emotional-intelligence'] | ['natural-language-processing'] | [-1.09853581e-01 1.15807407e-01 2.11675152e-01 -6.37105405e-01
-1.56163707e-01 -7.35161901e-02 1.63869202e-01 1.65868446e-01
-7.03059435e-01 3.12861294e-01 4.89592433e-01 -1.32741362e-01
-9.74334627e-02 -3.97576630e-01 -2.67260343e-01 -4.72700953e-01
1.30762577e-01 -1.68663844e-01 -3.66439372e-01 -3.90519142... | [13.768638610839844, 5.789379596710205] |
6dbe1494-b644-4cb5-8b95-10e53263c355 | xnap-making-lstm-based-next-activity | 2008.07993 | null | https://arxiv.org/abs/2008.07993v3 | https://arxiv.org/pdf/2008.07993v3.pdf | XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP | Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques` limit... | ['Jörg Becker', 'Martin Matzner', 'Sven Weinzierl', 'Jens Brunk', 'Kate Revoredo', 'Sandra Zilker'] | 2020-08-18 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 4.14101422e-01 5.78585386e-01 -2.07506627e-01 -5.12943804e-01
-1.59715042e-02 2.39433974e-01 7.99615085e-01 5.60116768e-01
1.95578247e-01 5.90840638e-01 3.69758815e-01 -5.20575881e-01
-8.23016524e-01 -1.12558532e+00 -6.33780658e-01 -2.38256574e-01
-1.68724805e-01 7.04774439e-01 5.53921275e-02 1.13794133... | [8.581814765930176, 5.972146987915039] |
a4c48e08-c8c1-4edb-806f-019ac8ffe400 | iteratively-improving-biomedical-entity | 2305.14645 | null | https://arxiv.org/abs/2305.14645v1 | https://arxiv.org/pdf/2305.14645v1.pdf | Iteratively Improving Biomedical Entity Linking and Event Extraction via Hard Expectation-Maximization | Biomedical entity linking and event extraction are two crucial tasks to support text understanding and retrieval in the biomedical domain. These two tasks intrinsically benefit each other: entity linking disambiguates the biomedical concepts by referring to external knowledge bases and the domain knowledge further prov... | ['Lifu Huang', 'Zhiyang Xu', 'Minqian Liu', 'Xiaochu Li'] | 2023-05-24 | null | null | null | null | ['event-extraction', 'entity-linking'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.92791060e-01 3.66842270e-01 -4.58149195e-01 -1.31111011e-01
-1.16291308e+00 -6.00638509e-01 4.22131568e-01 7.83151627e-01
-6.12326562e-01 1.04571283e+00 4.03553188e-01 -2.27118328e-01
-1.19723544e-01 -4.88017946e-01 -8.01784039e-01 -7.43028224e-01
1.77284628e-02 6.27392411e-01 2.56064147e-01 3.57284665... | [8.629146575927734, 8.836601257324219] |
69e09c50-4479-4c0e-b845-e77bc2c425c7 | two-stream-networks-for-weakly-supervised | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Two-Stream_Networks_for_Weakly-Supervised_Temporal_Action_Localization_With_Semantic-Aware_Mechanisms_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Two-Stream_Networks_for_Weakly-Supervised_Temporal_Action_Localization_With_Semantic-Aware_Mechanisms_CVPR_2023_paper.pdf | Two-Stream Networks for Weakly-Supervised Temporal Action Localization With Semantic-Aware Mechanisms | Weakly-supervised temporal action localization aims to detect action boundaries in untrimmed videos with only video-level annotations. Most existing schemes detect temporal regions that are most responsive to video-level classification, but they overlook the semantic consistency between frames. In this paper, we hy... | ['Hongbin Wang', 'Yadong Li', 'Yu Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['weakly-supervised-temporal-action', 'action-localization', 'action-recognition', 'multiple-instance-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 4.19230431e-01 6.80630654e-02 -7.22160995e-01 -3.52034390e-01
-5.49992383e-01 -4.01900381e-01 5.90481162e-01 1.55572668e-01
-3.06727380e-01 5.95296264e-01 6.59558117e-01 4.23919737e-01
-1.81053177e-01 -3.12644213e-01 -6.95231378e-01 -8.67299318e-01
-2.77452081e-01 -6.08219206e-02 6.73457980e-01 1.12953432... | [8.46787166595459, 0.6833968162536621] |
8d150bd2-d59c-422d-bf13-5ecbda349837 | refinements-in-motion-and-appearance-for | 2003.07177 | null | https://arxiv.org/abs/2003.07177v2 | https://arxiv.org/pdf/2003.07177v2.pdf | Refinements in Motion and Appearance for Online Multi-Object Tracking | Modern multi-object tracking (MOT) system usually involves separated modules, such as motion model for location and appearance model for data association. However, the compatible problems within both motion and appearance models are always ignored. In this paper, a general architecture named as MIF is presented by seam... | ['Donghaisheng Liu', 'En Yu', 'Shoudong Han', 'Piao Huang', 'Jun Zhao', 'Hongwei Wang', 'Alex ChiChung Kot'] | 2020-03-16 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-2.9352039e-01 -5.7933170e-01 -1.2275130e-01 -1.6887167e-01
-4.4911668e-01 -4.4854426e-01 5.3680295e-01 -2.4011883e-01
-4.2688769e-01 3.8077718e-01 -3.0585003e-01 -1.6588545e-01
-4.7612853e-02 -4.1496795e-01 -5.1691121e-01 -9.2758739e-01
2.0010136e-01 2.5024909e-01 8.0788195e-01 1.3932478e-01
5.8082029e-02... | [6.503890037536621, -2.023082971572876] |
407b8582-e8d2-458d-a7b4-e86f626770bb | spidercnn-deep-learning-on-point-sets-with | 1803.11527 | null | http://arxiv.org/abs/1803.11527v3 | http://arxiv.org/pdf/1803.11527v3.pdf | SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters | Deep neural networks have enjoyed remarkable success for various vision
tasks, however it remains challenging to apply CNNs to domains lacking a
regular underlying structures such as 3D point clouds. Towards this we propose
a novel convolutional architecture, termed SpiderCNN, to efficiently extract
geometric features ... | ['Tianqi Fan', 'Yu Qiao', 'Mingye Xu', 'Yifan Xu', 'Long Zeng'] | 2018-03-30 | spidercnn-deep-learning-on-point-sets-with-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Yifan_Xu_SpiderCNN_Deep_Learning_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yifan_Xu_SpiderCNN_Deep_Learning_ECCV_2018_paper.pdf | eccv-2018-9 | ['3d-part-segmentation'] | ['computer-vision'] | [-2.24567011e-01 5.01443967e-02 1.32402495e-01 -4.27994579e-01
-2.99347937e-01 -6.40022933e-01 6.74688339e-01 -2.54224092e-01
-4.93217438e-01 6.16784208e-02 -2.23347679e-01 -3.74679357e-01
-1.01058828e-02 -1.13583422e+00 -1.12221754e+00 -3.26834828e-01
-3.91375512e-01 2.57747948e-01 5.76030612e-01 -1.92484185... | [7.943106174468994, -3.634932279586792] |
594df015-8eee-4f29-9db9-da464e29f5fe | flexible-end-to-end-dialogue-system-for | 1709.04264 | null | http://arxiv.org/abs/1709.04264v1 | http://arxiv.org/pdf/1709.04264v1.pdf | Flexible End-to-End Dialogue System for Knowledge Grounded Conversation | In knowledge grounded conversation, domain knowledge plays an important role
in a special domain such as Music. The response of knowledge grounded
conversation might contain multiple answer entities or no entity at all.
Although existing generative question answering (QA) systems can be applied to
knowledge grounded co... | ['Xuezheng Peng', 'Kaixiang Mo', 'Wenya Zhu', 'Zhangbin Zhu', 'Qiang Yang', 'Yu Zhang'] | 2017-09-13 | null | null | null | null | ['generative-question-answering'] | ['natural-language-processing'] | [-6.46274490e-03 6.98831558e-01 2.36416951e-01 -2.83529103e-01
-1.12836671e+00 -6.98075116e-01 7.20457911e-01 -6.24395050e-02
-3.59209001e-01 1.15387988e+00 6.68990910e-01 9.26555321e-02
3.76002565e-02 -1.26981080e+00 -5.90500236e-01 -4.32384640e-01
4.64164734e-01 1.33786130e+00 5.82012653e-01 -8.73342752... | [12.443760871887207, 8.071685791015625] |
0182e204-f31d-41d7-8a34-23cb0bd87f13 | semantic-neighborhoods-as-hypergraphs | null | null | https://aclanthology.org/P13-2040 | https://aclanthology.org/P13-2040.pdf | Semantic Neighborhoods as Hypergraphs | null | ['Pallavi Choudhury', 'Chris Quirk'] | 2013-08-01 | null | null | null | acl-2013-8 | ['video-description'] | ['computer-vision'] | [-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... | [-7.294008731842041, 3.632223129272461] |
19efa00c-7b43-40c4-a012-7903e811af1e | transferring-procedural-knowledge-across | 2304.13867 | null | https://arxiv.org/abs/2304.13867v1 | https://arxiv.org/pdf/2304.13867v1.pdf | Transferring Procedural Knowledge across Commonsense Tasks | Stories about everyday situations are an essential part of human communication, motivating the need to develop AI agents that can reliably understand these stories. Despite the long list of supervised methods for story completion and procedural understanding, current AI has no mechanisms to automatically track and expl... | ['Kaixin Ma', 'Filip Ilievski', 'Yifan Jiang'] | 2023-04-26 | null | null | null | null | ['story-completion'] | ['natural-language-processing'] | [ 4.32310164e-01 6.78189337e-01 -1.68748215e-01 -4.89893347e-01
-5.52713037e-01 -6.78569198e-01 1.12459612e+00 1.26284957e-01
-1.10942528e-01 6.84703708e-01 7.81138897e-01 -1.57404572e-01
1.09988086e-01 -9.77266610e-01 -7.20665753e-01 2.58776098e-01
2.13426694e-01 6.59093320e-01 2.12015957e-01 -5.30317664... | [11.215782165527344, 8.756576538085938] |
74883d61-c759-4a05-b300-4901fd54ea80 | consistent-video-instance-segmentation-with | 2206.07011 | null | https://arxiv.org/abs/2206.07011v1 | https://arxiv.org/pdf/2206.07011v1.pdf | Consistent Video Instance Segmentation with Inter-Frame Recurrent Attention | Video instance segmentation aims at predicting object segmentation masks for each frame, as well as associating the instances across multiple frames. Recent end-to-end video instance segmentation methods are capable of performing object segmentation and instance association together in a direct parallel sequence decodi... | ['Zicheng Liu', 'Andre Abrantes', 'Peng Chu', 'Jiang Wang', 'Quanzeng You'] | 2022-06-14 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.77458489e-01 -5.66009842e-02 -7.51406193e-01 -5.96847415e-01
-9.21761155e-01 -4.06775773e-01 3.32331955e-01 -1.26308843e-01
-3.26419890e-01 4.72578049e-01 -1.02520131e-01 1.91583514e-01
1.79711133e-01 -3.18415135e-01 -8.79590333e-01 -3.45606357e-01
-2.43445709e-01 3.37194175e-01 7.51094341e-01 3.50054115... | [9.162773132324219, -0.09205283224582672] |
54d241bb-c7a7-438e-89b5-1e2b3eb41d89 | self-supervised-out-of-distribution-detection-1 | 2109.15222 | null | https://arxiv.org/abs/2109.15222v3 | https://arxiv.org/pdf/2109.15222v3.pdf | Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization | We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only normal training data. NSA integrates Poisson image editing to seamlessly blend scaled patches of various sizes from separate images. This creat... | ['Bernhard Kainz', 'Benjamin Hou', 'Jeremy Tan', 'Hannah M. Schlüter'] | 2021-09-30 | null | null | null | null | ['self-supervised-anomaly-detection', 'supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 4.12689924e-01 1.93072096e-01 4.50845599e-01 -4.03635561e-01
-9.67546523e-01 -1.31989613e-01 6.25762880e-01 3.31352770e-01
8.56617615e-02 1.67101473e-01 -3.96988153e-01 -1.88642845e-01
2.70814270e-01 -6.19535267e-01 -1.03742945e+00 -6.06442332e-01
-1.99137285e-01 5.93838573e-01 3.47100168e-01 -1.22308157... | [7.649716854095459, 2.1084816455841064] |
0b487137-4b8d-4520-bab4-d9e02342455d | acceleration-of-federated-learning-with-1 | 2203.02645 | null | https://arxiv.org/abs/2203.02645v1 | https://arxiv.org/pdf/2203.02645v1.pdf | Acceleration of Federated Learning with Alleviated Forgetting in Local Training | Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy by independently training local models on each client and then aggregating parameters on a central server, thereby producing an effective global model. Although a variety of FL algorithms have been proposed, the... | ['Tao Jiang', 'Minlie Huang', 'Zhiwei Hong', 'Chencheng Xu'] | 2022-03-05 | acceleration-of-federated-learning-with | https://openreview.net/forum?id=541PxiEKN3F | https://openreview.net/pdf?id=541PxiEKN3F | iclr-2022-4 | ['distributed-optimization'] | ['methodology'] | [-3.62859249e-01 -1.68012083e-01 -3.41980070e-01 -4.11883622e-01
-9.26070392e-01 -7.79750168e-01 2.26139814e-01 -2.05368742e-01
-6.31660581e-01 8.55996251e-01 -1.79142095e-04 -3.62611145e-01
1.65585894e-02 -8.02899361e-01 -1.04750133e+00 -1.13747215e+00
2.51835231e-02 3.91729772e-01 -1.78649038e-01 3.11121643... | [5.848702907562256, 6.462411880493164] |
fa5bf22c-37e4-4257-bd14-8acc28bd4c18 | a-bounded-operator-approach-to-technical | 2009.08821 | null | https://arxiv.org/abs/2009.08821v1 | https://arxiv.org/pdf/2009.08821v1.pdf | A bounded operator approach to technical indicators without lag | In the framework of technical analysis for algorithmic trading we use a linear algebra approach in order to define classical technical indicators as bounded operators of the space $l^\infty(\mathbb{N})$. This more abstract view enables us to define in a very simple way the no-lag versions of these tools. Then we apply ... | ['Frédéric Butin'] | 2020-09-18 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-5.71016610e-01 4.82427627e-02 2.03714147e-01 7.61218462e-03
-2.84950107e-01 -8.83458972e-01 4.23861474e-01 -1.33933589e-01
-6.50393784e-01 7.52517998e-01 -3.58419478e-01 -7.91298449e-01
-4.19020683e-01 -6.14470720e-01 -3.34086835e-01 -8.17668736e-01
-5.01055598e-01 2.39925921e-01 -1.10367030e-01 -4.31058347... | [5.017591953277588, 4.030539512634277] |
af84fc08-8ca1-4c40-b563-9b4929435ab0 | a-new-evaluation-method-evaluation-data-and | 2205.00217 | null | https://arxiv.org/abs/2205.00217v1 | https://arxiv.org/pdf/2205.00217v1.pdf | A New Evaluation Method: Evaluation Data and Metrics for Chinese Grammar Error Correction | As a fundamental task in natural language processing, Chinese Grammatical Error Correction (CGEC) has gradually received widespread attention and become a research hotspot. However, one obvious deficiency for the existing CGEC evaluation system is that the evaluation values are significantly influenced by the Chinese w... | ['Shengyi Jiang', 'Ziyu Yang', 'Xiaotian Lin', 'Nankai Lin'] | 2022-04-30 | null | null | null | null | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 1.60479862e-02 -4.41914111e-01 1.46924376e-01 -5.81464767e-01
-5.28349936e-01 -3.95098537e-01 2.51944274e-01 6.53443456e-01
-8.22731256e-01 6.34470344e-01 2.65366465e-01 -1.67066589e-01
5.69290072e-02 -8.44683647e-01 -2.49867842e-01 -3.82665336e-01
6.86876476e-01 -3.88846770e-02 5.30552924e-01 -2.93147832... | [10.985759735107422, 10.76330852508545] |
bf1653e5-490a-4458-9678-ed8e544465a5 | convex-combination-consistency-between | 2205.00400 | null | https://arxiv.org/abs/2205.00400v1 | https://arxiv.org/pdf/2205.00400v1.pdf | Convex Combination Consistency between Neighbors for Weakly-supervised Action Localization | In weakly-supervised temporal action localization (WS-TAL), the methods commonly follow the "localization by classification" procedure, which uses the snippet predictions to form video class scores and then optimizes a video classification loss. In this procedure, the snippet predictions (or snippet attention weights) ... | ['Zhilin Li', 'Ruoxi Chen', 'Zilei Wang', 'Qinying Liu'] | 2022-05-01 | null | null | null | null | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action', 'action-localization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.69279349e-01 -5.82345650e-02 -6.27692461e-01 -2.81255215e-01
-5.23983836e-01 -1.56423911e-01 3.91251951e-01 -2.23289147e-01
-1.80747509e-01 4.89985019e-01 3.74810547e-01 1.74509570e-01
-5.75649068e-02 -2.28898153e-01 -1.08558786e+00 -7.29166329e-01
-2.37403676e-01 -2.02697188e-01 6.31929994e-01 7.39449561... | [8.51846694946289, 0.675515353679657] |
386b7ae5-2a67-46d9-8351-c18868148803 | dynamic-kernel-distillation-for-efficient | 1908.09216 | null | https://arxiv.org/abs/1908.09216v1 | https://arxiv.org/pdf/1908.09216v1.pdf | Dynamic Kernel Distillation for Efficient Pose Estimation in Videos | Existing video-based human pose estimation methods extensively apply large networks onto every frame in the video to localize body joints, which suffer high computational cost and hardly meet the low-latency requirement in realistic applications. To address this issue, we propose a novel Dynamic Kernel Distillation (DK... | ['Yuncheng Li', 'Xuecheng Nie', 'Linjie Luo', 'Jiashi Feng', 'Ning Zhang'] | 2019-08-24 | dynamic-kernel-distillation-for-efficient-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Nie_Dynamic_Kernel_Distillation_for_Efficient_Pose_Estimation_in_Videos_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Nie_Dynamic_Kernel_Distillation_for_Efficient_Pose_Estimation_in_Videos_ICCV_2019_paper.pdf | iccv-2019-10 | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.88965395e-01 -1.55367330e-01 -2.85829216e-01 -1.88706398e-01
-5.78258812e-01 -2.90028691e-01 1.16587162e-01 -4.41937417e-01
-7.34684825e-01 5.93264461e-01 3.30421269e-01 3.25280666e-01
1.82552367e-01 -6.38213634e-01 -9.20618951e-01 -7.18648851e-01
-2.10457727e-01 1.75595194e-01 4.37454611e-01 -1.33283541... | [7.17518424987793, -0.6010617613792419] |
c0cad2bf-4c5a-43b8-b0da-1a6b60b650d3 | reconet-real-time-coherent-video-style | 1807.01197 | null | http://arxiv.org/abs/1807.01197v2 | http://arxiv.org/pdf/1807.01197v2.pdf | ReCoNet: Real-time Coherent Video Style Transfer Network | Image style transfer models based on convolutional neural networks usually
suffer from high temporal inconsistency when applied to videos. Some video
style transfer models have been proposed to improve temporal consistency, yet
they fail to guarantee fast processing speed, nice perceptual style quality and
high tempora... | ['Derun Gu', 'Yizhou Yu', 'Fangjun Zhang', 'Chang Gao'] | 2018-07-03 | null | null | null | null | ['video-style-transfer'] | ['computer-vision'] | [ 3.08923304e-01 -6.88444138e-01 4.34572771e-02 -3.37132424e-01
-1.10471763e-01 -5.19537449e-01 5.48755765e-01 -4.85901028e-01
-1.25359327e-01 6.98320329e-01 -1.20978185e-03 7.76337162e-02
-1.22748734e-02 -7.27012217e-01 -8.46729636e-01 -4.40282136e-01
3.11319202e-01 -4.94378984e-01 5.66968679e-01 -1.52573854... | [11.251672744750977, -0.8638311624526978] |
f48a1609-0001-41d5-9d9f-773fc974a689 | metaaudio-a-few-shot-audio-classification | 2204.02121 | null | https://arxiv.org/abs/2204.02121v2 | https://arxiv.org/pdf/2204.02121v2.pdf | MetaAudio: A Few-Shot Audio Classification Benchmark | Currently available benchmarks for few-shot learning (machine learning with few training examples) are limited in the domains they cover, primarily focusing on image classification. This work aims to alleviate this reliance on image-based benchmarks by offering the first comprehensive, public and fully reproducible aud... | ['Mehrdad Yaghoobi', 'Timothy Hospedales', 'Sam Budgett', 'Calum Heggan'] | 2022-04-05 | null | null | null | null | ['few-shot-audio-classification'] | ['audio'] | [ 5.03247261e-01 -4.77523357e-01 -6.41835332e-02 -2.63326973e-01
-1.48937237e+00 -2.84844190e-01 7.96966255e-01 5.26429750e-02
-5.23555756e-01 4.35743302e-01 3.45770657e-01 2.61408359e-01
-3.40366483e-01 -3.88533115e-01 -4.53273445e-01 -5.88097155e-01
-3.31547230e-01 2.73442000e-01 5.11121094e-01 -2.50348985... | [15.186585426330566, 5.075692176818848] |
b5c28f86-bbd0-4eae-a1b9-b98f116f67dc | semantic-segmentation-of-anaemic-rbcs-using | 2202.04650 | null | https://arxiv.org/abs/2202.04650v1 | https://arxiv.org/pdf/2202.04650v1.pdf | Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network | Pixel-level analysis of blood images plays a pivotal role in diagnosing blood-related diseases, especially Anaemia. These analyses mainly rely on an accurate diagnosis of morphological deformities like shape, size, and precise pixel counting. In traditional segmentation approaches, instance or object-based approaches h... | ['Israr Ahmed Shaikh', 'Syed Hamad Shirazi', 'Arif Iqbal Umar', 'Muhammad Shahzad'] | 2022-02-09 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [-2.47316897e-01 -2.04392195e-01 3.71411920e-01 -4.66965675e-01
-3.48012924e-01 1.70911085e-02 7.99474046e-02 7.21077919e-01
-5.99305391e-01 8.97682369e-01 -1.72364116e-01 8.28280225e-02
5.07119019e-03 -1.23409367e+00 -2.74829000e-01 -8.89544010e-01
-7.50854984e-02 6.92295849e-01 3.63426775e-01 6.97825775... | [14.894109725952148, -3.1129302978515625] |
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