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dc5efa7f-c842-429c-91af-4c47fc1c5e15 | learning-collaborative-generation-correction | 1807.11706 | null | http://arxiv.org/abs/1807.11706v1 | http://arxiv.org/pdf/1807.11706v1.pdf | Learning Collaborative Generation Correction Modules for Blind Image Deblurring and Beyond | Blind image deblurring plays a very important role in many vision and
multimedia applications. Most existing works tend to introduce complex priors
to estimate the sharp image structures for blur kernel estimation. However, it
has been verified that directly optimizing these models is challenging and easy
to fall into ... | ['Zhongxuan Luo', 'Yi He', 'Xin Fan', 'Shichao Cheng', 'Risheng Liu'] | 2018-07-31 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [-1.44433267e-02 -4.26227748e-01 7.22647309e-02 -2.64693618e-01
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-2.79354334e-01 -4.69307274e-01 -6.32131040e-01 -9.02700424e-01
1.10616505e-01 -1.48342013e-01 2.71284252e-01 1.75010972... | [11.538541793823242, -2.6795828342437744] |
9996075f-c522-4a52-94cc-b689eded11db | feature-engineering-in-the-nli-shared-task | null | null | https://aclanthology.info/papers/W13-1730/w13-1730 | https://www.aclweb.org/anthology/W13-1730v2 | Feature Engineering in the NLI Shared Task 2013: Charles University Submission Report | null | ['Barbora Hladka', 'Martin Holub', 'Vincent Kriz'] | 2013-06-01 | feature-engineering-in-the-nli-shared-task-1 | https://aclanthology.org/W13-1730 | https://aclanthology.org/W13-1730.pdf | ws-2013-6 | ['native-language-identification'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5391837358474731, 15.86918830871582] |
c2c80e11-330e-4719-ad44-fa63691a829b | where-we-are-and-what-we-re-looking-at-query | 2303.04249 | null | https://arxiv.org/abs/2303.04249v1 | https://arxiv.org/pdf/2303.04249v1.pdf | Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes | Determining the exact latitude and longitude that a photo was taken is a useful and widely applicable task, yet it remains exceptionally difficult despite the accelerated progress of other computer vision tasks. Most previous approaches have opted to learn a single representation of query images, which are then classif... | ['Mubarak Shah', 'Vicente Vivanco Cepeda', 'Parth Parag Kulkarni', 'Alec Kerrigan', 'Brandon Clark'] | 2023-03-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Clark_Where_We_Are_and_What_Were_Looking_At_Query_Based_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Clark_Where_We_Are_and_What_Were_Looking_At_Query_Based_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-based-localization', 'memorization'] | ['computer-vision', 'natural-language-processing'] | [-2.15176135e-01 -2.81641066e-01 -1.43985480e-01 -5.25768816e-01
-6.27348602e-01 -8.93429875e-01 1.02985191e+00 2.61638165e-01
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-1.42415896e-01 1.72197223e-01 2.66963750e-01 -2.39701137... | [7.7030463218688965, -1.869247555732727] |
4323d593-330f-4c89-8469-61d51160aa42 | efe-end-to-end-frame-to-gaze-estimation | 2305.05526 | null | https://arxiv.org/abs/2305.05526v1 | https://arxiv.org/pdf/2305.05526v1.pdf | EFE: End-to-end Frame-to-Gaze Estimation | Despite the recent development of learning-based gaze estimation methods, most methods require one or more eye or face region crops as inputs and produce a gaze direction vector as output. Cropping results in a higher resolution in the eye regions and having fewer confounding factors (such as clothing and hair) is beli... | ['Otmar Hilliges', 'Xucong Zhang', 'Xi Wang', 'Seonwook Park', 'Haldun Balim'] | 2023-05-09 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 1.83379963e-01 -5.39643597e-03 2.96088755e-02 -7.60029316e-01
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4.30186689e-01 -3.79793704e-01 1.12943046e-01 -1.29757956... | [14.1240234375, 0.08084197342395782] |
688a8b4d-188d-40fe-bd0e-9d4064368f85 | computing-star-discrepancies-with-numerical | 2306.16998 | null | https://arxiv.org/abs/2306.16998v1 | https://arxiv.org/pdf/2306.16998v1.pdf | Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms | The $L_{\infty}$ star discrepancy is a measure for the regularity of a finite set of points taken from $[0,1)^d$. Low discrepancy point sets are highly relevant for Quasi-Monte Carlo methods in numerical integration and several other applications. Unfortunately, computing the $L_{\infty}$ star discrepancy of a given po... | ['Carola Doerr', 'Luís Paquete', 'Alexandre D. Jesus', 'Jacob de Nobel', 'Diederick Vermetten', 'François Clément'] | 2023-06-29 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-4.81457412e-01 -1.22304037e-02 -7.26312175e-02 -1.95433432e-03
-1.07332277e+00 -4.03714716e-01 1.54697672e-01 3.52503181e-01
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-6.14359856e-01 9.15437698e-01 3.55898067e-02 -5.22126019... | [6.463342666625977, 4.452096462249756] |
e4cca3b8-5442-4ddb-8362-818177ffe229 | a-network-resource-allocation-recommendation | 2307.03399 | null | https://arxiv.org/abs/2307.03399v1 | https://arxiv.org/pdf/2307.03399v1.pdf | A Network Resource Allocation Recommendation Method with An Improved Similarity Measure | Recommender systems have been acknowledged as efficacious tools for managing information overload. Nevertheless, conventional algorithms adopted in such systems primarily emphasize precise recommendations and, consequently, overlook other vital aspects like the coverage, diversity, and novelty of items. This approach r... | ['Junhua Hu', 'Pei Liang', 'Huiyu Li'] | 2023-07-07 | null | null | null | null | ['recommendation-systems'] | ['miscellaneous'] | [-2.85994828e-01 -5.50258420e-02 -5.42228341e-01 -1.40683487e-01
2.71980405e-01 -4.89708841e-01 1.28563777e-01 4.31815922e-01
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-8.85461748e-01 -1.06012642e+00 -7.57575110e-02 -5.77221096e-01
-7.80108720e-02 1.72310367e-01 2.87375182e-01 -5.18699229... | [9.866291999816895, 5.721176624298096] |
b3c726a7-70f9-4072-a991-4629e114e617 | uiai-system-for-short-duration-speaker | 2007.13118 | null | https://arxiv.org/abs/2007.13118v1 | https://arxiv.org/pdf/2007.13118v1.pdf | UIAI System for Short-Duration Speaker Verification Challenge 2020 | In this work, we present the system description of the UIAI entry for the short-duration speaker verification (SdSV) challenge 2020. Our focus is on Task 1 dedicated to text-dependent speaker verification. We investigate different feature extraction and modeling approaches for automatic speaker verification (ASV) and u... | ['Zheng-Hua Tan', 'Xuechen Liu', 'Tomi Kinnunen', 'Achintya Kumar Sarkar', 'Emmanuel Vincent', 'Ville Vestman', 'Romain Serizel', 'Md Sahidullah'] | 2020-07-26 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 1.39107138e-01 3.70356701e-02 1.87445015e-01 -8.35446119e-01
-1.65049541e+00 -5.92202902e-01 7.78563261e-01 -1.15303896e-01
-4.67654735e-01 3.93032759e-01 4.07908261e-01 -5.47108531e-01
5.13631642e-01 4.24941242e-01 -2.76039869e-01 -6.55566216e-01
1.12418488e-01 2.83476859e-01 -5.93017712e-02 -1.54670075... | [14.40160846710205, 6.080440521240234] |
98484172-6fc0-4b33-8f5c-9f7506f33260 | probabilistic-conformal-prediction-using | 2206.06584 | null | https://arxiv.org/abs/2206.06584v2 | https://arxiv.org/pdf/2206.06584v2.pdf | Probabilistic Conformal Prediction Using Conditional Random Samples | This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set. Given inputs, PCP construct the predictive set based on random samples from an estimated generative model. It is efficient and compatible with either explici... | ['David M. Blei', 'Mingyuan Zhou', 'Mingzhang Yin', 'Ruijiang Gao', 'Zhendong Wang'] | 2022-06-14 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 4.34588850e-01 5.30331910e-01 -6.88507617e-01 -5.78596890e-01
-1.15106499e+00 -5.26113451e-01 9.72638845e-01 -3.89017254e-01
4.59050417e-01 1.20486927e+00 8.39514136e-02 -3.41876149e-01
-3.99400741e-01 -1.22191167e+00 -8.80051255e-01 -6.31571054e-01
-1.25762671e-01 1.35909462e+00 3.88840795e-01 5.45574427... | [7.566667556762695, 4.4221696853637695] |
6bbcfaed-abb2-41f9-8489-c43aa52ee5fe | neural-network-quantum-state-with-proximal | 2210.16493 | null | https://arxiv.org/abs/2210.16493v1 | https://arxiv.org/pdf/2210.16493v1.pdf | Neural network quantum state with proximal optimization: a ground-state searching scheme based on variational Monte Carlo | Neural network quantum states (NQS), incorporating with variational Monte Carlo (VMC) method, are shown to be a promising way to investigate quantum many-body physics. Whereas vanilla VMC methods perform one gradient update per sample, we introduce a novel objective function with proximal optimization (PO) that enables... | ['Ming Xue', 'Feng Chen'] | 2022-10-29 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 3.62863123e-01 -2.36417219e-01 3.00458120e-03 -1.41528070e-01
-8.60363066e-01 3.00161988e-02 6.00992322e-01 -1.94557354e-01
-8.80254686e-01 1.25902462e+00 -3.37895602e-02 -3.87653410e-01
-3.96204174e-01 -9.64509010e-01 -8.28696191e-01 -1.29380977e+00
-2.22805724e-01 7.25584865e-01 2.13089615e-01 -4.63748246... | [5.613711833953857, 4.923817157745361] |
2d65d842-b853-4e7c-b992-bb4e3c80f3ff | sampling-equivariant-self-attention-networks | 2111.03420 | null | https://arxiv.org/abs/2111.03420v1 | https://arxiv.org/pdf/2111.03420v1.pdf | Sampling Equivariant Self-attention Networks for Object Detection in Aerial Images | Objects in aerial images have greater variations in scale and orientation than in typical images, so detection is more difficult. Convolutional neural networks use a variety of frequency- and orientation-specific kernels to identify objects subject to different transformations; these require many parameters. Sampling e... | ['Shi-Min Hu', 'Ralph R. Martin', 'Xiang-Li Li', 'Guo-Ye Yang'] | 2021-11-05 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 3.21615577e-01 -2.81887978e-01 -1.17402792e-01 -4.01432276e-01
-7.25354701e-02 -8.47020447e-01 5.00435650e-01 -5.00058770e-01
-4.87534702e-01 2.64643639e-01 1.47809237e-01 4.28623483e-02
-1.38657093e-01 -1.09392905e+00 -9.58561599e-01 -7.38378584e-01
-4.86163199e-02 8.40219930e-02 6.10438287e-01 -3.32823873... | [9.09962272644043, 2.2704291343688965] |
6f5178e2-051e-4b47-bc59-a5644a0e1af3 | tgcf-texture-guided-color-fusion-for | 2207.12585 | null | https://arxiv.org/abs/2207.12585v2 | https://arxiv.org/pdf/2207.12585v2.pdf | PTGCF: Printing Texture Guided Color Fusion for Impressionism Oil Painting Style Rendering | As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses the computer algorithms to render a photo into an artistic painting. Recent work has shown that the extraction of style information such as stroke texture and color of the target style image is the key to image stylization. Given its... | ['Xiaoquan Li', "Li'e Ma", 'Yijun Yan', 'Jing Geng'] | 2022-07-26 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 5.80177546e-01 -1.87303603e-01 6.87745363e-02 1.07945241e-02
2.31504813e-01 -5.30509889e-01 7.27338672e-01 -5.40037632e-01
-7.76850581e-02 5.24188936e-01 1.11982226e-01 -1.86980829e-01
7.79035911e-02 -9.20997858e-01 -2.33017236e-01 -6.29236996e-01
6.22431934e-01 1.12305611e-01 2.51665175e-01 -4.29525048... | [11.540949821472168, -0.8383886218070984] |
a50baa1f-713f-43f7-b69d-05e9bde953d9 | tsdpmm-incorporating-prior-topic-knowledge | null | null | https://aclanthology.org/D15-1091 | https://aclanthology.org/D15-1091.pdf | TSDPMM: Incorporating Prior Topic Knowledge into Dirichlet Process Mixture Models for Text Clustering | null | ['Xiao-Li Li', 'Chao Shao', 'Xuzhong Wang', 'Linmei Hu', 'Juanzi Li'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['text-clustering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.418964385986328, 3.6275181770324707] |
095f4e95-756c-49ed-b5d4-acf1105d90dc | hypersf-spectral-hypergraph-coarsening-via | 2108.07901 | null | https://arxiv.org/abs/2108.07901v1 | https://arxiv.org/pdf/2108.07901v1.pdf | HyperSF: Spectral Hypergraph Coarsening via Flow-based Local Clustering | Hypergraphs allow modeling problems with multi-way high-order relationships. However, the computational cost of most existing hypergraph-based algorithms can be heavily dependent upon the input hypergraph sizes. To address the ever-increasing computational challenges, graph coarsening can be potentially applied for pre... | ['Zhuo Feng', 'Zhiqiang Zhao', 'Ali Aghdaei'] | 2021-08-17 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [ 3.03295970e-01 2.65488505e-01 -4.97735381e-01 3.37235965e-02
-6.27280593e-01 -9.14505303e-01 6.92374120e-03 3.44368011e-01
2.19002262e-01 5.75460970e-01 -1.93902940e-01 -5.94776630e-01
-7.44503498e-01 -1.20494831e+00 -5.82760751e-01 -7.72440374e-01
-3.60485494e-01 7.29242265e-01 5.86495578e-01 -7.07490966... | [7.026424407958984, 5.17626953125] |
2db15f1f-c652-43ad-b0f1-6c29cd807310 | deep-learning-inversion-of-seismic-data | 1901.07733 | null | https://arxiv.org/abs/1901.07733v2 | https://arxiv.org/pdf/1901.07733v2.pdf | Deep-Learning Inversion of Seismic Data | We propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i.e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs). The conventional way of addressing this ill-posed inversion problem is through iterative ... | ['Yunhai Wang', 'Yuxiao Ren', 'Bin Liu', 'Yangkang Chen', 'Peng Jiang', 'Shucai Li', 'Senlin Yang'] | 2019-01-23 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 1.18795864e-01 -1.10617109e-01 2.87250787e-01 -1.67293891e-01
-9.85332310e-01 -3.14668000e-01 4.10976946e-01 -2.10080504e-01
-5.42865574e-01 5.99082112e-01 3.00370783e-01 -1.68823212e-01
-6.37160063e-01 -1.06533873e+00 -8.80554676e-01 -1.06675565e+00
-2.07363427e-01 5.58968723e-01 3.63398910e-01 -5.07689595... | [6.861716270446777, 2.5284829139709473] |
699497af-e769-4860-8681-834b6c299130 | input-sensitive-dense-sparse-primitive | 2306.15155 | null | https://arxiv.org/abs/2306.15155v1 | https://arxiv.org/pdf/2306.15155v1.pdf | Input-sensitive dense-sparse primitive compositions for GNN acceleration | Graph neural networks (GNN) have become an important class of neural network models that have gained popularity in domains such as social and financial network analysis. Different phases of GNN computations can be modeled using both dense and sparse matrix operations. There have been many frameworks and optimization te... | ['Charith Mendis', 'Josep Torrellas', 'Serif Yesil', 'Gerasimos Gerogiannis', 'Vimarsh Sathia', 'Damitha Lenadora'] | 2023-06-27 | null | null | null | null | ['graph-embedding', 'graph-attention'] | ['graphs', 'graphs'] | [-1.03005268e-01 3.01651154e-02 2.71222722e-02 -2.56835043e-01
-7.41181076e-02 -3.18233073e-01 4.03143883e-01 4.68627244e-01
-7.16025591e-01 3.50233585e-01 -6.79690018e-02 -6.59080923e-01
-2.74979621e-01 -1.18114710e+00 -9.39941943e-01 -3.09108227e-01
-6.75575078e-01 2.64721870e-01 -4.78989668e-02 -4.60950941... | [7.0370097160339355, 5.65277624130249] |
14fc7068-17c8-4afa-92e1-a3dd41faa24d | one-shot-face-reenactment | 1908.03251 | null | https://arxiv.org/abs/1908.03251v1 | https://arxiv.org/pdf/1908.03251v1.pdf | One-shot Face Reenactment | To enable realistic shape (e.g. pose and expression) transfer, existing face reenactment methods rely on a set of target faces for learning subject-specific traits. However, in real-world scenario end-users often only have one target face at hand, rendering existing methods inapplicable. In this work, we bridge this ga... | ['Yunxuan Zhang', 'Siwei Zhang', 'Yue He', 'Chen Change Loy', 'Ziwei Liu', 'Cheng Li'] | 2019-08-05 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 3.40227783e-01 1.61783859e-01 -1.56108648e-01 -6.29697561e-01
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1.72316000e-01 -6.26137137e-01 -7.84429491e-01 -7.02981412e-01
2.43245408e-01 2.66807765e-01 -3.47866148e-01 -3.30434114... | [12.817489624023438, 0.0947776734828949] |
ac32f8d9-8426-4ba7-9e9e-71d1a4edc94c | traffic-net-3d-traffic-monitoring-using-a | 2109.09165 | null | https://arxiv.org/abs/2109.09165v2 | https://arxiv.org/pdf/2109.09165v2.pdf | Traffic-Net: 3D Traffic Monitoring Using a Single Camera | Computer Vision has played a major role in Intelligent Transportation Systems (ITS) and traffic surveillance. Along with the rapidly growing automated vehicles and crowded cities, the automated and advanced traffic management systems (ATMS) using video surveillance infrastructures have been evolved by the implementatio... | ['Farzam Mohammad Pour Mir', 'Mohsen Azarmi', 'Mahdi Rezaei'] | 2021-09-19 | null | null | null | null | ['camera-auto-calibration'] | ['computer-vision'] | [-2.48913765e-01 -6.01299226e-01 7.05770552e-02 -4.15445834e-01
-1.08044356e-01 -1.88878059e-01 6.29831851e-01 -6.94274232e-02
-6.14550292e-01 5.71548522e-01 -2.27046594e-01 -5.63537121e-01
-8.00449699e-02 -1.16974425e+00 -4.58462745e-01 -7.62564242e-01
-6.13180026e-02 6.63576603e-01 7.74404705e-01 -3.84926677... | [7.900379180908203, -0.9164146184921265] |
6399f53c-18d7-4fe9-8295-2ff186acaa75 | on-the-effectiveness-of-out-of-distribution | 2306.04934 | null | https://arxiv.org/abs/2306.04934v1 | https://arxiv.org/pdf/2306.04934v1.pdf | On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning | Though Self-supervised learning (SSL) has been widely studied as a promising technique for representation learning, it doesn't generalize well on long-tailed datasets due to the majority classes dominating the feature space. Recent work shows that the long-tailed learning performance could be boosted by sampling extra ... | ['Haoji Hu', 'Huanpeng Chu', 'Yang Feng', 'Jin Hao', 'Hualiang Wang', 'Zuozhu Liu', 'Jianhong Bai'] | 2023-06-08 | null | null | null | null | ['long-tail-learning'] | ['methodology'] | [-1.86046720e-01 -1.90873250e-01 -8.04670095e-01 -5.54203153e-01
-8.34478557e-01 -5.27557433e-01 6.20634377e-01 1.24914430e-01
-2.92539060e-01 8.16432178e-01 1.11028746e-01 -2.51883596e-01
-3.13111156e-01 -7.80773044e-01 -7.15873778e-01 -8.05374026e-01
-5.77100329e-02 5.11572599e-01 4.28314477e-01 8.08295384... | [9.617332458496094, 3.424311637878418] |
f539d30c-35fa-463d-8050-fb636d2fde47 | a-transfer-learning-and-optimized-cnn-based | 2201.11812 | null | https://arxiv.org/abs/2201.11812v1 | https://arxiv.org/pdf/2201.11812v1.pdf | A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles | Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-thre... | ['Abdallah Shami', 'Li Yang'] | 2022-01-27 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [-4.85836715e-01 -1.26640365e-01 -2.53996342e-01 -4.59400788e-02
1.16232418e-01 -5.80875993e-01 8.59959245e-01 -2.77994815e-02
-5.28542876e-01 4.88796264e-01 -5.65596700e-01 -9.51040864e-01
4.04917300e-02 -1.07900095e+00 -6.26834631e-01 -5.26886404e-01
-2.02241868e-01 -5.76085858e-02 5.72283864e-01 -3.69367033... | [5.298510551452637, 7.304529666900635] |
73d03af1-74da-40c5-8e63-48ac48c05a43 | effective-occlusion-handling-for-fast | 1807.04880 | null | https://arxiv.org/abs/1807.04880v3 | https://arxiv.org/pdf/1807.04880v3.pdf | Effective Occlusion Handling for Fast Correlation Filter-based Trackers | Correlation filter-based trackers heavily suffer from the problem of multiple peaks in their response maps incurred by occlusions. Moreover, the whole tracking pipeline may break down due to the uncertainties brought by shifting among peaks, which will further lead to the degraded correlation filter model. To alleviate... | ['T. T. Wong', 'Zheng Zhang'] | 2018-07-13 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [-2.90759206e-01 -5.91054201e-01 1.57648936e-01 -1.96160510e-01
-7.20492125e-01 -6.05876207e-01 6.64053380e-01 4.49981391e-02
-4.28592712e-01 5.60657561e-01 2.11434111e-01 1.63280576e-01
-7.06995353e-02 -3.26837182e-01 -5.06245315e-01 -8.10965776e-01
1.18369229e-01 1.34222344e-01 7.47010410e-01 1.26665562... | [6.365098476409912, -2.098672389984131] |
62b53329-8661-4d15-b5a9-e97a0fc45774 | conssed-at-semeval-2019-task-3-configurable | null | null | https://aclanthology.org/S19-2027 | https://aclanthology.org/S19-2027.pdf | ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector | This paper describes our system participating in the SemEval-2019 Task 3: EmoContext: Contextual Emotion Detection in Text. The goal was to for a given textual dialogue, i.e. a user utterance along with two turns of context, identify the emotion of user utterance as one of the emotion classes: Happy, Sad, Angry or Othe... | ["Rafa{\\l} Po{\\'s}wiata"] | 2019-06-01 | null | null | null | semeval-2019-6 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 5.16618378e-02 2.72854179e-01 2.92350292e-01 -9.12389219e-01
-3.54496151e-01 -5.14014661e-01 7.04093277e-01 1.79044619e-01
-4.57681268e-01 8.31621826e-01 4.10558850e-01 8.07219669e-02
6.36531293e-01 -1.52322710e-01 7.50585347e-02 -2.87763834e-01
5.53490929e-02 3.32020700e-01 -4.33020651e-01 -6.12113237... | [12.931099891662598, 6.2303266525268555] |
917273d3-5f91-41d1-bb87-1bee1696be62 | panoptic-narrative-grounding | 2109.04988 | null | https://arxiv.org/abs/2109.04988v1 | https://arxiv.org/pdf/2109.04988v1.pdf | Panoptic Narrative Grounding | This paper proposes Panoptic Narrative Grounding, a spatially fine and general formulation of the natural language visual grounding problem. We establish an experimental framework for the study of this new task, including new ground truth and metrics, and we propose a strong baseline method to serve as stepping stone f... | ['P. Arbeláez', 'J. Pont-Tuset', 'J. Hernández', 'I. Hernández', 'N. Ayobi', 'C. González'] | 2021-09-10 | null | null | null | null | ['natural-language-visual-grounding'] | ['reasoning'] | [ 2.31077433e-01 3.50487471e-01 -4.93911564e-01 -2.63666868e-01
-6.79538548e-01 -1.02747583e+00 1.11009455e+00 3.79643112e-01
-2.14699090e-01 4.00045604e-01 7.03038871e-01 -1.59297168e-01
1.47751674e-01 -1.01724267e+00 -6.36265337e-01 -4.50536191e-01
2.15718001e-02 3.72101277e-01 3.27539593e-01 -3.24329585... | [11.008648872375488, 1.0954896211624146] |
41da7b4a-bbb7-48e3-b064-77f6f52ddaf4 | deep-generative-modeling-for-protein-design | 2109.13754 | null | https://arxiv.org/abs/2109.13754v1 | https://arxiv.org/pdf/2109.13754v1.pdf | Deep Generative Modeling for Protein Design | Deep learning approaches have produced substantial breakthroughs in fields such as image classification and natural language processing and are making rapid inroads in the area of protein design. Many generative models of proteins have been developed that encompass all known protein sequences, model specific protein fa... | ['Philip M. Kim', 'Alexey Strokach'] | 2021-08-31 | null | null | null | null | ['protein-design'] | ['medical'] | [ 3.19169492e-01 1.49176583e-01 -2.57690281e-01 -5.90291798e-01
-3.33088994e-01 -7.41791606e-01 3.71690243e-01 3.60387564e-01
3.01167425e-02 9.23706412e-01 -3.12294532e-02 -3.56179208e-01
-8.00898746e-02 -7.55573750e-01 -9.88832355e-01 -9.30540085e-01
9.40935165e-02 7.57680178e-01 6.67627305e-02 -2.15033829... | [4.711837291717529, 5.618199348449707] |
af8932e7-da06-482d-bc0c-d4c218d195b1 | team-taurus-at-semeval-2019-task-9-expert | null | null | https://aclanthology.org/S19-2219 | https://aclanthology.org/S19-2219.pdf | Team Taurus at SemEval-2019 Task 9: Expert-informed pattern recognition for suggestion mining | This paper presents our submissions to SemEval-2019 Task9, Suggestion Mining. Our system is one in a series of systems in which we compare an approach using expert-defined rules with a comparable one using machine learning. We target tasks with a syntactic or semantic component that might be better described by a human... | ['Nelleke Oostdijk', 'Hans van Halteren'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [ 1.36377707e-01 5.89119494e-01 -1.80174857e-01 -7.84704745e-01
-4.22336876e-01 -3.57160270e-01 1.06111562e+00 4.98093367e-01
-8.16227078e-01 1.02600217e+00 8.42202976e-02 -7.38608360e-01
-5.41169822e-01 -5.89371443e-01 -5.27461767e-01 -7.08203623e-03
2.57858466e-02 1.10281873e+00 7.43055999e-01 -2.49789223... | [10.866368293762207, 7.570070266723633] |
8fbada1b-182f-4486-a57b-6452da4eddae | atrial-fibrillation-detection-using-weight | 2206.07649 | null | https://arxiv.org/abs/2206.07649v1 | https://arxiv.org/pdf/2206.07649v1.pdf | Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks | Deep neural networks (DNN) are a promising tool in medical applications. However, the implementation of complex DNNs on battery-powered devices is challenging due to high energy costs for communication. In this work, a convolutional neural network model is developed for detecting atrial fibrillation from electrocardiog... | ['Deepu John', 'Rajesh C. Panicker', 'Li Xiaolin', 'Wang He', 'Rui Han', 'Shuhui Wang', 'Benjamin Chen Ming Choong', 'Ann Feng Chew', 'Xiu Qi Chang'] | 2022-06-14 | null | null | null | null | ['atrial-fibrillation-detection'] | ['medical'] | [ 3.64977211e-01 -1.44996375e-01 -4.49736863e-01 -3.25819165e-01
-8.87911469e-02 4.07174975e-02 -3.90978903e-01 3.99502158e-01
-7.48670161e-01 9.56299245e-01 -2.37670183e-01 -4.13172960e-01
-2.60007858e-01 -7.80409694e-01 -2.13843390e-01 -6.10730290e-01
-3.78388703e-01 9.19494312e-03 -2.17018381e-01 4.79634523... | [13.984697341918945, 3.2672150135040283] |
d1a07499-2eb8-4f57-95d2-dea12f826650 | myope-models-are-face-presentation-attack | 2111.11127 | null | https://arxiv.org/abs/2111.11127v1 | https://arxiv.org/pdf/2111.11127v1.pdf | Myope Models -- Are face presentation attack detection models short-sighted? | Presentation attacks are recurrent threats to biometric systems, where impostors attempt to bypass these systems. Humans often use background information as contextual cues for their visual system. Yet, regarding face-based systems, the background is often discarded, since face presentation attack detection (PAD) model... | ['Jaime S. Cardoso', 'Ana F. Sequeira', 'Pedro C. Neto'] | 2021-11-22 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.10207903e-01 -2.44679809e-01 1.13246739e-02 1.71175718e-01
-5.49391568e-01 -6.74490333e-01 7.41782129e-01 -7.80736208e-02
-4.40218449e-01 4.99068618e-01 -2.97938734e-01 -2.61847228e-01
2.19747260e-01 -3.80433440e-01 -6.13682151e-01 -1.05538726e+00
-1.37458434e-02 5.09384237e-02 3.41331571e-01 -3.46777707... | [13.077624320983887, 1.0822629928588867] |
f0cce92c-7c45-4791-8b89-e5e90d84a07e | cramnet-camera-radar-fusion-with-ray | 2210.09267 | null | https://arxiv.org/abs/2210.09267v2 | https://arxiv.org/pdf/2210.09267v2.pdf | CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection | Robust 3D object detection is critical for safe autonomous driving. Camera and radar sensors are synergistic as they capture complementary information and work well under different environmental conditions. Fusing camera and radar data is challenging, however, as each of the sensors lacks information along a perpendicu... | ['Dragomir Anguelov', 'Tiffany Chen', 'Nicholas Armstrong-Crews', 'Sean Rafferty', 'Joshua Manela', 'Henrik Kretzschmar', 'Jyh-Jing Hwang'] | 2022-10-17 | null | null | null | null | ['monocular-3d-object-detection', 'robust-3d-object-detection'] | ['computer-vision', 'computer-vision'] | [ 4.01748121e-01 -2.64471591e-01 -1.24075361e-01 -6.51454926e-01
-1.17595983e+00 -8.49708200e-01 5.43726623e-01 -4.23262060e-01
-5.37265122e-01 7.49309221e-03 2.15019081e-02 -3.30301613e-01
-2.06224203e-01 -6.38580501e-01 -9.40922141e-01 -6.28753841e-01
3.24985147e-01 4.38961059e-01 2.09214509e-01 -2.18958333... | [7.72109842300415, -1.8380153179168701] |
8ad96060-fb2c-4225-8eb5-26e980066172 | at-human-speed-deep-reinforcement-learning | 1810.07286 | null | http://arxiv.org/abs/1810.07286v1 | http://arxiv.org/pdf/1810.07286v1.pdf | At Human Speed: Deep Reinforcement Learning with Action Delay | There has been a recent explosion in the capabilities of game-playing
artificial intelligence. Many classes of tasks, from video games to motor
control to board games, are now solvable by fairly generic algorithms, based on
deep learning and reinforcement learning, that learn to play from experience
with minimal prior ... | ['Tina Ju', 'Josh Tenenbaum', 'Vlad Firoiu'] | 2018-10-16 | null | null | null | null | ['board-games'] | ['playing-games'] | [-9.49071646e-02 2.15588108e-01 1.99947804e-02 1.45524785e-01
-1.38170198e-01 -7.40778029e-01 4.37192112e-01 -1.27404839e-01
-9.62833941e-01 8.14782500e-01 -3.96259815e-01 -4.81476247e-01
-3.42943698e-01 -8.90385628e-01 -5.78423798e-01 -4.46089298e-01
-3.18728030e-01 6.49303198e-01 4.00028706e-01 -9.33791578... | [3.652376890182495, 1.5000393390655518] |
cfe3c04c-0bb2-4994-9b82-0ac590e07e3b | dcid-deep-canonical-information-decomposition | 2306.15619 | null | https://arxiv.org/abs/2306.15619v1 | https://arxiv.org/pdf/2306.15619v1.pdf | DCID: Deep Canonical Information Decomposition | We consider the problem of identifying the signal shared between two one-dimensional target variables, in the presence of additional multivariate observations. Canonical Correlation Analysis (CCA)-based methods have traditionally been used to identify shared variables, however, they were designed for multivariate targe... | ['Christoph Lippert', 'Alexander Rakowski'] | 2023-06-27 | null | null | null | null | ['multi-task-learning', 'information-retrieval'] | ['methodology', 'natural-language-processing'] | [ 4.16846067e-01 -2.38131717e-01 -3.04071963e-01 -2.81142354e-01
-1.30939388e+00 -5.13028800e-01 7.88800776e-01 -1.56697929e-01
-1.95967183e-01 7.00504601e-01 2.67501980e-01 3.80855948e-02
-5.56643248e-01 -1.76208809e-01 -6.39753461e-01 -9.69730616e-01
-6.21077657e-01 3.52103680e-01 -3.66651058e-01 1.37943909... | [7.534479141235352, 4.611071586608887] |
0f34c0ff-d7f7-484e-9b34-91c3a1c638b6 | backdoor-attacks-on-crowd-counting | 2207.05641 | null | https://arxiv.org/abs/2207.05641v1 | https://arxiv.org/pdf/2207.05641v1.pdf | Backdoor Attacks on Crowd Counting | Crowd counting is a regression task that estimates the number of people in a scene image, which plays a vital role in a range of safety-critical applications, such as video surveillance, traffic monitoring and flow control. In this paper, we investigate the vulnerability of deep learning based crowd counting models to ... | ['Lichao', 'Yu Cheng', 'Xing Di', 'Zichuan Xu', 'Jian Lou', 'Pan Zhou', 'Xingjun Ma', 'Tailai Zhang', 'Yuhua Sun'] | 2022-07-12 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-1.47954330e-01 -2.62373209e-01 1.62979007e-01 3.84749360e-02
-2.70304561e-01 -6.32971287e-01 6.99582875e-01 1.06262274e-01
-7.71781564e-01 7.86137342e-01 -1.59971625e-01 -6.48281932e-01
2.79289931e-01 -1.22442961e+00 -8.51089001e-01 -8.71334195e-01
-5.79103351e-01 5.87312698e-01 4.99971509e-01 -1.36390388... | [5.575076103210449, 7.829614162445068] |
cb12648b-7e04-41d1-8534-3463f5a5276f | learning-high-level-representations-from | 1802.06604 | null | http://arxiv.org/abs/1802.06604v3 | http://arxiv.org/pdf/1802.06604v3.pdf | Learning High-level Representations from Demonstrations | Hierarchical learning (HL) is key to solving complex sequential decision
problems with long horizons and sparse rewards. It allows learning agents to
break-up large problems into smaller, more manageable subtasks. A common
approach to HL, is to provide the agent with a number of high-level skills that
solve small parts... | ['Haitham Bou-Ammar', 'Peter Vrancx', 'Garrett Andersen'] | 2018-02-19 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [ 1.21382743e-01 3.67947102e-01 -1.16691880e-01 -1.45458981e-01
-8.61084342e-01 -9.12591815e-01 4.86926049e-01 4.87847440e-03
-5.78653753e-01 1.06783903e+00 1.04949936e-01 -3.97608995e-01
-4.45962638e-01 -3.95847201e-01 -8.36499155e-01 -6.37648344e-01
-6.79867506e-01 7.08077133e-01 5.02659619e-01 -5.62387943... | [4.204461097717285, 1.246055245399475] |
de160972-17d7-40d9-b373-49e0bc28a0c1 | growing-a-brain-fine-tuning-by-increasing-1 | 1907.07844 | null | https://arxiv.org/abs/1907.07844v1 | https://arxiv.org/pdf/1907.07844v1.pdf | Growing a Brain: Fine-Tuning by Increasing Model Capacity | CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through f... | ['Yu-Xiong Wang', 'Deva Ramanan', 'Martial Hebert'] | 2019-07-18 | growing-a-brain-fine-tuning-by-increasing | http://openaccess.thecvf.com/content_cvpr_2017/html/Wang_Growing_a_Brain_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Growing_a_Brain_CVPR_2017_paper.pdf | cvpr-2017-7 | ['developmental-learning'] | ['robots'] | [ 4.26286101e-01 1.57174230e-01 -8.27311426e-02 -5.72513521e-01
-1.54513016e-01 -7.93353736e-01 5.51015735e-01 -4.70473878e-02
-7.67534196e-01 7.24010289e-01 1.02391116e-01 -5.71756065e-02
-1.14622802e-01 -9.75702763e-01 -1.17104292e+00 -4.23253477e-01
1.15236618e-01 4.76779550e-01 6.48534238e-01 -2.37193421... | [9.029719352722168, 3.005648136138916] |
23f8057e-b4b7-4f6b-bc35-aa83ac407dc7 | tackling-interpretability-in-audio | 2305.07132 | null | https://arxiv.org/abs/2305.07132v1 | https://arxiv.org/pdf/2305.07132v1.pdf | Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization | This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to interpret decisions of a network in terms of high-level audio objects that are also listenable for the end-user. This is extended to present an i... | ["Florence d'Alché-Buc", 'Gaël Richard', 'Pavlo Mozharovskyi', 'Sanjeel Parekh', 'Jayneel Parekh'] | 2023-05-11 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 9.86026347e-01 7.58157313e-01 1.34568617e-01 -7.99488962e-01
-6.98047280e-01 -6.01887286e-01 1.85998529e-01 9.53375623e-02
-1.64052472e-01 3.25738400e-01 4.47533339e-01 -2.57358819e-01
-3.47277254e-01 -3.02555919e-01 -6.44446850e-01 -5.19848466e-01
-2.68468112e-01 3.83689761e-01 -5.39287210e-01 -1.12247385... | [15.744776725769043, 5.280426979064941] |
26c9b03a-835c-4fd7-a0df-75f0a0dc088c | msctd-a-multimodal-sentiment-chat-translation | 2202.13645 | null | https://arxiv.org/abs/2202.13645v1 | https://arxiv.org/pdf/2202.13645v1.pdf | MSCTD: A Multimodal Sentiment Chat Translation Dataset | Multimodal machine translation and textual chat translation have received considerable attention in recent years. Although the conversation in its natural form is usually multimodal, there still lacks work on multimodal machine translation in conversations. In this work, we introduce a new task named Multimodal Chat Tr... | ['Jie zhou', 'Yufeng Chen', 'Jinan Xu', 'Fandong Meng', 'Yunlong Liang'] | 2022-02-28 | null | https://aclanthology.org/2022.acl-long.186 | https://aclanthology.org/2022.acl-long.186.pdf | acl-2022-5 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 2.00600147e-01 -2.09353477e-01 -5.40705621e-02 -4.88055676e-01
-1.25346351e+00 -7.03274667e-01 1.02798522e+00 -2.28640318e-01
-2.88646102e-01 9.07221019e-01 6.44166291e-01 -3.04673076e-01
8.70519519e-01 -3.74927044e-01 -3.00580949e-01 -5.45932889e-01
5.22029996e-01 5.23375928e-01 -2.46814623e-01 -6.57589495... | [11.54366397857666, 1.6783663034439087] |
9719c5cc-e11b-4702-aac6-04b8dbc0f9c3 | open-vocabulary-argument-role-prediction-for | 2211.01577 | null | https://arxiv.org/abs/2211.01577v1 | https://arxiv.org/pdf/2211.01577v1.pdf | Open-Vocabulary Argument Role Prediction for Event Extraction | The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies still depend on roles pre-defined by domain experts. These studies expose obvious weakness when extending to emerging event types or new dom... | ['Jiawei Han', 'Heng Ji', 'Ming Zhong', 'Yiqing Xie', 'Sha Li', 'Yizhu Jiao'] | 2022-11-03 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 4.99235123e-01 4.35589194e-01 -4.65801746e-01 -6.07053816e-01
-6.88080311e-01 -6.80734277e-01 8.50174785e-01 6.79713607e-01
-5.74796319e-01 9.87500846e-01 8.13384414e-01 -6.00767098e-02
-1.23880096e-01 -8.85036767e-01 -4.55661267e-01 -3.48726362e-01
1.39846981e-01 5.05781949e-01 5.49444020e-01 -2.24911720... | [9.17901611328125, 9.190762519836426] |
51ecb5a0-b51f-4ad2-82f6-4bcd5d33a75c | layoutxlm-multimodal-pre-training-for | 2104.08836 | null | https://arxiv.org/abs/2104.08836v3 | https://arxiv.org/pdf/2104.08836v3.pdf | LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding | Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document understanding tasks recently, which demonstrates the great potential for joint learning across different modalities. In this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document... | ['Furu Wei', 'Cha Zhang', 'Dinei Florencio', 'Yijuan Lu', 'Guoxin Wang', 'Lei Cui', 'Tengchao Lv', 'Yiheng Xu'] | 2021-04-18 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 1.66389808e-01 -1.32967830e-01 -2.35903725e-01 -2.99314499e-01
-1.27634072e+00 -9.86309171e-01 9.46126223e-01 -6.37446493e-02
-2.11362571e-01 5.82621634e-01 5.74934304e-01 -6.42343521e-01
1.68764099e-01 -4.31967080e-01 -9.63978052e-01 -1.82992354e-01
5.18218994e-01 6.76914036e-01 -5.95133305e-01 3.65001708... | [11.261201858520508, 1.9981497526168823] |
0eeea5e1-0e7e-4862-9153-8e874e5b5685 | hierarchical-gumbel-attention-network-for | null | null | https://www.researchgate.net/publication/346192190_Hierarchical_Gumbel_Attention_Network_for_Text-based_Person_Search | https://www.researchgate.net/publication/346192190_Hierarchical_Gumbel_Attention_Network_for_Text-based_Person_Search | Hierarchical Gumbel Attention Network for Text-based Person Search | Text-based person search aims to retrieve the pedestrian images that best match a given textual description from gallery images. Previous methods utilize the soft-attention mechanism to infer the semantic alignments between the regions of image and the corresponding words in sentence. However, these methods may fuse th... | ['Tao Mei', 'Zheng-Jun Zha', 'Jiawei Liu', 'Wu Liu', 'Kecheng Zheng'] | 2020-10-10 | null | null | null | null | ['nlp-based-person-retrival', 'person-search'] | ['computer-vision', 'computer-vision'] | [-1.88520120e-03 -5.64171255e-01 -1.49176881e-01 -4.69431609e-01
-1.52291751e+00 -1.09695338e-01 5.88884115e-01 -6.10238016e-02
-8.41485381e-01 4.48173195e-01 5.23109078e-01 2.56537884e-01
-2.87026227e-01 -5.11160135e-01 -5.90272903e-01 -8.42570961e-01
5.72441161e-01 3.75209749e-01 7.07751438e-02 -1.13206819... | [14.644928932189941, 0.8234220147132874] |
84530f40-16cd-446f-9672-e6ebc02d1645 | end-to-end-neural-ad-hoc-ranking-with-kernel | 1706.06613 | null | http://arxiv.org/abs/1706.06613v1 | http://arxiv.org/pdf/1706.06613v1.pdf | End-to-End Neural Ad-hoc Ranking with Kernel Pooling | This paper proposes K-NRM, a kernel based neural model for document ranking.
Given a query and a set of documents, K-NRM uses a translation matrix that
models word-level similarities via word embeddings, a new kernel-pooling
technique that uses kernels to extract multi-level soft match features, and a
learning-to-rank ... | ['Jamie Callan', 'Zhuyun Dai', 'Chenyan Xiong', 'Zhiyuan Liu', 'Russell Power'] | 2017-06-20 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-8.59806836e-02 -3.42660397e-01 -9.80736613e-01 -6.69543684e-01
-1.31502569e+00 -3.05302799e-01 7.74677098e-01 2.98297822e-01
-5.35663247e-01 -1.72789335e-01 6.83267772e-01 -8.44074190e-02
-6.73465729e-01 -8.14034045e-01 -3.93723577e-01 -2.83595830e-01
-5.34525454e-01 5.28992414e-01 3.78434837e-01 -3.68304163... | [11.406259536743164, 7.622568607330322] |
bebec923-a1a3-4bab-9d29-912e14c46ca9 | 3d-magic-mirror-clothing-reconstruction-from | 2204.13096 | null | https://arxiv.org/abs/2204.13096v2 | https://arxiv.org/pdf/2204.13096v2.pdf | 3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective | This research aims to study a self-supervised 3D clothing reconstruction method, which recovers the geometry shape and texture of human clothing from a single image. Compared with existing methods, we observe that three primary challenges remain: (1) 3D ground-truth meshes of clothing are usually inaccessible due to an... | ['Tat-Seng Chua', 'Yi Yang', 'Wei Ji', 'Jiayin Zhu', 'Zhedong Zheng'] | 2022-04-27 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 6.60178214e-02 1.02279022e-01 -1.82439566e-01 -2.44038343e-01
-2.11319983e-01 -5.84237456e-01 2.24986643e-01 -2.76862651e-01
1.87686101e-01 5.16726553e-01 2.69658715e-01 8.57804567e-02
-4.09650393e-02 -6.86506867e-01 -1.14134192e+00 -6.74998224e-01
3.19688857e-01 3.03681016e-01 -1.17202876e-02 -2.26319227... | [7.271237850189209, -1.2735695838928223] |
e6f3f1da-2552-46c3-ba7e-2146c1a2dcb5 | towards-robust-monocular-visual-odometry-for | 2109.05509 | null | https://arxiv.org/abs/2109.05509v1 | https://arxiv.org/pdf/2109.05509v1.pdf | Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions | In the future, extraterrestrial expeditions will not only be conducted by rovers but also by flying robots. The technical demonstration drone Ingenuity, that just landed on Mars, will mark the beginning of a new era of exploration unhindered by terrain traversability. Robust self-localization is crucial for that. Camer... | ['Wolfgang Stürzl', 'Daniel Cremers', 'Rudolph Triebel', 'Armin Wedler', 'Nikolaus Demmel', 'Marcus G. Müller', 'Martin Wudenka'] | 2021-09-12 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-6.50191233e-02 -2.58920938e-01 -1.20906380e-03 -1.96684167e-01
-1.80502549e-01 -6.95610940e-01 8.90345812e-01 -3.13019097e-01
-6.38569415e-01 7.91430891e-01 -3.61595452e-01 -6.92762434e-03
-2.69963205e-01 -5.76995194e-01 -5.80209851e-01 -5.75833797e-01
-3.19940418e-01 8.14575851e-01 3.84569287e-01 -5.85414290... | [7.417903900146484, -2.0445284843444824] |
117a0435-64cd-432d-ae41-b4e319373a0e | mixing-context-granularities-for-improved | 1804.08460 | null | http://arxiv.org/abs/1804.08460v1 | http://arxiv.org/pdf/1804.08460v1.pdf | Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories | The first stage of every knowledge base question answering approach is to
link entities in the input question. We investigate entity linking in the
context of a question answering task and present a jointly optimized neural
architecture for entity mention detection and entity disambiguation that models
the surrounding ... | ['Iryna Gurevych', 'Daniil Sorokin'] | 2018-04-23 | mixing-context-granularities-for-improved-1 | https://aclanthology.org/S18-2007 | https://aclanthology.org/S18-2007.pdf | semeval-2018-6 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-3.74905556e-01 7.21634448e-01 -1.96035117e-01 -2.95907855e-01
-1.09773719e+00 -5.73086977e-01 5.55832922e-01 8.16143394e-01
-9.01041031e-01 7.79897034e-01 4.34248269e-01 -2.45835498e-01
-1.86693832e-01 -1.03047645e+00 -8.68695021e-01 3.59571815e-01
4.92806919e-02 8.17381799e-01 8.98267031e-01 -6.53614819... | [10.518332481384277, 7.989806175231934] |
788919c0-8b6b-4640-9231-2f80aaabe573 | o-gnn-incorporating-ring-priors-into | null | null | https://openreview.net/forum?id=5cFfz6yMVPU | https://openreview.net/pdf?id=5cFfz6yMVPU | O-GNN: Incorporating Ring Priors into Molecular Modeling | Cyclic compounds that contain at least one ring play an important role in drug design. Despite the recent success of molecular modeling with graph neural networks (GNNs), few models explicitly take rings in compounds into consideration, consequently limiting the expressiveness of the models. In this work, we design a n... | ['Tie-Yan Liu', 'Houqiang Li', 'Wengang Zhou', 'Tao Qin', 'Lijun Wu', 'Qi Meng', 'Shufang Xie', 'Yingce Xia', 'Bohan Wang', 'Kehan Wu', 'Jinhua Zhu'] | 2023-05-01 | null | null | null | iclr-2023-5 | ['graph-regression', 'retrosynthesis', 'property-prediction', 'molecular-property-prediction'] | ['graphs', 'medical', 'medical', 'miscellaneous'] | [ 3.84761482e-01 3.07995975e-01 -7.98708200e-01 5.60664684e-02
-1.93171635e-01 -7.67848253e-01 4.87613916e-01 6.35955572e-01
-2.71483269e-02 9.08973336e-01 1.02654099e-01 -9.12810445e-01
-1.81935176e-01 -9.55609620e-01 -1.01377618e+00 -6.72076166e-01
-4.64926511e-01 3.95709544e-01 3.55251841e-02 -2.65999794... | [5.15720272064209, 5.8714823722839355] |
de0a735c-4fa4-4ede-a73e-c2d9834747ed | weakly-supervised-rotation-invariant-aerial | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Feng_Weakly_Supervised_Rotation-Invariant_Aerial_Object_Detection_Network_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Feng_Weakly_Supervised_Rotation-Invariant_Aerial_Object_Detection_Network_CVPR_2022_paper.pdf | Weakly Supervised Rotation-Invariant Aerial Object Detection Network | Object rotation is among long-standing, yet still unexplored, hard issues encountered in the task of weakly supervised object detection (WSOD) from aerial images. Existing predominant WSOD approaches built on regular CNNs which are not inherently designed to tackle object rotations without corresponding constraints... | ['Junwei Han', 'Gong Cheng', 'Xiwen Yao', 'Xiaoxu Feng'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 4.09212887e-01 1.15032047e-01 -3.56285363e-01 -2.13013560e-01
-3.87313575e-01 -5.35229266e-01 2.90718466e-01 -1.96942940e-01
-3.21310014e-01 3.38408053e-01 -7.88655430e-02 -3.60397175e-02
-2.44591489e-01 -5.50174594e-01 -7.16409624e-01 -8.49958003e-01
-2.80199423e-02 3.49774569e-01 6.76579177e-01 -3.33962888... | [8.813285827636719, -0.3057343065738678] |
dae22820-88e3-4b2b-90a5-c181656889d6 | img2pose-face-alignment-and-detection-via | 2012.07791 | null | https://arxiv.org/abs/2012.07791v2 | https://arxiv.org/pdf/2012.07791v2.pdf | img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation | We propose real-time, six degrees of freedom (6DoF), 3D face pose estimation without face detection or landmark localization. We observe that estimating the 6DoF rigid transformation of a face is a simpler problem than facial landmark detection, often used for 3D face alignment. In addition, 6DoF offers more informatio... | ['Vítor Albiero', 'Tal Hassner', 'Guan Pang', 'Xi Yin', 'Xingyu Chen'] | 2020-12-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Albiero_img2pose_Face_Alignment_and_Detection_via_6DoF_Face_Pose_Estimation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Albiero_img2pose_Face_Alignment_and_Detection_via_6DoF_Face_Pose_Estimation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['head-pose-estimation', 'face-alignment'] | ['computer-vision', 'computer-vision'] | [-1.67730346e-01 2.21759398e-02 -1.79212123e-01 -5.10899723e-01
-8.27810407e-01 -8.50032568e-01 7.23546743e-01 -5.42899966e-01
-1.87046751e-01 3.73065844e-02 7.57164806e-02 -5.06559759e-02
2.16299713e-01 -2.65210181e-01 -9.56786215e-01 -4.53725427e-01
-2.42378011e-01 9.09846663e-01 -1.09244280e-01 -4.14410383... | [13.391510963439941, 0.22522971034049988] |
1c1d2f41-4e57-4523-ab51-674a83e4fa3c | text-free-non-parallel-many-to-many-voice | 2203.08009 | null | https://arxiv.org/abs/2203.08009v1 | https://arxiv.org/pdf/2203.08009v1.pdf | Text-free non-parallel many-to-many voice conversion using normalising flows | Non-parallel voice conversion (VC) is typically achieved using lossy representations of the source speech. However, ensuring only speaker identity information is dropped whilst all other information from the source speech is retained is a large challenge. This is particularly challenging in the scenario where at infere... | ['Daniel Korzekwa', 'Roberto Barra-Chicote', 'Kamil Pokora', 'Magdalena Proszewska', 'Piotr Biliński', 'Abdelhamid Ezzerg', 'Thomas Merritt'] | 2022-03-15 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [ 4.50455815e-01 1.83746263e-01 -9.60951764e-03 -7.82458037e-02
-1.01516473e+00 -6.35984600e-01 6.87847733e-01 -1.32880360e-01
-3.61866951e-01 8.15207422e-01 8.02528501e-01 -2.47933879e-01
2.91134685e-01 -4.91367668e-01 -7.29226172e-01 -6.93815768e-01
3.55241239e-01 1.11125052e-01 -5.14175035e-02 -1.89830456... | [14.997023582458496, 6.032270431518555] |
01dd5f49-9e4a-41a3-b75d-90ac4196ee50 | natural-image-matting-via-guided-contextual | 2001.04069 | null | https://arxiv.org/abs/2001.04069v1 | https://arxiv.org/pdf/2001.04069v1.pdf | Natural Image Matting via Guided Contextual Attention | Over the last few years, deep learning based approaches have achieved outstanding improvements in natural image matting. Many of these methods can generate visually plausible alpha estimations, but typically yield blurry structures or textures in the semitransparent area. This is due to the local ambiguity of transpare... | ['Hongtao Lu', 'Yaoyi Li'] | 2020-01-13 | null | null | null | null | ['transparent-objects', 'semantic-image-matting'] | ['computer-vision', 'computer-vision'] | [ 9.94437113e-02 -8.65649059e-02 1.37776256e-01 -1.20012097e-01
-6.25490725e-01 -2.23225392e-02 3.58239621e-01 -1.80705875e-01
-6.53993264e-02 6.97203755e-01 3.87272626e-01 3.11295632e-02
2.55759716e-01 -9.29547727e-01 -1.08766031e+00 -7.84256935e-01
2.31669828e-01 2.90479094e-01 3.12847376e-01 -1.39583245... | [10.68094253540039, -0.9591020345687866] |
26885a5b-b66d-466b-8df0-e33fbf7c6e47 | vlpd-context-aware-pedestrian-detection-via | 2304.03135 | null | https://arxiv.org/abs/2304.03135v1 | https://arxiv.org/pdf/2304.03135v1.pdf | VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision | Detecting pedestrians accurately in urban scenes is significant for realistic applications like autonomous driving or video surveillance. However, confusing human-like objects often lead to wrong detections, and small scale or heavily occluded pedestrians are easily missed due to their unusual appearances. To address t... | ['Xu-Cheng Yin', 'Chao Zhu', 'Jie Jiang', 'Mengyin Liu'] | 2023-04-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_VLPD_Context-Aware_Pedestrian_Detection_via_Vision-Language_Semantic_Self-Supervision_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_VLPD_Context-Aware_Pedestrian_Detection_via_Vision-Language_Semantic_Self-Supervision_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pedestrian-detection'] | ['computer-vision'] | [ 6.93963096e-02 -4.16716635e-01 -2.59894371e-01 -7.00781703e-01
-4.86355066e-01 -2.81891018e-01 5.07311404e-01 7.92860985e-02
-4.99100745e-01 7.59848893e-01 -8.04725215e-02 -1.27855450e-01
6.22394323e-01 -7.87815690e-01 -7.87850082e-01 -7.75799632e-01
4.08843011e-01 2.16349155e-01 9.51696992e-01 -5.09942137... | [7.988702774047852, -0.6259140372276306] |
dd5a75f3-cf1d-4dc3-88ab-238c735e024c | towards-annotating-and-creating-sub-sentence | 1910.07659 | null | https://arxiv.org/abs/1910.07659v1 | https://arxiv.org/pdf/1910.07659v1.pdf | Towards Annotating and Creating Sub-Sentence Summary Highlights | Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences are more concise than whole sentences. They are also better suited than individual words and phrases that can potentially lead to disfluent,... | ['Fei Liu', 'Parminder Bhatia', 'Kristjan Arumae'] | 2019-10-17 | null | null | null | null | ['sentence-compression'] | ['natural-language-processing'] | [ 5.50798237e-01 2.88215131e-01 -3.71774912e-01 -2.95007050e-01
-1.42855299e+00 -5.54891348e-01 4.91549104e-01 8.11740160e-01
-3.38532537e-01 1.28961766e+00 9.19796288e-01 -1.57923251e-01
2.09562391e-01 -5.92253506e-01 -4.47508603e-01 -5.67930222e-01
1.24940770e-02 7.11137205e-02 -4.70046476e-02 -1.23864807... | [12.553327560424805, 9.510290145874023] |
d4b2db6b-0b88-4d5a-a759-2b2b2ede1d7f | modeling-unknown-semantic-labels-as | null | null | https://openreview.net/forum?id=-BBL3b4Tqfo | https://openreview.net/pdf?id=-BBL3b4Tqfo | Modeling Unknown Semantic Labels as Uncertainty in the Prediction: Evidential Deep Learning for Class-Incremental Semantic Segmentation | Class-Incremental Learning is an essential component for expanding the knowledge of previously trained neural networks.
This is especially useful if the system needs to be able to handle new objects but the original training data is unavailable.
While the semantic segmentation problem has received less attention than ... | ['Lena Klasen', 'Michael Felsberg', 'Karl Holmquist'] | 2021-09-29 | null | null | null | null | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 5.86715460e-01 3.56240273e-01 -5.95233962e-03 -5.98011851e-01
-7.11272418e-01 -6.02111280e-01 5.36005616e-01 9.97819304e-02
-5.53917766e-01 9.41278219e-01 -5.19967616e-01 -1.82245553e-01
9.64177996e-02 -6.94378436e-01 -8.15646827e-01 -1.10268736e+00
2.97694772e-01 9.58076596e-01 6.17756784e-01 5.38769305... | [9.323675155639648, 1.3426815271377563] |
9b6bf4a0-f2ba-4406-a64f-19053d1d2b13 | snapture-a-novel-neural-architecture-for | 2205.15862 | null | https://arxiv.org/abs/2205.15862v1 | https://arxiv.org/pdf/2205.15862v1.pdf | Snapture -- A Novel Neural Architecture for Combined Static and Dynamic Hand Gesture Recognition | As robots are expected to get more involved in people's everyday lives, frameworks that enable intuitive user interfaces are in demand. Hand gesture recognition systems provide a natural way of communication and, thus, are an integral part of seamless Human-Robot Interaction (HRI). Recent years have witnessed an immens... | ['Stefan Wermter', 'Doreen Jirak', 'Hassan Ali'] | 2022-05-28 | null | null | null | null | ['hand-gesture-recognition', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [-1.12236209e-01 -2.58770347e-01 -4.11743492e-01 -3.96697700e-01
-3.82049561e-01 -4.84174609e-01 8.55866134e-01 -5.30755818e-01
-5.95395088e-01 2.91009784e-01 4.90542203e-01 -6.73366431e-03
-6.77521899e-02 -5.07822096e-01 -3.59683663e-01 -8.50577772e-01
-2.64663219e-01 4.71161306e-01 1.52813960e-02 -4.75301147... | [6.6475300788879395, -0.2246188372373581] |
76a67f11-f28d-4cf1-a330-c340989491ae | colonmapper-topological-mapping-and | 2305.05546 | null | https://arxiv.org/abs/2305.05546v1 | https://arxiv.org/pdf/2305.05546v1.pdf | ColonMapper: topological mapping and localization for colonoscopy | Mapping and localization in endoluminal cavities from colonoscopies or gastroscopies has to overcome the challenge of significant shape and illumination changes between reobservations of the same endoluminal location. Instead of geometrical maps that strongly rely on a fixed scene geometry, topological maps are more ad... | ['J. M. M. Montiel', 'Juan D. Tardós', 'Javier Morlana'] | 2023-05-09 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 1.11502074e-01 5.87370172e-02 1.14233270e-01 -2.34909922e-01
-4.11710232e-01 -7.96440661e-01 4.78073210e-01 1.08095968e+00
-5.47038913e-01 2.39303589e-01 -2.04676121e-01 -3.69905233e-01
-4.02588159e-01 -9.73855138e-01 -9.39800322e-01 -4.31213826e-01
-3.76768947e-01 4.85551327e-01 5.47352076e-01 -4.80405875... | [13.955595016479492, -3.149299383163452] |
60dc78bf-79e4-49d1-a806-de863291720d | move-unsupervised-movable-object-segmentation | 2210.07920 | null | https://arxiv.org/abs/2210.07920v2 | https://arxiv.org/pdf/2210.07920v2.pdf | MOVE: Unsupervised Movable Object Segmentation and Detection | We introduce MOVE, a novel method to segment objects without any form of supervision. MOVE exploits the fact that foreground objects can be shifted locally relative to their initial position and result in realistic (undistorted) new images. This property allows us to train a segmentation model on a dataset of images wi... | ['Paolo Favaro', 'Adam Bielski'] | 2022-10-14 | null | null | null | null | ['single-object-discovery', 'object-discovery', 'class-agnostic-object-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.07694352e-01 7.39077032e-01 -6.95903897e-02 -1.77367955e-01
-1.03727186e+00 -5.80852449e-01 5.80537140e-01 -6.59895465e-02
-5.07412076e-01 5.75122476e-01 -3.68419468e-01 8.36842805e-02
3.95101726e-01 -6.89021468e-01 -1.21224618e+00 -8.63399625e-01
-3.91566642e-02 6.34906530e-01 1.00998211e+00 1.39205068... | [9.87429141998291, 0.27594879269599915] |
a325f861-d3cf-4c1c-99db-9f9c2d6d7f8a | abstractive-text-summarization-enhancing | null | null | https://aclanthology.org/2021.cl-4.27 | https://aclanthology.org/2021.cl-4.27.pdf | Abstractive Text Summarization: Enhancing Sequence-to-Sequence Models Using Word Sense Disambiguation and Semantic Content Generalization | Abstract Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based approaches that could further enhance their efficiency. In this direction, this work presents a novel framework that combines sequenc... | ['Andreas Stafylopatis', 'Georgios Alexandridis', 'Panagiotis Kouris'] | null | null | null | null | cl-acl-2021-12 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 7.17639208e-01 3.64683688e-01 -8.12933370e-02 -1.13235183e-01
-6.16836727e-01 -1.33511484e-01 7.83271194e-01 7.18881369e-01
-5.54826856e-01 9.75904405e-01 7.32034504e-01 -2.07513824e-01
-1.85712129e-01 -1.09376228e+00 -6.10856116e-01 -3.41908902e-01
2.22456053e-01 6.02565527e-01 1.67735279e-01 -4.62678283... | [12.434807777404785, 9.436675071716309] |
8e629c78-1bc5-4b5f-bf54-7b92ef61dbaf | on-decoding-strategies-for-neural-text | 2203.15721 | null | https://arxiv.org/abs/2203.15721v1 | https://arxiv.org/pdf/2203.15721v1.pdf | On Decoding Strategies for Neural Text Generators | When generating text from probabilistic models, the chosen decoding strategy has a profound effect on the resulting text. Yet the properties elicited by various decoding strategies do not always transfer across natural language generation tasks. For example, while mode-seeking methods like beam search perform remarkabl... | ['Ryan Cotterell', 'Clara Meister', 'Gian Wiher'] | 2022-03-29 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 5.66507339e-01 1.05282463e-01 -4.32132855e-02 -1.91256627e-01
-1.09622478e+00 -8.95147562e-01 1.35504055e+00 3.23051870e-01
-2.90419877e-01 9.51403439e-01 8.17077816e-01 -4.01055664e-01
-1.39653146e-01 -6.43420815e-01 -6.01719141e-01 -5.39280295e-01
4.41510469e-01 6.93391502e-01 -6.31429255e-02 -3.49419296... | [11.577939987182617, 9.14590072631836] |
14d00ec5-7cbe-49f9-a9ca-cf2e3f701533 | leveraging-weak-complementary-labels-to | 2302.01813 | null | https://arxiv.org/abs/2302.01813v1 | https://arxiv.org/pdf/2302.01813v1.pdf | Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides | In this paper, we present a deep learning segmentation approach to classify and quantify the two most prevalent primary liver cancers - hepatocellular carcinoma and intrahepatic cholangiocarcinoma - from hematoxylin and eosin (H&E) stained whole slide images. While semantic segmentation of medical images typically requ... | ['Frederick Klauschen', 'Frank Tacke', 'Christoph Roderburg', 'Adrien Guillot', 'Simon Schallenberg', 'Maximilian Alber', 'Lukas Ruff', 'Johannes Eschrich', 'Miriam Hägele'] | 2023-02-03 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [-1.30224451e-02 1.89405173e-01 -4.14310962e-01 -4.79871839e-01
-1.35457218e+00 -7.19084024e-01 2.59222180e-01 4.23795938e-01
-3.49296689e-01 6.48086548e-01 5.80889769e-02 -6.04922831e-01
1.03389420e-01 -5.55713534e-01 -3.71531665e-01 -1.19838607e+00
-1.98318154e-01 5.79242766e-01 -2.21231520e-01 7.17995405... | [14.714163780212402, -2.7108888626098633] |
e2cde3a5-5e65-4ddd-8906-ae9edf7d889e | masked-autoencoders-are-scalable-vision | 2111.06377 | null | https://arxiv.org/abs/2111.06377v2 | https://arxiv.org/pdf/2111.06377v2.pdf | Masked Autoencoders Are Scalable Vision Learners | This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an enco... | ['Ross Girshick', 'Piotr Dollár', 'Yanghao Li', 'Saining Xie', 'Xinlei Chen', 'Kaiming He'] | 2021-11-11 | null | http://openaccess.thecvf.com//content/CVPR2022/html/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.pdf | cvpr-2022-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 3.41584116e-01 6.84333980e-01 -3.48616898e-01 -3.46619338e-01
-8.52835178e-01 -4.04302716e-01 4.79366273e-01 -5.59401691e-01
-5.45117319e-01 5.60683608e-01 3.19019228e-01 -4.89185363e-01
6.15982473e-01 -3.80444288e-01 -1.40446806e+00 -7.24903286e-01
7.17167258e-02 2.90618360e-01 2.57551312e-01 1.16348006... | [9.56423568725586, 1.329418420791626] |
bca04ff6-183c-4433-afc6-8ec2b6ef3a64 | biomarker-clustering-of-colorectal-cancer | 1307.1601 | null | http://arxiv.org/abs/1307.1601v1 | http://arxiv.org/pdf/1307.1601v1.pdf | Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification | In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. Attempts are made
to cluster ... | ['Chris Roadknight', 'John Scholefield', 'Daniele Soria', 'Uwe Aickelin', 'Alex Ladas', 'Lindy Durrant'] | 2013-07-05 | null | null | null | null | ['tumour-classification'] | ['medical'] | [ 2.28339568e-01 -2.23887384e-01 -4.90780294e-01 -1.00617066e-01
-5.41605055e-01 -6.27457857e-01 6.05101943e-01 1.08496535e+00
-6.41543746e-01 5.57960331e-01 4.91445959e-01 -7.48504281e-01
-6.11935258e-01 -4.06681985e-01 2.71002978e-01 -1.06691790e+00
-3.70952040e-01 6.29015148e-01 -1.24234445e-01 -2.11338624... | [15.165657997131348, -3.0813775062561035] |
48711674-cfc2-401c-8785-4dd753a5101b | evaluation-and-generation-of-physical | 2203.04623 | null | https://arxiv.org/abs/2203.04623v2 | https://arxiv.org/pdf/2203.04623v2.pdf | Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition | Recent studies have revealed the vulnerability of face recognition models against physical adversarial patches, which raises security concerns about the deployed face recognition systems. However, it is still challenging to ensure the reproducibility for most attack algorithms under complex physical conditions, which l... | ['Jun Zhu', 'Hang Su', 'Zihao Xiao', 'Tianyu Pang', 'Yinpeng Dong', 'Xiao Yang'] | 2022-03-09 | null | null | null | null | ['3d-face-modeling'] | ['computer-vision'] | [-5.17537370e-02 -3.24168593e-01 3.94541234e-01 3.28068510e-02
-6.67502880e-02 -7.77344644e-01 7.58960605e-01 -8.11699629e-01
1.63572490e-01 5.61428845e-01 -4.62278843e-01 -2.83864141e-01
-1.54570520e-01 -8.79929602e-01 -7.42557824e-01 -1.01460350e+00
-3.52176160e-01 -2.43019357e-01 -1.86913256e-02 -1.89508602... | [12.900440216064453, 1.0840145349502563] |
bb82d35f-36eb-42ab-978a-6ce2264c0435 | deepfgs-fine-grained-scalable-coding-for | 2201.01173 | null | https://arxiv.org/abs/2201.01173v1 | https://arxiv.org/pdf/2201.01173v1.pdf | DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression | Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, the existing scalable compression methods face two challenges: reduced compression performance and insufficient scalability. In this paper, we propose the first learned fine-grained scalable i... | ['Ronggang Wang', 'Yongqi Zhai', 'Yi Ma'] | 2022-01-04 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 3.48882943e-01 -5.26641309e-01 -6.42182052e-01 -3.40511024e-01
-7.79527783e-01 -1.92369018e-02 8.00883919e-02 3.20603289e-02
-1.37430638e-01 7.37204313e-01 5.77969849e-01 -2.92147994e-01
-2.38910198e-01 -8.07813108e-01 -7.02186286e-01 -5.18759847e-01
-4.32767183e-01 -1.04578443e-01 4.69325662e-01 -9.52082574... | [11.350045204162598, -1.567292332649231] |
a23fb3d1-b861-49ea-ab09-f1ace34c4375 | wiris-transformer-for-ris-assisted-device | 2304.06475 | null | https://arxiv.org/abs/2304.06475v2 | https://arxiv.org/pdf/2304.06475v2.pdf | WiRiS: Transformer for RIS-Assisted Device-Free Sensing for Joint People Counting and Localization using Wi-Fi CSI | Channel State Information (CSI) is widely adopted as a feature for indoor localization. Taking advantage of the abundant information from the CSI, people can be accurately sensed even without equipped devices. However, the positioning error increases severely in non-line-of-sight (NLoS) regions. Reconfigurable intellig... | ['Sheng-Fuh Chang', 'Shih-Cheng Lin', 'Yuan-Chun Lin', 'Kai-Ten Feng', 'Li-Hsiang Shen', 'Wei-Yu Chung'] | 2023-03-25 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 2.82918721e-01 -4.80523020e-01 3.23329329e-01 -1.74641415e-01
-7.54406273e-01 -4.75843996e-01 3.66472840e-01 2.41854936e-02
-3.29267025e-01 8.56856942e-01 1.01923279e-01 -1.26936138e-01
-2.29713753e-01 -1.24468279e+00 -6.06356621e-01 -7.39522338e-01
5.12159877e-02 4.37311321e-01 4.29724753e-01 -1.63579762... | [6.465443134307861, 0.8940410017967224] |
6ee133fb-6b89-435d-8c9f-cf0d33c3d0ba | zero-shot-stance-detection-based-on-cross | 2210.03380 | null | https://arxiv.org/abs/2210.03380v1 | https://arxiv.org/pdf/2210.03380v1.pdf | Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning | Zero-shot stance detection is challenging because it requires detecting the stance of previously unseen targets in the inference phase. The ability to learn transferable target-invariant features is critical for zero-shot stance detection. In this work, we propose a stance detection approach that can efficiently adapt ... | ['Lei Tian', 'Bin Zhou', 'Feng Xie', 'Zhong Zhang', 'Jiaying Zou', 'Xuechen Zhao'] | 2022-10-07 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 5.38180232e-01 4.76380885e-02 -6.13372564e-01 -6.97022796e-01
-1.16246080e+00 -5.12585163e-01 6.49955034e-01 -5.39223372e-04
-4.30379957e-01 6.49107337e-01 4.88958061e-01 2.47973382e-01
4.66853380e-01 -7.77372003e-01 -5.87058961e-01 -6.29497826e-01
1.76874287e-02 3.81566375e-01 5.01863956e-01 -5.39009452... | [10.713313102722168, 7.865392684936523] |
b358951f-0585-48b6-b533-f1de65834115 | generalizable-metric-network-for-cross-domain | 2306.11991 | null | https://arxiv.org/abs/2306.11991v1 | https://arxiv.org/pdf/2306.11991v1.pdf | Generalizable Metric Network for Cross-domain Person Re-identification | Person Re-identification (Re-ID) is a crucial technique for public security and has made significant progress in supervised settings. However, the cross-domain (i.e., domain generalization) scene presents a challenge in Re-ID tasks due to unseen test domains and domain-shift between the training and test sets. To tackl... | ['Xin Geng', 'Yinghuan Shi', 'Ziang Liu', 'Lei Qi'] | 2023-06-21 | null | null | null | null | ['person-re-identification', 'domain-generalization'] | ['computer-vision', 'methodology'] | [ 1.82651907e-01 -3.63857001e-01 -1.05423607e-01 -5.50395727e-01
-4.78226364e-01 -6.00583255e-01 5.81728637e-01 4.11176197e-02
-4.04681474e-01 7.44505644e-01 -2.30592415e-02 4.64895554e-02
-2.99056321e-01 -6.50119603e-01 -4.34696525e-01 -6.79999530e-01
2.22836733e-01 3.37970138e-01 1.76376134e-01 -2.96779215... | [14.684263229370117, 1.064751148223877] |
42ff7f88-b333-440d-9101-47fe8844a1a4 | leveraging-skill-to-skill-supervision-for | 2306.06841 | null | https://arxiv.org/abs/2306.06841v1 | https://arxiv.org/pdf/2306.06841v1.pdf | Leveraging Skill-to-Skill Supervision for Knowledge Tracing | Knowledge tracing plays a pivotal role in intelligent tutoring systems. This task aims to predict the probability of students answering correctly to specific questions. To do so, knowledge tracing systems should trace the knowledge state of the students by utilizing their problem-solving history and knowledge about the... | ['Kyungwoo Song', 'Yun Jegal', 'Minjae Lee', 'Jinwoo Nam', 'Hyeondey Kim'] | 2023-06-12 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [ 4.46766764e-02 2.85494268e-01 -4.73812312e-01 -3.65043014e-01
-3.38650674e-01 -8.05583179e-01 2.98271537e-01 3.64069492e-01
-2.53341079e-01 7.86116660e-01 1.02338590e-01 -7.38778234e-01
-5.30890644e-01 -9.46317911e-01 -6.00324273e-01 2.77272537e-02
2.56631672e-01 3.48329186e-01 6.84552073e-01 -4.12677199... | [10.122260093688965, 7.20395565032959] |
35e57009-cfc3-4f93-8f0f-e49978f2b6f0 | novel-features-for-time-series-analysis-a | 2110.09888 | null | https://arxiv.org/abs/2110.09888v3 | https://arxiv.org/pdf/2110.09888v3.pdf | Novel Features for Time Series Analysis: A Complex Networks Approach | Being able to capture the characteristics of a time series with a feature vector is a very important task with a multitude of applications, such as classification, clustering or forecasting. Usually, the features are obtained from linear and nonlinear time series measures, that may present several data related drawback... | ['Fernando Silva', 'Pedro Ribeiro', 'Maria Eduarda Silva', 'Vanessa Freitas Silva'] | 2021-10-11 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-1.56268626e-02 -3.39796305e-01 -6.33058995e-02 -2.36260921e-01
1.71680059e-02 -8.56866300e-01 1.06769037e+00 7.25259542e-01
-2.93118417e-01 5.32887518e-01 1.38539702e-01 -9.13338587e-02
-1.07702446e+00 -1.02202487e+00 -1.33002639e-01 -6.86127961e-01
-9.18634951e-01 5.31281292e-01 3.24639231e-01 -5.87204039... | [7.307806491851807, 3.4327545166015625] |
a0cf13b5-d92a-4a93-bb7a-8dc6833deced | investigating-correlations-of-inter-coder | 1907.10450 | null | https://arxiv.org/abs/1907.10450v1 | https://arxiv.org/pdf/1907.10450v1.pdf | Investigating Correlations of Inter-coder Agreement and Machine Annotation Performance for Historical Video Data | Video indexing approaches such as visual concept classification and person recognition are essential to enable fine-grained semantic search in large-scale video archives such as the historical video collection of former German Democratic Republic (GDR) maintained by the German Broadcasting Archive (DRA). Typically, a l... | ['Angelika Hörth', 'Sabrina Bernhöft', 'Markus Mühling', 'Kader Pustu-Iren', 'Joanna Bars', 'Bernd Freisleben', 'Ralph Ewerth', 'Nikolaus Korfhage'] | 2019-07-24 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 1.99573506e-02 -2.38335729e-01 4.20739986e-02 -4.77376461e-01
-7.98158646e-01 -8.72308969e-01 5.49911380e-01 5.41886032e-01
-8.26012552e-01 5.70619702e-01 2.41056770e-01 2.43428815e-02
-2.30730608e-01 -5.73279023e-01 -9.96453539e-02 -4.48716968e-01
3.45629573e-01 5.06584823e-01 1.25428960e-01 8.87825266... | [10.66260814666748, 0.7031905651092529] |
5c93308f-bd7c-44c2-8845-c61c3ce6d10d | end-to-end-neural-pipeline-for-goal-oriented | null | null | https://aclanthology.org/2020.acl-main.54 | https://aclanthology.org/2020.acl-main.54.pdf | End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2 | The goal-oriented dialogue system needs to be optimized for tracking the dialogue flow and carrying out an effective conversation under various situations to meet the user goal. The traditional approach to build such a dialogue system is to take a pipelined modular architecture, where its modules are optimized individu... | ['Kee-Eung Kim', 'Jeong-Gwan Lee', 'Donghoon Ham', 'Youngsoo Jang'] | 2020-07-01 | null | null | null | acl-2020-6 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-8.89953747e-02 6.56322360e-01 4.54426169e-01 -6.98255599e-01
-6.49757504e-01 -6.21542454e-01 5.67542195e-01 1.24178439e-01
-5.38485467e-01 6.60877943e-01 4.70651448e-01 -3.34329188e-01
2.64600664e-01 -5.11116385e-01 1.12605371e-01 -1.11356504e-01
1.94949329e-01 8.84948254e-01 6.74045980e-02 -9.41341400... | [12.864176750183105, 7.981298923492432] |
9c4ce0c3-abcf-4c5a-9cee-2611faefc1d0 | inter-instance-similarity-modeling-for | 2306.12243 | null | https://arxiv.org/abs/2306.12243v3 | https://arxiv.org/pdf/2306.12243v3.pdf | Inter-Instance Similarity Modeling for Contrastive Learning | The existing contrastive learning methods widely adopt one-hot instance discrimination as pretext task for self-supervised learning, which inevitably neglects rich inter-instance similarities among natural images, then leading to potential representation degeneration. In this paper, we propose a novel image mix method,... | ['Jianxin Wang', 'Zhe Qu', 'Hao Tang', 'Dawei Liu', 'Chengchao Shen'] | 2023-06-21 | null | null | null | null | ['contrastive-learning', 'self-supervised-learning', 'instance-segmentation', 'contrastive-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 3.02660763e-01 -1.68642789e-01 -2.38268510e-01 -2.77373403e-01
-8.89122307e-01 -4.95196402e-01 5.57138026e-01 -3.29806089e-01
-5.78560889e-01 5.90658426e-01 -3.58246028e-01 -8.72008502e-02
7.77744427e-02 -5.22534132e-01 -9.60761189e-01 -7.78125048e-01
4.05495644e-01 2.28753030e-01 3.35112244e-01 -2.09170207... | [9.63626480102539, 0.5778481364250183] |
f76f1f07-701b-46f7-bf09-1cea2a4f9345 | knowledge-transfer-for-dynamic-multi | 2306.10668 | null | https://arxiv.org/abs/2306.10668v1 | https://arxiv.org/pdf/2306.10668v1.pdf | Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives | Different from most other dynamic multi-objective optimization problems (DMOPs), DMOPs with a changing number of objectives usually result in expansion or contraction of the Pareto front or Pareto set manifold. Knowledge transfer has been used for solving DMOPs, since it can transfer useful information from solving one... | ['Xin Yao', 'Bernhard Sendhoff', 'Stefan Menzel', 'Leandro L. Minku', 'Gan Ruan'] | 2023-06-19 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 2.18932584e-01 -5.85588753e-01 2.31176481e-01 1.98741361e-01
-5.81964254e-02 -5.67330539e-01 -1.43182352e-02 2.05384806e-01
-2.02112541e-01 1.17050302e+00 -1.68256596e-01 1.65131882e-01
-9.18590844e-01 -9.29891229e-01 -5.90607345e-01 -1.07910001e+00
-2.20332861e-01 7.56105721e-01 1.01798356e-01 -4.59372848... | [5.726919174194336, 3.503704071044922] |
6a820d00-2049-4342-b1a7-71c389fb6d00 | beyondpixels-a-comprehensive-review-of-the | 2306.03000 | null | https://arxiv.org/abs/2306.03000v1 | https://arxiv.org/pdf/2306.03000v1.pdf | BeyondPixels: A Comprehensive Review of the Evolution of Neural Radiance Fields | Neural rendering combines ideas from classical computer graphics and machine learning to synthesize images from real-world observations. NeRF, short for Neural Radiance Fields, is a recent innovation that uses AI algorithms to create 3D objects from 2D images. By leveraging an interpolation approach, NeRF can produce n... | ['Chengcui Zhang', 'Akm Shahariar Azad Rabby'] | 2023-06-05 | null | null | null | null | ['neural-rendering', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 3.51629555e-01 -9.70442742e-02 1.71442434e-01 -9.04991627e-02
-3.77657562e-01 -3.72821957e-01 8.50157797e-01 -5.62764347e-01
1.09241277e-01 6.03714347e-01 1.02061525e-01 -5.82429886e-01
1.44179031e-01 -1.04787278e+00 -6.97024882e-01 -6.88347161e-01
1.48638740e-01 1.51590258e-01 -1.23313658e-01 -3.48194093... | [9.392041206359863, -3.042092800140381] |
adea5fbf-224f-4f57-94fe-c325830c545e | mitigating-adversarial-attacks-in-deepfake | 2302.11704 | null | https://arxiv.org/abs/2302.11704v1 | https://arxiv.org/pdf/2302.11704v1.pdf | Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniques | Deep learning is a crucial aspect of machine learning, but it also makes these techniques vulnerable to adversarial examples, which can be seen in a variety of applications. These examples can even be targeted at humans, leading to the creation of false media, such as deepfakes, which are often used to shape public opi... | ['David Ada Adama', 'Farhad Fassihi Tash', 'Isibor Kennedy Ihianle', 'Pedro Machado', 'Laura Fontes', 'Saminder Dhesi'] | 2023-02-22 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-8.64538848e-02 1.63120523e-01 3.17584664e-01 -6.47549704e-02
-5.75241268e-01 -8.66795421e-01 9.03850436e-01 1.23164579e-01
-3.31153065e-01 9.26007926e-01 -9.91583839e-02 -2.19616547e-01
4.15039748e-01 -1.00009239e+00 -9.77912366e-01 -9.11251307e-01
-3.13910246e-02 -1.38796911e-01 8.41904432e-02 -1.69510961... | [5.711294174194336, 7.893290996551514] |
01f7453a-68c9-485a-9a03-65d16667ff08 | dynamic-temporal-alignment-of-speech-to-lips | 1808.06250 | null | http://arxiv.org/abs/1808.06250v1 | http://arxiv.org/pdf/1808.06250v1.pdf | Dynamic Temporal Alignment of Speech to Lips | Many speech segments in movies are re-recorded in a studio during
postproduction, to compensate for poor sound quality as recorded on location.
Manual alignment of the newly-recorded speech with the original lip movements
is a tedious task. We present an audio-to-video alignment method for automating
speech to lips ali... | ['Shmuel Peleg', 'Ariel Ephrat', 'Tavi Halperin'] | 2018-08-19 | null | null | null | null | ['video-alignment', 'lip-sync-1'] | ['computer-vision', 'computer-vision'] | [ 3.80401224e-01 -1.39005840e-01 -2.56019160e-02 -1.75887540e-01
-1.24494278e+00 -6.14067376e-01 2.22815201e-01 -2.90602632e-02
-1.78189605e-01 3.76071125e-01 5.52659869e-01 1.60166949e-01
2.01155841e-01 2.72345748e-02 -6.38975143e-01 -6.21296287e-01
3.08434129e-01 4.12902199e-02 3.42521876e-01 4.29924354... | [14.449265480041504, 5.113368988037109] |
d6204400-76e4-4670-962f-59fe78f6c947 | infobert-zero-shot-approach-to-natural | null | null | https://aclanthology.org/2021.ranlp-main.25 | https://aclanthology.org/2021.ranlp-main.25.pdf | InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding | Natural language understanding is an important task in modern dialogue systems. It becomes more important with the rapid extension of the dialogue systems’ functionality. In this work, we present an approach to zero-shot transfer learning for the tasks of intent classification and slot-filling based on pre-trained lang... | ['Irina Piontkovskaya', 'Valentin Malykh', 'Andrey Bout', 'Pavel Burnyshev'] | null | null | https://aclanthology.org/2021.ranlp-1.25 | https://aclanthology.org/2021.ranlp-1.25.pdf | ranlp-2021-9 | ['slot-filling'] | ['natural-language-processing'] | [ 1.83306932e-01 7.36787379e-01 -2.67865986e-01 -7.13158131e-01
-5.38772702e-01 -1.91285461e-01 1.01127231e+00 2.50475198e-01
-7.11681664e-01 8.29980969e-01 8.14960599e-01 -5.33590019e-01
4.02395248e-01 -6.99412227e-01 -2.22371325e-01 5.52136339e-02
1.14940099e-01 9.80008364e-01 3.24950039e-01 -9.05872047... | [12.591461181640625, 7.764108657836914] |
328e2d10-df80-4c0a-97b4-0c3dab31876a | poda-prompt-driven-zero-shot-domain | 2212.03241 | null | https://arxiv.org/abs/2212.03241v2 | https://arxiv.org/pdf/2212.03241v2.pdf | PØDA: Prompt-driven Zero-shot Domain Adaptation | Domain adaptation has been vastly investigated in computer vision but still requires access to target images at train time, which might be intractable in some uncommon conditions. In this paper, we propose the task of `Prompt-driven Zero-shot Domain Adaptation', where we adapt a model trained on a source domain using o... | ['Raoul de Charette', 'Patrick Pérez', 'Andrei Bursuc', 'Tuan-Hung Vu', 'Mohammad Fahes'] | 2022-12-06 | null | null | null | null | ['prompt-driven-zero-shot-domain-adaptation'] | ['computer-vision'] | [ 4.81665641e-01 1.56184882e-01 -2.93880284e-01 -5.02394021e-01
-1.11310577e+00 -9.06887889e-01 8.65393460e-01 -7.71244913e-02
-5.77806294e-01 4.61683035e-01 3.67787272e-01 2.62181610e-02
4.40903991e-01 -3.91167849e-01 -9.06335592e-01 -6.21578455e-01
7.15125024e-01 5.74162781e-01 4.34545338e-01 -1.83403984... | [10.075315475463867, 2.4546217918395996] |
6423008e-41f5-4e51-8f98-03b59d82bff7 | how-to-track-your-dragon-a-multi-attentional | 2004.10335 | null | https://arxiv.org/abs/2004.10335v3 | https://arxiv.org/pdf/2004.10335v3.pdf | How to track your dragon: A Multi-Attentional Framework for real-time RGB-D 6-DOF Object Pose Tracking | We present a novel multi-attentional convolutional architecture to tackle the problem of real-time RGB-D 6D object pose tracking of single, known objects. Such a problem poses multiple challenges originating both from the objects' nature and their interaction with their environment, which previous approaches have faile... | ['Georgios Retsinas', 'Nikos Kardaris', 'Georgia Chalvatzaki', 'Petros Maragos', 'Petros Koutras', 'Isidoros Marougkas'] | 2020-04-21 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 1.00765303e-01 -2.02341154e-01 1.25966474e-01 -1.87238809e-02
-4.53909993e-01 -4.58159655e-01 7.04652071e-01 -5.02317883e-02
-7.77603745e-01 2.46021196e-01 -6.34378344e-02 -1.40046448e-01
5.09814918e-02 -2.42192417e-01 -1.03836501e+00 -6.35835946e-01
-1.12506665e-01 5.91071427e-01 6.02977157e-01 -1.35928288... | [6.636395454406738, -2.1361565589904785] |
f1343e68-7940-4098-886c-6720f4aa5a2b | slimmable-encoders-for-flexible-split-dnns-in | 2306.12691 | null | https://arxiv.org/abs/2306.12691v1 | https://arxiv.org/pdf/2306.12691v1.pdf | Slimmable Encoders for Flexible Split DNNs in Bandwidth and Resource Constrained IoT Systems | The execution of large deep neural networks (DNN) at mobile edge devices requires considerable consumption of critical resources, such as energy, while imposing demands on hardware capabilities. In approaches based on edge computing the execution of the models is offloaded to a compute-capable device positioned at the ... | ['Marco Levorato', 'Eduardo Valle', 'J. C. S. Santos Filho', 'Juliano S. Assine'] | 2023-06-22 | null | null | null | null | ['edge-computing'] | ['time-series'] | [ 1.66420951e-01 -2.08334640e-01 -1.30166829e-01 -2.20452592e-01
-2.98905522e-02 -3.46768349e-01 1.71994686e-01 -9.43405628e-02
-6.97641492e-01 5.02251804e-01 -2.12702990e-01 -5.37533104e-01
-2.74401754e-01 -9.97532666e-01 -6.12386227e-01 -5.40218294e-01
-3.32325734e-02 4.41037655e-01 3.23508561e-01 -3.19752991... | [8.384544372558594, 2.8984556198120117] |
518d15f7-0b4c-4293-925d-a1912750f7eb | time-series-prediction-under-distribution | 2207.11486 | null | https://arxiv.org/abs/2207.11486v1 | https://arxiv.org/pdf/2207.11486v1.pdf | Time Series Prediction under Distribution Shift using Differentiable Forgetting | Time series prediction is often complicated by distribution shift which demands adaptive models to accommodate time-varying distributions. We frame time series prediction under distribution shift as a weighted empirical risk minimisation problem. The weighting of previous observations in the empirical risk is determine... | ['Jase Clarkson', 'Stefanos Bennett'] | 2022-07-23 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.42532632e-01 -5.99287525e-02 -3.29713106e-01 -3.68171901e-01
-2.74793029e-01 -2.25550950e-01 6.34280205e-01 3.57515126e-01
-8.60015094e-01 8.09623539e-01 5.47197983e-02 -4.70946819e-01
-5.51431954e-01 -8.29239011e-01 -2.50803471e-01 -7.73938656e-01
-2.64636457e-01 5.54645836e-01 3.85969341e-01 3.22809294... | [7.368536472320557, 3.181185483932495] |
2f87757a-ea6b-4207-a667-d5809b9a6acb | disentangling-sources-of-risk-for | null | null | https://openreview.net/forum?id=5qwA7LLbgP0 | https://openreview.net/pdf?id=5qwA7LLbgP0 | Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning | In cooperative multi-agent reinforcement learning, state transitions, rewards, and actions can all induce randomness (or uncertainty) in the observed long-term returns. These randomnesses are reflected from two risk sources: (a) agent-wise risk (i.e., how cooperative our teammates act for a given agent) and (b) environ... | ['Jinwoo Shin', 'Yung Yi', 'Junsu Kim', 'Kyunghwan Son'] | 2021-09-29 | null | null | null | null | ['smac-1'] | ['playing-games'] | [-4.68429327e-01 4.74790186e-02 -4.95272815e-01 1.75683856e-01
-1.02426243e+00 -7.51491666e-01 8.68987143e-01 2.36476704e-01
-3.90887618e-01 8.97307336e-01 5.29688478e-01 -3.89097095e-01
-4.99461323e-01 -7.94812202e-01 -5.76611698e-01 -9.16216969e-01
-6.44330144e-01 7.08168268e-01 -9.48046520e-02 -3.77130240... | [3.8429830074310303, 2.12790846824646] |
ce2159ee-adb9-4d44-9d80-5eecdf2c1b79 | weakly-supervised-dense-video-captioning-via | 2105.08252 | null | https://arxiv.org/abs/2105.08252v1 | https://arxiv.org/pdf/2105.08252v1.pdf | Weakly Supervised Dense Video Captioning via Jointly Usage of Knowledge Distillation and Cross-modal Matching | This paper proposes an approach to Dense Video Captioning (DVC) without pairwise event-sentence annotation. First, we adopt the knowledge distilled from relevant and well solved tasks to generate high-quality event proposals. Then we incorporate contrastive loss and cycle-consistency loss typically applied to cross-mod... | ['Hua Wu', 'Jian Zhang', 'Xinyan Xiao', 'Jun Yu', 'guocheng niu', 'Bofeng Wu'] | 2021-05-18 | null | null | null | null | ['dense-video-captioning'] | ['computer-vision'] | [ 3.13596040e-01 3.82835083e-02 -3.04224759e-01 -5.58273137e-01
-1.58138406e+00 -4.68015254e-01 9.47822392e-01 3.90571430e-02
-4.94896024e-01 8.91549826e-01 7.84301400e-01 3.37615401e-01
1.81947500e-01 -5.61082065e-01 -1.20873654e+00 -3.13770324e-01
-3.74363288e-02 5.95625460e-01 4.73335862e-01 -2.87871715... | [10.404794692993164, 0.6520432829856873] |
85c37a6b-7303-4570-98cf-6a7a45dff63a | learning-with-noisy-labels-by-targeted | 2110.08355 | null | https://arxiv.org/abs/2110.08355v2 | https://arxiv.org/pdf/2110.08355v2.pdf | Clean or Annotate: How to Spend a Limited Data Collection Budget | Crowdsourcing platforms are often used to collect datasets for training machine learning models, despite higher levels of inaccurate labeling compared to expert labeling. There are two common strategies to manage the impact of such noise. The first involves aggregating redundant annotations, but comes at the expense of... | ['Samuel R. Bowman', 'Zhou Yu', 'Derek Chen'] | 2021-10-15 | null | https://aclanthology.org/2022.deeplo-1.17 | https://aclanthology.org/2022.deeplo-1.17.pdf | deeplo-2022-7 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 3.24327111e-01 4.89836305e-01 7.85936564e-02 -8.38203669e-01
-1.41641927e+00 -7.58875370e-01 3.22878331e-01 7.73331702e-01
-8.28093529e-01 7.14950025e-01 1.89791217e-01 5.15607893e-02
4.44058686e-01 -4.43762660e-01 -5.98027408e-01 -6.17211282e-01
6.16327703e-01 6.96897209e-01 3.12807709e-01 1.14778958... | [9.617900848388672, 4.603750705718994] |
7ade7326-e182-42e1-8dbd-a5de46261f1b | compressive-visual-representations | 2109.12909 | null | https://arxiv.org/abs/2109.12909v3 | https://arxiv.org/pdf/2109.12909v3.pdf | Compressive Visual Representations | Learning effective visual representations that generalize well without human supervision is a fundamental problem in order to apply Machine Learning to a wide variety of tasks. Recently, two families of self-supervised methods, contrastive learning and latent bootstrapping, exemplified by SimCLR and BYOL respectively, ... | ['Ian Fischer', 'John Canny', 'Sergio Guadarrama', 'Anurag Arnab', 'Kuang-Huei Lee'] | 2021-09-27 | null | http://proceedings.neurips.cc/paper/2021/hash/a29a5ba2cb7bdeabba22de8c83321b46-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a29a5ba2cb7bdeabba22de8c83321b46-Paper.pdf | neurips-2021-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.80932149e-01 2.23970383e-01 -6.13178670e-01 -3.89007777e-01
-6.36990130e-01 -5.70614874e-01 7.83006251e-01 2.38916084e-01
-5.64204991e-01 7.48255432e-01 3.68667871e-01 -8.83420184e-02
-2.12183267e-01 -4.78914469e-01 -9.81223941e-01 -5.33254862e-01
-1.65817142e-01 2.37190828e-01 9.21893194e-02 -1.52260736... | [9.157881736755371, 3.055661916732788] |
a03d3005-c039-4f24-9cdf-4c3c17778d02 | learning-causal-representation-for-training | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_Learning_Causal_Representation_for_Training_Cross-Domain_Pose_Estimator_via_Generative_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_Learning_Causal_Representation_for_Training_Cross-Domain_Pose_Estimator_via_Generative_ICCV_2021_paper.pdf | Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions | 3D pose estimation has attracted increasing attention with the availability of high-quality benchmark datasets. However, prior works show that deep learning models tend to learn spurious correlations, which fail to generalize beyond the specific dataset they are trained on. In this work, we take a step towards trai... | ['Weidong Geng', 'Xiangdong Li', 'Mohan Kankanhalli', 'Juwei Lu', 'Xiaofei Wu', 'Yongkang Wong', 'Xiheng Zhang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['3d-pose-estimation'] | ['computer-vision'] | [ 2.58554876e-01 1.85321078e-01 -4.65254247e-01 -4.30453002e-01
-7.30602086e-01 -6.58802807e-01 8.49378586e-01 -5.44910252e-01
-7.69333774e-03 1.17028320e+00 5.10817111e-01 1.29256785e-01
-1.95292503e-01 -6.59492910e-01 -1.12633014e+00 -7.03692377e-01
-4.70521161e-03 6.06119514e-01 -5.55831604e-02 -3.21752205... | [10.306282997131348, 3.0199475288391113] |
93976464-24b7-48ee-b6f4-6f1a68921484 | self-training-with-dual-uncertainty-for-semi | 2304.04441 | null | https://arxiv.org/abs/2304.04441v1 | https://arxiv.org/pdf/2304.04441v1.pdf | Self-training with dual uncertainty for semi-supervised medical image segmentation | In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem. How to effectively learn image features from unlabeled images to improve segmentation accuracy is the main research direction in this field. Traditional self-training methods can partially solve the prob... | ['Zhi Yang', 'Zhongwei Huang', 'Ming Shi', 'Haitao Gan', 'Zhanhong Qiu'] | 2023-04-10 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 4.81517434e-01 6.10010087e-01 -7.31287420e-01 -8.37988853e-01
-1.19389045e+00 -3.68442625e-01 7.56078213e-02 1.79743707e-01
-6.06851995e-01 8.79085064e-01 -4.24811989e-02 -1.99094221e-01
1.94110706e-01 -7.11380780e-01 -8.94761205e-01 -8.84114504e-01
3.57927024e-01 7.17356741e-01 1.48576513e-01 4.74283129... | [14.638854026794434, -2.064882278442383] |
a0cd91b5-c238-433f-8ddb-015f8d4162b8 | bethe-admm-for-tree-decomposition-based | 1309.6829 | null | http://arxiv.org/abs/1309.6829v1 | http://arxiv.org/pdf/1309.6829v1.pdf | Bethe-ADMM for Tree Decomposition based Parallel MAP Inference | We consider the problem of maximum a posteriori (MAP) inference in discrete
graphical models. We present a parallel MAP inference algorithm called
Bethe-ADMM based on two ideas: tree-decomposition of the graph and the
alternating direction method of multipliers (ADMM). However, unlike the
standard ADMM, we use an inexa... | ['Qiang Fu', 'Huahua Wang', 'Arindam Banerjee'] | 2013-09-26 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 2.32333001e-02 2.80653507e-01 -5.83513686e-03 -5.57654500e-01
-1.00956225e+00 -6.36562258e-02 5.41006505e-01 -2.85206158e-02
-1.91464007e-01 8.46898973e-01 3.33038531e-02 -4.34330314e-01
-2.61840403e-01 -8.02833259e-01 -1.03792667e+00 -5.83295107e-01
-1.95570305e-01 9.59499359e-01 -1.63926437e-01 1.05590098... | [6.9583964347839355, 4.291372776031494] |
5c320f98-b96c-4585-a718-30ce942527f4 | deep-multimodal-fusion-for-generalizable | 2211.00933 | null | https://arxiv.org/abs/2211.00933v3 | https://arxiv.org/pdf/2211.00933v3.pdf | Deep Multimodal Fusion for Generalizable Person Re-identification | Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. Recently, leveraging the supervised or semi-unsupervised learning paradigms, which benefits from the large-scale datasets and strong computing performance, has achieved a c... | ['Zefang Yu', 'Wei Ran', 'Yuzhuo Fu', 'Dahong Qian', 'Ting Liu', 'Hao Chen', 'Suncheng Xiang'] | 2022-11-02 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 2.70371526e-01 -4.32361901e-01 -2.69815803e-01 -5.26198089e-01
-8.96778345e-01 -3.39234620e-01 6.65859282e-01 3.02065373e-03
-3.82094115e-01 6.76663995e-01 3.44528913e-01 1.32309258e-01
-5.24886921e-02 -6.46709204e-01 -4.00006086e-01 -7.09709644e-01
3.79575759e-01 3.78310502e-01 4.61706556e-02 -2.13353038... | [14.734374046325684, 1.07125723361969] |
5bab7ea6-7fdc-4d6a-9c7f-1376e5bf08bd | choosing-a-sampling-frequency-for-ecg-qrs | 2007.02052 | null | https://arxiv.org/abs/2007.02052v1 | https://arxiv.org/pdf/2007.02052v1.pdf | Choosing a sampling frequency for ECG QRS detection using convolutional networks | Automated QRS detection methods depend on the ECG data which is sampled at a certain frequency, irrespective of filter-based traditional methods or convolutional network (CNN) based deep learning methods. These methods require a selection of the sampling frequency at which they operate in the very first place. While wo... | ['John Yearwood', 'Chandan Karmakar', 'Ahsan Habib'] | 2020-07-04 | null | null | null | null | ['ecg-qrs-detection'] | ['medical'] | [ 9.95251834e-02 -4.18267161e-01 -1.12327442e-01 -2.07668051e-01
-5.37372172e-01 -4.09561098e-01 -1.18890733e-01 3.74944240e-01
-5.75806499e-01 5.68240643e-01 -5.25450930e-02 -5.55291831e-01
-5.58829010e-01 -6.69720888e-01 -2.52439022e-01 -6.06129587e-01
-4.48517114e-01 7.24081695e-02 1.56675190e-01 -6.14896417... | [14.322041511535645, 3.285440444946289] |
01c008e8-4e98-4760-9cb9-2fca2148216c | towards-controllable-and-interpretable-face | null | null | https://openreview.net/forum?id=ryxUkTVYvH | https://openreview.net/pdf?id=ryxUkTVYvH | Towards Controllable and Interpretable Face Completion via Structure-Aware and Frequency-Oriented Attentive GANs | Face completion is a challenging conditional image synthesis task. This paper proposes controllable and interpretable high-resolution and fast face completion by learning generative adversarial networks (GANs) progressively from low resolution to high resolution. We present structure-aware and frequency-oriented attent... | ['Christopher G. Healey', 'Tianfu Wu', 'Shaoliang Nie', 'Zeyuan Chen'] | 2019-09-25 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 2.18844861e-01 3.23074937e-01 1.17260784e-01 -5.23329675e-01
-9.85600173e-01 -2.21971512e-01 7.46352017e-01 -8.91056478e-01
-2.20299102e-02 8.44528317e-01 3.02445412e-01 4.35757130e-01
1.16503969e-01 -8.34206998e-01 -1.05308723e+00 -7.83132911e-01
-1.40928894e-01 3.50980163e-01 -2.30497211e-01 -2.10369125... | [12.708595275878906, -0.15235519409179688] |
29af88cc-31f2-4203-929f-23b96011fce0 | a-study-of-global-and-episodic-bonuses-for | 2306.03236 | null | https://arxiv.org/abs/2306.03236v1 | https://arxiv.org/pdf/2306.03236v1.pdf | A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs | Exploration in environments which differ across episodes has received increasing attention in recent years. Current methods use some combination of global novelty bonuses, computed using the agent's entire training experience, and \textit{episodic novelty bonuses}, computed using only experience from the current episod... | ['Roberta Raileanu', 'Minqi Jiang', 'Mikael Henaff'] | 2023-06-05 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-5.70110651e-03 -3.30937415e-01 -6.39877841e-02 -2.17611521e-01
-5.13348699e-01 -6.20403886e-01 1.00039113e+00 3.09719682e-01
-1.03112924e+00 8.83648872e-01 4.56616640e-01 -2.58242935e-01
-5.60344458e-01 -5.55768311e-01 -6.25016510e-01 -7.90513039e-01
-5.46602666e-01 2.35066637e-01 2.59742409e-01 -3.35759342... | [3.893474817276001, 1.6939244270324707] |
1d04092c-a65c-42a2-b836-9a7e6553ca5b | insight-1-at-semeval-2016-task-5-deep | 1609.02748 | null | http://arxiv.org/abs/1609.02748v2 | http://arxiv.org/pdf/1609.02748v2.pdf | INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis | This paper describes our deep learning-based approach to multilingual
aspect-based sentiment analysis as part of SemEval 2016 Task 5. We use a
convolutional neural network (CNN) for both aspect extraction and aspect-based
sentiment analysis. We cast aspect extraction as a multi-label classification
problem, outputting ... | ['Parsa Ghaffari', 'John G. Breslin', 'Sebastian Ruder'] | 2016-09-09 | insight-1-at-semeval-2016-task-5-deep-1 | https://aclanthology.org/S16-1053 | https://aclanthology.org/S16-1053.pdf | semeval-2016-6 | ['aspect-extraction', 'aspect-category-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.09924690e-02 2.03776881e-01 -4.37518448e-01 -6.79993927e-01
-1.15966606e+00 -1.02317548e+00 9.25241351e-01 4.79984909e-01
-7.07960844e-01 5.37690699e-01 4.72795695e-01 -6.93157494e-01
4.62301999e-01 -9.37121093e-01 -5.00281155e-01 -4.31638002e-01
2.36663356e-01 8.21681976e-01 -3.04820746e-01 -4.94445711... | [11.382570266723633, 6.678008079528809] |
9fc66c65-eee9-43d4-a86e-0dd7ac2d5b05 | handwritten-digit-recognition-using-improved | 2111.05483 | null | https://arxiv.org/abs/2111.05483v1 | https://arxiv.org/pdf/2111.05483v1.pdf | Handwritten Digit Recognition Using Improved Bounding Box Recognition Technique | The project comes with the technique of OCR (Optical Character Recognition) which includes various research sides of computer science. The project is to take a picture of a character and process it up to recognize the image of that character like a human brain recognize the various digits. The project contains the deep... | ['M. Sathya', 'Arkaprabha Basu'] | 2021-11-10 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.99989989e-01 -7.37661589e-03 3.31586689e-01 -7.21033454e-01
1.11481979e-01 -4.33307558e-01 2.32236430e-01 4.53848355e-02
-4.51331288e-01 4.43964779e-01 -2.81700075e-01 -4.74137217e-01
-8.00831541e-02 -1.11331081e+00 -6.69322014e-01 -6.08705342e-01
2.83093333e-01 9.20667648e-01 4.25756752e-01 -3.90068114... | [11.742912292480469, 2.7393839359283447] |
ffb23f31-a6a5-4573-a52d-42bd2e9f6dd0 | sotab-the-wdc-schema-org-table-annotation | null | null | https://ceur-ws.org/Vol-3320/paper1.pdf | https://ceur-ws.org/Vol-3320/paper1.pdf | SOTAB: The WDC Schema.org Table Annotation Benchmark | Understanding the semantics of table elements is a prerequisite for many data integration and data discovery tasks. Table annotation is the task of labeling table elements with terms from a given vocabulary. This paper presents the WDC Schema.org Table Annotation Benchmark (SOTAB) for comparing the performance of table... | ['Christian Bizer', 'Ralph Peeters', 'Keti Korini'] | 2023-01-09 | null | null | null | semtab-iswc-2023-1 | ['table-annotation', 'data-integration', 'table-annotation', 'column-type-annotation', 'columns-property-annotation'] | ['knowledge-base', 'knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.26360878e-01 2.51519412e-01 -4.80268627e-01 -2.32624203e-01
-8.28656137e-01 -1.21258056e+00 5.80280721e-01 1.32464278e+00
-2.85416842e-01 8.36692333e-01 3.15078884e-01 -1.11511461e-01
-4.15170461e-01 -9.03231859e-01 -7.72920012e-01 -1.10332564e-01
1.47360831e-03 9.63657439e-01 3.34784031e-01 -3.00241560... | [9.403702735900879, 7.971385478973389] |
1be69a93-2cd8-4bfe-99a6-a7c1f853dc33 | temporal-embeddings-and-transformer-models | 2003.08811 | null | https://arxiv.org/abs/2003.08811v1 | https://arxiv.org/pdf/2003.08811v1.pdf | Temporal Embeddings and Transformer Models for Narrative Text Understanding | We present two deep learning approaches to narrative text understanding for character relationship modelling. The temporal evolution of these relations is described by dynamic word embeddings, that are designed to learn semantic changes over time. An empirical analysis of the corresponding character trajectories shows ... | ['Simone Mellace', 'Vani K', 'Alessandro Antonucci'] | 2020-03-19 | null | null | null | null | ['de-aliasing', 'diachronic-word-embeddings'] | ['computer-vision', 'natural-language-processing'] | [ 4.61293124e-02 1.61752746e-01 -3.12846214e-01 -7.19738379e-02
-2.71611720e-01 -8.06382596e-01 1.42738688e+00 1.11321449e+00
-3.32003206e-01 3.48991305e-01 6.86159134e-01 -2.58244604e-01
-3.08773249e-01 -1.35925210e+00 -4.25264925e-01 -6.34639561e-01
-3.19374710e-01 9.36523974e-01 2.61030823e-01 -4.44614261... | [10.943673133850098, 8.92700481414795] |
8ffb5c65-56fd-4180-8f19-e4ebd8e358a9 | parameter-prediction-for-unseen-deep | 2110.13100 | null | https://arxiv.org/abs/2110.13100v1 | https://arxiv.org/pdf/2110.13100v1.pdf | Parameter Prediction for Unseen Deep Architectures | Deep learning has been successful in automating the design of features in machine learning pipelines. However, the algorithms optimizing neural network parameters remain largely hand-designed and computationally inefficient. We study if we can use deep learning to directly predict these parameters by exploiting the pas... | ['Adriana Romero-Soriano', 'Graham W. Taylor', 'Michal Drozdzal', 'Boris Knyazev'] | 2021-10-25 | null | http://proceedings.neurips.cc/paper/2021/hash/f6185f0ef02dcaec414a3171cd01c697-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/f6185f0ef02dcaec414a3171cd01c697-Paper.pdf | neurips-2021-12 | ['parameter-prediction'] | ['miscellaneous'] | [-2.59717315e-01 2.77965009e-01 -3.91183188e-03 -6.07543707e-01
-3.09332848e-01 -6.84999406e-01 5.19393981e-01 -1.97906289e-02
-6.21481895e-01 4.05622423e-01 -3.84923667e-02 -5.75718999e-01
-1.51890457e-01 -7.58209884e-01 -9.92592692e-01 -3.80936593e-01
-4.73393887e-01 7.58601069e-01 2.45539740e-01 -2.08726093... | [8.796030044555664, 3.299941062927246] |
34bf9b16-02c7-4b51-9797-c5d162c95806 | how-to-avoid-being-eaten-by-a-grue | 2002.08795 | null | https://arxiv.org/abs/2002.08795v1 | https://arxiv.org/pdf/2002.08795v1.pdf | How To Avoid Being Eaten By a Grue: Exploration Strategies for Text-Adventure Agents | Text-based games -- in which an agent interacts with the world through textual natural language -- present us with the problem of combinatorially-sized action-spaces. Most current reinforcement learning algorithms are not capable of effectively handling such a large number of possible actions per turn. Poor sample effi... | ['Zhaochen Luo', 'Ethan Tien', 'Prithviraj Ammanabrolu', 'Mark O. Riedl'] | 2020-02-19 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-5.48636029e-03 3.80571514e-01 -1.37511536e-01 2.66766697e-01
-6.24131382e-01 -8.82785141e-01 9.68573034e-01 7.45735317e-02
-1.04883981e+00 1.21198595e+00 2.68252820e-01 -8.59249771e-01
-2.02235058e-01 -1.21477938e+00 -4.93139446e-01 -3.79045367e-01
-5.90039253e-01 1.04590046e+00 5.34863710e-01 -7.36778498... | [3.7635598182678223, 1.4507441520690918] |
0d9bdfd2-3b3b-4dcd-8c6f-105ac248ae09 | knowledgenet-a-benchmark-dataset-for | null | null | https://aclanthology.org/D19-1069 | https://aclanthology.org/D19-1069.pdf | KnowledgeNet: A Benchmark Dataset for Knowledge Base Population | KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whol... | ['Filipe Mesquita', 'Paramita Mirza', 'Jordan Schmidek', 'Denilson Barbosa', 'Matteo Cannaviccio'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['knowledge-base-population'] | ['natural-language-processing'] | [-4.22154307e-01 7.38417208e-01 -5.90521872e-01 1.28382891e-02
-8.05927277e-01 -1.00400507e+00 8.87588561e-01 4.07563776e-01
-5.41946411e-01 1.25240028e+00 4.42553043e-01 -1.51344940e-01
-2.55016834e-01 -1.05340374e+00 -8.59058499e-01 -1.70630012e-02
5.22247441e-02 8.24479342e-01 6.01947308e-01 -4.84130234... | [9.404902458190918, 8.498964309692383] |
d9ebb936-c47f-4978-9f9a-d3fd78905e1a | speechlmscore-evaluating-speech-generation | 2212.04559 | null | https://arxiv.org/abs/2212.04559v1 | https://arxiv.org/pdf/2212.04559v1.pdf | SpeechLMScore: Evaluating speech generation using speech language model | While human evaluation is the most reliable metric for evaluating speech generation systems, it is generally costly and time-consuming. Previous studies on automatic speech quality assessment address the problem by predicting human evaluation scores with machine learning models. However, they rely on supervised learnin... | ['Shinji Watanabe', 'Takaaki Saeki', 'Yifan Peng', 'Soumi Maiti'] | 2022-12-08 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 1.30844399e-01 6.22675121e-02 -1.35126188e-01 -5.20735323e-01
-1.44879663e+00 -3.47721189e-01 4.49159473e-01 2.45011210e-01
-2.60821819e-01 7.04093218e-01 3.88444185e-01 -2.90760845e-01
2.13560298e-01 -5.23074567e-01 -1.59197450e-01 -5.47166944e-01
3.78910333e-01 2.94681758e-01 2.02551126e-01 -9.58047509... | [14.507465362548828, 6.609552383422852] |
db732e35-e53c-4a37-af5c-ce62eacd6e26 | the-effect-of-masking-strategies-on-knowledge | 2306.07185 | null | https://arxiv.org/abs/2306.07185v1 | https://arxiv.org/pdf/2306.07185v1.pdf | The Effect of Masking Strategies on Knowledge Retention by Language Models | Language models retain a significant amount of world knowledge from their pre-training stage. This allows knowledgeable models to be applied to knowledge-intensive tasks prevalent in information retrieval, such as ranking or question answering. Understanding how and which factual information is acquired by our models i... | ['Avishek Anand', 'Tianyi Zhang', 'Jonas Wallat'] | 2023-06-12 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [ 2.62541950e-01 3.68205875e-01 -9.65221748e-02 -2.59456933e-01
-4.45623070e-01 -6.43384516e-01 6.70218289e-01 4.30614084e-01
-5.87133110e-01 1.01873994e+00 1.76432565e-01 -6.27393186e-01
-4.56621200e-01 -8.80456567e-01 -1.11519551e+00 -2.80316919e-01
-1.20482169e-01 1.99410200e-01 3.82061899e-01 -1.73478603... | [10.503230094909668, 7.97879695892334] |
004e020b-0db2-4d55-8041-00bb22af586d | cluster-guided-asymmetric-contrastive | 2106.07846 | null | https://arxiv.org/abs/2106.07846v2 | https://arxiv.org/pdf/2106.07846v2.pdf | Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification | Unsupervised person re-identification (Re-ID) aims to match pedestrian images from different camera views in unsupervised setting. Existing methods for unsupervised person Re-ID are usually built upon the pseudo labels from clustering. However, the quality of clustering depends heavily on the quality of the learned fea... | ['Jun Guo', 'Chun-Guang Li', 'Mingkun Li'] | 2021-06-15 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.31713256e-01 -4.99599427e-01 -1.28896546e-03 -5.93875706e-01
-3.62025887e-01 -5.54552794e-01 5.92891693e-01 -3.04119196e-02
-5.63737810e-01 4.61362183e-01 4.12318021e-01 4.98790115e-01
-1.24656036e-01 -4.03538078e-01 -5.30771017e-01 -8.60192657e-01
4.24175829e-01 4.65105891e-01 -1.57907486e-01 8.80811885... | [14.795690536499023, 1.0472853183746338] |
ca71db2b-ce15-428e-8ee3-7a843a08bb30 | spherevlad-attention-based-and-signal | 2207.02958 | null | https://arxiv.org/abs/2207.02958v2 | https://arxiv.org/pdf/2207.02958v2.pdf | SphereVLAD++: Attention-based and Signal-enhanced Viewpoint Invariant Descriptor | LiDAR-based localization approach is a fundamental module for large-scale navigation tasks, such as last-mile delivery and autonomous driving, and localization robustness highly relies on viewpoints and 3D feature extraction. Our previous work provides a viewpoint-invariant descriptor to deal with viewpoint differences... | ['Sebastian Scherer', 'Ge Yi', 'Peng Yin', 'Shiqi Zhao'] | 2022-07-06 | null | null | null | null | ['3d-place-recognition'] | ['computer-vision'] | [-5.11534989e-01 -6.20074749e-01 -1.26613081e-02 -5.74614942e-01
-8.68025362e-01 -6.50747478e-01 6.76104307e-01 2.08417550e-01
-5.75409412e-01 3.72457504e-01 -2.02948645e-01 1.29695773e-01
-3.52293819e-01 -8.50130856e-01 -6.10624731e-01 -7.14771092e-01
-1.84814483e-01 7.07829654e-01 6.38066351e-01 -4.37474042... | [7.479350566864014, -2.1837549209594727] |
e842b6ec-fbd2-4756-86b6-75c7343c4952 | end-to-end-diarization-for-variable-number-of | 2105.02096 | null | https://arxiv.org/abs/2105.02096v1 | https://arxiv.org/pdf/2105.02096v1.pdf | End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings | We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings. End-to-end diarization models have the advantage of handling speaker overlap and enabling straightforward handling of discriminative training, unlike traditional clustering-based diarization methods. The ... | ['John R. Hershey', 'Shinji Watanabe', 'Scott Wisdom', 'Kevin Wilson', 'Hakan Erdogan', 'Soumi Maiti'] | 2021-05-05 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 1.04734875e-01 1.76897243e-01 7.22632885e-01 -7.02954590e-01
-1.44536388e+00 -4.43186939e-01 5.46344936e-01 5.19122556e-03
-4.56845045e-01 2.71316558e-01 2.73711115e-01 1.88136816e-01
-4.07301456e-01 -1.34596378e-01 -2.98460275e-01 -7.08991587e-01
-5.85155487e-01 1.07976425e+00 -1.61061555e-01 -3.75789441... | [14.62192440032959, 6.093745708465576] |
30062c5e-5c32-4e37-ad91-6417c1a17873 | deeplofargram-a-deep-learning-based | 1912.00605 | null | https://arxiv.org/abs/1912.00605v1 | https://arxiv.org/pdf/1912.00605v1.pdf | DeepLofargram: A Deep Learning based Fluctuating Dim Frequency Line Detection and Recovery | This paper investigates the problem of dim frequency line detection and recovery in the so-called lofargram. Theoretically, time integration long enough can always enhance the detection characteristic. But this does not hold for irregularly fluctuating lines. Deep learning has been shown to perform very well for sophis... | ['Yuyan Li', 'Yuanliang Ma', 'Yina Han', 'Qingyu Liu'] | 2019-12-02 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [-1.21177882e-01 -2.44653765e-02 -2.95467041e-02 -1.45876795e-01
-7.74200439e-01 -3.98653775e-01 3.42910856e-01 1.99559733e-01
-1.51248991e-01 7.83152580e-01 -8.28911066e-02 -2.35146612e-01
-4.73707877e-02 -4.62649643e-01 -8.37165654e-01 -7.96275616e-01
-4.12418664e-01 -1.52513012e-01 3.63671750e-01 -2.59903371... | [11.159549713134766, -2.081875801086426] |
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