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e1e9fd84-8bf2-495b-82da-510f03a1fa98 | clip-models-are-few-shot-learners-empirical | 2203.07190 | null | https://arxiv.org/abs/2203.07190v1 | https://arxiv.org/pdf/2203.07190v1.pdf | CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment | CLIP has shown a remarkable zero-shot capability on a wide range of vision tasks. Previously, CLIP is only regarded as a powerful visual encoder. However, after being pre-trained by language supervision from a large amount of image-caption pairs, CLIP itself should also have acquired some few-shot abilities for vision-... | ['Furu Wei', 'Ting Liu', 'Wei-Nan Zhang', 'Li Dong', 'Haoyu Song'] | 2022-03-14 | null | https://aclanthology.org/2022.acl-long.421 | https://aclanthology.org/2022.acl-long.421.pdf | acl-2022-5 | ['visual-entailment'] | ['reasoning'] | [ 1.76163111e-02 2.59146504e-02 -1.54573724e-01 -4.66140330e-01
-1.22594416e+00 -3.17622453e-01 9.10031080e-01 -1.75090700e-01
-6.01363063e-01 4.08598125e-01 2.02374816e-01 -4.66330141e-01
3.30598146e-01 -4.20883685e-01 -1.08790386e+00 -2.81938374e-01
3.81634116e-01 2.03494489e-01 2.63518274e-01 -3.09686154... | [10.769235610961914, 1.6640204191207886] |
e16309be-c0be-4e82-baed-82fb272d6d01 | diffpose-toward-more-reliable-3d-pose | 2211.16940 | null | https://arxiv.org/abs/2211.16940v3 | https://arxiv.org/pdf/2211.16940v3.pdf | DiffPose: Toward More Reliable 3D Pose Estimation | Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity and occlusion, which often lead to high uncertainty and indeterminacy. On the other hand, diffusion models have recently emerged as an effective tool for generating high-quality images from noise. Inspired by their capability, we expl... | ['Jun Liu', 'Hossein Rahmani', 'Qiuhong Ke', 'Zhipeng Fan', 'Lin Geng Foo', 'Jia Gong'] | 2022-11-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gong_DiffPose_Toward_More_Reliable_3D_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gong_DiffPose_Toward_More_Reliable_3D_Pose_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-pose-estimation', '3d-human-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.93446714e-01 -1.21387690e-01 3.00602973e-01 -2.34331846e-01
-8.31042707e-01 -4.80461150e-01 7.11201251e-01 -5.13368189e-01
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3.04750174e-01 9.53120470e-01 1.35292679e-01 -8.88389070... | [6.991943359375, -1.06046724319458] |
cbb1cb29-e9b6-47e3-a4dc-9e34392aaa4a | rakutens-participation-in-wat-2021-examining | null | null | https://aclanthology.org/2021.wat-1.9 | https://aclanthology.org/2021.wat-1.9.pdf | Rakuten’s Participation in WAT 2021: Examining the Effectiveness of Pre-trained Models for Multilingual and Multimodal Machine Translation | This paper introduces our neural machine translation systems’ participation in the WAT 2021 shared translation tasks (team ID: sakura). We participated in the (i) NICT-SAP, (ii) Japanese-English multimodal translation, (iii) Multilingual Indic, and (iv) Myanmar-English translation tasks. Multilingual approaches such as... | ['Ohnmar Htun', 'Mausam Jain', 'Sunil Yadav', 'Dongzhe Wang', 'Raymond Hendy Susanto'] | null | null | null | null | acl-wat-2021-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 5.39927445e-02 -3.08025628e-01 -1.24327101e-01 -1.24963284e-01
-1.57668674e+00 -8.09642851e-01 8.94712806e-01 -3.06659371e-01
-6.12637341e-01 1.01397204e+00 1.68910056e-01 -9.54364181e-01
6.05990291e-01 -2.69879431e-01 -9.00324821e-01 -5.84277153e-01
4.52261627e-01 9.12191510e-01 -4.27959412e-01 -4.98038113... | [11.510578155517578, 1.569311499595642] |
5d874651-e8c6-4063-9ccf-a4a77d585000 | exposure-correction-model-to-enhance-image | 2204.10648 | null | https://arxiv.org/abs/2204.10648v1 | https://arxiv.org/pdf/2204.10648v1.pdf | Exposure Correction Model to Enhance Image Quality | Exposure errors in an image cause a degradation in the contrast and low visibility in the content. In this paper, we address this problem and propose an end-to-end exposure correction model in order to handle both under- and overexposure errors with a single model. Our model contains an image encoder, consecutive resid... | ['Alexander Waibel', 'Hazim Kemal Ekenel', 'Dogucan Yaman', 'Fevziye Irem Eyiokur'] | 2022-04-22 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 5.67471862e-01 -3.58516991e-01 1.29054412e-01 -3.49650860e-01
-6.29169106e-01 -3.22540969e-01 2.85087079e-01 -3.18321317e-01
-8.92709941e-02 3.90033215e-01 2.02357277e-01 1.43480271e-01
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5.58359802e-01 -5.96194446e-01 2.68524826e-01 -1.04654901... | [10.899675369262695, -2.1522066593170166] |
192732ec-4f2d-43a1-8617-1641a0074e2b | a-multi-modal-neural-geometric-solver-with | 2302.11097 | null | https://arxiv.org/abs/2302.11097v2 | https://arxiv.org/pdf/2302.11097v2.pdf | A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram | Geometry problem solving (GPS) is a high-level mathematical reasoning requiring the capacities of multi-modal fusion and geometric knowledge application. Recently, neural solvers have shown great potential in GPS but still be short in diagram presentation and modal fusion. In this work, we convert diagrams into basic t... | ['Cheng-Lin Liu', 'Fei Yin', 'Ming-Liang Zhang'] | 2023-02-22 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-2.72210032e-01 3.22263598e-01 -9.17199254e-02 -4.13338482e-01
-7.42189765e-01 -4.96786565e-01 1.77769676e-01 -1.76306237e-02
4.20618832e-01 6.31892622e-01 2.39618674e-01 -4.81640100e-01
-3.77878696e-01 -1.38371229e+00 -1.14322317e+00 -3.52103561e-01
-2.42253728e-02 5.95090806e-01 -8.98401663e-02 -3.70762944... | [9.418240547180176, 7.4313201904296875] |
412cad54-3b4b-4cc6-9b55-162c6a2cab87 | gspmd-general-and-scalable-parallelization | 2105.04663 | null | https://arxiv.org/abs/2105.04663v2 | https://arxiv.org/pdf/2105.04663v2.pdf | GSPMD: General and Scalable Parallelization for ML Computation Graphs | We present GSPMD, an automatic, compiler-based parallelization system for common machine learning computations. It allows users to write programs in the same way as for a single device, then give hints through a few annotations on how to distribute tensors, based on which GSPMD will parallelize the computation. Its rep... | ['Zhifeng Chen', 'Yonghui Wu', 'Tao Wang', 'Shibo Wang', 'Noam Shazeer', 'Ruoming Pang', 'Marcello Maggioni', 'Andy Ly', 'Dmitry Lepikhin', 'Maxim Krikun', 'Rahul Joshi', 'Yanping Huang', 'Blake Hechtman', 'Dehao Chen', 'HyoukJoong Lee', 'Yuanzhong Xu'] | 2021-05-10 | null | null | null | null | ['2048'] | ['playing-games'] | [-5.12876332e-01 -2.23067731e-01 -4.52198893e-01 -3.17847550e-01
-5.76396704e-01 -5.97988725e-01 6.53103650e-01 1.98654041e-01
-1.03076838e-01 4.67490070e-02 -6.23515993e-03 -8.73555362e-01
9.09718103e-04 -6.53935969e-01 -4.57224905e-01 -5.37295461e-01
-7.94864073e-02 1.04120755e+00 1.87173173e-01 2.78207306... | [8.47344970703125, 3.4983651638031006] |
e63951f1-2e76-44dd-af93-5da1eca53674 | sigmorphon-2022-task-0-submission-description | null | null | https://aclanthology.org/2022.sigmorphon-1.20 | https://aclanthology.org/2022.sigmorphon-1.20.pdf | SIGMORPHON 2022 Task 0 Submission Description: Modelling Morphological Inflection with Data-Driven and Rule-Based Approaches | This paper describes our participation in the 2022 SIGMORPHON-UniMorph Shared Task on Typologically Diverse and AcquisitionInspired Morphological Inflection Generation. We present two approaches: one being a modification of the neural baseline encoderdecoder model, the other being hand-coded morphological analyzers usi... | ['Ryan Soh-Eun Shim', 'Jingwen Li', 'Leander Girrbach', 'Nkonye Gbadegoye', 'Tatiana Merzhevich'] | null | null | null | null | naacl-sigmorphon-2022-7 | ['morphological-inflection'] | ['natural-language-processing'] | [ 1.48771495e-01 3.02669883e-01 -2.17240855e-01 -3.41019243e-01
-8.80913317e-01 -9.79320467e-01 6.15534782e-01 1.64986834e-01
-9.29288864e-01 9.13592041e-01 4.19455111e-01 -6.36277199e-01
9.63967890e-02 -4.80893642e-01 -6.06526732e-01 -3.13197792e-01
2.02052221e-01 8.71065140e-01 -6.56578541e-02 -4.99568671... | [10.727738380432129, 10.0040922164917] |
837b349d-3245-412b-9c88-61668cb8503f | self-supervised-classification-network | 2103.10994 | null | https://arxiv.org/abs/2103.10994v3 | https://arxiv.org/pdf/2103.10994v3.pdf | Self-Supervised Classification Network | We present Self-Classifier -- a novel self-supervised end-to-end classification learning approach. Self-Classifier learns labels and representations simultaneously in a single-stage end-to-end manner by optimizing for same-class prediction of two augmented views of the same sample. To guarantee non-degenerate solutions... | ['Alex Bronstein', 'Leonid Karlinsky', 'Elad Amrani'] | 2021-03-19 | null | null | null | null | ['self-supervised-image-classification', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision'] | [ 4.52994704e-01 4.32130158e-01 -4.51093346e-01 -8.26896787e-01
-8.59133184e-01 -6.16951942e-01 3.83364052e-01 2.49575496e-01
-2.68355280e-01 4.07643050e-01 6.00414276e-02 -1.16335526e-01
-6.36093318e-02 -5.30242443e-01 -5.67619145e-01 -7.62629867e-01
4.90524694e-02 7.37866998e-01 -1.10964663e-01 2.82672286... | [9.309185028076172, 2.9721572399139404] |
d227c901-e97e-4ac8-83f3-7743bfdea82f | federated-nearest-neighbor-machine | 2302.12211 | null | https://arxiv.org/abs/2302.12211v1 | https://arxiv.org/pdf/2302.12211v1.pdf | Federated Nearest Neighbor Machine Translation | To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional FL algorithm (e.g., FedAvg) typically relies on multi-round model-based interactions. However, it is impractical and inefficient for machine ... | ['Enhong Chen', 'Tong Xu', 'Lemao Liu', 'Bingzhe Wu', 'Zhirui Zhang', 'Yichao Du'] | 2023-02-23 | null | null | null | null | ['nmt', 'memorization'] | ['computer-code', 'natural-language-processing'] | [ 1.29458949e-01 -1.21115409e-01 -5.76632619e-01 -6.24813676e-01
-1.23965812e+00 -9.43246424e-01 5.22261858e-01 -6.33883178e-02
-5.44267833e-01 8.41680646e-01 -1.86991394e-01 -8.14242840e-01
9.58951861e-02 -8.00558805e-01 -1.05171382e+00 -7.85748541e-01
4.36812848e-01 2.55966306e-01 -3.81779730e-01 1.71031013... | [5.829962730407715, 6.505829334259033] |
2684c411-996f-46f6-8e11-a5b6202654cf | efficiently-discovering-locally-exceptional | 1709.07941 | null | http://arxiv.org/abs/1709.07941v1 | http://arxiv.org/pdf/1709.07941v1.pdf | Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups | Subgroup discovery is a local pattern mining technique to find interpretable
descriptions of sub-populations that stand out on a given target variable. That
is, these sub-populations are exceptional with regard to the global
distribution. In this paper we argue that in many applications, such as
scientific discovery, s... | ['Janis Kalofolias', 'Mario Boley', 'Jilles Vreeken'] | 2017-09-22 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 1.45598143e-01 3.66296411e-01 -7.18020916e-01 -3.83836895e-01
-5.61931074e-01 -5.14935315e-01 3.76286983e-01 5.97028971e-01
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-6.65758073e-01 -9.33483899e-01 -7.49107122e-01 -9.58200157e-01
-4.79612440e-01 1.09545362e+00 2.41006881e-01 1.18242443... | [7.679121971130371, 4.760691165924072] |
fd974aa1-b129-4d93-a911-53ae2dcb46d8 | clustering-aided-weakly-supervised-training | 2203.13704 | null | https://arxiv.org/abs/2203.13704v1 | https://arxiv.org/pdf/2203.13704v1.pdf | Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos | Formulating learning systems for the detection of real-world anomalous events using only video-level labels is a challenging task mainly due to the presence of noisy labels as well as the rare occurrence of anomalous events in the training data. We propose a weakly supervised anomaly detection system which has multiple... | ['Seung-Ik Lee', 'Marcella Astrid', 'Arif Mahmood', 'Muhammad Zaigham Zaheer'] | 2022-03-25 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 1.95384920e-01 -1.94568679e-01 6.62142038e-02 -5.62364221e-01
-5.66672385e-01 -1.67269737e-01 3.55863065e-01 5.29645741e-01
-2.75899619e-01 3.91449392e-01 1.21536113e-01 1.13881201e-01
-3.20140906e-02 -3.86128247e-01 -5.46756446e-01 -8.11827600e-01
-5.28431296e-01 7.99825415e-02 3.45230460e-01 -3.43036503... | [7.831862926483154, 1.6581337451934814] |
5e0b7369-07c7-4d3e-b3c0-ba9c66775693 | outlier-robust-extreme-learning-machine-for | 1905.09368 | null | https://arxiv.org/abs/1905.09368v2 | https://arxiv.org/pdf/1905.09368v2.pdf | Outlier Robust Extreme Learning Machine for Multi-Target Regression | The popularity of algorithms based on Extreme Learning Machine (ELM), which can be used to train Single Layer Feedforward Neural Networks (SLFN), has increased in the past years. They have been successfully applied to a wide range of classification and regression tasks. The most commonly used methods are the ones based... | ['Bruno Légora Souza da Silva', 'Patrick Marques Ciarelli', 'Fernando Kentaro Inaba', 'Evandro Ottoni Teatini Salles'] | 2019-05-22 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [-2.29494244e-01 -1.28543496e-01 9.00839344e-02 -3.32730711e-01
-3.03703874e-01 2.05121294e-01 1.11259468e-01 1.23510838e-01
-6.99101269e-01 9.13275301e-01 -2.75353253e-01 -7.10563213e-02
-4.08156067e-01 -6.70315504e-01 -8.70445549e-01 -7.97504008e-01
-1.88563108e-01 3.16483676e-01 -1.03395857e-01 -2.03293219... | [7.729569911956787, 2.849924087524414] |
ba7d94be-b795-4586-825c-346078750b8b | the-role-of-global-and-local-context-in-named | 2305.03132 | null | https://arxiv.org/abs/2305.03132v2 | https://arxiv.org/pdf/2305.03132v2.pdf | The Role of Global and Local Context in Named Entity Recognition | Pre-trained transformer-based models have recently shown great performance when applied to Named Entity Recognition (NER). As the complexity of their self-attention mechanism prevents them from processing long documents at once, these models are usually applied in a sequential fashion. Such an approach unfortunately on... | ['Richard Dufour', 'Vincent Labatut', 'Arthur Amalvy'] | 2023-05-04 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [ 2.49515474e-02 -1.66835785e-01 -2.52123475e-01 -3.49099517e-01
-9.16444242e-01 -7.52160192e-01 8.90100718e-01 5.34124672e-01
-9.48852897e-01 5.18700421e-01 7.30399609e-01 -8.02648544e-01
4.18932177e-02 -6.18680894e-01 -4.53121483e-01 -2.63319343e-01
1.70986265e-01 1.84657127e-01 2.58958697e-01 -1.62543803... | [10.795117378234863, 8.505051612854004] |
f8339d2d-2e6a-43db-a178-31e298b7716f | vision-transformer-adapter-for-dense | 2205.08534 | null | https://arxiv.org/abs/2205.08534v4 | https://arxiv.org/pdf/2205.08534v4.pdf | Vision Transformer Adapter for Dense Predictions | This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT). Unlike recently advanced variants that incorporate vision-specific inductive biases into their architectures, the plain ViT suffers inferior performance on dense predictions due to weak prior assumptions. To address... | ['Yu Qiao', 'Jifeng Dai', 'Tong Lu', 'Junjun He', 'Wenhai Wang', 'Yuchen Duan', 'Zhe Chen'] | 2022-05-17 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 1.85610831e-01 3.42476070e-01 -9.40500107e-03 -4.45145160e-01
-8.55795622e-01 -3.74139488e-01 6.70905173e-01 -5.34753799e-01
-2.99194455e-01 3.03581685e-01 1.44427449e-01 -4.10572588e-01
4.74973917e-01 -6.55230105e-01 -1.04772449e+00 -6.18490040e-01
5.72917998e-01 5.14008105e-01 5.37365675e-01 -4.75700535... | [9.760509490966797, 1.3476005792617798] |
2e8a1b0c-e54c-4bbf-8134-56c6fe491ddd | icta-system-combination-for-chinese-semantic | null | null | https://aclanthology.org/S12-1073 | https://aclanthology.org/S12-1073.pdf | ICT:A System Combination for Chinese Semantic Dependency Parsing | null | ['Qun Liu', 'Hao Xiong'] | 2012-07-01 | null | null | null | semeval-2012-7 | ['semantic-dependency-parsing'] | ['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... | [-7.270680904388428, 3.7627005577087402] |
6ec5d7ae-fbca-4b72-9551-ae9066958c4d | towards-multi-domain-single-image-dehazing | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Towards_Multi-Domain_Single_Image_Dehazing_via_Test-Time_Training_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_Towards_Multi-Domain_Single_Image_Dehazing_via_Test-Time_Training_CVPR_2022_paper.pdf | Towards Multi-Domain Single Image Dehazing via Test-Time Training | Recent years have witnessed significant progress in the area of single image dehazing, thanks to the employment of deep neural networks and diverse datasets. Most of the existing methods perform well when the training and testing are conducted on a single dataset. However, they are not able to handle different type... | ['Keyan Wang', 'Jun Chen', 'Sadaf Salehkalaibar', 'Liangyan Li', 'Zijun Wu', 'Huan Liu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['image-dehazing'] | ['computer-vision'] | [ 3.03718150e-01 -1.76557451e-01 -7.91959986e-02 -3.34220350e-01
-4.08018798e-01 -2.08963856e-01 5.04743934e-01 1.17223173e-01
-3.01780313e-01 4.77518827e-01 -1.41862154e-01 -9.13123041e-02
-1.70404673e-01 -8.67312849e-01 -6.52387977e-01 -1.00140142e+00
3.46373618e-01 1.44304588e-01 4.24721271e-01 -3.45080316... | [10.944283485412598, -3.0267539024353027] |
c7889580-743e-45ea-b39a-075cf42dad98 | enabling-weakly-supervised-temporal-action | 2208.12673 | null | https://arxiv.org/abs/2208.12673v1 | https://arxiv.org/pdf/2208.12673v1.pdf | Enabling Weakly-Supervised Temporal Action Localization from On-Device Learning of the Video Stream | Detecting actions in videos have been widely applied in on-device applications. Practical on-device videos are always untrimmed with both action and background. It is desirable for a model to both recognize the class of action and localize the temporal position where the action happens. Such a task is called temporal a... | ['Jingtong Hu', 'Peipei Zhou', 'Yawen Wu', 'Yue Tang'] | 2022-08-25 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 0.37980536 -0.19723947 -0.5666264 -0.28603348 -0.7064961 -0.60501426
0.02747823 -0.17954212 -0.29059818 0.3299041 -0.19973788 -0.24200776
0.12039312 -0.414344 -0.8807798 -0.658428 -0.22102194 0.04155475
0.5060434 0.35870153 -0.06121735 0.32716718 -1.4703604 0.6325117
0.55480736 1.3480175 0.3... | [8.552701950073242, 0.6540181040763855] |
7d3a9468-2f6b-4ec8-9d31-79178ca24317 | enhancing-transformer-for-end-to-end-speech | null | null | https://aclanthology.org/W19-6603 | https://aclanthology.org/W19-6603.pdf | Enhancing Transformer for End-to-end Speech-to-Text Translation | null | ['Roldano Cattoni', 'Roberto Dessi', 'Mattia Antonino Di Gangi', 'Marco Turchi', 'Matteo Negri'] | 2019-08-01 | null | null | null | ws-2019-8 | ['speech-to-text-translation'] | ['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
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c91ed3ee-3c89-4a13-99c3-d857999cb8fe | evolving-domain-generalization | 2206.00047 | null | https://arxiv.org/abs/2206.00047v2 | https://arxiv.org/pdf/2206.00047v2.pdf | Evolving Domain Generalization | Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data. Existing domain generalization methods ignore the relationship between tasks, implicitly assuming that all the tasks are sample... | ['William Wei Wang', 'Boyu Wang', 'Christian Gagné', 'Charles Ling', 'Changjian Shui', 'Fan Zhou', 'Jiaqi Li', 'Ruizhi Pu', 'Gezheng Xu'] | 2022-05-31 | null | null | null | null | ['evolving-domain-generalization'] | ['computer-vision'] | [ 5.46667099e-01 7.12455958e-02 -3.18618976e-02 -3.59995633e-01
-9.17003751e-02 -7.00491130e-01 8.38555098e-01 -3.27251386e-04
-1.74674913e-01 9.40823197e-01 -5.68479896e-02 -9.39924270e-02
-5.57875991e-01 -5.78284979e-01 -7.05580354e-01 -7.87506282e-01
-2.21200392e-01 6.38383687e-01 2.91240782e-01 -4.14833069... | [10.17762565612793, 3.1112685203552246] |
13be319b-b53d-4697-9e9f-5be726bba964 | using-set-covering-to-generate-databases-for | 2211.03447 | null | https://arxiv.org/abs/2211.03447v1 | https://arxiv.org/pdf/2211.03447v1.pdf | Using Set Covering to Generate Databases for Holistic Steganalysis | Within an operational framework, covers used by a steganographer are likely to come from different sensors and different processing pipelines than the ones used by researchers for training their steganalysis models. Thus, a performance gap is unavoidable when it comes to out-of-distributions covers, an extremely freque... | ['Tomáš Pevný', 'Patrick Bas', 'Jérémie Boulanger', 'Vincent Itier', 'Rony Abecidan'] | 2022-11-07 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 5.37851632e-01 5.84865138e-02 5.17031774e-02 1.91416815e-02
-9.02427316e-01 -5.91471732e-01 4.98179287e-01 2.22070083e-01
-9.27819312e-02 3.49912465e-01 -6.20080903e-02 8.17100611e-03
-8.99559110e-02 -1.01371133e+00 -1.01314402e+00 -9.24127758e-01
-1.85789779e-01 3.91570181e-01 3.24980408e-01 -3.42895687... | [4.3997578620910645, 7.982824325561523] |
3bdb12f9-d00e-454b-8ccd-3f88d40ebff4 | sst-reversiblenet-reversible-prior-based | 2305.04054 | null | https://arxiv.org/abs/2305.04054v1 | https://arxiv.org/pdf/2305.04054v1.pdf | SST-ReversibleNet: Reversible-prior-based Spectral-Spatial Transformer for Efficient Hyperspectral Image Reconstruction | Spectral image reconstruction is an important task in snapshot compressed imaging. This paper aims to propose a new end-to-end framework with iterative capabilities similar to a deep unfolding network to improve reconstruction accuracy, independent of optimization conditions, and to reduce the number of parameters. A n... | ['Feipeng Da', 'Chengqian Jin', 'Ziyu Zhang', 'Jian Yu', 'Zeyu Cai'] | 2023-05-06 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 4.51465279e-01 -3.96642417e-01 2.99332947e-01 -4.45760101e-01
-5.93578696e-01 -1.81378409e-01 8.34704712e-02 -5.15094817e-01
-3.81637007e-01 5.39882839e-01 3.30653310e-01 1.98940616e-02
-4.82101619e-01 -9.08196211e-01 -5.65922976e-01 -1.08587039e+00
1.43810421e-01 -1.54965445e-01 1.28450215e-01 -1.63911939... | [10.799715995788574, -2.288935661315918] |
c33fbf66-54c1-4c46-a0fe-b754bf0248eb | unsupervised-audio-source-separation-using-1 | 2201.09592 | null | https://arxiv.org/abs/2201.09592v2 | https://arxiv.org/pdf/2201.09592v2.pdf | Unsupervised Music Source Separation Using Differentiable Parametric Source Models | Supervised deep learning approaches to underdetermined audio source separation achieve state-of-the-art performance but require a dataset of mixtures along with their corresponding isolated source signals. Such datasets can be extremely costly to obtain for musical mixtures. This raises a need for unsupervised methods.... | ['Liam Kelley', 'Gaël Richard', 'Roland Badeau', 'Clement S. J. Doire', 'Kilian Schulze-Forster'] | 2022-01-24 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 3.07619333e-01 -1.29942670e-01 4.64019775e-02 -2.39876106e-01
-1.41359508e+00 -6.87053800e-01 4.36941147e-01 -2.71285236e-01
-8.66791680e-02 5.71002603e-01 3.30340445e-01 1.22118860e-01
-3.91800135e-01 -2.24178493e-01 -7.60315061e-01 -9.38371003e-01
6.75970037e-03 5.69840193e-01 -4.52545434e-01 -5.15914857... | [15.397066116333008, 5.582434177398682] |
21f3c233-3b5e-4722-9529-0c03b34a14dc | webcpm-interactive-web-search-for-chinese | 2305.06849 | null | https://arxiv.org/abs/2305.06849v2 | https://arxiv.org/pdf/2305.06849v2.pdf | WebCPM: Interactive Web Search for Chinese Long-form Question Answering | Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two procedures: information retrieval, which searches for relevant supporting facts, and information synthesis, which integrates these facts into a co... | ['Jie zhou', 'Maosong Sun', 'Zhiyuan Liu', 'Fanchao Qi', 'Ruobing Xie', 'Huadong Wang', 'Ning Ding', 'Xu Han', 'Yankai Lin', 'Kunlun Zhu', 'Shihao Liang', 'Lan Yan', 'Dian Jin', 'Zihan Cai', 'Yujia Qin'] | 2023-05-11 | null | null | null | null | ['long-form-question-answering'] | ['natural-language-processing'] | [-2.00268716e-01 2.54242241e-01 9.79649555e-03 -3.87268364e-01
-1.87430346e+00 -1.18949056e+00 4.01056647e-01 1.22326948e-01
-5.64637482e-01 7.73679852e-01 5.41433096e-01 -4.97347444e-01
-3.25175710e-02 -8.64662230e-01 -6.04314506e-01 1.62666589e-01
5.65593302e-01 1.02405059e+00 5.82064807e-01 -5.90863824... | [11.503085136413574, 8.017892837524414] |
895e2d70-d29a-45a3-ad7d-390556b3fceb | hybrid-and-collaborative-passage-reranking | 2305.09313 | null | https://arxiv.org/abs/2305.09313v1 | https://arxiv.org/pdf/2305.09313v1.pdf | Hybrid and Collaborative Passage Reranking | In passage retrieval system, the initial passage retrieval results may be unsatisfactory, which can be refined by a reranking scheme. Existing solutions to passage reranking focus on enriching the interaction between query and each passage separately, neglecting the context among the top-ranked passages in the initial ... | ['Houqiang Li', 'Jiaxin Shi', 'Wengang Zhou', 'Zongmeng Zhang'] | 2023-05-16 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-3.55116218e-01 -5.20635068e-01 -2.62638032e-01 1.36666685e-01
-1.26854312e+00 -7.29502618e-01 7.42926419e-01 8.23185980e-01
-4.13961977e-01 9.15873468e-01 1.00062680e+00 1.07693616e-02
-8.19820583e-01 -8.54188740e-01 -2.60200381e-01 -4.07554388e-01
-3.87475431e-01 4.36399043e-01 8.04955542e-01 -7.16214895... | [11.46517276763916, 7.668643951416016] |
d2558e97-44f9-44d5-a3c8-a1a05292ef9a | boundary-enhanced-co-training-for-weakly | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Rong_Boundary-Enhanced_Co-Training_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Rong_Boundary-Enhanced_Co-Training_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | Boundary-Enhanced Co-Training for Weakly Supervised Semantic Segmentation | The existing weakly supervised semantic segmentation (WSSS) methods pay much attention to generating accurate and complete class activation maps (CAMs) as pseudo-labels, while ignoring the importance of training the segmentation networks. In this work, we observe that there is an inconsistency between the quality o... | ['Junjie Li', 'Zilei Wang', 'Bohai Tu', 'Shenghai Rong'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['weakly-supervised-semantic-segmentation'] | ['computer-vision'] | [ 4.49000865e-01 4.08251941e-01 -1.06270820e-01 -6.66284442e-01
-8.44511807e-01 -4.06489640e-01 3.48562628e-01 -5.53929880e-02
-4.31137979e-01 4.89630878e-01 -2.04375744e-01 -1.90843105e-01
1.46030501e-01 -6.33060515e-01 -9.08347011e-01 -6.97878063e-01
4.11518604e-01 3.16049695e-01 8.32747757e-01 -7.29756877... | [9.576639175415039, 0.6250138878822327] |
72965288-aeb4-4677-a0e9-71a318c92a2d | spectral-aware-softmax-for-visible-infrared | 2302.01512 | null | https://arxiv.org/abs/2302.01512v1 | https://arxiv.org/pdf/2302.01512v1.pdf | Spectral Aware Softmax for Visible-Infrared Person Re-Identification | Visible-infrared person re-identification (VI-ReID) aims to match specific pedestrian images from different modalities. Although suffering an extra modality discrepancy, existing methods still follow the softmax loss training paradigm, which is widely used in single-modality classification tasks. The softmax loss lacks... | ['Rongrong Ji', 'Yongjian Wu', 'Mingliang Xu', 'Qixiang Ye', 'Pingyang Dai', 'Lei Tan'] | 2023-02-03 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 2.83987612e-01 -2.85933614e-01 -1.07990876e-01 -5.84186435e-01
-6.50216639e-01 -1.51612163e-01 5.18008351e-01 -3.14133823e-01
-7.03313231e-01 6.50507510e-01 3.39355975e-01 3.34000662e-02
2.76047084e-02 -4.78943676e-01 -6.23967826e-01 -9.60500062e-01
4.60669190e-01 -3.51467043e-01 -2.49057338e-01 -1.62133768... | [14.69723129272461, 0.945119321346283] |
bc8e47e9-75b8-4a5b-af6c-2c77d6e1ee35 | a-real-time-automated-point-process-method | null | null | https://ieeexplore.ieee.org/document/6257443/authors#authors | https://ieeexplore.ieee.org/document/6257443/authors#authors | A Real-Time Automated Point-Process Method for the Detection and Correction of Erroneous and Ectopic Heartbeats | The presence of recurring arrhythmic events (also known as cardiac dysrhythmia or irregular heartbeats), as well as erroneous beat detection due to low signal quality, significantly affects estimation of both time and frequency domain indices of heart rate variability (HRV). A reliable, real-time classification and cor... | ['Riccardo Barbieri', 'Emery N. Brown', 'Luca Citi'] | 2012-08-02 | null | null | null | null | ['heart-rate-variability', 'heartbeat-classification'] | ['medical', 'medical'] | [ 4.88780260e-01 -2.64330208e-01 2.84845531e-01 -2.04200000e-01
-4.14584249e-01 -7.19700575e-01 7.24020973e-02 4.79783595e-01
-2.00467706e-01 9.02554095e-01 -8.98546502e-02 -2.50413477e-01
-2.83435792e-01 -6.02254152e-01 -2.22929910e-01 -5.39145291e-01
-5.99484921e-01 5.14638364e-01 -1.71978865e-02 2.12359235... | [14.18603515625, 3.175640106201172] |
a4920eba-cf8a-4186-b9a9-489db5e6f3c9 | divergence-regulated-encoder-network-for | 2012.15764 | null | https://arxiv.org/abs/2012.15764v6 | https://arxiv.org/pdf/2012.15764v6.pdf | Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification | Feature representation is an important aspect of remote-sensing based image classification. While deep convolutional neural networks are able to effectively amalgamate information, large numbers of parameters often make learned features inscrutable and difficult to transfer to alternative models. In order to better rep... | ['Weihuang Xu', 'James Keller', 'Alina Zare', 'Connor McCurley', 'Sarah Walker', 'Joshua Peeples'] | 2020-12-31 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 3.22287977e-01 -2.86514848e-01 2.04076841e-02 -6.38038993e-01
-8.12944353e-01 -3.29616517e-01 7.59403765e-01 3.41630071e-01
-5.13240457e-01 6.60668373e-01 1.83130741e-01 -5.11236042e-02
-7.72224844e-01 -1.22305059e+00 -3.88820767e-01 -1.19610035e+00
-4.01453465e-01 9.10235047e-02 -1.67092100e-01 2.33156681... | [9.387486457824707, 2.304417133331299] |
772c9e97-1bfe-4c83-b716-dae7760d74b2 | an-adversarial-multi-task-learning-method-for | 2306.16313 | null | https://arxiv.org/abs/2306.16313v1 | https://arxiv.org/pdf/2306.16313v1.pdf | An Adversarial Multi-Task Learning Method for Chinese Text Correction with Semantic Detection | Text correction, especially the semantic correction of more widely used scenes, is strongly required to improve, for the fluency and writing efficiency of the text. An adversarial multi-task learning method is proposed to enhance the modeling and detection ability of character polysemy in Chinese sentence context. Wher... | ['Zhenping Xie', 'Fanyu Wang'] | 2023-06-28 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 3.72136474e-01 -4.91576940e-01 1.42788678e-01 -1.97273433e-01
-8.51258993e-01 -7.29335621e-02 6.04023039e-01 -1.14406973e-01
-6.69986904e-01 9.50672984e-01 5.68668902e-01 -6.31930232e-02
3.24506283e-01 -5.38662910e-01 -4.49794292e-01 -6.04332268e-01
9.82065380e-01 2.73047984e-01 4.45048153e-01 -2.74185002... | [10.963138580322266, 10.82551383972168] |
b7598349-2c23-41e9-abfe-7a3eabf3dede | contrastively-enforcing-distinctiveness-for | null | null | https://openreview.net/forum?id=jNsynsmDkl | https://openreview.net/pdf?id=jNsynsmDkl | Contrastively Enforcing Distinctiveness for Multi-Label Classification | Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains. The success of constrastive learning in single-label classifications motivates us to leverage this learning framework to enhance distinctiveness for better performance ... | ['Jianfei Cai', 'Dinh Phung', 'He Zhao', 'Son Duy Dao'] | 2021-09-29 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 6.54421687e-01 -3.36272180e-01 -6.12248898e-01 -4.89669442e-01
-1.06872880e+00 -5.86192012e-01 6.62459970e-01 3.19570869e-01
-3.07282239e-01 4.60632831e-01 -2.42992446e-01 -4.03521359e-02
-1.99198455e-01 -3.03182989e-01 -3.53947461e-01 -1.03432870e+00
2.83149719e-01 1.32657677e-01 -1.40681639e-01 6.20165318... | [9.660860061645508, 4.185973167419434] |
5ed7011d-778a-4840-bafa-0dfb8bad4901 | full-duplex-strategy-for-video-object | 2108.03151 | null | https://arxiv.org/abs/2108.03151v3 | https://arxiv.org/pdf/2108.03151v3.pdf | Full-Duplex Strategy for Video Object Segmentation | Previous video object segmentation approaches mainly focus on using simplex solutions between appearance and motion, limiting feature collaboration efficiency among and across these two cues. In this work, we study a novel and efficient full-duplex strategy network (FSNet) to address this issue, by considering a better... | ['Ling Shao', 'Jianbing Shen', 'Deng-Ping Fan', 'Zhe Wu', 'Keren Fu', 'Ge-Peng Ji'] | 2021-08-06 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ji_Full-Duplex_Strategy_for_Video_Object_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ji_Full-Duplex_Strategy_for_Video_Object_Segmentation_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-salient-object-detection', 'unsupervised-video-object-segmentation', 'video-polyp-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.71829298e-01 -1.39288232e-01 -3.15938264e-01 -2.54936278e-01
-4.62291837e-01 -4.06394929e-01 6.16470873e-01 -4.07210886e-01
-4.71525311e-01 4.82318670e-01 3.60586703e-01 -6.96643814e-02
-2.82580964e-04 -4.26493496e-01 -7.47594774e-01 -8.22541177e-01
-8.10031444e-02 -2.31764480e-01 5.92231035e-01 1.06368981... | [9.275411605834961, -0.15825045108795166] |
afd1a5b5-9868-4b76-a73c-52e6bc3e54e7 | equiangular-basis-vectors | 2303.11637 | null | https://arxiv.org/abs/2303.11637v2 | https://arxiv.org/pdf/2303.11637v2.pdf | Equiangular Basis Vectors | We propose Equiangular Basis Vectors (EBVs) for classification tasks. In deep neural networks, models usually end with a k-way fully connected layer with softmax to handle different classification tasks. The learning objective of these methods can be summarized as mapping the learned feature representations to the samp... | ['Xiu-Shen Wei', 'Xuhao Sun', 'Yang shen'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shen_Equiangular_Basis_Vectors_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shen_Equiangular_Basis_Vectors_CVPR_2023_paper.pdf | cvpr-2023-1 | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-1.31238863e-01 1.07185863e-01 -4.32017028e-01 -8.39903891e-01
-3.00552368e-01 -4.68517244e-01 5.62337160e-01 -4.05747369e-02
-5.56225777e-01 6.98359132e-01 4.84368168e-02 -9.61357355e-02
-3.47595334e-01 -8.62079442e-01 -7.28452861e-01 -8.07488263e-01
3.36872190e-02 6.36508644e-01 -1.99351996e-01 1.36350915... | [9.451921463012695, 3.074852228164673] |
2a841b38-d3a2-46bf-98f4-a7b234003780 | dan-deep-attention-neural-network-for-news | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/4549 | https://ojs.aaai.org/index.php/AAAI/article/view/4549/4427 | Dan: Deep attention neural network for news recommendation | With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem. Many existing recommendation methods that regard the recommendation procedure as the static process, have achieved better recommendation performance. However, they usually fail wi... | ['Qiannan Zhu'] | 2019-07-17 | null | null | null | aaai-2019-7 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-3.47320676e-01 -5.44076979e-01 -4.27997023e-01 -5.13380408e-01
-8.12647119e-02 -8.65601227e-02 5.64504385e-01 -3.35786730e-01
-3.38496596e-01 4.93647128e-01 1.05712438e+00 -3.68216008e-01
-2.39524513e-01 -8.99975061e-01 -4.72596884e-01 -3.39856386e-01
3.82043235e-02 1.58095852e-01 2.52098590e-01 -4.46531832... | [10.156679153442383, 5.598781585693359] |
8a0e19b5-4694-4521-be41-bbe2636e321c | multi-target-multiplicity-flexibility-and | 2306.13738 | null | https://arxiv.org/abs/2306.13738v1 | https://arxiv.org/pdf/2306.13738v1.pdf | Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints | Prediction models have been widely adopted as the basis for decision-making in domains as diverse as employment, education, lending, and health. Yet, few real world problems readily present themselves as precisely formulated prediction tasks. In particular, there are often many reasonable target variable options. Prior... | ['Alexandra Chouldechova', 'Jake M. Hofman', 'Solon Barocas', 'Jamelle Watson-Daniels'] | 2023-06-23 | null | null | null | null | ['fairness', 'fairness', 'decision-making'] | ['computer-vision', 'miscellaneous', 'reasoning'] | [ 4.54602540e-01 1.26271188e-01 -9.06019330e-01 -2.85089642e-01
-3.97131294e-01 -2.37677351e-01 5.78619361e-01 4.14362311e-01
-4.72341210e-01 8.65696073e-01 3.88301671e-01 -5.63418448e-01
-7.13025868e-01 -7.25811899e-01 -5.03993630e-01 -5.80421031e-01
2.95040399e-01 5.25145411e-01 -1.77549869e-01 -2.41336212... | [8.486918449401855, 5.370142459869385] |
53d39896-c604-4192-b545-dfde9e861ebf | pento-diaref-a-diagnostic-dataset-for | 2305.15087 | null | https://arxiv.org/abs/2305.15087v1 | https://arxiv.org/pdf/2305.15087v1.pdf | Pento-DIARef: A Diagnostic Dataset for Learning the Incremental Algorithm for Referring Expression Generation from Examples | NLP tasks are typically defined extensionally through datasets containing example instantiations (e.g., pairs of image i and text t), but motivated intensionally through capabilities invoked in verbal descriptions of the task (e.g., "t is a description of i, for which the content of i needs to be recognised and underst... | ['David Schlangen', 'Philipp Sadler'] | 2023-05-24 | null | null | null | null | ['referring-expression-generation', 'referring-expression'] | ['computer-vision', 'computer-vision'] | [ 7.86878169e-01 6.54970109e-01 2.58614700e-02 -5.41166365e-01
-6.95939660e-01 -1.02874207e+00 1.19125795e+00 -6.03680797e-02
-2.04656333e-01 5.25724113e-01 4.90705818e-01 -3.77073556e-01
-3.31912220e-01 -5.06502450e-01 -9.08399999e-01 -6.05694115e-01
8.47693905e-02 7.27275372e-01 -1.64250553e-01 -3.18332523... | [10.715112686157227, 1.8249149322509766] |
1f5481e5-1c08-40cc-bb5f-beb47ecdc3b2 | localization-distillation-for-object | 2102.12252 | null | https://arxiv.org/abs/2102.12252v4 | https://arxiv.org/pdf/2102.12252v4.pdf | Localization Distillation for Dense Object Detection | Knowledge distillation (KD) has witnessed its powerful capability in learning compact models in object detection. Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logit due to its inefficiency in distilling localization inf... | ['Ming-Ming Cheng', 'Qibin Hou', 'Dongwei Ren', 'WangMeng Zuo', 'Ping Wang', 'Rongguang Ye', 'Zhaohui Zheng'] | 2021-02-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zheng_Localization_Distillation_for_Dense_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zheng_Localization_Distillation_for_Dense_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['dense-object-detection'] | ['computer-vision'] | [-5.50165951e-01 1.95270523e-01 -2.16063425e-01 -9.68451947e-02
-7.44827688e-01 -7.16801643e-01 4.63553280e-01 -1.68369170e-02
-7.29949176e-01 6.04906619e-01 -1.93159640e-01 -2.12120429e-01
-4.80578579e-02 -7.02251673e-01 -1.10562396e+00 -6.90837979e-01
8.74259546e-02 4.07011896e-01 6.14678442e-01 1.50430068... | [9.325974464416504, 1.3807191848754883] |
88076794-0d2a-4ea9-ac6b-ee512881a9d0 | me-gcn-multi-dimensional-edge-enhanced-graph | null | null | https://openreview.net/forum?id=pY5QUHPRhNN | https://openreview.net/pdf?id=pY5QUHPRhNN | ME-GCN: Multi-dimensional Edge-Enhanced Graph Convolutional Networks for Semi-supervised Text Classification | Compared to sequential learning models, graph-based neural networks exhibit excellent ability in capturing global information and have been used for semi-supervised learning tasks, including citation network analysis or text classification. Most Graph Convolutional Networks are designed with the single-dimensional edge... | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [-1.95251429e-03 2.66746506e-02 -1.74359024e-01 -8.94868895e-02
6.22959137e-02 -3.21816742e-01 8.42051268e-01 5.24275959e-01
-1.39175102e-01 1.43213466e-01 2.43318439e-01 -5.13297141e-01
-1.99158773e-01 -1.20658970e+00 -2.81827569e-01 -4.79395509e-01
-4.44836527e-01 4.26612735e-01 2.01966465e-01 -2.20311448... | [9.945577621459961, 6.757925033569336] |
baf77286-6720-4d20-b974-aaf90d348664 | schema-inference-for-interpretable-image | 2303.06635 | null | https://arxiv.org/abs/2303.06635v1 | https://arxiv.org/pdf/2303.06635v1.pdf | Schema Inference for Interpretable Image Classification | In this paper, we study a novel inference paradigm, termed as schema inference, that learns to deductively infer the explainable predictions by rebuilding the prior deep neural network (DNN) forwarding scheme, guided by the prevalent philosophical cognitive concept of schema. We strive to reformulate the conventional m... | ['Mingli Song', 'Jie Song', 'KaiXuan Chen', 'Xiaokang Liu', 'Mengqi Xue', 'Haofei Zhang'] | 2023-03-12 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 2.66238302e-01 7.28911459e-01 -1.45382240e-01 -7.06862867e-01
9.18891504e-02 -4.96859550e-01 1.06032336e+00 9.93496552e-02
2.94796795e-01 3.15375149e-01 4.56572741e-01 -4.26855624e-01
-4.04244542e-01 -1.21071863e+00 -1.12566185e+00 -3.47041994e-01
2.54236400e-01 4.56544250e-01 7.89457094e-03 -3.97054166... | [10.55648422241211, 1.7776635885238647] |
b4e212e5-9f29-4ec4-a4e8-c2aaf25890c9 | toward-automatic-misinformation-detection | null | null | https://openreview.net/forum?id=bzqte0FZQH | https://openreview.net/pdf?id=bzqte0FZQH | Toward Automatic Misinformation Detection Utilizing Fact-checked Information | We proposed a new task FCCKB: Fact-checking by Claim Knowledge Base. The goal was to fact-check a sentence utilizing verified claims stored in the database. To retrieve relevant claims from the large database, we proposed applying Semantic Role Labeling(SRL) on the input sentence having rich semantics and then encoding... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 4.98711795e-01 6.01493061e-01 -5.88614643e-01 -5.52110851e-01
-1.42142117e+00 -4.90421683e-01 4.49412197e-01 9.28363979e-01
-6.92072570e-01 1.15267789e+00 9.46847856e-01 -8.81731585e-02
-3.48599941e-01 -9.76292312e-01 -1.05613613e+00 2.19316617e-01
2.50577539e-01 3.62160951e-01 7.79224634e-01 -6.22921705... | [9.741408348083496, 8.47488784790039] |
3a78a030-58b0-4a1e-b895-0eb23550e178 | predicting-classification-accuracy-when-1 | 2010.15011 | null | https://arxiv.org/abs/2010.15011v3 | https://arxiv.org/pdf/2010.15011v3.pdf | Predicting Classification Accuracy When Adding New Unobserved Classes | Multiclass classifiers are often designed and evaluated only on a sample from the classes on which they will eventually be applied. Hence, their final accuracy remains unknown. In this work we study how a classifier's performance over the initial class sample can be used to extrapolate its expected accuracy on a larger... | ['Yuval Benjamini', 'Yuli Slavutsky'] | 2020-10-28 | predicting-classification-accuracy-when | https://openreview.net/forum?id=Y9McSeEaqUh | https://openreview.net/pdf?id=Y9McSeEaqUh | iclr-2021-1 | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 5.99579036e-01 4.84630093e-02 -3.32754791e-01 -8.95335138e-01
-6.36330903e-01 -4.99902517e-01 5.75149715e-01 2.01828182e-01
-4.85753238e-01 9.16374147e-01 -3.89568716e-01 -1.47401065e-01
-1.62481919e-01 -5.28105021e-01 -8.91815662e-01 -8.82298827e-01
2.10623555e-02 5.47366679e-01 3.64122659e-01 3.18922520... | [8.7008638381958, 4.049533843994141] |
98cc1661-b49c-4464-aaaf-ebd235e90cee | a-policy-guided-imitation-approach-for | 2210.08323 | null | https://arxiv.org/abs/2210.08323v3 | https://arxiv.org/pdf/2210.08323v3.pdf | A Policy-Guided Imitation Approach for Offline Reinforcement Learning | Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution generalization but suffer from erroneous off-policy evaluation. Imitation-based methods avoid off-policy evaluation but are too conservativ... | ['Xianyuan Zhan', 'Jianxiong Li', 'Li Jiang', 'Haoran Xu'] | 2022-10-15 | null | null | null | null | ['d4rl'] | ['robots'] | [-9.63488147e-02 2.00529158e-01 -5.70911705e-01 -1.25972643e-01
-8.50518405e-01 -1.09864640e+00 8.01302433e-01 -1.16878174e-01
-8.99369538e-01 1.13042259e+00 -1.34824663e-01 -4.43419784e-01
-1.23709276e-01 -5.04051566e-01 -8.85399818e-01 -9.03545141e-01
-1.57761499e-01 6.71566606e-01 8.21656585e-02 -4.20913309... | [4.091348648071289, 2.0806221961975098] |
25c3a3a3-b888-486d-b78a-5a7991e3eeec | generalization-challenges-for-neural | 1803.08629 | null | http://arxiv.org/abs/1803.08629v2 | http://arxiv.org/pdf/1803.08629v2.pdf | Generalization Challenges for Neural Architectures in Audio Source Separation | Recent work has shown that recurrent neural networks can be trained to
separate individual speakers in a sound mixture with high fidelity. Here we
explore convolutional neural network models as an alternative and show that
they achieve state-of-the-art results with an order of magnitude fewer
parameters. We also charac... | ['Bruno Olshausen', 'Brian Cheung', 'Shariq Mobin'] | 2018-03-23 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 1.57623395e-01 -4.13914531e-01 3.17141980e-01 -3.64811569e-01
-1.12311995e+00 -7.60887325e-01 3.39805841e-01 -2.38851294e-01
-2.63218254e-01 4.84072357e-01 3.44573945e-01 -4.09642428e-01
-1.76644206e-01 -2.84996092e-01 -7.22171307e-01 -5.63775122e-01
-3.06503296e-01 1.42905265e-01 1.66212648e-01 -1.03843503... | [15.044066429138184, 5.758535385131836] |
0dc0fac5-c88e-4fb4-8f31-c7bdab0c1f50 | face-presentation-attack-detection-using | 2111.11046 | null | https://arxiv.org/abs/2111.11046v2 | https://arxiv.org/pdf/2111.11046v2.pdf | FRT-PAD: Effective Presentation Attack Detection Driven by Face Related Task | The robustness and generalization ability of Presentation Attack Detection (PAD) methods is critical to ensure the security of Face Recognition Systems (FRSs). However, in a real scenario, Presentation Attacks (PAs) are various and it is hard to predict the Presentation Attack Instrument (PAI) species that will be used... | ['Raghavendra Ramachandra', 'Feng Liu', 'Christoph Busch', 'Haozhe Liu', 'Wentian Zhang'] | 2021-11-22 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 2.32804537e-01 -3.40831548e-01 2.40659371e-01 -2.67544180e-01
-2.94921726e-01 -5.74337900e-01 4.75283831e-01 -4.38853592e-01
1.09076845e-02 2.17206612e-01 -3.34245384e-01 -1.40084639e-01
-2.82252319e-02 -7.87633955e-01 -6.02496743e-01 -6.14456296e-01
-2.83246368e-01 1.33187711e-01 1.03670791e-01 -3.73835951... | [13.061765670776367, 1.1314411163330078] |
3ed50582-2c24-467c-a687-b61d34b6e1aa | representation-and-synthesis-of-geometric | null | null | https://aclanthology.org/2022.signlang-1.9 | https://aclanthology.org/2022.signlang-1.9.pdf | Representation and Synthesis of Geometric Relocations | One of the key features of signed discourse is the geometric placements of gestural units in signing space. Signers use the geometry of signing space to describe the placements and forms of objects and also use it to contrast participants or locales in a story. Depending on the specific functions of the placement in th... | ['John McDonald', 'Michael Filhol'] | null | null | null | null | signlang-lrec-2022-6 | ['gaze-redirection'] | ['computer-vision'] | [ 2.74083674e-01 2.64429718e-01 -5.16336691e-03 -4.75309342e-01
-3.17295432e-01 -1.19323838e+00 1.23241103e+00 1.42302826e-01
9.14342254e-02 2.79224664e-01 9.38301444e-01 -3.21532309e-01
-4.89414372e-02 -4.46186155e-01 -3.62822920e-01 -1.66689873e-01
-5.73836081e-02 2.36850977e-01 2.15555459e-01 -6.48531735... | [11.200165748596191, 0.8048818707466125] |
4f7665b1-915d-4672-b661-257ec1f8b4e0 | wave-simulation-in-non-smooth-media-by-pinn | 2208.08276 | null | https://arxiv.org/abs/2208.08276v2 | https://arxiv.org/pdf/2208.08276v2.pdf | Wave simulation in non-smooth media by PINN with quadratic neural network and PML condition | Frequency-domain simulation of seismic waves plays an important role in seismic inversion, but it remains challenging in large models. The recently proposed physics-informed neural network (PINN), as an effective deep learning method, has achieved successful applications in solving a wide range of partial differential ... | ['Jianwei Ma', 'Stephane Operto', 'Hossein S. Aghamiry', 'Yanqi Wu'] | 2022-08-16 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 2.79504657e-01 -1.40255019e-01 7.04041362e-01 1.18731298e-01
-7.33703911e-01 2.21194960e-02 1.04215592e-01 -2.54863858e-01
-6.01197183e-01 9.52121079e-01 -3.03349085e-03 -2.32969016e-01
-5.96748888e-01 -8.56466651e-01 -7.33653128e-01 -1.04699898e+00
-3.35589856e-01 3.97225618e-01 4.63817000e-01 -5.17537713... | [6.795405387878418, 2.5864317417144775] |
4442ae7b-94ca-44e5-a3bf-9949a55ab34f | neural-collaborative-graph-machines-for-table | 2111.13359 | null | https://arxiv.org/abs/2111.13359v2 | https://arxiv.org/pdf/2111.13359v2.pdf | Neural Collaborative Graph Machines for Table Structure Recognition | Recently, table structure recognition has achieved impressive progress with the help of deep graph models. Most of them exploit single visual cues of tabular elements or simply combine visual cues with other modalities via early fusion to reason their graph relationships. However, neither early fusion nor individually ... | ['Bo Ren', 'Yinsong Liu', 'Deqiang Jiang', 'Bing Liu', 'Xin Li', 'Hao liu'] | 2021-11-26 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Neural_Collaborative_Graph_Machines_for_Table_Structure_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_Neural_Collaborative_Graph_Machines_for_Table_Structure_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['table-recognition'] | ['computer-vision'] | [ 2.94678450e-01 -2.24696714e-02 -3.66822958e-01 -1.43959031e-01
-5.97251713e-01 -7.14262307e-01 5.52427113e-01 3.35567981e-01
2.13271290e-01 3.53662372e-01 4.13234591e-01 -1.06106274e-01
-2.73891062e-01 -8.31504107e-01 -7.44803965e-01 -5.15950501e-01
2.60668784e-01 6.46546602e-01 2.10077643e-01 -4.91113216... | [11.098712921142578, 2.4537389278411865] |
b760117d-13eb-46ec-9038-b415d40f2b37 | decoupling-the-skeleton-parsing-and-schema | 2302.05965 | null | https://arxiv.org/abs/2302.05965v3 | https://arxiv.org/pdf/2302.05965v3.pdf | RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL | One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i.e., tables and columns) and the skeleton (i.e., SQL keywords). Such coupled targets increase the difficulty ... | ['Hong Chen', 'Cuiping Li', 'Jing Zhang', 'Haoyang Li'] | 2023-02-12 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [ 5.30557968e-02 1.50475368e-01 -1.45721167e-01 -5.50293207e-01
-1.00533116e+00 -8.39774489e-01 1.58795491e-01 3.08060080e-01
-3.31804492e-02 3.03984851e-01 4.19477403e-01 -5.54720938e-01
2.34153152e-01 -1.10630357e+00 -1.08289623e+00 -1.33550838e-01
1.74500883e-01 3.40814024e-01 4.79239792e-01 -2.93646634... | [9.919173240661621, 7.826427459716797] |
5b3fcafc-2797-4648-97ed-4f41c6a12810 | epg-mgcn-ego-planning-guided-multi-graph | 2303.17027 | null | https://arxiv.org/abs/2303.17027v1 | https://arxiv.org/pdf/2303.17027v1.pdf | EPG-MGCN: Ego-Planning Guided Multi-Graph Convolutional Network for Heterogeneous Agent Trajectory Prediction | To drive safely in complex traffic environments, autonomous vehicles need to make an accurate prediction of the future trajectories of nearby heterogeneous traffic agents (i.e., vehicles, pedestrians, bicyclists, etc). Due to the interactive nature, human drivers are accustomed to infer what the future situations will ... | ['Sikai Chen', 'Zilin Huang', 'Zihao Sheng'] | 2023-03-29 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-4.35927480e-01 3.61612618e-01 -6.91521317e-02 -4.20971364e-01
-2.40305245e-01 -2.08116934e-01 8.76103461e-01 -1.63427144e-01
-1.72398929e-02 6.16004407e-01 3.66357863e-01 -5.73915720e-01
7.40990639e-02 -1.22374892e+00 -8.76639962e-01 -5.93990624e-01
-4.84809101e-01 7.18406439e-01 6.21040404e-01 -5.30198812... | [5.948138236999512, 0.9262908697128296] |
2506af5c-f9b7-428f-8ca7-055f80c2611f | a-closer-look-at-novel-class-discovery-from | 2209.09120 | null | https://arxiv.org/abs/2209.09120v4 | https://arxiv.org/pdf/2209.09120v4.pdf | A Closer Look at Novel Class Discovery from the Labeled Set | Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising disjoint but related classes. Existing research focuses primarily on utilizing the labeled set at the methodological level, with less emphasis on the analysis of the labeled set itse... | ['Haojin Yang', 'Christoph Meinel', 'Di Hu', 'Ben Dai', 'Jona Otholt', 'Ziyun Li'] | 2022-09-19 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 2.98000127e-01 1.93846852e-01 -3.85058075e-01 -6.73269749e-01
-2.95671076e-01 -8.95499945e-01 6.29848182e-01 2.99164802e-01
-3.04168701e-01 6.60114467e-01 3.55108157e-02 -1.45787865e-01
-2.87841707e-01 -8.24701905e-01 -5.38882554e-01 -5.91181397e-01
1.94817737e-01 2.14814842e-01 1.76144555e-01 -1.49822803... | [9.656194686889648, 2.9676802158355713] |
303e9950-43f0-4e33-b474-457478f2861d | enhancing-general-face-forgery-detection-via | 2303.00917 | null | https://arxiv.org/abs/2303.00917v2 | https://arxiv.org/pdf/2303.00917v2.pdf | Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation | Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc. Despite the demonstrated success in intra-domain face forgery detection, existing detection methods lack generalization capability and tend to suffer from dramatic performance drops when deployed to unforeseen domains. To... | ['Shiqi Wang', 'Haoliang Li', 'Chenqi Kong'] | 2023-03-02 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 2.31488109e-01 -2.37540424e-01 -1.49628073e-01 -5.31322777e-01
-4.38251644e-01 -2.03482106e-01 7.66553760e-01 -1.31798968e-01
-3.23513359e-01 4.86978412e-01 -1.08033895e-01 -6.11710958e-02
6.69681802e-02 -5.46624959e-01 -4.26662683e-01 -6.23057425e-01
1.37213498e-01 -9.97485872e-03 1.34323463e-01 -2.15254903... | [12.75704288482666, 1.0083798170089722] |
3a29cdcc-0151-4434-8ac4-7c00ddfb81b6 | noisy-boundaries-lemon-or-lemonade-for-semi | 2203.13427 | null | https://arxiv.org/abs/2203.13427v1 | https://arxiv.org/pdf/2203.13427v1.pdf | Noisy Boundaries: Lemon or Lemonade for Semi-supervised Instance Segmentation? | Current instance segmentation methods rely heavily on pixel-level annotated images. The huge cost to obtain such fully-annotated images restricts the dataset scale and limits the performance. In this paper, we formally address semi-supervised instance segmentation, where unlabeled images are employed to boost the perfo... | ['Shengjin Wang', 'YaLi Li', 'Zhenyu Wang'] | 2022-03-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Noisy_Boundaries_Lemon_or_Lemonade_for_Semi-Supervised_Instance_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Noisy_Boundaries_Lemon_or_Lemonade_for_Semi-Supervised_Instance_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-instance-segmentation'] | ['computer-vision'] | [ 4.72653508e-01 5.12704313e-01 -3.50680977e-01 -4.43855345e-01
-1.21658826e+00 -7.51948059e-01 4.37650681e-01 -4.99664433e-02
-6.94173753e-01 5.90572000e-01 -1.21181887e-02 -1.49532303e-01
2.62763292e-01 -5.18774152e-01 -8.24595869e-01 -6.70220256e-01
1.31826937e-01 2.82554775e-01 6.55157268e-01 1.32148668... | [9.535964965820312, 0.5929101705551147] |
a6c04aa7-21eb-485a-8984-87a0d696626f | physiological-and-affective-computing-through | 1908.10307 | null | https://arxiv.org/abs/1908.10307v1 | https://arxiv.org/pdf/1908.10307v1.pdf | Physiological and Affective Computing through Thermal Imaging: A Survey | Thermal imaging-based physiological and affective computing is an emerging research area enabling technologies to monitor our bodily functions and understand psychological and affective needs in a contactless manner. However, up to recently, research has been mainly carried out in very controlled lab settings. As small... | ['Nadia Bianchi-Berthouze', 'Youngjun Cho'] | 2019-08-27 | null | null | null | null | ['physiological-computing'] | ['computer-vision'] | [ 5.89341342e-01 -3.71407390e-01 1.31563663e-01 -4.65373397e-01
-1.58231527e-01 -5.54355681e-01 2.91150689e-01 -7.93932229e-02
-5.52106082e-01 4.18789178e-01 -1.65667683e-01 4.63032097e-01
4.13145512e-01 -2.43741661e-01 5.22414185e-02 -8.49610507e-01
3.29789892e-02 -4.06120449e-01 -3.05619240e-01 1.38668492... | [13.586858749389648, 2.524019241333008] |
e3ac909e-3cb0-4dc7-995a-5a1134a31d2c | cross-diffusion-model-makes-controllable | 2305.16936 | null | https://arxiv.org/abs/2305.16936v1 | https://arxiv.org/pdf/2305.16936v1.pdf | CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography | Current image steganography techniques are mainly focused on cover-based methods, which commonly have the risk of leaking secret images and poor robustness against degraded container images. Inspired by recent developments in diffusion models, we discovered that two properties of diffusion models, the ability to achiev... | ['Jian Zhang', 'Youmin Xu', 'Xuanyu Zhang', 'Jiwen Yu'] | 2023-05-26 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 6.60961747e-01 5.55153796e-03 3.80728841e-02 3.14989984e-01
1.80804223e-01 -5.67393541e-01 6.76172435e-01 -2.48768926e-01
-1.60753772e-01 5.49209237e-01 -1.20319039e-01 -6.62512243e-01
-8.24289545e-02 -1.04636264e+00 -4.82852906e-01 -9.42820549e-01
-5.14914989e-01 -3.99283350e-01 4.07188237e-01 -5.91357172... | [4.316466331481934, 8.053154945373535] |
688e2665-0c68-4cce-8685-7a983a173843 | beir-pl-zero-shot-information-retrieval | 2305.19840 | null | https://arxiv.org/abs/2305.19840v1 | https://arxiv.org/pdf/2305.19840v1.pdf | BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language | The BEIR dataset is a large, heterogeneous benchmark for Information Retrieval (IR) in zero-shot settings, garnering considerable attention within the research community. However, BEIR and analogous datasets are predominantly restricted to the English language. Our objective is to establish extensive large-scale resour... | ['Maciej Piasecki', 'Arkadiusz Janz', 'Kacper Wołowiec', 'Vadim Shishkin', 'Konrad Wojtasik'] | 2023-05-31 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [ 7.63585344e-02 -5.47565036e-02 -4.44527566e-01 1.15224749e-01
-1.34330714e+00 -7.91372538e-01 9.30107236e-01 3.90377641e-01
-7.82771170e-01 5.38451791e-01 4.83879209e-01 -2.92917401e-01
-6.16154194e-01 -6.56573534e-01 -1.75920501e-01 -3.44890058e-01
2.85722405e-01 7.77525902e-01 3.21411192e-02 -7.62248695... | [11.357308387756348, 9.83283805847168] |
664edaa2-06ac-4261-853e-ca0babf665b1 | signal-enhancement-for-two-dimensional-cryo | 2212.01421 | null | https://arxiv.org/abs/2212.01421v1 | https://arxiv.org/pdf/2212.01421v1.pdf | Signal enhancement for two-dimensional cryo-EM data processing | Different tasks in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM) require enhancing the quality of the highly noisy raw images. To this end, we develop an efficient algorithm for signal enhancement of cryo-EM images. The enhanced images can be used for a variety of downstream tasks, su... | ['Tamir Bendory', 'Yoel Shkolnisky', 'Guy Sharon'] | 2022-12-02 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 5.49745798e-01 -3.76582354e-01 7.12926865e-01 -4.98983115e-01
-1.17639458e+00 -5.14535308e-01 2.23909557e-01 3.76397759e-01
-6.64274573e-01 7.39263058e-01 -2.75784165e-01 -1.83934659e-01
-1.98491421e-02 -3.76212716e-01 -5.21008909e-01 -1.14958835e+00
3.74542177e-02 5.92031658e-01 2.07070187e-01 1.60691649... | [13.393728256225586, -3.069051742553711] |
87df80cd-2459-4309-a7c3-15fd1ffdb4b3 | pmp-net-point-cloud-completion-by-learning | 2012.03408 | null | https://arxiv.org/abs/2012.03408v3 | https://arxiv.org/pdf/2012.03408v3.pdf | PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths | The task of point cloud completion aims to predict the missing part for an incomplete 3D shape. A widely used strategy is to generate a complete point cloud from the incomplete one. However, the unordered nature of point clouds will degrade the generation of high-quality 3D shapes, as the detailed topology and structur... | ['Yu-Shen Liu', 'Wen Zheng', 'Pengfei Wan', 'Yan-Pei Cao', 'Zhizhong Han', 'Peng Xiang', 'Xin Wen'] | 2020-12-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wen_PMP-Net_Point_Cloud_Completion_by_Learning_Multi-Step_Point_Moving_Paths_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wen_PMP-Net_Point_Cloud_Completion_by_Learning_Multi-Step_Point_Moving_Paths_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-completion'] | ['computer-vision'] | [-1.47029892e-01 -2.95195859e-02 1.57611415e-01 -1.25760198e-01
-3.66010994e-01 -5.12588978e-01 3.75429720e-01 -2.81341970e-01
2.11615562e-01 3.22415382e-01 -1.75603017e-01 -1.59593374e-01
-7.88348019e-02 -1.13275468e+00 -1.01286042e+00 -5.97581744e-01
1.62048057e-01 1.08668303e+00 1.33380219e-01 -2.00253233... | [8.39932632446289, -3.5729141235351562] |
cc5550f3-9b43-4aaf-b9e3-9bcbfb4dbab2 | near-optimal-heteroscedastic-regression-with | 2306.14288 | null | https://arxiv.org/abs/2306.14288v2 | https://arxiv.org/pdf/2306.14288v2.pdf | Near Optimal Heteroscedastic Regression with Symbiotic Learning | We consider the problem of heteroscedastic linear regression, where, given $n$ samples $(\mathbf{x}_i, y_i)$ from $y_i = \langle \mathbf{w}^{*}, \mathbf{x}_i \rangle + \epsilon_i \cdot \langle \mathbf{f}^{*}, \mathbf{x}_i \rangle$ with $\mathbf{x}_i \sim N(0,\mathbf{I})$, $\epsilon_i \sim N(0,1)$, we aim to estimate $\... | ['Praneeth Netrapalli', 'Dheeraj Nagaraj', 'Aniket Das', 'Dheeraj Baby'] | 2023-06-25 | null | null | null | null | ['retrieval', 'econometrics', 'time-series'] | ['methodology', 'miscellaneous', 'time-series'] | [ 3.22550982e-01 1.77633658e-01 9.27419215e-02 -2.35861093e-01
-1.31848598e+00 -6.08715594e-01 -6.09100536e-02 3.59129049e-02
-8.59190702e-01 1.11440122e+00 -5.48945725e-01 -4.72237051e-01
-1.00204456e+00 -9.60211515e-01 -9.70053852e-01 -1.22632897e+00
-7.69202232e-01 3.71347576e-01 -1.31525531e-01 -3.33972871... | [6.427547931671143, 4.557592868804932] |
870464f9-fc31-445e-b592-540688d9f7aa | potato-the-portable-text-annotation-tool | 2212.08620 | null | https://arxiv.org/abs/2212.08620v2 | https://arxiv.org/pdf/2212.08620v2.pdf | POTATO: The Portable Text Annotation Tool | We present POTATO, the Portable text annotation tool, a free, fully open-sourced annotation system that 1) supports labeling many types of text and multimodal data; 2) offers easy-to-configure features to maximize the productivity of both deployers and annotators (convenient templates for common ML/NLP tasks, active le... | ['David Jurgens', 'Apostolos Dedeloudis', 'Jackson Sargent', 'Naitian Zhou', 'Xingyao Wang', 'Aparna Ananthasubramaniam', 'Jiaxin Pei'] | 2022-12-16 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [-4.19743359e-02 4.02864456e-01 -3.34728658e-01 -4.20553386e-01
-1.10077477e+00 -1.27365172e+00 2.94331610e-01 6.45251274e-01
-4.99486655e-01 6.92346931e-01 3.53207350e-01 -5.47827601e-01
-2.72938788e-01 9.63286385e-02 9.73392352e-02 -1.20661549e-01
3.38802151e-02 1.01544726e+00 3.50145191e-01 -1.91903040... | [9.246037483215332, 8.724334716796875] |
c9eeebca-5ba5-4077-8856-29bc45ea81ed | cross-domain-face-presentation-attack | 2004.01959 | null | https://arxiv.org/abs/2004.01959v1 | https://arxiv.org/pdf/2004.01959v1.pdf | Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning | Face presentation attack detection (PAD) has been an urgent problem to be solved in the face recognition systems. Conventional approaches usually assume the testing and training are within the same domain; as a result, they may not generalize well into unseen scenarios because the representations learned for PAD may ov... | ['Xilin Chen', 'Shiguang Shan', 'Guoqing Wang', 'Hu Han'] | 2020-04-04 | cross-domain-face-presentation-attack-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Cross-Domain_Face_Presentation_Attack_Detection_via_Multi-Domain_Disentangled_Representation_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Cross-Domain_Face_Presentation_Attack_Detection_via_Multi-Domain_Disentangled_Representation_Learning_CVPR_2020_paper.pdf | cvpr-2020-6 | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 1.33777842e-01 -2.66423430e-02 -2.90952742e-01 -5.53622544e-01
-1.09441960e+00 -6.24558568e-01 5.13153911e-01 -4.71432358e-01
2.46783033e-01 8.65225971e-01 2.22094819e-01 2.98148394e-02
-2.43270993e-01 -7.93764353e-01 -5.58566868e-01 -8.90583038e-01
-1.33868799e-01 5.31732976e-01 -2.58734703e-01 -4.05982025... | [13.087055206298828, 1.183016061782837] |
9d469bf8-3d1c-4dde-9082-7e55d4981bca | multimodal-gpt-a-vision-and-language-model | 2305.04790 | null | https://arxiv.org/abs/2305.04790v3 | https://arxiv.org/pdf/2305.04790v3.pdf | MultiModal-GPT: A Vision and Language Model for Dialogue with Humans | We present a vision and language model named MultiModal-GPT to conduct multi-round dialogue with humans. MultiModal-GPT can follow various instructions from humans, such as generating a detailed caption, counting the number of interested objects, and answering general questions from users. MultiModal-GPT is parameter-e... | ['Kai Chen', 'Ping Luo', 'Wenwei Zhang', 'Kuikun Liu', 'Qian Zhao', 'Miao Zheng', 'Yudong Wang', 'Shilong Zhang', 'Chengqi Lyu', 'Tao Gong'] | 2023-05-08 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-3.59468430e-01 6.63854554e-02 3.37804973e-01 -5.36485910e-01
-8.16137612e-01 -7.14156449e-01 5.81722140e-01 -2.44724751e-01
-6.33522272e-01 4.02058423e-01 4.91995692e-01 -3.20425868e-01
3.94653082e-01 -4.33978736e-01 -3.99520487e-01 -3.36696208e-01
4.83882129e-01 9.41252112e-01 1.40340418e-01 -7.61667490... | [10.936434745788574, 1.412665605545044] |
a4fa52c3-c2f8-4b30-b217-c2fe2c050ff2 | discovering-optimal-scoring-mechanisms-in | 2302.06804 | null | https://arxiv.org/abs/2302.06804v2 | https://arxiv.org/pdf/2302.06804v2.pdf | Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction | Faced with data-driven policies, individuals will manipulate their features to obtain favorable decisions. While earlier works cast these manipulations as undesirable gaming, recent works have adopted a more nuanced causal framing in which manipulations can improve outcomes of interest, and setting coherent mechanisms ... | ['Zachary Lipton', 'Shantanu Gupta', 'Tom Yan'] | 2023-02-14 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.53793174e-01 6.94253385e-01 -9.95562613e-01 -3.94369572e-01
-1.49004117e-01 -6.81720018e-01 5.56292892e-01 1.26300499e-01
-3.41957271e-01 9.04131174e-01 6.23936594e-01 -4.12628680e-01
-7.00161815e-01 -1.08627641e+00 -6.97391808e-01 -3.20307851e-01
-3.76446456e-01 1.66179538e-01 -2.88837880e-01 4.59018238... | [8.136791229248047, 5.393988132476807] |
f6b42750-10c5-4acf-b4b3-3deb8bb7b46a | longt5-efficient-text-to-text-transformer-for | 2112.07916 | null | https://arxiv.org/abs/2112.07916v2 | https://arxiv.org/pdf/2112.07916v2.pdf | LongT5: Efficient Text-To-Text Transformer for Long Sequences | Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models. In this paper, we present a new model, called LongT5, with which we explore the effects of scaling both the input length and model size at the same time. Specifi... | ['Yinfei Yang', 'Yun-Hsuan Sung', 'Jianmo Ni', 'Santiago Ontanon', 'David Uthus', 'Joshua Ainslie', 'Mandy Guo'] | 2021-12-15 | null | https://aclanthology.org/2022.findings-naacl.55 | https://aclanthology.org/2022.findings-naacl.55.pdf | findings-naacl-2022-7 | ['long-range-modeling'] | ['natural-language-processing'] | [ 2.95052469e-01 5.82307696e-01 1.49781823e-01 -2.10061267e-01
-8.25324774e-01 -4.56110299e-01 7.11224198e-01 1.39666572e-01
-5.04682064e-01 3.79982501e-01 5.77249706e-01 -6.50802314e-01
1.10330749e-02 -7.23367274e-01 -9.21067059e-01 -1.86751485e-01
2.83699989e-01 5.98220766e-01 5.09970427e-01 -3.04145396... | [11.254128456115723, 8.428555488586426] |
e70255c2-39a5-4a18-ac96-1c52e7a2eca6 | scalable-matching-and-clustering-of-entities | null | null | https://csimq-journals.rtu.lv/article/view/csimq.2018-16.04/0 | https://csimq-journals.rtu.lv/article/view/csimq.2018-16.04/1276 | Scalable Matching and Clustering of Entities with FAMER | Entity resolution identifies semantically equivalent entities, e.g. describing the same product or customer. It is especially challenging for Big Data applications where large volumes of data from many sources have to be matched and integrated. We therefore introduce a scalable entity resolution framework called FAMER ... | ['Erhard Rahm', 'Eric Peukert', 'Markus Nentwig', 'Alieh Saeedi'] | 2018-10-01 | null | null | null | complex-systems-informatics-and-modeling | ['clustering-algorithms-evaluation', 'entity-resolution'] | ['methodology', 'natural-language-processing'] | [-6.08926296e-01 -9.59115401e-02 9.80784837e-03 -1.41030565e-01
-9.72328961e-01 -8.20550084e-01 4.68564779e-01 1.14858735e+00
-3.45293641e-01 7.43032694e-01 1.00306801e-01 4.38957840e-01
-5.08289516e-01 -1.34471667e+00 -3.41597080e-01 -4.09452617e-02
-2.22430587e-01 1.13515282e+00 7.21120119e-01 -1.37249827... | [9.206306457519531, 7.99027156829834] |
a02d0ef3-b7b6-454b-bf52-579400808e4c | few-shot-adversarial-domain-adaptation | 1711.02536 | null | http://arxiv.org/abs/1711.02536v1 | http://arxiv.org/pdf/1711.02536v1.pdf | Few-Shot Adversarial Domain Adaptation | This work provides a framework for addressing the problem of supervised
domain adaptation with deep models. The main idea is to exploit adversarial
learning to learn an embedded subspace that simultaneously maximizes the
confusion between two domains while semantically aligning their embedding. The
supervised setting b... | ['Seyed Mehdi Iranmanesh', 'Saeid Motiian', 'Gianfranco Doretto', 'Quinn Jones'] | 2017-11-05 | few-shot-adversarial-domain-adaptation-1 | http://papers.nips.cc/paper/7244-few-shot-adversarial-domain-adaptation | http://papers.nips.cc/paper/7244-few-shot-adversarial-domain-adaptation.pdf | neurips-2017-12 | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 4.28193003e-01 -2.37977393e-02 -2.31919065e-01 -3.71603668e-01
-8.07384253e-01 -7.67158628e-01 7.79178858e-01 4.44300286e-03
-6.35791957e-01 7.97539473e-01 -1.74686298e-01 -1.12116067e-02
-5.58809098e-03 -6.55205667e-01 -6.27116323e-01 -8.72975767e-01
2.86966056e-01 7.89647341e-01 2.79749781e-01 -7.90917054... | [9.904926300048828, 2.7128396034240723] |
95fa74ab-eeb7-4226-bc82-403ba925b13e | dualfair-fair-representation-learning-at-both | 2303.08403 | null | https://arxiv.org/abs/2303.08403v1 | https://arxiv.org/pdf/2303.08403v1.pdf | DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision | Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications. This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Unlike existing models that target a single type o... | ['Meeyoung Cha', 'Xing Xie', 'Xiting Wang', 'Chuhan Wu', 'Sundong Kim', 'Fangzhao Wu', 'Seungeon Lee', 'Sungwon Han'] | 2023-03-15 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [-2.44982451e-01 5.40983140e-01 -7.28173673e-01 -8.33187759e-01
-3.64646822e-01 -3.95037681e-01 8.75136077e-01 3.66935223e-01
-6.19895339e-01 1.02526426e+00 5.80099463e-01 -4.06509399e-01
-1.67542547e-01 -8.73257339e-01 -2.17449203e-01 -1.59853593e-01
4.63948660e-02 3.07648093e-01 -4.25797701e-01 -2.93057859... | [8.952082633972168, 5.317002773284912] |
e93c1622-77da-4cd1-90c2-487faf235f94 | ctrl-connect-tabular-and-language-model-for | 2306.02841 | null | https://arxiv.org/abs/2306.02841v2 | https://arxiv.org/pdf/2306.02841v2.pdf | CTRL: Connect Tabular and Language Model for CTR Prediction | Traditional click-through rate (CTR) prediction models convert the tabular data into one-hot vectors and leverage the collaborative relations among features for inferring user's preference over items. This modeling paradigm discards the essential semantic information. Though some recent works like P5 and M6-Rec have ex... | ['Ruiming Tang', 'Lu Hou', 'Bo Chen', 'Xiangyang Li'] | 2023-06-05 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 2.68070903e-02 -5.57923019e-01 -6.73412085e-01 -5.13985813e-01
-7.58348942e-01 -5.22348225e-01 3.21503758e-01 -1.97750121e-01
-2.32072741e-01 3.35784286e-01 4.07040626e-01 -1.94709122e-01
-5.12615025e-01 -7.80389786e-01 -4.67626721e-01 -4.22415107e-01
2.51421988e-01 4.01595950e-01 -7.41085187e-02 -3.98211718... | [10.176714897155762, 5.529623031616211] |
bcd64d7d-bd27-4c28-9d1c-7b24c133452e | few-shot-representation-learning-for-out-of | 1907.00505 | null | https://arxiv.org/abs/1907.00505v1 | https://arxiv.org/pdf/1907.00505v1.pdf | Few-Shot Representation Learning for Out-Of-Vocabulary Words | Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts. However, in real-world scenarios, out-of-vocabulary (a.k.a. OOV) words that do not appear in training corpus ... | ['Kai-Wei Chang', 'Ziniu Hu', 'Ting Chen', 'Yizhou Sun'] | 2019-07-01 | few-shot-representation-learning-for-out-of-1 | https://aclanthology.org/P19-1402 | https://aclanthology.org/P19-1402.pdf | acl-2019-7 | ['learning-word-embeddings'] | ['methodology'] | [-4.10813978e-03 -1.44233434e-02 -5.23514271e-01 -4.86700326e-01
-8.71064961e-01 -3.85773003e-01 6.08051300e-01 4.49684471e-01
-6.12730145e-01 3.50541711e-01 4.55124378e-01 -2.35622510e-01
2.09521964e-01 -6.46010280e-01 -6.69339895e-01 -4.88166362e-01
1.67284027e-01 3.74068618e-01 -8.98385197e-02 -1.86832145... | [10.530954360961914, 8.644791603088379] |
10916bc0-f6cf-403d-a642-3502629b2f1a | deep-attention-recognition-for-attack | 2303.12947 | null | https://arxiv.org/abs/2303.12947v1 | https://arxiv.org/pdf/2303.12947v1.pdf | Deep Attention Recognition for Attack Identification in 5G UAV scenarios: Novel Architecture and End-to-End Evaluation | Despite the robust security features inherent in the 5G framework, attackers will still discover ways to disrupt 5G unmanned aerial vehicle (UAV) operations and decrease UAV control communication performance in Air-to-Ground (A2G) links. Operating under the assumption that the 5G UAV communications infrastructure will ... | ['Rui Dinis', 'Sandra Lagen', 'Biljana Bojovic', 'Katerina Koutlia', 'Luis Miguel Campos', 'Pedro Sebastiao', 'Hamed Farkhari', 'Joseanne Viana'] | 2023-03-03 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-1.53824657e-01 -1.44822858e-02 2.38592215e-02 2.81127036e-01
6.04459569e-02 -8.99619043e-01 4.54762638e-01 -1.89789250e-01
-4.72644418e-01 7.59069860e-01 -4.82480526e-01 -1.07451129e+00
-5.14253020e-01 -1.17729485e+00 -6.56834900e-01 -9.22538161e-01
-6.27064466e-01 -2.85307378e-01 5.75031191e-02 -3.69922727... | [5.457822322845459, 7.896629333496094] |
b195ac6a-4626-4ed5-a327-af6083f85522 | taming-transformers-for-high-resolution-image | 2012.09841 | null | https://arxiv.org/abs/2012.09841v3 | https://arxiv.org/pdf/2012.09841v3.pdf | Taming Transformers for High-Resolution Image Synthesis | Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This makes them expressive, but also computationally infeasible for long sequences, su... | ['Björn Ommer', 'Robin Rombach', 'Patrick Esser'] | 2020-12-17 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Esser_Taming_Transformers_for_High-Resolution_Image_Synthesis_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Esser_Taming_Transformers_for_High-Resolution_Image_Synthesis_CVPR_2021_paper.pdf | cvpr-2021-1 | ['image-outpainting'] | ['computer-vision'] | [ 4.96632695e-01 2.00007841e-01 -8.12187791e-02 -3.71332705e-01
-8.74489069e-01 -5.95895827e-01 9.33546662e-01 -5.07011056e-01
-1.72078490e-01 6.27938211e-01 3.05278093e-01 -2.98445731e-01
1.32203773e-01 -1.10330927e+00 -1.22498417e+00 -8.84034812e-01
2.20923856e-01 4.40038145e-01 4.10001963e-01 -3.13004136... | [11.371524810791016, -0.4429171681404114] |
2d1bc10f-0769-4b30-b741-6fd70dcd22e0 | end-to-end-trainable-network-for-degraded | 2010.14266 | null | https://arxiv.org/abs/2010.14266v1 | https://arxiv.org/pdf/2010.14266v1.pdf | End-to-end trainable network for degraded license plate detection via vehicle-plate relation mining | License plate detection is the first and essential step of the license plate recognition system and is still challenging in real applications, such as on-road scenarios. In particular, small-sized and oblique license plates, mainly caused by the distant and mobile camera, are difficult to detect. In this work, we propo... | ['Xu-Cheng Yin', 'Feng Chen', 'Chun Yang', 'Qi Liu', 'Jia-Wei Ma', 'Shu Tian', 'Song-Lu Chen'] | 2020-10-27 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [-2.14201197e-01 -4.68971550e-01 8.28072522e-03 -2.07688168e-01
-9.05137599e-01 -8.59973490e-01 2.20837981e-01 -6.16755545e-01
-9.65209007e-02 2.74367720e-01 -3.65724295e-01 -2.82646090e-01
2.34194532e-01 -7.33714342e-01 -8.09767485e-01 -5.95484376e-01
5.11977911e-01 3.49278420e-01 8.08397651e-01 -1.34576783... | [9.851032257080078, -4.92311954498291] |
fa8ef771-21a7-4ece-ac64-4ed1a943b509 | a-hierarchical-variable-autonomy-mixed | 2211.14095 | null | https://arxiv.org/abs/2211.14095v1 | https://arxiv.org/pdf/2211.14095v1.pdf | A Hierarchical Variable Autonomy Mixed-Initiative Framework for Human-Robot Teaming in Mobile Robotics | This paper presents a Mixed-Initiative (MI) framework for addressing the problem of control authority transfer between a remote human operator and an AI agent when cooperatively controlling a mobile robot. Our Hierarchical Expert-guided Mixed-Initiative Control Switcher (HierEMICS) leverages information on the human op... | ['Manolis Chiou', 'Rustam Stolkin', 'Grigoris Nikolaou', 'Tianshu Ruan', 'Aniketh Ramesh', 'Giannis Petousakis', 'Dimitris Panagopoulos'] | 2022-11-25 | null | null | null | null | ['robot-navigation'] | ['robots'] | [ 2.31279328e-01 6.40357792e-01 5.94240008e-03 -1.67151704e-01
-6.04178131e-01 -5.18124104e-01 5.77919602e-01 1.28857896e-01
-7.19255149e-01 7.72834480e-01 6.96182102e-02 -5.64786971e-01
-7.39247441e-01 -5.72926998e-01 -5.81449866e-01 -6.86555624e-01
-3.31002086e-01 6.99132025e-01 4.06236202e-01 -8.94472837... | [4.827230930328369, 1.3341054916381836] |
07555cc6-cb90-4f2d-80b7-9fd437755f1a | merging-diverging-hybrid-transformer-networks | 2307.03427 | null | https://arxiv.org/abs/2307.03427v1 | https://arxiv.org/pdf/2307.03427v1.pdf | Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer | Survival prediction is crucial for cancer patients as it provides early prognostic information for treatment planning. Recently, deep survival models based on deep learning and medical images have shown promising performance for survival prediction. However, existing deep survival models are not well developed in utili... | ['Jinman Kim', 'Dagan Feng', 'Michael Fulham', 'Lei Bi', 'Mingyuan Meng'] | 2023-07-07 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 2.50934139e-02 6.24046996e-02 -6.44652665e-01 -4.72197950e-01
-1.57841027e+00 -7.19396397e-02 3.10438454e-01 1.97651163e-01
-5.14684618e-01 7.95759022e-01 7.04184234e-01 -6.19507968e-01
-8.67574066e-02 -7.10609674e-01 -3.63100618e-01 -1.19852698e+00
2.70288847e-02 6.45156324e-01 2.78765589e-01 -2.11462870... | [14.983065605163574, -2.5990729331970215] |
46ba42e2-137c-4918-94c9-3a47c73cb6b2 | beyond-spectral-gap-extended-the-role-of-the | 2301.02151 | null | https://arxiv.org/abs/2301.02151v1 | https://arxiv.org/pdf/2301.02151v1.pdf | Beyond spectral gap (extended): The role of the topology in decentralized learning | In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers communicate over a sparse graph, current theory fails to capture impo... | ['Martin Jaggi', 'Hadrien Hendrikx', 'Thijs Vogels'] | 2023-01-05 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-5.25739491e-01 4.04008180e-01 -1.28833190e-01 -1.59328684e-01
-5.25952578e-01 -4.88025993e-01 4.15754706e-01 5.22327423e-01
-4.99147147e-01 8.39652956e-01 1.60554379e-01 -3.59166741e-01
-4.69712436e-01 -6.34062588e-01 -9.69144821e-01 -6.77598417e-01
-6.39799297e-01 7.55289853e-01 -1.86936378e-01 -1.00022301... | [6.445381164550781, 5.213738918304443] |
dbf234f2-0114-4b92-856d-7d6defa4a7af | gandlf-a-generally-nuanced-deep-learning | 2103.01006 | null | https://arxiv.org/abs/2103.01006v4 | https://arxiv.org/pdf/2103.01006v4.pdf | GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging | Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their reproducibility, translation, and deployment. Here we present the community-driven Gene... | ['Peter Mattson', 'Renato Umeton', 'Alexandros Karargyris', 'Prashant Shah', 'Yong Fan', 'Joel H. Saltz', 'Sezgin Er', 'Aimilia Gastounioti', 'Tahsin M. Kurc', 'Shahira Abousamra', 'Rhea Chitalia', 'Babak Haghighi', 'Yuemeng Li', 'Chunrui Zou', 'Vinayak Ahluwalia', 'Ravi Panchumarthy', 'Deepthi Karkada', 'Junwen Wu', '... | 2021-02-26 | null | null | null | null | ['unet-segmentation'] | ['computer-vision'] | [-4.95089814e-02 2.26106048e-01 -2.74660796e-01 -6.15744948e-01
-1.12287295e+00 -5.16923070e-01 1.71700731e-01 4.91488338e-01
-2.89888918e-01 5.66557646e-01 3.37731868e-01 -6.56806231e-01
-2.69653827e-01 -4.78139579e-01 -4.96272773e-01 -5.04690111e-01
-1.26092404e-01 6.80296004e-01 -3.97462249e-01 2.58066863... | [14.834442138671875, -2.428736925125122] |
429288c9-717a-4be3-a4c4-ea9b01408365 | ju_cse_nlp-multi-grade-classification-of | null | null | https://aclanthology.org/S12-1083 | https://aclanthology.org/S12-1083.pdf | JU\_CSE\_NLP: Multi-grade Classification of Semantic Similarity between Text Pairs | null | ['er', 'B', 'Alex Gelbukh', 'Snehasis Neogi', 'Partha Pakray', 'Sivaji yopadhyay'] | 2012-07-01 | null | null | null | semeval-2012-7 | ['video-description'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.315940856933594, 3.831088066101074] |
55ffeab7-d31f-45d0-991b-905ce76d5a66 | distributed-contrastive-learning-for-medical | 2208.03808 | null | https://arxiv.org/abs/2208.03808v1 | https://arxiv.org/pdf/2208.03808v1.pdf | Distributed Contrastive Learning for Medical Image Segmentation | Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective. Federated learning (FL) can learn a shared model from decentralized data. But traditional FL req... | ['Jingtong Hu', 'Yiyu Shi', 'Zhepeng Wang', 'Dewen Zeng', 'Yawen Wu'] | 2022-08-07 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [-1.68522701e-01 6.27183774e-03 -4.31558907e-01 -7.16288090e-01
-7.87120759e-01 -4.12991196e-01 -2.03913331e-01 1.39604464e-01
-4.66086358e-01 7.82274008e-01 -1.87235922e-01 -3.32456231e-01
-2.55828857e-01 -8.73528242e-01 -6.46718383e-01 -1.02049375e+00
-3.56387272e-02 5.76188684e-01 4.92244363e-01 4.41251665... | [6.074367046356201, 6.476650714874268] |
84801c94-ce28-4e29-950a-fbce58fb061c | ada-tta-towards-adaptive-high-quality-text-to | 2306.03504 | null | https://arxiv.org/abs/2306.03504v1 | https://arxiv.org/pdf/2306.03504v1.pdf | Ada-TTA: Towards Adaptive High-Quality Text-to-Talking Avatar Synthesis | We are interested in a novel task, namely low-resource text-to-talking avatar. Given only a few-minute-long talking person video with the audio track as the training data and arbitrary texts as the driving input, we aim to synthesize high-quality talking portrait videos corresponding to the input text. This task has br... | ['Zhou Zhao', 'Zejun Ma', 'Xiang Yin', 'Chen Zhang', 'Jinglin Liu', 'Yi Ren', 'Ziyue Jiang', 'Zhenhui Ye'] | 2023-06-06 | null | null | null | null | ['zero-shot-multi-speaker-tts', 'video-generation', 'neural-rendering'] | ['audio', 'computer-vision', 'computer-vision'] | [ 5.54737806e-01 -8.78122598e-02 2.20950544e-01 -1.37681276e-01
-1.20601106e+00 -3.27551931e-01 6.30955815e-01 -8.85474980e-01
2.60466129e-01 4.78976101e-01 6.14515007e-01 1.54196441e-01
2.32292667e-01 -2.70778507e-01 -6.00425184e-01 -7.92374313e-01
5.02748072e-01 3.11820477e-01 -9.78521928e-02 -4.12531734... | [13.230497360229492, -0.4383385181427002] |
bb638376-4ae8-4808-b59f-ab6ae8ddc131 | development-of-a-hand-gesture-based-control | null | null | https://link.springer.com/chapter/10.1007%2F978-3-030-46140-9_14 | https://dennishnf.bitbucket.io/research/2019%20-%20simbig%202019%20-%20development%20of%20a%20hand%20gesture%20interface.pdf | Development of a hand gesture based control interface using Deep Learning | This paper describes the implementation of a control system based on ten different hand gestures, providing a useful approach for the implementation of better user-friendly human-machine interfaces. Hand detection is achieved using fast detection and tracking algorithms, and classification by a light convolutional neur... | ['Dennis Núñez-Fernández'] | 2020-04-23 | null | null | null | international-conference-on-information | ['hand-detection'] | ['computer-vision'] | [-2.92234063e-01 -5.91383338e-01 -4.60353851e-01 -2.68373907e-01
4.87231106e-01 -3.35487902e-01 6.28115773e-01 -6.85419619e-01
-8.77326548e-01 5.16479433e-01 -4.28571999e-01 -5.35508752e-01
-7.49811381e-02 -6.43254936e-01 -5.62035851e-02 -5.62155604e-01
-6.88285977e-02 3.70322078e-01 5.62755883e-01 -3.44165742... | [6.447032928466797, -0.31830930709838867] |
5b858c82-da64-496a-977e-b9ac4bfddebd | a-human-centered-safe-robot-reinforcement | 2302.13137 | null | https://arxiv.org/abs/2302.13137v2 | https://arxiv.org/pdf/2302.13137v2.pdf | A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors | Deployment of reinforcement learning algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment. Safe robot reinforcement learning (SRRL) is a crucial step towards achieving human-robot coexistence. In this paper, we envision a human-centered SRRL framework cons... | ['Alois Knoll', 'Jan Peters', 'Guang Chen', 'Yali Du', 'Alap Kshirsagar', 'Shangding Gu'] | 2023-02-25 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-3.15033734e-01 9.44182336e-01 -2.37936556e-01 -3.21383737e-02
-3.09150636e-01 -4.35398668e-01 5.62607646e-01 -2.84056336e-01
-3.59494686e-01 1.10330236e+00 1.51965931e-01 -2.40346909e-01
-2.03264207e-01 -4.57270712e-01 -4.69558716e-01 -5.62545538e-01
-6.83802664e-01 2.07493737e-01 2.49836788e-01 -6.96563840... | [4.681305885314941, 1.210062026977539] |
b475d5d3-8d8f-478a-93e1-3476c3969b11 | modeling-the-mistakes-of-boundedly-rational | 2106.13249 | null | https://arxiv.org/abs/2106.13249v1 | https://arxiv.org/pdf/2106.13249v1.pdf | Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind | When inferring the goals that others are trying to achieve, people intuitively understand that others might make mistakes along the way. This is crucial for activities such as teaching, offering assistance, and deciding between blame or forgiveness. However, Bayesian models of theory of mind have generally not accounte... | ['Joshua B. Tenenbaum', 'Vikash K. Mansinghka', 'Tan Zhi-Xuan', 'Joie Le', 'Gloria Z. Lin', 'Arwa Alanqary'] | 2021-06-24 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [-8.99610817e-02 5.34022987e-01 2.45382801e-01 -3.31574768e-01
-1.49785176e-01 -3.71584713e-01 5.78369021e-01 2.73321986e-01
-3.89016062e-01 7.50113368e-01 3.16783905e-01 -5.15935242e-01
-2.93274224e-01 -8.32675815e-01 -3.06815922e-01 -3.47402602e-01
2.66808599e-01 8.06826890e-01 5.45129366e-02 -2.32105702... | [9.520100593566895, 7.234376430511475] |
39e0a94b-1eb3-44b5-8b8e-965ffaeca8d3 | visual-story-post-editing | 1906.01764 | null | https://arxiv.org/abs/1906.01764v1 | https://arxiv.org/pdf/1906.01764v1.pdf | Visual Story Post-Editing | We introduce the first dataset for human edits of machine-generated visual stories and explore how these collected edits may be used for the visual story post-editing task. The dataset, VIST-Edit, includes 14,905 human edited versions of 2,981 machine-generated visual stories. The stories were generated by two state-of... | ["Ting-Hao 'Kenneth' Huang", 'Yen-Chia Hsu', 'Chieh-Yang Huang', 'Ting-Yao Hsu'] | 2019-06-05 | visual-story-post-editing-1 | https://aclanthology.org/P19-1658 | https://aclanthology.org/P19-1658.pdf | acl-2019-7 | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.20914200e-01 5.70679665e-01 -1.80159643e-01 -3.09054703e-01
-6.27802730e-01 -8.82436931e-01 1.18818176e+00 3.44297707e-01
-2.21795246e-01 5.87144673e-01 9.87135887e-01 1.31670618e-02
4.19256896e-01 -5.06925344e-01 -8.39795053e-01 1.98375627e-01
5.18925153e-02 4.79142904e-01 3.62767816e-01 -1.94634423... | [11.199010848999023, 0.865492045879364] |
6546f864-c575-4b66-b1ba-e71d920556a9 | end-to-end-segmentation-with-recurrent | 1812.02068 | null | https://arxiv.org/abs/1812.02068v2 | https://arxiv.org/pdf/1812.02068v2.pdf | Brain Segmentation from k-space with End-to-end Recurrent Attention Network | The task of medical image segmentation commonly involves an image reconstruction step to convert acquired raw data to images before any analysis. However, noises, artifacts and loss of information due to the reconstruction process are almost inevitable, which compromises the final performance of segmentation. We presen... | ['Mariappan S. Nadar', 'Xiao Chen', 'Qiaoying Huang', 'Dimitris Metaxas'] | 2018-12-05 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 6.20540679e-01 2.16897860e-01 2.49745294e-01 -7.74036467e-01
-1.16649365e+00 -2.23307863e-01 8.39877278e-02 -6.00009002e-02
-7.80789375e-01 5.37991345e-01 -4.05427031e-02 -2.71792024e-01
1.19595401e-01 -4.23306972e-01 -6.45494282e-01 -7.66808331e-01
2.46954784e-01 4.05887097e-01 9.13949460e-02 2.84464240... | [14.544795989990234, -2.2090208530426025] |
dbffc506-8709-4631-8b1e-01823992f3ec | the-power-of-typed-affine-decision-structures | 2304.14888 | null | https://arxiv.org/abs/2304.14888v1 | https://arxiv.org/pdf/2304.14888v1.pdf | The Power of Typed Affine Decision Structures: A Case Study | TADS are a novel, concise white-box representation of neural networks. In this paper, we apply TADS to the problem of neural network verification, using them to generate either proofs or concise error characterizations for desirable neural network properties. In a case study, we consider the robustness of neural networ... | ['Bernhard Steffen', 'Alnis Murtovi', 'Maximilian Schlüter', 'Gerrit Nolte'] | 2023-04-28 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 4.94121939e-01 3.20276678e-01 -8.27094316e-02 -1.24748953e-01
-1.63668260e-01 -9.49497759e-01 5.03739774e-01 -8.80618915e-02
-1.28116772e-01 6.22002780e-01 -4.91398335e-01 -9.90631461e-01
-2.48479083e-01 -6.61227643e-01 -1.28324234e+00 -7.50874937e-01
-2.83147603e-01 8.12501535e-02 8.49400535e-02 -1.15482032... | [6.1553730964660645, 7.565533638000488] |
f820ef54-d11a-4366-aa68-0cea1d037c5f | localizing-objects-with-self-supervised | 2109.14279 | null | https://arxiv.org/abs/2109.14279v1 | https://arxiv.org/pdf/2109.14279v1.pdf | Localizing Objects with Self-Supervised Transformers and no Labels | Localizing objects in image collections without supervision can help to avoid expensive annotation campaigns. We propose a simple approach to this problem, that leverages the activation features of a vision transformer pre-trained in a self-supervised manner. Our method, LOST, does not require any external object propo... | ['Jean Ponce', 'Renaud Marlet', 'Patrick Pérez', 'Andrei Bursuc', 'Spyros Gidaris', 'Simon Roburin', 'Huy V. Vo', 'Gilles Puy', 'Oriane Siméoni'] | 2021-09-29 | null | null | null | null | ['single-object-discovery'] | ['computer-vision'] | [-9.82093662e-02 2.88551241e-01 -1.86540201e-01 -4.77307200e-01
-8.85345638e-01 -7.89121211e-01 8.68014336e-01 1.17928624e-01
-6.32057071e-01 4.23882365e-01 -1.58600882e-01 1.74400121e-01
6.40926836e-03 -4.21242923e-01 -1.15043116e+00 -5.46464145e-01
1.12519510e-01 7.51996756e-01 7.85970807e-01 8.89319479... | [9.365816116333008, 1.1099289655685425] |
a97f8585-20d4-4ecc-a0e9-95231d2e534d | ranking-sentences-for-extractive | 1802.08636 | null | http://arxiv.org/abs/1802.08636v2 | http://arxiv.org/pdf/1802.08636v2.pdf | Ranking Sentences for Extractive Summarization with Reinforcement Learning | Single document summarization is the task of producing a shorter version of a
document while preserving its principal information content. In this paper we
conceptualize extractive summarization as a sentence ranking task and propose a
novel training algorithm which globally optimizes the ROUGE evaluation metric
throug... | ['Mirella Lapata', 'Shay B. Cohen', 'Shashi Narayan'] | 2018-02-23 | ranking-sentences-for-extractive-1 | https://aclanthology.org/N18-1158 | https://aclanthology.org/N18-1158.pdf | naacl-2018-6 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 3.51704657e-01 5.18900454e-01 -1.95456430e-01 -3.75956267e-01
-9.87829804e-01 -5.59708178e-01 6.17804766e-01 6.00552440e-01
-6.83010638e-01 8.67719531e-01 1.07513952e+00 -6.72860965e-02
2.33211983e-02 -5.34454703e-01 -8.22991073e-01 -1.75759763e-01
6.14913628e-02 5.59269905e-01 -2.89204001e-01 -3.37941021... | [12.559900283813477, 9.525269508361816] |
bc3d0382-8b81-4413-a38a-1debbbb55d47 | mmface4d-a-large-scale-multi-modal-4d-face | 2303.09797 | null | https://arxiv.org/abs/2303.09797v1 | https://arxiv.org/pdf/2303.09797v1.pdf | MMFace4D: A Large-Scale Multi-Modal 4D Face Dataset for Audio-Driven 3D Face Animation | Audio-Driven Face Animation is an eagerly anticipated technique for applications such as VR/AR, games, and movie making. With the rapid development of 3D engines, there is an increasing demand for driving 3D faces with audio. However, currently available 3D face animation datasets are either scale-limited or quality-un... | ['Jelo Wang', 'Xiangyuan Wang', 'Hongwei Xu', 'Junliang Xing', 'Jia Jia', 'Haozhe Wu'] | 2023-03-17 | null | null | null | null | ['3d-face-animation'] | ['computer-vision'] | [-7.72983208e-02 -2.94422776e-01 1.34255186e-01 -2.85732776e-01
-7.50391781e-01 -2.57656366e-01 6.15546346e-01 -8.31844032e-01
2.41255328e-01 4.01483983e-01 4.26823288e-01 6.80363178e-02
2.88709164e-01 -5.42395651e-01 -6.50864959e-01 -6.90583169e-01
-4.57980424e-01 5.62035561e-01 -1.55429453e-01 -3.42354685... | [13.132710456848145, -0.3872746527194977] |
22a88137-8a6c-4bdc-8626-1805ad794f2f | a-universal-parent-model-for-low-resource | 1909.06516 | null | https://arxiv.org/abs/1909.06516v2 | https://arxiv.org/pdf/1909.06516v2.pdf | A Universal Parent Model for Low-Resource Neural Machine Translation Transfer | Transfer learning from a high-resource language pair `parent' has been proven to be an effective way to improve neural machine translation quality for low-resource language pairs `children.' However, previous approaches build a custom parent model or at least update an existing parent model's vocabulary for each child ... | ['Mozhdeh Gheini', 'Jonathan May'] | 2019-09-14 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 8.05506185e-02 -1.00646345e-02 -4.32144821e-01 -4.92502242e-01
-1.18110693e+00 -8.02496195e-01 3.94254357e-01 1.54537395e-01
-7.20384061e-01 1.04764152e+00 2.81834155e-01 -6.73134625e-01
3.87280285e-01 -8.72197986e-01 -8.05613279e-01 -3.59737426e-01
4.72983360e-01 1.11080849e+00 -7.09149763e-02 -7.76707947... | [11.557246208190918, 10.32866096496582] |
9b68fc89-d294-43c0-bffa-a18726d4692c | ssd-monodtr-supervised-scale-constrained | 2305.07270 | null | https://arxiv.org/abs/2305.07270v3 | https://arxiv.org/pdf/2305.07270v3.pdf | SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection | Transformer-based methods have demonstrated superior performance for monocular 3D object detection recently, which aims at predicting 3D attributes from a single 2D image. Most existing transformer-based methods leverage both visual and depth representations to explore valuable query points on objects, and the quality ... | ['Meng Wang', 'Haolong Fu', 'Jiacheng Lin', 'Zhiyong Li', 'Kailun Yang', 'Jin Yuan', 'Fan Yang', 'Xuan He'] | 2023-05-12 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [-5.16674109e-02 -3.00421119e-01 -1.98523223e-01 -3.34668696e-01
-7.42736280e-01 -4.19607282e-01 4.29610312e-01 -3.26860137e-02
-2.60536700e-01 1.12155266e-01 5.80404792e-03 2.47721355e-02
-2.32149921e-02 -5.39122462e-01 -7.86710441e-01 -7.22387016e-01
1.42698035e-01 3.66193384e-01 7.67002761e-01 2.63294950... | [7.944461345672607, -2.5206286907196045] |
6d882678-471a-4aa7-9bbf-aa9fd8686330 | learning-multilingual-word-embeddings-using | 1905.12260 | null | https://arxiv.org/abs/1905.12260v1 | https://arxiv.org/pdf/1905.12260v1.pdf | Learning Multilingual Word Embeddings Using Image-Text Data | There has been significant interest recently in learning multilingual word embeddings -- in which semantically similar words across languages have similar embeddings. State-of-the-art approaches have relied on expensive labeled data, which is unavailable for low-resource languages, or have involved post-hoc unification... | ['Balder ten Cate', 'Karthik Raman', 'Karan Singhal'] | 2019-05-29 | learning-multilingual-word-embeddings-using-1 | https://aclanthology.org/W19-1807 | https://aclanthology.org/W19-1807.pdf | ws-2019-6 | ['multilingual-word-embeddings'] | ['methodology'] | [-3.26834857e-01 -1.21090733e-01 -3.38517845e-01 -4.24870551e-01
-1.03431427e+00 -5.67172885e-01 8.98768127e-01 5.21335244e-01
-8.56865823e-01 3.06785136e-01 6.54205561e-01 -2.78489918e-01
2.75928706e-01 -4.42618966e-01 -6.83216095e-01 -2.43872851e-01
1.96513772e-01 4.51000750e-01 2.11470691e-03 -2.03526810... | [11.19913387298584, 1.746645450592041] |
801ad881-a319-447a-8409-ef27c2eac77d | embedding-structured-dictionary-entries | null | null | https://aclanthology.org/2020.insights-1.18 | https://aclanthology.org/2020.insights-1.18.pdf | Embedding Structured Dictionary Entries | Previous work has shown how to effectively use external resources such as dictionaries to improve English-language word embeddings, either by manipulating the training process or by applying post-hoc adjustments to the embedding space. We experiment with a multi-task learning approach for explicitly incorporating the s... | ['Gareth Tyson', 'Barbara McGillivray', 'Walid Magdy', 'Steven Wilson'] | null | null | null | null | emnlp-insights-2020-11 | ['learning-word-embeddings'] | ['methodology'] | [-9.82778799e-03 -7.62202293e-02 -4.59259242e-01 -3.96114558e-01
-5.56289017e-01 -7.59470403e-01 5.66378653e-01 6.20312572e-01
-1.16936815e+00 4.03635263e-01 8.70300174e-01 -6.64144754e-01
9.11798999e-02 -6.04294598e-01 -3.24908406e-01 -3.68112713e-01
-5.00060171e-02 4.96156782e-01 -2.41241366e-01 -4.86347497... | [10.523515701293945, 8.818354606628418] |
5770f2e0-5e42-4fc6-bb4b-1baed0f9d496 | towards-high-performance-exploratory-data | 2306.04425 | null | https://arxiv.org/abs/2306.04425v1 | https://arxiv.org/pdf/2306.04425v1.pdf | Towards High-Performance Exploratory Data Analysis (EDA) Via Stable Equilibrium Point | Exploratory data analysis (EDA) is a vital procedure for data science projects. In this work, we introduce a stable equilibrium point (SEP) - based framework for improving the efficiency and solution quality of EDA. By exploiting the SEPs to be the representative points, our approach aims to generate high-quality clust... | ['Yongyu Wang', 'Yuxuan Song'] | 2023-06-07 | null | null | null | null | ['data-visualization', 'data-visualization'] | ['methodology', 'miscellaneous'] | [-4.89482403e-01 -3.19532365e-01 2.73748636e-01 -1.44521952e-01
-2.96699136e-01 -5.12883604e-01 4.58373874e-01 4.24150556e-01
3.30661535e-02 2.70645201e-01 1.62795648e-01 -2.58514643e-01
-7.05275714e-01 -9.34985340e-01 -1.57247066e-01 -9.42956328e-01
-3.18154007e-01 3.80170614e-01 1.37256026e-01 1.39888942... | [7.888458728790283, 4.569932460784912] |
e77b1418-9659-4c32-a862-620cf46b251b | vision-transformer-with-attentive-pooling-for | 2212.05463 | null | https://arxiv.org/abs/2212.05463v1 | https://arxiv.org/pdf/2212.05463v1.pdf | Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition | Facial Expression Recognition (FER) in the wild is an extremely challenging task. Recently, some Vision Transformers (ViT) have been explored for FER, but most of them perform inferiorly compared to Convolutional Neural Networks (CNN). This is mainly because the new proposed modules are difficult to converge well from ... | ['Guodong Guo', 'Zhongsong Ma', 'Zichang Tan', 'Qiangchang Wang', 'Fanglei Xue'] | 2022-12-11 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.01347715e-01 1.71445478e-02 5.51915020e-02 -3.93055350e-01
-6.15110278e-01 -1.09955361e-02 2.86971748e-01 -3.43532562e-01
-5.24600029e-01 7.14131892e-01 2.40954444e-01 3.56215894e-01
2.69696526e-02 -7.45200217e-01 -6.53901815e-01 -9.70930636e-01
-5.22801019e-02 -2.96650261e-01 2.41735905e-01 -3.24621499... | [13.557586669921875, 1.5777231454849243] |
e74217cf-f1cc-47e8-8eb8-7f4996a84263 | max-margin-contrastive-learning | 2112.11450 | null | https://arxiv.org/abs/2112.11450v1 | https://arxiv.org/pdf/2112.11450v1.pdf | Max-Margin Contrastive Learning | Standard contrastive learning approaches usually require a large number of negatives for effective unsupervised learning and often exhibit slow convergence. We suspect this behavior is due to the suboptimal selection of negatives used for offering contrast to the positives. We counter this difficulty by taking inspirat... | ['Anoop Cherian', 'Rama Chellappa', 'Suvrit Sra', 'Anshul Shah'] | 2021-12-21 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 4.42263901e-01 -6.29691407e-02 -4.80811596e-01 -3.29789788e-01
-7.46015728e-01 -6.24592960e-01 8.68618011e-01 1.92633107e-01
-6.70928061e-01 6.25286877e-01 -3.57412882e-02 -2.19872430e-01
1.20064449e-02 -2.63823837e-01 -5.46027958e-01 -6.43530488e-01
-1.14448711e-01 3.68582308e-01 1.23716310e-01 2.14492977... | [9.315892219543457, 2.951799154281616] |
4d0fffbf-cb14-4f70-b11c-e6291545c2c7 | ddipnet-and-ddipnet-discriminant-deep-image | 2212.10411 | null | https://arxiv.org/abs/2212.10411v1 | https://arxiv.org/pdf/2212.10411v1.pdf | DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification | Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technology and the availability of many high-quality remote sensing images create a demand for reliable automation methods. The current paper propose... | ['João P. Papa', 'Leandro A. Passos', 'Rafael G. Pires', 'Daniel F. S. Santos'] | 2022-12-20 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 6.33747280e-01 -3.55153561e-01 -1.70068577e-01 -6.10266387e-01
-5.37290275e-01 -2.46950805e-01 7.69611835e-01 -1.29090294e-01
-4.94927794e-01 5.51578879e-01 -4.20057684e-01 -5.58696806e-01
-4.17560726e-01 -1.01500642e+00 -3.34896237e-01 -9.97434199e-01
-1.12347670e-01 4.14719373e-01 -4.15355027e-01 -1.94452614... | [9.625642776489258, -1.4939337968826294] |
aca393ec-dfc3-4386-8287-c0a58dd26f86 | trade-the-event-corporate-events-detection | 2105.12825 | null | https://arxiv.org/abs/2105.12825v2 | https://arxiv.org/pdf/2105.12825v2.pdf | Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading | In this paper, we introduce an event-driven trading strategy that predicts stock movements by detecting corporate events from news articles. Unlike existing models that utilize textual features (e.g., bag-of-words) and sentiments to directly make stock predictions, we consider corporate events as the driving force behi... | ['Han Liu', 'Liqian Ma', 'Zhihan Zhou'] | 2021-05-26 | null | https://aclanthology.org/2021.findings-acl.186 | https://aclanthology.org/2021.findings-acl.186.pdf | findings-acl-2021-8 | ['text-based-stock-prediction', 'event-driven-trading', 'stock-trend-prediction', 'stock-market-prediction', 'stock-price-prediction', 'stock-prediction'] | ['natural-language-processing', 'natural-language-processing', 'time-series', 'time-series', 'time-series', 'time-series'] | [-6.77056909e-01 -2.77917027e-01 -6.14372671e-01 -2.42738962e-01
-9.03615057e-01 -8.25350285e-01 1.04434109e+00 6.29193068e-01
-3.72412890e-01 7.54627407e-01 6.83411419e-01 -2.07266226e-01
3.89594793e-01 -1.38254535e+00 -6.30670905e-01 -1.66421488e-01
-3.27555746e-01 1.79929763e-01 5.89050770e-01 -1.31161869... | [4.41933012008667, 4.283189296722412] |
a53468cb-bbec-4ff4-949e-4aa52f5a8d8e | single-mr-image-super-resolution-using | 2207.08036 | null | https://arxiv.org/abs/2207.08036v1 | https://arxiv.org/pdf/2207.08036v1.pdf | Single MR Image Super-Resolution using Generative Adversarial Network | Spatial resolution of medical images can be improved using super-resolution methods. Real Enhanced Super Resolution Generative Adversarial Network (Real-ESRGAN) is one of the recent effective approaches utilized to produce higher resolution images, given input images of lower resolution. In this paper, we apply this me... | ['Mehran Ebrahimi', 'Elham Shakibapour', 'Shawkh Ibne Rashid'] | 2022-07-16 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 8.15010369e-01 5.95107615e-01 1.18851528e-01 3.53047401e-02
-1.09389758e+00 -2.63812512e-01 5.43618679e-01 -4.60809737e-01
-5.93787074e-01 1.14440262e+00 2.59240836e-01 -4.61268611e-02
-6.29572123e-02 -7.94202209e-01 -5.40567815e-01 -8.96477520e-01
-1.45042464e-01 1.95398882e-01 3.98280233e-01 -4.03755635... | [13.739777565002441, -2.2646074295043945] |
897ba227-343b-4ae4-9e79-6766ce538195 | bert-hlstms-bert-and-hierarchical-lstms-for | 2012.02128 | null | https://arxiv.org/abs/2012.02128v1 | https://arxiv.org/pdf/2012.02128v1.pdf | BERT-hLSTMs: BERT and Hierarchical LSTMs for Visual Storytelling | Visual storytelling is a creative and challenging task, aiming to automatically generate a story-like description for a sequence of images. The descriptions generated by previous visual storytelling approaches lack coherence because they use word-level sequence generation methods and do not adequately consider sentence... | ['Mian Zhou', 'Frank Guerin', 'Qingyun Dai', 'Jing Su'] | 2020-12-03 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 1.67084590e-01 7.30734095e-02 -4.60610203e-02 -1.60714999e-01
-6.87340915e-01 -4.06738281e-01 1.07934165e+00 2.02560257e-02
-8.08810219e-02 6.92904055e-01 7.06514359e-01 -1.14679851e-01
5.48588216e-01 -8.73723209e-01 -8.13738823e-01 -6.17455542e-01
1.78989246e-01 3.46724600e-01 7.16589019e-02 -7.43399039... | [11.180764198303223, 0.7461451292037964] |
0c3c7568-3f77-4bc1-8f18-74f8c79c2c46 | a-step-towards-digital-operations-a-novel | 2306.07772 | null | https://arxiv.org/abs/2306.07772v1 | https://arxiv.org/pdf/2306.07772v1.pdf | A step towards digital operations -- A novel grey-box approach for modelling the heat dynamics of Ultra-low temperature freezing chambers | Ultra-low temperature (ULT) freezers store perishable bio-contents and have high energy consumption, which highlight a demand for reliable methods for intelligent surveillance and smart energy management. This study introduces a novel grey-box modelling approach based on stochastic differential equations to describe th... | ['Wiebke Brix Markussen', "Francesco D'Ettorre", 'Jan Kloppenborg Møller', 'Peder Bacher', 'Tao Huang'] | 2023-06-13 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 1.34078190e-01 -4.61513638e-01 -5.64046204e-02 -4.83794101e-02
-5.34700379e-02 -7.50763893e-01 4.36861515e-01 2.74249882e-01
4.14589979e-02 6.41099215e-01 -5.11252940e-01 -3.40556115e-01
-1.19053684e-01 -7.60010481e-01 -3.91255975e-01 -1.11089969e+00
-1.45197436e-01 7.98371881e-02 3.00915297e-02 -1.16654215... | [5.738037586212158, 2.48437762260437] |
271d87fe-fd8e-496a-be67-32c061a44cdf | supervised-video-summarization-via-multiple | 2104.11530 | null | https://arxiv.org/abs/2104.11530v2 | https://arxiv.org/pdf/2104.11530v2.pdf | Supervised Video Summarization via Multiple Feature Sets with Parallel Attention | The assignment of importance scores to particular frames or (short) segments in a video is crucial for summarization, but also a difficult task. Previous work utilizes only one source of visual features. In this paper, we suggest a novel model architecture that combines three feature sets for visual content and motion ... | ['Ralph Ewerth', 'Sherzod Hakimov', 'Junaid Ahmed Ghauri'] | 2021-04-23 | null | null | null | null | ['supervised-video-summarization', 'automated-feature-engineering'] | ['computer-vision', 'methodology'] | [ 3.02078545e-01 -2.09971860e-01 -3.69904995e-01 -2.52904147e-01
-6.87713861e-01 -3.62690836e-01 8.18572819e-01 4.06375527e-01
-6.82253420e-01 7.33725548e-01 8.30771029e-01 1.65518224e-01
-1.79284647e-01 -4.25934643e-01 -4.69600767e-01 -6.48531735e-01
-2.62595892e-01 -1.37660978e-02 5.43599069e-01 -5.28120762... | [10.456260681152344, 0.45033958554267883] |
0f8e5610-4001-4b67-8f95-adeb45f8f425 | text-segmentation-by-cross-segment-attention | 2004.14535 | null | https://arxiv.org/abs/2004.14535v2 | https://arxiv.org/pdf/2004.14535v2.pdf | Text Segmentation by Cross Segment Attention | Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents, which are commonly used to help downstream tasks such as information retrieval or text summarization. In this work, we propose three transformer-based architectures and provide comprehensive comparisons w... | ['Gonçalo Simões', 'Kishore Papineni', 'Boris Dadachev', 'Michal Lukasik'] | 2020-04-30 | null | https://aclanthology.org/2020.emnlp-main.380 | https://aclanthology.org/2020.emnlp-main.380.pdf | emnlp-2020-11 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 5.25713265e-01 4.11774814e-01 -5.63858032e-01 -2.55021483e-01
-1.30890954e+00 -8.91292274e-01 7.90683270e-01 4.76745635e-01
-4.07602966e-01 8.05890799e-01 6.46768630e-01 -7.16401875e-01
1.08082600e-01 -4.17888373e-01 -4.40495402e-01 -4.22623217e-01
1.23969004e-01 7.93549538e-01 5.67212820e-01 -2.74556309... | [12.240033149719238, 9.405409812927246] |
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