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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8b33e049-9a37-486f-99bb-0e3f6cf91fee | scalable-scene-flow-from-point-clouds-in-the | 2103.01306 | null | https://arxiv.org/abs/2103.01306v5 | https://arxiv.org/pdf/2103.01306v5.pdf | Scalable Scene Flow from Point Clouds in the Real World | Autonomous vehicles operate in highly dynamic environments necessitating an accurate assessment of which aspects of a scene are moving and where they are moving to. A popular approach to 3D motion estimation, termed scene flow, is to employ 3D point cloud data from consecutive LiDAR scans, although such approaches have... | ['Jonathon Shlens', 'Zhifeng Chen', 'Nichola Abdo', 'Chris Sweeney', 'Philipp Jund'] | 2021-03-01 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-9.04723778e-02 -2.93819994e-01 -2.41347522e-01 -3.46437544e-01
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-3.59739304e-01 7.18547821e-01 9.06855464e-01 -1.13362566... | [7.990083694458008, -2.3660106658935547] |
4e7fc372-c802-4dc7-af62-d2cf12958293 | tbrats-trusted-brain-tumor-segmentation | 2206.09309 | null | https://arxiv.org/abs/2206.09309v3 | https://arxiv.org/pdf/2206.09309v3.pdf | TBraTS: Trusted Brain Tumor Segmentation | Despite recent improvements in the accuracy of brain tumor segmentation, the results still exhibit low levels of confidence and robustness. Uncertainty estimation is one effective way to change this situation, as it provides a measure of confidence in the segmentation results. In this paper, we propose a trusted brain ... | ['Huazhu Fu', 'Meng Wang', 'Xiaojing Shen', 'Xuedong Yuan', 'Ke Zou'] | 2022-06-19 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [-1.44003540e-01 8.95958483e-01 -4.68581349e-01 -8.20248604e-01
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2.66502887e-01 5.58456719e-01 3.75004619e-01 3.33282322... | [14.405646324157715, -2.088986873626709] |
c43171cf-7f7c-4d35-8978-8c9f06edc65f | single-stage-uav-detection-and-classification | null | null | https://scholar.google.com/citations?view_op=view_citation&hl=en&user=ZztEI20AAAAJ&citation_for_view=ZztEI20AAAAJ:qUcmZB5y_30C | https://ieeexplore.ieee.org/iel7/9663572/9663735/09663841.pdf | Single-stage uav detection and classification with yolov5: Mosaic data augmentation and panet | In Drone-vs-Bird Detection Challenge in conjunction with the 4th International Workshop on Small-Drone Surveillance, Detection and Counteraction Techniques at IEEE AVSS 2021, we proposed a YOLOV5-based object detection model for small UAV detection and classification. YOLOV5 leverages PANet neck and mosaic augmentation... | ['Miodrag Bolic', 'Varun Mehta', 'Vaibhav Patel', 'Fardad Dadboud'] | 2021-11-16 | null | null | null | 2021-17th-ieee-international-conference-on | ['2d-object-detection'] | ['computer-vision'] | [-4.80470990e-05 -2.72894382e-01 7.83190429e-02 1.41841723e-02
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94382065-f888-4459-ac09-96a655e4672c | ell-p-slack-norm-support-vector-data | 2203.08932 | null | https://arxiv.org/abs/2203.08932v1 | https://arxiv.org/pdf/2203.08932v1.pdf | $\ell_p$ Slack Norm Support Vector Data Description | The support vector data description (SVDD) approach serves as a de facto standard for one-class classification where the learning task entails inferring the smallest hyper-sphere to enclose target objects while linearly penalising any errors/slacks via an $\ell_1$-norm penalty term. In this study, we generalise this mo... | ['Shervin R. Arashloo'] | 2022-03-16 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 3.72666180e-01 3.93212497e-01 -1.85210153e-01 -2.34337494e-01
-5.57990789e-01 -2.38292500e-01 5.12060106e-01 2.63803631e-01
-5.58372557e-01 7.59349704e-01 -2.51784205e-01 -1.20691746e-01
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-3.48371774e-01 2.07091704e-01 -2.34156638e-01 -1.75421104... | [7.8457746505737305, 4.111073017120361] |
08ad331a-834d-4a76-ab43-0256aa5f0f40 | generating-natural-language-adversarial-3 | 2003.10388 | null | https://arxiv.org/abs/2003.10388v1 | https://arxiv.org/pdf/2003.10388v1.pdf | Generating Natural Language Adversarial Examples on a Large Scale with Generative Models | Today text classification models have been widely used. However, these classifiers are found to be easily fooled by adversarial examples. Fortunately, standard attacking methods generate adversarial texts in a pair-wise way, that is, an adversarial text can only be created from a real-world text by replacing a few word... | ['Jun Zhou', 'Yankun Ren', 'Siliang Tang', 'Xiang Ren', 'Jianbin Lin', 'Yuan Qi', 'Shuang Yang'] | 2020-03-10 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 2.00020224e-01 2.12926954e-01 3.27144355e-01 -9.33139324e-02
-5.01561046e-01 -9.24734890e-01 9.06761885e-01 -2.78947502e-01
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6.62735939e-01 6.53302610e-01 -1.00810803e-01 -6.73820913... | [5.958520412445068, 8.079705238342285] |
86199ef8-8363-4568-b4e2-39296f3b814c | federated-learning-in-the-presence-of | 2305.19971 | null | https://arxiv.org/abs/2305.19971v1 | https://arxiv.org/pdf/2305.19971v1.pdf | Federated Learning in the Presence of Adversarial Client Unavailability | Federated learning is a decentralized machine learning framework wherein not all clients are able to participate in each round. An emerging line of research is devoted to tackling arbitrary client unavailability. Existing theoretical analysis imposes restrictive structural assumptions on the unavailability patterns, an... | ['Pengkun Yang', 'Jiaming Xu', 'Lili Su'] | 2023-05-31 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-0.31104746 0.18078372 -0.10299103 -0.21432748 -1.2020569 -0.97462326
-0.08346 0.13333924 -0.7268543 0.7892822 -0.34468332 -0.6095022
-0.47577986 -0.8173212 -1.1391151 -1.1224045 -0.58559674 0.45936057
-0.13368347 0.06232153 -0.07940972 0.38036478 -0.9391768 0.03744726
0.715499 1.1039414 -0.3... | [6.070937633514404, 5.2093329429626465] |
71684804-d698-4dde-9cc0-0f72bd78a238 | a-knowledge-graph-perspective-on-supply-chain | 2305.08506 | null | https://arxiv.org/abs/2305.08506v1 | https://arxiv.org/pdf/2305.08506v1.pdf | A Knowledge Graph Perspective on Supply Chain Resilience | Global crises and regulatory developments require increased supply chain transparency and resilience. Companies do not only need to react to a dynamic environment but have to act proactively and implement measures to prevent production delays and reduce risks in the supply chains. However, information about supply chai... | ['Volker Tresp', 'Martin Berbalk', 'Dagmar Beyer', 'Emanuel Weigel', 'Roger Wernert', 'Daniela Inzko', 'Maximilian Buchner', 'Marcel Hildebrandt', 'Bailan He', 'Yushan Liu'] | 2023-05-15 | null | null | null | null | ['knowledge-graph-completion'] | ['knowledge-base'] | [-2.24172205e-01 3.88485372e-01 -4.21193123e-01 7.33012054e-03
-1.75638497e-01 -1.20998967e+00 6.99826106e-02 6.83193624e-01
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-7.43118227e-01 -1.18124652e+00 -4.58465129e-01 -2.10820973e-01
-2.53403068e-01 8.00983846e-01 -5.09707667e-02 -2.54341632... | [6.5946760177612305, 5.935716152191162] |
ac101df0-3527-4eed-8c11-4a60684a380f | mllp-vrain-upv-systems-for-the-iwslt-2022 | null | null | https://aclanthology.org/2022.iwslt-1.22 | https://aclanthology.org/2022.iwslt-1.22.pdf | MLLP-VRAIN UPV systems for the IWSLT 2022 Simultaneous Speech Translation and Speech-to-Speech Translation tasks | This work describes the participation of the MLLP-VRAIN research group in the two shared tasks of the IWSLT 2022 conference: Simultaneous Speech Translation and Speech-to-Speech Translation. We present our streaming-ready ASR, MT and TTS systems for Speech Translation and Synthesis from English into German. Our submiss... | ['Alfons Juan', 'Albert Sanchis', 'Jorge Civera Saiz', 'Joan Albert Silvestre-Cerdà', 'Pau Baquero-Arnal', 'Gonçal Garcés Díaz-Munío', 'Adrián Giménez Pastor', 'Alejandro Pérez-González-de-Martos', 'Javier Jorge Cano', 'Javier Iranzo-Sánchez'] | null | null | null | null | iwslt-acl-2022-5 | ['simultaneous-speech-to-text-translation', 'speech-to-speech-translation'] | ['natural-language-processing', 'speech'] | [ 3.39170069e-01 1.70460761e-01 -1.55481875e-01 -5.19002914e-01
-1.44977653e+00 -7.03256428e-01 7.79122114e-01 -7.93863758e-02
-2.49077410e-01 7.23027468e-01 4.64271516e-01 -1.18433273e+00
4.93767709e-01 9.16026309e-02 -7.17832863e-01 1.38668092e-02
3.08965504e-01 1.00378978e+00 4.35787328e-02 -4.79676127... | [14.498761177062988, 7.175112724304199] |
fd06bad4-59b0-4e4d-ae1f-2badc686feab | a-multi-horizon-quantile-recurrent-forecaster | 1711.11053 | null | http://arxiv.org/abs/1711.11053v2 | http://arxiv.org/pdf/1711.11053v2.pdf | A Multi-Horizon Quantile Recurrent Forecaster | We propose a framework for general probabilistic multi-step time series
regression. Specifically, we exploit the expressiveness and temporal nature of
Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional
structures), the nonparametric nature of Quantile Regression and the efficiency
of Direct Multi-Ho... | ['Ruofeng Wen', 'Kari Torkkola', 'Balakrishnan Narayanaswamy', 'Dhruv Madeka'] | 2017-11-29 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 7.92675614e-02 -3.34235787e-01 -4.15489376e-01 -7.93428600e-01
-9.85507965e-01 -7.50799775e-01 6.15813553e-01 6.23172801e-03
-1.55914858e-01 9.03796673e-01 4.59907591e-01 -5.85553765e-01
-3.31454366e-01 -6.83547199e-01 -7.07389176e-01 -5.73447227e-01
-8.20796013e-01 3.33674431e-01 -2.53178895e-01 -3.01298320... | [6.89016580581665, 3.1092891693115234] |
a471f019-4839-4189-a643-74dc7945d6cb | deepflorist-rethinking-deep-neural-networks | 2307.01806 | null | https://arxiv.org/abs/2307.01806v1 | https://arxiv.org/pdf/2307.01806v1.pdf | DeepFlorist: Rethinking Deep Neural Networks and Ensemble Learning as A Meta-Classifier For Object Classification | In this paper, we propose a novel learning paradigm called "DeepFlorist" for flower classification using ensemble learning as a meta-classifier. DeepFlorist combines the power of deep learning with the robustness of ensemble methods to achieve accurate and reliable flower classification results. The proposed network ar... | ['Afshin Khadangi'] | 2023-07-04 | null | null | null | null | ['classification-1'] | ['methodology'] | [-1.21702418e-01 -5.19002259e-01 -2.58753777e-01 -3.50605547e-01
1.14712231e-01 -8.31961095e-01 4.38359320e-01 8.55486095e-02
2.32134700e-01 6.98266625e-01 -3.38496417e-01 -2.65640020e-01
-2.09389806e-01 -1.07812309e+00 -4.96860862e-01 -9.61131275e-01
-1.80706352e-01 -2.88907200e-01 -2.09990382e-01 -2.71790236... | [9.435924530029297, -1.2391856908798218] |
bfcda236-1177-4569-8b00-95019e84703e | deep-learning-based-novel-cascaded-approach | 2301.06226 | null | https://arxiv.org/abs/2301.06226v1 | https://arxiv.org/pdf/2301.06226v1.pdf | Deep Learning based Novel Cascaded Approach for Skin Lesion Analysis | Automatic lesion analysis is critical in skin cancer diagnosis and ensures effective treatment. The computer aided diagnosis of such skin cancer in dermoscopic images can significantly reduce the clinicians workload and help improve diagnostic accuracy. Although researchers are working extensively to address this probl... | ['Sanjay Talbar', 'Ujjwal Baid', 'Bhakti Baheti', 'Prasad Dutande', 'Shubham Innani'] | 2023-01-16 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.10108173e-01 1.68435857e-01 -4.61731762e-01 -3.44464749e-01
-7.82454431e-01 -4.26196337e-01 2.71421403e-01 3.43915671e-01
-4.85651374e-01 4.38920945e-01 -1.23394154e-01 -3.85580122e-01
-5.52223362e-02 -6.66652739e-01 -1.39124528e-01 -7.87612557e-01
1.38737470e-01 1.10551454e-01 1.67664632e-01 2.06026852... | [15.64657974243164, -3.0121207237243652] |
d1940ceb-2341-401a-a621-c14f02a61518 | ortho-ode-enhancing-robustness-and-of-neural | 2305.09179 | null | https://arxiv.org/abs/2305.09179v1 | https://arxiv.org/pdf/2305.09179v1.pdf | Ortho-ODE: Enhancing Robustness and of Neural ODEs against Adversarial Attacks | Neural Ordinary Differential Equations (NODEs) probed the usage of numerical solvers to solve the differential equation characterized by a Neural Network (NN), therefore initiating a new paradigm of deep learning models with infinite depth. NODEs were designed to tackle the irregular time series problem. However, NODEs... | ['Vishal Purohit'] | 2023-05-16 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [-2.09067732e-01 4.94100988e-01 4.40619051e-01 1.07959248e-02
-1.30363375e-01 -7.41987050e-01 4.92672801e-01 -4.20698613e-01
-7.05314219e-01 8.15455616e-01 -1.01680897e-01 -2.63216645e-01
-3.52340281e-01 -4.36173618e-01 -8.83739233e-01 -9.52552021e-01
-6.53445423e-01 -1.25436842e-01 9.00651440e-02 -4.49399799... | [6.885190963745117, 3.557990789413452] |
8e572cd8-8e58-41a7-9c56-abfc5cfe831a | analyzing-vietnamese-legal-questions-using-1 | 2304.14447 | null | https://arxiv.org/abs/2304.14447v1 | https://arxiv.org/pdf/2304.14447v1.pdf | Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine Classifiers | In this paper, we propose using deep neural networks to extract important information from Vietnamese legal questions, a fundamental task towards building a question answering system in the legal domain. Given a legal question in natural language, the goal is to extract all the segments that contain the needed informat... | ['Ngo Xuan Bach', 'Tu Minh Phuong', 'Hoang Thi Thu Uyen', 'Nguyen Anh Tu'] | 2023-04-27 | analyzing-vietnamese-legal-questions-using | https://link.springer.com/chapter/10.1007/978-3-030-92270-2_44 | https://link.springer.com/chapter/10.1007/978-3-030-92270-2_44 | iconip-2021-12 | ['dependency-parsing'] | ['natural-language-processing'] | [ 7.03457296e-02 3.47724736e-01 -5.09623706e-01 -6.81320012e-01
-1.25554681e+00 -5.31020582e-01 4.43974316e-01 3.88940752e-01
-8.14428210e-01 5.42591810e-01 8.41847122e-01 -6.65359855e-01
9.21365172e-02 -9.82616663e-01 -6.54066205e-01 -2.95842707e-01
6.29578009e-02 3.23475182e-01 2.24182755e-01 -5.46531737... | [11.054187774658203, 8.047601699829102] |
d06719af-a145-44cf-8710-deab01f6e9aa | weakly-supervised-segmentation-with-point | 2304.03572 | null | https://arxiv.org/abs/2304.03572v1 | https://arxiv.org/pdf/2304.03572v1.pdf | Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model | Image segmentation is a fundamental task in the field of imaging and vision. Supervised deep learning for segmentation has achieved unparalleled success when sufficient training data with annotated labels are available. However, annotation is known to be expensive to obtain, especially for histopathology images where t... | ['Yalin Zheng', 'Ke Chen', 'Sarah E Coupland', 'Abhik Mukherjee', 'Declan Sculthorpe', 'Yanda Meng', 'Liam Burrows', 'Hongrun Zhang'] | 2023-04-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Weakly_Supervised_Segmentation_With_Point_Annotations_for_Histopathology_Images_via_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Weakly_Supervised_Segmentation_With_Point_Annotations_for_Histopathology_Images_via_CVPR_2023_paper.pdf | cvpr-2023-1 | ['weakly-supervised-segmentation'] | ['computer-vision'] | [ 3.93894136e-01 2.02622592e-01 -2.55219430e-01 -5.01508892e-01
-1.10765648e+00 -4.91476923e-01 2.80359417e-01 4.45172071e-01
-5.72304904e-01 7.64770508e-01 -2.46766284e-01 -1.00213006e-01
1.77760780e-01 -4.43963438e-01 -5.63554287e-01 -1.09913671e+00
2.44750187e-01 6.53382897e-01 3.93106103e-01 -7.80449554... | [14.673062324523926, -2.4502429962158203] |
80fb69d2-4f21-447e-9e0e-23fddb0e0ee9 | a-new-approach-for-texture-based-script | 2009.07435 | null | https://arxiv.org/abs/2009.07435v1 | https://arxiv.org/pdf/2009.07435v1.pdf | A New Approach for Texture based Script Identification At Block Level using Quad Tree Decomposition | A considerable amount of success has been achieved in developing monolingual OCR systems for Indic scripts. But in a country like India, where multi-script scenario is prevalent, identifying scripts beforehand becomes obligatory. In this paper, we present the significance of Gabor wavelets filters in extracting directi... | ['Ram Sarkar', 'Pawan Kumar Singh', 'Mita Nasipuri', 'Supratim Das'] | 2020-09-16 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [-3.87548804e-02 -5.11046410e-01 1.75116599e-01 -1.54066190e-01
-4.41789269e-01 -6.93696022e-01 6.24311388e-01 -5.40379528e-03
-3.52634847e-01 9.76449370e-01 7.61273205e-02 -4.29473460e-01
-2.36471385e-01 -5.89422762e-01 -1.02995355e-02 -1.02701080e+00
-6.10702559e-02 2.83737838e-01 -1.25435337e-01 -2.66247809... | [11.844944953918457, 2.677154541015625] |
7c348b7d-43c1-4a03-b7be-4937b87fdadc | is-self-attention-powerful-to-learn-code | 2212.10017 | null | https://arxiv.org/abs/2212.10017v2 | https://arxiv.org/pdf/2212.10017v2.pdf | Are Code Pre-trained Models Powerful to Learn Code Syntax and Semantics? | Analysis of pre-trained code models also has revealed that they can effectively learn program syntax. However, these works are limited in analyzing code syntax and their distance-based approaches are not accurate due to the curse of high dimensionality. Furthermore, the study of the learnt program semantics of these mo... | ['Yang Liu', 'Wenhan Wang', 'Jie Zhang', 'Shangqing Liu', 'Qiang Hu', 'Xiaofei Xie', 'Mengjie Zhao', 'Wei Ma'] | 2022-12-20 | null | null | null | null | ['program-synthesis', 'code-search', 'code-search'] | ['computer-code', 'computer-code', 'computer-vision'] | [-2.53103197e-01 1.57905370e-02 -5.49408138e-01 -6.28070712e-01
-5.24776317e-02 -5.42731166e-01 9.21362415e-02 5.20908892e-01
1.57330140e-01 -6.99264109e-02 2.37632632e-01 -8.55220675e-01
8.78788456e-02 -1.03175867e+00 -1.01552784e+00 -1.71375170e-01
-2.63626724e-01 4.52118069e-02 3.85309905e-01 -2.44150802... | [7.527394771575928, 7.893374443054199] |
99dd238f-7d39-43af-83cf-dbebb7e81944 | multilingual-dictionary-based-construction-of | null | null | https://aclanthology.org/2020.lrec-1.519 | https://aclanthology.org/2020.lrec-1.519.pdf | Multilingual Dictionary Based Construction of Core Vocabulary | We propose a new functional definition and construction method for core vocabulary sets for multiple applications based on the relative coverage of a target concept in thousands of bilingual dictionaries. Our newly developed core concept vocabulary list derived from these dictionary consensus methods achieves high over... | ['Winston Wu', 'Garrett Nicolai', 'David Yarowsky'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cognate-prediction'] | ['natural-language-processing'] | [ 1.76974759e-01 3.42095308e-02 -7.70487785e-01 -2.19946414e-01
-9.13954973e-01 -1.16658294e+00 6.31214321e-01 3.22412282e-01
-6.01769984e-01 9.15689528e-01 6.89959466e-01 -9.58961844e-01
2.01174729e-02 -5.44457257e-01 -4.42479759e-01 -1.17836669e-01
4.23008829e-01 9.76987123e-01 -2.75695115e-01 -8.55329216... | [10.913200378417969, 9.890629768371582] |
4f0ebc4c-ae1a-4df9-9b44-eec080301909 | natural-scene-recognition-based-on | 1506.07271 | null | http://arxiv.org/abs/1506.07271v1 | http://arxiv.org/pdf/1506.07271v1.pdf | Natural Scene Recognition Based on Superpixels and Deep Boltzmann Machines | The Deep Boltzmann Machines (DBM) is a state-of-the-art unsupervised learning
model, which has been successfully applied to handwritten digit recognition
and, as well as object recognition. However, the DBM is limited in scene
recognition due to the fact that natural scene images are usually very large.
In this paper, ... | ['Guanghui Wang', 'Shanshan Zhang', 'Jinfu Yang', 'Jingyu Gao'] | 2015-06-24 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 3.53645116e-01 -6.37502253e-01 -1.13318503e-01 -5.64353585e-01
-4.02510881e-01 8.97335187e-02 5.53959846e-01 -3.39306325e-01
-8.85748386e-01 2.81442851e-01 -1.53370872e-01 -1.00625753e-01
-2.33026911e-02 -7.00720429e-01 -5.19177794e-01 -1.18957007e+00
3.03339064e-01 2.32136309e-01 4.64295566e-01 5.21084666... | [9.923293113708496, -0.36613568663597107] |
537d095f-d552-4f78-9a1d-75fcb4e2d37b | scalable-mutual-information-estimation-using | 1801.09125 | null | http://arxiv.org/abs/1801.09125v2 | http://arxiv.org/pdf/1801.09125v2.pdf | Scalable Mutual Information Estimation using Dependence Graphs | The Mutual Information (MI) is an often used measure of dependency between
two random variables utilized in information theory, statistics and machine
learning. Recently several MI estimators have been proposed that can achieve
parametric MSE convergence rate. However, most of the previously proposed
estimators have th... | ['Alfred O. Hero III', 'Morteza Noshad', 'Yu Zeng'] | 2018-01-27 | null | null | null | null | ['mutual-information-estimation', 'information-plane'] | ['methodology', 'methodology'] | [ 5.68026416e-02 -3.62219065e-02 -1.79268539e-01 -4.71091837e-01
-8.58050704e-01 -2.25669459e-01 1.11426122e-01 1.54760808e-01
-9.05580997e-01 9.07596350e-01 -2.09752783e-01 -3.43784660e-01
-7.35374510e-01 -7.37541556e-01 -9.94644821e-01 -1.09489572e+00
-7.42600143e-01 4.81555343e-01 1.06744424e-01 1.35717675... | [7.815914154052734, 3.675879955291748] |
94afdbe6-6bc1-47dc-a3ab-81d722cac5ff | very-lightweight-photo-retouching-network | 2104.06279 | null | https://arxiv.org/abs/2104.06279v2 | https://arxiv.org/pdf/2104.06279v2.pdf | Very Lightweight Photo Retouching Network with Conditional Sequential Modulation | Photo retouching aims at improving the aesthetic visual quality of images that suffer from photographic defects, especially for poor contrast, over/under exposure, and inharmonious saturation. In practice, photo retouching can be accomplished by a series of image processing operations. As most commonly-used retouching ... | ['Yu Qiao', 'Chao Dong', 'Hengyuan Zhao', 'Zhengwen Zhang', 'Xiangyu Chen', 'Jingwen He', 'Yihao Liu'] | 2021-04-13 | null | null | null | null | ['photo-retouching', 'image-retouching'] | ['computer-vision', 'computer-vision'] | [ 4.17227477e-01 -1.53114110e-01 -7.03505874e-02 -7.45303556e-02
-5.09580016e-01 -1.61520541e-01 2.20153570e-01 -6.82459325e-02
-5.80109775e-01 6.73439980e-01 -5.75694963e-02 -3.57810348e-01
2.27723122e-01 -7.15753436e-01 -1.07938623e+00 -9.19078350e-01
2.29132652e-01 -6.38145566e-01 2.55576938e-01 -1.10705331... | [10.91137409210205, -2.122277021408081] |
3885366f-d32c-42a6-aa5e-6a71c3cbf397 | ssul-semantic-segmentation-with-unknown-label | 2106.11562 | null | https://arxiv.org/abs/2106.11562v3 | https://arxiv.org/pdf/2106.11562v3.pdf | SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning | This paper introduces a solid state-of-the-art baseline for a class-incremental semantic segmentation (CISS) problem. While the recent CISS algorithms utilize variants of the knowledge distillation (KD) technique to tackle the problem, they failed to fully address the critical challenges in CISS causing the catastrophi... | ['Beomyoung Kim', 'Sungmin Cha', 'Taesup Moon', 'Youngjoon Yoo'] | 2021-06-22 | null | http://proceedings.neurips.cc/paper/2021/hash/5a9542c773018268fc6271f7afeea969-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/5a9542c773018268fc6271f7afeea969-Paper.pdf | neurips-2021-12 | ['overlapped-100-5', 'overlapped-10-1', 'disjoint-15-5', 'disjoint-10-1', 'disjoint-15-1', 'overlapped-15-5', 'class-incremental-semantic-segmentation', 'overlapped-15-1', 'overlapped-50-50', 'overlapped-100-50', 'continual-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 5.79309642e-01 3.28283370e-01 -3.77880692e-01 -3.76113027e-01
-7.57384837e-01 -5.73996246e-01 3.84532124e-01 1.67834148e-01
-4.07784373e-01 8.91424596e-01 -2.72118092e-01 -1.43385842e-01
-5.02300411e-02 -4.96074528e-01 -1.05115950e+00 -7.45181561e-01
2.25073814e-01 6.39854133e-01 8.84738505e-01 1.92943200... | [9.392409324645996, 2.242628812789917] |
34e2694f-fc2f-445a-891c-029143d49680 | mitos-rcnn-a-novel-approach-to-mitotic-figure | 1807.01788 | null | http://arxiv.org/abs/1807.01788v1 | http://arxiv.org/pdf/1807.01788v1.pdf | MITOS-RCNN: A Novel Approach to Mitotic Figure Detection in Breast Cancer Histopathology Images using Region Based Convolutional Neural Networks | Studies estimate that there will be 266,120 new cases of invasive breast
cancer and 40,920 breast cancer induced deaths in the year of 2018 alone.
Despite the pervasiveness of this affliction, the current process to obtain an
accurate breast cancer prognosis is tedious and time consuming, requiring a
trained pathologis... | ['Siddhant Rao'] | 2018-07-04 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 3.23491126e-01 3.52213651e-01 -5.00620723e-01 -1.65022060e-01
-1.27533507e+00 -4.02241230e-01 5.56993306e-01 8.16472113e-01
-6.09550834e-01 7.34140694e-01 4.21577096e-02 -7.84070909e-01
7.59177357e-02 -8.47593307e-01 -4.58503038e-01 -8.03231359e-01
2.74846256e-02 6.40811384e-01 -1.04386069e-01 5.27985059... | [15.133021354675293, -3.0637359619140625] |
0e413068-9f21-4888-bc71-2b21c4961623 | self-supervised-super-plane-for-neural-3d | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ye_Self-Supervised_Super-Plane_for_Neural_3D_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ye_Self-Supervised_Super-Plane_for_Neural_3D_Reconstruction_CVPR_2023_paper.pdf | Self-Supervised Super-Plane for Neural 3D Reconstruction | Neural implicit surface representation methods show impressive reconstruction results but struggle to handle texture-less planar regions that widely exist in indoor scenes. Existing approaches addressing this leverage image prior that requires assistive networks trained with large-scale annotated datasets. In this ... | ['Ming-Hsuan Yang', 'Xueting Li', 'Sifei Liu', 'Botao Ye'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-reconstruction'] | ['computer-vision'] | [ 5.40951908e-01 5.61085403e-01 -1.49552330e-01 -5.33154368e-01
-8.72270107e-01 -3.74314487e-01 5.43222547e-01 -3.00010949e-01
-4.50455509e-02 4.11519587e-01 -1.57074872e-02 -1.63639978e-01
1.64246976e-01 -9.40856695e-01 -1.11013770e+00 -5.50461292e-01
2.02326536e-01 7.12442040e-01 6.76710546e-01 -1.32796690... | [8.815401077270508, -2.905479669570923] |
970d7c1f-4c5e-4a3a-9d1d-4255bdaa0d49 | animesr-learning-real-world-super-resolution | 2206.07038 | null | https://arxiv.org/abs/2206.07038v3 | https://arxiv.org/pdf/2206.07038v3.pdf | AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos | This paper studies the problem of real-world video super-resolution (VSR) for animation videos, and reveals three key improvements for practical animation VSR. First, recent real-world super-resolution approaches typically rely on degradation simulation using basic operators without any learning capability, such as blu... | ['Ying Shan', 'Gen Li', 'Xintao Wang', 'Yanze Wu'] | 2022-06-14 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 6.98108897e-02 -4.57059145e-01 -2.16593042e-01 1.39676496e-01
-6.89952970e-01 -1.89858899e-01 2.73650557e-01 -6.34787261e-01
-6.49698526e-02 6.34056985e-01 5.74012458e-01 -3.40881459e-02
1.68485388e-01 -5.38854897e-01 -6.81244552e-01 -6.62855625e-01
-3.12198400e-01 -1.76967442e-01 5.52582622e-01 -5.62231481... | [11.089649200439453, -1.9186567068099976] |
82272371-ba28-459a-804b-df3f7734e105 | connecting-the-dots-between-audio-and-text-1 | null | null | https://openreview.net/forum?id=o1LLnx21iVj | https://openreview.net/pdf?id=o1LLnx21iVj | Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer | Machines that can represent and describe environmental soundscapes have practical potential, e.g., for audio tagging and captioning. Prevailing learning paradigms of audio-text connections have been relying on parallel audio-text data, which is, however, scarcely available on the web. We propose VIP-ANT that induces Au... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['audio-tagging'] | ['audio'] | [ 4.60200906e-01 6.53572083e-02 -1.42900869e-01 -2.32913867e-01
-1.53912795e+00 -6.43263638e-01 5.57010353e-01 2.48291507e-01
-4.91987765e-01 3.64445210e-01 3.35648954e-01 1.49225052e-02
-9.24816206e-02 -2.70328045e-01 -9.27728891e-01 -5.43955147e-01
-1.44155696e-01 4.51206714e-01 2.17952102e-01 -2.44722471... | [15.261110305786133, 5.0199360847473145] |
a87c0e42-9318-4c3b-95b8-027c3bdab91e | group-invariant-global-pooling | 2305.19207 | null | https://arxiv.org/abs/2305.19207v1 | https://arxiv.org/pdf/2305.19207v1.pdf | Group Invariant Global Pooling | Much work has been devoted to devising architectures that build group-equivariant representations, while invariance is often induced using simple global pooling mechanisms. Little work has been done on creating expressive layers that are invariant to given symmetries, despite the success of permutation invariant poolin... | ['Pietro Liò', 'Chaitanya K. Joshi', 'Yonatan Gideoni', 'Kamil Bujel'] | 2023-05-30 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 4.75552976e-01 3.87909919e-01 -5.58812581e-02 -4.55725789e-01
-5.32350540e-01 -6.72618091e-01 1.01590312e+00 -1.45080969e-01
-3.27143103e-01 8.64003479e-01 6.12814486e-01 1.78898778e-02
-3.97174984e-01 -6.57677948e-01 -6.25879467e-01 -8.50241303e-01
-5.60630918e-01 1.95648402e-01 2.55568564e-01 -2.75811076... | [8.762832641601562, 2.5842156410217285] |
d8934b9b-a4df-47f0-9442-514a0c80f8cf | fast-preprocessing-for-robust-face-sketch | 1708.00224 | null | http://arxiv.org/abs/1708.00224v1 | http://arxiv.org/pdf/1708.00224v1.pdf | Fast Preprocessing for Robust Face Sketch Synthesis | Exemplar-based face sketch synthesis methods usually meet the challenging
problem that input photos are captured in different lighting conditions from
training photos. The critical step causing the failure is the search of similar
patch candidates for an input photo patch. Conventional illumination invariant
patch dist... | ['Linchao Bao', 'Yibing Song', 'Qingxiong Yang', 'Jiawei Zhang'] | 2017-08-01 | null | null | null | null | ['face-sketch-synthesis'] | ['computer-vision'] | [ 5.37759006e-01 -4.85950351e-01 4.76598740e-02 -3.95380020e-01
-4.78304356e-01 -5.34995914e-01 5.91355741e-01 -4.58190739e-01
-6.20641299e-02 8.33523452e-01 -2.82890588e-01 -3.25417668e-02
3.10014412e-02 -8.49250317e-01 -7.56997168e-01 -6.81892633e-01
6.26296103e-01 -2.78536715e-02 2.43424281e-01 -5.63824698... | [12.67293930053711, -0.018292201682925224] |
cc272a36-83d5-4efb-814b-5e81e623ccd2 | xdoc-unified-pre-training-for-cross-format | 2210.02849 | null | https://arxiv.org/abs/2210.02849v1 | https://arxiv.org/pdf/2210.02849v1.pdf | XDoc: Unified Pre-training for Cross-Format Document Understanding | The surge of pre-training has witnessed the rapid development of document understanding recently. Pre-training and fine-tuning framework has been effectively used to tackle texts in various formats, including plain texts, document texts, and web texts. Despite achieving promising performance, existing pre-trained model... | ['Furu Wei', 'Cha Zhang', 'Lei Cui', 'Tengchao Lv', 'Jingye Chen'] | 2022-10-06 | null | null | null | null | ['semantic-entity-labeling'] | ['natural-language-processing'] | [ 1.86365675e-02 -2.64007479e-01 -2.05634683e-01 -3.32769483e-01
-7.86221027e-01 -8.28319848e-01 8.13480616e-01 1.82515025e-01
-4.89014357e-01 3.75169009e-01 4.39227879e-01 -5.56133151e-01
-1.42386422e-01 -6.64020061e-01 -4.86308306e-01 -3.43959153e-01
1.92660019e-01 2.75069445e-01 2.34925792e-01 -2.59289861... | [11.01025390625, 8.413555145263672] |
346f0094-40f3-4ee2-a20d-55e722cd8556 | visual-grounding-of-inter-lingual-word | 2209.03714 | null | https://arxiv.org/abs/2209.03714v2 | https://arxiv.org/pdf/2209.03714v2.pdf | Visual Grounding of Inter-lingual Word-Embeddings | Visual grounding of Language aims at enriching textual representations of language with multiple sources of visual knowledge such as images and videos. Although visual grounding is an area of intense research, inter-lingual aspects of visual grounding have not received much attention. The present study investigates the... | ['R. Harald Baayen', 'Hendrik P. A. Lensch', 'Hassan Shahmohammadi', 'Wafaa Mohammed'] | 2022-09-08 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [-3.93714756e-01 -8.74971896e-02 -3.60077798e-01 -2.53126711e-01
-4.35510129e-01 -7.68495858e-01 1.13081551e+00 6.26805246e-01
-7.38547504e-01 1.75430790e-01 5.94585419e-01 -3.81380349e-01
3.22074950e-01 -7.69101858e-01 -6.07797384e-01 -5.07805169e-01
7.76032582e-02 -9.74912494e-02 1.59792274e-01 -4.34354097... | [10.79731273651123, 1.8134679794311523] |
5990a4d8-001a-47ca-878a-36b1df345a95 | authorship-attribution-by-consensus-among | null | null | https://aclanthology.org/C18-1234 | https://aclanthology.org/C18-1234.pdf | Authorship Attribution By Consensus Among Multiple Features | Most existing research on authorship attribution uses various lexical, syntactic and semantic features. In this paper we demonstrate an effective template-based approach for combining various syntactic features of a document for authorship analysis. The parse-tree based features that we propose are independent of the t... | ['Jagadeesh Patchala', 'Raj Bhatnagar'] | 2018-08-01 | authorship-attribution-by-consensus-among-1 | https://aclanthology.org/C18-1234 | https://aclanthology.org/C18-1234.pdf | coling-2018-8 | ['author-attribution'] | ['natural-language-processing'] | [-6.22148626e-03 -1.91653937e-01 -3.43209594e-01 -5.45690775e-01
-2.82985419e-01 -8.28118265e-01 9.60361719e-01 4.71371949e-01
-3.72096479e-01 6.28963530e-01 4.02330905e-01 -4.51829582e-01
-4.67523545e-01 -5.16115665e-01 7.13800043e-02 1.79335643e-02
3.50421369e-01 5.61841846e-01 1.03370376e-01 -1.25503168... | [9.579859733581543, 10.571958541870117] |
fc5d4c31-b4d8-40cd-8cb1-eb41c8b304cb | efficient-contrastive-learning-via-novel-data | 2109.05941 | null | https://arxiv.org/abs/2109.05941v2 | https://arxiv.org/pdf/2109.05941v2.pdf | Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning | We introduce EfficientCL, a memory-efficient continual pretraining method that applies contrastive learning with novel data augmentation and curriculum learning. For data augmentation, we stack two types of operation sequentially: cutoff and PCA jittering. While pretraining steps proceed, we apply curriculum learning b... | ['Alice Oh', 'Jiseon Kim', 'Seonghyeon Ye'] | 2021-09-10 | null | https://aclanthology.org/2021.emnlp-main.138 | https://aclanthology.org/2021.emnlp-main.138.pdf | emnlp-2021-11 | ['continual-pretraining'] | ['methodology'] | [ 4.17018414e-01 -6.39426038e-02 -3.21430087e-01 -3.59883517e-01
-9.63762164e-01 -6.62721395e-01 5.85367560e-01 5.14599085e-01
-1.09015179e+00 6.50178373e-01 3.70917380e-01 -7.71405160e-01
3.05640548e-01 -6.11830235e-01 -8.06855798e-01 -4.17361617e-01
-4.22496982e-02 6.72380567e-01 1.46417201e-01 -1.74704492... | [10.842473983764648, 8.362937927246094] |
98b887a2-b475-4d9f-91ec-2c77ed5febf8 | zero-shot-slot-filling-with-dpr-and-rag | 2104.08610 | null | https://arxiv.org/abs/2104.08610v1 | https://arxiv.org/pdf/2104.08610v1.pdf | Zero-shot Slot Filling with DPR and RAG | The ability to automatically extract Knowledge Graphs (KG) from a given collection of documents is a long-standing problem in Artificial Intelligence. One way to assess this capability is through the task of slot filling. Given an entity query in form of [Entity, Slot, ?], a system is asked to `fill' the slot by genera... | ['Alfio Gliozzo', 'Gaetano Rossiello', 'Michael Glass'] | 2021-04-17 | null | null | null | null | ['knowledge-base-population', 'zero-shot-slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.92072168e-01 5.98555803e-01 -3.16503912e-01 -6.59644306e-02
-1.09790289e+00 -7.05622256e-01 7.92068064e-01 4.51605380e-01
-4.02982265e-01 1.03555179e+00 2.57032067e-01 -4.19303536e-01
-3.52355719e-01 -1.06942463e+00 -6.89470112e-01 -1.71413377e-01
1.03151731e-01 1.11327374e+00 5.52902341e-01 -6.05745494... | [9.684585571289062, 8.472850799560547] |
d63b2b49-0a95-44ec-8042-289eaa9edbe7 | on-complex-valued-convolutional-neural | 1602.09046 | null | http://arxiv.org/abs/1602.09046v1 | http://arxiv.org/pdf/1602.09046v1.pdf | On Complex Valued Convolutional Neural Networks | Convolutional neural networks (CNNs) are the cutting edge model for
supervised machine learning in computer vision. In recent years CNNs have
outperformed traditional approaches in many computer vision tasks such as
object detection, image classification and face recognition. CNNs are
vulnerable to overfitting, and a l... | ['Nitzan Guberman'] | 2016-02-29 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.46149921e-01 3.25475842e-01 -1.19932063e-01 -3.30036670e-01
3.57587226e-02 -2.20982164e-01 5.25816083e-01 8.69824290e-02
-7.99895763e-01 7.31720984e-01 -3.15205991e-01 -2.94109464e-01
-1.06006801e-01 -6.43105209e-01 -7.89149582e-01 -9.00457859e-01
-1.03889056e-01 2.51453161e-01 4.82730478e-01 -3.61996710... | [9.076118469238281, 2.391167163848877] |
5aeef28a-7539-450d-b899-1d5ef649ac57 | 3d-shapenets-a-deep-representation-for | 1406.5670 | null | http://arxiv.org/abs/1406.5670v3 | http://arxiv.org/pdf/1406.5670v3.pdf | 3D ShapeNets: A Deep Representation for Volumetric Shapes | 3D shape is a crucial but heavily underutilized cue in today's computer
vision systems, mostly due to the lack of a good generic shape representation.
With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft
Kinect), it is becoming increasingly important to have a powerful 3D shape
representation ... | ['Jianxiong Xiao', 'Fisher Yu', 'Aditya Khosla', 'Zhirong Wu', 'Xiaoou Tang', 'Shuran Song', 'Linguang Zhang'] | 2014-06-22 | 3d-shapenets-a-deep-representation-for-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Wu_3D_ShapeNets_A_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Wu_3D_ShapeNets_A_2015_CVPR_paper.pdf | cvpr-2015-6 | ['3d-shape-representation'] | ['computer-vision'] | [-1.55027444e-02 1.55079484e-01 -1.61631033e-01 -5.78818262e-01
-6.83887124e-01 -7.23790944e-01 6.18084669e-01 -2.21779067e-02
2.18595304e-02 -1.41784619e-03 3.07421476e-01 -1.50133759e-01
5.84826954e-02 -9.54153657e-01 -8.53936374e-01 -3.04744214e-01
1.17650636e-01 1.26460302e+00 1.96411118e-01 1.21317901... | [8.31525993347168, -3.276215076446533] |
d4d71e2a-e4fa-4918-b885-b9eba3d714b9 | deepigeos-a-deep-interactive-geodesic | 1707.00652 | null | http://arxiv.org/abs/1707.00652v3 | http://arxiv.org/pdf/1707.00652v3.pdf | DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation | Accurate medical image segmentation is essential for diagnosis, surgical
planning and many other applications. Convolutional Neural Networks (CNNs) have
become the state-of-the-art automatic segmentation methods. However, fully
automatic results may still need to be refined to become accurate and robust
enough for clin... | ['Jan Deprest', 'Tom Vercauteren', 'Sebastien Ourselin', 'Michael Aertsen', 'Maria A. Zuluaga', 'Guotai Wang', 'Wenqi Li', 'Rosalind Pratt', 'Anna L. David', 'Tom Doel', 'Premal A. Patel'] | 2017-07-03 | null | null | null | null | ['placenta-segmentation'] | ['medical'] | [ 2.59513795e-01 5.67159891e-01 9.23480541e-02 -8.14367890e-01
-6.29099727e-01 -3.14582348e-01 1.35266259e-01 3.47503990e-01
-7.02683449e-01 5.71547270e-01 -8.02302361e-02 -4.64379400e-01
-5.58262281e-02 -9.56524968e-01 -7.43303478e-01 -5.20799994e-01
2.97651570e-02 8.36215317e-01 7.26259351e-01 1.82669461... | [14.506421089172363, -2.6016182899475098] |
69d1d23e-34b5-46ce-af7a-59dde353c2a7 | deep-learning-of-high-order-interactions-for | 2007.09334 | null | https://arxiv.org/abs/2007.09334v1 | https://arxiv.org/pdf/2007.09334v1.pdf | Deep Learning of High-Order Interactions for Protein Interface Prediction | Protein interactions are important in a broad range of biological processes. Traditionally, computational methods have been developed to automatically predict protein interface from hand-crafted features. Recent approaches employ deep neural networks and predict the interaction of each amino acid pair independently. Ho... | ['Yi Liu', 'Hao Yuan', 'Lei Cai', 'Shuiwang Ji'] | 2020-07-18 | null | null | null | null | ['protein-interface-prediction'] | ['miscellaneous'] | [ 1.02342516e-01 -2.28934377e-01 -1.66078851e-01 -4.89609361e-01
-1.86093107e-01 -1.83552116e-01 1.34387031e-01 3.41276348e-01
-1.75234184e-01 7.67794847e-01 2.11340934e-01 -3.96393895e-01
-1.52426036e-02 -7.43705571e-01 -9.85060513e-01 -7.86483943e-01
-3.70136827e-01 6.57498419e-01 1.69783115e-01 -1.12542026... | [4.800875663757324, 5.7116923332214355] |
9c978e15-4461-4f84-8f16-5ccfe01305b6 | prioritized-trajectory-replay-a-replay-memory | 2306.15503 | null | https://arxiv.org/abs/2306.15503v1 | https://arxiv.org/pdf/2306.15503v1.pdf | Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning | In recent years, data-driven reinforcement learning (RL), also known as offline RL, have gained significant attention. However, the role of data sampling techniques in offline RL has been overlooked despite its potential to enhance online RL performance. Recent research suggests applying sampling techniques directly to... | ['Changjie Fan', 'Tangjie Lv', 'Yan Zheng', 'Yujing Hu', 'Jianye Hao', 'Yi Ma', 'Jinyi Liu'] | 2023-06-27 | null | null | null | null | ['offline-rl', 'd4rl'] | ['playing-games', 'robots'] | [-1.24284647e-01 -1.01793438e-01 -9.75840688e-01 -2.48788998e-01
-8.54912341e-01 -7.03141034e-01 6.86904311e-01 1.84754536e-01
-7.28228748e-01 9.23762918e-01 5.22452772e-01 -5.59661686e-01
-4.28703547e-01 -7.15579748e-01 -5.75623393e-01 -6.08559310e-01
-2.23731801e-01 1.78190634e-01 2.21576408e-01 -6.78484067... | [4.061812400817871, 2.1586945056915283] |
7622551b-d20a-4cc1-a798-43090170e88f | kvasir-instrument-diagnostic-and-therapeutic | 2011.08065 | null | https://arxiv.org/abs/2011.08065v1 | https://arxiv.org/pdf/2011.08065v1.pdf | Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy | Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. Usually the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development and amount and size of the resecte... | ['Pål Halvorsen', 'Dag Johansen', 'Håvard D. Johansen', 'Peter T. Schmidt', 'Thomas de Lange', 'Michael A. Riegler', 'Enrique Garcia-Ceja', 'VajiraThambawita', 'Steven A. Hicks', 'Krister Emanuelsen', 'Sharib Ali', 'Debesh Jha'] | 2020-10-23 | null | null | null | null | ['instrument-recognition'] | ['audio'] | [-2.20424756e-01 2.60516673e-01 -2.04113498e-01 8.08337629e-02
-3.61375600e-01 -1.07402670e+00 5.59330583e-02 4.38227892e-01
-4.38256145e-01 3.52760226e-01 1.54118054e-02 -5.99546194e-01
-2.07801044e-01 -6.72270656e-01 -5.37080407e-01 -6.97670877e-01
-4.08034116e-01 3.36983562e-01 4.44648683e-01 1.32455658... | [14.083377838134766, -3.1371309757232666] |
414585cf-a088-4af9-bea6-8c7a7f036270 | lr-to-hr-face-hallucination-with-an | 2109.14690 | null | https://arxiv.org/abs/2109.14690v1 | https://arxiv.org/pdf/2109.14690v1.pdf | LR-to-HR Face Hallucination with an Adversarial Progressive Attribute-Induced Network | Face super-resolution is a challenging and highly ill-posed problem since a low-resolution (LR) face image may correspond to multiple high-resolution (HR) ones during the hallucination process and cause a dramatic identity change for the final super-resolved results. Thus, to address this problem, we propose an end-to-... | ['Rama Chellappa', 'Jun-Cheng Chen', 'Nitin Balachandran'] | 2021-09-29 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 2.90606797e-01 1.44105032e-01 9.23675671e-02 -4.95273560e-01
-1.03839350e+00 -2.05717623e-01 2.99874991e-01 -7.31045783e-01
-1.11296527e-01 8.57621610e-01 4.44760978e-01 5.25448143e-01
-4.60610092e-02 -4.66271192e-01 -6.33474112e-01 -8.24565530e-01
2.97297984e-02 2.39965007e-01 -1.06198534e-01 -1.91064328... | [12.833574295043945, -0.08613074570894241] |
7334556e-71c0-458d-b677-ac1e25cebfa6 | fast-online-clustering-with-randomized | 1506.03425 | null | http://arxiv.org/abs/1506.03425v1 | http://arxiv.org/pdf/1506.03425v1.pdf | Fast Online Clustering with Randomized Skeleton Sets | We present a new fast online clustering algorithm that reliably recovers
arbitrary-shaped data clusters in high throughout data streams. Unlike the
existing state-of-the-art online clustering methods based on k-means or
k-medoid, it does not make any restrictive generative assumptions. In addition,
in contrast to exist... | ['Xiaofeng Liu', 'Krzysztof Choromanski', 'Sanjiv Kumar'] | 2015-06-10 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-3.51134866e-01 -1.87858954e-01 1.18086375e-02 -3.79735649e-01
-7.47528315e-01 -5.38246870e-01 2.29470983e-01 6.94947362e-01
-3.65740806e-01 2.45472461e-01 -1.14390813e-01 -4.11881357e-02
-5.16860604e-01 -9.25151825e-01 -6.74132109e-01 -9.86834288e-01
-6.46234155e-01 1.23470759e+00 7.12261856e-01 4.04308379... | [7.309330940246582, 4.419173717498779] |
8a0c251c-02ef-4d26-a4ac-7dc7b0cedf70 | naist-at-the-hoo-2012-shared-task | null | null | https://aclanthology.org/W12-2033 | https://aclanthology.org/W12-2033.pdf | NAIST at the HOO 2012 Shared Task | null | ['Yuji Matsumoto', 'Yuta Hayashibe', 'Tomoya Mizumoto', 'Lis Kanashiro', 'Shuhei Kondo', 'Mamoru Komachi', 'Keisuke Sakaguchi'] | 2012-06-01 | null | null | null | ws-2012-6 | ['grammatical-error-detection'] | ['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... | [-7.231553077697754, 3.8342316150665283] |
942a8472-a698-43fc-81dc-2a7e65b340be | neurocs-neural-nocs-supervision-for-monocular-1 | 2305.17763 | null | https://arxiv.org/abs/2305.17763v1 | https://arxiv.org/pdf/2305.17763v1.pdf | NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization | Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature. Estimating 3D coordinates for each pixel on the object surface holds great potential as it provides dense 2D-3D geometric constraints for the underlying PnP problem. However, high-quality ground truth supe... | ['Manmohan Chandraker', 'Enrique Dunn', 'Buyu Liu', 'Samuel Schulter', 'Bingbing Zhuang', 'Zhixiang Min'] | 2023-05-28 | neurocs-neural-nocs-supervision-for-monocular | http://openaccess.thecvf.com//content/CVPR2023/html/Min_NeurOCS_Neural_NOCS_Supervision_for_Monocular_3D_Object_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Min_NeurOCS_Neural_NOCS_Supervision_for_Monocular_3D_Object_Localization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['monocular-3d-object-localization', 'object-localization'] | ['computer-vision', 'computer-vision'] | [ 1.30162016e-01 -4.87890206e-02 -6.04505576e-02 -4.08512473e-01
-8.06539714e-01 -7.65748858e-01 6.72957957e-01 -3.41371417e-01
-2.04475373e-01 5.52830100e-01 -2.30706736e-01 -2.22037479e-01
-1.43387690e-01 -5.05535185e-01 -1.16042125e+00 -5.81274152e-01
2.73947537e-01 7.53093004e-01 1.79945931e-01 1.29336417... | [7.845257759094238, -2.6394922733306885] |
0fdceb76-4d03-46bd-a883-100b62fddc01 | scalable-sparse-subspace-clustering-by | 1507.01238 | null | http://arxiv.org/abs/1507.01238v3 | http://arxiv.org/pdf/1507.01238v3.pdf | Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit | Subspace clustering methods based on $\ell_1$, $\ell_2$ or nuclear norm
regularization have become very popular due to their simplicity, theoretical
guarantees and empirical success. However, the choice of the regularizer can
greatly impact both theory and practice. For instance, $\ell_1$ regularization
is guaranteed t... | ['Chong You', 'Rene Vidal', 'Daniel P. Robinson'] | 2015-07-05 | scalable-sparse-subspace-clustering-by-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/You_Scalable_Sparse_Subspace_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/You_Scalable_Sparse_Subspace_CVPR_2016_paper.pdf | cvpr-2016-6 | ['face-clustering'] | ['computer-vision'] | [-2.45739833e-01 -3.73322845e-01 -2.00933382e-01 -2.15584740e-01
-5.20726383e-01 -5.10618091e-01 -6.29370064e-02 -3.18107277e-01
-1.51367858e-01 5.34095645e-01 -5.71819430e-04 -1.28444089e-02
-3.54439884e-01 -4.17891443e-01 -5.63675582e-01 -1.23643661e+00
5.02297245e-02 2.35859066e-01 -1.42835617e-01 1.18724205... | [7.675570487976074, 4.417294025421143] |
c2aa4081-bd69-4743-9356-b2109be55d9e | ps-nerv-patch-wise-stylized-neural | 2208.03742 | null | https://arxiv.org/abs/2208.03742v1 | https://arxiv.org/pdf/2208.03742v1.pdf | PS-NeRV: Patch-wise Stylized Neural Representations for Videos | We study how to represent a video with implicit neural representations (INRs). Classical INRs methods generally utilize MLPs to map input coordinates to output pixels. While some recent works have tried to directly reconstruct the whole image with CNNs. However, we argue that both the above pixel-wise and image-wise st... | ['Cairong Wang', 'Chao Dong', 'Yunpeng Bai'] | 2022-08-07 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 3.49711329e-01 -3.57200690e-02 -3.87494326e-01 -1.32942557e-01
-4.23911721e-01 3.00566736e-03 3.46321940e-01 -2.23237410e-01
-2.86384225e-01 7.92822123e-01 1.85693890e-01 -8.11770093e-03
1.56554312e-01 -1.00438881e+00 -1.28221524e+00 -7.18534589e-01
1.95798799e-01 -2.68540204e-01 1.22432731e-01 -9.99971405... | [11.248608589172363, -1.5030841827392578] |
392996d4-6dbd-4d9b-b121-3c2fdf3c9178 | fine-grained-classification-of-solder-joints | 2209.09857 | null | https://arxiv.org/abs/2209.09857v1 | https://arxiv.org/pdf/2209.09857v1.pdf | Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence | Solder joint inspection (SJI) is a critical process in the production of printed circuit boards (PCB). Detection of solder errors during SJI is quite challenging as the solder joints have very small sizes and can take various shapes. In this study, we first show that solders have low feature diversity, and that the SJI... | ['Dincer Gokcen', 'Atila Yilmaz', 'Seniha Esen Yuksel', 'Furkan Ulger'] | 2022-09-20 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 1.72029182e-01 1.87866524e-01 1.93196252e-01 -3.87009919e-01
-9.49373603e-01 -1.07248381e-01 5.81053942e-02 5.88584458e-03
-7.85510540e-02 9.63569522e-01 -3.12834322e-01 1.95033535e-01
-4.70690936e-01 -5.87711215e-01 -7.56453037e-01 -9.39001739e-01
1.28024340e-01 4.18552279e-01 6.30905926e-01 1.55195937... | [7.578438758850098, 1.8695734739303589] |
4f86aec7-745b-47e2-9f61-be9bb5bcc109 | physics-informed-machine-learning-for-3 | 2306.13867 | null | https://arxiv.org/abs/2306.13867v1 | https://arxiv.org/pdf/2306.13867v1.pdf | Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems | Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to purely data-driven methods, PIML models can be trained from addi... | ['Draguna L. Vrabie', 'Wenceslao Shaw Cortez', 'Melanie N. Zeilinger', 'Andrea Carron', 'Joel A. Paulson', 'Stefano Di Cairano', 'Ankush Chakrabarty', 'Biswadip Dey', 'Roland Schwan', 'Zoltan Nagy', 'Colin Jones', 'Ján Drgoňa', 'Truong X. Nghiem'] | 2023-06-24 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-9.77077633e-02 -8.36158618e-02 -7.32177615e-01 1.24042153e-01
-1.30978078e-01 -4.76264596e-01 7.39898264e-01 2.79772162e-01
3.52920681e-01 8.50466967e-01 -4.71093029e-01 -3.48346770e-01
-5.95082402e-01 -5.24581313e-01 -8.19013238e-01 -9.00430918e-01
-2.85724908e-01 3.64184648e-01 -1.66643992e-01 -1.67479336... | [6.4650702476501465, 3.592512845993042] |
703acb15-1b58-437f-a7f0-cfa71759a407 | chatgpt-as-a-mapping-assistant-a-novel-method | 2306.03204 | null | https://arxiv.org/abs/2306.03204v1 | https://arxiv.org/pdf/2306.03204v1.pdf | ChatGPT as a mapping assistant: A novel method to enrich maps with generative AI and content derived from street-level photographs | This paper explores the concept of leveraging generative AI as a mapping assistant for enhancing the efficiency of collaborative mapping. We present results of an experiment that combines multiple sources of volunteered geographic information (VGI) and large language models (LLMs). Three analysts described the content ... | ['Boyuan Guan', 'Hartwig H. Hochmair', 'Peter Mooney', 'Levente Juhász'] | 2023-06-05 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 1.44780293e-01 8.80249083e-01 3.92229706e-01 -4.19170409e-01
-1.03534067e+00 -6.86907113e-01 8.55249882e-01 4.39998180e-01
-4.29320514e-01 6.69238687e-01 6.83784664e-01 -4.79056954e-01
-1.06979571e-01 -1.04263508e+00 -6.36117816e-01 -2.96784997e-01
-1.24847509e-01 9.55734313e-01 2.48084053e-01 -3.19772780... | [9.366415023803711, 9.160676956176758] |
cac7ce69-bf05-4fcb-bf12-db32064c22fc | full-capacity-unitary-recurrent-neural | 1611.00035 | null | http://arxiv.org/abs/1611.00035v1 | http://arxiv.org/pdf/1611.00035v1.pdf | Full-Capacity Unitary Recurrent Neural Networks | Recurrent neural networks are powerful models for processing sequential data,
but they are generally plagued by vanishing and exploding gradient problems.
Unitary recurrent neural networks (uRNNs), which use unitary recurrence
matrices, have recently been proposed as a means to avoid these issues.
However, in previous ... | ['Thomas Powers', 'Scott Wisdom', 'John R. Hershey', 'Les Atlas', 'Jonathan Le Roux'] | 2016-10-31 | full-capacity-unitary-recurrent-neural-1 | http://papers.nips.cc/paper/6327-full-capacity-unitary-recurrent-neural-networks | http://papers.nips.cc/paper/6327-full-capacity-unitary-recurrent-neural-networks.pdf | neurips-2016-12 | ['sequential-image-classification'] | ['computer-vision'] | [ 4.06994253e-01 3.17683637e-01 -2.11838618e-01 1.12909205e-01
-7.66998708e-01 -5.41822910e-01 4.58875924e-01 -3.22313815e-01
-5.80961764e-01 6.86359942e-01 2.82887191e-01 -8.55571926e-01
8.23997557e-02 -6.36307955e-01 -7.14863837e-01 -6.58194244e-01
-1.58824146e-01 1.77151226e-02 1.23692073e-01 -3.61539364... | [7.804925918579102, 3.5862061977386475] |
e19af55d-5ed1-4d58-bae8-519b0f31dd64 | unsupervised-and-semi-supervised-anomaly | 1710.09207 | null | http://arxiv.org/abs/1710.09207v1 | http://arxiv.org/pdf/1710.09207v1.pdf | Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks | We investigate anomaly detection in an unsupervised framework and introduce
Long Short Term Memory (LSTM) neural network based algorithms. In particular,
given variable length data sequences, we first pass these sequences through our
LSTM based structure and obtain fixed length sequences. We then find a decision
functi... | ['Suleyman Serdar Kozat', 'Tolga Ergen', 'Ali Hassan Mirza'] | 2017-10-25 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 3.60762924e-01 -2.67096072e-01 5.34456298e-02 -2.65859425e-01
-3.15842479e-01 -4.52964008e-01 3.83012146e-01 4.36384737e-01
-7.43061364e-01 5.01252353e-01 -3.36897075e-01 -4.79617089e-01
-1.36334538e-01 -6.84683144e-01 -5.34192681e-01 -6.95695400e-01
-4.94593084e-01 3.98895621e-01 1.92854017e-01 -3.18162560... | [7.5516767501831055, 2.458076238632202] |
272c94fa-0faa-48d6-bb76-f9fc22a6aafe | simplistic-collection-and-labeling-practices | 2301.07015 | null | https://arxiv.org/abs/2301.07015v2 | https://arxiv.org/pdf/2301.07015v2.pdf | Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection | Accurate bot detection is necessary for the safety and integrity of online platforms. It is also crucial for research on the influence of bots in elections, the spread of misinformation, and financial market manipulation. Platforms deploy infrastructure to flag or remove automated accounts, but their tools and data are... | ['Philipp Zimmer', 'Erin Walk', 'Manish Raghavan', 'Zachary Schutzman', 'Chris Hays'] | 2023-01-17 | null | null | null | null | ['twitter-bot-detection'] | ['miscellaneous'] | [-8.81168768e-02 -3.14689815e-01 -6.99049532e-01 -9.67978127e-03
-3.21537733e-01 -1.01687396e+00 7.64922440e-01 3.77550274e-01
-7.65814185e-01 6.24077797e-01 1.51403069e-01 -1.01424348e+00
3.02981049e-01 -7.79150784e-01 -3.46257597e-01 -1.32635012e-01
2.71282405e-01 3.78047466e-01 2.97954321e-01 -1.77225649... | [8.232937812805176, 10.131983757019043] |
11c4366c-2cdb-4a8d-9b20-e2f3fb9982bf | keep-the-primary-rewrite-the-secondary-a-two | null | null | https://aclanthology.org/2021.findings-acl.50 | https://aclanthology.org/2021.findings-acl.50.pdf | Keep the Primary, Rewrite the Secondary: A Two-Stage Approach for Paraphrase Generation | null | ['Nigel Collier', 'Yan Wang', 'Simon Baker', 'David Vandyke', 'Yixuan Su'] | null | null | null | null | findings-acl-2021-8 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', '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... | [-7.3846917152404785, 3.682955503463745] |
7603c3a5-cc08-498e-99cf-ee0455a7fe65 | the-elements-of-temporal-sentence-grounding | 2201.08071 | null | https://arxiv.org/abs/2201.08071v3 | https://arxiv.org/pdf/2201.08071v3.pdf | Temporal Sentence Grounding in Videos: A Survey and Future Directions | Temporal sentence grounding in videos (TSGV), \aka natural language video localization (NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that semantically corresponds to a language query from an untrimmed video. Connecting computer vision and natural language, TSGV has drawn significant attenti... | ['Joey Tianyi Zhou', 'Wei Jing', 'Aixin Sun', 'Hao Zhang'] | 2022-01-20 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 2.63756990e-01 -1.35726333e-01 -6.96428359e-01 -2.17909798e-01
-8.69768202e-01 -9.03424382e-01 6.70168042e-01 -1.25961423e-01
-2.62459278e-01 2.64320195e-01 6.45939529e-01 -1.66105870e-02
-1.56615913e-01 -1.22286774e-01 -4.69810724e-01 -4.25619543e-01
-2.55403131e-01 -2.84850180e-01 2.04664618e-01 -8.11632946... | [10.118961334228516, 0.8265277147293091] |
ab26ab44-3cdc-48cd-9068-f9ed1f6f98bd | momentum-calibration-for-text-generation | 2212.04257 | null | https://arxiv.org/abs/2212.04257v1 | https://arxiv.org/pdf/2212.04257v1.pdf | Momentum Calibration for Text Generation | The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as Transformers. These models are usually trained by maximizing the likelihood the output text sequence and assumes the input sequence and all ... | ['Furu Wei', 'Wayne Xiong', 'Si-Qing Chen', 'Yang Yu', 'Pengcheng He', 'Xun Wang', 'Yiran Liu', 'Xingxing Zhang'] | 2022-12-08 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.01494712e-01 2.68492222e-01 -3.69227111e-01 -1.57035798e-01
-8.18910182e-01 -5.72214901e-01 1.03962708e+00 -1.35715172e-01
-2.31967986e-01 1.12914646e+00 3.79887372e-01 -2.91540951e-01
2.81349421e-01 -9.05757129e-01 -9.23288226e-01 -6.55292749e-01
3.38496506e-01 1.00127292e+00 -2.53740624e-02 -3.12597752... | [11.820849418640137, 9.0680570602417] |
ed35f8ce-08d0-4cc3-8070-8d80fc3681bb | an-in-network-data-cleaning-approach-for | null | null | http://www.tandfonline.com/loi/tasj20 | http://www.tandfonline.com/loi/tasj20 | An in-network data cleaning approach for wireless sensor networks | Wireless Sensor Networks (WSNs) are widely used for monitoring physical happenings of the
environment. However, the data gathered by the WSNs may be inaccurate and unreliable due to power
exhaustion, noise and other reasons. Unnecessary data such as erroneous data and redundant data
transmission causes a lot of extr... | ['Jun Huanga and Haeyoung Baeb', 'Haiyang Bia', 'Ying Xiaa', 'Jianjun Leia'] | 2016-03-17 | null | null | null | journal-2016-3 | ['clustering-algorithms-evaluation'] | ['methodology'] | [ 1.36291340e-01 3.08249831e-01 2.31361762e-01 -5.61744452e-01
-1.05992584e-02 -1.65667161e-01 -1.38985500e-01 9.32358027e-01
-5.29318988e-01 9.62570906e-01 -1.55717835e-01 1.66489020e-01
-5.85701942e-01 -1.21510828e+00 -3.23303729e-01 -1.16971672e+00
-5.18767774e-01 3.19859385e-02 5.24809361e-01 3.81701253... | [5.930065155029297, 1.7409032583236694] |
9386e334-2508-4deb-b4fc-98c53a457477 | towards-sustainable-deep-learning-for | 2201.09071 | null | https://arxiv.org/abs/2201.09071v1 | https://arxiv.org/pdf/2201.09071v1.pdf | Towards Sustainable Deep Learning for Wireless Fingerprinting Localization | Location based services, already popular with end users, are now inevitably becoming part of new wireless infrastructures and emerging business processes. The increasingly popular Deep Learning (DL) artificial intelligence methods perform very well in wireless fingerprinting localization based on extensive indoor radio... | ['Carolina Fortuna', 'Marko Meža', 'Mihael Mohorčič', 'Gregor Cerar', 'Blaž Bertalanič', 'Anže Pirnat'] | 2022-01-22 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-9.65299681e-02 2.09304571e-01 -4.08497542e-01 -2.17406675e-01
-4.45203751e-01 -4.37713265e-01 5.25188923e-01 1.31898791e-01
-6.06415749e-01 1.14596772e+00 -3.01991671e-01 -7.96605051e-01
-3.81549776e-01 -1.16886210e+00 -6.39627457e-01 -6.44940317e-01
-2.82598108e-01 3.26787680e-01 -1.17342971e-01 4.66574758... | [6.393052101135254, 0.9661210179328918] |
68321dfe-46ef-4f18-b913-b1114fa4f819 | the-role-of-context-types-and-dimensionality | 1601.00893 | null | http://arxiv.org/abs/1601.00893v2 | http://arxiv.org/pdf/1601.00893v2.pdf | The Role of Context Types and Dimensionality in Learning Word Embeddings | We provide the first extensive evaluation of how using different types of
context to learn skip-gram word embeddings affects performance on a wide range
of intrinsic and extrinsic NLP tasks. Our results suggest that while intrinsic
tasks tend to exhibit a clear preference to particular types of contexts and
higher dime... | ['David McClosky', 'Oren Melamud', 'Mohit Bansal', 'Siddharth Patwardhan'] | 2016-01-05 | the-role-of-context-types-and-dimensionality-1 | https://aclanthology.org/N16-1118 | https://aclanthology.org/N16-1118.pdf | naacl-2016-6 | ['learning-word-embeddings'] | ['methodology'] | [-1.65480152e-01 -2.25275517e-01 -5.00236511e-01 -5.33203959e-01
-7.93050349e-01 -6.73320472e-01 7.85463095e-01 2.65294075e-01
-9.40720737e-01 5.95915914e-01 6.80920720e-01 -4.02243376e-01
3.12968977e-02 -6.25139654e-01 -2.89386213e-01 -4.85025436e-01
-4.10916805e-02 1.68406829e-01 8.00523758e-02 -2.73464561... | [10.5645751953125, 8.59926700592041] |
58f18441-f350-4e5c-805c-258797b30aed | rc-qed-evaluating-natural-language | 1910.04601 | null | https://arxiv.org/abs/1910.04601v2 | https://arxiv.org/pdf/1910.04601v2.pdf | R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason | Recent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets. This prevents the community from reliably measuring the progress of RC systems. To address this issue, we introduce R4C, a new task for evaluating RC systems' internal reasoni... | ['Pontus Stenetorp', 'Naoya Inoue', 'Kentaro Inui'] | 2019-10-10 | r4c-a-benchmark-for-evaluating-rc-systems-to | https://aclanthology.org/2020.acl-main.602 | https://aclanthology.org/2020.acl-main.602.pdf | acl-2020-6 | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 1.40920699e-01 7.61558473e-01 1.28780693e-01 -5.28051734e-01
-1.24940515e+00 -1.17137444e+00 4.70023632e-01 2.86682606e-01
-2.82400697e-01 7.31950879e-01 6.37652993e-01 -6.87700033e-01
-6.86565191e-02 -4.88918424e-01 -9.82661664e-01 2.81056389e-03
5.56853831e-01 5.52466989e-01 2.95569479e-01 -3.01028669... | [11.05859375, 7.985897541046143] |
850c7a4c-3b20-470e-88b9-61f01c9fd40a | collecting-language-resources-for-the-latvian | null | null | https://aclanthology.org/L16-1202 | https://aclanthology.org/L16-1202.pdf | Collecting Language Resources for the Latvian e-Government Machine Translation Platform | This paper describes corpora collection activity for building large machine translation systems for Latvian e-Government platform. We describe requirements for corpora, selection and assessment of data sources, collection of the public corpora and creation of new corpora from miscellaneous sources. Methodology, tools a... | ['Raivis Skadi{\\c{n}}{\\v{s}}', 'Andrejs Vasi{\\c{l}}jevs', 'Roberts Rozis'] | 2016-05-01 | collecting-language-resources-for-the-latvian-1 | https://aclanthology.org/L16-1202 | https://aclanthology.org/L16-1202.pdf | lrec-2016-5 | ['miscellaneous'] | ['miscellaneous'] | [-3.06017488e-01 2.23295212e-01 -4.55470890e-01 -3.07738990e-01
-1.41243792e+00 -1.16949022e+00 1.15547645e+00 7.51556233e-02
-6.51118696e-01 1.19177186e+00 7.48662174e-01 -7.62118161e-01
1.77086800e-01 -4.63767558e-01 -1.65237740e-01 -4.86122578e-01
6.80279076e-01 1.18398297e+00 -2.04987898e-01 -5.99279404... | [11.197053909301758, 10.412286758422852] |
058bb359-510f-40c3-81b6-ea4647e79b11 | learning-to-represent-programs-with-1 | 2012.04188 | null | https://arxiv.org/abs/2012.04188v3 | https://arxiv.org/pdf/2012.04188v3.pdf | Learning to Represent Programs with Heterogeneous Graphs | Program source code contains complex structure information, which can be represented in structured data forms like trees or graphs. To acquire the structural information in source code, most existing researches use abstract syntax trees (AST). A group of works add additional edges to ASTs to convert source code into gr... | ['Huangzhao Zhang', 'Kechi Zhang', 'Zhi Jin', 'Ge Li', 'Wenhan Wang'] | 2020-12-08 | learning-to-represent-programs-with | null | null | null | ['code-comment-generation', 'comment-generation'] | ['computer-code', 'natural-language-processing'] | [-1.32803634e-01 3.04058462e-01 -7.13910758e-01 -5.38149893e-01
1.64235234e-02 -8.08500826e-01 1.00823119e-01 7.43301272e-01
2.89519131e-01 9.97382253e-02 2.32325971e-01 -8.52296591e-01
3.64794075e-01 -1.35120857e+00 -9.21225548e-01 -1.53672195e-03
-2.84491092e-01 -5.17388321e-02 5.90776563e-01 -3.59926701... | [7.5148725509643555, 7.873141288757324] |
d2fac47b-96db-4597-a548-d229223096cc | optimal-transport-for-unsupervised-1 | 2212.09631 | null | https://arxiv.org/abs/2212.09631v2 | https://arxiv.org/pdf/2212.09631v2.pdf | Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation | Neural machine translation (NMT) has become the de-facto standard in real-world machine translation applications. However, NMT models can unpredictably produce severely pathological translations, known as hallucinations, that seriously undermine user trust. It becomes thus crucial to implement effective preventive stra... | ['André F. T. Martins', 'Pablo Piantanida', 'Pierre Colombo', 'Nuno M. Guerreiro'] | 2022-12-19 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 1.60823837e-01 3.80610108e-01 -2.40171120e-01 5.28759547e-02
-1.01686561e+00 -4.38003719e-01 7.36939728e-01 2.95569357e-02
-7.59780779e-02 7.97653019e-01 3.84910703e-01 -4.88049775e-01
6.60528779e-01 -2.18716323e-01 -9.40932214e-01 -2.93958515e-01
3.14276427e-01 8.81054401e-01 -2.37215802e-01 -3.81800771... | [11.741525650024414, 9.968893051147461] |
e1eb3103-e0f0-4933-bc70-cac73cc3f073 | video-based-pedestrian-attribute-recognition | 1901.05742 | null | https://arxiv.org/abs/1901.05742v2 | https://arxiv.org/pdf/1901.05742v2.pdf | A Temporal Attentive Approach for Video-Based Pedestrian Attribute Recognition | In this paper, we first tackle the problem of pedestrian attribute recognition by video-based approach. The challenge mainly lies in spatial and temporal modeling and how to integrating them for effective and dynamic pedestrian representation. To solve this problem, a novel multi-task model based on the conventional ne... | ['Yunhong Wang', 'Zhiyuan Chen', 'Annan Li'] | 2019-01-17 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [ 4.72153053e-02 -8.27490151e-01 -3.75278652e-01 -5.60930848e-01
-7.47028351e-01 -6.89421296e-02 6.26561284e-01 2.14546826e-02
-5.75239897e-01 1.17332017e+00 1.76012993e-01 1.71821490e-01
-1.20487839e-01 -7.02627718e-01 -4.45680231e-01 -8.31136346e-01
-1.70623973e-01 3.97412270e-01 1.14228003e-01 -2.54188865... | [14.466917991638184, 0.9694693684577942] |
a7f63a10-4854-469d-b068-32cd879979df | imitation-and-supervised-learning-of | 2111.10488 | null | https://arxiv.org/abs/2111.10488v1 | https://arxiv.org/pdf/2111.10488v1.pdf | Imitation and Supervised Learning of Compliance for Robotic Assembly | We present the design of a learning-based compliance controller for assembly operations for industrial robots. We propose a solution within the general setting of learning from demonstration (LfD), where a nominal trajectory is provided through demonstration by an expert teacher. This can be used to learn a suitable re... | ['Daniel Nikovski', 'William Yerazunis', 'Diego Romeres', 'Devesh K. Jha'] | 2021-11-20 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 3.91593665e-01 6.03604615e-01 1.91633016e-01 1.21729217e-01
-3.29757810e-01 -6.46579325e-01 1.04580685e-01 3.34342048e-02
-3.65013123e-01 6.13500178e-01 -6.89076960e-01 -1.18304558e-01
-6.02486789e-01 -3.83248866e-01 -1.31587839e+00 -8.26328933e-01
1.83753759e-01 9.34758425e-01 1.05712168e-01 -2.40540192... | [4.815544605255127, 1.3684022426605225] |
c57af8b4-5c19-4cf0-9e25-92fb97f5fcfc | towards-speech-to-text-translation-without | 1702.03856 | null | http://arxiv.org/abs/1702.03856v1 | http://arxiv.org/pdf/1702.03856v1.pdf | Towards speech-to-text translation without speech recognition | We explore the problem of translating speech to text in low-resource
scenarios where neither automatic speech recognition (ASR) nor machine
translation (MT) are available, but we have training data in the form of audio
paired with text translations. We present the first system for this problem
applied to a realistic mu... | ['Sameer Bansal', 'Sharon Goldwater', 'Adam Lopez', 'Herman Kamper'] | 2017-02-13 | towards-speech-to-text-translation-without-1 | https://aclanthology.org/E17-2076 | https://aclanthology.org/E17-2076.pdf | eacl-2017-4 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 6.78361356e-01 1.22103244e-01 1.16377868e-01 -4.36422288e-01
-1.94478691e+00 -8.00239265e-01 8.00065041e-01 -2.40167201e-01
-2.89416641e-01 6.23336017e-01 5.13698459e-01 -7.25956321e-01
3.57344449e-01 -8.38983357e-02 -6.74649000e-01 -3.62964362e-01
2.86698997e-01 1.00303209e+00 6.57658055e-02 -3.82596403... | [14.464219093322754, 7.155285835266113] |
06175ad2-3249-4fc7-9a2e-9a4c8e9944bb | explaining-predictions-from-tree-based | 1907.02582 | null | https://arxiv.org/abs/1907.02582v1 | https://arxiv.org/pdf/1907.02582v1.pdf | Explaining Predictions from Tree-based Boosting Ensembles | Understanding how "black-box" models arrive at their predictions has sparked significant interest from both within and outside the AI community. Our work focuses on doing this by generating local explanations about individual predictions for tree-based ensembles, specifically Gradient Boosting Decision Trees (GBDTs). G... | ['Maarten de Rijke', 'Hinda Haned', 'Ana Lucic'] | 2019-07-04 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 6.12324595e-01 7.47221053e-01 -3.39434683e-01 -4.55609918e-01
-4.91728336e-01 -5.01725256e-01 8.74634206e-01 3.50223295e-02
9.27707404e-02 1.19877660e+00 4.06132519e-01 -9.97021139e-01
9.04283114e-03 -1.01680553e+00 -8.75991940e-01 -7.96018958e-01
6.14946149e-02 5.91593206e-01 1.91450819e-01 -1.56836659... | [8.672211647033691, 5.617580413818359] |
1e82bcd4-adea-45e8-933d-2c1ff2959dfe | parser-free-virtual-try-on-via-distilling | 2103.04559 | null | https://arxiv.org/abs/2103.04559v2 | https://arxiv.org/pdf/2103.04559v2.pdf | Parser-Free Virtual Try-on via Distilling Appearance Flows | Image virtual try-on aims to fit a garment image (target clothes) to a person image. Prior methods are heavily based on human parsing. However, slightly-wrong segmentation results would lead to unrealistic try-on images with large artifacts. Inaccurate parsing misleads parser-based methods to produce visually unrealist... | ['Ping Luo', 'Wei Liu', 'Chongjian Ge', 'Ruimao Zhang', 'Yibing Song', 'Yuying Ge'] | 2021-03-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ge_Parser-Free_Virtual_Try-On_via_Distilling_Appearance_Flows_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ge_Parser-Free_Virtual_Try-On_via_Distilling_Appearance_Flows_CVPR_2021_paper.pdf | cvpr-2021-1 | ['human-parsing'] | ['computer-vision'] | [ 2.98609614e-01 7.09654212e-01 2.26806968e-01 -2.33405992e-01
-5.34327745e-01 -4.37913626e-01 1.95763290e-01 -5.56768596e-01
-2.11123034e-01 6.64140940e-01 -5.60844481e-01 -2.98313260e-01
5.03980935e-01 -9.40124035e-01 -1.26998711e+00 -5.91637313e-01
7.97582209e-01 3.31320286e-01 3.67829055e-01 -1.01038568... | [11.168296813964844, -0.4297358989715576] |
8f9936cb-4277-436a-856b-d82faf4a2509 | snu-ids-at-semeval-2019-task-3-addressing | null | null | https://aclanthology.org/S19-2054 | https://aclanthology.org/S19-2054.pdf | SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification | We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem. Reducing the distance between the distribution of prediction and ground truth, they consiste... | ['Sang-goo Lee', 'Sanghwan Bae', 'Jihun Choi'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 4.94690947e-02 2.27688393e-03 -1.77371517e-01 -1.08436334e+00
-9.29023325e-01 -1.99092180e-01 1.69114381e-01 2.26110488e-01
-4.63157088e-01 9.78392780e-01 1.27812639e-01 2.10047707e-01
3.16449225e-01 -3.92772883e-01 -5.25802135e-01 -4.89698648e-01
1.17324449e-01 2.67128438e-01 -2.66233057e-01 -2.90453345... | [13.51669979095459, 5.712762832641602] |
05c54d65-141f-40de-82ba-c058973ef11d | deep-neural-network-based-on-f-neurons-and | null | null | https://www.researchsquare.com/article/rs-2032768/v1 | https://assets.researchsquare.com/files/rs-2032768/v1_covered.pdf?c=1667512127 | Deep neural network based on F-neurons and its learning | Artificial neural networks are widely used in data processing and Data Mining. In contrast to traditional artificial neural networks, deep neural networks employ many artificial neuron blocks and more than three layers. An increase in the number of neuron blocks and layers improves the approximation capabilities but le... | ['Serhii Kostiuk', 'Yevgeniy Bodyanskiy'] | 2022-11-03 | null | null | null | research-square-pre-print-2022-11 | ['activation-function-synthesis', 'architecture-search'] | ['methodology', 'methodology'] | [-2.88876861e-01 -2.16156945e-01 5.05674668e-02 -6.36380494e-01
1.80620641e-01 -1.18694127e-01 1.73868388e-01 -2.48944256e-02
-9.34471130e-01 5.40380776e-01 -2.09954426e-01 -3.05807859e-01
1.05510220e-01 -9.19058800e-01 -9.57697809e-01 -3.76395524e-01
-1.12071007e-01 1.31258681e-01 4.05399263e-01 -4.05691952... | [8.830869674682617, 2.7464919090270996] |
2b174bc9-c837-4fda-bb99-7e444a684ebc | exploit-fully-automatic-low-level-segmented | 1903.02871 | null | http://arxiv.org/abs/1903.02871v1 | http://arxiv.org/pdf/1903.02871v1.pdf | Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data | We present an approach for fully automatic urinary bladder segmentation in CT
images with artificial neural networks in this study. Automatic medical image
analysis has become an invaluable tool in the different treatment stages of
diseases. Especially medical image segmentation plays a vital role, since
segmentation i... | ['Jürgen Wallner', 'Peter M. Roth', 'Jan Egger', 'Christina Gsaxner'] | 2019-03-07 | null | null | null | null | ['bladder-segmentation'] | ['medical'] | [ 5.95471442e-01 4.66728300e-01 -2.64452189e-01 -6.35237336e-01
-3.40728283e-01 -2.11874381e-01 3.52221370e-01 4.47459430e-01
-1.13030231e+00 6.77860141e-01 -2.37393931e-01 -3.74955118e-01
4.62292805e-02 -9.91326332e-01 -6.05642259e-01 -5.20769835e-01
-7.18223527e-02 1.04467833e+00 5.30088581e-02 -1.27910171... | [14.474397659301758, -2.476074457168579] |
fa9dcd3c-42d0-43a5-a259-87a66d475a27 | head-and-body-unified-detector-and-graph | 2111.13888 | null | https://arxiv.org/abs/2111.13888v1 | https://arxiv.org/pdf/2111.13888v1.pdf | Head and Body: Unified Detector and Graph Network for Person Search in Media | Person search in media has seen increasing potential in Internet applications, such as video clipping and character collection. This task is common but overlooked by previous person search works which focus on surveillance scenes. The media scenarios have some different challenges from surveillance scenes. For example,... | ['Bo Ren', 'Wei Wen', 'Bo Ke', 'Ruizhi Qiao', 'Yusheng Tao', 'Xiujun Shu'] | 2021-11-27 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-2.48863265e-01 -4.24000442e-01 -2.04534605e-01 -1.57542869e-01
-4.48260427e-01 -4.22223657e-01 4.05675471e-01 -3.54856178e-02
-4.17044461e-01 3.14625949e-01 4.14532959e-01 3.49541396e-01
8.98213312e-02 -7.76516676e-01 -4.03749943e-01 -4.76384789e-01
-3.67813148e-02 3.17241341e-01 6.25364184e-01 -2.55395293... | [14.829538345336914, 0.7823842763900757] |
d5fc1c97-9f21-4649-80b3-8fe81f22fd04 | geometrics-exploiting-geometric-structure-for | 1901.11461 | null | http://arxiv.org/abs/1901.11461v1 | http://arxiv.org/pdf/1901.11461v1.pdf | GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects | Mesh models are a promising approach for encoding the structure of 3D
objects. Current mesh reconstruction systems predict uniformly distributed
vertex locations of a predetermined graph through a series of graph
convolutions, leading to compromises with respect to performance or resolution.
In this paper, we argue tha... | ['Scott Fujimoto', 'David Meger', 'Edward J. Smith', 'Adriana Romero'] | 2019-01-31 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 2.31605902e-01 5.23623347e-01 2.68649548e-01 -1.31941959e-01
-4.06142712e-01 -4.58669960e-01 7.05507755e-01 5.31708121e-01
1.95599943e-01 2.56378978e-01 1.56016842e-01 -2.18974218e-01
-3.58378552e-02 -1.18436515e+00 -1.05616271e+00 -4.82274354e-01
-3.39535445e-01 7.23319232e-01 3.10540110e-01 -1.57044321... | [8.527009963989258, -3.631091594696045] |
2b872471-761a-4a24-9f63-576a6744ec3e | exploiting-data-characteristics-for-document | null | null | https://openreview.net/forum?id=tVF2KuTau5g | https://openreview.net/pdf?id=tVF2KuTau5g | Exploiting Data Characteristics for Document-level Event Extraction | Document-level event extraction (DEE) extracts structured information of events from a document. Previous studies focus on improving the model architecture. We propose to exploit data characteristics: 1) we utilize more coreference information to obtain better document-level entity representations; 2) we manually ident... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['document-level-event-extraction'] | ['natural-language-processing'] | [-3.65761598e-03 1.64286956e-01 -6.92402124e-01 -5.25118649e-01
-9.47778285e-01 -7.20025241e-01 6.93004489e-01 7.88708091e-01
-5.62408030e-01 7.01752841e-01 7.92484641e-01 -2.19524298e-02
-2.14349292e-02 -8.59050751e-01 -6.22181296e-01 -1.22047644e-02
-1.48577243e-01 2.80808479e-01 5.12136340e-01 1.37093171... | [9.167539596557617, 9.20262622833252] |
a834a685-d2f7-427f-8138-c178f0299184 | a-gold-standard-to-measure-relative | null | null | https://aclanthology.org/W18-4601 | https://aclanthology.org/W18-4601.pdf | A Gold Standard to Measure Relative Linguistic Complexity with a Grounded Language Learning Model | This paper focuses on linguistic complexity from a relative perspective. It presents a grounded language learning system that can be used to study linguistic complexity from a developmental point of view and introduces a tool for generating a gold standard in order to evaluate the performance of the learning system. In... | ["M. Dolores Jim{\\'e}nez-L{\\'o}pez", 'Leonor Becerra-Bonache', 'Henning Christiansen'] | 2018-08-01 | null | null | null | ws-2018-8 | ['grounded-language-learning'] | ['natural-language-processing'] | [-3.07367086e-01 2.67800897e-01 -1.42447904e-01 -4.66931850e-01
-3.88033628e-01 -5.21205783e-01 3.86812985e-01 1.05100572e+00
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1.36895075e-01 4.78900015e-01 2.01898977e-01 -4.95069116... | [10.570271492004395, 9.936635971069336] |
074173dc-23b9-4333-8d4d-999155022882 | multi-granularity-prediction-for-scene-text | 2209.03592 | null | https://arxiv.org/abs/2209.03592v2 | https://arxiv.org/pdf/2209.03592v2.pdf | Multi-Granularity Prediction for Scene Text Recognition | Scene text recognition (STR) has been an active research topic in computer vision for years. To tackle this challenging problem, numerous innovative methods have been successively proposed and incorporating linguistic knowledge into STR models has recently become a prominent trend. In this work, we first draw inspirati... | ['Cong Yao', 'Cheng Da', 'Peng Wang'] | 2022-09-08 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.57210779e-01 -4.51249987e-01 -1.69329330e-01 -1.29512146e-01
-7.57093787e-01 -3.66617769e-01 9.38572288e-01 1.05679035e-01
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9.12629902e-01 1.98945895e-01 3.80498528e-01 -7.66896009... | [11.791440963745117, 2.115125894546509] |
80f74173-05c6-4369-a0cf-18f24a6fc870 | shadocnet-learning-spatial-aware-tokens-in | 2211.16675 | null | https://arxiv.org/abs/2211.16675v2 | https://arxiv.org/pdf/2211.16675v2.pdf | ShaDocNet: Learning Spatial-Aware Tokens in Transformer for Document Shadow Removal | Shadow removal improves the visual quality and legibility of digital copies of documents. However, document shadow removal remains an unresolved subject. Traditional techniques rely on heuristics that vary from situation to situation. Given the quality and quantity of current public datasets, the majority of neural net... | ['Shuqiang Wang', 'Chi-Man Pun', 'Xiaodong Cun', 'Xuhang Chen'] | 2022-11-30 | null | null | null | null | ['shadow-removal', 'shadow-detection', 'image-shadow-removal'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.50470793e-01 -5.29325247e-01 9.34544280e-02 -2.78560668e-01
-3.62996250e-01 -5.40685177e-01 5.74609280e-01 -9.14054215e-02
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3.26732963e-01 -1.04709320e-01 5.05328655e-01 -1.43758491... | [10.86196231842041, -4.042856693267822] |
04222e42-955e-4f87-b9d9-039940c45d71 | vast-a-vision-audio-subtitle-text-omni | 2305.18500 | null | https://arxiv.org/abs/2305.18500v1 | https://arxiv.org/pdf/2305.18500v1.pdf | VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset | Vision and text have been fully explored in contemporary video-text foundational models, while other modalities such as audio and subtitles in videos have not received sufficient attention. In this paper, we resort to establish connections between multi-modality video tracks, including Vision, Audio, and Subtitle, and ... | ['Jing Liu', 'Xinxin Zhu', 'Mingzhen Sun', 'Zijia Zhao', 'Qunbo Wang', 'Handong Li', 'Sihan Chen'] | 2023-05-29 | null | null | null | null | ['audio-captioning', 'video-captioning', 'video-question-answering', 'video-retrieval', 'image-captioning', 'cross-modal-retrieval', 'zero-shot-cross-modal-retrieval'] | ['audio', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous', 'miscellaneous'] | [ 2.76358128e-01 -1.83802262e-01 -1.91446349e-01 -2.10116550e-01
-1.35663092e+00 -5.32520652e-01 7.25875020e-01 -3.39348704e-01
-2.63443649e-01 4.73123997e-01 7.20904052e-01 -1.49010584e-01
3.06838274e-01 -1.11029215e-01 -1.21301162e+00 -3.17465186e-01
3.21335852e-01 2.85057813e-01 1.09978877e-01 7.73489997... | [10.549964904785156, 1.0510869026184082] |
190b2102-12a5-4a5e-a0fc-929129fc7e00 | competing-models | 1907.03809 | null | https://arxiv.org/abs/1907.03809v5 | https://arxiv.org/pdf/1907.03809v5.pdf | Competing Models | Different agents need to make a prediction. They observe identical data, but have different models: they predict using different explanatory variables. We study which agent believes they have the best predictive ability -- as measured by the smallest subjective posterior mean squared prediction error -- and show how it... | ['Mallesh M. Pai', 'Andrea Prat', 'Jose Luis Montiel Olea', 'Pietro Ortoleva'] | 2019-07-08 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-2.06704125e-01 5.79807222e-01 -6.35926664e-01 -2.71141618e-01
-2.58130074e-01 -4.95192945e-01 5.31453133e-01 -2.78331131e-01
-4.62419271e-01 8.43849003e-01 2.75133163e-01 -6.16780102e-01
-5.94026864e-01 -7.04176009e-01 -3.79776955e-01 -5.58975875e-01
-2.64405981e-02 8.61170173e-01 -2.25618929e-01 -7.49787614... | [4.409146785736084, 3.085740566253662] |
61d64ed0-8565-42cb-92f0-5523bbe39d75 | halting-in-random-walk-kernels | null | null | http://papers.nips.cc/paper/5688-halting-in-random-walk-kernels | http://papers.nips.cc/paper/5688-halting-in-random-walk-kernels.pdf | Halting in Random Walk Kernels | Random walk kernels measure graph similarity by counting matching walks in two graphs. In their most popular form of geometric random walk kernels, longer walks of length $k$ are downweighted by a factor of $\lambda^k$ ($\lambda < 1$) to ensure convergence of the corresponding geometric series. We know from the field o... | ['Karsten Borgwardt', 'Mahito Sugiyama'] | 2015-12-01 | null | null | null | neurips-2015-12 | ['graph-similarity'] | ['graphs'] | [ 7.34373406e-02 3.22608322e-01 -1.50605112e-01 -2.26121694e-01
-3.87745142e-01 -7.68624008e-01 4.06592637e-01 7.29378402e-01
-2.87694484e-01 4.26299959e-01 -2.31263831e-01 -7.57409990e-01
-2.56673843e-01 -1.38181674e+00 -5.79559207e-01 -5.15062630e-01
-8.98927212e-01 4.51339006e-01 6.73502564e-01 -4.94539440... | [6.975478649139404, 5.4732666015625] |
2688f7a5-1576-4ccb-9ffc-74d8f8521a1a | collaboratively-boosting-data-driven-deep | 2010.02451 | null | https://arxiv.org/abs/2010.02451v1 | https://arxiv.org/pdf/2010.02451v1.pdf | Collaboratively boosting data-driven deep learning and knowledge-guided ontological reasoning for semantic segmentation of remote sensing imagery | As one kind of architecture from the deep learning family, deep semantic segmentation network (DSSN) achieves a certain degree of success on the semantic segmentation task and obviously outperforms the traditional methods based on hand-crafted features. As a classic data-driven technique, DSSN can be trained by an end-... | ['Yongjun Zhang', 'Song Ouyang', 'Yansheng Li'] | 2020-10-06 | null | null | null | null | ['segmentation-of-remote-sensing-imagery'] | ['miscellaneous'] | [ 2.25585774e-01 3.49747986e-01 -2.18778178e-01 -6.70500994e-01
-1.10706620e-01 -1.45464659e-01 5.18296063e-01 4.55789566e-02
-4.78199661e-01 3.65302205e-01 -1.91548586e-01 -2.56357431e-01
-3.17358255e-01 -1.04649282e+00 -4.93013024e-01 -4.72421616e-01
3.18629354e-01 4.74902958e-01 3.14245403e-01 -5.96909106... | [9.679390907287598, 0.6810242533683777] |
c2fe9dda-0f0d-463c-a164-5358a1892676 | where-will-players-move-next-dynamic-graphs | 2211.12217 | null | https://arxiv.org/abs/2211.12217v2 | https://arxiv.org/pdf/2211.12217v2.pdf | Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badminton | Sports analytics has captured increasing attention since analysis of the various data enables insights for training strategies, player evaluation, etc. In this paper, we focus on predicting what types of returning strokes will be made, and where players will move to based on previous strokes. As this problem has not be... | ['Wen-Chih Peng', 'Wei-Yao Wang', 'Kai-Shiang Chang'] | 2022-11-22 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 5.91177009e-02 -2.73501754e-01 -4.68205631e-01 2.20719695e-01
-4.94234897e-02 -5.13658524e-01 6.02626145e-01 -9.19845998e-02
-8.47260505e-02 4.01377797e-01 6.92401826e-01 -2.19525337e-01
-3.98338199e-01 -1.11876988e+00 -3.23660493e-01 -3.38357866e-01
-9.70769674e-02 4.75588590e-01 6.45685136e-01 -8.16478968... | [6.720944881439209, 0.3468337655067444] |
a3d36659-c36f-45cd-a102-13d9944225bd | multimodal-cnn-networks-for-brain-tumor | 2212.09310 | null | https://arxiv.org/abs/2212.09310v1 | https://arxiv.org/pdf/2212.09310v1.pdf | Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution | Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Chal... | ['Franziska Mathis-Ullrich', 'Oliver Burgert', 'Mohamed E. Karar', 'Ramy A. Zeineldin'] | 2022-12-19 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [-1.18410833e-01 -1.29706133e-03 1.95185483e-01 -2.26839721e-01
-1.14104950e+00 -6.21047854e-01 5.14820337e-01 4.15946931e-01
-6.84646010e-01 9.47422445e-01 1.78397343e-01 -4.40785617e-01
-4.05232787e-01 -4.70382303e-01 -2.20299080e-01 -1.12714958e+00
-3.57079715e-01 7.32580304e-01 2.65129358e-01 1.42431051... | [14.619728088378906, -2.460951328277588] |
30d37faa-5667-47f4-a41f-63ff5cb1632e | semi-parametric-video-grounded-text | 2301.11507 | null | https://arxiv.org/abs/2301.11507v1 | https://arxiv.org/pdf/2301.11507v1.pdf | Semi-Parametric Video-Grounded Text Generation | Efficient video-language modeling should consider the computational cost because of a large, sometimes intractable, number of video frames. Parametric approaches such as the attention mechanism may not be ideal since its computational cost quadratically increases as the video length increases. Rather, previous studies ... | ['Minjoon Seo', 'Jiyoung Lee', 'Jin-Hwa Kim', 'Sungdong Kim'] | 2023-01-27 | null | null | null | null | ['video-question-answering', 'video-understanding'] | ['computer-vision', 'computer-vision'] | [-4.32770364e-02 -2.45545387e-01 -4.58847106e-01 -2.67461181e-01
-1.34094071e+00 -3.67188901e-01 5.21305621e-01 -3.84146482e-01
-3.34917605e-01 5.13750732e-01 4.80480731e-01 -7.09031075e-02
3.02446812e-01 -3.26939732e-01 -1.14467752e+00 -5.37712872e-01
-2.26474658e-01 5.34237325e-01 2.36152232e-01 1.74219549... | [10.178637504577637, 0.7176439762115479] |
bc02c5d5-85d4-4ebf-adf5-548dca5fea7a | heart-darts-classification-of-heartbeats | 2105.00693 | null | https://arxiv.org/abs/2105.00693v1 | https://arxiv.org/pdf/2105.00693v1.pdf | Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search | Arrhythmia is a cardiovascular disease that manifests irregular heartbeats. In arrhythmia detection, the electrocardiogram (ECG) signal is an important diagnostic technique. However, manually evaluating ECG signals is a complicated and time-consuming task. With the application of convolutional neural networks (CNNs), t... | ['Jiancheng Lv', 'Juan Zhao', 'Yanan sun', 'Qing Ye', 'Jindi Lv'] | 2021-05-03 | null | null | null | null | ['arrhythmia-detection', 'ecg-classification'] | ['medical', 'medical'] | [ 1.09626845e-01 -4.62754995e-01 3.02665353e-01 -1.83379441e-01
-2.86336690e-01 -4.49747294e-01 -2.84304440e-01 1.93664789e-01
-3.68207723e-01 7.70110369e-01 -4.26781654e-01 -3.68809104e-01
-2.48027220e-01 -6.59780741e-01 -1.29768908e-01 -7.01526046e-01
-2.76853323e-01 3.58207703e-01 -2.42547944e-01 -4.16267514... | [14.273576736450195, 3.2698471546173096] |
8bd152ee-1bb2-4969-bd7a-52750aef25ba | self-supervised-mean-teacher-for-semi | 2103.03629 | null | https://arxiv.org/abs/2103.03629v3 | https://arxiv.org/pdf/2103.03629v3.pdf | Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification | The training of deep learning models generally requires a large amount of annotated data for effective convergence and generalisation. However, obtaining high-quality annotations is a laboursome and expensive process due to the need of expert radiologists for the labelling task. The study of semi-supervised learning in... | ['Gustavo Carneiro', 'Ian Reid', 'Vasileios Belagiannis', 'Filipe R. Cordeiro', 'Yu Tian', 'Fengbei Liu'] | 2021-03-05 | null | null | null | null | ['semi-supervised-medical-image-classification'] | ['medical'] | [ 5.28847098e-01 5.57860255e-01 -2.36023337e-01 -8.04422975e-01
-1.41799688e+00 -2.88902998e-01 4.61369574e-01 1.30082548e-01
-8.14966261e-01 7.56412625e-01 -3.72300208e-01 -3.23348850e-01
-5.44848382e-01 -4.80057031e-01 -7.76003540e-01 -9.99212801e-01
-1.18759170e-01 7.84591138e-01 2.90749013e-01 4.17523496... | [14.693458557128906, -2.2140378952026367] |
ad841b67-4167-4928-8526-7cb648db8e6c | why-so-deep-towards-boosting-previously | 2201.03212 | null | https://arxiv.org/abs/2201.03212v1 | https://arxiv.org/pdf/2201.03212v1.pdf | Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition | Deep learning-based image retrieval techniques for the loop closure detection demonstrate satisfactory performance. However, it is still challenging to achieve high-level performance based on previously trained models in different geographical regions. This paper addresses the problem of their deployment with simultane... | ['Ming Liu', 'Darwin Lau', 'Yuxiang Sun', 'M. Usman Maqbool Bhutta'] | 2022-01-10 | null | null | null | null | ['loop-closure-detection', 'visual-place-recognition'] | ['computer-vision', 'computer-vision'] | [-2.78275251e-01 -5.34108281e-01 -1.08126119e-01 -2.52461433e-01
-1.26938355e+00 -5.30115426e-01 8.86783361e-01 5.18315673e-01
-9.64735746e-01 3.91812205e-01 -2.63195425e-01 -8.69264677e-02
-3.86992425e-01 -7.19065964e-01 -7.25617826e-01 -4.57760900e-01
-3.54136378e-01 7.42311537e-01 3.81324738e-01 -4.40782368... | [7.491269111633301, -2.1169350147247314] |
c29ce70f-93ab-4527-9152-625b566262c4 | discourse-representation-structure-parsing-2 | 2306.09725 | null | https://arxiv.org/abs/2306.09725v1 | https://arxiv.org/pdf/2306.09725v1.pdf | Discourse Representation Structure Parsing for Chinese | Previous work has predominantly focused on monolingual English semantic parsing. We, instead, explore the feasibility of Chinese semantic parsing in the absence of labeled data for Chinese meaning representations. We describe the pipeline of automatically collecting the linearized Chinese meaning representation data fo... | ['Johan Bos', 'Xiao Zhang', 'Chunliu Wang'] | 2023-06-16 | null | null | null | null | ['machine-translation', 'semantic-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.18334490e-01 5.95559657e-01 2.59851688e-04 -7.77908206e-01
-1.18555486e+00 -7.39468753e-01 2.17996791e-01 -1.28700221e-02
-5.97446084e-01 6.86987936e-01 5.95432997e-01 -8.20407510e-01
6.64048314e-01 -8.18863988e-01 -7.19513655e-01 -1.06939234e-01
2.46394321e-01 5.40835977e-01 3.83177423e-03 -2.97564548... | [10.479972839355469, 9.410812377929688] |
916d5821-f466-4246-975b-ef5c6c79779d | direct-quantification-for-coronary-artery | 1907.10032 | null | https://arxiv.org/abs/1907.10032v3 | https://arxiv.org/pdf/1907.10032v3.pdf | Direct Quantification for Coronary Artery Stenosis Using Multiview Learning | The quantification of the coronary artery stenosis is of significant clinical importance in coronary artery disease diagnosis and intervention treatment. It aims to quantify the morphological indices of the coronary artery lesions such as minimum lumen diameter, reference vessel diameter, lesion length, and these indic... | ['Heye Zhang', 'Shu Zhao', 'Dong Zhang', 'Yanping Zhang', 'Shuo Li', 'Guang Yang'] | 2019-07-20 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [-2.39009291e-01 -1.19308777e-01 -3.74809444e-01 -4.54361290e-01
-1.26665008e+00 -3.41274142e-01 2.64971144e-02 5.10720070e-03
-6.11376995e-03 3.49005848e-01 3.89039457e-01 -4.77668405e-01
-1.25156373e-01 -6.95295155e-01 -1.70850590e-01 -7.05469310e-01
-1.10397287e-01 2.82997876e-01 1.87988758e-01 1.71795875... | [14.45499038696289, -2.4046082496643066] |
f32d75ff-53bf-42c4-b008-a7c71be110bd | counterfactual-explanations-can-be | 2106.02666 | null | https://arxiv.org/abs/2106.02666v2 | https://arxiv.org/pdf/2106.02666v2.pdf | Counterfactual Explanations Can Be Manipulated | Counterfactual explanations are emerging as an attractive option for providing recourse to individuals adversely impacted by algorithmic decisions. As they are deployed in critical applications (e.g. law enforcement, financial lending), it becomes important to ensure that we clearly understand the vulnerabilities of th... | ['Sameer Singh', 'Himabindu Lakkaraju', 'Sophie Hilgard', 'Dylan Slack'] | 2021-06-04 | null | http://proceedings.neurips.cc/paper/2021/hash/009c434cab57de48a31f6b669e7ba266-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/009c434cab57de48a31f6b669e7ba266-Paper.pdf | neurips-2021-12 | ['counterfactual-explanation', 'crime-prediction'] | ['miscellaneous', 'miscellaneous'] | [ 1.58867076e-01 5.22582054e-01 -5.42924583e-01 -5.40211916e-01
-4.30422336e-01 -4.54953551e-01 7.49406636e-01 1.24083772e-01
-3.75821888e-01 1.18167114e+00 6.86372042e-01 -1.04660869e+00
-4.47754055e-01 -6.65540993e-01 -5.92365801e-01 -4.01626348e-01
-8.11152384e-02 2.30843320e-01 -4.09789890e-01 -2.62234598... | [8.67993450164795, 5.591487407684326] |
c0441a04-9b6c-4600-90ae-5afcd2a8f0ef | end-to-end-emotion-cause-pair-extraction-with | null | null | https://aclanthology.org/2020.coling-main.17 | https://aclanthology.org/2020.coling-main.17.pdf | End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network | Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages. Considering that most emotions usually have few causes mentioned in their contexts, we present a no... | ['Xiaoqiang Zhang', 'Caicong Wu', 'Shoushan Li', 'Wenjun Hou', 'Ying Chen'] | 2020-12-01 | null | null | null | coling-2020-8 | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [-1.01200394e-01 -1.65824831e-01 2.42061969e-02 -7.57467866e-01
-5.06453514e-01 -5.89476883e-01 6.58679247e-01 5.59375346e-01
-9.77678075e-02 6.75750732e-01 6.93079531e-01 -1.99018605e-02
-9.36624929e-02 -7.05515087e-01 -3.94927561e-01 -3.77023309e-01
-1.36212915e-01 -1.16007052e-01 -1.81911811e-01 -5.01969695... | [12.623649597167969, 6.216344356536865] |
a790bec4-3fc2-491e-9fbd-2322d2abb45a | ernie-search-bridging-cross-encoder-with-dual | 2205.09153 | null | https://arxiv.org/abs/2205.09153v1 | https://arxiv.org/pdf/2205.09153v1.pdf | ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval | Neural retrievers based on pre-trained language models (PLMs), such as dual-encoders, have achieved promising performance on the task of open-domain question answering (QA). Their effectiveness can further reach new state-of-the-arts by incorporating cross-architecture knowledge distillation. However, most of the exist... | ['Haifeng Wang', 'Dawei Yin', 'Shuaiqiang Wang', 'Hua Wu', 'Hao Tian', 'Shikun Feng Yu Sun', 'Zhengjie Huang', 'Yunsheng Shi', 'Jiaxiang Liu', 'Yiding Liu', 'Yuxiang Lu'] | 2022-05-18 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-7.96800256e-02 2.76018173e-01 4.34962213e-02 -4.56557870e-01
-1.38786721e+00 -6.52905703e-01 6.44797087e-01 1.59033202e-02
-5.91272712e-01 6.64031148e-01 3.15747082e-01 -7.75577664e-01
2.17370629e-01 -9.05582130e-01 -1.16306400e+00 -2.65944809e-01
4.79935229e-01 1.06582236e+00 2.25207910e-01 -7.16677904... | [11.241779327392578, 8.015488624572754] |
dd7c7a63-1df3-4c9e-b90d-5e417fdc9c6d | cross-domain-medical-image-translation-by | 2007.07230 | null | https://arxiv.org/abs/2007.07230v1 | https://arxiv.org/pdf/2007.07230v1.pdf | Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Model | Current deep learning based segmentation models often generalize poorly between domains due to insufficient training data. In real-world clinical applications, cross-domain image analysis tools are in high demand since medical images from different domains are often needed to achieve a precise diagnosis. An important e... | ['Yu-Xing Tang', 'Sung-Won Lee', 'You-Bao Tang', 'Yingying Zhu', 'Perry J. Pickhardt', 'Ronald M. Summers', 'Daniel C. Elton'] | 2020-07-14 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 3.37685972e-01 3.03533673e-01 -8.11112151e-02 -4.12952751e-01
-1.03327239e+00 -6.07387960e-01 3.72025788e-01 1.33218810e-01
-2.23546699e-01 6.20129883e-01 5.86080551e-02 -2.15641543e-01
-1.06090471e-01 -8.87638092e-01 -8.99746239e-01 -7.68257320e-01
-5.09840138e-02 9.83991981e-01 4.58167315e-01 -1.56469420... | [14.357365608215332, -2.2397756576538086] |
49e90bae-1975-45ca-9577-fb65edc2131a | physics-coupled-spatio-temporal-active | 2108.05385 | null | https://arxiv.org/abs/2108.05385v1 | https://arxiv.org/pdf/2108.05385v1.pdf | Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems | Spatio-temporal forecasting is of great importance in a wide range of dynamical systems applications from atmospheric science, to recent COVID-19 spread modeling. These applications rely on accurate predictions of spatio-temporal structured data reflecting real-world phenomena. A stunning characteristic is that the dyn... | ['Laurent Cherubin', 'Hanqi Zhuang', 'Ali Muhamed Ali', 'Min Shi', 'Xingquan Zhu', 'Yufei Tang', 'Yu Huang'] | 2021-08-11 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [ 1.45239606e-01 -5.45423441e-02 -9.96238738e-02 -1.74062252e-01
-1.77659646e-01 -4.78521109e-01 9.14517045e-01 3.57213855e-01
5.43735549e-02 9.42575574e-01 1.21028282e-01 -4.43220168e-01
-8.03677619e-01 -9.27297473e-01 -7.72472918e-01 -1.09869659e+00
-7.07283020e-01 4.70460385e-01 4.35282707e-01 -3.68028045... | [6.7163848876953125, 3.025892734527588] |
70b1b1ca-1804-4514-a7a6-1e2c36311ed4 | parrottts-text-to-speech-synthesis-by | 2303.01261 | null | https://arxiv.org/abs/2303.01261v1 | https://arxiv.org/pdf/2303.01261v1.pdf | ParrotTTS: Text-to-Speech synthesis by exploiting self-supervised representations | Text-to-speech (TTS) systems are modelled as mel-synthesizers followed by speech-vocoders since the era of statistical TTS that is carried forward into neural designs. We propose an alternative approach to TTS modelling referred to as ParrotTTS borrowing from self-supervised learning (SSL) methods. ParrotTTS takes a tw... | ['Vineet Gandhi', 'Neha Sherin', 'Vishal Tambrahalli', 'Neil Kumar Shah', 'Saiteja Kosgi'] | 2023-03-01 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 4.06913728e-01 7.41362929e-01 -1.95176318e-01 -6.84610009e-01
-1.41251040e+00 -5.39561629e-01 7.79745817e-01 -1.63785830e-01
-2.03162059e-01 4.13124263e-01 8.30877185e-01 -7.79314160e-01
6.04915619e-01 -8.51440132e-02 -5.99725783e-01 -4.52820510e-01
3.78580570e-01 4.76786971e-01 -4.14751060e-02 -3.51044118... | [14.563797950744629, 7.0904221534729] |
7f08e5fc-7bc1-4a12-92ed-d957357e1c49 | tpa-net-generate-a-dataset-for-text-to | 2211.13887 | null | https://arxiv.org/abs/2211.13887v1 | https://arxiv.org/pdf/2211.13887v1.pdf | TPA-Net: Generate A Dataset for Text to Physics-based Animation | Recent breakthroughs in Vision-Language (V&L) joint research have achieved remarkable results in various text-driven tasks. High-quality Text-to-video (T2V), a task that has been long considered mission-impossible, was proven feasible with reasonably good results in latest works. However, the resulting videos often hav... | ['Chenfanfu Jiang', 'Yin Yang', 'Govind Thattai', 'Minchen Li', 'Feng Gao', 'Yuxing Qiu'] | 2022-11-25 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 1.51391765e-02 -4.41919655e-01 5.37667274e-01 -3.08409631e-02
-7.14002073e-01 -3.96484673e-01 1.02069604e+00 -9.32875946e-02
-1.17226407e-01 7.12725639e-01 1.43707562e-02 -2.86020875e-01
-5.79328649e-02 -9.25587475e-01 -7.95903802e-01 -7.31792748e-01
-1.07459791e-01 5.69203615e-01 5.71211398e-01 -6.33669674... | [9.157235145568848, -2.9653632640838623] |
db7b95d3-9bf4-4609-a7c5-b43973122534 | context-aware-alignment-and-mutual-masking | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jin_Context-Aware_Alignment_and_Mutual_Masking_for_3D-Language_Pre-Training_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jin_Context-Aware_Alignment_and_Mutual_Masking_for_3D-Language_Pre-Training_CVPR_2023_paper.pdf | Context-Aware Alignment and Mutual Masking for 3D-Language Pre-Training | 3D visual language reasoning plays an important role in effective human-computer interaction. The current approaches for 3D visual reasoning are task-specific, and lack pre-training methods to learn generic representations that can transfer across various tasks. Despite the encouraging progress in vision-language p... | ['Yinjie Lei', 'Yulan Guo', 'Yuwei Yang', 'Munawar Hayat', 'Zhao Jin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['dense-captioning', 'visual-grounding', 'visual-reasoning', '3d-dense-captioning', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'reasoning'] | [-0.06354461 0.22568032 -0.12398522 -0.4897935 -0.85541993 -0.7337215
0.8293168 0.3214245 0.05469055 0.1282397 0.41935754 -0.6050696
0.02241159 -0.6068243 -1.0451119 -0.16615215 0.3984772 1.0092075
0.24810699 -0.53833306 0.19167371 0.9792589 -1.5303826 0.9088278
0.43989313 0.72846496 0.4800... | [8.167732238769531, -3.3418478965759277] |
15a937a3-748e-45aa-b545-32e4d02604d4 | semantic-aware-knowledge-preservation-for | 1904.03208 | null | https://arxiv.org/abs/1904.03208v3 | https://arxiv.org/pdf/1904.03208v3.pdf | Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval | Sketch-based image retrieval (SBIR) is widely recognized as an important vision problem which implies a wide range of real-world applications. Recently, research interests arise in solving this problem under the more realistic and challenging setting of zero-shot learning. In this paper, we investigate this problem fro... | ['Lingxi Xie', 'Huiyu Wang', 'Qing Liu', 'Alan Yuille'] | 2019-04-05 | semantic-aware-knowledge-preservation-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_Semantic-Aware_Knowledge_Preservation_for_Zero-Shot_Sketch-Based_Image_Retrieval_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Semantic-Aware_Knowledge_Preservation_for_Zero-Shot_Sketch-Based_Image_Retrieval_ICCV_2019_paper.pdf | iccv-2019-10 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.82414472e-01 -1.52953953e-01 -4.00912672e-01 -1.72466576e-01
-6.95014477e-01 -3.47692966e-01 8.12258780e-01 -2.21707493e-01
-4.14795309e-01 5.01484275e-01 4.38118696e-01 1.15302637e-01
-5.71350813e-01 -8.47284675e-01 -7.00661361e-01 -7.14670241e-01
3.80590618e-01 -4.59074639e-02 2.80816764e-01 -4.10035670... | [11.535587310791016, 0.7832457423210144] |
f8b4ef30-c352-4ab2-96e0-88804f00fce9 | advancing-covid-19-diagnosis-with-privacy | 2111.09461 | null | https://arxiv.org/abs/2111.09461v1 | https://arxiv.org/pdf/2111.09461v1.pdf | Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence | Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To... | ['Tian Xia', 'Carola-Bibiane Schönlieb', 'Zhen Li', 'Jianming Wang', 'Chuangsheng Zheng', 'Joan Lasenby', 'Adrian Weller', 'Daniel Rubin', 'Evis Sala', 'Lorena Escudero Sanchez', 'Lucian Beer', 'Zhongzhao Teng', 'Michael Roberts', 'Weiyang Liu', 'Jianjun Zhang', 'Pattanasak Mongkolwat', 'Jianbo Shao', 'Xiang Wang', 'Xu... | 2021-11-18 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 2.24590644e-01 4.66679543e-01 -3.42088193e-01 -4.10945714e-01
-1.22670019e+00 -6.16281629e-01 2.60688692e-01 3.57887566e-01
-6.48939788e-01 7.19123304e-01 2.42063195e-01 -9.83621240e-01
-6.34012818e-01 -5.50883949e-01 -7.63532579e-01 -9.61770773e-01
-3.69614333e-01 8.06326032e-01 -4.08588141e-01 6.12332344... | [6.151970863342285, 6.563205718994141] |
bf131733-4cc1-4fb1-882f-2af0ee9bc168 | do-as-i-can-not-as-i-say-grounding-language | 2204.01691 | null | https://arxiv.org/abs/2204.01691v2 | https://arxiv.org/pdf/2204.01691v2.pdf | Do As I Can, Not As I Say: Grounding Language in Robotic Affordances | Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, w... | ['Andy Zeng', 'Mengyuan Yan', 'Sichun Xu', 'Peng Xu', 'Ted Xiao', 'Fei Xia', 'Vincent Vanhoucke', 'Alexander Toshev', 'Clayton Tan', 'Nicolas Sievers', 'Pierre Sermanet', 'Diego Reyes', 'Jarek Rettinghouse', 'Kanishka Rao', 'Jornell Quiambao', 'Peter Pastor', 'Carolina Parada', 'Linda Luu', 'Yao Lu', 'Sergey Levine', '... | 2022-04-04 | null | null | null | null | ['robot-task-planning'] | ['robots'] | [ 3.89653817e-02 4.54865456e-01 -1.73864678e-01 -1.79336473e-01
-2.66602457e-01 -7.95022011e-01 6.95955992e-01 1.09621145e-01
-3.43436331e-01 6.35810733e-01 1.16308644e-01 -5.92515171e-01
-2.58596629e-01 -9.18366790e-01 -8.40811253e-01 -2.37924308e-01
-1.80147201e-01 5.54170609e-01 2.41878226e-01 -4.32717025... | [4.449063777923584, 0.9351276755332947] |
991f5bfd-d7cb-4c60-9129-d457492d8303 | editable-graph-neural-network-for-node | 2305.15529 | null | https://arxiv.org/abs/2305.15529v1 | https://arxiv.org/pdf/2305.15529v1.pdf | Editable Graph Neural Network for Node Classifications | Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-based learning problem, such as credit risk assessment in financial networks and fake news detection in social networks. However, the trained GNNs still make errors and these errors may cause serious negative impact on society. \textit{M... | ['Xia Hu', 'Soo-Hyun Choi', 'Rui Chen', 'Li Li', 'Kaixiong Zhou', 'Shaochen Zhong', 'Zhimeng Jiang', 'Zirui Liu'] | 2023-05-24 | null | null | null | null | ['fake-news-detection', 'model-editing'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.21006322e-01 8.41699362e-01 -3.35010231e-01 -3.15200567e-01
1.74957111e-01 -2.86968231e-01 4.32866782e-01 4.22340482e-01
-3.56129818e-02 8.28426838e-01 -3.36143374e-01 -4.57114160e-01
6.77425563e-02 -1.30040312e+00 -1.05124283e+00 -3.37862402e-01
-9.25078392e-02 3.74404252e-01 3.22435379e-01 -3.10986578... | [6.998199939727783, 6.111227512359619] |
96266538-9610-4481-853f-b46502afb9c0 | dialogue-act-classification-in-team | null | null | https://aclanthology.org/W19-5946 | https://aclanthology.org/W19-5946.pdf | Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response | We present the results we obtained on the classification of dialogue acts in a corpus of human-human team communication in the domain of robot-assisted disaster response. We annotated dialogue acts according to the ISO 24617-2 standard scheme and carried out experiments using the FastText linear classifier as well as s... | ['Ivana Kruijff-Korbayova', 'Tatiana Anikina'] | 2019-09-01 | null | null | null | ws-2019-9 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 8.02538693e-02 8.97652507e-01 5.36483049e-01 -4.53670353e-01
-4.22272712e-01 -1.00178085e-01 9.35613215e-01 3.88219714e-01
-1.01674759e+00 9.75005209e-01 1.04718208e+00 -2.89713234e-01
-3.35140228e-02 -6.13248885e-01 1.23905733e-01 -5.47376513e-01
-4.90970105e-01 8.62862647e-01 -5.58000132e-02 -8.43236148... | [12.812413215637207, 7.781406879425049] |
eccf4154-aa00-470b-a61e-d7f9b5d38781 | toward-creating-subsurface-camera | 1810.12271 | null | http://arxiv.org/abs/1810.12271v1 | http://arxiv.org/pdf/1810.12271v1.pdf | Toward Creating Subsurface Camera | In this article, the framework and architecture of Subsurface Camera (SAMERA)
is envisioned and described for the first time. A SAMERA is a geophysical
sensor network that senses and processes geophysical sensor signals, and
computes a 3D subsurface image in-situ in real-time. The basic mechanism is:
geophysical waves ... | [] | 2018-10-29 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 4.25008565e-01 -1.20180883e-01 9.92896557e-01 -2.30788499e-01
-6.88018575e-02 -4.66895610e-01 3.67707163e-01 -2.41453841e-01
-3.02327991e-01 5.14140837e-02 3.01366895e-01 -2.21243545e-01
-1.51571348e-01 -1.34061110e+00 -3.17737281e-01 -9.85388339e-01
-6.23784363e-01 1.82714731e-01 7.25299478e-01 -4.33784723... | [6.918558120727539, 2.4451820850372314] |
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