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b7db5826-76aa-474b-bf2e-f8c47c947c7f | context-aware-neural-based-dialog-act | 1902.11060 | null | http://arxiv.org/abs/1902.11060v1 | http://arxiv.org/pdf/1902.11060v1.pdf | Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions | This paper presents our latest investigations on dialog act (DA)
classification on automatically generated transcriptions. We propose a novel
approach that combines convolutional neural networks (CNNs) and conditional
random fields (CRFs) for context modeling in DA classification. We explore the
impact of transcription... | ['Gisela Vallejo', 'Chia-Yu Li', 'Daniel Ortega', 'Pavel Denisov', 'Ngoc Thang Vu'] | 2019-02-28 | null | null | null | null | ['dialog-act-classification'] | ['natural-language-processing'] | [-1.54568523e-01 1.52874380e-01 1.20534867e-01 -8.49294007e-01
-6.45488143e-01 -5.65861821e-01 1.05421984e+00 -1.17523121e-02
-7.14739084e-01 9.61169720e-01 6.90538168e-01 -4.10305589e-01
5.41728973e-01 -4.04939145e-01 1.97164699e-01 -4.94619548e-01
3.00720543e-01 8.39094341e-01 1.07439056e-01 -4.32756007... | [12.810647964477539, 7.730242729187012] |
4508e971-2302-48db-89e3-ae7ec17eae62 | revisit-dictionary-learning-for-video | 2110.04966 | null | https://arxiv.org/abs/2110.04966v2 | https://arxiv.org/pdf/2110.04966v2.pdf | Revisit Dictionary Learning for Video Compressive Sensing under the Plug-and-Play Framework | Aiming at high-dimensional (HD) data acquisition and analysis, snapshot compressive imaging (SCI) obtains the 2D compressed measurement of HD data with optical imaging systems and reconstructs HD data using compressive sensing algorithms. While the Plug-and-Play (PnP) framework offers an emerging solution to SCI recons... | ['Yaping Zhao', 'Qing Yang'] | 2021-10-11 | null | null | null | null | ['video-compressive-sensing'] | ['computer-vision'] | [ 3.56769860e-01 -5.33682227e-01 -1.14771418e-01 1.32823944e-01
-9.28178370e-01 -3.49416643e-01 9.27901939e-02 -4.47199225e-01
1.74321979e-02 4.71330285e-01 4.06700701e-01 -2.43274972e-01
-3.71953815e-01 -3.63790929e-01 -6.95773780e-01 -8.63675714e-01
-7.06914440e-02 -2.97964569e-02 1.69822928e-02 4.15924452... | [11.141098022460938, -2.132469892501831] |
7ff58bf6-4693-4d7b-9ccd-8c0f5f425ee1 | attribute-surrogates-learning-and-spectral | 2203.09064 | null | https://arxiv.org/abs/2203.09064v1 | https://arxiv.org/pdf/2203.09064v1.pdf | Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning | This paper presents new hierarchically cascaded transformers that can improve data efficiency through attribute surrogates learning and spectral tokens pooling. Vision transformers have recently been thought of as a promising alternative to convolutional neural networks for visual recognition. But when there is no suff... | ['Wenqiang Zhang', 'Yizhou Yu', 'Weifeng Ge', 'Hong-Yu Zhou', 'Dongyang Zhao', 'Weihan Liang', 'Yangji He'] | 2022-03-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/He_Attribute_Surrogates_Learning_and_Spectral_Tokens_Pooling_in_Transformers_for_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/He_Attribute_Surrogates_Learning_and_Spectral_Tokens_Pooling_in_Transformers_for_CVPR_2022_paper.pdf | cvpr-2022-1 | ['few-shot-image-classification'] | ['computer-vision'] | [ 5.69067299e-02 -1.38228610e-01 -4.13902521e-01 -4.94075805e-01
-7.83828259e-01 -2.72569567e-01 6.10225677e-01 -1.79331213e-01
-5.89141071e-01 3.92631292e-01 2.12293521e-01 8.89577263e-04
4.89143245e-02 -7.15725124e-01 -7.09921658e-01 -8.39946747e-01
9.44999680e-02 1.11772813e-01 3.87787372e-01 -7.16046840... | [9.874216079711914, 2.6029934883117676] |
9106637d-e5fa-4faa-a286-afd120aa32eb | 2d-human-pose-estimation-a-survey | 2204.07370 | null | https://arxiv.org/abs/2204.07370v1 | https://arxiv.org/pdf/2204.07370v1.pdf | 2D Human Pose Estimation: A Survey | Human pose estimation aims at localizing human anatomical keypoints or body parts in the input data (e.g., images, videos, or signals). It forms a crucial component in enabling machines to have an insightful understanding of the behaviors of humans, and has become a salient problem in computer vision and related fields... | ['Zhenguang Liu', 'Fengcheng Zhou', 'Hao Xu', 'Sifan Wu', 'Runyang Feng', 'Haoming Chen'] | 2022-04-15 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 1.73148289e-01 1.28368931e-02 -5.89343309e-01 -1.74487278e-01
-2.90610820e-01 -3.27929705e-01 2.86819875e-01 9.07391757e-02
-7.12408602e-01 2.85559386e-01 2.82548338e-01 2.78216422e-01
-2.05724493e-01 -4.04206216e-01 -4.86287892e-01 -4.36073869e-01
-4.22353476e-01 4.48551655e-01 1.05638251e-01 -2.09661081... | [7.066722869873047, -0.7958130240440369] |
802d6725-2791-43ec-b94b-96fadc83878e | supervised-raw-video-denoising-with-a | 2003.14013 | null | https://arxiv.org/abs/2003.14013v1 | https://arxiv.org/pdf/2003.14013v1.pdf | Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes | In recent years, the supervised learning strategy for real noisy image denoising has been emerging and has achieved promising results. In contrast, realistic noise removal for raw noisy videos is rarely studied due to the lack of noisy-clean pairs for dynamic scenes. Clean video frames for dynamic scenes cannot be capt... | ['Jingyu Yang', 'Ronghe Chu', 'Huanjing Yue', 'Lei Liao', 'Cong Cao'] | 2020-03-31 | supervised-raw-video-denoising-with-a-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.pdf | cvpr-2020-6 | ['video-denoising'] | ['computer-vision'] | [ 4.00138110e-01 -6.82246327e-01 3.95903498e-01 -1.63885728e-01
-8.64998698e-01 -3.79711241e-01 2.39026785e-01 -4.60597754e-01
-4.69456285e-01 7.97450662e-01 1.33107632e-01 2.30831839e-02
-7.10582510e-02 -6.00208998e-01 -9.04314756e-01 -1.18815577e+00
-9.36308280e-02 -6.22584343e-01 1.47726730e-01 -1.01890832... | [11.312362670898438, -2.1949517726898193] |
1defe6c1-c2d9-43ea-913d-1c57872f0cc0 | texture-representation-via-analysis-and | 2212.09983 | null | https://arxiv.org/abs/2212.09983v1 | https://arxiv.org/pdf/2212.09983v1.pdf | Texture Representation via Analysis and Synthesis with Generative Adversarial Networks | We investigate data-driven texture modeling via analysis and synthesis with generative adversarial networks. For network training and testing, we have compiled a diverse set of spatially homogeneous textures, ranging from stochastic to regular. We adopt StyleGAN3 for synthesis and demonstrate that it produces diverse t... | ['Thrasyvoulos N. Pappas', 'Gaurav Sharma', 'Jue Lin'] | 2022-12-20 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 6.83820188e-01 2.77416885e-01 -9.51117948e-02 -1.91250771e-01
-7.14335203e-01 -7.34286249e-01 9.93940651e-01 -7.38675654e-01
3.78041804e-01 8.75932515e-01 3.56877029e-01 -1.89478412e-01
-7.81248733e-02 -8.99704456e-01 -8.27254295e-01 -1.07673800e+00
-1.72168631e-02 5.05182862e-01 -2.45085403e-01 -1.93534002... | [11.573887825012207, -0.5502592325210571] |
ebb65d25-e566-40c1-94d0-959cd3435997 | rsfnet-a-white-box-image-retouching-approach | 2303.08682 | null | https://arxiv.org/abs/2303.08682v1 | https://arxiv.org/pdf/2303.08682v1.pdf | RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters | Retouching images is an essential aspect of enhancing the visual appeal of photos. Although users often share common aesthetic preferences, their retouching methods may vary based on their individual preferences. Therefore, there is a need for white-box approaches that produce satisfying results and enable users to con... | ['Xuansong Xie', 'Xin Xu', 'Xiaoyang Kang', 'Peiran Ren', 'Yi Dong', 'Wenqi Ouyang'] | 2023-03-15 | null | null | null | null | ['photo-retouching', 'image-retouching'] | ['computer-vision', 'computer-vision'] | [ 3.48239899e-01 -1.22570992e-01 2.73232609e-02 -3.83780688e-01
-7.93785080e-02 -7.85954773e-01 5.10688305e-01 4.84517924e-02
-2.44373053e-01 4.12831694e-01 3.21014732e-01 -3.48395556e-01
2.14239731e-01 -8.87119114e-01 -5.67507744e-01 -2.73917854e-01
4.55892086e-01 -4.30610269e-01 4.25986081e-01 -4.82129157... | [11.36823558807373, -0.9740555286407471] |
d66c2322-90c7-46aa-b2a9-2d64ef74ca72 | delog-a-privacy-preserving-log-filtering | 1902.04843 | null | https://arxiv.org/abs/1902.04843v3 | https://arxiv.org/pdf/1902.04843v3.pdf | Delog: A Privacy Preserving Log Filtering Framework for Online Compute Platforms | In many software applications, logs serve as the only interface between the application and the developer. However, navigating through the logs of long-running applications is often challenging. Logs from previously successful application runs can be leveraged to automatically identify errors and provide users with onl... | ['Amey Agrawal', 'Rajat Gupta', 'Namrata Shettar', 'Darshil Kapadia', 'Vikram Agrawal', 'Rohit Karlupia', 'Abhishek Dixit'] | 2019-02-13 | null | null | null | null | ['log-parsing'] | ['computer-code'] | [-3.07763427e-01 -4.84091431e-01 -3.97104084e-01 -4.04991657e-01
-1.31339991e+00 -1.18982089e+00 -5.12120873e-02 6.28910542e-01
-6.86183050e-02 1.88367829e-01 -8.68362486e-02 -6.33915067e-01
2.15211779e-01 -4.60124075e-01 -9.58831787e-01 -4.79205437e-02
-5.94228327e-01 3.08579672e-02 5.55457532e-01 2.90275931... | [6.408806324005127, 6.859131813049316] |
53742da7-9a4e-44c6-bf1f-65c966fc7864 | context-aware-cascade-attention-based-rnn-for | 1805.12098 | null | http://arxiv.org/abs/1805.12098v1 | http://arxiv.org/pdf/1805.12098v1.pdf | Context-aware Cascade Attention-based RNN for Video Emotion Recognition | Emotion recognition can provide crucial information about the user in many
applications when building human-computer interaction (HCI) systems. Most of
current researches on visual emotion recognition are focusing on exploring
facial features. However, context information including surrounding environment
and human bod... | ['Jen-Hsien Chien', 'Min-Chun Yang', 'Shih-Huan Hsu', 'Man-Chin Sun'] | 2018-05-30 | null | null | null | null | ['video-emotion-recognition', 'multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'computer-vision', 'speech'] | [ 4.85639006e-01 -2.12703049e-01 1.30902946e-01 -6.14885926e-01
-2.23786980e-01 -2.32528523e-01 4.18955624e-01 -4.60708916e-01
-6.53294504e-01 3.83926004e-01 3.77911538e-01 -3.01558506e-02
8.21439862e-01 -1.18852936e-01 -5.64405382e-01 -6.07836485e-01
2.55624264e-01 -3.33364129e-01 -5.64742386e-01 -2.77075887... | [13.283439636230469, 5.040441989898682] |
6252049b-2265-4601-911e-39a8408b5219 | edge-weighted-pfista-net-for-mri | 2302.07468 | null | https://arxiv.org/abs/2302.07468v1 | https://arxiv.org/pdf/2302.07468v1.pdf | Edge-weighted pFISTA-Net for MRI Reconstruction | Deep learning based on unrolled algorithm has served as an effective method for accelerated magnetic resonance imaging (MRI). However, many methods ignore the direct use of edge information to assist MRI reconstruction. In this work, we present the edge-weighted pFISTA-Net that directly applies the detected edge map to... | ['Jianpeng Cao'] | 2023-02-15 | null | null | null | null | ['edge-detection', 'mri-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.45094776e-01 -2.13525787e-01 1.33709639e-01 -5.32705724e-01
-4.94516224e-01 2.82669775e-02 -5.71224540e-02 -8.22171196e-02
-8.37418675e-01 7.14091182e-01 2.76696868e-02 -3.28264952e-01
-7.56622180e-02 -7.28612125e-01 -4.48485702e-01 -8.38226676e-01
-4.50500399e-01 2.55585581e-01 7.40678787e-01 4.04387340... | [14.097617149353027, -2.4156830310821533] |
9acf617c-c930-4ffd-8be7-3942bdc2ba6b | a-perspective-on-neural-capacity-estimation | 2203.11793 | null | https://arxiv.org/abs/2203.11793v2 | https://arxiv.org/pdf/2203.11793v2.pdf | A Perspective on Neural Capacity Estimation: Viability and Reliability | Recently, several methods have been proposed for estimating the mutual information from sample data using deep neural networks. These estimators ar referred to as neural mutual information estimation (NMIE)s. NMIEs differ from other approaches as they are data-driven estimators. As such, they have the potential to perf... | ['Nariman Farsad', 'Stefano Rini', 'Farhad Mirkarimi'] | 2022-03-22 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 3.69727880e-01 -5.36856577e-02 5.65169938e-02 -1.59645900e-01
-8.04234326e-01 -2.14910775e-01 5.05649090e-01 -2.76128232e-01
-6.96020782e-01 1.25672936e+00 -3.89983878e-02 -6.06954336e-01
-7.22934186e-01 -6.20642722e-01 -5.51991343e-01 -9.53751624e-01
-7.14342058e-01 1.56103805e-01 -2.87346572e-01 2.87458509... | [6.440377712249756, 1.7513105869293213] |
c47f63ac-75a7-4f22-a54f-d092d6fb84f1 | what-does-plate-glass-reveal-about-camera | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_What_Does_Plate_Glass_Reveal_About_Camera_Calibration_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_What_Does_Plate_Glass_Reveal_About_Camera_Calibration_CVPR_2020_paper.pdf | What Does Plate Glass Reveal About Camera Calibration? | This paper aims to calibrate the orientation of glass and the field of view of the camera from a single reflection-contaminated image. We show how a reflective amplitude coefficient map can be used as a calibration cue. Different from existing methods, the proposed solution is free from image contents. To reduce the im... | [' Alex C. Kot', ' Ling-Yu Duan', ' Kim-Hui Yap', ' Xudong Jiang', ' Boxin Shi', ' Zhan Lu', ' Jinnan Chen', 'Qian Zheng'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['reflection-removal'] | ['computer-vision'] | [ 5.28880358e-01 -1.75611377e-02 2.94556111e-01 -3.31120282e-01
-1.27283716e+00 -6.78050756e-01 2.96959907e-01 -4.61383432e-01
-3.25808793e-01 2.90825456e-01 1.85157314e-01 -2.38564551e-01
5.53868487e-02 -4.84795362e-01 -9.63254333e-01 -9.73803103e-01
4.98162419e-01 -5.74446656e-02 2.81422347e-01 6.69842064... | [9.43759822845459, -2.660884141921997] |
5dcbbaca-4a14-454e-9bfc-8c2bb79f042c | fourier-analysis-on-robustness-of-graph | 2305.17939 | null | https://arxiv.org/abs/2305.17939v1 | https://arxiv.org/pdf/2305.17939v1.pdf | Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action Recognition | Using Fourier analysis, we explore the robustness and vulnerability of graph convolutional neural networks (GCNs) for skeleton-based action recognition. We adopt a joint Fourier transform (JFT), a combination of the graph Fourier transform (GFT) and the discrete Fourier transform (DFT), to examine the robustness of adv... | ['Kazuhiko Kawamoto', 'Hiroshi Kera', 'Nariki Tanaka'] | 2023-05-29 | null | null | null | null | ['skeleton-based-action-recognition', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 8.71856153e-01 1.27871022e-01 1.18601536e-02 1.40553921e-01
-4.43026811e-01 -5.74940264e-01 5.43079793e-01 -3.49122226e-01
-1.81706965e-01 3.09987038e-01 3.99408937e-01 -3.80990505e-01
-1.29662335e-01 -1.01496112e+00 -9.26870763e-01 -7.84007370e-01
-4.95473295e-01 -4.11381483e-01 2.21274585e-01 -3.11627567... | [5.553149700164795, 7.763954162597656] |
e401c704-a568-4e91-a07f-f12b00bb45e5 | two-stage-framework-for-optic-disc | 2005.14284 | null | https://arxiv.org/abs/2005.14284v1 | https://arxiv.org/pdf/2005.14284v1.pdf | Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning | With the advancement of powerful image processing and machine learning techniques, CAD has become ever more prevalent in all fields of medicine including ophthalmology. Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detect... | ['Sheraz Ahmed', 'Wolfgang Neumeier', 'Shoaib Ahmed Siddiqui', 'Muhammad Naseer Bajwa', 'Faisal Shafait', 'Muhammad Imran Malik', 'Andreas Dengel'] | 2020-05-28 | null | null | null | null | ['optic-disc-detection'] | ['medical'] | [ 1.20535761e-01 1.58552408e-01 -7.21101984e-02 -2.12775439e-01
-8.57419133e-01 -4.89445806e-01 2.98687994e-01 1.67399213e-01
-5.08281827e-01 8.32811832e-01 2.62269139e-01 -5.41708231e-01
-5.76936424e-01 -5.07142901e-01 -1.96155995e-01 -6.47845805e-01
-2.82804638e-01 6.74340069e-01 2.71442026e-01 3.17719728... | [15.818700790405273, -3.991852283477783] |
7d1a7b2b-6e04-4ecb-9bde-59be4593c6cc | mmdialog-a-large-scale-multi-turn-dialogue | 2211.05719 | null | https://arxiv.org/abs/2211.05719v3 | https://arxiv.org/pdf/2211.05719v3.pdf | MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation | Responding with multi-modal content has been recognized as an essential capability for an intelligent conversational agent. In this paper, we introduce the MMDialog dataset to better facilitate multi-modal conversation. MMDialog is composed of a curated set of 1.08 million real-world dialogues with 1.53 million unique ... | ['QIngwei Lin', 'Dongyan Zhao', 'Chongyang Tao', 'Yaming Yang', 'Pu Zhao', 'Can Xu', 'Qingfeng Sun', 'Jiazhan Feng'] | 2022-11-10 | null | null | null | null | ['multimodal-intent-recognition'] | ['miscellaneous'] | [-2.85656720e-01 -6.74448535e-03 9.52171460e-02 -6.55654669e-01
-1.31008911e+00 -6.43321991e-01 1.20124495e+00 -5.53604126e-01
-3.55329961e-01 8.85948300e-01 9.52077329e-01 2.61981547e-01
3.43047440e-01 -6.34685159e-01 -3.64970975e-02 -6.87100172e-01
4.73347366e-01 1.12606525e+00 1.03477187e-01 -6.09573007... | [12.81352424621582, 7.918302536010742] |
bf71048d-02bd-43ad-b548-abd9546c6023 | open-set-recognition-via-augmentation-based | 2203.13238 | null | https://arxiv.org/abs/2203.13238v3 | https://arxiv.org/pdf/2203.13238v3.pdf | Open-set Recognition via Augmentation-based Similarity Learning | The primary assumption of conventional supervised learning or classification is that the test samples are drawn from the same distribution as the training samples, which is called closed set learning or classification. In many practical scenarios, this is not the case because there are unknowns or unseen class samples ... | ['Lei Shu', 'Bing Liu', 'Sepideh Esmaeilpour'] | 2022-03-24 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 6.50645554e-01 1.09888189e-01 -4.14298140e-02 -4.78616685e-01
-6.87879145e-01 -8.18684697e-01 5.77810884e-01 2.98432738e-01
-5.09236120e-02 1.03862619e+00 -2.50598162e-01 -1.06788956e-01
-3.40001017e-01 -8.48171115e-01 -8.08755338e-01 -1.04084802e+00
1.49812967e-01 8.84793103e-01 2.35470325e-01 -3.90554853... | [9.789421081542969, 2.956890821456909] |
d4dbf85d-035b-4e92-be5b-8c9c9e207a91 | unsupervised-domain-adaptation-for-robust | 1707.06265 | null | http://arxiv.org/abs/1707.06265v2 | http://arxiv.org/pdf/1707.06265v2.pdf | Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation | Domain mismatch between training and testing can lead to significant
degradation in performance in many machine learning scenarios. Unfortunately,
this is not a rare situation for automatic speech recognition deployments in
real-world applications. Research on robust speech recognition can be regarded
as trying to over... | ['Yu Zhang', 'Wei-Ning Hsu', 'James Glass'] | 2017-07-19 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 6.96968675e-01 4.09465492e-01 8.61020163e-02 -6.55677736e-01
-1.24189723e+00 -7.20680833e-01 6.43379807e-01 -8.44665766e-02
-4.96182799e-01 8.03031862e-01 4.29435104e-01 -5.13856173e-01
2.62857050e-01 -2.93300450e-01 -5.54813504e-01 -8.75005364e-01
5.25770903e-01 6.16529524e-01 1.39824376e-01 -1.72299445... | [14.474555969238281, 6.577635765075684] |
ebd22bed-5d14-4e24-b94b-dbc8cea74acc | blind-predicting-similar-quality-map-for | 1805.08493 | null | http://arxiv.org/abs/1805.08493v2 | http://arxiv.org/pdf/1805.08493v2.pdf | Blind Predicting Similar Quality Map for Image Quality Assessment | A key problem in blind image quality assessment (BIQA) is how to effectively
model the properties of human visual system in a data-driven manner. In this
paper, we propose a simple and efficient BIQA model based on a novel framework
which consists of a fully convolutional neural network (FCNN) and a pooling
network to ... | ['Ming Hou', 'Ping Shi', 'Yuan Zhang', 'Zefeng Ying', 'Sizhe Fu', 'Da Pan'] | 2018-05-22 | blind-predicting-similar-quality-map-for-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Pan_Blind_Predicting_Similar_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Pan_Blind_Predicting_Similar_CVPR_2018_paper.pdf | cvpr-2018-6 | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 7.92410970e-02 -4.11492288e-01 4.18209791e-01 -6.20796561e-01
-1.02795029e+00 -3.36126745e-01 4.85172987e-01 -4.06716585e-01
-2.91561306e-01 5.28486431e-01 4.05917108e-01 -1.33449525e-01
-2.56339848e-01 -7.83411324e-01 -6.26192570e-01 -5.47362924e-01
2.05097422e-01 -2.55336761e-01 2.90043414e-01 -2.33484536... | [11.857734680175781, -1.833631157875061] |
99ff7318-b9af-4e43-8b79-e9d913ef82a1 | a-practical-toolkit-for-multilingual-question | 2305.17416 | null | https://arxiv.org/abs/2305.17416v1 | https://arxiv.org/pdf/2305.17416v1.pdf | A Practical Toolkit for Multilingual Question and Answer Generation | Generating questions along with associated answers from a text has applications in several domains, such as creating reading comprehension tests for students, or improving document search by providing auxiliary questions and answers based on the query. Training models for question and answer generation (QAG) is not str... | ['Jose Camacho-Collados', 'Fernando Alva-Manchego', 'Asahi Ushio'] | 2023-05-27 | null | null | null | null | ['reading-comprehension', 'answer-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.75162731e-02 5.41121840e-01 4.07497704e-01 -4.09939826e-01
-1.63168335e+00 -9.41646576e-01 4.88185346e-01 3.09551716e-01
-1.71984538e-01 8.46652329e-01 3.81887347e-01 -9.04771745e-01
1.39045805e-01 -1.04263353e+00 -6.10708952e-01 6.35485947e-02
4.82996613e-01 8.38893175e-01 2.22368971e-01 -6.99422896... | [11.469834327697754, 8.292272567749023] |
4dcb6603-0109-4aaf-9431-2ce560897aee | adaptive-graph-convolutional-networks-for | 2202.06503 | null | https://arxiv.org/abs/2202.06503v3 | https://arxiv.org/pdf/2202.06503v3.pdf | Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos | For weakly supervised anomaly detection, most existing work is limited to the problem of inadequate video representation due to the inability of modeling long-term contextual information. To solve this, we propose a novel weakly supervised adaptive graph convolutional network (WAGCN) to model the complex contextual rel... | ['Yanning Zhang', 'Peng Wang', 'Shizhou Zhang', 'Xin Zhang', 'Congqi Cao'] | 2022-02-14 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [-4.49131280e-02 -3.16772968e-01 -1.32714644e-01 -2.61560261e-01
-7.53244609e-02 -3.30053627e-01 2.79650748e-01 3.96974057e-01
-2.74063081e-01 2.65498757e-01 3.52211475e-01 -2.38235459e-01
-9.56045240e-02 -7.49277294e-01 -7.23416984e-01 -5.82163155e-01
-5.36190689e-01 -1.02225952e-01 5.05222142e-01 -1.28564656... | [7.842347145080566, 1.6342964172363281] |
d8108ed1-8219-48ba-86c4-ee1af3f68ddb | human-machine-co-adaption-interface-via | 2305.02058 | null | https://arxiv.org/abs/2305.02058v1 | https://arxiv.org/pdf/2305.02058v1.pdf | Human Machine Co-adaption Interface via Cooperation Markov Decision Process System | This paper aims to develop a new human-machine interface to improve rehabilitation performance from the perspective of both the user (patient) and the machine (robot) by introducing the co-adaption techniques via model-based reinforcement learning. Previous studies focus more on robot assistance, i.e., to improve the c... | ['Steven W. Su', 'Rob Duffield', 'Jun Li', 'Yaqi Li', 'Adrian Cheng', 'Kairui Guo'] | 2023-05-03 | null | null | null | null | ['multi-agent-reinforcement-learning', 'model-based-reinforcement-learning'] | ['methodology', 'reasoning'] | [-2.19739050e-01 5.92347860e-01 -2.75650322e-01 5.83056360e-02
-2.84789562e-01 1.46276698e-01 1.27520069e-01 6.31182343e-02
-6.94257498e-01 1.15729856e+00 2.74392277e-01 -6.65917024e-02
-5.84744096e-01 -5.69986343e-01 -2.84931451e-01 -9.42246735e-01
-1.14420697e-01 6.56254470e-01 1.05938487e-01 -6.85266316... | [4.224880695343018, 1.910614252090454] |
1e4afd04-aea6-4ddd-ac8a-55695a1ed8df | let-s-verify-step-by-step-1 | 2305.20050 | null | https://arxiv.org/abs/2305.20050v1 | https://arxiv.org/pdf/2305.20050v1.pdf | Let's Verify Step by Step | In recent years, large language models have greatly improved in their ability to perform complex multi-step reasoning. However, even state-of-the-art models still regularly produce logical mistakes. To train more reliable models, we can turn either to outcome supervision, which provides feedback for a final result, or ... | ['Karl Cobbe', 'Ilya Sutskever', 'John Schulman', 'Jan Leike', 'Teddy Lee', 'Bowen Baker', 'Harri Edwards', 'Yura Burda', 'Vineet Kosaraju', 'Hunter Lightman'] | 2023-05-31 | let-s-verify-step-by-step | https://cdn.openai.com/improving-mathematical-reasoning-with-process-supervision/Lets_Verify_Step_by_Step.pdf | https://cdn.openai.com/improving-mathematical-reasoning-with-process-supervision/Lets_Verify_Step_by_Step.pdf | preprint-2023-5 | ['math-word-problem-solving', 'active-learning', 'active-learning', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'methodology', 'natural-language-processing', 'reasoning', 'time-series'] | [ 1.50706679e-01 3.35724264e-01 -1.98521003e-01 -7.05112398e-01
-1.18951976e+00 -6.69183791e-01 4.33798283e-01 6.37639642e-01
-5.18293858e-01 8.87500226e-01 2.73317754e-01 -5.67192614e-01
-1.75898001e-01 -8.52563441e-01 -6.47953391e-01 -4.08726037e-02
2.38388881e-01 8.16686749e-01 1.90394253e-01 -2.37623930... | [9.740891456604004, 7.404353141784668] |
0d9fb503-d14d-43e4-9101-6a507445c87c | early-melanoma-diagnosis-with-sequential | 2110.05976 | null | https://arxiv.org/abs/2110.05976v1 | https://arxiv.org/pdf/2110.05976v1.pdf | Early Melanoma Diagnosis with Sequential Dermoscopic Images | Dermatologists often diagnose or rule out early melanoma by evaluating the follow-up dermoscopic images of skin lesions. However, existing algorithms for early melanoma diagnosis are developed using single time-point images of lesions. Ignoring the temporal, morphological changes of lesions can lead to misdiagnosis in ... | ['ZongYuan Ge', 'Victoria Mar', 'Lei Zhang', 'Paul Bonnington', 'Catriona Mclean', 'John Kelly', 'Toan D Nguyen', 'Jennifer Nguyen', 'Zhen Yu'] | 2021-10-12 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 7.85242498e-01 -3.30683351e-01 -3.12767386e-01 -7.57982507e-02
-3.75473469e-01 -6.07594728e-01 4.90872681e-01 1.23763904e-01
-6.73833847e-01 4.88880754e-01 -2.68640935e-01 -4.03805166e-01
-3.43262494e-01 -7.08797514e-01 1.26602482e-02 -9.57961142e-01
7.36555830e-02 8.02807510e-02 4.13366497e-01 1.55021161... | [15.645354270935059, -2.9877421855926514] |
2ab3818c-d5e7-48b0-b423-c5a9ffc9ffcb | cluster-forests | 1104.2930 | null | http://arxiv.org/abs/1104.2930v3 | http://arxiv.org/pdf/1104.2930v3.pdf | Cluster Forests | With inspiration from Random Forests (RF) in the context of classification, a
new clustering ensemble method---Cluster Forests (CF) is proposed.
Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good
local clusterings" and then aggregates via spectral clustering to obtain
cluster assignments fo... | ['Michael. I. Jordan', 'Aiyou Chen', 'Donghui Yan'] | 2011-04-14 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [ 1.84693381e-01 -2.39888191e-01 -2.07768127e-01 -2.89086431e-01
-7.59989798e-01 -8.12508941e-01 4.82765764e-01 1.01782451e-03
-6.84139505e-02 6.68628037e-01 1.24612838e-01 -3.43166679e-01
-4.47733432e-01 -8.94961178e-01 -2.98858434e-01 -1.48914027e+00
-3.84884655e-01 5.30998766e-01 2.83686608e-01 4.07121480... | [7.5567402839660645, 4.585978031158447] |
716c857a-82aa-4a6e-ae03-b22e399444e7 | native-language-identification-using-a | null | null | https://aclanthology.org/W17-5022 | https://aclanthology.org/W17-5022.pdf | Native Language Identification Using a Mixture of Character and Word N-grams | Native language identification (NLI) is the task of determining an author{'}s native language, based on a piece of his/her writing in a second language. In recent years, NLI has received much attention due to its challenging nature and its applications in language pedagogy and forensic linguistics. We participated in t... | ['Elham Mohammadi', 'Hadi Veisi', 'Hessam Amini'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [ 3.36103663e-02 -2.60446906e-01 -4.27424729e-01 -1.64768249e-01
-1.09786141e+00 -9.74477768e-01 8.34884644e-01 2.66679555e-01
-7.28402972e-01 6.20998144e-01 3.10247749e-01 -7.87511289e-01
1.08990431e-01 -1.64386272e-01 -2.12902695e-01 -2.35552698e-01
5.78177035e-01 3.44692409e-01 -1.90945014e-01 2.13274464... | [10.388199806213379, 10.52476978302002] |
23478c1d-efba-4fb5-a797-9d913067dbea | follownet-a-comprehensive-benchmark-for-car | 2306.05381 | null | https://arxiv.org/abs/2306.05381v1 | https://arxiv.org/pdf/2306.05381v1.pdf | FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling | Car-following is a control process in which a following vehicle (FV) adjusts its acceleration to keep a safe distance from the lead vehicle (LV). Recently, there has been a booming of data-driven models that enable more accurate modeling of car-following through real-world driving datasets. Although there are several p... | ['Yinhai Wang', 'Xu Han', 'Hui Zhong', 'Hongliang Lu', 'Pengqin Wang', 'Kehua Chen', 'Meixin Zhu', 'Xianda Chen'] | 2023-05-25 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [-3.63513976e-01 -5.88510573e-01 -6.47459924e-01 -5.79445064e-01
-3.81139666e-01 -2.56800443e-01 7.62502432e-01 -3.19040358e-01
-7.54743516e-01 6.26001418e-01 -1.40289739e-01 -9.03649390e-01
-3.06397881e-02 -9.74403560e-01 -9.50576961e-01 -6.14814162e-01
-1.00800171e-01 3.22175086e-01 5.81926465e-01 -5.45393288... | [6.101931571960449, 0.8346863389015198] |
e43ce53c-7df7-4828-957b-b6865fc59b8f | operation-wise-attention-network-for | 2105.05515 | null | https://arxiv.org/abs/2105.05515v2 | https://arxiv.org/pdf/2105.05515v2.pdf | Operation-wise Attention Network for Tampering Localization Fusion | In this work, we present a deep learning-based approach for image tampering localization fusion. This approach is designed to combine the outcomes of multiple image forensics algorithms and provides a fused tampering localization map, which requires no expert knowledge and is easier to interpret by end users. Our fusio... | ['Ioannis Kompatsiaris', 'Symeon Papadopoulos', 'Giorgos Kordopatis-Zilos', 'Polychronis Charitidis'] | 2021-05-12 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 2.76340038e-01 -5.42941928e-01 3.58537465e-01 9.58980247e-03
-1.21330059e+00 -4.99754876e-01 7.31020689e-01 2.51438051e-01
-5.36247730e-01 4.25225407e-01 9.42431390e-02 -3.57642084e-01
-1.18867442e-01 -5.24524868e-01 -7.42186129e-01 -9.12335396e-01
1.43328413e-01 3.00539672e-01 2.18037277e-01 -5.59395440... | [12.410805702209473, 1.0147883892059326] |
d853c3dc-02f9-4531-8ad3-3f6344273180 | modified-qpsk-partition-algorithm-based-on | 2010.10106 | null | https://arxiv.org/abs/2010.10106v1 | https://arxiv.org/pdf/2010.10106v1.pdf | Modified QPSK Partition Algorithm Based on MAP Estimation for Probabilistically-Shaped 16-QAM | Probabilistic shaping (PS) is investigated as a potential technique to approach the Shannon limit. However, it has been proved that conventional carrier phase recovery (CPR) algorithm designed for uniform distribution may have extra penalty in PS systems. In this paper, we find that the performance of QPSK partition al... | ['Yaojun Qiao', 'Yueming Lu', 'Xizi Tang', 'Mengqi Guo', 'Xuekai Xu', 'Zhongliang Sun', 'Jin Hu'] | 2020-10-20 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 4.74608868e-01 1.52751029e-01 1.39002129e-02 8.98044705e-02
-6.05643868e-01 -2.65163273e-01 4.31361496e-02 7.42422566e-02
-4.61092800e-01 1.17038035e+00 -1.19984902e-01 -5.88734984e-01
-4.57002431e-01 -6.59973562e-01 -1.72856048e-01 -1.37734830e+00
-2.90338606e-01 -1.48051217e-01 3.60721678e-01 9.93682817... | [6.362705230712891, 1.280679702758789] |
eadc0339-a8ea-4f18-b859-3dd2c2191358 | focus-effective-embedding-initialization-for | 2305.14481 | null | https://arxiv.org/abs/2305.14481v1 | https://arxiv.org/pdf/2305.14481v1.pdf | FOCUS: Effective Embedding Initialization for Specializing Pretrained Multilingual Models on a Single Language | Using model weights pretrained on a high-resource language as a warm start can reduce the need for data and compute to obtain high-quality language models in low-resource languages. To accommodate the new language, the pretrained vocabulary and embeddings need to be adapted. Previous work on embedding initialization fo... | ['Gerard de Melo', 'Konstantin Dobler'] | 2023-05-23 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-0.433459 -0.21803297 -0.63326406 -0.2976352 -1.099041 -0.71310747
0.42788133 0.15614085 -1.0228633 0.74991244 0.70788056 -0.21072936
0.20678149 -0.59595585 -0.43984145 -0.2981911 0.05496901 0.64439297
-0.10112511 -0.5024713 -0.24194269 0.15267132 -1.1276172 0.05924038
0.932841 0.206148 0.... | [11.059049606323242, 9.974159240722656] |
905f4432-cca4-4442-ab91-b32263f63a4e | deep-attention-unet-a-network-model-with | 2304.10829 | null | https://arxiv.org/abs/2304.10829v1 | https://arxiv.org/pdf/2304.10829v1.pdf | Deep Attention Unet: A Network Model with Global Feature Perception Ability | Remote sensing image segmentation is a specific task of remote sensing image interpretation. A good remote sensing image segmentation algorithm can provide guidance for environmental protection, agricultural production, and urban construction. This paper proposes a new type of UNet image segmentation algorithm based on... | ['Jiacheng Li'] | 2023-04-21 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 4.96018529e-01 -5.13612591e-02 -3.91484529e-01 -3.71936560e-01
3.93042594e-01 -3.03485394e-01 -2.04040781e-01 7.34615624e-02
-4.88080353e-01 4.93253022e-01 -1.76715046e-01 -8.59739125e-01
-2.58919686e-01 -1.48380697e+00 -4.23064798e-01 -5.85314751e-01
-1.01945862e-01 1.48592636e-01 7.25200325e-02 -2.58272558... | [9.3312349319458, -1.3879891633987427] |
2121e676-53d3-4ed2-922f-5ab59c15754d | considering-image-information-and-self | 2209.06417 | null | https://arxiv.org/abs/2209.06417v1 | https://arxiv.org/pdf/2209.06417v1.pdf | Considering Image Information and Self-similarity: A Compositional Denoising Network | Recently, convolutional neural networks (CNNs) have been widely used in image denoising. Existing methods benefited from residual learning and achieved high performance. Much research has been paid attention to optimizing the network architecture of CNN but ignored the limitations of residual learning. This paper sugge... | ['Jingning Ma', 'Wenshu Yu', 'Yonggui Zhu', 'Jiahong Zhang'] | 2022-09-14 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 1.45602301e-01 -3.72079641e-01 2.22490430e-01 -3.49859059e-01
-4.66451406e-01 8.29884876e-03 2.13187978e-01 -4.02206004e-01
-3.98870796e-01 3.38067383e-01 1.40332147e-01 -4.08232883e-02
-2.56562978e-02 -9.48693395e-01 -5.14761925e-01 -1.11542904e+00
2.14940414e-01 -4.59146649e-01 2.78265715e-01 -3.39380354... | [11.390170097351074, -2.35683536529541] |
ed42c669-7c14-4659-889b-8cf858b91d33 | recognizing-involuntary-actions-from-3d | 1708.06227 | null | http://arxiv.org/abs/1708.06227v1 | http://arxiv.org/pdf/1708.06227v1.pdf | Recognizing Involuntary Actions from 3D Skeleton Data Using Body States | Human action recognition has been one of the most active fields of research
in computer vision for last years. Two dimensional action recognition methods
are facing serious challenges such as occlusion and missing the third dimension
of data. Development of depth sensors has made it feasible to track positions
of human... | ['Benyamin Ghojogh', 'Hoda Mohammadzade', 'Mozhgan Mokari'] | 2017-08-21 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 9.64010805e-02 -3.01412374e-01 -3.94248098e-01 -2.95294464e-01
-4.10443395e-01 -1.21787146e-01 5.83881974e-01 -2.69645393e-01
-5.85634172e-01 4.90831554e-01 3.85493517e-01 6.96084425e-02
-7.00867772e-02 -4.89422768e-01 9.09586325e-02 -7.54330039e-01
-3.22618186e-02 5.38348675e-01 5.33391893e-01 -1.78995579... | [7.758524417877197, 0.32074931263923645] |
a7bcf718-ef2b-4757-bec5-27b49cc880ef | unsupervised-dependency-parsing-using | null | null | https://aclanthology.org/W12-1911 | https://aclanthology.org/W12-1911.pdf | Unsupervised Dependency Parsing using Reducibility and Fertility features | null | ["Zden{\\v{e}}k {\\v{Z}}abokrtsk{\\'y}", 'David Mare{\\v{c}}ek'] | 2012-06-01 | null | null | null | ws-2012-6 | ['unsupervised-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.285802364349365, 3.733289957046509] |
2db5f232-6bd9-43af-9294-5e0822f1a3e5 | sem-pos-grammatically-and-semantically | 2303.14829 | null | https://arxiv.org/abs/2303.14829v2 | https://arxiv.org/pdf/2303.14829v2.pdf | SEM-POS: Grammatically and Semantically Correct Video Captioning | Generating grammatically and semantically correct captions in video captioning is a challenging task. The captions generated from the existing methods are either word-by-word that do not align with grammatical structure or miss key information from the input videos. To address these issues, we introduce a novel global-... | ['Armin Mustafa', 'Graham Thomas', 'Robert Dawes', 'Adrian Hilton', 'Asmar Nadeem'] | 2023-03-26 | null | null | null | null | ['video-captioning'] | ['computer-vision'] | [ 2.11064771e-01 1.96380705e-01 2.39163879e-02 -5.72441161e-01
-1.01791215e+00 -7.21882999e-01 5.92850804e-01 8.29140469e-02
-9.50426981e-02 7.71131814e-01 6.18649423e-01 -8.87811780e-02
3.36932510e-01 -3.23617369e-01 -1.11796105e+00 -5.50049245e-01
2.90742069e-01 2.46722788e-01 2.36097962e-01 -3.27816963... | [10.703654289245605, 0.8071751594543457] |
82057e8a-74e1-40a9-bd6a-7407f5242916 | cross-corpus-native-language-identification | null | null | https://aclanthology.org/W18-1605 | https://aclanthology.org/W18-1605.pdf | Cross-corpus Native Language Identification via Statistical Embedding | In this paper, we approach the task of native language identification in a realistic cross-corpus scenario where a model is trained with available data and has to predict the native language from data of a different corpus. The motivation behind this study is to investigate native language identification in the Austral... | ['ra', 'Julian Brooke', 'Francisco Rangel', 'Alex Uitdenbogerd', 'Paolo Rosso'] | 2018-06-01 | null | null | null | ws-2018-6 | ['cross-corpus', 'native-language-identification'] | ['computer-vision', 'natural-language-processing'] | [-1.16878025e-01 -1.16590008e-01 -2.58704036e-01 -2.78499097e-01
-9.84755754e-01 -7.18147457e-01 6.77185833e-01 3.09800714e-01
-9.12519872e-01 8.61178041e-01 3.93348277e-01 -5.64605951e-01
1.92225292e-01 -5.47363997e-01 -1.51218981e-01 -4.29562956e-01
2.13863090e-01 7.93165267e-01 -4.68502045e-01 -4.04496908... | [10.332315444946289, 10.503157615661621] |
04527bc4-7517-4e33-bc57-374ceb8069cd | background-modeling-via-uncertainty | 2006.07006 | null | https://arxiv.org/abs/2006.07006v3 | https://arxiv.org/pdf/2006.07006v3.pdf | Weakly-supervised Temporal Action Localization by Uncertainty Modeling | Weakly-supervised temporal action localization aims to learn detecting temporal intervals of action classes with only video-level labels. To this end, it is crucial to separate frames of action classes from the background frames (i.e., frames not belonging to any action classes). In this paper, we present a new perspec... | ['Pilhyeon Lee', 'Yan Lu', 'Jinglu Wang', 'Hyeran Byun'] | 2020-06-12 | null | null | null | null | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 1.71560884e-01 1.46444574e-01 -6.74152076e-01 -3.30799967e-01
-1.07730854e+00 -4.09108579e-01 4.85179543e-01 -2.21633404e-01
-1.68941662e-01 9.25949991e-01 1.52832955e-01 -2.41765771e-02
1.63460255e-01 -4.62925375e-01 -1.09004235e+00 -1.05070722e+00
-2.65744567e-01 6.55865744e-02 5.58993638e-01 5.39760113... | [8.503457069396973, 0.6667400002479553] |
d4d49087-b07c-4aa3-9edd-b888d049440f | deep-co-attention-based-comparators-for | 1804.11027 | null | http://arxiv.org/abs/1804.11027v1 | http://arxiv.org/pdf/1804.11027v1.pdf | Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification | Person re-identification (re-ID) requires rapid, flexible yet discriminant
representations to quickly generalize to unseen observations on-the-fly and
recognize the same identity across disjoint camera views. Recent effective
methods are developed in a pair-wise similarity learning system to detect a
fixed set of featu... | ['DaCheng Tao', 'Lin Wu', 'Yang Wang', 'Junbin Gao'] | 2018-04-30 | null | null | null | null | ['foveation'] | ['computer-vision'] | [ 1.57066043e-02 -3.72744918e-01 2.06931576e-01 -5.12620866e-01
-6.64097667e-01 -5.54342091e-01 8.22254300e-01 1.46169409e-01
-8.11909020e-01 5.76961219e-01 -8.23196843e-02 1.94601104e-01
-3.32389399e-02 -6.17721379e-01 -7.24948645e-01 -6.49098754e-01
-4.06715348e-02 3.50203097e-01 3.11347634e-01 -1.01013832... | [14.697342872619629, 0.9457085728645325] |
76685bb2-e6d6-4b03-abe2-92ccc55d3739 | semeval-2015-task-5-qa-tempeval-evaluating | null | null | https://aclanthology.org/S15-2134 | https://aclanthology.org/S15-2134.pdf | SemEval-2015 Task 5: QA TempEval - Evaluating Temporal Information Understanding with Question Answering | null | ['Nasrin Mostafazadeh', 'James Allen', 'Naushad UzZaman', 'Nathanael Chambers', 'Hector Llorens', 'James Pustejovsky'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['temporal-information-extraction'] | ['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... | [-7.224506855010986, 3.7446227073669434] |
0db149b2-8dd4-4a5e-9f7a-a85d3974a1e2 | on-the-relationship-between-normalising-flows | null | null | https://openreview.net/forum?id=HklKEUUY_E | https://openreview.net/pdf?id=HklKEUUY_E | On the relationship between Normalising Flows and Variational- and Denoising Autoencoders | Normalising Flows (NFs) are a class of likelihood-based generative models that have recently gained popularity. They are based on the idea of transforming a simple density into that of the data. We seek to better understand this class of models, and how they compare to previously proposed techniques for generative mode... | ['Tim Salimans', 'Jasper Snoek', 'Alexey A. Gritsenko'] | 2019-03-27 | null | null | null | iclr-workshop-deepgenstruct-2019 | ['normalising-flows'] | ['methodology'] | [ 1.16526475e-02 2.14934200e-01 2.00565353e-01 -4.79372919e-01
3.12252194e-02 -3.86033267e-01 1.17355251e+00 -6.96863055e-01
-4.25496027e-02 6.41435325e-01 7.12552130e-01 -2.68387437e-01
-3.35073978e-01 -1.07102108e+00 -6.77771270e-01 -8.25695872e-01
2.01384887e-01 5.40549219e-01 -2.51655821e-02 -2.01758012... | [11.5596342086792, 0.033370330929756165] |
4b11913d-8197-4879-a28d-6348539bfd79 | hierarchical-semantic-contrast-for-scene | 2303.13051 | null | https://arxiv.org/abs/2303.13051v1 | https://arxiv.org/pdf/2303.13051v1.pdf | Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection | Increasing scene-awareness is a key challenge in video anomaly detection (VAD). In this work, we propose a hierarchical semantic contrast (HSC) method to learn a scene-aware VAD model from normal videos. We first incorporate foreground object and background scene features with high-level semantics by taking advantage o... | ['Xiaojin Gong', 'Shengyang Sun'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_Hierarchical_Semantic_Contrast_for_Scene-Aware_Video_Anomaly_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_Hierarchical_Semantic_Contrast_for_Scene-Aware_Video_Anomaly_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-anomaly-detection'] | ['computer-vision'] | [ 3.91724110e-01 -2.18830600e-01 -1.57559797e-01 -4.40853983e-01
-2.57527947e-01 -1.80794865e-01 5.44001222e-01 -5.21485955e-02
-6.39102012e-02 1.67352363e-01 3.69093984e-01 1.08858114e-02
7.16300402e-03 -7.41554558e-01 -8.50632906e-01 -6.37035131e-01
3.64507586e-02 9.57501456e-02 5.74426830e-01 1.95394590... | [8.163847923278809, 1.235967993736267] |
02410799-967f-4aea-aa99-69f5083f1e95 | continual-causal-inference-with-incremental | 2303.01775 | null | https://arxiv.org/abs/2303.01775v1 | https://arxiv.org/pdf/2303.01775v1.pdf | Continual Causal Inference with Incremental Observational Data | The era of big data has witnessed an increasing availability of observational data from mobile and social networking, online advertising, web mining, healthcare, education, public policy, marketing campaigns, and so on, which facilitates the development of causal effect estimation. Although significant advances have be... | ['Sheng Li', 'Stephen Rathbun', 'Ruopeng Li', 'Zhixuan Chu'] | 2023-03-03 | null | null | null | null | ['marketing', 'selection-bias'] | ['miscellaneous', 'natural-language-processing'] | [ 2.97210962e-01 -1.88267112e-01 -8.05284023e-01 -4.02953833e-01
-4.84209120e-01 -1.30173579e-01 6.67692363e-01 3.60231936e-01
-2.53734529e-01 1.11394989e+00 8.74992430e-01 -4.16593283e-01
-6.13912880e-01 -1.00049293e+00 -7.78315783e-01 -5.70466936e-01
-4.04212743e-01 8.37957934e-02 -1.48962140e-01 4.70475517... | [8.04596996307373, 5.3937668800354] |
ad30dbc8-4638-44f6-8d37-8550269edf89 | learning-pose-invariant-3d-object | 2004.01347 | null | https://arxiv.org/abs/2004.01347v2 | https://arxiv.org/pdf/2004.01347v2.pdf | Learning Pose-invariant 3D Object Reconstruction from Single-view Images | Learning to reconstruct 3D shapes using 2D images is an active research topic, with benefits of not requiring expensive 3D data. However, most work in this direction requires multi-view images for each object instance as training supervision, which oftentimes does not apply in practice. In this paper, we relax the comm... | ['Tieniu Tan', 'Jing Dong', 'Bo Peng', 'Wei Wang'] | 2020-04-03 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 8.41463730e-02 -5.82131669e-02 -5.95894866e-02 -1.80097774e-01
-8.79142642e-01 -9.36588407e-01 4.64154184e-01 -4.01077420e-01
-1.08515799e-01 5.27448416e-01 1.74170479e-01 -5.35510108e-02
-7.64547512e-02 -7.08631158e-01 -8.96456122e-01 -8.81875515e-01
5.68474531e-01 7.46713758e-01 -5.14887720e-02 -9.97261703... | [8.510246276855469, -3.103480577468872] |
ee9edb43-85a1-4028-8978-5356f880ea0f | multilingual-sequence-to-sequence-speech | 1810.03459 | null | http://arxiv.org/abs/1810.03459v1 | http://arxiv.org/pdf/1810.03459v1.pdf | Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling | Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively
new direction in speech research. The approach benefits by performing model
training without using lexicon and alignments. However, this poses a new
problem of requiring more data compared to conventional DNN-HMM systems. In
this work, we atte... | ['Shinji Watanabe', 'Matthew Wiesner', 'Murali Karthick Baskar', 'Sri Harish Mallidi', 'Takaaki Hori', 'Jaejin Cho', 'Ruizhi Li', 'Nelson Yalta', 'Martin Karafiat'] | 2018-10-04 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 2.92407181e-02 1.13157727e-01 -6.79011643e-02 -5.50872922e-01
-1.55632353e+00 -6.82674229e-01 5.23407280e-01 -4.77241516e-01
-6.54422939e-01 9.43786383e-01 6.22767806e-01 -6.80576801e-01
6.85495675e-01 -2.07684398e-01 -6.95642710e-01 -5.15560448e-01
2.45280534e-01 7.37841964e-01 5.30466512e-02 -5.71188569... | [14.348320007324219, 7.009493350982666] |
5881450c-196d-417f-9b5a-2b6778deeaa8 | video-instance-segmentation-using-inter-frame | 2106.03299 | null | https://arxiv.org/abs/2106.03299v1 | https://arxiv.org/pdf/2106.03299v1.pdf | Video Instance Segmentation using Inter-Frame Communication Transformers | We propose a novel end-to-end solution for video instance segmentation (VIS) based on transformers. Recently, the per-clip pipeline shows superior performance over per-frame methods leveraging richer information from multiple frames. However, previous per-clip models require heavy computation and memory usage to achiev... | ['Seon Joo Kim', 'Seoung Wug Oh', 'Miran Heo', 'Sukjun Hwang'] | 2021-06-07 | null | http://proceedings.neurips.cc/paper/2021/hash/6f2688a5fce7d48c8d19762b88c32c3b-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/6f2688a5fce7d48c8d19762b88c32c3b-Paper.pdf | neurips-2021-12 | ['video-instance-segmentation'] | ['computer-vision'] | [ 3.51556152e-01 -1.81749761e-01 -2.89742589e-01 -4.51504827e-01
-1.00396478e+00 -4.91645694e-01 3.70312512e-01 1.98924959e-01
-5.56081355e-01 5.50844729e-01 2.44432613e-02 -2.15655133e-01
1.80959001e-01 -7.72697151e-01 -9.98572230e-01 -4.27128673e-01
-2.44789764e-01 2.53857493e-01 9.26232100e-01 2.59687990... | [9.105510711669922, -0.08536838740110397] |
238987f7-46bf-4d9b-9ca6-a46419c5cc3d | typeface-completion-with-generative | 1811.03762 | null | http://arxiv.org/abs/1811.03762v2 | http://arxiv.org/pdf/1811.03762v2.pdf | Typeface Completion with Generative Adversarial Networks | The mood of a text and the intention of the writer can be reflected in the
typeface. However, in designing a typeface, it is difficult to keep the style
of various characters consistent, especially for languages with lots of
morphological variations such as Chinese. In this paper, we propose a Typeface
Completion Netwo... | ['Yookyung Koh', 'Junhyun Lee', 'Jinhyuk Lee', 'Inyeop Lee', 'Jaewoo Kang', 'Yonggyu Park'] | 2018-11-09 | null | null | null | null | ['font-style-transfer', 'typeface-completion'] | ['computer-vision', 'computer-vision'] | [ 3.74144644e-01 -3.12137753e-01 -1.20322891e-01 -4.30758357e-01
-3.98253649e-01 -7.65614510e-01 5.78727067e-01 -5.15917838e-01
-4.06087160e-01 4.33964401e-01 2.80581146e-01 -1.66705817e-01
6.11692250e-01 -6.71130836e-01 -9.46105957e-01 -5.70002556e-01
9.99584496e-01 4.58821595e-01 -3.53217542e-01 -1.13608450... | [11.532158851623535, -0.21959125995635986] |
74faf6b6-a452-4b33-867a-e5d0ba16a78e | a-template-based-abstractive-meeting | null | null | https://aclanthology.org/W14-4407 | https://aclanthology.org/W14-4407.pdf | A Template-based Abstractive Meeting Summarization: Leveraging Summary and Source Text Relationships | null | ['Giuseppe Carenini', 'Yashar Mehdad', 'Tatsuro Oya', 'Raymond Ng'] | 2014-06-01 | null | null | null | ws-2014-6 | ['meeting-summarization'] | ['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... | [-7.264395713806152, 3.7835240364074707] |
f07fb436-8404-40ee-9eae-6cd14fab04eb | deep-reinforcement-learning-in-quantitative | 2106.00123 | null | https://arxiv.org/abs/2106.00123v1 | https://arxiv.org/pdf/2106.00123v1.pdf | Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review | Algorithmic stock trading has become a staple in today's financial market, the majority of trades being now fully automated. Deep Reinforcement Learning (DRL) agents proved to be to a force to be reckon with in many complex games like Chess and Go. We can look at the stock market historical price series and movements a... | ['Tidor-Vlad Pricope'] | 2021-05-31 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-8.46947789e-01 -1.77269638e-01 -1.37180984e-01 -1.66006405e-02
-4.04544741e-01 -7.44520783e-01 7.55499601e-01 -9.78438333e-02
-7.38642633e-01 1.16233385e+00 -3.15709859e-01 -4.32963729e-01
-3.42220873e-01 -1.03982544e+00 -6.09413087e-01 -6.34968042e-01
-6.26918912e-01 1.01861537e+00 3.35824698e-01 -9.14291441... | [4.4298834800720215, 3.9193644523620605] |
896683e2-e0f8-48b1-be6a-a4ff0da185fe | sample-level-deep-convolutional-neural | 1703.01789 | null | http://arxiv.org/abs/1703.01789v2 | http://arxiv.org/pdf/1703.01789v2.pdf | Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms | Recently, the end-to-end approach that learns hierarchical representations
from raw data using deep convolutional neural networks has been successfully
explored in the image, text and speech domains. This approach was applied to
musical signals as well but has been not fully explored yet. To this end, we
propose sample... | ['Keunhyoung Luke Kim', 'Jiyoung Park', 'Juhan Nam', 'Jongpil Lee'] | 2017-03-06 | null | null | null | null | ['music-auto-tagging', 'music-classification'] | ['music', 'music'] | [ 2.56276459e-01 -1.82645142e-01 1.61896840e-01 -1.93978310e-01
-8.35762918e-01 -7.90034711e-01 5.02135336e-01 5.46691306e-02
-2.64202386e-01 3.16291541e-01 5.46866953e-01 2.79087752e-01
-2.56386399e-01 -7.33708084e-01 -7.87889957e-01 -4.86199349e-01
-6.20678842e-01 2.15391517e-01 2.09537745e-01 -2.66791940... | [15.723793029785156, 5.231079578399658] |
21423684-46c4-4e62-bfc2-15fe93a65304 | renderme-360-a-large-digital-asset-library | 2305.13353 | null | https://arxiv.org/abs/2305.13353v1 | https://arxiv.org/pdf/2305.13353v1.pdf | RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars | Synthesizing high-fidelity head avatars is a central problem for computer vision and graphics. While head avatar synthesis algorithms have advanced rapidly, the best ones still face great obstacles in real-world scenarios. One of the vital causes is inadequate datasets -- 1) current public datasets can only support res... | ['Kwan-Yee Lin', 'Dahua Lin', 'Wayne Wu', 'Chen Qian', 'Chen Change Loy', 'Ziwei Liu', 'Bo Dai', 'Lei Yang', 'Shengqi Liu', 'Siming Fan', 'Yuxin Wang', 'Wei Cheng', 'Huiwen Luo', 'Jingtan Piao', 'Long Zhuo', 'Dongwei Pan'] | 2023-05-22 | null | null | null | null | ['talking-head-generation', 'image-matting', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.30720818e-01 7.04564378e-02 5.78910261e-02 -3.34797800e-01
-6.50724590e-01 -3.22396100e-01 4.82034266e-01 -7.97964573e-01
-6.59677610e-02 5.38955271e-01 7.08531797e-01 5.26252389e-01
5.29268682e-01 -3.18959534e-01 -4.84456956e-01 -7.83475757e-01
2.15150341e-01 6.04081869e-01 4.85396432e-03 -5.34263551... | [12.894146919250488, -0.42454051971435547] |
401269a8-4b42-44d8-9be6-70b7b6fa83e2 | data-assemble-leveraging-multiple-datasets | 2109.12265 | null | https://arxiv.org/abs/2109.12265v4 | https://arxiv.org/pdf/2109.12265v4.pdf | Label-Assemble: Leveraging Multiple Datasets with Partial Labels | The success of deep learning relies heavily on large labeled datasets, but we often only have access to several small datasets associated with partial labels. To address this problem, we propose a new initiative, "Label-Assemble", that aims to unleash the full potential of partial labels from an assembly of public data... | ['Elliot K. Fishman', 'Yongyi Lu', 'Zengle Zhu', 'Bowen Li', 'Zongwei Zhou', 'Alan L. Yuille', 'Mintong Kang'] | 2021-09-25 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 9.12310034e-02 3.36681038e-01 -5.26480615e-01 -3.77757221e-01
-1.31156468e+00 -8.37715268e-01 2.72626907e-01 6.37832224e-01
-4.13601816e-01 8.40863526e-01 1.46082550e-01 -5.85964799e-01
2.80361064e-02 -8.12130809e-01 -5.98951817e-01 -7.37015247e-01
-1.06093064e-01 6.55511081e-01 -3.27807277e-01 4.49936092... | [15.073539733886719, -2.85795521736145] |
710b1ec5-6422-4383-b06e-82354dc0b1ee | enhancing-dynamic-mode-decomposition-workflow | 2208.07767 | null | https://arxiv.org/abs/2208.07767v1 | https://arxiv.org/pdf/2208.07767v1.pdf | Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compression | Modern computational science and engineering applications are being improved by the advances in scientific machine learning. Data-driven methods such as Dynamic Mode Decomposition (DMD) can extract coherent structures from spatio-temporal data generated from dynamical systems and infer different scenarios for said syst... | ['Alvaro L. G. A. Coutinho', 'José J. Camata', 'Malú Grave', 'Gabriel F. Barros'] | 2022-08-16 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 5.11500090e-02 -4.93370682e-01 5.52846134e-01 2.59981185e-01
-4.13405985e-01 -6.55845344e-01 6.67651057e-01 1.54551640e-01
-3.94067585e-01 7.37398207e-01 -6.51628152e-02 -4.80827510e-01
-3.74262959e-01 -6.84679210e-01 -5.95506549e-01 -9.52811360e-01
-6.62668347e-01 6.17240965e-01 1.44953832e-01 8.61041155... | [6.520654678344727, 3.4390180110931396] |
533a3e79-9ea8-4019-ac85-861aa26e8ba2 | twitch-plays-pokemon-machine-learns-twitch | 1902.06208 | null | http://arxiv.org/abs/1902.06208v1 | http://arxiv.org/pdf/1902.06208v1.pdf | Twitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming Data | With the increasing importance of online communities, discussion forums, and
customer reviews, Internet "trolls" have proliferated thereby making it
difficult for information seekers to find relevant and correct information. In
this paper, we consider the problem of detecting and identifying Internet
trolls, almost all... | ['Albert Haque'] | 2019-02-17 | null | null | null | null | ['contextual-anomaly-detection'] | ['miscellaneous'] | [-1.43541634e-01 -5.50316930e-01 -3.39547843e-02 -1.27965525e-01
-1.81271106e-01 -6.58533037e-01 7.57953167e-01 8.76492977e-01
-5.34901977e-01 5.31913161e-01 -4.64868546e-02 -4.73562330e-01
-1.84890240e-01 -6.35067403e-01 -1.20790012e-01 -4.59545642e-01
-2.16637775e-01 7.99666345e-01 3.28769952e-01 -2.68601239... | [8.255522727966309, 10.19342041015625] |
01014767-6b73-4efc-ad8e-865d7715ba39 | multi-density-sketch-to-image-translation | 2006.10649 | null | https://arxiv.org/abs/2006.10649v1 | https://arxiv.org/pdf/2006.10649v1.pdf | Multi-Density Sketch-to-Image Translation Network | Sketch-to-image (S2I) translation plays an important role in image synthesis and manipulation tasks, such as photo editing and colorization. Some specific S2I translation including sketch-to-photo and sketch-to-painting can be used as powerful tools in the art design industry. However, previous methods only support S2I... | ['Zhifeng Tan', 'Sam Kwong', 'Jing Liao', 'Jialu Huang'] | 2020-06-18 | null | null | null | null | ['sketch-to-image-translation'] | ['computer-vision'] | [ 3.34124148e-01 -2.36323029e-01 -1.27070099e-01 -1.38949275e-01
-3.16443443e-01 -6.50281608e-01 8.40589464e-01 -5.49122691e-01
8.12292695e-02 6.24551892e-01 -1.93060189e-01 -7.06067532e-02
-1.21017814e-01 -1.06048024e+00 -6.29138291e-01 -6.65901601e-01
5.76360464e-01 6.40775919e-01 1.15864091e-01 -9.42252055... | [12.09615707397461, -0.3913973271846771] |
76b665dc-6849-4818-9c5c-143262b76126 | pixel-objectness-learning-to-segment-generic | 1808.04702 | null | http://arxiv.org/abs/1808.04702v2 | http://arxiv.org/pdf/1808.04702v2.pdf | Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos | We propose an end-to-end learning framework for segmenting generic objects in
both images and videos. Given a novel image or video, our approach produces a
pixel-level mask for all "object-like" regions---even for object categories
never seen during training. We formulate the task as a structured prediction
problem of ... | ['Kristen Grauman', 'Suyog Dutt Jain', 'Bo Xiong'] | 2018-08-11 | null | null | null | null | ['image-retargeting', 'foreground-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.64671004e-01 1.64740726e-01 -3.61649156e-01 -3.03100109e-01
-1.12143505e+00 -9.26706612e-01 4.79913831e-01 -2.45273158e-01
-3.82705629e-01 3.99025917e-01 -1.07828021e-01 -2.20670536e-01
4.55935866e-01 -4.58279401e-01 -1.29932284e+00 -7.10806310e-01
-1.11745141e-01 3.74908388e-01 7.33724177e-01 1.91880777... | [9.222951889038086, -0.0640270933508873] |
5910f8ad-21c0-44a2-8611-ecfc1f407085 | fairgen-towards-fair-graph-generation | 2303.17743 | null | https://arxiv.org/abs/2303.17743v1 | https://arxiv.org/pdf/2303.17743v1.pdf | FairGen: Towards Fair Graph Generation | There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks, from gene regulatory networks to online transaction networks. Despite the remarkable success, the vast majority of these works are unsuperv... | ['Jingrui He', 'Yada Zhu', 'Jiejun Xu', 'Hanghang Tong', 'Dawei Zhou', 'Lecheng Zheng'] | 2023-03-30 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 4.65368241e-01 6.32919371e-01 -5.28243780e-01 -2.53998160e-01
-3.61210495e-01 -3.08319092e-01 6.53221846e-01 2.32460067e-01
9.06184018e-02 8.71408999e-01 1.08811162e-01 -5.35931766e-01
-1.48715973e-01 -1.32830143e+00 -6.21866107e-01 -6.61460876e-01
-2.28245586e-01 5.87457120e-01 -8.29683021e-02 -2.13196903... | [7.385289669036865, 6.2834062576293945] |
f8a60459-6878-480a-98b9-115532f19e21 | videberta-a-powerful-pre-trained-language | 2301.10439 | null | https://arxiv.org/abs/2301.10439v2 | https://arxiv.org/pdf/2301.10439v2.pdf | ViDeBERTa: A powerful pre-trained language model for Vietnamese | This paper presents ViDeBERTa, a new pre-trained monolingual language model for Vietnamese, with three versions - ViDeBERTa_xsmall, ViDeBERTa_base, and ViDeBERTa_large, which are pre-trained on a large-scale corpus of high-quality and diverse Vietnamese texts using DeBERTa architecture. Although many successful pre-tra... | ['Tu Vu', 'Truong Son Hy', 'Anh Nguyen', 'Nhut Huy Pham', 'Cong Dao Tran'] | 2023-01-25 | null | null | null | null | ['part-of-speech-tagging'] | ['natural-language-processing'] | [-6.36127591e-01 8.53169486e-02 -1.89329222e-01 -6.05990648e-01
-1.38584375e+00 -8.14109266e-01 4.19254303e-01 1.06609657e-01
-9.03919876e-01 8.33166540e-01 4.36252862e-01 -8.81034672e-01
4.21010554e-01 -7.02205360e-01 -6.17060065e-01 -1.57392144e-01
1.07107766e-01 8.18022966e-01 3.21811199e-01 -8.01540732... | [10.768633842468262, 9.569822311401367] |
38f83262-79fa-4cfb-bcb4-4b66be0e1180 | occdepth-a-depth-aware-method-for-3d-semantic | 2302.13540 | null | https://arxiv.org/abs/2302.13540v1 | https://arxiv.org/pdf/2302.13540v1.pdf | OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion | 3D Semantic Scene Completion (SSC) can provide dense geometric and semantic scene representations, which can be applied in the field of autonomous driving and robotic systems. It is challenging to estimate the complete geometry and semantics of a scene solely from visual images, and accurate depth information is crucia... | ['Shuchang Zhou', 'Chen Hu', 'Weixin Xu', 'Zheng Gong', 'Mingrui Chen', 'Weizhou Liu', 'Ruihang Miao'] | 2023-02-27 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 2.21940964e-01 1.60087422e-01 -4.33300734e-02 -5.49664199e-01
-6.68012679e-01 -4.64671999e-01 5.98930836e-01 -1.59057498e-01
-3.48375201e-01 3.49754900e-01 2.23357901e-01 -2.05271974e-01
1.86633363e-01 -1.04889405e+00 -9.16200042e-01 -5.61803877e-01
5.06303549e-01 4.94402945e-01 4.50152159e-01 -4.10836875... | [8.562618255615234, -2.752880096435547] |
2ed13589-eda7-4e82-8501-9d874ce6fc8c | hub-at-semeval-2021-task-7-fusion-of-albert | null | null | https://aclanthology.org/2021.semeval-1.160 | https://aclanthology.org/2021.semeval-1.160.pdf | hub at SemEval-2021 Task 7: Fusion of ALBERT and Word Frequency Information Detecting and Rating Humor and Offense | This paper introduces the system description of the hub team, which explains the related work and experimental results of our team{'}s participation in SemEval 2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense. We successfully submitted the test set prediction results of the two subtasks in the task. The... | ['Yang Bai', 'Bo Huang'] | 2021-08-01 | null | null | null | semeval-2021 | ['humor-detection'] | ['natural-language-processing'] | [-3.84212494e-01 -6.69100285e-02 1.50011033e-01 -7.91206583e-02
-4.35212821e-01 -4.47421491e-01 5.93672812e-01 2.44356513e-01
-2.87403435e-01 7.37862825e-01 5.99350572e-01 -8.48187581e-02
1.01982757e-01 -6.02629006e-01 -1.72410414e-01 -4.19582725e-01
2.97681034e-01 2.79404402e-01 9.45252329e-02 -7.33273089... | [8.862120628356934, 11.072988510131836] |
76404b7d-420d-43cb-9af6-dc4a8260de55 | mamadroid2-0-the-holes-of-control-flow-graphs | 2202.13922 | null | https://arxiv.org/abs/2202.13922v1 | https://arxiv.org/pdf/2202.13922v1.pdf | MaMaDroid2.0 -- The Holes of Control Flow Graphs | Android malware is a continuously expanding threat to billions of mobile users around the globe. Detection systems are updated constantly to address these threats. However, a backlash takes the form of evasion attacks, in which an adversary changes malicious samples such that those samples will be misclassified as beni... | ['Amit Dvir', 'Enrico Mariconti', 'Chen Hajaj', 'Harel Berger'] | 2022-02-28 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 1.46281824e-01 3.73980477e-02 -4.14164603e-01 4.36938629e-02
-1.65941179e-01 -9.85628903e-01 7.91835427e-01 -1.58493258e-02
-1.78197369e-01 4.46978837e-01 -4.11840200e-01 -8.35111499e-01
1.02507034e-02 -9.62204099e-01 -7.81661332e-01 -5.10718882e-01
-2.51097202e-01 5.63659891e-02 8.21786106e-01 -2.75331408... | [14.409613609313965, 9.674015998840332] |
79684ad9-6d86-4714-a094-087e173032b3 | ibiscape-a-simulated-benchmark-for-multi | 2206.13455 | null | https://arxiv.org/abs/2206.13455v2 | https://arxiv.org/pdf/2206.13455v2.pdf | IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments | The development process of high-fidelity SLAM systems depends on their validation upon reliable datasets. Towards this goal, we propose IBISCape, a simulated benchmark that includes data synchronization and acquisition APIs for telemetry from heterogeneous sensors: stereo-RGB/DVS, Depth, IMU, and GPS, along with the gr... | ['Samia Bouchafa', 'Désiré Sidibé', 'Fabien Bonardi', 'Abanob Soliman'] | 2022-06-27 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [-4.83868927e-01 -4.38285142e-01 3.34950149e-01 -5.89939177e-01
-4.77442861e-01 -8.69347036e-01 7.40432680e-01 -2.78140008e-01
-5.00065625e-01 6.12948656e-01 -2.69977570e-01 -3.61809283e-01
-4.95683812e-02 -8.72247815e-01 -9.32938755e-01 -5.63376665e-01
-1.24068804e-01 9.96509790e-01 4.70201403e-01 -6.28365636... | [7.393765449523926, -2.151695728302002] |
5d61fbb3-3b2b-4eae-9c91-8210517124d3 | saibersoc-synthetic-attack-injection-to | 2010.08453 | null | https://arxiv.org/abs/2010.08453v1 | https://arxiv.org/pdf/2010.08453v1.pdf | SAIBERSOC: Synthetic Attack Injection to Benchmark and Evaluate the Performance of Security Operation Centers | In this paper we introduce SAIBERSOC, a tool and methodology enabling security researchers and operators to evaluate the performance of deployed and operational Security Operation Centers (SOCs) (or any other security monitoring infrastructure). The methodology relies on the MITRE ATT&CK Framework to define a procedure... | ['Luca Allodi', 'Ganduulga Gankhuyag', 'Michele Campobasso', 'Martin Rosso'] | 2020-10-16 | null | null | null | null | ['cyber-attack-investigation'] | ['miscellaneous'] | [-7.41657522e-03 -3.77746046e-01 2.41264120e-01 -1.61212876e-01
-4.32150990e-01 -9.26944613e-01 2.10673407e-01 3.04965436e-01
-1.64073557e-01 8.93846810e-01 -6.91950321e-01 -7.76885152e-01
-9.77348909e-03 -7.32134223e-01 -4.71286267e-01 -4.03785735e-01
-3.57055783e-01 2.02646717e-01 7.64746785e-01 -2.79608607... | [5.4200921058654785, 7.253251075744629] |
5db2e9a4-a691-47d3-9283-71a606e838ba | complex-mixer-for-medmnist-classification | 2304.10054 | null | https://arxiv.org/abs/2304.10054v1 | https://arxiv.org/pdf/2304.10054v1.pdf | Complex Mixer for MedMNIST Classification Decathlon | With the development of the medical image field, researchers seek to develop a class of datasets to block the need for medical knowledge, such as \text{MedMNIST} (v2). MedMNIST (v2) includes a large number of small-sized (28 $\times$ 28 or 28 $\times$ 28 $\times$ 28) medical samples and the corresponding expert annotat... | ['Xiuyi Jia', 'Zhuoran Zheng'] | 2023-04-20 | null | null | null | null | ['image-enhancement', 'automl'] | ['computer-vision', 'methodology'] | [ 4.08999026e-01 5.52135825e-01 -2.38996267e-01 -5.55382609e-01
-9.64692056e-01 -2.98955232e-01 2.10511521e-01 -1.52166691e-02
-5.44929862e-01 6.28365695e-01 8.83966908e-02 -2.81694680e-01
-2.42414102e-01 -5.22681713e-01 -2.94175595e-01 -5.24300158e-01
1.85834363e-01 3.58684599e-01 4.94452864e-02 -1.37041077... | [14.858917236328125, -2.1711230278015137] |
8ca034cb-c88e-43b3-8701-2e73378b763d | human-in-the-loop-automatic-program-repair | 1912.07758 | null | https://arxiv.org/abs/1912.07758v1 | https://arxiv.org/pdf/1912.07758v1.pdf | Human-In-The-Loop Automatic Program Repair | We introduce Learn2fix, the first human-in-the-loop, semi-automatic repair technique when no bug oracle--except for the user who is reporting the bug--is available. Our approach negotiates with the user the condition under which the bug is observed. Only when a budget of queries to the user is exhausted, it attempts to... | ['Van-Thuan Pham', 'Marcel Böhme', 'Charaka Geethal'] | 2019-12-16 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-2.07965195e-01 4.75277364e-01 -3.08982521e-01 -3.89424562e-01
-1.70272052e+00 -9.21289802e-01 -4.50853735e-01 1.85807467e-01
2.31908023e-01 7.71106660e-01 -3.62834483e-01 -8.02664101e-01
9.44878608e-02 -9.01256561e-01 -9.15603817e-01 -1.69199914e-01
-1.64806396e-01 5.67351162e-01 3.96937102e-01 1.82358935... | [7.626348972320557, 7.71376371383667] |
182e93bc-377e-41bc-bfcc-8d5c0058be25 | functional-constrained-optimization-for-risk | 2210.05108 | null | https://arxiv.org/abs/2210.05108v1 | https://arxiv.org/pdf/2210.05108v1.pdf | Functional Constrained Optimization for Risk Aversion and Sparsity Control | Risk and sparsity requirements often need to be enforced simultaneously in many applications, e.g., in portfolio optimization, assortment planning, and treatment planning. Properly balancing these potentially conflicting requirements entails the formulation of functional constrained optimization with either convex or n... | ['H. Edwin Romeijn', 'Guanghui Lan', 'Yi Cheng'] | 2022-10-11 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [ 2.79180139e-01 3.67043912e-01 -7.31087625e-02 1.04995638e-01
-1.24699116e+00 -3.49446088e-01 -1.31920278e-01 3.59903485e-01
-5.21683276e-01 9.63151395e-01 1.32790193e-01 -7.12032318e-01
-8.81724656e-01 -6.81177855e-01 -7.81874418e-01 -9.40349162e-01
-4.07360196e-01 3.32143217e-01 -3.83507341e-01 -1.63458720... | [6.493074417114258, 4.518100261688232] |
c73022dc-308a-4595-a823-f86354eebc7f | why-do-deepfake-detectors-fail | 2302.13156 | null | https://arxiv.org/abs/2302.13156v1 | https://arxiv.org/pdf/2302.13156v1.pdf | Why Do Deepfake Detectors Fail? | Recent rapid advancements in deepfake technology have allowed the creation of highly realistic fake media, such as video, image, and audio. These materials pose significant challenges to human authentication, such as impersonation, misinformation, or even a threat to national security. To keep pace with these rapid adv... | ['Simon Woo', 'Kristen Moore', 'Alsharif Abuadbba', 'Shahroz Tariq', 'Binh Le'] | 2023-02-25 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-1.21050151e-02 -3.45133960e-01 9.76540223e-02 -1.51508898e-02
-5.26259959e-01 -7.51679301e-01 9.24045205e-01 2.96008676e-01
-2.88504511e-01 5.02888381e-01 2.86120445e-01 -3.93854171e-01
3.59601587e-01 -6.60806775e-01 -3.92433912e-01 -3.64777178e-01
4.31224629e-02 -1.22936293e-01 5.45953929e-01 -1.99819244... | [12.561349868774414, 1.1561734676361084] |
258344d8-508d-4620-8ec8-767b7ef1d07f | mdm-molecular-diffusion-model-for-3d-molecule | 2209.05710 | null | https://arxiv.org/abs/2209.05710v1 | https://arxiv.org/pdf/2209.05710v1.pdf | MDM: Molecular Diffusion Model for 3D Molecule Generation | Molecule generation, especially generating 3D molecular geometries from scratch (i.e., 3D \textit{de novo} generation), has become a fundamental task in drug designs. Existing diffusion-based 3D molecule generation methods could suffer from unsatisfactory performances, especially when generating large molecules. At the... | ['Ka-Chun Wong', 'Tingyang Xu', 'Hengtong Zhang', 'Lei Huang'] | 2022-09-13 | null | null | null | null | ['3d-molecule-generation'] | ['medical'] | [ 2.16055095e-01 -1.49328172e-01 -2.43905276e-01 -1.10951148e-01
-7.31886506e-01 -6.79073215e-01 7.62137651e-01 2.97079265e-01
-6.14871830e-02 1.33918357e+00 1.94998264e-01 -4.23163414e-01
-6.85706362e-03 -1.17483985e+00 -8.73961806e-01 -1.03783774e+00
2.14181006e-01 5.02484322e-01 -5.79522774e-02 -3.55727077... | [5.011490821838379, 5.718114376068115] |
2233a86a-7b5e-45f3-814d-aebd03545a1d | personalized-predictive-asr-for-latency | 2305.13794 | null | https://arxiv.org/abs/2305.13794v1 | https://arxiv.org/pdf/2305.13794v1.pdf | Personalized Predictive ASR for Latency Reduction in Voice Assistants | Streaming Automatic Speech Recognition (ASR) in voice assistants can utilize prefetching to partially hide the latency of response generation. Prefetching involves passing a preliminary ASR hypothesis to downstream systems in order to prefetch and cache a response. If the final ASR hypothesis after endpoint detection m... | ['Ariya Rastrow', 'Mohammed Hethnawi', 'Maarten Van Segbroeck', 'Di He', 'Andreas Schwarz'] | 2023-05-23 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 5.29392481e-01 5.04917562e-01 -1.18374281e-01 -4.51013714e-01
-1.29672492e+00 -4.39092636e-01 2.15262234e-01 1.30358115e-01
-5.71868956e-01 4.76774722e-01 7.13018596e-01 -5.31808794e-01
2.49727473e-01 -2.62311816e-01 -2.74769634e-01 -5.15370667e-01
-2.26767778e-01 5.42937398e-01 5.88641286e-01 -2.61867996... | [14.447023391723633, 6.8845014572143555] |
6616c3ab-472a-42b6-b6d5-50be62fbaa49 | a-method-for-expressing-and-displaying-the | 1904.11786 | null | https://arxiv.org/abs/1904.11786v1 | https://arxiv.org/pdf/1904.11786v1.pdf | A Method for Expressing and Displaying the Vehicle Behavior Distribution in Maintenance Work Zones | Maintenance work zones on the road network have impacts on the normal travelling of vehicles, which increase the risk of traffic accidents. The traffic characteristic analysis in maintenance work zones is a basis for maintenance work zone related research such as layout design, traffic control and safety assessment. Du... | ['Ping Wang', 'Saravanan Gurupackiam', 'Zhepu Xu', 'Qun Yang'] | 2019-04-25 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-1.66472316e-01 -6.88054323e-01 -1.79675385e-01 -1.21775486e-01
1.13931052e-01 -2.36993685e-01 6.16719723e-01 3.88535947e-01
-1.12729199e-01 3.87731910e-01 -1.50939137e-01 -7.60698557e-01
-7.80668259e-01 -1.25183511e+00 -1.09313942e-01 -1.20920599e+00
-1.46185532e-01 3.85225147e-01 6.80315316e-01 -5.08347750... | [5.898136615753174, 1.2763071060180664] |
9ebf0d61-8748-49b5-8d68-33ffd03304c8 | sina-bert-a-pre-trained-language-model-for-1 | null | null | https://openreview.net/forum?id=YSPukpxgWsU | https://openreview.net/pdf?id=YSPukpxgWsU | SINA-BERT: A Pre-Trained Language Model for Analysis of Medical Texts in Persian | We have released SINA-BERT, a language model pre-trained on BERT to address the lack of a high-quality Persian language model in the medical domain. SINA-BERT utilizes pre-training on a large-scale corpus of medical contents including formal and informal texts collected from various online resources in order to improve... | ['Anonymous'] | 2021-05-16 | null | null | null | acl-arr-may-2021-5 | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [-5.91218509e-02 6.55010164e-01 -2.76730537e-01 -4.52622294e-01
-1.32229555e+00 -4.18208420e-01 2.01677740e-01 6.51595473e-01
-1.13259459e+00 8.91147554e-01 5.65408111e-01 -7.10644603e-01
-2.04474851e-01 -4.32769865e-01 -1.26747340e-01 -1.04633853e-01
-2.83432566e-02 1.32784343e+00 1.37384906e-01 -3.25478703... | [8.68275260925293, 8.765933990478516] |
04d8bb3c-5d96-4f06-90c1-97a418c2a000 | a-deep-learning-based-gpr-forward-solver-for | 2207.06527 | null | https://arxiv.org/abs/2207.06527v1 | https://arxiv.org/pdf/2207.06527v1.pdf | A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects | The forward full-wave modeling of ground-penetrating radar (GPR) facilitates the understanding and interpretation of GPR data. Traditional forward solvers require excessive computational resources, especially when their repetitive executions are needed in signal processing and/or machine learning algorithms for GPR dat... | ['Abdulkadir C. Yucel', 'Mohamed Lokman Mohd Yusof', 'Genevieve Ow', 'Jiwei Qian', 'Hai-Han Sun', 'Yee Hui Lee', 'Qiqi Dai'] | 2022-07-13 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 4.89680260e-01 3.17729525e-02 8.21297228e-01 -5.19575715e-01
-1.17621601e+00 2.84298211e-01 -1.23723492e-01 -6.36712974e-03
-1.62127331e-01 7.78345108e-01 -1.79942608e-01 -5.49673319e-01
-3.24078113e-01 -1.15063071e+00 -7.65364408e-01 -1.00367308e+00
-5.30842185e-01 4.23244029e-01 -4.22738679e-02 -2.34299451... | [6.85938835144043, 1.87955904006958] |
5dbc4c4f-1a3e-4912-86e7-72ab9bf53c85 | multi-temporal-and-multi-source-remote | 2012.04469 | null | https://arxiv.org/abs/2012.04469v1 | https://arxiv.org/pdf/2012.04469v1.pdf | Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization | Remote sensing image classification exploiting multiple sensors is a very challenging problem: data from different modalities are affected by spectral distortions and mis-alignments of all kinds, and this hampers re-using models built for one image to be used successfully in other scenes. In order to adapt and transfer... | ['Gustau Camps-Valls', 'Diego Marcos', 'Devis Tuia'] | 2020-12-07 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 6.26152754e-01 -5.08795500e-01 1.50544196e-01 -1.93517968e-01
-5.10501802e-01 -7.09989786e-01 6.58516347e-01 2.10752890e-01
-5.27732313e-01 5.71899533e-01 -2.13387519e-01 1.21456925e-02
-6.59846902e-01 -8.55355561e-01 -4.87824529e-01 -1.12019575e+00
1.55325219e-01 5.51621377e-01 2.08049398e-02 -1.50423631... | [10.009339332580566, -2.0534842014312744] |
7aea6cf4-3478-40e8-bc52-24197bbb939e | infoctm-a-mutual-information-maximization | 2304.03544 | null | https://arxiv.org/abs/2304.03544v1 | https://arxiv.org/pdf/2304.03544v1.pdf | InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling | Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing aligned latent topics. However, most existing methods suffer from producing repetitive topics that hinder further analysis and performance decline caused by low-coverage dictionaries. In this paper, we propose the Cross-lingual ... | ['Anh Tuan Luu', 'Liangming Pan', 'Chaoqun Liu', 'Thong Nguyen', 'Xinshuai Dong', 'Xiaobao Wu'] | 2023-04-07 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-2.06636727e-01 6.83962703e-02 -7.56937683e-01 -3.45365375e-01
-1.35208189e+00 -5.36896348e-01 6.98496699e-01 1.12451993e-01
-8.82444009e-02 6.09369338e-01 6.41890824e-01 -2.40528062e-01
8.68593380e-02 -5.34731686e-01 -5.42113423e-01 -5.92879951e-01
2.44810939e-01 5.76174557e-01 1.85757577e-01 1.27553968... | [10.41346263885498, 7.015490531921387] |
895ff113-3012-47bd-9c83-b0e3b2633905 | pure-passive-multi-person-identification-via | 2104.07177 | null | https://arxiv.org/abs/2104.07177v1 | https://arxiv.org/pdf/2104.07177v1.pdf | PURE: Passive mUlti-peRson idEntification via Deep Footstep Separation and Recognition | Recently, \textit{passive behavioral biometrics} (e.g., gesture or footstep) have become promising complements to conventional user identification methods (e.g., face or fingerprint) under special situations, yet existing sensing technologies require lengthy measurement traces and cannot identify multiple users at the ... | ['Jun Luo', 'Hongbo Jiang', 'Liyuan Ye', 'Peng Wang', 'Ruinan Jin', 'Chao Cai'] | 2021-04-15 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 4.58583593e-01 -5.09715021e-01 -3.36645216e-01 -2.46467769e-01
-6.83602810e-01 -9.06394839e-01 1.94111556e-01 -2.96456337e-01
-4.46187586e-01 8.57805729e-01 -3.79248530e-01 -3.00607532e-01
8.99229497e-02 -7.93873370e-01 -5.23315310e-01 -4.88437831e-01
5.57529293e-02 1.09956965e-01 -5.62145002e-02 -7.25472867... | [14.098567008972168, 1.537129521369934] |
64dde0a9-327f-4182-bf29-adf1ff066cf9 | training-on-polar-image-transformations | null | null | https://ieeexplore.ieee.org/document/9551998 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9551998 | Training on Polar Image Transformations Improves Biomedical Image Segmentation | A key step in medical image-based diagnosis is image segmentation. A common use case for medical image segmentation is the identification of single structures of an elliptical shape. Most organs like the heart and kidneys fall into this category, as well as skin lesions, polyps, and other types of abnormalities. Neural... | ['Danilo Babin', 'Marija Habijan', 'Irena Galić', 'Marin Benčević'] | 2021-09-29 | null | null | null | ieee-access-2021-9 | ['skin-cancer-segmentation', 'liver-segmentation'] | ['medical', 'medical'] | [ 3.59113246e-01 3.17462593e-01 -1.58114687e-01 -3.13132316e-01
-7.46189475e-01 -6.13911510e-01 2.08553299e-01 4.34299588e-01
-5.20654202e-01 1.23281822e-01 -1.74342796e-01 -5.42413235e-01
2.56083190e-01 -6.66648090e-01 -7.19732106e-01 -7.08650172e-01
-5.48695624e-02 8.14500153e-01 2.69508809e-01 3.04062188... | [14.557918548583984, -2.590419054031372] |
d659d5b7-18ae-4614-a786-b68358bf041b | towards-building-automatic-medical | null | null | https://openreview.net/forum?id=q9uLLvoLUWD | https://openreview.net/pdf?id=q9uLLvoLUWD | Towards Building Automatic Medical Consultation System: Framework, Task and Dataset | In this paper, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and diagnosis-oriented interaction. A new medical dialogue dataset with multi-level fine-grained annotations is introduced and five evaluation tasks are established, including medical named e... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['medical-report-generation', 'dialogue-understanding', 'medical-named-entity-recognition', 'dialogue-act-classification'] | ['medical', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.89158911e-01 1.23906219e+00 -3.06823641e-01 -8.58200371e-01
-9.00218189e-01 -3.59281689e-01 7.94262230e-01 6.31508470e-01
-3.70187253e-01 1.21740794e+00 8.78811479e-01 -3.65958482e-01
-1.20018691e-01 -3.78239274e-01 6.48678720e-01 -3.32967669e-01
-3.44899185e-02 1.39966428e+00 8.05226639e-02 -4.01529133... | [12.458284378051758, 8.384528160095215] |
eadc501e-77b1-4054-9566-e07d18427e6d | deep-density-ratio-estimation-for-change | 1905.09876 | null | https://arxiv.org/abs/1905.09876v1 | https://arxiv.org/pdf/1905.09876v1.pdf | Deep density ratio estimation for change point detection | In this work, we propose new objective functions to train deep neural network based density ratio estimators and apply it to a change point detection problem. Existing methods use linear combinations of kernels to approximate the density ratio function by solving a convex constrained minimization problem. Approximating... | ['Bülent Yener', 'Lara Marcuse', 'Haidar Khan'] | 2019-05-23 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-3.88772994e-01 -4.46647406e-02 -3.39207679e-01 -4.94891524e-01
-9.86519217e-01 -1.80755749e-01 1.94905013e-01 8.93197581e-02
-6.76268220e-01 9.54097092e-01 -7.37236291e-02 -2.40112454e-01
-6.67562932e-02 -7.00945497e-01 -6.00728095e-01 -4.13874090e-01
-4.71608400e-01 6.10554814e-01 4.00020890e-02 1.62530109... | [7.699991226196289, 3.715602397918701] |
f78e11a0-95f2-4e44-bd62-bd32c3d56933 | conservative-q-learning-for-offline | 2006.04779 | null | https://arxiv.org/abs/2006.04779v3 | https://arxiv.org/pdf/2006.04779v3.pdf | Conservative Q-Learning for Offline Reinforcement Learning | Effectively leveraging large, previously collected datasets in reinforcement learning (RL) is a key challenge for large-scale real-world applications. Offline RL algorithms promise to learn effective policies from previously-collected, static datasets without further interaction. However, in practice, offline RL presen... | ['Sergey Levine', 'Aurick Zhou', 'George Tucker', 'Aviral Kumar'] | 2020-06-08 | null | http://proceedings.neurips.cc/paper/2020/hash/0d2b2061826a5df3221116a5085a6052-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/0d2b2061826a5df3221116a5085a6052-Paper.pdf | neurips-2020-12 | ['dqn-replay-dataset', 'dqn-replay-dataset'] | ['miscellaneous', 'playing-games'] | [-2.53617525e-01 2.18917936e-01 -6.89264178e-01 7.44200274e-02
-1.27622497e+00 -7.09249020e-01 3.94083053e-01 3.21209431e-01
-9.37723100e-01 1.36756361e+00 1.08854480e-01 -5.58168232e-01
-2.21160904e-01 -6.37970030e-01 -9.63645279e-01 -7.44628131e-01
-3.65610808e-01 6.55881405e-01 2.34153755e-02 -2.21459836... | [4.077636241912842, 2.2752068042755127] |
64e6c4b0-c254-40ba-b2f4-24161f70d0ce | an-image-fusion-scheme-for-single-shot-high | 1908.08195 | null | https://arxiv.org/abs/1908.08195v1 | https://arxiv.org/pdf/1908.08195v1.pdf | An Image Fusion Scheme for Single-Shot High Dynamic Range Imaging with Spatially Varying Exposures | This paper proposes a novel multi-exposure image fusion (MEF) scheme for single-shot high dynamic range imaging with spatially varying exposures (SVE). Single-shot imaging with SVE enables us not only to produce images without color saturation regions from a single-shot image, but also to avoid ghost artifacts in the p... | ['Sayaka Shiota', 'Hitoshi Kiya', 'Chihiro Go', 'Yuma Kinoshita'] | 2019-08-22 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 8.24337423e-01 -6.60790741e-01 3.86718094e-01 -2.23235071e-01
-2.97935784e-01 -2.42519036e-01 1.82949185e-01 -3.56487334e-01
-6.87768638e-01 5.33869863e-01 -2.02469915e-01 -2.90578101e-02
-2.32972220e-01 -8.52676034e-01 -2.78162360e-01 -9.10106063e-01
5.84159613e-01 -1.54039577e-01 8.02618802e-01 -4.61431108... | [10.923429489135742, -2.4970462322235107] |
f252d2c9-d12d-45ab-9163-66022d7d37b4 | motionbert-unified-pretraining-for-human | 2210.06551 | null | https://arxiv.org/abs/2210.06551v2 | https://arxiv.org/pdf/2210.06551v2.pdf | Learning Human Motion Representations: A Unified Perspective | We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources. Specifically, we propose a pretraining stage in which a motion encoder is trained to recover the underlying 3D motion from noisy partial 2D observati... | ['Yizhou Wang', 'Wayne Wu', 'Libin Liu', 'Zhaoyang Liu', 'Xiaoxuan Ma', 'Wentao Zhu'] | 2022-10-12 | null | null | null | null | ['3d-pose-estimation', 'one-shot-3d-action-recognition', '3d-human-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.60746112e-01 -2.21890919e-02 -4.47039157e-01 -7.45080933e-02
-6.41205609e-01 -3.23659390e-01 6.77882969e-01 -5.70325673e-01
-5.89896977e-01 4.21496749e-01 6.37277126e-01 2.08555788e-01
1.48428366e-01 -5.58168530e-01 -1.02116358e+00 -5.08486390e-01
-7.88415894e-02 3.82127017e-01 3.79674315e-01 -2.18797132... | [7.371423721313477, -0.3036661744117737] |
05ef9c0a-b941-4190-a23c-84d81f22c657 | 3d-human-pose-estimation-in-multi-view | 2210.11826 | null | https://arxiv.org/abs/2210.11826v1 | https://arxiv.org/pdf/2210.11826v1.pdf | 3D Human Pose Estimation in Multi-View Operating Room Videos Using Differentiable Camera Projections | 3D human pose estimation in multi-view operating room (OR) videos is a relevant asset for person tracking and action recognition. However, the surgical environment makes it challenging to find poses due to sterile clothing, frequent occlusions, and limited public data. Methods specifically designed for the OR are gener... | ['Ivo A. M. J. Broeders', 'Jelmer M. Wolterink', 'Beerend G. A. Gerats'] | 2022-10-21 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 2.55496055e-01 1.54525355e-01 -1.36616141e-01 -8.75731930e-02
-1.10704935e+00 -6.49968684e-01 2.28362650e-01 1.79920867e-01
-9.37032223e-01 3.81536186e-01 3.08823317e-01 -9.07847658e-02
-2.08208747e-02 -1.07738636e-01 -8.60899329e-01 -4.92573351e-01
-2.34646454e-01 3.55258822e-01 -1.60839990e-01 3.93368676... | [6.848818302154541, -1.0308849811553955] |
d7e3c8b0-5522-4372-aeef-c33adf88789c | dilation-erosion-for-single-frame-supervised | 2212.06348 | null | https://arxiv.org/abs/2212.06348v1 | https://arxiv.org/pdf/2212.06348v1.pdf | Dilation-Erosion for Single-Frame Supervised Temporal Action Localization | To balance the annotation labor and the granularity of supervision, single-frame annotation has been introduced in temporal action localization. It provides a rough temporal location for an action but implicitly overstates the supervision from the annotated-frame during training, leading to the confusion between action... | ['Yan Rui', 'Xiangbo Shu', 'Yang Zhao', 'Fanming Wang', 'Yan Song', 'Bin Wang'] | 2022-12-13 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 4.89742279e-01 3.08718443e-01 -4.23131764e-01 -2.55317837e-01
-7.67757177e-01 -2.84756958e-01 4.32952374e-01 8.79488215e-02
-4.48508501e-01 7.19758451e-01 2.09692225e-01 1.27745271e-01
2.30693340e-01 -5.64672530e-01 -5.64995944e-01 -9.45587695e-01
3.56899261e-01 -1.66812047e-01 8.49640429e-01 2.17065305... | [8.48918628692627, 0.6401223540306091] |
edd4a0ba-56a7-4c54-bcb0-c904b08ed6b5 | modeling-dynamic-heterogeneous-graph-and-node | 2305.17417 | null | https://arxiv.org/abs/2305.17417v1 | https://arxiv.org/pdf/2305.17417v1.pdf | Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction | Accurate citation count prediction of newly published papers could help editors and readers rapidly figure out the influential papers in the future. Though many approaches are proposed to predict a paper's future citation, most ignore the dynamic heterogeneous graph structure or node importance in academic networks. To... | ['Rui Liu', 'Haolong Guo', 'Ting Jiang', 'Chenguang Du', 'Xuehua Ming', 'Fuzhen Zhuang', 'Deqing Wang', 'Hao Geng'] | 2023-05-27 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-8.01782072e-01 -6.32921755e-02 -3.75963360e-01 1.41893998e-01
-3.06325871e-03 -5.14885902e-01 6.85143471e-01 4.54688519e-01
-8.80168471e-03 5.94959259e-01 4.18119133e-01 -3.83929938e-01
-5.62924623e-01 -1.08671975e+00 -2.44770810e-01 -6.44769490e-01
-1.30011320e-01 4.30344313e-01 8.10713843e-02 7.43213668... | [7.210330009460449, 6.158230781555176] |
09a633c3-3489-4c83-9ba4-590ef2b0fe90 | tempcaps-a-capsule-network-based-embedding | null | null | https://aclanthology.org/2022.spnlp-1.3 | https://aclanthology.org/2022.spnlp-1.3.pdf | TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion | Temporal knowledge graphs store the dynamics of entities and relations during a time period. However, typical temporal knowledge graphs often suffer from incomplete dynamics with missing facts in real-world scenarios. Hence, modeling temporal knowledge graphs to complete the missing facts is important. In this paper, w... | ['Roger Wattenhofer', 'Volker Tresp', 'Matthias Schubert', 'Yunpu Ma', 'Zifeng Ding', 'Zhen Han', 'Zhao Meng', 'Guirong Fu'] | null | null | null | null | spnlp-acl-2022-5 | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-8.24527919e-01 1.43908337e-01 -4.71829534e-01 4.55918461e-02
-7.63683170e-02 -8.92296970e-01 6.15713775e-01 4.28281218e-01
-1.15530729e-01 6.34404361e-01 6.12184286e-01 -6.13228716e-02
-8.13644826e-01 -1.15267515e+00 -7.25865722e-01 -3.56204718e-01
-7.50828922e-01 3.90718013e-01 3.92668366e-01 -1.69207469... | [8.509096145629883, 7.879737377166748] |
ab0164f4-59d1-46bb-b86a-7fd0d8e9f9c1 | exploiting-partially-annotated-data-for | 1804.08420 | null | http://arxiv.org/abs/1804.08420v2 | http://arxiv.org/pdf/1804.08420v2.pdf | Exploiting Partially Annotated Data for Temporal Relation Extraction | Annotating temporal relations (TempRel) between events described in natural
language is known to be labor intensive, partly because the total number of
TempRels is quadratic in the number of events. As a result, only a small number
of documents are typically annotated, limiting the coverage of various
lexical/semantic ... | ['Qiang Ning', 'Dan Roth', 'Zhongzhi Yu', 'Chuchu Fan'] | 2018-04-18 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 3.54348607e-02 3.78590226e-01 -5.41024625e-01 -3.07476193e-01
-7.76069164e-01 -8.92103314e-01 6.61293507e-01 6.70252919e-01
-4.56649333e-01 1.28695130e+00 2.45878264e-01 -6.90084174e-02
5.17910086e-02 -6.01803243e-01 -5.97182930e-01 -5.10745347e-01
-7.00922385e-02 6.93530381e-01 6.39521658e-01 -1.39308691... | [9.100269317626953, 9.219077110290527] |
63388cf2-7b6c-4dfa-9bd2-632f55b1ca49 | deft-detection-embeddings-for-tracking | 2102.02267 | null | https://arxiv.org/abs/2102.02267v2 | https://arxiv.org/pdf/2102.02267v2.pdf | DEFT: Detection Embeddings for Tracking | Most modern multiple object tracking (MOT) systems follow the tracking-by-detection paradigm, consisting of a detector followed by a method for associating detections into tracks. There is a long history in tracking of combining motion and appearance features to provide robustness to occlusions and other challenges, bu... | ["Stephen O'Hara", 'J. Ross Beveridge', 'Peter Zhang', 'Mohamed Chaabane'] | 2021-02-03 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-9.45250243e-02 -4.72142726e-01 -3.91310692e-01 7.94227142e-03
-7.08440781e-01 -8.01858664e-01 8.02277565e-01 -6.57366961e-03
-6.68478251e-01 3.50849152e-01 -2.34368183e-02 -1.77268963e-02
3.79528664e-02 -2.61658996e-01 -7.65624166e-01 -4.94244993e-01
-7.47979581e-02 5.02499938e-01 8.60398173e-01 1.11004539... | [6.369413375854492, -2.0913760662078857] |
f7198d97-e8a6-4925-93f6-c7fd0eaab31d | learning-to-hallucinate-face-images-via | 1708.00223 | null | http://arxiv.org/abs/1708.00223v1 | http://arxiv.org/pdf/1708.00223v1.pdf | Learning to Hallucinate Face Images via Component Generation and Enhancement | We propose a two-stage method for face hallucination. First, we generate
facial components of the input image using CNNs. These components represent the
basic facial structures. Second, we synthesize fine-grained facial structures
from high resolution training images. The details of these structures are
transferred int... | ['Qingxiong Yang', 'Linchao Bao', 'Shengfeng He', 'Jiawei Zhang', 'Yibing Song'] | 2017-08-01 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 2.96764672e-01 5.66552401e-01 2.03385338e-01 -4.52709258e-01
-4.77619469e-01 -1.19309895e-01 6.02184772e-01 -7.86000311e-01
-6.35046214e-02 7.53576398e-01 4.89108860e-01 4.98537093e-01
5.22395611e-01 -1.06239951e+00 -8.30271482e-01 -6.07378125e-01
3.28785986e-01 2.70621628e-02 -1.50699407e-01 -3.38033855... | [12.768369674682617, -0.10806423425674438] |
b7082c5d-6370-48b0-aa27-fb132131d497 | max-margin-structured-output-regression-for | null | null | http://papers.nips.cc/paper/4794-max-margin-structured-output-regression-for-spatio-temporal-action-localization | http://papers.nips.cc/paper/4794-max-margin-structured-output-regression-for-spatio-temporal-action-localization.pdf | Max-Margin Structured Output Regression for Spatio-Temporal Action Localization | Structured output learning has been successfully applied to object localization, where the mapping between an image and an object bounding box can be well captured. Its extension to action localization in videos, however, is much more challenging, because one needs to predict the locations of the action patterns both s... | ['Du Tran', 'Junsong Yuan'] | 2012-12-01 | null | null | null | neurips-2012-12 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 4.15021449e-01 -2.88420022e-01 -5.85846364e-01 -1.70570716e-01
-8.93883526e-01 -6.20590448e-01 3.48633260e-01 8.32561255e-02
-4.64138418e-01 6.19180977e-01 1.93784103e-01 1.07715249e-01
-2.54169852e-01 -3.18607301e-01 -1.11984348e+00 -8.38765740e-01
-4.09735203e-01 2.23533422e-01 6.51827633e-01 5.33258736... | [8.490264892578125, 0.5932130217552185] |
b11f0b3b-d78b-42dc-a39b-b150b82cb9a5 | uabcoral-a-preliminary-study-for-resolving | null | null | https://aclanthology.org/S12-1036 | https://aclanthology.org/S12-1036.pdf | UABCoRAL: A Preliminary study for Resolving the Scope of Negation | null | ['Binod Gyawali', 'Thamar Solorio'] | 2012-07-01 | null | null | null | semeval-2012-7 | ['negation-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... | [-7.230929374694824, 3.7111520767211914] |
4f7675a0-1b8c-4229-843e-fae7a6435787 | resmlp-feedforward-networks-for-image | 2105.03404 | null | https://arxiv.org/abs/2105.03404v2 | https://arxiv.org/pdf/2105.03404v2.pdf | ResMLP: Feedforward networks for image classification with data-efficient training | We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. It is a simple residual network that alternates (i) a linear layer in which image patches interact, independently and identically across channels, and (ii) a two-layer feed-forward network in which channels interact... | ['Armand Joulin', 'Gautier Izacard', 'Hervé Jégou', 'Jakob Verbeek', 'Gabriel Synnaeve', 'Edouard Grave', 'Alaaeldin El-Nouby', 'Matthieu Cord', 'Mathilde Caron', 'Piotr Bojanowski', 'Hugo Touvron'] | 2021-05-07 | null | https://openreview.net/forum?id=K9uApq7iyyI | https://openreview.net/pdf?id=K9uApq7iyyI | neurips-2021-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 6.10153079e-01 2.55007386e-01 -5.44085130e-02 -4.43709910e-01
-8.78952861e-01 -4.83045846e-01 9.91842985e-01 -3.24404716e-01
-6.71497107e-01 5.89383483e-01 1.92869842e-01 -5.70980310e-01
5.24910092e-01 -5.08786917e-01 -1.39711368e+00 -6.56980038e-01
-5.03945015e-02 5.29478669e-01 1.59068152e-01 3.65696102... | [9.542190551757812, 1.4730764627456665] |
9b5a6535-7883-49fd-b562-b3bf01a05710 | mfm-net-unpaired-shape-completion-network | 2111.11976 | null | https://arxiv.org/abs/2111.11976v3 | https://arxiv.org/pdf/2111.11976v3.pdf | KTNet: Knowledge Transfer for Unpaired 3D Shape Completion | Unpaired 3D object completion aims to predict a complete 3D shape from an incomplete input without knowing the correspondence between the complete and incomplete shapes. In this paper, we propose the novel KTNet to solve this task from the new perspective of knowledge transfer. KTNet elaborates a teacher-assistant-stud... | ['Bisheng Yang', 'Xiongwu Xiao', 'Yu-Shen Liu', 'Zhen Dong', 'Xin Wen', 'Wenxiao Zhang', 'Zhen Cao'] | 2021-11-23 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-1.26702815e-01 1.81436166e-01 -5.24118692e-02 -4.28648591e-01
-5.74473500e-01 -6.37481630e-01 2.26543754e-01 -1.92402929e-01
-9.09041520e-03 5.50045550e-01 -9.07768980e-02 -2.03055099e-01
-1.24111339e-01 -1.16143811e+00 -1.11412096e+00 -6.81642056e-01
4.75014716e-01 9.00508702e-01 3.16349149e-01 -1.34508431... | [8.484164237976074, -3.6140987873077393] |
f039879b-4c8d-45d3-8df6-d59cbd9594e3 | video-acceleration-magnification | 1704.04186 | null | http://arxiv.org/abs/1704.04186v2 | http://arxiv.org/pdf/1704.04186v2.pdf | Video Acceleration Magnification | The ability to amplify or reduce subtle image changes over time is useful in
contexts such as video editing, medical video analysis, product quality control
and sports. In these contexts there is often large motion present which
severely distorts current video amplification methods that magnify change
linearly. In this... | ['Jan C. van Gemert', 'Silvia L. Pintea', 'Yichao Zhang'] | 2017-04-13 | video-acceleration-magnification-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Video_Acceleration_Magnification_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Video_Acceleration_Magnification_CVPR_2017_paper.pdf | cvpr-2017-7 | ['motion-magnification'] | ['computer-vision'] | [ 5.68629622e-01 -1.13359511e-01 -6.86000362e-02 9.54047143e-02
-5.66521548e-02 -7.76092350e-01 5.89029670e-01 5.25795668e-03
-4.94534016e-01 5.41598201e-01 1.54711604e-01 -3.11864406e-01
1.14531882e-01 -5.03870726e-01 -6.88865185e-01 -3.84752482e-01
-3.52574140e-01 -2.58316785e-01 7.86584437e-01 -3.58514577... | [10.829383850097656, -1.4417403936386108] |
71084e0d-5111-4ece-b276-9178327b9aca | enriched-music-representations-with-multiple | 2104.00437 | null | https://arxiv.org/abs/2104.00437v1 | https://arxiv.org/pdf/2104.00437v1.pdf | Enriched Music Representations with Multiple Cross-modal Contrastive Learning | Modeling various aspects that make a music piece unique is a challenging task, requiring the combination of multiple sources of information. Deep learning is commonly used to obtain representations using various sources of information, such as the audio, interactions between users and songs, or associated genre metadat... | ['Dmitry Bogdanov', 'Yuntae Kim', 'Konstantinos Drossos', 'Xavier Favory', 'Andres Ferraro'] | 2021-04-01 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 2.84933537e-01 -3.47083390e-01 -3.11534584e-01 -2.95839965e-01
-1.29978979e+00 -7.12384641e-01 5.98667920e-01 4.92830873e-01
-3.39455366e-01 3.29005301e-01 8.40938270e-01 6.14882946e-01
-3.04016978e-01 -5.45539439e-01 -7.31851459e-01 -6.02351367e-01
-3.85505781e-02 2.45909870e-01 1.38229936e-01 -7.68934786... | [15.633539199829102, 5.167294025421143] |
e212c4c5-efa2-4d34-b654-6a2dc7d0f8d0 | growsp-unsupervised-semantic-segmentation-of-1 | 2305.16404 | null | https://arxiv.org/abs/2305.16404v1 | https://arxiv.org/pdf/2305.16404v1.pdf | GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds | We study the problem of 3D semantic segmentation from raw point clouds. Unlike existing methods which primarily rely on a large amount of human annotations for training neural networks, we propose the first purely unsupervised method, called GrowSP, to successfully identify complex semantic classes for every point in 3... | ['Bo Li', 'Bing Wang', 'Bo Yang', 'Zihui Zhang'] | 2023-05-25 | growsp-unsupervised-semantic-segmentation-of | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_GrowSP_Unsupervised_Semantic_Segmentation_of_3D_Point_Clouds_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_GrowSP_Unsupervised_Semantic_Segmentation_of_3D_Point_Clouds_CVPR_2023_paper.pdf | cvpr-2023-1 | ['unsupervised-semantic-segmentation', '3d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.99441597e-01 3.84619266e-01 -2.03400657e-01 -6.46461964e-01
-8.52453470e-01 -8.21929932e-01 7.28678226e-01 4.08190936e-01
-2.47495323e-01 -1.24411145e-03 -2.31900290e-01 -2.98824042e-01
5.34327067e-02 -6.58746421e-01 -9.48192716e-01 -2.19639957e-01
-1.54006615e-01 1.03922284e+00 7.80827403e-01 5.73427901... | [8.013278007507324, -3.2727761268615723] |
69f6c3d3-597f-4ba4-babc-56caa60e2f57 | molecular-docking-studies-on-jensenone-from | 2004.00217 | null | http://arxiv.org/abs/2004.00217v2 | http://arxiv.org/pdf/2004.00217v2.pdf | Molecular docking studies on Jensenone from eucalyptus essential oil as a potential inhibitor of COVID 19 corona virus infection | COVID-19, a member of corona virus family is spreading its tentacles across
the world due to lack of drugs at present. However, the main viral proteinase
(Mpro/3CLpro) has recently been regarded as a suitable target for drug design
against SARS infection due to its vital role in polyproteins processing
necessary for co... | [] | 2020-04-17 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 5.04550450e-02 -5.14164746e-01 -1.32379234e-01 -4.91115414e-02
1.26830682e-01 -5.65704763e-01 2.13845238e-01 3.99585605e-01
-4.05558914e-01 1.02245772e+00 2.77471300e-02 -4.75508302e-01
2.57034987e-01 -3.55405778e-01 -4.00203824e-01 -9.44077015e-01
-2.58914292e-01 3.80455106e-01 4.77977283e-02 -2.32892334... | [4.667364120483398, 5.106382846832275] |
1f77b5b4-b321-4ec3-a86d-419e788b21bb | maximum-entropy-heterogeneous-agent-mirror | 2306.10715 | null | https://arxiv.org/abs/2306.10715v1 | https://arxiv.org/pdf/2306.10715v1.pdf | Maximum Entropy Heterogeneous-Agent Mirror Learning | Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years. However, existing state-of-the-art methods face challenges related to sample inefficiency, brittleness regarding hyperparameters, and the risk of converging to a suboptimal Nash Equilibrium. To resolve these issues... | ['Yaodong Yang', 'Xiaojun Chang', 'Qiang Fu', 'Haobo Fu', 'Siyi Hu', 'Yifan Zhong', 'Jiarong Liu'] | 2023-06-19 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-4.45618957e-01 2.45462283e-01 -5.56017637e-01 4.00288731e-01
-1.08991134e+00 -4.73262936e-01 7.58573174e-01 -4.60346416e-02
-5.84604263e-01 1.20608938e+00 3.41831923e-01 -1.42024413e-01
-2.89675862e-01 -4.02467906e-01 -6.83589399e-01 -8.21143568e-01
-3.40338409e-01 4.95776802e-01 1.21512283e-02 -6.66945040... | [3.8038694858551025, 1.97822105884552] |
f7adeea0-08dc-4f62-af59-6230beba6691 | unsupervised-opinion-summarization-with-1 | 2012.07808 | null | https://arxiv.org/abs/2012.07808v1 | https://arxiv.org/pdf/2012.07808v1.pdf | Unsupervised Opinion Summarization with Content Planning | The recent success of deep learning techniques for abstractive summarization is predicated on the availability of large-scale datasets. When summarizing reviews (e.g., for products or movies), such training data is neither available nor can be easily sourced, motivating the development of methods which rely on syntheti... | ['Mirella Lapata', 'Stefanos Angelidis', 'Reinald Kim Amplayo'] | 2020-12-14 | null | null | null | null | ['unsupervised-opinion-summarization'] | ['natural-language-processing'] | [ 2.90769130e-01 7.18663394e-01 -2.00172186e-01 -5.77093899e-01
-1.23186886e+00 -7.93061078e-01 1.19705617e+00 6.05675459e-01
-1.34837002e-01 1.07077229e+00 1.07843554e+00 -3.55399996e-02
3.90821934e-01 -7.27623880e-01 -7.67368734e-01 -1.91129684e-01
3.49026769e-01 9.90485013e-01 -1.68188646e-01 -2.40531445... | [12.391692161560059, 9.343610763549805] |
8aff332a-15e3-4dc4-b2b9-57e41cbbdb13 | how-good-is-the-model-in-model-in-the-loop | 2306.05434 | null | https://arxiv.org/abs/2306.05434v1 | https://arxiv.org/pdf/2306.05434v1.pdf | How Good is the Model in Model-in-the-loop Event Coreference Resolution Annotation? | Annotating cross-document event coreference links is a time-consuming and cognitively demanding task that can compromise annotation quality and efficiency. To address this, we propose a model-in-the-loop annotation approach for event coreference resolution, where a machine learning model suggests likely corefering even... | ['James H. Martin', 'Nikhil Krishnaswamy', 'Adam Pollins', 'Michael Regan', 'Abhijnan Nath', 'Shafiuddin Rehan Ahmed'] | 2023-06-06 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 1.05941892e-01 6.59036994e-01 -2.10371166e-01 -5.58601618e-01
-1.38289583e+00 -9.77833390e-01 5.28513491e-01 5.98157942e-01
-6.45620465e-01 7.94275999e-01 5.32589555e-01 -1.49095565e-01
-1.85225725e-01 -3.72118413e-01 -4.26122844e-01 -2.31348038e-01
1.48934096e-01 9.74426150e-01 3.55345488e-01 1.15589000... | [9.283900260925293, 9.448452949523926] |
937fa682-c086-4189-bfbc-c96771b9431e | rotation-synchronization-via-deep-matrix | 2305.05268 | null | https://arxiv.org/abs/2305.05268v1 | https://arxiv.org/pdf/2305.05268v1.pdf | Rotation Synchronization via Deep Matrix Factorization | In this paper we address the rotation synchronization problem, where the objective is to recover absolute rotations starting from pairwise ones, where the unknowns and the measures are represented as nodes and edges of a graph, respectively. This problem is an essential task for structure from motion and simultaneous l... | ['Federica Arrigoni', 'Elisa Ricci', 'Andrea Fusiello', 'Paolo Rota', 'Giacomo Zara', 'Gk Tejus'] | 2023-05-09 | null | null | null | null | ['simultaneous-localization-and-mapping', 'matrix-completion'] | ['computer-vision', 'methodology'] | [-9.78428796e-02 1.01949545e-02 -3.14481616e-01 -2.02393401e-02
-2.98651874e-01 -4.62696135e-01 6.58432424e-01 -1.80788845e-01
-5.75079501e-01 3.71065378e-01 2.31144637e-01 1.18528761e-01
-2.26405933e-01 -2.79821366e-01 -7.26257920e-01 -7.61811435e-01
-1.29624531e-01 4.74988341e-01 -3.12425166e-01 -2.54943281... | [8.125958442687988, -2.180600166320801] |
b16fc2d5-e522-49d3-9c48-ad342097444e | recursive-construction-of-stable-assemblies | 2106.08928 | null | https://arxiv.org/abs/2106.08928v6 | https://arxiv.org/pdf/2106.08928v6.pdf | RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks | Recurrent neural networks (RNNs) are widely used throughout neuroscience as models of local neural activity. Many properties of single RNNs are well characterized theoretically, but experimental neuroscience has moved in the direction of studying multiple interacting areas, and RNN theory needs to be likewise extended.... | ['Leo Kozachkov', 'Jean-Jacques Slotine', 'Michaela Ennis'] | 2021-06-16 | recursive-construction-of-stable-assemblies-1 | https://openreview.net/forum?id=qTBC7E4c454 | https://openreview.net/pdf?id=qTBC7E4c454 | null | ['sequential-image-classification'] | ['computer-vision'] | [ 2.69659519e-01 9.18413624e-02 1.31101891e-01 7.39055406e-03
-1.68772861e-01 -6.20481074e-01 6.77994251e-01 -3.01196426e-01
-4.87804383e-01 6.65546060e-01 2.70929337e-01 -3.43244046e-01
-2.19897881e-01 -1.69357345e-01 -6.81895196e-01 -1.12583375e+00
-4.09521997e-01 8.75443816e-02 2.19551668e-01 -6.26014113... | [8.21005630493164, 3.281325578689575] |
0b31d1eb-d953-4246-9ce5-b4b6c515d2e3 | tourist-attractions-recommendation-based-on | 2306.10946 | null | https://arxiv.org/abs/2306.10946v4 | https://arxiv.org/pdf/2306.10946v4.pdf | Att-KGCN: Tourist Attractions Recommendation System by using Attention mechanism and Knowledge Graph Convolution Network | The recommendation algorithm based on knowledge graphs is at a relatively mature stage. However, there are still some problems in the recommendation of specific areas. For example, in the tourism field, selecting suitable tourist attraction attributes process is complicated as the recommendation basis for tourist attra... | ['Han Cao', 'Jingjing Li', 'Ahmad A. Mubarak'] | 2023-06-19 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-7.79187560e-01 -2.30747536e-01 -3.00275743e-01 -5.59073806e-01
2.10015729e-01 -2.51533866e-01 2.91585922e-01 4.64639366e-02
-3.37394327e-01 4.38158959e-01 6.02476299e-01 -3.81316662e-01
-7.55120337e-01 -1.44132626e+00 -4.79145557e-01 -7.63141394e-01
-2.13465169e-01 5.57566643e-01 9.66171548e-02 -7.37341642... | [10.24071979522705, 5.619266033172607] |
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