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9498585d-85b6-4851-b936-8de8e7d1b293 | gmss-graph-based-multi-task-self-supervised | 2205.01030 | null | https://arxiv.org/abs/2205.01030v1 | https://arxiv.org/pdf/2205.01030v1.pdf | GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition | Previous electroencephalogram (EEG) emotion recognition relies on single-task learning, which may lead to overfitting and learned emotion features lacking generalization. In this paper, a graph-based multi-task self-supervised learning model (GMSS) for EEG emotion recognition is proposed. GMSS has the ability to learn ... | ['Wenming Zheng', 'Guangming Shi', 'Yi Niu', 'Yijin Zhou', 'Youshuo Ji', 'Hao Wu', 'Boxun Fu', 'Fu Li', 'Ji Chen', 'Yang Li'] | 2022-04-12 | null | null | null | null | ['eeg-emotion-recognition'] | ['miscellaneous'] | [ 1.38046890e-01 -1.75952017e-01 1.33311570e-01 -4.83309925e-01
-2.07493261e-01 2.48873625e-02 4.86254618e-02 5.31061478e-02
-2.70947009e-01 9.04304981e-01 2.53277104e-02 4.25206155e-01
-7.96410084e-01 -4.86716241e-01 -3.59564602e-01 -8.74925971e-01
-4.14531887e-01 -2.21480444e-01 -2.98082352e-01 -2.71512449... | [13.109867095947266, 3.4790215492248535] |
341c8586-bc39-4d6e-b5b8-5d10246c81a8 | semantic-white-balance-semantic-color | 1802.00153 | null | https://arxiv.org/abs/1802.00153v5 | https://arxiv.org/pdf/1802.00153v5.pdf | Semantic White Balance: Semantic Color Constancy Using Convolutional Neural Network | The goal of computational color constancy is to preserve the perceptive colors of objects under different lighting conditions by removing the effect of color casts caused by the scene's illumination. With the rapid development of deep learning based techniques, significant progress has been made in image semantic segme... | ['Mahmoud Afifi'] | 2018-02-01 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 3.21564764e-01 -4.57786471e-01 3.36507231e-01 -5.31256318e-01
4.98976558e-02 -4.62404877e-01 2.17865840e-01 2.38837432e-02
-6.11712992e-01 3.84926736e-01 -2.27924898e-01 5.58675407e-03
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5.56235492e-01 -2.78992821e-02 4.32439059e-01 -6.59482181... | [10.550889015197754, -2.455578565597534] |
dec4e46f-41ef-48a7-bab9-bba2cfda254a | transsleep-transitioning-aware-attention | 2203.12590 | null | https://arxiv.org/abs/2203.12590v1 | https://arxiv.org/pdf/2203.12590v1.pdf | TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging | Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine learning as well as deep learning architectures for sleep staging. However, two key challenges hinder the practical use of these architectures: effectively capturing salient wav... | ['Heung-Il Suk', 'Eunjin Jeon', 'Wonjun Ko', 'Jauen Phyo'] | 2022-03-22 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 4.05102670e-02 -4.08836305e-01 -3.26638699e-01 -6.15212083e-01
-6.98970079e-01 -1.62401780e-01 1.74132586e-01 -4.92449030e-02
-4.93544608e-01 4.83626038e-01 4.22603607e-01 -1.39084995e-01
-4.01345780e-03 -1.48752585e-01 4.53481227e-02 -6.53074801e-01
-2.68287182e-01 -1.30323365e-01 2.26471901e-01 -8.29196274... | [13.502568244934082, 3.534496307373047] |
ad504a4d-607c-493d-99f3-9a605b469f1a | decentralized-vehicle-coordination-the | 2209.08763 | null | https://arxiv.org/abs/2209.08763v2 | https://arxiv.org/pdf/2209.08763v2.pdf | Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset | Decentralized multiagent planning has been an important field of research in robotics. An interesting and impactful application in the field is decentralized vehicle coordination in understructured road environments. For example, in an intersection, it is useful yet difficult to deconflict multiple vehicles of intersec... | ['Alexandre Bayen', 'Trevor Darrell', 'Christopher Chou', 'Jiamu Zhang', 'Jiawei Lu', 'Chenhui Hao', 'Minjune Hwang', 'Dequan Wang', 'Fangyu Wu'] | 2022-09-19 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-3.07103200e-03 3.90488803e-01 8.68140012e-02 -5.05128264e-01
-2.91566402e-01 -1.01580215e+00 7.56555319e-01 1.70040697e-01
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-2.52815843e-01 8.47119987e-01 3.22268426e-01 -8.64706039... | [5.597462177276611, 0.8961154818534851] |
357fa228-4150-4894-9fb4-bc056a414dd0 | benchmarking-robustness-in-neural-radiance | 2301.04075 | null | https://arxiv.org/abs/2301.04075v1 | https://arxiv.org/pdf/2301.04075v1.pdf | Benchmarking Robustness in Neural Radiance Fields | Neural Radiance Field (NeRF) has demonstrated excellent quality in novel view synthesis, thanks to its ability to model 3D object geometries in a concise formulation. However, current approaches to NeRF-based models rely on clean images with accurate camera calibration, which can be difficult to obtain in the real worl... | ['Cihang Xie', 'Alan Yuille', 'Junbo Li', 'Angtian Wang', 'Chen Wang'] | 2023-01-10 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [ 2.66499698e-01 -3.06384176e-01 2.50099391e-01 -2.48714030e-01
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3.16411048e-01 -2.55691200e-01 -1.09179750e-01 -4.86616910... | [9.589327812194824, -2.8330371379852295] |
4d4122fd-cc76-40f1-900b-898b2fa3e8f2 | hybrids-of-constraint-based-and-noise-based | 2306.08765 | null | https://arxiv.org/abs/2306.08765v1 | https://arxiv.org/pdf/2306.08765v1.pdf | Hybrids of Constraint-based and Noise-based Algorithms for Causal Discovery from Time Series | Constraint-based and noise-based methods have been proposed to discover summary causal graphs from observational time series under strong assumptions which can be violated or impossible to verify in real applications. Recently, a hybrid method (Assaad et al, 2021) that combines these two approaches, proved to be robust... | ['Wilfried Thuiller', 'Eric Gaussier', 'Emilie Devijver', 'Julyan Arbel', 'Daria Bystrova', 'Charles K. Assaad'] | 2023-06-14 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 2.08656862e-01 -2.59766310e-01 -1.42071381e-01 1.52769417e-01
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-1.02997422e+00 1.65683240e-01 7.95621157e-01 -7.66754150... | [7.676187992095947, 5.2455878257751465] |
11d446ac-fcc5-4513-b87e-1b3620f790b4 | etpnav-evolving-topological-planning-for | 2304.03047 | null | https://arxiv.org/abs/2304.03047v2 | https://arxiv.org/pdf/2304.03047v2.pdf | ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments | Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. It becomes increasingly crucial in the field of embodied AI, with potential applications in autonomous navigation, search and rescue, and human-robot interaction. In this paper, we propose to address a more p... | ['Liang Wang', 'Keji He', 'Yan Huang', 'Zun Wang', 'Wenguan Wang', 'Hanqing Wang', 'Dong An'] | 2023-04-06 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [ 7.40478113e-02 2.68350631e-01 3.98793519e-01 -2.36059159e-01
-4.67667609e-01 -6.51952565e-01 7.98668921e-01 -1.82680681e-01
-7.11813986e-01 5.69789350e-01 3.16279009e-02 -4.56471920e-01
-2.69613624e-01 -9.41299260e-01 -7.90679157e-01 -5.33074021e-01
-2.33600572e-01 6.14998460e-01 3.55372399e-01 -8.08658600... | [4.616539478302002, 0.6431280970573425] |
d5ce0542-5c46-4bc9-a4c5-554108f71284 | what-is-essential-for-unseen-goal | 2305.18882 | null | https://arxiv.org/abs/2305.18882v2 | https://arxiv.org/pdf/2305.18882v2.pdf | What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL? | Offline goal-conditioned RL (GCRL) offers a way to train general-purpose agents from fully offline datasets. In addition to being conservative within the dataset, the generalization ability to achieve unseen goals is another fundamental challenge for offline GCRL. However, to the best of our knowledge, this problem has... | ['Tong Zhang', 'Chongjie Zhang', 'Hao Hu', 'Xiaoteng Ma', 'Yong Lin', 'Rui Yang'] | 2023-05-30 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.66700715e-01 9.68182236e-02 -3.63283485e-01 -2.83665329e-01
-8.89906287e-01 -7.43124545e-01 4.85007674e-01 -1.66939989e-01
-6.92954957e-01 9.67544794e-01 -1.02808483e-01 -3.81249666e-01
-3.78858745e-01 -2.10480675e-01 -9.30000901e-01 -9.04232562e-01
-6.62839234e-01 4.20186847e-01 2.08595786e-02 -2.15259001... | [4.125502586364746, 2.150444984436035] |
3ae12208-5b85-40ec-ab0e-12d2aeec5f26 | a-call-for-more-rigor-in-unsupervised-cross | 2004.14958 | null | https://arxiv.org/abs/2004.14958v1 | https://arxiv.org/pdf/2004.14958v1.pdf | A Call for More Rigor in Unsupervised Cross-lingual Learning | We review motivations, definition, approaches, and methodology for unsupervised cross-lingual learning and call for a more rigorous position in each of them. An existing rationale for such research is based on the lack of parallel data for many of the world's languages. However, we argue that a scenario without any par... | ['Mikel Artetxe', 'Sebastian Ruder', 'Gorka Labaka', 'Eneko Agirre', 'Dani Yogatama'] | 2020-04-30 | a-call-for-more-rigor-in-unsupervised-cross-1 | https://aclanthology.org/2020.acl-main.658 | https://aclanthology.org/2020.acl-main.658.pdf | acl-2020-6 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-2.19256148e-01 -1.19930334e-01 -6.27023697e-01 -5.69578767e-01
-1.18764412e+00 -7.98841000e-01 8.75607073e-01 2.26385176e-01
-8.99585128e-01 7.48314440e-01 5.50950885e-01 -8.39707971e-01
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1.77793071e-01 6.82482123e-01 -2.11142287e-01 -2.88190365... | [10.863261222839355, 9.955353736877441] |
a87b24af-df71-46ba-a8eb-b6757f770a80 | when-bioprocess-engineering-meets-machine | 2209.01083 | null | https://arxiv.org/abs/2209.01083v2 | https://arxiv.org/pdf/2209.01083v2.pdf | When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development | Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation has been accelerated by increasing levels of lab automation, experimental planning and data modeling are still largerly depend on human inte... | ['Thorben Werner', 'Mariano Nicolas Cruz-Bournazou', 'Peter Neubauer', 'Lars Schmidt-Thieme', 'Randolf Scholz', 'Ernesto Martinez', 'Maxim Borisyak', 'Katharina Paulick', 'Marie-Therese Schermeyer', 'Jong Woo Kim', 'Stefan Born', 'Nghia Duong-Trung'] | 2022-09-02 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 5.19941509e-01 2.39141613e-01 -4.98902099e-03 -1.64160863e-01
-1.45062432e-01 -4.88133997e-01 3.32564414e-01 6.94709122e-01
-1.94272831e-01 8.62119913e-01 -5.40034652e-01 -6.38007998e-01
-5.78848124e-01 -7.37078369e-01 -4.43216950e-01 -8.83492172e-01
-5.70702739e-02 9.93030787e-01 -2.88399626e-02 1.10653587... | [6.094482898712158, 4.035916805267334] |
cfad41af-3038-4c4d-800d-629c143e9b7b | visual-boundary-knowledge-translation-for | 2108.00379 | null | https://arxiv.org/abs/2108.00379v1 | https://arxiv.org/pdf/2108.00379v1.pdf | Visual Boundary Knowledge Translation for Foreground Segmentation | When confronted with objects of unknown types in an image, humans can effortlessly and precisely tell their visual boundaries. This recognition mechanism and underlying generalization capability seem to contrast to state-of-the-art image segmentation networks that rely on large-scale category-aware annotated training s... | ['Mingli Song', 'Xiangtong Du', 'Yajie Liu', 'Xiang Wang', 'Xinchao Wang', 'Lechao Cheng', 'Zunlei Feng'] | 2021-08-01 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 7.40812123e-01 5.82605183e-01 -1.96821317e-01 -5.78386009e-01
-4.49971229e-01 -8.43765438e-01 5.39293468e-01 -3.13997865e-01
-3.84290844e-01 5.53657055e-01 -5.75964928e-01 -3.71521711e-01
4.15068001e-01 -8.03329468e-01 -9.28770304e-01 -6.46604717e-01
4.07244474e-01 7.44730055e-01 5.59880912e-01 1.67817727... | [9.740591049194336, 0.7024540305137634] |
5bf2e0a0-062c-4ad5-9c9a-4b1fefbfd6ac | exploring-stereovision-based-3-d-scene | 1902.06255 | null | http://arxiv.org/abs/1902.06255v1 | http://arxiv.org/pdf/1902.06255v1.pdf | Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality | Three-dimensional (3-D) scene reconstruction is one of the key techniques in
Augmented Reality (AR), which is related to the integration of image processing
and display systems of complex information. Stereo matching is a computer
vision based approach for 3-D scene reconstruction. In this paper, we explore
an improved... | ['Guang-Yu Nie', 'Cong Wang', 'Yongtian Wang', 'Yun Liu', 'Yue Liu'] | 2019-02-17 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 2.80442744e-01 -3.70489389e-01 -6.43358603e-02 -2.93465406e-01
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-3.99019048e-02 -6.12471402e-01 -7.97859788e-01 -1.46023169e-01
3.98788005e-01 8.84217918e-02 4.99757558e-01 -3.47222388... | [8.748719215393066, -2.345165252685547] |
5dc2c7aa-7e21-4492-9f11-0a07731a9d96 | conditional-sound-generation-using-neural | 2107.09998 | null | https://arxiv.org/abs/2107.09998v3 | https://arxiv.org/pdf/2107.09998v3.pdf | Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning | Deep generative models have recently achieved impressive performance in speech and music synthesis. However, compared to the generation of those domain-specific sounds, generating general sounds (such as siren, gunshots) has received less attention, despite their wide applications. In previous work, the SampleRNN metho... | ['Wenwu Wang', 'Mark D. Plumbley', 'Qiushi Huang', 'Jinzheng Zhao', 'Turab Iqbal', 'Xubo Liu'] | 2021-07-21 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 5.68116754e-02 -3.82967591e-01 3.13697308e-01 -1.12769626e-01
-9.63219881e-01 -4.83919799e-01 7.09353268e-01 -5.74968085e-02
-2.36488413e-02 7.10412562e-01 5.84320843e-01 5.62754944e-02
-3.81616771e-01 -9.51063991e-01 -4.70740527e-01 -7.62501895e-01
-3.15233201e-01 1.99696064e-01 1.92368850e-01 -5.96991554... | [15.598160743713379, 5.83946418762207] |
5d4db664-030c-4b1b-b2f3-313fabc421df | rethinking-neural-operations-for-diverse | 2103.15798 | null | https://arxiv.org/abs/2103.15798v2 | https://arxiv.org/pdf/2103.15798v2.pdf | Rethinking Neural Operations for Diverse Tasks | An important goal of AutoML is to automate-away the design of neural networks on new tasks in under-explored domains. Motivated by this goal, we study the problem of enabling users to discover the right neural operations given data from their specific domain. We introduce a search space of operations called XD-Operatio... | ['Ameet Talwalkar', 'Christopher Ré', 'Liam Li', 'Tri Dao', 'Mikhail Khodak', 'Nicholas Roberts'] | 2021-03-29 | null | http://proceedings.neurips.cc/paper/2021/hash/84fdbc3ac902561c00871c9b0c226756-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/84fdbc3ac902561c00871c9b0c226756-Paper.pdf | neurips-2021-12 | ['music-modeling'] | ['music'] | [ 3.26985747e-01 6.62467629e-02 -4.74145487e-02 -5.08927047e-01
-3.65814507e-01 -6.42909586e-01 4.00166899e-01 -9.18386057e-02
-8.17554355e-01 5.95554471e-01 9.88036096e-02 -5.62195301e-01
-2.05929205e-01 -5.50203502e-01 -1.04663873e+00 -3.48848283e-01
-1.60971373e-01 5.86659133e-01 8.90590698e-02 -3.15614939... | [8.806130409240723, 3.239421844482422] |
1aaab332-b110-4c2c-b537-f5c335385bd5 | fast-bayesian-optimization-of-needle-in-a | 2208.13771 | null | https://arxiv.org/abs/2208.13771v2 | https://arxiv.org/pdf/2208.13771v2.pdf | Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI) | Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the datas... | ['Tonio Buonassisi', 'Qianxiao Li', 'Zekun Ren', 'Alexander E. Siemenn'] | 2022-08-26 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 2.72513293e-02 -5.21074951e-01 -8.51540715e-02 -2.50361919e-01
-7.54701138e-01 -6.09551072e-01 -2.03542151e-02 2.50856251e-01
-7.62467384e-01 9.64667559e-01 -4.85429138e-01 -5.59393644e-01
-8.33216071e-01 -8.80156219e-01 -9.87532020e-01 -9.54009950e-01
-4.60808694e-01 1.19451869e+00 1.74263105e-01 1.37262970... | [6.520457744598389, 4.112582206726074] |
776e998c-0898-462b-bb3d-e039069e3e7b | an-adaptive-graph-learning-method-for | null | null | https://www.nature.com/articles/s42256-022-00501-8 | https://www.researchsquare.com/article/rs-1172418/v1.pdf?c=1656084202000 | An adaptive graph learning method for automated molecular interactions and properties predictions | Improving drug discovery efficiency is a core and long-standing challenge in drug discovery. For this purpose, many graph learning methods have been developed to search potential drug candidates with fast speed and low cost. In fact, the pursuit of high prediction performance on a limited number of datasets has crystal... | ['Xiaojun Yao', 'Shengyu Zhang', 'Huanxiang Liu', 'Qifeng Bai', 'Jiaxian Yan', 'Dejun Jiang', 'Yanan Tian', 'Shuo Liu', 'Pengyong Li', 'Xiaorui Wang', 'Xiaoqing Gong', 'Ruiqiang Lu', 'Chang-Yu Hsieh', 'Yuquan Li'] | 2022-06-23 | null | null | null | nature-machine-intelligence-2022-6 | ['molecular-property-prediction'] | ['miscellaneous'] | [ 2.49848545e-01 -2.00197950e-01 -5.39242208e-01 -3.70906949e-01
-3.49718571e-01 -3.64999652e-01 1.05427079e-01 5.94421387e-01
-3.89292240e-02 1.09633589e+00 -2.42025197e-01 -6.30538762e-01
-4.80829895e-01 -7.83485591e-01 -4.79305238e-01 -7.44536936e-01
-5.50954901e-02 6.05765700e-01 2.28621095e-01 -1.50347397... | [5.153038501739502, 5.803918361663818] |
10812015-3fb8-4d5f-b0f7-78bbf10d638b | end-to-end-complex-valued-multidilated | 2110.00745 | null | https://arxiv.org/abs/2110.00745v3 | https://arxiv.org/pdf/2110.00745v3.pdf | End-to-End Complex-Valued Multidilated Convolutional Neural Network for Joint Acoustic Echo Cancellation and Noise Suppression | Echo and noise suppression is an integral part of a full-duplex communication system. Many recent acoustic echo cancellation (AEC) systems rely on a separate adaptive filtering module for linear echo suppression and a neural module for residual echo suppression. However, not only do adaptive filtering modules require c... | ['Bin Ma', 'Shengkui Zhao', 'Woon-Seng Gan', 'Thi Ngoc Tho Nguyen', 'Karn N. Watcharasupat'] | 2021-10-02 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.36064523e-01 -1.31793484e-01 7.53025413e-01 -3.59939247e-01
-4.78093296e-01 -5.01510859e-01 3.73523176e-01 -4.91289347e-02
-9.19600666e-01 4.02574509e-01 3.61315429e-01 -1.28377348e-01
-2.05614924e-01 -5.62011302e-01 -3.77321273e-01 -7.24224389e-01
-4.44952607e-01 -4.64240938e-01 4.47447896e-01 -2.74165362... | [15.06296157836914, 5.95719575881958] |
22977478-142b-4fe2-9615-964d56e0781a | augstatic-a-light-weight-image-augmentation | null | null | https://www.jetir.org/view?paper=JETIR2205199 | https://www.jetir.org/papers/JETIR2205199.pdf | AugStatic - A Light-Weight Image Augmentation Library | The rapid exponential increase in the data led to an abrupt mix of various data types, leading to a deficiency of helpful information. Creating new data with the existing different types of data are presented in this paper. Augmentation is adding up or modifying the dataset with extra data. There are many types of augm... | ['Dr. Venkateswara Rao Gurrala', 'Allena Venkata Sai Abhishek'] | 2022-05-01 | null | null | null | journal-of-emerging-technologies-and | ['image-manipulation-detection', 'image-smoothing', 'image-matting', 'image-variation', 'image-stitching', 'image-augmentation', 'image-manipulation', 'image-morphing', 'roi-based-image-generation', 'image-cropping', 'detecting-image-manipulation', 'data-visualization', 'data-visualization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [ 8.59419778e-02 -2.00577796e-01 3.18770915e-01 -6.62581682e-01
-6.97008073e-02 -2.81716317e-01 7.82181144e-01 2.91901767e-01
-2.89778233e-01 4.98241782e-01 5.18803954e-01 -2.96374470e-01
4.27833557e-01 -1.00568628e+00 -3.68885696e-01 -7.48835921e-01
4.87013794e-02 3.56597155e-01 1.50305122e-01 -4.59549218... | [9.443737030029297, 2.057987928390503] |
e5a870dd-f885-4eca-b252-dee719007f28 | relaxed-transformer-decoders-for-direct | 2102.01894 | null | https://arxiv.org/abs/2102.01894v3 | https://arxiv.org/pdf/2102.01894v3.pdf | Relaxed Transformer Decoders for Direct Action Proposal Generation | Temporal action proposal generation is an important and challenging task in video understanding, which aims at detecting all temporal segments containing action instances of interest. The existing proposal generation approaches are generally based on pre-defined anchor windows or heuristic bottom-up boundary matching s... | ['Gangshan Wu', 'LiMin Wang', 'Jiaqi Tang', 'Jing Tan'] | 2021-02-03 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Tan_Relaxed_Transformer_Decoders_for_Direct_Action_Proposal_Generation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Tan_Relaxed_Transformer_Decoders_for_Direct_Action_Proposal_Generation_ICCV_2021_paper.pdf | iccv-2021-1 | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 0.571864 0.04715557 -0.5085007 -0.15007949 -0.7634664 -0.32628378
0.65210295 -0.22512367 -0.26469 0.61919683 0.5367159 -0.18261534
0.01718873 -0.5550658 -0.6372055 -0.49403256 -0.06097094 0.08033273
0.911356 -0.03833305 0.26607728 0.13605867 -1.5146356 0.53555405
0.7338354 1.1748425 0.... | [8.438102722167969, 0.49097076058387756] |
407191a7-4233-4ff6-aecb-56d769e16f4f | objective-surgical-skills-assessment-and-tool | 2212.04448 | null | https://arxiv.org/abs/2212.04448v1 | https://arxiv.org/pdf/2212.04448v1.pdf | Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge | Timely and effective feedback within surgical training plays a critical role in developing the skills required to perform safe and efficient surgery. Feedback from expert surgeons, while especially valuable in this regard, is challenging to acquire due to their typically busy schedules, and may be subject to biases. Fo... | ['Anthony Jarc', 'Stefanie Speidel', 'Danail Stoyanov', 'Lena Maier-Hein', 'Evangelos Mazomenos', 'Jinfan Zhou', 'Yueming Jin', 'Dimitris Psychogyios', 'Emanuele Colleoni', 'Satoshi Kondo', 'Max Berniker', 'Ziheng Wang', 'Xi Liu', 'Kiran Bhattacharyya', 'Aneeq Zia'] | 2022-12-08 | null | null | null | null | ['skills-assessment'] | ['computer-vision'] | [-2.22892240e-01 1.45721629e-01 -4.22224432e-01 -1.74695909e-01
-1.15973794e+00 -8.93486202e-01 3.01034786e-02 5.03607094e-01
-6.90453351e-01 5.41574180e-01 4.55937296e-01 -9.14873600e-01
-4.00537223e-01 -2.71278441e-01 -6.40103221e-01 -3.66501629e-01
-1.14567839e-01 3.43395263e-01 -1.84734866e-01 -2.74545163... | [14.062792778015137, -3.385866165161133] |
3e236d3d-2f81-482d-b297-2b0245c9823b | multi-level-graph-matching-networks-for-deep | null | null | https://openreview.net/forum?id=65_RUwah5kr | https://openreview.net/pdf?id=65_RUwah5kr | Multi-level Graph Matching Networks for Deep and Robust Graph Similarity Learning | While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to graph similarity learning. Recent works have considered either global-level graph-graph interactions or low-level node-node interactions, ignoring the rich c... | ['Shouling Ji', 'Chunming Wu', 'Alex X. Liu', 'Fangli Xu', 'Tengfei Ma', 'Saizhuo Wang', 'Lingfei Wu', 'Xiang Ling'] | 2021-01-01 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-2.65155882e-02 3.07020098e-01 -1.49874195e-01 -2.60673672e-01
-1.94292784e-01 -4.46683377e-01 6.40207171e-01 8.15538883e-01
-3.02271675e-02 9.20653641e-02 -8.91014934e-02 -2.37414569e-01
-2.54126757e-01 -1.22314095e+00 -7.18481421e-01 -5.12211561e-01
-5.97294927e-01 4.89996791e-01 3.75609308e-01 -3.18931878... | [7.164551734924316, 6.2913498878479] |
68e7846f-1ba1-402d-b159-0757f0a998c0 | exploring-body-texture-from-mmw-images-for | 2203.15618 | null | https://arxiv.org/abs/2203.15618v1 | https://arxiv.org/pdf/2203.15618v1.pdf | Exploring Body Texture from mmW Images for Person Recognition | Imaging using millimeter waves (mmWs) has many advantages including the ability to penetrate obscurants such as clothes and polymers. After having explored shape information retrieved from mmW images for person recognition, in this work we aim to gain some insight about the potential of using mmW texture information fo... | ['V. M. Patel', 'F. Alonso-Fernandez', 'R. Vera-Rodrıguez', 'J. Fierrez', 'E. Gonzalez-Sosa'] | 2022-03-27 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 1.20935841e-02 8.88623148e-02 6.92906678e-02 -3.45536113e-01
-4.38067168e-01 -3.78336400e-01 5.37508845e-01 -2.36092389e-01
-3.46821845e-01 4.55881089e-01 2.14922205e-01 1.52670979e-01
-5.00072658e-01 -1.03576767e+00 -3.47721100e-01 -9.27672565e-01
-1.80987135e-01 4.06310529e-01 -3.20576996e-01 -4.06766832... | [13.48478889465332, 0.912346601486206] |
3eb9031e-08f1-452f-8233-5e99e4def33f | modeling-molecular-structures-with-intrinsic | 2302.12255 | null | https://arxiv.org/abs/2302.12255v1 | https://arxiv.org/pdf/2302.12255v1.pdf | Modeling Molecular Structures with Intrinsic Diffusion Models | Since its foundations, more than one hundred years ago, the field of structural biology has strived to understand and analyze the properties of molecules and their interactions by studying the structure that they take in 3D space. However, a fundamental challenge with this approach has been the dynamic nature of these ... | ['Gabriele Corso'] | 2023-02-23 | null | null | null | null | ['molecular-docking'] | ['medical'] | [-1.17582656e-01 -1.66515261e-01 -2.35478450e-02 4.71608266e-02
-1.96494699e-01 -8.25873375e-01 8.38685811e-01 3.36700439e-01
-2.99764872e-01 9.76882637e-01 1.63039282e-01 -4.61264670e-01
-1.88117549e-01 -8.56277883e-01 -6.37587488e-01 -1.11711013e+00
-6.34062514e-02 8.13729644e-01 4.86602411e-02 -2.42433876... | [5.096661567687988, 5.262852191925049] |
e9474c41-d36b-4a02-ad87-92704e9e6eb0 | high-dimensional-classification-for-brain | 1504.02800 | null | http://arxiv.org/abs/1504.02800v1 | http://arxiv.org/pdf/1504.02800v1.pdf | High-Dimensional Classification for Brain Decoding | Brain decoding involves the determination of a subject's cognitive state or
an associated stimulus from functional neuroimaging data measuring brain
activity. In this setting the cognitive state is typically characterized by an
element of a finite set, and the neuroimaging data comprise voluminous amounts
of spatiotemp... | ['Jiguo Cao', 'Nicole Croteau', 'Ryan Budney', 'Farouk S. Nathoo'] | 2015-04-10 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 4.26198423e-01 -3.99396345e-02 2.57309899e-02 -7.28946626e-01
-5.16035438e-01 -4.05889362e-01 6.75426781e-01 -4.37167659e-02
-7.63969004e-01 7.76499629e-01 2.74884552e-01 -8.49961564e-02
-4.50378805e-01 -3.26589048e-01 -5.80690980e-01 -8.47549438e-01
-4.27347362e-01 2.67144024e-01 -3.10235053e-01 4.51036692... | [12.771493911743164, 3.4121453762054443] |
c247db44-c37b-40f2-bde9-e354403f6c51 | adaptpose-cross-dataset-adaptation-for-3d | 2112.11593 | null | https://arxiv.org/abs/2112.11593v2 | https://arxiv.org/pdf/2112.11593v2.pdf | AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation | This paper addresses the problem of cross-dataset generalization of 3D human pose estimation models. Testing a pre-trained 3D pose estimator on a new dataset results in a major performance drop. Previous methods have mainly addressed this problem by improving the diversity of the training data. We argue that diversity ... | ['Z. Jane Wang', 'Rabab Ward', 'Helge Rhodin', 'Bastian Wandt', 'Mohsen Gholami'] | 2021-12-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Gholami_AdaptPose_Cross-Dataset_Adaptation_for_3D_Human_Pose_Estimation_by_Learnable_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gholami_AdaptPose_Cross-Dataset_Adaptation_for_3D_Human_Pose_Estimation_by_Learnable_CVPR_2022_paper.pdf | cvpr-2022-1 | ['weakly-supervised-3d-human-pose-estimation'] | ['computer-vision'] | [ 2.07682535e-01 2.13949814e-01 2.73284055e-02 -4.71970648e-01
-8.31032753e-01 -7.24581122e-01 4.78333592e-01 -4.18911994e-01
-5.45773625e-01 7.47324228e-01 2.53694445e-01 3.01414490e-01
5.29675245e-01 -3.31784368e-01 -1.13325179e+00 -4.21132833e-01
9.87453759e-02 1.19611263e+00 3.71937275e-01 -2.56197333... | [6.935800075531006, -1.0075664520263672] |
2e44053f-124e-4d98-9708-59634fed5ccf | dan-danish-nested-named-entities-and-lexical | 2105.11301 | null | https://arxiv.org/abs/2105.11301v1 | https://arxiv.org/pdf/2105.11301v1.pdf | DaN+: Danish Nested Named Entities and Lexical Normalization | This paper introduces DaN+, a new multi-domain corpus and annotation guidelines for Danish nested named entities (NEs) and lexical normalization to support research on cross-lingual cross-domain learning for a less-resourced language. We empirically assess three strategies to model the two-layer Named Entity Recognitio... | ['Rob van der Goot', 'Kristian Nørgaard Jensen', 'Barbara Plank'] | 2021-05-24 | null | https://aclanthology.org/2020.coling-main.583 | https://aclanthology.org/2020.coling-main.583.pdf | coling-2020-8 | ['lexical-normalization'] | ['natural-language-processing'] | [-4.07388091e-01 -7.52707720e-02 -3.36743295e-01 -4.52829987e-01
-1.40982354e+00 -1.01048112e+00 5.94974577e-01 3.90212357e-01
-1.36006057e+00 1.06148732e+00 6.08614266e-01 -2.89868444e-01
5.84434308e-02 -3.08660030e-01 -5.27485549e-01 -1.92826569e-01
2.46474162e-01 8.51717710e-01 3.36833447e-01 -4.60359216... | [10.10705852508545, 9.782964706420898] |
e73c216a-e4f1-47f5-8781-a94d044ce5d6 | weakly-supervised-cloud-detection-with-fixed | 2111.11879 | null | https://arxiv.org/abs/2111.11879v1 | https://arxiv.org/pdf/2111.11879v1.pdf | Weakly-Supervised Cloud Detection with Fixed-Point GANs | The detection of clouds in satellite images is an essential preprocessing task for big data in remote sensing. Convolutional neural networks (CNNs) have greatly advanced the state-of-the-art in the detection of clouds in satellite images, but existing CNN-based methods are costly as they require large amounts of traini... | ['Ira Assent', 'Joachim Nyborg'] | 2021-11-23 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 3.67056102e-01 -2.36477703e-01 2.08610177e-01 -4.29323524e-01
-9.90224898e-01 -8.28396738e-01 5.03895104e-01 -5.01906089e-02
-3.73489946e-01 2.43278205e-01 -4.51635361e-01 -4.46100950e-01
3.06827515e-01 -1.06354940e+00 -1.06466544e+00 -7.47556567e-01
-1.21937983e-01 4.31583077e-01 2.13125125e-01 3.88646033... | [9.646495819091797, -1.5995991230010986] |
9fcadbf0-a6f2-4cc8-956e-36534d7f4aff | wish-you-were-here-context-aware-human | 2005.10663 | null | https://arxiv.org/abs/2005.10663v1 | https://arxiv.org/pdf/2005.10663v1.pdf | Wish You Were Here: Context-Aware Human Generation | We present a novel method for inserting objects, specifically humans, into existing images, such that they blend in a photorealistic manner, while respecting the semantic context of the scene. Our method involves three subnetworks: the first generates the semantic map of the new person, given the pose of the other pers... | ['Oran Gafni', 'Lior Wolf'] | 2020-05-21 | wish-you-were-here-context-aware-human-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Gafni_Wish_You_Were_Here_Context-Aware_Human_Generation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Gafni_Wish_You_Were_Here_Context-Aware_Human_Generation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['pose-transfer'] | ['computer-vision'] | [ 6.32233381e-01 5.31816006e-01 4.48670864e-01 -5.20008087e-01
-2.22809315e-01 -6.09018028e-01 6.69927537e-01 -4.37005192e-01
-3.35592598e-01 5.89625835e-01 2.40780160e-01 5.20205498e-01
1.60472900e-01 -6.37245893e-01 -8.80178094e-01 -4.32723612e-01
2.44801313e-01 9.17866230e-01 4.73653018e-01 -1.66080713... | [11.681981086730957, -0.7159238457679749] |
f98561a9-48a6-4b60-b486-ce936886a5e0 | darknet-traffic-classification-and | 2206.06371 | null | https://arxiv.org/abs/2206.06371v1 | https://arxiv.org/pdf/2206.06371v1.pdf | Darknet Traffic Classification and Adversarial Attacks | The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning techniques to automate the detection of darknet traffic in an attempt to block these criminal activities. This research aims to improve darknet traffic detection by assessing ... | ['Mark Stamp', 'Nhien Rust-Nguyen'] | 2022-06-12 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 2.24176735e-01 1.83583647e-02 -2.49588132e-01 -9.97421741e-02
-2.82788306e-01 -1.11288905e+00 1.21740985e+00 -5.66976547e-01
-1.44981787e-01 8.49530935e-01 -1.72275662e-01 -1.31499851e+00
5.09254932e-01 -1.03546762e+00 -4.04768616e-01 -4.44325447e-01
-3.05240769e-02 3.65927994e-01 4.54749137e-01 -2.09353775... | [5.50093936920166, 7.607359409332275] |
6546f84b-538f-47bb-a0ea-b2c1f16c4d3f | cmvae-causal-meta-vae-for-unsupervised-meta | 2302.09731 | null | https://arxiv.org/abs/2302.09731v1 | https://arxiv.org/pdf/2302.09731v1.pdf | CMVAE: Causal Meta VAE for Unsupervised Meta-Learning | Unsupervised meta-learning aims to learn the meta knowledge from unlabeled data and rapidly adapt to novel tasks. However, existing approaches may be misled by the context-bias (e.g. background) from the training data. In this paper, we abstract the unsupervised meta-learning problem into a Structural Causal Model (SCM... | ['Huimin Yu', 'Guodong Qi'] | 2023-02-20 | null | null | null | null | ['few-shot-image-classification', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 4.23691899e-01 2.90003657e-01 -6.82387888e-01 -4.88395274e-01
-6.66971087e-01 -1.28212482e-01 7.65456736e-01 -1.24858148e-01
-6.54973090e-02 9.69990194e-01 7.03368425e-01 -9.59246531e-02
-2.11582482e-01 -6.47994936e-01 -1.08017242e+00 -8.83782089e-01
6.06405959e-02 2.04259112e-01 -2.10622013e-01 9.87440720... | [9.050933837890625, 1.2985789775848389] |
b76af7c4-fb6b-4305-8095-e0ff1552483e | on-interference-rejection-using-riemannian | 2301.03399 | null | https://arxiv.org/abs/2301.03399v1 | https://arxiv.org/pdf/2301.03399v1.pdf | On Interference-Rejection using Riemannian Geometry for Direction of Arrival Estimation | We consider the problem of estimating the direction of arrival of desired acoustic sources in the presence of multiple acoustic interference sources. All the sources are located in noisy and reverberant environments and are received by a microphone array. We propose a new approach for designing beamformers based on the... | ['Ronen Talmon', 'Amitay Bar'] | 2023-01-09 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 1.47400558e-01 -1.45019948e-01 9.74971294e-01 -8.22253302e-02
-5.60914814e-01 -5.22490799e-01 3.72253917e-02 -4.76880193e-01
-2.90509790e-01 2.56306201e-01 5.48324227e-01 -3.24678719e-01
-6.22652650e-01 -3.16256940e-01 -3.72666955e-01 -9.06503439e-01
-5.15959382e-01 -4.34481263e-01 -1.11039497e-01 -1.22042730... | [15.183661460876465, 5.697331428527832] |
823e266a-6bc8-48d2-ae30-237e06160794 | segdiscover-visual-concept-discovery-via | 2204.10926 | null | https://arxiv.org/abs/2204.10926v1 | https://arxiv.org/pdf/2204.10926v1.pdf | SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation | Visual concept discovery has long been deemed important to improve interpretability of neural networks, because a bank of semantically meaningful concepts would provide us with a starting point for building machine learning models that exhibit intelligible reasoning process. Previous methods have disadvantages: either ... | ['Cynthia Rudin', 'Zhi Chen', 'Haiyang Huang'] | 2022-04-22 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 5.02531350e-01 7.59131491e-01 -1.82355881e-01 -6.75421894e-01
-2.30292723e-01 -5.88220477e-01 8.42935383e-01 4.87195790e-01
-2.60219604e-01 4.08892184e-01 3.15433890e-01 -4.36808050e-01
-1.46634012e-01 -8.16079497e-01 -8.55231822e-01 -4.80232090e-01
-1.26549557e-01 8.40699971e-01 2.23450184e-01 -1.10576920... | [9.770822525024414, 0.9866703152656555] |
e3ad0c93-ac93-4e31-92fd-2bd951a92930 | cultural-aware-machine-learning-based | 2304.06953 | null | https://arxiv.org/abs/2304.06953v1 | https://arxiv.org/pdf/2304.06953v1.pdf | Cultural-aware Machine Learning based Analysis of COVID-19 Vaccine Hesitancy | Understanding the COVID-19 vaccine hesitancy, such as who and why, is very crucial since a large-scale vaccine adoption remains as one of the most efficient methods of controlling the pandemic. Such an understanding also provides insights into designing successful vaccination campaigns for future pandemics. Unfortunate... | ['My T. Thai', 'Huan Chen', 'Sylvia Chan-Olmsted', 'Raed Alharbi'] | 2023-04-14 | null | null | null | null | ['culture'] | ['speech'] | [-1.01963796e-01 2.49743000e-01 -1.02118790e+00 -3.93226683e-01
1.85297757e-01 -1.93143889e-01 3.64216745e-01 5.75399697e-01
-3.19341183e-01 5.53213894e-01 9.03224051e-01 -9.00224805e-01
-2.87729073e-02 -8.19473922e-01 -8.43667567e-01 -5.82903743e-01
-8.76124017e-03 3.52583557e-01 -6.42700374e-01 -4.81124401... | [8.024704933166504, 5.4857892990112305] |
e5cfa607-7a90-4edd-95af-5638abb261a0 | sceneflowfields-multi-frame-matching | 1902.10099 | null | https://arxiv.org/abs/1902.10099v2 | https://arxiv.org/pdf/1902.10099v2.pdf | SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation | State-of-the-art scene flow algorithms pursue the conflicting targets of accuracy, run time, and robustness. With the successful concept of pixel-wise matching and sparse-to-dense interpolation, we push the limits of scene flow estimation. Avoiding strong assumptions on the domain or the problem yields a more robust al... | ['Oliver Wasenmüller', 'René Schuster', 'Didier Stricker', 'Christian Unger', 'Georg Kuschk'] | 2019-02-26 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.06861703e-01 -4.91384506e-01 -2.48372272e-01 -2.09157355e-02
-3.96707177e-01 -6.05519235e-01 6.03552818e-01 -1.50050715e-01
-3.45334470e-01 7.89250851e-01 3.01348835e-01 -2.49645039e-01
1.94085501e-02 -7.93018460e-01 -3.40530187e-01 -4.46578622e-01
-2.82152116e-01 3.00950736e-01 8.48146915e-01 -2.06467256... | [8.716327667236328, -1.9074554443359375] |
e8acd350-64aa-479c-8424-ad55f7d785de | deepar-probabilistic-forecasting-with | 1704.04110 | null | http://arxiv.org/abs/1704.04110v3 | http://arxiv.org/pdf/1704.04110v3.pdf | DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks | Probabilistic forecasting, i.e. estimating the probability distribution of a
time series' future given its past, is a key enabler for optimizing business
processes. In retail businesses, for example, forecasting demand is crucial for
having the right inventory available at the right time at the right place. In
this pap... | ['Valentin Flunkert', 'Jan Gasthaus', 'David Salinas'] | 2017-04-13 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-2.50669003e-01 -1.89506292e-01 -7.11249709e-02 -5.50777674e-01
-7.25421190e-01 -3.59810978e-01 7.75925279e-01 -3.48005816e-03
-3.88849638e-02 7.56262839e-01 3.34800273e-01 -6.38314009e-01
-1.86835006e-01 -8.27762723e-01 -8.67897153e-01 -6.70747697e-01
-2.32276425e-01 7.47166693e-01 -3.42766702e-01 -2.65237838... | [6.948117733001709, 3.1435611248016357] |
1d58dc60-5757-4f7d-92dc-0fe49175a5ff | towards-feature-space-adversarial-attack-1 | 2004.12385 | null | https://arxiv.org/abs/2004.12385v2 | https://arxiv.org/pdf/2004.12385v2.pdf | Towards Feature Space Adversarial Attack | We propose a new adversarial attack to Deep Neural Networks for image classification. Different from most existing attacks that directly perturb input pixels, our attack focuses on perturbing abstract features, more specifically, features that denote styles, including interpretable styles such as vivid colors and sharp... | ['Qiu-Ling Xu', 'Xiangyu Zhang', 'Siyuan Cheng', 'Guanhong Tao'] | 2020-04-26 | null | https://openreview.net/forum?id=S1eqj1SKvr | https://openreview.net/pdf?id=S1eqj1SKvr | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 7.63116777e-01 1.96206659e-01 4.50806320e-01 -4.01977032e-01
-2.10395724e-01 -1.35047865e+00 7.05381870e-01 -5.24146616e-01
-2.38674104e-01 6.25721276e-01 -2.98315257e-01 -5.73005915e-01
3.95542324e-01 -1.05750120e+00 -1.10375845e+00 -5.37621796e-01
4.83649373e-02 -2.24980235e-01 -1.05797611e-01 -4.85592335... | [5.655569553375244, 7.963830947875977] |
225f963a-f548-4c78-981b-d44fdf97f836 | reiterative-domain-aware-multi-target | 2109.00919 | null | https://arxiv.org/abs/2109.00919v2 | https://arxiv.org/pdf/2109.00919v2.pdf | Reiterative Domain Aware Multi-Target Adaptation | Most domain adaptation methods focus on single-source-single-target adaptation settings. Multi-target domain adaptation is a powerful extension in which a single classifier is learned for multiple unlabeled target domains. To build a multi-target classifier, it is important to have: a feature extractor that generalizes... | ['Xiao Xiang Zhu', 'Nasrullah Sheikh', 'Shan Zhao', 'Sudipan Saha'] | 2021-08-26 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 2.92441338e-01 1.99833378e-01 -3.04885775e-01 -7.25319326e-01
-8.65359962e-01 -4.77005243e-01 3.52190495e-01 1.01405576e-01
-4.03342396e-01 1.21833396e+00 -1.40458435e-01 -3.59928655e-03
-2.72568837e-02 -8.83631706e-01 -6.41223133e-01 -6.43292248e-01
5.58810048e-02 8.25128078e-01 7.08932459e-01 -9.04570222... | [10.349178314208984, 3.1364495754241943] |
5c89b276-3a99-4766-b0e1-0c15e9fc9162 | visual-relationship-detection-with-relative | 1911.00713 | null | https://arxiv.org/abs/1911.00713v1 | https://arxiv.org/pdf/1911.00713v1.pdf | Visual Relationship Detection with Relative Location Mining | Visual relationship detection, as a challenging task used to find and distinguish the interactions between object pairs in one image, has received much attention recently. In this work, we propose a novel visual relationship detection framework by deeply mining and utilizing relative location of object-pair in every st... | ['Hao Zhou', 'Chuanping Hu', 'Chongyang Zhang'] | 2019-11-02 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 2.00113252e-01 7.34117534e-03 -4.39342529e-01 -3.96961153e-01
-1.42286107e-01 -2.17339456e-01 4.58746642e-01 6.55136943e-01
-3.08290273e-01 5.26857257e-01 -1.39841363e-01 -9.22217891e-02
-5.11516988e-01 -1.04676247e+00 -6.42904282e-01 -6.33537948e-01
-3.53740931e-01 2.88219273e-01 6.91659272e-01 1.03674263... | [10.082143783569336, 1.703153133392334] |
159c7533-4efa-4653-80b0-c7c217ab0824 | spherical-formulation-of-geometric-motion | 2104.12404 | null | https://arxiv.org/abs/2104.12404v1 | https://arxiv.org/pdf/2104.12404v1.pdf | Spherical formulation of geometric motion segmentation constraints in fisheye cameras | We introduce a visual motion segmentation method employing spherical geometry for fisheye cameras and automoated driving. Three commonly used geometric constraints in pin-hole imagery (the positive height, positive depth and epipolar constraints) are reformulated to spherical coordinates, making them invariant to speci... | ['Ciaran Eising', 'Letizia Mariotti'] | 2021-04-26 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-6.28726855e-02 -1.32049471e-01 -1.62618414e-01 -3.06668043e-01
1.95335537e-01 -8.30748558e-01 7.32355237e-01 -3.97672296e-01
-6.50469363e-01 4.14531648e-01 -4.02630448e-01 -3.78015518e-01
-4.29781936e-02 -4.21152800e-01 -6.05255485e-01 -6.68664038e-01
1.58681735e-01 1.14493355e-01 7.84224331e-01 -1.85353979... | [8.11725902557373, -2.269369602203369] |
e86fd509-1664-4bf9-9fa7-d74a33150f11 | multi-armed-bandits-with-generalized | 2303.00620 | null | https://arxiv.org/abs/2303.00620v1 | https://arxiv.org/pdf/2303.00620v1.pdf | Multi-Armed Bandits with Generalized Temporally-Partitioned Rewards | Decision-making problems of sequential nature, where decisions made in the past may have an impact on the future, are used to model many practically important applications. In some real-world applications, feedback about a decision is delayed and may arrive via partial rewards that are observed with different delays. M... | ['Pratik Gajane', 'Nina Verbeeke', 'Luc Siecker', 'Tobias Sagis', 'Rik Litjens', 'Ronald C. van den Broek'] | 2023-03-01 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 3.08867633e-01 5.89477159e-02 -6.23731911e-01 -3.21455002e-01
-6.60471499e-01 -6.96857512e-01 2.72838742e-01 5.23097217e-01
-5.99776268e-01 1.38518310e+00 -1.14261284e-01 -6.23407841e-01
-6.39562130e-01 -7.93711543e-01 -7.52263248e-01 -8.60491037e-01
-4.47970092e-01 5.08719504e-01 1.52193278e-01 -3.42580378... | [4.512691020965576, 3.2605862617492676] |
dd38ed55-2088-479b-a03a-676c02e58eb3 | e-inu-simulating-a-quadruped-robot-with | 2301.00964 | null | https://arxiv.org/abs/2301.00964v1 | https://arxiv.org/pdf/2301.00964v1.pdf | e-Inu: Simulating A Quadruped Robot With Emotional Sentience | Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detectin... | ['Annapurna Jonnalagadda', 'Firuz Kamalov', 'S. Anitha', 'Aswani Kumar Cherukuri', 'Sibi Chakkaravarthy S', 'Jatin Karthik Tripathy', 'Abhiruph Chakravarty'] | 2023-01-03 | null | null | null | null | ['video-emotion-detection'] | ['computer-vision'] | [-2.17839524e-01 4.06524718e-01 4.62282419e-01 -4.77054901e-02
9.77058038e-02 -2.31185645e-01 1.94861636e-01 -3.06069225e-01
-2.42461786e-01 7.72631168e-01 -6.05327785e-01 2.30182514e-01
2.96475202e-01 -8.60073388e-01 -5.37961483e-01 -7.85775185e-01
-3.07339966e-01 4.69427407e-01 2.26289779e-01 -6.87460959... | [4.821213245391846, 1.0997895002365112] |
78679ffb-efad-4a49-a5b8-dea22424ef4f | circa-comprehensible-online-system-in-support | 2210.05440 | null | https://arxiv.org/abs/2210.05440v1 | https://arxiv.org/pdf/2210.05440v1.pdf | CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis | Due to the large accumulation of patients requiring hospitalization, the COVID-19 pandemic disease caused a high overload of health systems, even in developed countries. Deep learning techniques based on medical imaging data can help in the faster detection of COVID-19 cases and monitoring of disease progression. Regar... | ['Joanna Polanska', 'Michal Marczyk', 'POLCOVID Study Group', 'Edyta Szurowska', 'Barbara Gizycka', 'Gabriela Zapolska', 'Krzysztof Simon', 'Robert Flisiak', 'Malgorzata Pawlowska', 'Mateusz Nowak', 'Andrzej Cieszanowski', 'Grzegorz Przybylski', 'Tadeusz Popiela', 'Jerzy Walecki', 'Magdalena Sliwinska', 'Katarzyna Grus... | 2022-10-11 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-1.61080882e-01 -1.34917572e-01 -1.56171143e-01 -6.99933171e-02
-7.74705112e-01 -5.57210326e-01 2.63854355e-01 2.14287475e-01
-4.32974219e-01 7.80924797e-01 -5.22027276e-02 -7.52068341e-01
-3.17497551e-01 -8.59797537e-01 -4.66179401e-01 -8.52061629e-01
6.32098839e-02 1.23877478e+00 4.23342735e-01 7.03885138... | [15.486405372619629, -1.831817865371704] |
d822bc54-0672-415c-a8fe-afa41f77bc17 | attention-mechanism-based-cognition-level | 2204.08027 | null | https://arxiv.org/abs/2204.08027v2 | https://arxiv.org/pdf/2204.08027v2.pdf | Attention Mechanism based Cognition-level Scene Understanding | Given a question-image input, the Visual Commonsense Reasoning (VCR) model can predict an answer with the corresponding rationale, which requires inference ability from the real world. The VCR task, which calls for exploiting the multi-source information as well as learning different levels of understanding and extensi... | ['Wenbin Zhang', 'Chris Brown', 'Peng Xu', 'Israat Haque', 'Vasile Palade', 'Kea Turner', 'Eirini Ntoutsi', 'Tai Le Quy', 'Xuejiao Tang'] | 2022-04-17 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 4.41388100e-01 9.43506658e-02 -1.44876376e-01 -3.57537866e-01
-4.51604217e-01 -3.71327370e-01 5.82725167e-01 2.90252090e-01
-1.33478150e-01 5.28686821e-01 4.77686703e-01 -7.44661212e-01
-1.46269649e-01 -8.81467164e-01 -5.48183084e-01 -2.10924715e-01
4.71820623e-01 2.47287154e-01 2.97542572e-01 -3.49931687... | [10.746161460876465, 1.7623708248138428] |
92379fd8-7188-4f25-899c-7837105769f2 | poserac-pose-saliency-transformer-for | 2303.08450 | null | https://arxiv.org/abs/2303.08450v2 | https://arxiv.org/pdf/2303.08450v2.pdf | PoseRAC: Pose Saliency Transformer for Repetitive Action Counting | This paper presents a significant contribution to the field of repetitive action counting through the introduction of a new approach called Pose Saliency Representation. The proposed method efficiently represents each action using only two salient poses instead of redundant frames, which significantly reduces the compu... | ['Yuexian Zou', 'Xuxin Cheng', 'Ziyu Yao'] | 2023-03-15 | null | null | null | null | ['repetitive-action-counting'] | ['computer-vision'] | [ 3.68734986e-01 7.43068457e-02 -3.96654546e-01 1.89903826e-02
-8.90240550e-01 -2.72510141e-01 5.34781814e-01 3.38598974e-02
-3.77885342e-01 5.03213286e-01 5.09263337e-01 1.03951022e-01
2.33037889e-01 -3.41270298e-01 -5.82857788e-01 -4.26184356e-01
1.62304137e-02 3.30862284e-01 1.00812781e+00 -2.70564646... | [8.37866497039795, 0.49341458082199097] |
2d14bbe4-33c2-481e-bb9b-bed859d410e4 | touchless-palmprint-recognition-based-on-3d | 2103.02167 | null | https://arxiv.org/abs/2103.02167v3 | https://arxiv.org/pdf/2103.02167v3.pdf | Touchless Palmprint Recognition based on 3D Gabor Template and Block Feature Refinement | With the growing demand for hand hygiene and convenience of use, palmprint recognition with touchless manner made a great development recently, providing an effective solution for person identification. Despite many efforts that have been devoted to this area, it is still uncertain about the discriminative ability of t... | ['David Zhang', 'Wei Jia', 'Jinxing Li', 'Dandan Fan', 'Xu Liang', 'Zhaoqun Li'] | 2021-03-03 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 9.12974924e-02 -8.69573712e-01 -5.91312498e-02 -2.07820088e-01
-5.60467422e-01 -6.47293448e-01 6.14265442e-01 -4.30941552e-01
-3.33252698e-01 3.29159886e-01 -1.51881099e-01 -2.21736863e-01
-3.23233515e-01 -8.17201138e-01 -3.74963284e-01 -8.90398860e-01
6.49398938e-02 8.65303278e-02 -9.89439562e-02 1.13368034... | [12.988265037536621, 0.982180655002594] |
d51e027a-9f33-47aa-8321-f87c5dfb3941 | photometric-redshift-estimation-with | 2202.09964 | null | https://arxiv.org/abs/2202.09964v1 | https://arxiv.org/pdf/2202.09964v1.pdf | Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods | Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental for subsequent analyses. In this work, we investigate two major forms of biases, i.e., class-dependent residuals and mode collapse, in a case study of estimat... | ['O. Ilbert', 'S. Arnouts', 'R. Ait Ouahmed', 'M. Treyer', 'J. Pasquet', 'D. Fouchez', 'Q. Lin'] | 2022-02-21 | null | null | null | null | ['photometric-redshift-estimation'] | ['miscellaneous'] | [ 9.67851952e-02 -4.18646157e-01 1.06363788e-01 -8.47843051e-01
-5.37407577e-01 -6.24612331e-01 1.07890892e+00 -1.37746140e-01
-6.18539274e-01 6.57065094e-01 -1.16868079e-01 -2.37299278e-01
-5.72390795e-01 -9.14477706e-01 -5.73642254e-01 -1.23606145e+00
1.13071732e-01 4.42333817e-01 2.89784253e-01 -2.21034840... | [7.559234619140625, 3.1819393634796143] |
813be88f-22b2-439e-8d6c-9b4046fdf850 | adversarial-image-perturbation-for-privacy | 1703.09471 | null | http://arxiv.org/abs/1703.09471v2 | http://arxiv.org/pdf/1703.09471v2.pdf | Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective | Users like sharing personal photos with others through social media. At the
same time, they might want to make automatic identification in such photos
difficult or even impossible. Classic obfuscation methods such as blurring are
not only unpleasant but also not as effective as one would expect. Recent
studies on adver... | ['Seong Joon Oh', 'Mario Fritz', 'Bernt Schiele'] | 2017-03-28 | adversarial-image-perturbation-for-privacy-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Oh_Adversarial_Image_Perturbation_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Oh_Adversarial_Image_Perturbation_ICCV_2017_paper.pdf | iccv-2017-10 | ['person-recognition'] | ['computer-vision'] | [ 3.88904363e-01 3.32147837e-01 4.14645344e-01 1.21831506e-01
-4.19502288e-01 -9.98844028e-01 7.03729212e-01 -1.25773296e-01
-5.71743011e-01 7.21723676e-01 -1.39039889e-01 -3.15577567e-01
-2.08663344e-01 -6.64270043e-01 -4.79034573e-01 -7.98559666e-01
-7.12052360e-02 2.44613498e-01 -2.03228816e-01 -2.61658102... | [12.697261810302734, 1.014221429824829] |
553b8b4a-5b6c-4a01-9ad0-010d799b85a7 | global-selector-a-new-benchmark-dataset-and | 2106.01263 | null | https://arxiv.org/abs/2106.01263v5 | https://arxiv.org/pdf/2106.01263v5.pdf | Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems | Sample-and-rank is a key decoding strategy for modern generation-based dialogue systems. It helps achieve diverse and high-quality responses by selecting an answer from a small pool of generated candidates. The current state-of-the-art ranking methods mainly use an encoding paradigm called Cross-Encoder, which separate... | ['Leyang Cui', 'Pengfei Fang', 'Haofei Yu', 'Zhenzhong Lan', 'Hongliang He', 'Chiyu Song'] | 2021-06-02 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [ 1.97110668e-01 -1.03593148e-01 -2.95885563e-01 -4.70618993e-01
-1.19920194e+00 -5.06848693e-01 4.54278558e-01 1.99478328e-01
-6.71451032e-01 8.96400869e-01 5.87106526e-01 -8.34812671e-02
1.68837413e-01 -7.80337334e-01 -4.85801160e-01 -5.06185234e-01
2.28416339e-01 7.87818849e-01 4.76108342e-01 -5.81159115... | [12.307746887207031, 8.063586235046387] |
08e5c6d9-e788-4826-a7f0-a852e8dc5a0a | 3d-graph-convolutional-networks-with-temporal | 1903.00919 | null | http://arxiv.org/abs/1903.00919v1 | http://arxiv.org/pdf/1903.00919v1.pdf | 3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting | Spatio-temporal prediction plays an important role in many application areas
especially in traffic domain. However, due to complicated spatio-temporal
dependency and high non-linear dynamics in road networks, traffic prediction
task is still challenging. Existing works either exhibit heavy training cost or
fail to accu... | ['Mengzhang Li', 'Jiyong Zhang', 'Bing Yu', 'Zhanxing Zhu'] | 2019-03-03 | null | null | null | null | ['4d-spatio-temporal-semantic-segmentation'] | ['computer-vision'] | [-3.48239005e-01 -3.83833498e-01 -3.22948009e-01 -3.79547983e-01
3.01276781e-02 -2.75898486e-01 6.66045964e-01 -6.25414625e-02
-7.87697732e-02 5.54520965e-01 2.85593241e-01 -8.05557013e-01
-4.46957469e-01 -1.26115823e+00 -7.51265764e-01 -2.45263204e-01
-3.09581548e-01 1.91733137e-01 8.34315419e-01 -3.81402194... | [6.4863972663879395, 2.0426876544952393] |
76aef325-6e57-4eae-83bb-6cfba5925b22 | cloze-evaluation-for-deeper-understanding-of-1 | null | null | https://aclanthology.org/2022.csrr-1.2 | https://aclanthology.org/2022.csrr-1.2.pdf | Cloze Evaluation for Deeper Understanding of Commonsense Stories in Indonesian | Story comprehension that involves complex causal and temporal relations is a critical task in NLP, but previous studies have focused predominantly on English, leaving open the question of how the findings generalize to other languages, such as Indonesian. In this paper, we follow the Story Cloze Test framework of Mosta... | ['Jey Han Lau', 'Timothy Baldwin', 'Fajri Koto'] | null | null | null | null | csrr-acl-2022-5 | ['cloze-test'] | ['natural-language-processing'] | [ 5.71746588e-01 1.48923501e-01 3.74139324e-02 -2.56059229e-01
-8.91807735e-01 -7.69155622e-01 1.01443350e+00 2.44407132e-01
-3.27580661e-01 1.00517786e+00 8.21017206e-01 -7.18961239e-01
3.59761827e-02 -7.67520010e-01 -9.10405099e-01 -2.31147856e-01
9.61378887e-02 4.68695223e-01 -3.99963781e-02 -3.21944565... | [11.207513809204102, 8.826971054077148] |
e9b97670-0bba-49df-a522-968360d54e85 | ensemble-sequence-level-training-for | 1808.10592 | null | http://arxiv.org/abs/1808.10592v1 | http://arxiv.org/pdf/1808.10592v1.pdf | Ensemble Sequence Level Training for Multimodal MT: OSU-Baidu WMT18 Multimodal Machine Translation System Report | This paper describes multimodal machine translation systems developed jointly
by Oregon State University and Baidu Research for WMT 2018 Shared Task on
multimodal translation. In this paper, we introduce a simple approach to
incorporate image information by feeding image features to the decoder side. We
also explore di... | ['Renjie Zheng', 'Mingbo Ma', 'Yilin Yang', 'Liang Huang'] | 2018-08-31 | ensemble-sequence-level-training-for-1 | https://aclanthology.org/W18-6443 | https://aclanthology.org/W18-6443.pdf | ws-2018-10 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.77835208e-01 6.28600940e-02 -3.82698357e-01 -4.26408380e-01
-1.44046497e+00 -5.78781366e-01 1.02583039e+00 -4.97265369e-01
-5.85440516e-01 8.47780824e-01 4.12214339e-01 -5.34137845e-01
7.54971862e-01 -1.60153672e-01 -1.08501899e+00 -2.75741249e-01
4.05460417e-01 6.52839541e-01 -2.17460662e-01 -4.52399850... | [11.4453706741333, 1.5088586807250977] |
a2c5b086-2b0d-4fe3-b809-2923b70dda1f | visual-cue-integration-for-small-target | 1903.07546 | null | http://arxiv.org/abs/1903.07546v1 | http://arxiv.org/pdf/1903.07546v1.pdf | Visual Cue Integration for Small Target Motion Detection in Natural Cluttered Backgrounds | The robust detection of small targets against cluttered background is
important for future artificial visual systems in searching and tracking
applications. The insects' visual systems have demonstrated excellent ability
to avoid predators, find prey or identify conspecifics - which always appear as
small dim speckles ... | ['Huatian Wang', 'Hongxin Wang', 'Qinbing Fu', 'Shigang Yue', 'Jigen Peng'] | 2019-03-18 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 1.77013397e-01 -9.70925033e-01 5.29496977e-03 -4.60526068e-03
2.07134292e-01 -9.58431482e-01 3.35633069e-01 -3.15613091e-01
-8.43252122e-01 6.91065192e-01 -4.25286621e-01 3.27297561e-02
4.43066180e-01 -4.62522507e-01 -1.88695386e-01 -9.61843312e-01
-4.19140309e-01 -3.71319205e-01 1.35062778e+00 1.04548372... | [8.443037033081055, -0.8305386900901794] |
84b684e8-4f17-4878-9f73-067a4c46f3f1 | swinfir-revisiting-the-swinir-with-fast | 2208.11247 | null | https://arxiv.org/abs/2208.11247v2 | https://arxiv.org/pdf/2208.11247v2.pdf | SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution | Transformer-based methods have achieved impressive image restoration performance due to their capacities to model long-range dependency compared to CNN-based methods. However, advances like SwinIR adopts the window-based and local attention strategy to balance the performance and computational overhead, which restricts... | ['Zhezhu Jin', 'Xiaobing Wang', 'Shizhuo Liu', 'Feiyu Huang', 'Dafeng Zhang'] | 2022-08-24 | null | null | null | null | ['stereo-image-super-resolution'] | ['computer-vision'] | [ 7.69877806e-02 -5.80724418e-01 1.12736166e-01 -2.09111303e-01
-7.36146986e-01 7.83872157e-02 3.75001848e-01 -3.18943262e-01
-5.01069367e-01 5.70344210e-01 5.75969934e-01 -1.32462308e-01
-1.06288880e-01 -7.77903259e-01 -7.00894535e-01 -9.44330931e-01
-1.32564887e-01 -5.11593878e-01 4.77550805e-01 -3.84027511... | [11.116073608398438, -2.098097801208496] |
609f9abb-fd00-4690-a14f-cdcbeffc9dad | explainable-recommender-with-geometric | 2305.05331 | null | https://arxiv.org/abs/2305.05331v1 | https://arxiv.org/pdf/2305.05331v1.pdf | Explainable Recommender with Geometric Information Bottleneck | Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanation generation or leverage the attention mechanism to extract important text spans from reviews as e... | ['Yulan He', 'Kun Zhang', 'Menghan Wang', 'Lin Gui', 'Hanqi Yan'] | 2023-05-09 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 1.65270209e-01 9.80446517e-01 -4.43095624e-01 -7.46956348e-01
-5.71714103e-01 -6.37973785e-01 6.33396029e-01 2.14489107e-03
3.14081341e-01 5.51519394e-01 7.49210954e-01 -4.10951644e-01
-4.65543419e-01 -5.16786695e-01 -6.62219048e-01 -2.62971669e-01
1.90917924e-01 6.93080604e-01 -2.49039367e-01 -2.18004853... | [9.9165620803833, 5.791503429412842] |
3d7f0585-07e4-4526-9eee-8f0284eb1f0d | a-multi-task-joint-framework-for-real-time | 2012.06418 | null | https://arxiv.org/abs/2012.06418v1 | https://arxiv.org/pdf/2012.06418v1.pdf | A Multi-task Joint Framework for Real-time Person Search | Person search generally involves three important parts: person detection, feature extraction and identity comparison. However, person search integrating detection, extraction and comparison has the following drawbacks. Firstly, the accuracy of detection will affect the accuracy of comparison. Secondly, it is difficult ... | ['Guangqiang Yin', 'Chunyu Wang', 'Jie Liang', 'Kangning Yin', 'Ye Li'] | 2020-12-11 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.53782517e-01 -9.34648216e-01 1.44425169e-01 -1.99242979e-01
-1.88604638e-01 -2.65865088e-01 4.69582647e-01 -2.51046568e-01
-9.69043553e-01 4.33516830e-01 4.26891586e-03 2.57875174e-01
-5.87555766e-02 -7.86952794e-01 1.35334909e-01 -6.43706679e-01
2.67794222e-01 3.08838308e-01 3.26922685e-01 -1.26471609... | [14.753421783447266, 0.8826547861099243] |
614dc53a-9db9-4813-8c76-fdb12509792c | riffled-independence-for-ranked-data | null | null | http://papers.nips.cc/paper/3775-riffled-independence-for-ranked-data | http://papers.nips.cc/paper/3775-riffled-independence-for-ranked-data.pdf | Riffled Independence for Ranked Data | Representing distributions over permutations can be a daunting task due to the fact that the number of permutations of n objects scales factorially in n. One recent way that has been used to reduce storage complexity has been to exploit probabilistic independence, but as we argue, full independence assumptions impose s... | ['Carlos Guestrin', 'Jonathan Huang'] | 2009-12-01 | null | null | null | neurips-2009-12 | ['card-games'] | ['playing-games'] | [ 4.50827241e-01 -2.91395694e-01 -9.05710682e-02 -3.38818312e-01
-3.81698251e-01 -9.80771542e-01 7.95264244e-01 -2.85418600e-01
-2.64744401e-01 9.49618280e-01 4.72052962e-01 -2.23114133e-01
-1.06552744e+00 -8.23861241e-01 -4.98741925e-01 -8.89983952e-01
-3.75985920e-01 9.79351997e-01 8.15133974e-02 1.58318430... | [7.410732746124268, 4.42526388168335] |
caa6156e-214f-412c-9a14-54112727f4b2 | fable-fabric-anomaly-detection-automation | 2306.10089 | null | https://arxiv.org/abs/2306.10089v1 | https://arxiv.org/pdf/2306.10089v1.pdf | FABLE : Fabric Anomaly Detection Automation Process | Unsupervised anomaly in industry has been a concerning topic and a stepping stone for high performance industrial automation process. The vast majority of industry-oriented methods focus on learning from good samples to detect anomaly notwithstanding some specific industrial scenario requiring even less specific traini... | ['Mahmoud Soua', 'Hichem Snoussi', 'Simon Thomine'] | 2023-06-16 | null | null | null | null | ['defect-detection', 'domain-generalization', 'anomaly-detection', 'specificity'] | ['computer-vision', 'methodology', 'methodology', 'natural-language-processing'] | [ 4.21337783e-01 -2.14693509e-02 5.49268663e-01 -4.04568166e-01
-2.67183837e-02 -2.26177916e-01 2.96305209e-01 4.83551025e-01
-9.04306248e-02 4.50592399e-01 -8.81754994e-01 -1.02703482e-01
-6.87396765e-01 -8.75512838e-01 -4.72277433e-01 -7.31919050e-01
-1.26365900e-01 9.44960713e-01 4.06231225e-01 -4.64480549... | [7.381244659423828, 2.0326685905456543] |
b56f8aeb-d557-4808-8f66-be7f7a0f305a | treedqn-learning-to-minimize-branch-and-bound | 2306.05905 | null | https://arxiv.org/abs/2306.05905v1 | https://arxiv.org/pdf/2306.05905v1.pdf | TreeDQN: Learning to minimize Branch-and-Bound tree | Combinatorial optimization problems require an exhaustive search to find the optimal solution. A convenient approach to solving combinatorial optimization tasks in the form of Mixed Integer Linear Programs is Branch-and-Bound. Branch-and-Bound solver splits a task into two parts dividing the domain of an integer variab... | ['Alexander Kostin', 'Dmitry Sorokin'] | 2023-06-09 | null | null | null | null | ['combinatorial-optimization', 'variable-selection'] | ['methodology', 'methodology'] | [ 3.39746028e-01 5.60188591e-01 -8.68522584e-01 -1.40008628e-01
-8.85169327e-01 -8.12208474e-01 -1.73543245e-01 2.66313776e-02
-3.37801099e-01 1.32989657e+00 -3.25686932e-01 -6.07754707e-01
-3.88805240e-01 -9.58036363e-01 -6.62337720e-01 -8.75278771e-01
-1.50657699e-01 1.10625219e+00 5.92253730e-02 1.15839593... | [5.134770393371582, 2.929753303527832] |
db2e2957-8bd4-4657-9b2d-771b2b932ffd | log-based-anomaly-detection-without-log | 2108.01955 | null | https://arxiv.org/abs/2108.01955v3 | https://arxiv.org/pdf/2108.01955v3.pdf | Log-based Anomaly Detection Without Log Parsing | Software systems often record important runtime information in system logs for troubleshooting purposes. There have been many studies that use log data to construct machine learning models for detecting system anomalies. Through our empirical study, we find that existing log-based anomaly detection approaches are signi... | ['Hongyu Zhang', 'Van-Hoang Le'] | 2021-08-04 | null | null | null | null | ['log-parsing'] | ['computer-code'] | [ 8.12664777e-02 -3.46586317e-01 -1.47088706e-01 -4.85137790e-01
-2.97005624e-01 -2.54824191e-01 2.43571773e-01 9.26510930e-01
2.27734938e-01 3.99461575e-02 1.00264557e-01 -7.59799242e-01
2.79807270e-01 -7.40638852e-01 -6.90782070e-01 -5.72822057e-02
-4.25567210e-01 -3.08679584e-02 6.44015193e-01 -8.69742930... | [7.454946517944336, 2.652639627456665] |
31e09361-8d85-43fc-8198-93376a000a5c | heuristic-free-optimization-of-force | 2207.07524 | null | https://arxiv.org/abs/2207.07524v1 | https://arxiv.org/pdf/2207.07524v1.pdf | Heuristic-free Optimization of Force-Controlled Robot Search Strategies in Stochastic Environments | In both industrial and service domains, a central benefit of the use of robots is their ability to quickly and reliably execute repetitive tasks. However, even relatively simple peg-in-hole tasks are typically subject to stochastic variations, requiring search motions to find relevant features such as holes. While sear... | ['Michael Beetz', 'Rainer Jäkel', 'Darko Katic', 'Benjamin Alt'] | 2022-07-15 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 3.08963388e-01 6.13931799e-03 1.16561547e-01 -1.05519705e-01
-5.76791704e-01 -6.52026713e-01 2.28003308e-01 4.80730623e-01
-5.11758924e-01 7.56340683e-01 -5.95843256e-01 -2.99706846e-01
-7.93163717e-01 -6.05227292e-01 -6.80733860e-01 -7.01496661e-01
-1.25641286e-01 1.17436171e+00 5.04873812e-01 -2.08661675... | [4.791365146636963, 1.5378655195236206] |
8e211fb9-4af7-41ba-99fe-c2e3480bcb96 | whats-in-a-name-answer-equivalence-for-open | null | null | https://aclanthology.org/2021.emnlp-main.757 | https://aclanthology.org/2021.emnlp-main.757.pdf | What’s in a Name? Answer Equivalence For Open-Domain Question Answering | A flaw in QA evaluation is that annotations often only provide one gold answer. Thus, model predictions semantically equivalent to the answer but superficially different are considered incorrect. This work explores mining alias entities from knowledge bases and using them as additional gold answers (i.e., equivalent an... | ['Jordan Boyd-Graber', 'Chen Zhao', 'Chenglei Si'] | null | null | null | null | emnlp-2021-11 | ['triviaqa'] | ['miscellaneous'] | [-3.46905701e-02 7.93131948e-01 -2.15584233e-01 -5.83120763e-01
-1.29710531e+00 -9.57583368e-01 6.28557801e-01 6.81313097e-01
-6.92767024e-01 1.08788371e+00 5.77692986e-01 -5.86110890e-01
-4.39106897e-02 -1.01665783e+00 -8.43515813e-01 2.25368723e-01
4.30508673e-01 9.62983668e-01 9.21763778e-01 -6.01195753... | [11.046798706054688, 7.964628219604492] |
ab8f7bef-98a9-43eb-b27b-bbc907d9d8f2 | gated-dilated-networks-for-lung-nodule | 1901.00120 | null | https://arxiv.org/abs/1901.00120v2 | https://arxiv.org/pdf/1901.00120v2.pdf | Gated-Dilated Networks for Lung Nodule Classification in CT scans | Different types of Convolutional Neural Networks (CNNs) have been applied to detect cancerous lung nodules from computed tomography (CT) scans. However, the size of a nodule is very diverse and can range anywhere between 3 and 30 millimeters. The high variation of nodule sizes makes classifying them a difficult and cha... | ['Mundher Al-Shabi', 'Maxine Tan', 'Hwee Kuan Lee'] | 2019-01-01 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 2.39770450e-02 1.99343279e-01 -3.53237480e-01 -2.34180138e-01
-5.72384655e-01 -3.39609325e-01 3.74317944e-01 -3.49441051e-01
-4.20000643e-01 2.73809552e-01 2.73323804e-01 -5.29069185e-01
-9.10366606e-03 -8.04614782e-01 -4.55841362e-01 -7.81711400e-01
2.64727455e-02 2.28975981e-01 7.04680860e-01 6.27850592... | [15.340513229370117, -2.1889116764068604] |
eaed4825-9b43-4b6a-9eb1-3b789007b0bb | jpeg-artifact-correction-using-denoising | 2209.11888 | null | https://arxiv.org/abs/2209.11888v2 | https://arxiv.org/pdf/2209.11888v2.pdf | JPEG Artifact Correction using Denoising Diffusion Restoration Models | Diffusion models can be used as learned priors for solving various inverse problems. However, most existing approaches are restricted to linear inverse problems, limiting their applicability to more general cases. In this paper, we build upon Denoising Diffusion Restoration Models (DDRM) and propose a method for solvin... | ['Michael Elad', 'Stefano Ermon', 'Jiaming Song', 'Bahjat Kawar'] | 2022-09-23 | null | null | null | null | ['jpeg-artifact-correction'] | ['computer-vision'] | [ 4.39782470e-01 -1.55517772e-01 4.37028073e-02 -1.94056198e-01
-1.00606823e+00 -2.86114693e-01 5.85767388e-01 -3.60004246e-01
-3.59551877e-01 4.77345675e-01 5.42252302e-01 -2.60021895e-01
-2.57302940e-01 -4.90156084e-01 -5.77384651e-01 -7.74319768e-01
-5.29222526e-02 1.09632656e-01 2.90211886e-01 -2.38036111... | [11.701738357543945, -2.399709701538086] |
18cabd58-82f1-4f87-9791-6e97254ef6a9 | ressl-relational-self-supervised-learning | 2107.09282 | null | https://arxiv.org/abs/2107.09282v2 | https://arxiv.org/pdf/2107.09282v2.pdf | ReSSL: Relational Self-Supervised Learning with Weak Augmentation | Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most of methods mainly focus on the instance level information (\ie, the different augmented images of the same instance should have the same feat... | ['Chang Xu', 'Xiaogang Wang', 'ChangShui Zhang', 'Chen Qian', 'Fei Wang', 'Shan You', 'Mingkai Zheng'] | 2021-07-20 | null | http://proceedings.neurips.cc/paper/2021/hash/14c4f36143b4b09cbc320d7c95a50ee7-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/14c4f36143b4b09cbc320d7c95a50ee7-Paper.pdf | neurips-2021-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [-1.82727799e-02 9.11355019e-02 -5.02114952e-01 -3.88045311e-01
-5.38792908e-01 -2.62729704e-01 5.86508930e-01 2.73466736e-01
-5.81414439e-02 5.45433640e-01 3.26861680e-01 9.01726931e-02
-3.35580677e-01 -6.42331660e-01 -8.26586246e-01 -7.79467702e-01
2.29101554e-01 6.82899132e-02 -3.16057056e-02 -3.07816956... | [9.67431926727295, 2.6610279083251953] |
0a6259e7-dc6e-4ed9-91ac-904edcb198ae | adversarial-text-to-image-synthesis-a-review | 2101.09983 | null | https://arxiv.org/abs/2101.09983v2 | https://arxiv.org/pdf/2101.09983v2.pdf | Adversarial Text-to-Image Synthesis: A Review | With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area. It is a flexible and intuitive way for conditional image generation with significant progress in the last years regarding visual realism, diversity, and semantic alignment. Howe... | ['Andreas Dengel', 'Jörn Hees', 'Federico Raue', 'Tobias Hinz', 'Stanislav Frolov'] | 2021-01-25 | null | null | null | null | ['adversarial-text', 'conditional-image-generation'] | ['adversarial', 'computer-vision'] | [ 7.00198054e-01 1.74054950e-01 1.01861112e-01 -4.64708418e-01
-6.63179636e-01 -7.55217373e-01 1.01655996e+00 -6.28865540e-01
-2.14291667e-03 7.28745699e-01 2.95502990e-01 -1.66884422e-01
1.41025692e-01 -8.02420974e-01 -6.25697076e-01 -4.75485504e-01
3.20453376e-01 3.77967715e-01 9.44064651e-03 -5.14696717... | [11.687969207763672, -0.3230242431163788] |
f9c80a5e-7526-4498-a9aa-8acb586af6c7 | fedadmm-a-federated-primal-dual-algorithm | 2203.15104 | null | https://arxiv.org/abs/2203.15104v1 | https://arxiv.org/pdf/2203.15104v1.pdf | FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation | Federated learning is a framework for distributed optimization that places emphasis on communication efficiency. In particular, it follows a client-server broadcast model and is particularly appealing because of its ability to accommodate heterogeneity in client compute and storage resources, non-i.i.d. data assumption... | ['James Anderson', 'Siddartha Marella', 'Han Wang'] | 2022-03-28 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-4.95803893e-01 -1.83800161e-01 -3.48581135e-01 -5.24110496e-01
-1.24320400e+00 -5.56691349e-01 3.41463238e-01 1.10945806e-01
-2.13494778e-01 7.82433867e-01 5.49073517e-01 -2.52559602e-01
-4.52376634e-01 -7.24392235e-01 -6.09735370e-01 -1.03672266e+00
-4.40149188e-01 6.21901214e-01 -3.75211567e-01 1.21752732... | [5.91078519821167, 6.145359516143799] |
85e50d1a-4c85-468c-ad16-4f6c7d5c3753 | kgi-an-integrated-framework-for-knowledge | 2204.03985 | null | https://arxiv.org/abs/2204.03985v2 | https://arxiv.org/pdf/2204.03985v2.pdf | KGI: An Integrated Framework for Knowledge Intensive Language Tasks | In this paper, we present a system to showcase the capabilities of the latest state-of-the-art retrieval augmented generation models trained on knowledge-intensive language tasks, such as slot filling, open domain question answering, dialogue, and fact-checking. Moreover, given a user query, we show how the output from... | ['Nandana Mihindukulasooriya', 'Alfio Gliozzo', 'Gaetano Rossiello', 'Michael Glass', 'Md Faisal Mahbub Chowdhury'] | 2022-04-08 | null | null | null | null | ['zero-shot-slot-filling', 'passage-retrieval', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-4.86823618e-02 9.78989899e-01 6.13377728e-02 -1.31679416e-01
-1.33949649e+00 -7.83669710e-01 1.02146518e+00 3.25149029e-01
-1.92862436e-01 1.28601706e+00 4.34996307e-01 -6.44606650e-01
1.60964206e-02 -7.35333502e-01 -5.80949664e-01 2.46515274e-01
3.15980345e-01 1.20745802e+00 5.71897030e-01 -1.00470173... | [11.428264617919922, 8.07784366607666] |
9e3c1c71-16c7-4ead-aded-ad57a5b0b4bf | subspace-clustering-via-optimal-direction | 1706.03860 | null | http://arxiv.org/abs/1706.03860v4 | http://arxiv.org/pdf/1706.03860v4.pdf | Subspace Clustering via Optimal Direction Search | This letter presents a new spectral-clustering-based approach to the subspace
clustering problem. Underpinning the proposed method is a convex program for
optimal direction search, which for each data point d finds an optimal
direction in the span of the data that has minimum projection on the other data
points and non... | ['Mostafa Rahmani', 'George Atia'] | 2017-06-12 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 1.62612215e-01 -2.35313162e-01 -3.88615519e-01 -5.52947298e-02
-7.53899217e-01 -7.36767709e-01 2.37674505e-01 -3.12170267e-01
-1.59087867e-01 1.65636346e-01 2.41282552e-01 -1.00834928e-01
-5.13366222e-01 -9.58988443e-02 -2.40208969e-01 -1.23521054e+00
-9.33843851e-02 3.81238848e-01 -3.63911331e-01 2.48401180... | [7.69588041305542, 4.443843364715576] |
0583a5e0-bf62-404f-ada9-90094f5661bd | scene-graph-generation-a-comprehensive-survey | 2201.00443 | null | https://arxiv.org/abs/2201.00443v2 | https://arxiv.org/pdf/2201.00443v2.pdf | Scene Graph Generation: A Comprehensive Survey | Deep learning techniques have led to remarkable breakthroughs in the field of generic object detection and have spawned a lot of scene-understanding tasks in recent years. Scene graph has been the focus of research because of its powerful semantic representation and applications to scene understanding. Scene Graph Gene... | ['Mohammed Bennamoun', 'Syed Afaq Ali Shah', 'Qiguang Miao', 'Xia Zhao', 'Mingtao Feng', 'Peiyi Shen', 'Haoran Hou', 'Yixuan Dang', 'Youliang Jiang', 'Liang Zhang', 'Guangming Zhu'] | 2022-01-03 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 5.20660639e-01 1.40963709e-02 -1.44522190e-01 -4.81690496e-01
-1.04169518e-01 -3.00042212e-01 5.52001536e-01 3.08050036e-01
5.19719794e-02 3.05266023e-01 8.70614350e-02 -4.05402444e-02
-2.19187766e-01 -1.04920280e+00 -4.97986972e-01 -5.96107543e-01
-5.13985679e-02 1.79542407e-01 4.22122359e-01 -1.03428066... | [10.316658020019531, 1.5343457460403442] |
4f4d0dd6-397d-42e0-83e7-a539496d4ced | general-transformation-for-consistent-online | 2306.07163 | null | https://arxiv.org/abs/2306.07163v1 | https://arxiv.org/pdf/2306.07163v1.pdf | General Transformation for Consistent Online Approximation Algorithms | We introduce a transformation framework that can be utilized to develop online algorithms with low $\epsilon$-approximate regret in the random-order model from offline approximation algorithms. We first give a general reduction theorem that transforms an offline approximation algorithm with low average sensitivity to a... | ['Yuichi Yoshida', 'Jing Dong'] | 2023-06-12 | null | null | null | null | ['clustering'] | ['methodology'] | [-2.17296943e-01 3.92314315e-01 -1.91374540e-01 -1.98161110e-01
-1.16676855e+00 -9.48304057e-01 -5.66334367e-01 4.99429256e-01
-4.55028474e-01 7.25809157e-01 -4.21233177e-01 -5.96752107e-01
-7.63699710e-01 -8.42714965e-01 -1.32049978e+00 -6.26928091e-01
-4.38538015e-01 7.84224927e-01 5.15337475e-02 -4.40622605... | [6.548680782318115, 4.842085361480713] |
11a6c53d-6a31-408d-b783-60ebfb68685f | combining-human-parsing-with-analytical | 2207.14243 | null | https://arxiv.org/abs/2207.14243v1 | https://arxiv.org/pdf/2207.14243v1.pdf | Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification | Person reidentification (re-ID) has been receiving increasing attention in recent years due to its importance for both science and society. Machine learning and particularly Deep Learning (DL) has become the main re-id tool that allowed researches to achieve unprecedented accuracy levels on benchmark datasets. However,... | ['Nikita Gabdullin'] | 2022-07-28 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [-2.41959602e-01 -1.20059013e-01 -1.46585137e-01 -5.79406381e-01
-8.99096727e-01 -5.43104529e-01 7.22466946e-01 2.72511452e-01
-8.70295346e-01 1.04072940e+00 -9.31605101e-02 -1.31317740e-02
-2.81958073e-01 -7.80047774e-01 -6.38993382e-01 -5.80195665e-01
4.15766574e-02 7.44033754e-01 6.29751906e-02 -1.53714225... | [14.721607208251953, 1.0288594961166382] |
f58bb7ab-4c83-4644-a18e-9238e67ee60c | 3d-cinemagraphy-from-a-single-image | 2303.05724 | null | https://arxiv.org/abs/2303.05724v1 | https://arxiv.org/pdf/2303.05724v1.pdf | 3D Cinemagraphy from a Single Image | We present 3D Cinemagraphy, a new technique that marries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion. We empirically find that naively combining existing 2D image animation and 3D photography m... | ['Guosheng Lin', 'Ke Xian', 'Jianming Zhang', 'Huiqiang Sun', 'Zhiguo Cao', 'Xingyi Li'] | 2023-03-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_3D_Cinemagraphy_From_a_Single_Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_3D_Cinemagraphy_From_a_Single_Image_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-animation'] | ['computer-vision'] | [ 3.21519196e-01 1.14213765e-01 2.87556738e-01 2.75138561e-02
-1.85853943e-01 -6.98901296e-01 7.08619893e-01 -5.27274311e-01
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5.68289161e-01 -6.50707901e-01 -6.73502266e-01 -3.51718545e-01
2.29298454e-02 1.66671142e-01 3.87040645e-01 6.45140046... | [9.54411792755127, -2.5839974880218506] |
01aaa343-fd74-4211-b465-2447c2e375de | lamp-hq-a-large-scale-multi-pose-high-quality | 1912.07809 | null | https://arxiv.org/abs/1912.07809v2 | https://arxiv.org/pdf/1912.07809v2.pdf | LAMP-HQ: A Large-Scale Multi-Pose High-Quality Database and Benchmark for NIR-VIS Face Recognition | Near-infrared-visible (NIR-VIS) heterogeneous face recognition matches NIR to corresponding VIS face images. However, due to the sensing gap, NIR images often lose some identity information so that the recognition issue is more difficult than conventional VIS face recognition. Recently, NIR-VIS heterogeneous face recog... | ['Zhen Lei', 'Huaibo Huang', 'Haoxue Wu', 'Ran He', 'Aijing Yu'] | 2019-12-17 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 2.88705498e-01 -5.34172654e-01 1.73605412e-01 -5.16562343e-01
-9.52784419e-01 -2.12905645e-01 1.13068633e-01 -1.00733423e+00
-5.48513159e-02 6.69748962e-01 4.85837609e-02 2.03399405e-01
-7.26053119e-02 -6.73403740e-01 -7.49868393e-01 -1.30749214e+00
7.89387643e-01 1.46925822e-01 -8.11859012e-01 -3.01190674... | [13.10386848449707, 0.41572463512420654] |
ccf7df5f-da28-493c-b8a3-351e47c77462 | targeted-collapse-regularized-autoencoder-for | 2306.12627 | null | https://arxiv.org/abs/2306.12627v1 | https://arxiv.org/pdf/2306.12627v1.pdf | Targeted collapse regularized autoencoder for anomaly detection: black hole at the center | Autoencoders have been extensively used in the development of recent anomaly detection techniques. The premise of their application is based on the notion that after training the autoencoder on normal training data, anomalous inputs will exhibit a significant reconstruction error. Consequently, this enables a clear dif... | ['Iman Soltani Bozchalooi', 'Dimitar Filev', 'Rajesh Gupta', 'Devesh Upadhyay', 'Huanyi Shui', 'Amin Ghafourian'] | 2023-06-22 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 7.17627779e-02 -1.13121688e-01 1.77688748e-01 -2.87382811e-01
-1.59818679e-01 -4.39722419e-01 6.67588472e-01 2.06260145e-01
-3.28807086e-01 3.57513309e-01 -1.89732462e-01 -3.54287386e-01
-2.03306168e-01 -7.34293520e-01 -5.59868097e-01 -9.76780713e-01
-2.61709809e-01 1.21753812e-01 1.80327386e-01 -1.96682692... | [7.640632152557373, 2.4257099628448486] |
b5bfa3d2-7a42-46cc-a930-53907b8b3649 | improved-prediction-of-soil-properties-with | 2002.04312 | null | https://arxiv.org/abs/2002.04312v1 | https://arxiv.org/pdf/2002.04312v1.pdf | Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectra | Machine Learning (ML) algorithms have been used for assessing soil quality parameters along with non-destructive methodologies. Among spectroscopic analytical methodologies, energy dispersive X-ray fluorescence (EDXRF) is one of the more quick, environmentally friendly and less expensive when compared to conventional m... | ['Saulo Martiello Mastelini', 'Everton Jose Santana', 'Sylvio Barbon Jr', 'Felipe Rodrigues dos Santos', 'Fabio Luiz Melquiades'] | 2020-02-11 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 4.41012055e-01 -3.56709033e-01 -2.28898779e-01 -2.58651488e-02
-4.84460026e-01 -1.78676710e-01 4.65233505e-01 5.13049603e-01
-3.45731080e-01 1.25447345e+00 -4.11736071e-01 -5.30095160e-01
-9.00669038e-01 -1.20724106e+00 -5.58497250e-01 -1.11024618e+00
-3.08316708e-01 3.46926093e-01 1.89333171e-01 -4.12216276... | [9.601502418518066, -1.7028635740280151] |
8dc883e5-d656-4460-9d88-f5a86220fa67 | a-deep-learning-approach-for-fetal-qrs | null | null | https://iopscience.iop.org/article/10.1088/1361-6579/aab297/meta | https://iopscience.iop.org/article/10.1088/1361-6579/aab297/meta | A deep learning approach for fetal QRS complex detection | Objective: Non-invasive foetal electrocardiography (NI-FECG) has the potential to provide
more additional clinical information for detecting and diagnosing fetal diseases. We propose and
demonstrate a deep learning approach for fetal QRS complex detection from raw NI-FECG signals
by using a convolutional neural n... | ['Xuemei Guo and GuoliWang', 'Lijuan Liao', 'Wei Zhong'] | 2018-04-20 | null | null | null | physiol-meas-39-2018-045004-9pp-2018-4 | ['qrs-complex-detection'] | ['medical'] | [ 1.97262704e-01 5.96095696e-02 1.24100320e-01 -4.50215101e-01
-4.32305336e-01 -3.74978304e-01 -2.52717018e-01 3.11975777e-01
-4.14479822e-01 6.83002055e-01 -4.32185978e-01 -4.22676951e-01
-5.00980735e-01 -7.26718724e-01 -5.33521950e-01 -8.23061287e-01
-4.66137141e-01 1.07823901e-01 -2.45235354e-01 4.77401912... | [14.319201469421387, 3.2436037063598633] |
6e47121a-0487-476b-931e-2feed4378984 | automatic-semantic-modeling-for-structural | 2212.10915 | null | https://arxiv.org/abs/2212.10915v1 | https://arxiv.org/pdf/2212.10915v1.pdf | Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base | A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach fo... | ['Zaiwen Feng', 'Keqing He', 'Hongyu Zhang', 'Wolfgang Mayer', 'Jiakang Xu'] | 2022-12-21 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 1.41269088e-01 6.31134391e-01 -5.94447792e-01 -5.17920494e-01
-4.77423221e-01 -4.19518888e-01 4.69169259e-01 7.91135967e-01
-5.57022989e-02 7.82618821e-01 2.17032447e-01 -5.68981171e-02
-3.79676044e-01 -1.20922315e+00 -6.58535242e-01 8.18284675e-02
1.78943589e-01 5.95854700e-01 9.57497478e-01 -3.78484488... | [9.237641334533691, 8.011149406433105] |
b2a94e9e-d35f-4201-bcdc-a3ab455d9a68 | fg-net-fast-large-scale-lidar-point | 2012.09439 | null | https://arxiv.org/abs/2012.09439v2 | https://arxiv.org/pdf/2012.09439v2.pdf | FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling | This work presents FG-Net, a general deep learning framework for large-scale point clouds understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 GPU. First, a novel noise and outlier filtering method is designed to facilitate subsequent high-level tasks. For... | ['Ben M. Chen', 'Feng Lin', 'Zhi Gao', 'Kangcheng Liu'] | 2020-12-17 | null | null | null | null | ['3d-part-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [-4.20794994e-01 -5.42901576e-01 1.52861103e-01 -3.30675423e-01
-5.09658635e-01 4.61730957e-02 3.87371421e-01 5.12688747e-03
-3.27258378e-01 5.18744946e-01 -3.09439898e-01 -1.57032996e-01
-1.34568781e-01 -1.33061671e+00 -1.08056355e+00 -4.72930223e-01
-2.90109992e-01 4.70928967e-01 4.02536899e-01 1.61214918... | [7.908173561096191, -3.597503185272217] |
b170094b-510d-41c9-9348-15e66e29f53e | instance-based-inductive-deep-transfer | 1802.05934 | null | http://arxiv.org/abs/1802.05934v1 | http://arxiv.org/pdf/1802.05934v1.pdf | Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing | Supervised learning models are typically trained on a single dataset and the
performance of these models rely heavily on the size of the dataset, i.e.,
amount of data available with the ground truth. Learning algorithms try to
generalize solely based on the data that is presented with during the training.
In this work,... | ['K. M. Annervaz', 'Ambedkar Dukkipati', 'Somnath Basu Roy Chowdhury'] | 2018-02-16 | instance-based-inductive-deep-transfer-1 | https://aclanthology.org/D19-6120 | https://aclanthology.org/D19-6120.pdf | ws-2019-11 | ['news-classification'] | ['natural-language-processing'] | [ 3.58948618e-01 2.86501616e-01 -5.08960783e-01 -7.00648069e-01
-1.44023085e+00 -7.01679289e-01 8.45999420e-01 7.05594659e-01
-5.75860441e-01 9.94255424e-01 2.69258708e-01 1.65685847e-01
-9.66493860e-02 -8.70121121e-01 -1.26004076e+00 -6.80598795e-01
2.25637287e-01 9.17740643e-01 3.38378429e-01 2.47371737... | [9.671740531921387, 3.1278436183929443] |
96d2da24-cd53-4ff4-b12f-ad8e9a6bbfc5 | local-aggregation-for-unsupervised-learning | 1903.12355 | null | http://arxiv.org/abs/1903.12355v2 | http://arxiv.org/pdf/1903.12355v2.pdf | Local Aggregation for Unsupervised Learning of Visual Embeddings | Unsupervised approaches to learning in neural networks are of substantial
interest for furthering artificial intelligence, both because they would enable
the training of networks without the need for large numbers of expensive
annotations, and because they would be better models of the kind of
general-purpose learning ... | ['Alex Lin Zhai', 'Daniel Yamins', 'Chengxu Zhuang'] | 2019-03-29 | local-aggregation-for-unsupervised-learning-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhuang_Local_Aggregation_for_Unsupervised_Learning_of_Visual_Embeddings_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhuang_Local_Aggregation_for_Unsupervised_Learning_of_Visual_Embeddings_ICCV_2019_paper.pdf | iccv-2019-10 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.61622182e-01 8.39523450e-02 -2.00357996e-02 -8.42846096e-01
-4.10265923e-01 -5.35417736e-01 8.29182506e-01 3.85586590e-01
-8.82962883e-01 4.09784704e-01 1.35346860e-01 -9.97383744e-02
-1.83249190e-01 -6.57620251e-01 -8.11109304e-01 -7.17807293e-01
-3.99515629e-01 7.43747950e-01 3.26420963e-01 1.74613506... | [9.510017395019531, 2.4668617248535156] |
25683ef5-da26-4536-9ea0-e538a135c5a1 | greedy-approximate-projection-for-magnetic | 1807.06912 | null | http://arxiv.org/abs/1807.06912v2 | http://arxiv.org/pdf/1807.06912v2.pdf | Greedy Approximate Projection for Magnetic Resonance Fingerprinting with Partial Volumes | In quantitative Magnetic Resonance Imaging, traditional methods suffer from
the so-called Partial Volume Effect (PVE) due to spatial resolution
limitations. As a consequence of PVE, the parameters of the voxels containing
more than one tissue are not correctly estimated. Magnetic Resonance
Fingerprinting (MRF) is not a... | [] | 2018-11-28 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 3.78580838e-01 7.99992681e-02 1.85287327e-01 -2.95829624e-01
-9.52325642e-01 -1.54825553e-01 1.56489044e-01 5.16655855e-02
-4.80783999e-01 8.34524214e-01 1.31238803e-01 1.46625653e-01
-5.21462321e-01 -1.96486607e-01 -7.76651859e-01 -8.16114426e-01
-3.57204020e-01 5.79308689e-01 3.27610672e-01 2.67028838... | [13.386689186096191, -2.482456684112549] |
ebf2eecf-cd87-43c7-800c-786e305059d8 | grounded-language-learning-fast-and-slow | 2009.01719 | null | https://arxiv.org/abs/2009.01719v4 | https://arxiv.org/pdf/2009.01719v4.pdf | Grounded Language Learning Fast and Slow | Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot learning. Here, we show that an embodied agent situated in a simulated 3D world, and endowed with a novel dual-coding external memory, can e... | ['Felix Hill', 'Stephen Clark', 'Olivier Tieleman', 'Tamara von Glehn', 'Hamza Merzic', 'Nathaniel Wong'] | 2020-09-03 | null | https://openreview.net/forum?id=wpSWuz_hyqA | https://openreview.net/pdf?id=wpSWuz_hyqA | iclr-2021-1 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.55620113e-01 6.31615072e-02 6.20878302e-03 7.55448341e-02
-2.96030790e-02 -7.21402645e-01 9.60875988e-01 2.70132482e-01
-7.57144988e-01 5.09161055e-01 1.84613809e-01 -3.95744555e-02
-2.94242799e-01 -9.78413224e-01 -9.96512234e-01 -6.64347053e-01
-1.61075175e-01 7.68356919e-01 1.63890645e-02 -5.76748073... | [4.378801345825195, 1.1530869007110596] |
11601902-1afb-45a5-be4f-4286f028bce5 | image-completion-with-heterogeneously | 2211.03700 | null | https://arxiv.org/abs/2211.03700v1 | https://arxiv.org/pdf/2211.03700v1.pdf | Image Completion with Heterogeneously Filtered Spectral Hints | Image completion with large-scale free-form missing regions is one of the most challenging tasks for the computer vision community. While researchers pursue better solutions, drawbacks such as pattern unawareness, blurry textures, and structure distortion remain noticeable, and thus leave space for improvement. To over... | ['Humphrey Shi', 'Yadong Mu', 'Andranik Sargsyan', 'Vahram Tadevosyan', 'Shant Navasardyan', 'Xingqian Xu'] | 2022-11-07 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 3.70623738e-01 7.21958093e-03 1.22383341e-01 -2.11550176e-01
-9.01139021e-01 -4.69627857e-01 5.80823660e-01 -5.71451187e-01
-9.41312835e-02 4.63528752e-01 3.29541177e-01 -2.13732928e-01
4.27286550e-02 -3.81470740e-01 -8.12281549e-01 -7.88937330e-01
1.76107511e-01 -3.59559476e-01 3.87278870e-02 -1.68626651... | [11.283541679382324, -1.8823668956756592] |
d7bfdb1d-347b-4e0b-93f0-0b42a284d36d | barycenters-of-natural-images-constrained-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Simon_Barycenters_of_Natural_Images__Constrained_Wasserstein_Barycenters_for_Image_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Simon_Barycenters_of_Natural_Images__Constrained_Wasserstein_Barycenters_for_Image_CVPR_2020_paper.pdf | Barycenters of Natural Images Constrained Wasserstein Barycenters for Image Morphing | Image interpolation, or image morphing, refers to a visual transition between two (or more) input images. For such a transition to look visually appealing, its desirable properties are (i) to be smooth; (ii) to apply the minimal required change in the image; and (iii) to seem "real", avoiding unnatural artifacts in eac... | [' Aviad Aberdam', 'Dror Simon'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['image-morphing'] | ['computer-vision'] | [ 5.69100916e-01 3.77516121e-01 2.51802385e-01 -2.33149186e-01
-4.23576623e-01 -5.27527869e-01 6.86712205e-01 -9.50973928e-02
-4.83041778e-02 6.74735725e-01 -1.93186224e-01 -3.65783274e-01
6.78923279e-02 -8.05151463e-01 -9.54896629e-01 -5.80878615e-01
2.26726249e-01 -1.61458608e-02 3.25095475e-01 -1.78290114... | [11.584016799926758, -0.6316536068916321] |
e41d7629-c3d8-45bf-80d1-d3249cb3ae2d | similarity-metric-learning-for-rgb-infrared | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xiong_Similarity_Metric_Learning_for_RGB-Infrared_Group_Re-Identification_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xiong_Similarity_Metric_Learning_for_RGB-Infrared_Group_Re-Identification_CVPR_2023_paper.pdf | Similarity Metric Learning for RGB-Infrared Group Re-Identification | Group re-identification (G-ReID) aims to re-identify a group of people that is observed from non-overlapping camera systems. The existing literature has mainly addressed RGB-based problems, but RGB-infrared (RGB-IR) cross-modality matching problem has not been studied yet. In this paper, we propose a metric learnin... | ['JianHuang Lai', 'Jianghao Xiong'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.49231458e-02 -4.35724616e-01 5.57588041e-02 -4.93867934e-01
-5.06823659e-01 -7.27766573e-01 5.70110500e-01 -6.12157844e-02
-3.28367472e-01 1.23299591e-01 2.93397874e-01 1.60063460e-01
-5.26010871e-01 -7.62460470e-01 -5.38811803e-01 -7.17829049e-01
3.36041339e-02 1.91642970e-01 -1.04553960e-01 -3.47259231... | [14.734392166137695, 0.9121596813201904] |
89c01871-4da2-4a5f-9d7c-6f776c7bd9f7 | stacked-semantic-guided-network-for-zero-shot | 1904.01971 | null | https://arxiv.org/abs/1904.01971v2 | https://arxiv.org/pdf/1904.01971v2.pdf | Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval | Zero-shot sketch-based image retrieval (ZS-SBIR) is a task of cross-domain image retrieval from a natural image gallery with free-hand sketch under a zero-shot scenario. Previous works mostly focus on a generative approach that takes a highly abstract and sparse sketch as input and then synthesizes the corresponding na... | ['DaCheng Tao', 'Xinxu Xu', 'Cheng Deng', 'Wei Liu', 'Hao Wang', 'Xinbo Gao'] | 2019-04-03 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 4.36632961e-01 -4.34786648e-01 -2.89690733e-01 -3.37524593e-01
-1.28072786e+00 -4.21241432e-01 7.69504189e-01 -4.79078293e-01
1.34428255e-02 5.78151226e-01 1.17692843e-01 3.02114516e-01
-2.55286843e-01 -8.36908579e-01 -8.03786993e-01 -6.79948509e-01
3.95547032e-01 2.74798721e-01 7.29078799e-02 -2.35400438... | [11.631340026855469, 0.6723492741584778] |
3c6d9f7c-5830-4c34-96db-24e8174a61d5 | image-reconstruction-from-events-why-learn-it | 2112.06242 | null | https://arxiv.org/abs/2112.06242v3 | https://arxiv.org/pdf/2112.06242v3.pdf | Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow | Event cameras are novel bio-inspired sensors that measure per-pixel brightness differences asynchronously. Recovering brightness from events is appealing since the reconstructed images inherit the high dynamic range (HDR) and high-speed properties of events; hence they can be used in many robotic vision applications an... | ['Guillermo Gallego', 'Anthony Yezzi', 'Zelin Zhang'] | 2021-12-12 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 5.87793529e-01 -6.96481168e-02 2.20139697e-01 -2.83750415e-01
-7.22724617e-01 -1.32475048e-01 4.54713076e-01 -2.67701566e-01
-7.04095244e-01 7.92229056e-01 -2.91019857e-01 1.48337677e-01
-1.95060477e-01 -7.56712735e-01 -1.04010642e+00 -1.14201236e+00
2.13221565e-01 1.87888008e-03 2.60611445e-01 -1.84192821... | [11.203699111938477, -2.2075119018554688] |
fe41defa-2a9d-4688-99c2-e4e3553553d8 | decomposed-diffusion-models-for-high-quality | 2303.08320 | null | https://arxiv.org/abs/2303.08320v3 | https://arxiv.org/pdf/2303.08320v3.pdf | VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation | A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution. Despite its recent success in image synthesis, applying DPMs to video generat... | ['Jingren Zhou', 'Tieniu Tan', 'Deli Zhao', 'Yujun Shen', 'Liang Wang', 'Yan Huang', 'Yingya Zhang', 'Dayou Chen', 'Zhengxiong Luo'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Luo_VideoFusion_Decomposed_Diffusion_Models_for_High-Quality_Video_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Luo_VideoFusion_Decomposed_Diffusion_Models_for_High-Quality_Video_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation'] | ['computer-vision'] | [ 3.31835836e-01 -5.50324284e-02 9.87450704e-02 -8.04376900e-02
-7.23424017e-01 -4.44597989e-01 1.01181269e+00 -6.41492188e-01
-2.17130795e-01 6.83955312e-01 6.03803217e-01 1.38581023e-01
9.44580808e-02 -6.72080278e-01 -7.90667653e-01 -1.01792049e+00
1.97545558e-01 3.30551028e-01 7.68022463e-02 1.51551748... | [10.965965270996094, -0.7692634463310242] |
4a51fc82-e441-4099-b6a0-2266de1bb264 | myfood-a-food-segmentation-and-classification | 2012.03087 | null | https://arxiv.org/abs/2012.03087v1 | https://arxiv.org/pdf/2012.03087v1.pdf | MyFood: A Food Segmentation and Classification System to Aid Nutritional Monitoring | The absence of food monitoring has contributed significantly to the increase in the population's weight. Due to the lack of time and busy routines, most people do not control and record what is consumed in their diet. Some solutions have been proposed in computer vision to recognize food images, but few are specialized... | ['Valmir Macario', 'Filipe R. Cordeiro', 'Charles N. C. Freitas'] | 2020-12-05 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [-1.69927984e-01 -2.11851388e-01 -4.23652768e-01 -3.85798037e-01
1.06918067e-01 -3.71016741e-01 8.56275707e-02 9.00939405e-01
-5.95240295e-01 2.54735827e-01 7.08428845e-02 -6.56271279e-02
-5.82118891e-02 -1.15105939e+00 -4.72495914e-01 -4.92857665e-01
1.41984940e-01 5.07707894e-01 8.66704360e-02 -1.13738015... | [11.560966491699219, 4.4075751304626465] |
5c8cd72d-c213-4f23-90be-a5578fbb625c | table-retrieval-may-not-necessitate-table | 2205.09843 | null | https://arxiv.org/abs/2205.09843v1 | https://arxiv.org/pdf/2205.09843v1.pdf | Table Retrieval May Not Necessitate Table-specific Model Design | Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts. Recent work has designed special models and training paradigms for table-related tasks such as table-based question answering and table retrieval. Th... | ['Graham Neubig', 'Eric Nyberg', 'Zhengbao Jiang', 'Zhiruo Wang'] | 2022-05-19 | null | https://aclanthology.org/2022.suki-1.5 | https://aclanthology.org/2022.suki-1.5.pdf | naacl-suki-2022-7 | ['hard-attention', 'natural-questions', 'table-retrieval'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [-3.72548960e-02 6.56808019e-02 -8.69747028e-02 -3.28344524e-01
-1.33908510e+00 -8.15978467e-01 3.99096847e-01 7.82978296e-01
-4.97651130e-01 5.60983837e-01 5.26397228e-01 -7.60649562e-01
-1.49704069e-01 -9.85567629e-01 -9.58182037e-01 -8.23950320e-02
2.71954477e-01 8.56307387e-01 3.47006887e-01 -8.32709193... | [10.148246765136719, 7.880138397216797] |
c015a232-4e22-4fcb-9fa4-e4557e891470 | altered-topological-structure-of-the-brain | 2304.05908 | null | https://arxiv.org/abs/2304.05908v1 | https://arxiv.org/pdf/2304.05908v1.pdf | Altered Topological Structure of the Brain White Matter in Maltreated Children through Topological Data Analysis | Childhood maltreatment may adversely affect brain development and consequently behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter structure in maltreated and typically developing childr... | ['Seth Pollak', 'Richard Davidson', 'Andrew Alexander', 'Thomas Burns', 'Jamie Hanson', 'Moo K. Chung', 'Tahmineh Azizi'] | 2023-04-12 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [-4.86411341e-02 1.59346551e-01 2.48031974e-01 -3.12147558e-01
3.60020101e-01 -5.65033734e-01 3.22228998e-01 4.85851645e-01
-2.12037817e-01 -3.04773133e-02 4.14148688e-01 -1.32642135e-01
-6.60452724e-01 -8.47340107e-01 -3.85573775e-01 -5.92430830e-01
-9.93117332e-01 3.77777457e-01 -1.33140057e-01 6.34273216... | [12.43104362487793, 3.3434207439422607] |
8c08a69d-dbaf-4034-b49b-864aca6af4e4 | a-survey-of-trustworthy-federated-learning | 2302.10637 | null | https://arxiv.org/abs/2302.10637v1 | https://arxiv.org/pdf/2302.10637v1.pdf | A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy | Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands out as a promising solution for diverse real-world scenarios, ranging from risk evaluation systems in finance to cutting-edge technologies ... | ['Irwin King', 'Zenglin Xu', 'Jinglong Luo', 'Dun Zeng', 'Yifei Zhang'] | 2023-02-21 | null | null | null | null | ['drug-discovery'] | ['medical'] | [-3.06586981e-01 3.69452219e-03 -1.83713540e-01 -3.70633811e-01
-6.10342741e-01 -1.03260589e+00 5.56163371e-01 1.60178140e-01
-3.22988600e-01 7.60150254e-01 -2.22011223e-01 -8.58955562e-01
-2.50188351e-01 -8.03231359e-01 -5.91604710e-01 -7.50221312e-01
-5.78414463e-02 -7.49001876e-02 -2.28289783e-01 -1.15851248... | [5.725090980529785, 6.911311149597168] |
a80de58d-1fd5-4f2b-8564-73053487257c | neural-document-expansion-for-ad-hoc | 2012.14005 | null | https://arxiv.org/abs/2012.14005v1 | https://arxiv.org/pdf/2012.14005v1.pdf | Neural document expansion for ad-hoc information retrieval | Recently, Nogueira et al. [2019] proposed a new approach to document expansion based on a neural Seq2Seq model, showing significant improvement on short text retrieval task. However, this approach needs a large amount of in-domain training data. In this paper, we show that this neural document expansion approach can be... | ['Andrew Arnold', 'Cheng Tang'] | 2020-12-27 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 3.18812340e-01 3.90932150e-03 -4.11187291e-01 -3.34522724e-01
-9.22300279e-01 -7.78975725e-01 7.05452919e-01 2.59051025e-01
-8.18859339e-01 1.01542616e+00 4.71910596e-01 -2.72368997e-01
-8.94708335e-02 -5.94997823e-01 -3.80662173e-01 -3.15554470e-01
3.29324529e-02 1.10805786e+00 1.38487414e-01 -6.95130706... | [11.52189826965332, 7.691163063049316] |
a1f2a1b7-f36b-41bb-8abc-16332fc7ee85 | multitask-learning-for-network-traffic | 1906.05248 | null | https://arxiv.org/abs/1906.05248v2 | https://arxiv.org/pdf/1906.05248v2.pdf | Multitask Learning for Network Traffic Classification | Traffic classification has various applications in today's Internet, from resource allocation, billing and QoS purposes in ISPs to firewall and malware detection in clients. Classical machine learning algorithms and deep learning models have been widely used to solve the traffic classification task. However, training s... | ['Shahbaz Rezaei', 'Xin Liu'] | 2019-06-12 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.92247760e-02 -5.84323168e-01 -5.92377305e-01 -7.80294776e-01
-4.07342345e-01 -6.94688261e-01 4.61639091e-02 5.79434223e-02
-3.62070262e-01 8.48146737e-01 -5.53055644e-01 -1.03426075e+00
-8.72364044e-02 -9.90511179e-01 -1.73441812e-01 -4.71448958e-01
5.04665673e-02 8.66684914e-01 5.79725325e-01 2.47864574... | [5.007819175720215, 7.246011734008789] |
c7213099-63dc-4cf9-8a7d-bb6447c78f62 | dialogue-policies-for-learning-board-games | null | null | https://aclanthology.org/2020.sigdial-1.41 | https://aclanthology.org/2020.sigdial-1.41.pdf | Dialogue Policies for Learning Board Games through Multimodal Communication | This paper presents MDP policy learning for agents to learn strategic behavior–how to play board games–during multimodal dialogues. Policies are trained offline in simulation, with dialogues carried out in a formal language. The agent has a temporary belief state for the dialogue, and a persistent knowledge store repre... | ['Rebecca Passonneau', 'Alan Wagner', 'Sweekar Sudhakara', 'Aishan Liu', 'Ali Ayub', 'Maryam Zare'] | null | null | null | null | sigdial-acl-2020-7 | ['board-games'] | ['playing-games'] | [-1.80205107e-01 1.09416389e+00 -1.26851812e-01 -3.29758883e-01
-7.34219015e-01 -9.52619314e-01 8.85664999e-01 -7.63532007e-03
-7.63857186e-01 1.06931269e+00 4.67963427e-01 -3.39920521e-01
2.08472952e-01 -7.50466585e-01 -1.35033086e-01 -4.56678540e-01
-1.02858998e-01 1.30587137e+00 2.46537313e-01 -9.23852265... | [13.044123649597168, 8.040255546569824] |
af0c26fb-c529-4786-9b8c-b2c6f270fa70 | part-guided-relational-transformers-for-fine | 2212.13685 | null | https://arxiv.org/abs/2212.13685v1 | https://arxiv.org/pdf/2212.13685v1.pdf | Part-guided Relational Transformers for Fine-grained Visual Recognition | Fine-grained visual recognition is to classify objects with visually similar appearances into subcategories, which has made great progress with the development of deep CNNs. However, handling subtle differences between different subcategories still remains a challenge. In this paper, we propose to solve this issue in o... | ['Yonghong Tian', 'Xiaowu Chen', 'Jia Li', 'Yifan Zhao'] | 2022-12-28 | null | null | null | null | ['fine-grained-visual-recognition', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.72582781e-02 -3.59969139e-01 -9.74728018e-02 -5.08309782e-01
-4.57522810e-01 -4.36144203e-01 6.49668932e-01 -5.73450848e-02
-7.55381957e-02 3.21728945e-01 2.89233208e-01 1.40262738e-01
-4.51588809e-01 -9.12395358e-01 -7.08311558e-01 -8.89560282e-01
2.11545616e-01 1.05200909e-01 3.95688474e-01 -5.62385377... | [9.685392379760742, 1.9942779541015625] |
a30ad240-a6d8-41f1-93e3-3ec331a8ccae | scene-labeling-using-gated-recurrent-units | 1611.07485 | null | http://arxiv.org/abs/1611.07485v2 | http://arxiv.org/pdf/1611.07485v2.pdf | Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning | Recurrent neural network (RNN), as a powerful contextual dependency modeling
framework, has been widely applied to scene labeling problems. However, this
work shows that directly applying traditional RNN architectures, which unfolds
a 2D lattice grid into a sequence, is not sufficient to model structure
dependencies in... | ['Suya You', 'Kevin Zhou', 'Weiyue Wang', 'Qiangui Huang', 'Ulrich Neumann'] | 2016-11-22 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 5.35818160e-01 -8.83353967e-03 4.49356958e-02 -4.28953350e-01
-4.54589933e-01 -2.76383460e-01 4.53794509e-01 -3.67218882e-01
-3.93606752e-01 4.98681217e-01 6.56381965e-01 -6.21344030e-01
2.81376958e-01 -6.65839970e-01 -7.22503901e-01 -8.15021932e-01
1.43613532e-01 -1.41348690e-02 1.98451400e-01 -3.58875036... | [9.576040267944336, 0.40264612436294556] |
b49eae0c-024f-46d7-bf24-87450b014b8a | revealing-single-frame-bias-for-video-and | 2206.03428 | null | https://arxiv.org/abs/2206.03428v1 | https://arxiv.org/pdf/2206.03428v1.pdf | Revealing Single Frame Bias for Video-and-Language Learning | Training an effective video-and-language model intuitively requires multiple frames as model inputs. However, it is unclear whether using multiple frames is beneficial to downstream tasks, and if yes, whether the performance gain is worth the drastically-increased computation and memory costs resulting from using more ... | ['Mohit Bansal', 'Tamara L. Berg', 'Jie Lei'] | 2022-06-07 | null | null | null | null | ['video-question-answering', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.42225623e-01 -4.39209640e-01 -4.69473094e-01 -4.71551180e-01
-1.09667063e+00 -3.12063128e-01 6.94441497e-01 -1.99294135e-01
-4.88114387e-01 5.92642069e-01 2.86170751e-01 -4.19744253e-01
1.75890908e-01 -4.12065893e-01 -9.73475873e-01 -6.52456880e-01
2.50906171e-03 2.49768868e-02 3.84746999e-01 1.01298969... | [10.025918006896973, 0.836377739906311] |
bac117fd-ff20-49ac-a73a-1b83448ccc06 | pairwise-geometric-matching-for-large-scale | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Li_Pairwise_Geometric_Matching_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Li_Pairwise_Geometric_Matching_2015_CVPR_paper.pdf | Pairwise Geometric Matching for Large-Scale Object Retrieval | Spatial verification is a key step in boosting the performance of object-based image retrieval. It serves to eliminate unreliable correspondences between salient points in a given pair of images and is typically performed by analyzing the consistency of spatial transformations between the image regions involved in indi... | ['Xinchao Li', 'Martha Larson', 'Alan Hanjalic'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['geometric-matching'] | ['computer-vision'] | [ 9.64801162e-02 -3.70998830e-01 -5.43387160e-02 -2.54279166e-01
-1.05028975e+00 -5.77987790e-01 7.44442225e-01 5.27499557e-01
-3.19839388e-01 4.23791528e-01 -1.61799669e-01 1.84217189e-02
-4.56072927e-01 -6.26988173e-01 -5.41730285e-01 -6.77992880e-01
1.76729456e-01 2.03250721e-01 5.85093081e-01 -8.43525901... | [8.171894073486328, -2.2921676635742188] |
e361b4ee-af62-420b-a660-108a4f9bfe63 | latent-filter-scaling-for-multimodal | 1812.09877 | null | http://arxiv.org/abs/1812.09877v3 | http://arxiv.org/pdf/1812.09877v3.pdf | Latent Filter Scaling for Multimodal Unsupervised Image-to-Image Translation | In multimodal unsupervised image-to-image translation tasks, the goal is to
translate an image from the source domain to many images in the target domain.
We present a simple method that produces higher quality images than current
state-of-the-art while maintaining the same amount of multimodal diversity.
Previous meth... | ['Peter Wonka', 'Yazeed Alharbi', 'Neil Smith'] | 2018-12-24 | latent-filter-scaling-for-multimodal-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Alharbi_Latent_Filter_Scaling_for_Multimodal_Unsupervised_Image-To-Image_Translation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Alharbi_Latent_Filter_Scaling_for_Multimodal_Unsupervised_Image-To-Image_Translation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multimodal-unsupervised-image-to-image'] | ['computer-vision'] | [ 6.01455629e-01 3.45161617e-01 1.34281009e-01 -4.21619684e-01
-1.05854881e+00 -9.91512418e-01 8.41148973e-01 -4.10201401e-01
-3.31768632e-01 6.86514378e-01 5.69851436e-02 -7.04888403e-02
3.81797850e-01 -6.97065115e-01 -1.10763276e+00 -8.06597829e-01
4.61228758e-01 5.02128720e-01 -2.51403544e-02 -2.72416443... | [11.693009376525879, -0.36958858370780945] |
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