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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
81b77345-3554-4407-938f-3ffecee70ff6 | comparing-software-developers-with-chatgpt-an | 2305.11837 | null | https://arxiv.org/abs/2305.11837v2 | https://arxiv.org/pdf/2305.11837v2.pdf | Comparing Software Developers with ChatGPT: An Empirical Investigation | The advent of automation in particular Software Engineering (SE) tasks has transitioned from theory to reality. Numerous scholarly articles have documented the successful application of Artificial Intelligence to address issues in areas such as project management, modeling, testing, and development. A recent innovation... | ['Donald Cowan', 'Paulo Alencar', 'Nathalia Nascimento'] | 2023-05-19 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-1.20648019e-01 4.09730732e-01 1.05520487e-01 -1.08521357e-01
-2.05459803e-01 -4.15749818e-01 1.84030399e-01 1.79360285e-01
1.14947729e-01 4.47463512e-01 -3.17838192e-01 -6.97666705e-01
-3.97399604e-01 -5.51630318e-01 -4.19067591e-01 -2.34661564e-01
4.15299088e-01 4.07075167e-01 -4.12423968e-01 -2.18252376... | [8.965889930725098, 6.56572151184082] |
b28e5a97-99e8-4edb-8c1b-67b97c13412d | self-supervised-blind-motion-deblurring-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Self-Supervised_Blind_Motion_Deblurring_With_Deep_Expectation_Maximization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Self-Supervised_Blind_Motion_Deblurring_With_Deep_Expectation_Maximization_CVPR_2023_paper.pdf | Self-Supervised Blind Motion Deblurring With Deep Expectation Maximization | When taking a picture, any camera shake during the shutter time can result in a blurred image. Recovering a sharp image from the one blurred by camera shake is a challenging yet important problem. Most existing deep learning methods use supervised learning to train a deep neural network (DNN) on a dataset of many p... | ['Hui Ji', 'Yuesong Nan', 'Weixi Wang', 'Ji Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deblurring'] | ['computer-vision'] | [ 1.42619893e-01 -5.40495455e-01 2.18611360e-01 -3.66966993e-01
-4.67258483e-01 -4.68761742e-01 5.50803483e-01 -9.72901940e-01
-5.39705813e-01 9.68484044e-01 2.56781220e-01 -6.39572069e-02
-1.30236313e-01 -2.07380459e-01 -9.65399861e-01 -1.11361706e+00
3.38455707e-01 4.08072919e-02 -1.17201339e-02 5.12118936... | [11.52645206451416, -2.6796648502349854] |
96644aad-c097-4c27-b1d5-ffaa2f64a554 | cluster-based-feature-importance-learning-for | null | null | https://openreview.net/forum?id=kroqZZb-6s | https://openreview.net/pdf?id=kroqZZb-6s | Cluster-based Feature Importance Learning for Electronic Health Record Time-series | The recent availability of Electronic Health Records (EHR) has allowed for the development of algorithms predicting inpatient risk of deterioration and trajectory evolution. However, prediction of disease progression with EHR is challenging since these data are sparse, heterogeneous, multi-dimensional, and multi-modal ... | ['Tingting Zhu', 'Peter Watkinson', 'Mauro Santos', 'Henrique Aguiar'] | 2021-09-29 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [-1.05486542e-01 -2.11929917e-01 -7.02462345e-02 -6.43591106e-01
-1.06917655e+00 -6.02030456e-01 -9.40458849e-02 1.12591982e+00
-6.36640191e-02 5.00904799e-01 5.87442458e-01 -2.77025819e-01
-8.58862877e-01 -4.13487852e-01 -3.65460187e-01 -4.05680537e-01
-6.73463523e-01 9.62670982e-01 -4.94366974e-01 3.02154481... | [7.918510437011719, 6.169245719909668] |
df9aa202-555b-4b95-b04a-1861938ff969 | environmental-effects-on-emergent-strategy-in | 2307.00994 | null | https://arxiv.org/abs/2307.00994v1 | https://arxiv.org/pdf/2307.00994v1.pdf | Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning | Multi-Agent Reinforcement Learning (MARL) is a promising candidate for realizing efficient control of microscopic particles, of which micro-robots are a subset. However, the microscopic particles' environment presents unique challenges, such as Brownian motion at sufficiently small length-scales. In this work, we explo... | ['Christian Holm', 'Clemens Bechinger', 'Veit-Lorenz Heuthe', 'Simon Koppenhoefer', 'Tobias Merkt', 'Christoph Lohrmann', 'David Zimmer', 'Samuel Tovey'] | 2023-07-03 | null | null | null | null | ['multi-agent-reinforcement-learning', 'reinforcement-learning-1'] | ['methodology', 'methodology'] | [-2.16793716e-01 -4.34891433e-01 1.81171164e-01 4.83409375e-01
-3.52871269e-01 -5.75387955e-01 7.06645966e-01 4.74867761e-01
-9.42790866e-01 1.16128266e+00 -4.06608552e-01 -1.38322443e-01
-1.01923116e-01 -8.38786483e-01 -8.16334307e-01 -1.42910492e+00
-3.48935783e-01 6.32624984e-01 4.16216910e-01 -5.89057565... | [3.944105625152588, 1.9652583599090576] |
32286e1a-8bae-4604-ae80-2ce0388eff5c | deep-optimization-model-for-screen-content | 1903.00705 | null | http://arxiv.org/abs/1903.00705v1 | http://arxiv.org/pdf/1903.00705v1.pdf | Deep Optimization model for Screen Content Image Quality Assessment using Neural Networks | In this paper, we propose a novel quadratic optimized model based on the deep
convolutional neural network (QODCNN) for full-reference and no-reference
screen content image (SCI) quality assessment. Unlike traditional CNN methods
taking all image patches as training data and using average quality pooling,
our model is ... | ['Xuhao Jiang', 'Liangwei Yu', 'Guorui Feng', 'Ping An', 'Liquan Shen'] | 2019-03-02 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.82482347e-01 -5.48798323e-01 -2.44073458e-02 -3.79768431e-01
-7.44566679e-01 -2.13068455e-01 -4.15291861e-02 -5.77245504e-02
-3.95965755e-01 5.26271224e-01 2.04993784e-01 4.77541834e-02
-3.71503443e-01 -1.00685704e+00 -5.71594059e-01 -7.29283810e-01
9.56761762e-02 -6.83922648e-01 2.97619104e-01 -3.44907880... | [11.759227752685547, -1.9047365188598633] |
7d0e0047-81ce-4ff3-8d5a-e2d5679634ec | spatially-adaptive-filter-units-for-compact | 1902.07474 | null | https://arxiv.org/abs/1902.07474v2 | https://arxiv.org/pdf/1902.07474v2.pdf | Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks | Convolutional neural networks excel in a number of computer vision tasks. One of their most crucial architectural elements is the effective receptive field size, that has to be manually set to accommodate a specific task. Standard solutions involve large kernels, down/up-sampling and dilated convolutions. These require... | ['Aleš Leonardis', 'Matej Kristan', 'Domen Tabernik'] | 2019-02-20 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.71267837e-01 -3.25264871e-01 5.17458916e-01 -4.68734384e-01
1.48831636e-01 -5.90878367e-01 4.98301625e-01 -2.97354698e-01
-9.71332312e-01 5.83248436e-01 7.18290657e-02 -4.88943636e-01
2.02125516e-02 -7.14279175e-01 -6.79439902e-01 -4.30988252e-01
1.04688615e-01 -1.01353578e-01 6.95245087e-01 -2.00674847... | [9.062902450561523, 2.175624132156372] |
f9854f24-a623-4ae6-8ae0-fc72d9320e23 | music-artist-classification-with | 1901.04555 | null | http://arxiv.org/abs/1901.04555v2 | http://arxiv.org/pdf/1901.04555v2.pdf | Music Artist Classification with Convolutional Recurrent Neural Networks | Previous attempts at music artist classification use frame level audio
features which summarize frequency content within short intervals of time.
Comparatively, more recent music information retrieval tasks take advantage of
temporal structure in audio spectrograms using deep convolutional and recurrent
models. This pa... | ['Zain Nasrullah', 'Yue Zhao'] | 2019-01-14 | null | null | null | null | ['artist-classification'] | ['computer-vision'] | [ 3.67861450e-01 -4.89336282e-01 -5.80763333e-02 -1.01654017e-02
-8.93121779e-01 -7.98828483e-01 5.46241343e-01 5.70405647e-02
-3.73462677e-01 4.46386546e-01 3.67828071e-01 1.94689691e-01
-5.67795515e-01 -3.72927874e-01 -3.91114980e-01 -5.22604406e-01
-4.90188837e-01 -5.88960908e-02 -5.88277504e-02 -8.60026851... | [15.77303695678711, 5.287568092346191] |
d30b05b1-9ea9-4bd4-9724-0fb4140f1ff5 | anatomically-parameterized-statistical-shape | 2202.08580 | null | https://arxiv.org/abs/2202.08580v1 | https://arxiv.org/pdf/2202.08580v1.pdf | Anatomically Parameterized Statistical Shape Model: Explaining Morphometry through Statistical Learning | Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape representations through SSMs are based on shape coefficients and lack an explicit one-to-one relationship with anatomical measures of clinical relev... | ['Bhushan Borotikar', 'Valérie Burdin', 'Asma Salhi', 'Arnaud Boutillon'] | 2022-02-17 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [ 5.45124598e-02 2.61194557e-01 -1.80607706e-01 -2.51240790e-01
-5.52046895e-01 -4.95194435e-01 5.19484818e-01 5.11730313e-01
-4.18538660e-01 5.35897732e-01 1.76759407e-01 -2.13601306e-01
-8.03188920e-01 -7.62613416e-01 -4.98403400e-01 -7.87894547e-01
-2.66958743e-01 8.92639339e-01 4.01630640e-01 -3.67137969... | [14.073051452636719, -2.509040355682373] |
332b09ee-0768-4dbd-a7ba-24c27d4b3e50 | an-exploration-of-neural-sequence-to-sequence | 1706.04138 | null | http://arxiv.org/abs/1706.04138v2 | http://arxiv.org/pdf/1706.04138v2.pdf | An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing | In this work, we explore multiple neural architectures adapted for the task
of automatic post-editing of machine translation output. We focus on neural
end-to-end models that combine both inputs $mt$ (raw MT output) and $src$
(source language input) in a single neural architecture, modeling $\{mt, src\}
\rightarrow pe$... | ['Roman Grundkiewicz', 'Marcin Junczys-Dowmunt'] | 2017-06-13 | an-exploration-of-neural-sequence-to-sequence-1 | https://aclanthology.org/I17-1013 | https://aclanthology.org/I17-1013.pdf | ijcnlp-2017-11 | ['hard-attention'] | ['methodology'] | [ 4.31289285e-01 3.38560343e-01 1.10407777e-01 -3.67417753e-01
-1.05251169e+00 -6.26287520e-01 9.91761982e-01 -1.01047166e-01
-9.58731949e-01 7.28173852e-01 3.33606601e-01 -7.58377314e-01
2.75538713e-01 -3.03153843e-01 -1.13569808e+00 -2.36166045e-01
6.21321261e-01 8.30658734e-01 -1.36069670e-01 -7.52186239... | [11.623140335083008, 10.255240440368652] |
feed4125-51d3-41af-a6b1-8584a4e69533 | kuielab-mdx-net-a-two-stream-neural-network | 2111.12203 | null | https://arxiv.org/abs/2111.12203v1 | https://arxiv.org/pdf/2111.12203v1.pdf | KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing | Recently, many methods based on deep learning have been proposed for music source separation. Some state-of-the-art methods have shown that stacking many layers with many skip connections improve the SDR performance. Although such a deep and complex architecture shows outstanding performance, it usually requires numero... | ['Soonyoung Jung', 'Daewon Lee', 'Jaehwa Chung', 'Woosung Choi', 'Minseok Kim'] | 2021-11-24 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 1.7351797e-01 -6.5127414e-01 -2.5180599e-01 -1.8164292e-01
-9.3087828e-01 -3.8837424e-01 2.6311967e-01 -2.3053053e-01
-1.3619405e-01 5.2617168e-01 3.1763250e-01 -1.0216203e-01
-3.4707084e-01 -3.9386851e-01 -6.4485502e-01 -5.7009643e-01
-2.5967929e-01 2.2407559e-01 -7.7408649e-02 -1.9441891e-01
9.9174209e-02... | [15.531474113464355, 5.452165603637695] |
fa4e7d5a-83dc-48cd-be0f-8642d25cad76 | nexus-sine-qua-non-essentially-connected | 2307.01482 | null | https://arxiv.org/abs/2307.01482v1 | https://arxiv.org/pdf/2307.01482v1.pdf | Nexus sine qua non: Essentially connected neural networks for spatial-temporal forecasting of multivariate time series | Modeling and forecasting multivariate time series not only facilitates the decision making of practitioners, but also deepens our scientific understanding of the underlying dynamical systems. Spatial-temporal graph neural networks (STGNNs) are emerged as powerful predictors and have become the de facto models for learn... | ['Jian Sun', 'Yunpeng Wang', 'Guoyang Qin', 'Tong Nie'] | 2023-07-04 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 6.06112704e-02 -2.75859758e-02 -2.14649469e-01 -2.69494236e-01
1.19742095e-01 -3.01933259e-01 9.36328709e-01 -1.58772290e-01
7.00533837e-02 5.68737090e-01 3.57168883e-01 -6.06299520e-01
-3.31783861e-01 -7.61290610e-01 -6.94001436e-01 -6.89487457e-01
-6.11801326e-01 1.07692681e-01 2.05785587e-01 -5.16626239... | [6.798316478729248, 2.736260175704956] |
cba69810-f187-4949-afc4-1cc8f91feef8 | wechat-ai-s-submission-for-dstc9-interactive | 2101.07947 | null | https://arxiv.org/abs/2101.07947v2 | https://arxiv.org/pdf/2101.07947v2.pdf | WeChat AI & ICT's Submission for DSTC9 Interactive Dialogue Evaluation Track | We participate in the DSTC9 Interactive Dialogue Evaluation Track (Gunasekara et al. 2020) sub-task 1 (Knowledge Grounded Dialogue) and sub-task 2 (Interactive Dialogue). In sub-task 1, we employ a pre-trained language model to generate topic-related responses and propose a response ensemble method for response selecti... | ['Jie zhou', 'Yang Feng', 'Jinchao Zhang', 'Zongjia Li', 'Zekang Li'] | 2021-01-20 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 1.44751057e-01 8.47629130e-01 3.85211200e-01 -6.52267158e-01
-8.97220910e-01 -6.18248284e-01 1.12386322e+00 -1.40576931e-02
-2.99490601e-01 1.04475451e+00 8.91899943e-01 -5.76874167e-02
2.88844913e-01 -5.23340225e-01 3.88610423e-01 6.97638094e-02
2.23956913e-01 1.15769184e+00 4.41082925e-01 -8.02976489... | [12.891340255737305, 8.061114311218262] |
5c57addf-a7b1-484f-80f4-feccc941f691 | modern-strategies-for-time-series-regression | 2010.15997 | null | https://arxiv.org/abs/2010.15997v1 | https://arxiv.org/pdf/2010.15997v1.pdf | Modern strategies for time series regression | This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared... | ['Louise M Ryan', 'Dan Pagendam', 'Rob J Hyndman', 'Stephanie Clark'] | 2020-10-29 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 1.32392809e-01 -4.23156321e-01 -2.14943275e-01 -7.34025538e-01
-2.77951807e-01 -2.57212222e-01 7.25134552e-01 4.08092022e-01
-5.11554360e-01 1.02292216e+00 1.21841960e-01 -7.73836911e-01
-5.55113137e-01 -8.35642636e-01 -6.74405918e-02 -9.78090346e-01
-8.92958939e-01 1.92420006e-01 -1.56454891e-01 -5.87670326... | [6.684452533721924, 3.1458027362823486] |
671c89ac-10de-43f7-98f8-4069a8900732 | an-indexing-scheme-and-descriptor-for-3d | 2008.02916 | null | https://arxiv.org/abs/2008.02916v1 | https://arxiv.org/pdf/2008.02916v1.pdf | An Indexing Scheme and Descriptor for 3D Object Retrieval Based on Local Shape Querying | A binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented. A new binary clutter resistant descriptor named Quick Intersection Count Change Image (QUICCI) is also introduced. This local shape descriptor is extremely small and fast to compare. Additionally,... | ['Theoharis Theoharis', 'Bart Iver van Blokland'] | 2020-08-07 | null | null | null | null | ['3d-object-retrieval'] | ['computer-vision'] | [ 2.06795409e-02 -8.63787055e-01 -9.40555632e-02 -2.97190189e-01
-9.52091336e-01 -7.52396107e-01 7.65285552e-01 4.31594521e-01
-4.92779613e-01 3.59028727e-01 8.76089111e-02 3.50961573e-02
-4.95636046e-01 -8.05283725e-01 -3.36362600e-01 -7.26375639e-01
-4.59613681e-01 2.38896489e-01 6.19514704e-01 -2.14982867... | [10.577027320861816, 0.28074365854263306] |
e0cb06cc-73d4-4b0b-bb6c-22ce6665c1ac | what-is-this-article-about-extreme | 1907.08722 | null | https://arxiv.org/abs/1907.08722v1 | https://arxiv.org/pdf/1907.08722v1.pdf | What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks | We introduce 'extreme summarization', a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question ``What is the article about?''. We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling ap... | ['Mirella Lapata', 'Shay B. Cohen', 'Shashi Narayan'] | 2019-07-19 | null | null | null | null | ['extreme-summarization'] | ['natural-language-processing'] | [ 6.94633007e-01 7.24461079e-01 -7.63846412e-02 -3.58410805e-01
-1.54120302e+00 -7.74079800e-01 8.15252542e-01 6.36849761e-01
-4.38671440e-01 9.95370269e-01 1.23475051e+00 -4.20971572e-01
-5.68647273e-02 -4.73723173e-01 -1.04472280e+00 -9.08437893e-02
-8.18427131e-02 8.61469924e-01 -5.17798699e-02 -5.14120281... | [12.519540786743164, 9.496709823608398] |
5484a068-0c2c-4a7c-a8a8-41bc91f8c44f | story-shaping-teaching-agents-human-like | 2301.10107 | null | https://arxiv.org/abs/2301.10107v1 | https://arxiv.org/pdf/2301.10107v1.pdf | Story Shaping: Teaching Agents Human-like Behavior with Stories | Reward design for reinforcement learning agents can be difficult in situations where one not only wants the agent to achieve some effect in the world but where one also cares about how that effect is achieved. For example, we might wish for an agent to adhere to a tacit understanding of commonsense, align itself to a p... | ['Mark Riedl', 'Renee Jia', 'Wei Zhou', 'Christopher Cui', 'Xiangyu Peng'] | 2023-01-24 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 4.04319793e-01 6.43044710e-01 5.88210337e-02 -2.75165915e-01
-8.76723751e-02 -6.24334276e-01 8.21564853e-01 3.46190512e-01
-3.90443772e-01 9.68891203e-01 2.78417677e-01 -2.22176567e-01
-1.77965045e-01 -1.22168386e+00 -6.11014307e-01 -4.49989766e-01
1.37551511e-02 6.49768889e-01 2.65317082e-01 -5.41520655... | [3.9476191997528076, 1.29319167137146] |
47cffa5e-5ecc-4b5f-9acb-56fdde88e7e2 | mmnet-muscle-motion-guided-network-for-micro | 2201.05297 | null | https://arxiv.org/abs/2201.05297v2 | https://arxiv.org/pdf/2201.05297v2.pdf | MMNet: Muscle motion-guided network for micro-expression recognition | Facial micro-expressions (MEs) are involuntary facial motions revealing peoples real feelings and play an important role in the early intervention of mental illness, the national security, and many human-computer interaction systems. However, existing micro-expression datasets are limited and usually pose some challeng... | ['Feng Zhao', 'Zhaoqing Zhu', 'Mingzhe Sui', 'Hanting Li'] | 2022-01-14 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [-1.06448665e-01 -8.55014771e-02 -3.98739517e-01 -5.43685794e-01
-2.00464964e-01 2.75551118e-02 4.60669041e-01 -9.34366107e-01
-3.30912381e-01 3.15265477e-01 3.55886400e-01 3.71935457e-01
3.90217602e-01 -3.87945712e-01 -4.05860841e-01 -1.10565650e+00
1.54709503e-01 -1.97542563e-01 -1.83364674e-01 -4.45787817... | [13.620123863220215, 1.6658926010131836] |
cb661fd4-e1cb-431f-b00f-85d35bde4a64 | kernel-density-bayesian-inverse-reinforcement | 2303.06827 | null | https://arxiv.org/abs/2303.06827v1 | https://arxiv.org/pdf/2303.06827v1.pdf | Kernel Density Bayesian Inverse Reinforcement Learning | Inverse reinforcement learning~(IRL) is a powerful framework to infer an agent's reward function by observing its behavior, but IRL algorithms that learn point estimates of the reward function can be misleading because there may be several functions that describe an agent's behavior equally well. A Bayesian approach to... | ['Barbara E. Engelhardt', 'Andrew Jones', 'Diana Cai', 'Didong Li', 'Aishwarya Mandyam'] | 2023-03-13 | null | null | null | null | ['birl-cima'] | ['medical'] | [-2.91836709e-01 -2.22380646e-02 -4.53116924e-01 -3.62022370e-01
-9.10116494e-01 -3.74041229e-01 2.63382196e-01 3.27081800e-01
-8.33834887e-01 1.27273166e+00 -1.26621053e-01 -3.33682120e-01
-4.51178461e-01 -5.55100381e-01 -7.32052207e-01 -6.68500543e-01
-5.51001310e-01 6.46091163e-01 2.14469001e-01 9.06314850... | [4.133194446563721, 2.158266305923462] |
67cc2b5c-dccf-4820-a0e2-b7b1f6a713be | on-the-intrinsic-privacy-of-stochastic | 1912.02919 | null | https://arxiv.org/abs/1912.02919v4 | https://arxiv.org/pdf/1912.02919v4.pdf | An Empirical Study on the Intrinsic Privacy of SGD | Introducing noise in the training of machine learning systems is a powerful way to protect individual privacy via differential privacy guarantees, but comes at a cost to utility. This work looks at whether the inherent randomness of stochastic gradient descent (SGD) could contribute to privacy, effectively reducing the... | ['Stephanie L. Hyland', 'Shruti Tople'] | 2019-12-05 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 2.58913279e-01 1.71630651e-01 1.89589225e-02 -4.00712550e-01
-1.10381770e+00 -1.00962961e+00 4.05047715e-01 1.38330132e-01
-8.01695704e-01 5.92779458e-01 -9.15790349e-02 -8.71557236e-01
-7.50874132e-02 -6.00854516e-01 -9.93125141e-01 -8.47475410e-01
-3.84244293e-01 1.65431708e-01 -2.51034826e-01 1.65505454... | [5.954126834869385, 6.967641830444336] |
a1453638-267b-4672-b3cf-d7ad33073907 | visual-reasoning-from-state-to-transformation | 2305.01668 | null | https://arxiv.org/abs/2305.01668v1 | https://arxiv.org/pdf/2305.01668v1.pdf | Visual Reasoning: from State to Transformation | Most existing visual reasoning tasks, such as CLEVR in VQA, ignore an important factor, i.e.~transformation. They are solely defined to test how well machines understand concepts and relations within static settings, like one image. Such \textbf{state driven} visual reasoning has limitations in reflecting the ability t... | ['Xueqi Cheng', 'Jiafeng Guo', 'Liang Pang', 'Yanyan Lan', 'Xin Hong'] | 2023-05-02 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-2.08829343e-01 -1.64836254e-02 -3.19942832e-02 -2.58677453e-01
-7.44159669e-02 -7.83615530e-01 9.35089111e-01 -2.50084758e-01
-1.59128785e-01 4.48950499e-01 1.32259965e-01 -7.18348622e-01
1.03461884e-01 -8.53107929e-01 -8.15218151e-01 -4.56959158e-01
2.87141860e-01 6.56142235e-01 4.33514655e-01 -5.63638330... | [10.736919403076172, 1.7540137767791748] |
211c63d0-57b1-40c9-9c15-f7355ba5d399 | jointly-dynamic-topic-model-for-recognition | 2111.10846 | null | https://arxiv.org/abs/2111.10846v1 | https://arxiv.org/pdf/2111.10846v1.pdf | Jointly Dynamic Topic Model for Recognition of Lead-lag Relationship in Two Text Corpora | Topic evolution modeling has received significant attentions in recent decades. Although various topic evolution models have been proposed, most studies focus on the single document corpus. However in practice, we can easily access data from multiple sources and also observe relationships between them. Then it is of gr... | ['Feifei Wang', 'Jingya Hong', 'Xiaoling Lu', 'Yandi Zhu'] | 2021-11-21 | null | null | null | null | ['dynamic-topic-modeling'] | ['natural-language-processing'] | [-8.63082260e-02 -2.41451010e-01 -1.96243867e-01 -1.52652249e-01
-2.65841901e-01 -3.78459334e-01 8.91767383e-01 4.26258624e-01
-1.92614757e-02 5.47340333e-01 2.20520288e-01 2.49246368e-03
-3.08126777e-01 -9.14672136e-01 -3.93844098e-01 -9.43379462e-01
-1.58814564e-02 5.58013082e-01 6.53728962e-01 -2.22360611... | [10.37291431427002, 6.9492411613464355] |
9a375a5e-9c13-4daa-ab17-5ae97d17da60 | pushing-the-envelope-for-depth-based-semi | 2303.15147 | null | https://arxiv.org/abs/2303.15147v1 | https://arxiv.org/pdf/2303.15147v1.pdf | Pushing the Envelope for Depth-Based Semi-Supervised 3D Hand Pose Estimation with Consistency Training | Despite the significant progress that depth-based 3D hand pose estimation methods have made in recent years, they still require a large amount of labeled training data to achieve high accuracy. However, collecting such data is both costly and time-consuming. To tackle this issue, we propose a semi-supervised method to ... | ['Vassilis Athitsos', 'Alex Dillhoff', 'Farnaz Farahanipad', 'Mohammad Rezaei'] | 2023-03-27 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [ 1.67071179e-01 1.94631621e-01 -5.94896734e-01 -6.49078608e-01
-8.99590671e-01 -5.05992353e-01 2.85107821e-01 -3.20601702e-01
-5.53068995e-01 8.72623980e-01 7.91672245e-03 -1.67014390e-01
3.09465677e-01 -4.91836518e-01 -8.13341618e-01 -6.23570263e-01
2.60687113e-01 7.69480109e-01 1.18738338e-01 4.26913768... | [7.029334545135498, -1.0707480907440186] |
848d7b7b-d484-439a-9924-6dd5c9683eb9 | all-e-aesthetics-guided-low-light-image | 2304.14610 | null | https://arxiv.org/abs/2304.14610v2 | https://arxiv.org/pdf/2304.14610v2.pdf | ALL-E: Aesthetics-guided Low-light Image Enhancement | Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into image enhancement a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training enhancement models. In this paper, we propo... | ['Songcan Chen', 'Sheng-Jun Huang', 'Yuanhang Gao', 'Dong Liang', 'Ling Li'] | 2023-04-28 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 3.77473712e-01 -1.14237085e-01 -6.86534941e-02 -4.97873366e-01
-6.93405449e-01 -2.60364950e-01 2.47218475e-01 7.27548404e-03
-5.88786900e-01 6.41330183e-01 9.01211873e-02 -6.98138475e-02
3.68467793e-02 -6.63163900e-01 -4.75854605e-01 -8.61973763e-01
1.49504676e-01 -3.30316424e-01 -1.28523424e-01 -3.26582283... | [11.372795104980469, -1.4504843950271606] |
253b3af1-bdb1-4cc3-96da-66f7e3bfb054 | entity-identification-as-multitasking | 1612.02706 | null | http://arxiv.org/abs/1612.02706v2 | http://arxiv.org/pdf/1612.02706v2.pdf | Entity Identification as Multitasking | Standard approaches in entity identification hard-code boundary detection and
type prediction into labels (e.g., John/B-PER Smith/I-PER) and then perform
Viterbi. This has two disadvantages: 1. the runtime complexity grows
quadratically in the number of types, and 2. there is no natural segment-level
representation. In... | ['Karl Stratos'] | 2016-12-08 | entity-identification-as-multitasking-1 | https://aclanthology.org/W17-4302 | https://aclanthology.org/W17-4302.pdf | ws-2017-9 | ['type-prediction'] | ['computer-code'] | [ 5.43549769e-02 3.54384631e-01 -4.88096267e-01 -3.60668391e-01
-9.88588810e-01 -7.45833039e-01 1.69981673e-01 4.79731470e-01
-8.10401499e-01 8.85723174e-01 -9.29236487e-02 -6.87487900e-01
4.53030378e-01 -8.06545377e-01 -9.11261141e-01 -3.77581120e-01
5.88888377e-02 6.00323856e-01 2.43344784e-01 2.78071135... | [9.714268684387207, 9.403746604919434] |
a18c38c4-e480-4a22-8538-7f629de5cc90 | farsight-a-physics-driven-whole-body | 2306.17206 | null | https://arxiv.org/abs/2306.17206v1 | https://arxiv.org/pdf/2306.17206v1.pdf | FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude | Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance. This paper presents the end-to-end design, development and evaluation of FarSight, an innovative software system designed for whole-body (fusion of face, gait and body sh... | ['Xiaoming Liu', 'Anil Jain', 'Zhangyang Wang', 'Humphrey Shi', 'Arun Ross', 'Stanley Chan', 'Xingguang Zhang', 'Kai Wang', 'Pegah Varghaei', 'Yiyang Su', 'Zhiyuan Ren', 'Christopher Perry', 'Zhiyuan Mao', 'Minchul Kim', 'Ajay Jaiswal', 'Ali Hassani', 'Najmul Hassan', 'Nicholas Chimitt', 'Ryan Ashbaugh', 'Feng Liu'] | 2023-06-29 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 7.78172463e-02 -4.51015919e-01 -3.94531526e-02 -4.16496873e-01
-7.28948593e-01 -6.15349889e-01 3.53983849e-01 -4.00053173e-01
-2.83627272e-01 3.37400943e-01 1.40659526e-01 2.03924000e-01
-3.32015514e-01 -5.11587918e-01 -2.99437463e-01 -6.70412838e-01
-2.01660991e-01 1.38336346e-01 -4.80637968e-01 -2.22330883... | [13.708536148071289, 1.0110222101211548] |
88048a93-b367-4ed0-bec8-c0b1b4f414f2 | comet-qe-and-active-learning-for-low-resource | 2210.15696 | null | https://arxiv.org/abs/2210.15696v1 | https://arxiv.org/pdf/2210.15696v1.pdf | COMET-QE and Active Learning for Low-Resource Machine Translation | Active learning aims to deliver maximum benefit when resources are scarce. We use COMET-QE, a reference-free evaluation metric, to select sentences for low-resource neural machine translation. Using Swahili, Kinyarwanda and Spanish for our experiments, we show that COMET-QE significantly outperforms two variants of Rou... | ['Bruce A. Bassett', 'Everlyn Asiko Chimoto'] | 2022-10-27 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.30073982e-01 6.20120652e-02 -6.17242098e-01 -3.58320296e-01
-2.20208359e+00 -5.50050855e-01 7.48213053e-01 -7.93666020e-03
-1.09387004e+00 1.57301974e+00 4.15868998e-01 -6.70899689e-01
1.07245751e-01 -3.53025883e-01 -7.49070644e-01 -3.79980534e-01
4.67348881e-02 8.34599018e-01 -1.69142291e-01 -6.20717466... | [11.671956062316895, 10.236418724060059] |
0dcb6450-5028-44a3-8408-0debb996a527 | reference-based-color-transfer-for-medical | 2210.08083 | null | https://arxiv.org/abs/2210.08083v1 | https://arxiv.org/pdf/2210.08083v1.pdf | Reference Based Color Transfer for Medical Volume Rendering | The benefits of medical imaging are enormous. Medical images provide considerable amounts of anatomical information and this facilitates medical practitioners in performing effective disease diagnosis and deciding upon the best course of medical treatment. A transition from traditional monochromatic medical images like... | ['Summanta Pattanaik', 'Sudarshan Devkota'] | 2022-10-14 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 3.89579386e-01 -2.67951433e-02 2.46705323e-01 -3.12699229e-01
-5.76364219e-01 -4.31668937e-01 4.54911649e-01 2.31830224e-01
-5.61750114e-01 5.50601244e-01 -1.10007174e-01 -6.03978992e-01
-5.36040962e-02 -1.02635229e+00 -1.56237140e-01 -6.25400186e-01
-1.25386879e-01 5.53788424e-01 2.80441314e-01 -1.77948609... | [14.4525728225708, -2.6814751625061035] |
1faf37ef-53e8-41b3-a171-5c9394a4ea75 | bayesian-predictive-beamforming-for-vehicular | 2005.07698 | null | https://arxiv.org/abs/2005.07698v1 | https://arxiv.org/pdf/2005.07698v1.pdf | Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach | The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a syst... | ['Nuria Gonzalez-Prelcic', 'Derrick Wing Kwan Ng', 'Jinhong Yuan', 'Christos Masouros', 'Fan Liu', 'Weijie Yuan'] | 2020-05-15 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [-5.23397028e-02 -2.44791135e-01 -3.74730378e-02 -2.97940195e-01
-6.61012113e-01 -3.24078768e-01 6.19759023e-01 -3.00757140e-01
-4.34132487e-01 8.86502147e-01 -1.23503760e-01 -7.18115926e-01
-4.88979220e-01 -9.44975138e-01 -5.61515510e-01 -9.65896904e-01
-3.12285691e-01 5.87524995e-02 2.71731675e-01 -1.16818108... | [6.303452491760254, 1.2630431652069092] |
99d55fc4-118f-4019-9089-7e4c03bdbaf2 | autodial-efficient-asynchronous-task-oriented | 2303.06245 | null | https://arxiv.org/abs/2303.06245v3 | https://arxiv.org/pdf/2303.06245v3.pdf | AUTODIAL: Efficient Asynchronous Task-Oriented Dialogue Model | As large dialogue models become commonplace in practice, the problems surrounding high compute requirements for training, inference and larger memory footprint still persists. In this work, we present AUTODIAL, a multi-task dialogue model that addresses the challenges of deploying dialogue model. AUTODIAL utilizes para... | ['Chinnadhurai Sankar', 'Shahin Shayandeh', 'Pooyan Amini', 'Prajjwal Bhargava'] | 2023-03-10 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [-1.40099138e-01 5.32354534e-01 1.66493878e-01 -3.56255442e-01
-1.03237295e+00 -7.29379773e-01 8.29523325e-01 -3.16369794e-02
-2.67018884e-01 8.80970478e-01 4.96735156e-01 -5.07797718e-01
5.17137349e-01 -6.68352246e-01 -1.13671094e-01 -1.18025750e-01
2.27950543e-01 1.13964987e+00 1.90680459e-01 -5.64858794... | [12.846858978271484, 7.936420440673828] |
c8186fd1-9cf7-4661-8ab1-2ed90d937f53 | denoising-based-turbo-message-passing-for | 2012.05626 | null | https://arxiv.org/abs/2012.05626v1 | https://arxiv.org/pdf/2012.05626v1.pdf | Denoising-based Turbo Message Passing for Compressed Video Background Subtraction | In this paper, we consider the compressed video background subtraction problem that separates the background and foreground of a video from its compressed measurements. The background of a video usually lies in a low dimensional space and the foreground is usually sparse. More importantly, each video frame is a natural... | ['Yang Yang', 'Xiaojun Yuan', 'Zhipeng Xue'] | 2020-12-10 | null | null | null | null | ['video-background-subtraction'] | ['computer-vision'] | [ 5.09220362e-01 -4.72251892e-01 4.96772230e-02 2.37970781e-02
-4.51498419e-01 -3.23801100e-01 3.72997165e-01 -1.66495204e-01
-4.47896034e-01 3.69621396e-01 -2.04584435e-01 -3.49848092e-01
1.13711447e-01 -5.38925588e-01 -7.99235165e-01 -1.18715072e+00
-2.79939890e-01 -2.94083506e-01 7.03977585e-01 9.90578979... | [9.032997131347656, -0.8348363637924194] |
5e180f89-be57-46ad-8db5-2ac89f33bbbc | consistency-based-self-supervised-learning | 2208.05251 | null | https://arxiv.org/abs/2208.05251v1 | https://arxiv.org/pdf/2208.05251v1.pdf | Consistency-based Self-supervised Learning for Temporal Anomaly Localization | This work tackles Weakly Supervised Anomaly detection, in which a predictor is allowed to learn not only from normal examples but also from a few labeled anomalies made available during training. In particular, we deal with the localization of anomalous activities within the video stream: this is a very challenging sce... | ['Rita Cucchiara', 'Simone Calderara', 'Angelo Porrello', 'Aniello Panariello'] | 2022-08-10 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'supervised-anomaly-detection', 'weakly-supervised-temporal-action', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 4.52980399e-01 1.26449674e-01 -1.58229217e-01 -5.32966912e-01
-5.18956542e-01 -2.87706524e-01 5.21234274e-01 5.10840833e-01
-6.13110662e-01 5.23081362e-01 1.61286116e-01 7.76687860e-02
1.87599644e-01 -3.51416588e-01 -1.05919909e+00 -6.86932206e-01
-4.23898906e-01 1.73554227e-01 3.93017560e-01 -1.36191949... | [7.854655742645264, 1.591782808303833] |
fe29831f-bee3-4e37-b527-2dec348d0aee | experimental-evaluation-of-quantum-bayesian | 2005.12474 | null | https://arxiv.org/abs/2005.12474v1 | https://arxiv.org/pdf/2005.12474v1.pdf | Experimental evaluation of quantum Bayesian networks on IBM QX hardware | Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian Network (QBN) is a quantum version of the Bayesian network that utilizes the principles of quantum mechanical systems to improve the computationa... | ['Saideep Nannapaneni', 'Sima E. Borujeni', 'Elizabeth C. Behrman', 'Nam H. Nguyen', 'James E. Steck'] | 2020-05-26 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-1.60996243e-01 -1.07694209e-01 8.18802640e-02 -4.64802116e-01
-7.18459547e-01 -5.04568875e-01 5.45076966e-01 1.04525290e-01
-2.81634033e-01 9.82400417e-01 -1.35811776e-01 -8.64499390e-01
-4.20863688e-01 -1.05656588e+00 -4.86076206e-01 -5.06559491e-01
-3.43305916e-01 6.36271536e-01 5.46500802e-01 -1.78843036... | [5.628036975860596, 4.868324279785156] |
dadb296a-7b90-42a8-9b9a-ee38da06c027 | detecting-images-generated-by-deep-diffusion | 2307.02347 | null | https://arxiv.org/abs/2307.02347v1 | https://arxiv.org/pdf/2307.02347v1.pdf | Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality | Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally... | ['Janis Keuper', 'Ricard Durall', 'Peter Lorenz'] | 2023-07-05 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 2.10699111e-01 -1.91899866e-01 2.10890725e-01 3.37241828e-01
-9.60188031e-01 -8.66689682e-01 1.19672906e+00 -2.78533548e-01
-1.70063317e-01 4.25661325e-01 -8.74027684e-02 -2.99236178e-01
1.76526174e-01 -5.50936401e-01 -4.10076708e-01 -9.25478637e-01
-8.57407153e-02 4.00541544e-01 2.22029567e-01 -3.96345221... | [12.439366340637207, 1.0809638500213623] |
35e4be72-bd91-4f0e-9857-06e1ecf93d4e | escape-room-a-configurable-testbed-for | 1812.09521 | null | http://arxiv.org/abs/1812.09521v1 | http://arxiv.org/pdf/1812.09521v1.pdf | Escape Room: A Configurable Testbed for Hierarchical Reinforcement Learning | Recent successes in Reinforcement Learning have encouraged a fast-growing
network of RL researchers and a number of breakthroughs in RL research. As the
RL community and the body of RL work grows, so does the need for widely
applicable benchmarks that can fairly and effectively evaluate a variety of RL
algorithms.
Th... | ['Jacob Menashe', 'Peter Stone'] | 2018-12-22 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-2.79833257e-01 -2.75308676e-02 -3.08719784e-01 -2.53762484e-01
-8.84382069e-01 -8.99499059e-01 5.36613584e-01 -2.39096850e-01
-5.74532688e-01 1.25952566e+00 9.99309346e-02 -5.24451137e-01
-3.75830203e-01 -6.48497462e-01 -4.59764540e-01 -5.19115984e-01
-5.46894133e-01 7.35596776e-01 3.16543609e-01 -6.46559656... | [4.0591607093811035, 1.5172829627990723] |
cf352ff4-53e0-4542-93e9-89fb904393f7 | iiitd-20k-dense-captioning-for-text-image | 2305.04497 | null | https://arxiv.org/abs/2305.04497v1 | https://arxiv.org/pdf/2305.04497v1.pdf | IIITD-20K: Dense captioning for Text-Image ReID | Text-to-Image (T2I) ReID has attracted a lot of attention in the recent past. CUHK-PEDES, RSTPReid and ICFG-PEDES are the three available benchmarks to evaluate T2I ReID methods. RSTPReid and ICFG-PEDES comprise of identities from MSMT17 but due to limited number of unique persons, the diversity is limited. On the othe... | ['Brejesh lall', 'Vibhu Dubey', 'Niranjan Sundararajan', 'A V Subramanyam'] | 2023-05-08 | null | null | null | null | ['dense-captioning'] | ['computer-vision'] | [ 7.98045546e-02 -2.44739741e-01 4.57281321e-02 -5.10719180e-01
-7.60688782e-01 -7.41691589e-01 1.10377383e+00 -4.78012741e-01
-4.00079042e-01 9.71094489e-01 3.16960633e-01 8.61460268e-02
1.78982645e-01 -5.25215685e-01 -9.74535942e-01 -6.01185262e-01
3.87399614e-01 7.22432256e-01 -2.45878935e-01 -1.62288398... | [14.659137725830078, 0.9361302256584167] |
cffee839-c043-4df4-9327-1ec107f3e8db | rethinking-performance-gains-in-image | 2209.11448 | null | https://arxiv.org/abs/2209.11448v1 | https://arxiv.org/pdf/2209.11448v1.pdf | Rethinking Performance Gains in Image Dehazing Networks | Image dehazing is an active topic in low-level vision, and many image dehazing networks have been proposed with the rapid development of deep learning. Although these networks' pipelines work fine, the key mechanism to improving image dehazing performance remains unclear. For this reason, we do not target to propose a ... | ['Xin Du', 'Hui Qian', 'Yang Zhou', 'Yuda Song'] | 2022-09-23 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 3.99827391e-01 1.69032559e-01 1.40619911e-02 -9.65655670e-02
-1.00834154e-01 -1.79728895e-01 4.76895303e-01 -1.39987782e-01
-5.19891977e-01 4.26908761e-01 1.39281347e-01 -1.41642779e-01
1.08290695e-01 -9.40686762e-01 -7.48197913e-01 -7.53476441e-01
8.96782503e-02 -7.31565475e-01 6.83682024e-01 -2.18299165... | [10.9475736618042, -3.0106990337371826] |
2fb2024c-095a-4a81-afa0-60fcdd901003 | end-to-end-automatic-sleep-stage | 2005.05437 | null | https://arxiv.org/abs/2005.05437v1 | https://arxiv.org/pdf/2005.05437v1.pdf | End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features | Sleep disorder is one of many neurological diseases that can affect greatly the quality of daily life. It is very burdensome to manually classify the sleep stages to detect sleep disorders. Therefore, the automatic sleep stage classification techniques are needed. However, the previous automatic sleep scoring methods u... | ['Seong-Whan Lee', 'Minji Lee', 'Hyeong-Jin Kim'] | 2020-05-04 | null | null | null | null | ['sleep-staging', 'automatic-sleep-stage-classification'] | ['medical', 'medical'] | [ 1.62580609e-01 -3.67809147e-01 -5.00094779e-02 -5.17239034e-01
-3.56473848e-02 -2.90077776e-02 7.98316002e-02 9.25321802e-02
-9.44248617e-01 8.58916283e-01 3.72450203e-02 1.10805236e-01
-1.42760202e-01 -4.53037649e-01 3.03552687e-01 -7.89098442e-01
-9.40091982e-02 1.15941487e-01 4.42378044e-01 -8.08546394... | [13.54723072052002, 3.472133159637451] |
afb29b54-6374-419a-a6a5-d8a0a466ec9a | cross-domain-document-layout-analysis-via | 2201.09407 | null | https://arxiv.org/abs/2201.09407v1 | https://arxiv.org/pdf/2201.09407v1.pdf | Cross-Domain Document Layout Analysis via Unsupervised Document Style Guide | The document layout analysis (DLA) aims to decompose document images into high-level semantic areas (i.e., figures, tables, texts, and background). Creating a DLA framework with strong generalization capabilities is a challenge due to document objects are diversity in layout, size, aspect ratio, texture, etc. Many rese... | ['Liang He', 'Tianlong Ma', 'Xin Li', 'Yingbin Zheng', 'Xiangcheng Du', 'Luwei Xiao', 'Xingjiao Wu'] | 2022-01-24 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 2.55534232e-01 -5.12691498e-01 1.77955985e-01 -3.21073681e-01
-5.05681396e-01 -7.77240634e-01 5.79494894e-01 8.18918869e-02
2.17791796e-01 2.37979740e-01 2.08982334e-01 -3.05675715e-01
-3.48754823e-01 -1.11573577e+00 -4.20313776e-01 -6.21167183e-01
5.32191694e-01 2.71676421e-01 2.23588571e-01 -1.35678411... | [11.662835121154785, 2.254664897918701] |
a2d0dda9-9a1d-48f5-b2a6-d4d5e421b539 | spatio-temporal-parking-behaviour-forecasting | 2108.07731 | null | https://arxiv.org/abs/2108.07731v1 | https://arxiv.org/pdf/2108.07731v1.pdf | Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19 | Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours. However, previous studies usually only consider temporal dependence but ignore the spatial correlations among parking ... | ['Ruibin Bai', 'Wei Tu', 'Yu Liu', 'Rui Cao', 'Xiaopeng Mo', 'Shuhui Gong'] | 2021-08-15 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-3.84342611e-01 -2.62184292e-01 -4.61935163e-01 -1.74220100e-01
-1.87701359e-01 -6.25758097e-02 7.31438994e-01 3.50663364e-01
-4.76584822e-01 8.34143221e-01 5.88617742e-01 -9.88839865e-01
-5.10059655e-01 -1.26400304e+00 -3.84287387e-01 -5.68762243e-01
-3.12947124e-01 4.51520741e-01 2.79130369e-01 -3.91766995... | [6.232601642608643, 1.855434775352478] |
7f274dbb-e52e-4083-b419-74c03405a80e | english-contrastive-learning-can-learn | 2211.06127 | null | https://arxiv.org/abs/2211.06127v1 | https://arxiv.org/pdf/2211.06127v1.pdf | English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings | Universal cross-lingual sentence embeddings map semantically similar cross-lingual sentences into a shared embedding space. Aligning cross-lingual sentence embeddings usually requires supervised cross-lingual parallel sentences. In this work, we propose mSimCSE, which extends SimCSE to multilingual settings and reveal ... | ['Graham Neubig', 'Ashley Wu', 'Yau-Shian Wang'] | 2022-11-11 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-3.58289540e-01 -3.59335124e-01 -6.08485281e-01 -3.42025131e-01
-1.54396653e+00 -8.81968796e-01 6.49104297e-01 4.17607754e-01
-8.67781997e-01 5.26565075e-01 7.44462073e-01 -3.43148053e-01
1.10999025e-01 -5.09173751e-01 -6.38752759e-01 -2.67511457e-01
2.52387166e-01 5.55549264e-01 -4.61184420e-02 -3.53906333... | [11.119160652160645, 9.850624084472656] |
a14557e5-b3b5-42de-a0df-74c4dfa83026 | generalized-multichannel-variational | 1810.00223 | null | http://arxiv.org/abs/1810.00223v1 | http://arxiv.org/pdf/1810.00223v1.pdf | Generalized Multichannel Variational Autoencoder for Underdetermined Source Separation | This paper deals with a multichannel audio source separation problem under
underdetermined conditions. Multichannel Non-negative Matrix Factorization
(MNMF) is one of powerful approaches, which adopts the NMF concept for source
power spectrogram modeling. This concept is also employed in Independent
Low-Rank Matrix Ana... | ['Tomoki Toda', 'Shogo Seki', 'Li Li', 'Kazuya Takeda', 'Hirokazu Kameoka'] | 2018-09-29 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 2.75278658e-01 -4.32207525e-01 2.40773648e-01 1.42946303e-01
-7.82756329e-01 -5.67131698e-01 4.17681932e-01 -2.20983222e-01
-3.86106670e-01 7.90389955e-01 2.30919331e-01 -2.35656738e-01
-4.75318164e-01 -2.40855768e-01 -3.96185189e-01 -9.96780217e-01
1.09748438e-01 9.15691033e-02 -3.18297863e-01 -2.17791975... | [15.233511924743652, 5.656243324279785] |
6088f6ca-dd76-439e-be6b-10473c344b9c | multifix-learning-to-repair-multiple-errors | null | null | https://aclanthology.org/2021.findings-emnlp.417 | https://aclanthology.org/2021.findings-emnlp.417.pdf | MultiFix: Learning to Repair Multiple Errors by Optimal Alignment Learning | We consider the problem of learning to repair erroneous C programs by learning optimal alignments with correct programs. Since the previous approaches fix a single error in a line, it is inevitable to iterate the fixing process until no errors remain. In this work, we propose a novel sequence-to-sequence learning frame... | ['Sang-Ki Ko', 'Yo-Sub Han', 'HyeonTae Seo'] | null | null | null | null | findings-emnlp-2021-11 | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 3.69265109e-01 -1.59215406e-01 -2.59669334e-01 -6.95372581e-01
-1.08423090e+00 -7.12127566e-01 -2.62001425e-01 1.11214256e+00
-3.37333322e-01 4.87875700e-01 -2.54192501e-01 -8.36271226e-01
3.15973699e-01 -6.74624681e-01 -1.45491290e+00 -2.72807293e-02
-1.28122106e-01 1.13956101e-01 3.54941100e-01 -3.39513272... | [7.6830573081970215, 7.745344638824463] |
3a05e39b-2e47-451b-bf1d-f0afa4ed98c6 | estimating-task-completion-times-for-network | 2211.10866 | null | https://arxiv.org/abs/2211.10866v2 | https://arxiv.org/pdf/2211.10866v2.pdf | Estimating Task Completion Times for Network Rollouts using Statistical Models within Partitioning-based Regression Methods | This paper proposes a data and Machine Learning-based forecasting solution for the Telecommunications network-rollout planning problem. Milestone completion-time estimation is crucial to network-rollout planning; accurate estimates enable better crew utilisation and optimised cost of materials and logistics. Using hist... | ['Thalanayar Muthukumar', 'Shrihari Vasudevan', 'Venkatachalam Natchiappan'] | 2022-11-20 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-1.62456017e-02 -2.64874279e-01 -1.69647232e-01 -8.01279902e-01
-7.46277511e-01 -2.19779268e-01 5.25558710e-01 2.25022301e-01
-1.19030572e-01 8.59438598e-01 -1.37379393e-01 -7.61461020e-01
-8.99817467e-01 -9.95564759e-01 -2.69765317e-01 -6.13897383e-01
-1.57869071e-01 9.51875985e-01 -3.86952877e-01 -3.45439941... | [6.851808547973633, 3.141873359680176] |
6cd4d5f5-ae94-4972-9ab6-a7d7a4f8bedf | dlpalign-a-deep-learning-based-progressive | null | null | https://dl.acm.org/doi/10.1145/3429210.3429221 | https://dl.acm.org/doi/pdf/10.1145/3429210.3429221 | DLPAlign: A Deep Learning based Progressive Alignment Method for Multiple Protein Sequences | This paper proposed a novel and straightforward approach to improve the accuracy of progressive multiple protein sequence alignment method. We trained a decision-making model based on the convolutional neural networks and bi-directional long short term memory networks, and progressively aligned the input protein sequen... | ['Lufei Gao', 'Yong liu', 'Mengmeng Kuang'] | 2020-11-21 | null | null | null | null | ['protein-secondary-structure-prediction', 'multiple-sequence-alignment'] | ['medical', 'medical'] | [ 4.29847091e-01 -3.21805865e-01 -9.16881040e-02 -4.04626489e-01
-5.66333711e-01 -3.52982640e-01 5.82458526e-02 1.91557392e-01
-8.03405404e-01 1.48025334e+00 -2.78974652e-01 -5.56614876e-01
-1.07464947e-01 -4.21780676e-01 -9.98972476e-01 -9.63948071e-01
-2.63501465e-01 9.34833467e-01 2.73387223e-01 -3.12486917... | [4.732826232910156, 5.572817325592041] |
02da21ee-bbb7-4436-9ecc-81d4e0e0c150 | transppg-two-stream-transformer-for-remote | 2201.10873 | null | https://arxiv.org/abs/2201.10873v1 | https://arxiv.org/pdf/2201.10873v1.pdf | TransPPG: Two-stream Transformer for Remote Heart Rate Estimate | Non-contact facial video-based heart rate estimation using remote photoplethysmography (rPPG) has shown great potential in many applications (e.g., remote health care) and achieved creditable results in constrained scenarios. However, practical applications require results to be accurate even under complex environment ... | ['Weishan Zhang', 'Su Yang', 'Jiaqi Kang'] | 2022-01-26 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 3.31858397e-01 -1.63628295e-01 3.06687266e-01 -1.36808634e-01
-3.95899296e-01 -6.83893114e-02 1.63325861e-01 -5.37822962e-01
-8.09037462e-02 6.12463474e-01 4.15076524e-01 2.91413903e-01
2.29744628e-01 -4.93691862e-01 -3.78161907e-01 -1.28736317e+00
-1.34757701e-02 -4.40149635e-01 7.85833895e-02 -2.00039297... | [13.858447074890137, 2.6636757850646973] |
906dabf6-8985-41a1-8736-da5629c4d7cc | mmgp-a-mesh-morphing-gaussian-process-based | 2305.12871 | null | https://arxiv.org/abs/2305.12871v1 | https://arxiv.org/pdf/2305.12871v1.pdf | MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability | When learning simulations for modeling physical phenomena in industrial designs, geometrical variabilities are of prime interest. For parameterized geometries, classical regression techniques can be successfully employed. However, in practice, the shape parametrization is generally not available in the inference stage ... | ['Xavier Roynard', 'Brian Staber', 'Fabien Casenave'] | 2023-05-22 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-8.00285563e-02 1.37211621e-01 1.11613669e-01 -5.33397449e-03
-4.25553054e-01 -2.02675804e-01 6.38343513e-01 7.48546362e-01
-2.73072332e-01 9.87218022e-01 -7.49721289e-01 -3.11227262e-01
-7.23027706e-01 -1.06584024e+00 -1.00929689e+00 -8.61784875e-01
-3.58120464e-02 1.14339483e+00 -8.55668932e-02 -2.13043794... | [6.4481096267700195, 3.3029651641845703] |
760cf513-755e-4048-af6f-052dd72969d8 | is-attention-better-than-matrix-decomposition-1 | 2109.04553 | null | https://arxiv.org/abs/2109.04553v2 | https://arxiv.org/pdf/2109.04553v2.pdf | Is Attention Better Than Matrix Decomposition? | As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery. However, is hand-crafted attention irreplaceable when modeling the global context? Our intriguing finding is that self-attention is not better than the matrix decom... | ['Zhouchen Lin', 'Ke Wei', 'Xia Li', 'Hongxu Chen', 'Meng-Hao Guo', 'Zhengyang Geng'] | 2021-09-09 | is-attention-better-than-matrix-decomposition | https://openreview.net/forum?id=1FvkSpWosOl | https://openreview.net/pdf?id=1FvkSpWosOl | iclr-2021-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 8.42181519e-02 5.34381829e-02 -1.57624319e-01 -4.85849291e-01
-7.42747545e-01 -2.76415765e-01 6.14536345e-01 -9.88483131e-02
-5.00400603e-01 3.93327177e-01 7.15985537e-01 -1.72198445e-01
-4.35885042e-01 -5.23931623e-01 -8.99562359e-01 -9.52603936e-01
-4.79383906e-03 4.99112129e-01 -2.59120077e-01 -2.80169368... | [9.687402725219727, 0.606458306312561] |
26ccf15d-6a0d-48c6-ad7b-ee23b13f1561 | an-animated-picture-says-at-least-a-thousand | 2109.12212 | null | https://arxiv.org/abs/2109.12212v2 | https://arxiv.org/pdf/2109.12212v2.pdf | An animated picture says at least a thousand words: Selecting Gif-based Replies in Multimodal Dialog | Online conversations include more than just text. Increasingly, image-based responses such as memes and animated gifs serve as culturally recognized and often humorous responses in conversation. However, while NLP has broadened to multimodal models, conversational dialog systems have largely focused only on generating ... | ['David Jurgens', 'Xingyao Wang'] | 2021-09-24 | null | https://aclanthology.org/2021.findings-emnlp.276 | https://aclanthology.org/2021.findings-emnlp.276.pdf | findings-emnlp-2021-11 | ['multimodal-gif-dialog'] | ['natural-language-processing'] | [ 8.80625471e-02 3.15127015e-01 1.35988012e-01 -4.93902355e-01
-1.23547077e+00 -7.41996408e-01 9.87403214e-01 -2.00980023e-01
-1.76464781e-01 1.07316804e+00 1.25152445e+00 3.65731567e-02
3.93920302e-01 -4.53096002e-01 -2.80038834e-01 -2.70333469e-01
2.07152009e-01 9.94476616e-01 -2.58202821e-01 -7.56817043... | [12.847004890441895, 8.07721996307373] |
ab42fbe1-c389-45ad-8946-ec1d84605f97 | learning-to-refactor-action-and-co-occurrence-1 | 2206.11493 | null | https://arxiv.org/abs/2206.11493v1 | https://arxiv.org/pdf/2206.11493v1.pdf | Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization | The main challenge of Temporal Action Localization is to retrieve subtle human actions from various co-occurring ingredients, e.g., context and background, in an untrimmed video. While prior approaches have achieved substantial progress through devising advanced action detectors, they still suffer from these co-occurri... | ['Wei Tang', 'Nanning Zheng', 'Sanping Zhou', 'Le Wang', 'Kun Xia'] | 2022-06-23 | learning-to-refactor-action-and-co-occurrence | http://openaccess.thecvf.com//content/CVPR2022/html/Xia_Learning_To_Refactor_Action_and_Co-Occurrence_Features_for_Temporal_Action_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xia_Learning_To_Refactor_Action_and_Co-Occurrence_Features_for_Temporal_Action_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-localization'] | ['computer-vision'] | [ 6.21753931e-01 -4.27567333e-01 -4.95220274e-01 -1.31286308e-01
-5.47636211e-01 -5.10360599e-01 6.13367856e-01 -2.75429096e-02
-3.01118106e-01 4.57767785e-01 8.92346919e-01 3.17436486e-01
1.51476875e-01 -3.41308445e-01 -4.85470593e-01 -7.46198535e-01
-6.20307624e-02 -5.12815535e-01 6.39242291e-01 4.82679084... | [8.42738151550293, 0.6272853016853333] |
4520e430-d3f6-447c-a75e-1963c9fe068a | augmenting-training-data-for-massive-semantic | null | null | https://aclanthology.org/2022.naacl-industry.19 | https://aclanthology.org/2022.naacl-industry.19.pdf | Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores | Extreme multi-label classification (XMC) systems have been successfully applied in e-commerce (Shen et al., 2020; Dahiya et al., 2021) for retrieving products based on customer behavior. Such systems require large amounts of customer behavior data (e.g. queries, clicks, purchases) for training. However, behavioral data... | ['Jonathan May', 'Rahul Bhagat', 'Vishy Vishwanathan', 'Vaclav Petricek', 'Choon Teo', 'Shankar Vishwanath', 'Ashutosh Joshi'] | null | null | null | null | naacl-acl-2022-7 | ['extreme-multi-label-classification'] | ['methodology'] | [-5.21901138e-02 -3.47809821e-01 -3.73742938e-01 -9.40745056e-01
-1.02894175e+00 -8.65558803e-01 4.48287457e-01 6.15294099e-01
-5.67343652e-01 2.69147336e-01 -1.92743316e-01 -3.73246759e-01
-1.90562859e-01 -8.11598122e-01 -8.16375017e-01 -1.04563043e-01
2.38736928e-01 1.03673804e+00 1.57882199e-01 -4.88181740... | [9.605263710021973, 4.519785404205322] |
30664257-3da6-48fc-8f47-2d8a02b6f199 | group-activity-prediction-with-sequential | 2008.02441 | null | https://arxiv.org/abs/2008.02441v1 | https://arxiv.org/pdf/2008.02441v1.pdf | Group Activity Prediction with Sequential Relational Anticipation Model | In this paper, we propose a novel approach to predict group activities given the beginning frames with incomplete activity executions. Existing action prediction approaches learn to enhance the representation power of the partial observation. However, for group activity prediction, the relation evolution of people's ac... | ['Yu Kong', 'Wentao Bao', 'Junwen Chen'] | 2020-08-06 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3803_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660579.pdf | eccv-2020-8 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 3.14732373e-01 2.66798586e-01 -8.59574437e-01 -4.04686540e-01
-1.17619962e-01 -1.11050397e-01 1.03090489e+00 4.53671068e-01
-1.17438473e-02 6.44172132e-01 9.38017964e-01 1.12666138e-01
-1.73096150e-01 -8.18744957e-01 -4.87073123e-01 -3.49885643e-01
-6.96730733e-01 3.01310807e-01 4.01583731e-01 -7.99255967... | [8.121192932128906, 0.5860447287559509] |
02e30c37-caf1-4fa4-88cc-e2b8ac974562 | skill-decision-transformer | 2301.13573 | null | https://arxiv.org/abs/2301.13573v1 | https://arxiv.org/pdf/2301.13573v1.pdf | Skill Decision Transformer | Recent work has shown that Large Language Models (LLMs) can be incredibly effective for offline reinforcement learning (RL) by representing the traditional RL problem as a sequence modelling problem (Chen et al., 2021; Janner et al., 2021). However many of these methods only optimize for high returns, and may not extra... | ['Sebastian Risi', 'Shyam Sudhakaran'] | 2023-01-31 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.85899988e-01 -1.65787395e-02 -6.23130739e-01 -4.91379797e-02
-1.05331767e+00 -9.31729376e-01 8.71112823e-01 -1.82707012e-01
-5.55407047e-01 8.15987766e-01 3.75982225e-01 -3.60276312e-01
-2.79105455e-01 -3.88285220e-01 -7.10298598e-01 -5.08286119e-01
-3.64784658e-01 6.74625337e-01 2.97613256e-02 -1.98471949... | [4.121525287628174, 1.756835699081421] |
c01ec839-9e8d-45cb-9d1b-c6b7fac1d68e | accurate-2d-soft-segmentation-of-medical | 2007.14556 | null | https://arxiv.org/abs/2007.14556v3 | https://arxiv.org/pdf/2007.14556v3.pdf | Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision | Accurate lung lesion segmentation from Computed Tomography (CT) images is crucial to the analysis and diagnosis of lung diseases such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeling make the accurate lung nodule segmentation difficult. To address the... | ['Xiaopeng Zhang', 'Jun Xiao', 'Weiliang Meng', 'Rongtao Xu', 'Changwei Wang', 'Shibiao Xu'] | 2020-07-29 | null | null | null | null | ['lung-nodule-segmentation'] | ['medical'] | [ 3.22868794e-01 2.54736960e-01 -3.83136779e-01 -3.24662149e-01
-8.31768513e-01 -2.61421710e-01 1.26682967e-01 -6.01317823e-01
-2.33604982e-01 4.90694553e-01 -1.01101547e-01 -4.92432147e-01
2.02013418e-01 -7.64640033e-01 -4.68750060e-01 -7.43125558e-01
4.04640019e-01 6.76028252e-01 1.13814962e+00 2.90149629... | [15.32339859008789, -2.0741524696350098] |
c206ea4a-70bb-486b-bfb7-4c27a39fa367 | multi-view-photometric-stereo-revisited | 2210.07670 | null | https://arxiv.org/abs/2210.07670v1 | https://arxiv.org/pdf/2210.07670v1.pdf | Multi-View Photometric Stereo Revisited | Multi-view photometric stereo (MVPS) is a preferred method for detailed and precise 3D acquisition of an object from images. Although popular methods for MVPS can provide outstanding results, they are often complex to execute and limited to isotropic material objects. To address such limitations, we present a simple, p... | ['Luc van Gool', 'Vittorio Ferrari', 'Carlos Oliveira', 'Suryansh Kumar', 'Berk Kaya'] | 2022-10-14 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 2.91555643e-01 -4.05287892e-02 5.38430095e-01 -4.20846105e-01
-6.31824672e-01 -5.42948917e-02 6.82540476e-01 -9.13321823e-02
-2.61243999e-01 8.09875846e-01 -3.21134955e-01 -4.03047092e-02
-2.13241100e-01 -9.39021349e-01 -9.68921602e-01 -8.77661526e-01
2.76523024e-01 6.97573066e-01 4.20413971e-01 -2.32875571... | [9.024271011352539, -3.2841477394104004] |
584f3d33-9317-4349-a2ae-d83139a46e7c | transparent-interpretation-with-knockouts | 2011.00639 | null | https://arxiv.org/abs/2011.00639v3 | https://arxiv.org/pdf/2011.00639v3.pdf | Model-Agnostic Explanations using Minimal Forcing Subsets | How can we find a subset of training samples that are most responsible for a specific prediction made by a complex black-box machine learning model? More generally, how can we explain the model's decisions to end-users in a transparent way? We propose a new model-agnostic algorithm to identify a minimal set of training... | ['Joydeep Ghosh', 'Xing Han'] | 2020-11-01 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 1.37821525e-01 7.34703913e-02 -5.08626342e-01 -6.28326893e-01
-2.28647903e-01 -4.40746307e-01 5.57476059e-02 4.44477916e-01
-3.93031612e-02 5.40247440e-01 -4.30169284e-01 -8.51993442e-01
-2.09634662e-01 -6.63040102e-01 -8.21126759e-01 -4.47170168e-01
1.78269953e-01 6.73392951e-01 -2.00442765e-02 3.41605060... | [8.790215492248535, 5.7928948402404785] |
6f47603f-10e9-4453-a2d7-844e83196628 | fitness-for-duty-classification-using | 2304.11858 | null | https://arxiv.org/abs/2304.11858v1 | https://arxiv.org/pdf/2304.11858v1.pdf | Fitness-for-Duty Classification using Temporal Sequences of Iris Periocular images | Fitness for Duty (FFD) techniques detects whether a subject is Fit to perform their work safely, which means no reduced alertness condition and security, or if they are Unfit, which means alertness condition reduced by sleepiness or consumption of alcohol and drugs. Human iris behaviour provides valuable information to... | ['Juan E. Tapia', 'Daniel P. Benalcazar', 'Pamela C. Zurita'] | 2023-04-24 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [ 1.37267590e-01 -2.83898145e-01 -3.63925666e-01 -3.09223592e-01
6.03605449e-01 -2.28255615e-01 2.24604502e-01 1.70736089e-02
-3.80312651e-01 6.63087726e-01 -1.38037214e-02 -2.16393426e-01
-4.40401018e-01 -3.47313285e-01 -1.61990058e-02 -8.83924901e-01
-4.21006568e-02 -1.13645129e-01 -4.11183417e-01 8.42031091... | [3.762969493865967, -3.616880178451538] |
11042fae-80d4-4863-8345-5574e91b4240 | nonlinear-unmixing-of-hyperspectral-images | 1310.8612 | null | http://arxiv.org/abs/1310.8612v1 | http://arxiv.org/pdf/1310.8612v1.pdf | Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization | Incorporating spatial information into hyperspectral unmixing procedures has
been shown to have positive effects, due to the inherent spatial-spectral
duality in hyperspectral scenes. Current research works that consider spatial
information are mainly focused on the linear mixing model. In this paper, we
investigate a ... | ['Alfred O. Hero III', 'Cédric Richard', 'Jie Chen'] | 2013-10-31 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.32267570e-01 -4.91748720e-01 -1.11647040e-01 -1.64303526e-01
-6.29073858e-01 -4.49759722e-01 4.22078878e-01 -6.34525478e-01
-1.37756586e-01 9.51085389e-01 9.53951925e-02 -1.18785605e-01
-5.54109216e-01 -4.56222802e-01 -2.79876977e-01 -1.28324902e+00
1.21111684e-01 -2.29501456e-01 -5.72697341e-01 1.67649850... | [10.07553768157959, -2.0440587997436523] |
37a706ba-9e90-48da-b238-a310ba5add63 | focused-meeting-summarization-via | 1606.07849 | null | http://arxiv.org/abs/1606.07849v1 | http://arxiv.org/pdf/1606.07849v1.pdf | Focused Meeting Summarization via Unsupervised Relation Extraction | We present a novel unsupervised framework for focused meeting summarization
that views the problem as an instance of relation extraction. We adapt an
existing in-domain relation learner (Chen et al., 2011) by exploiting a set of
task-specific constraints and features. We evaluate the approach on a decision
summarizatio... | ['Lu Wang', 'Claire Cardie'] | 2016-06-24 | focused-meeting-summarization-via-1 | https://aclanthology.org/W12-1642 | https://aclanthology.org/W12-1642.pdf | ws-2012-7 | ['meeting-summarization'] | ['natural-language-processing'] | [ 8.19777429e-01 1.04782486e+00 -6.05078697e-01 -3.13112140e-01
-1.45156813e+00 -5.60928822e-01 1.02370763e+00 9.49905515e-01
-3.23020071e-01 1.06131244e+00 1.30829096e+00 -4.65758517e-02
-1.19397156e-01 -5.12981474e-01 -3.99354428e-01 -1.05385438e-01
-5.24155013e-02 8.00154507e-01 2.15629995e-01 -5.27370095... | [12.55440616607666, 9.537837982177734] |
9941b45c-3a54-475e-94fe-4707fbf14fc2 | selective-sparse-sampling-for-fine-grained | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Ding_Selective_Sparse_Sampling_for_Fine-Grained_Image_Recognition_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Ding_Selective_Sparse_Sampling_for_Fine-Grained_Image_Recognition_ICCV_2019_paper.pdf | Selective Sparse Sampling for Fine-Grained Image Recognition | Fine-grained recognition poses the unique challenge of capturing subtle inter-class differences under considerable intra-class variances (e.g., beaks for bird species). Conventional approaches crop local regions and learn detailed representation from those regions, but suffer from the fixed number of parts and missing ... | [' Jianbin Jiao', ' Qixiang Ye', ' Yi Zhu', ' Yanzhao Zhou', 'Yao Ding'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 3.56600732e-01 -3.57907712e-01 -5.29363692e-01 -6.18955135e-01
-7.69419372e-01 -5.13448238e-01 4.41486657e-01 -3.10448229e-01
7.79941585e-03 6.92321122e-01 4.79865640e-01 4.22399104e-01
-1.18702702e-01 -5.05780756e-01 -9.67755020e-01 -7.59456635e-01
2.01051962e-02 -5.82178961e-03 3.70105118e-01 -1.75873786... | [9.612882614135742, 1.9946829080581665] |
48c089f9-0b77-46fb-a419-d792e6f3ec39 | generalizable-synthetic-image-detection-via | 2305.13800 | null | https://arxiv.org/abs/2305.13800v1 | https://arxiv.org/pdf/2305.13800v1.pdf | Generalizable Synthetic Image Detection via Language-guided Contrastive Learning | The heightened realism of AI-generated images can be attributed to the rapid development of synthetic models, including generative adversarial networks (GANs) and diffusion models (DMs). The malevolent use of synthetic images, such as the dissemination of fake news or the creation of fake profiles, however, raises sign... | ['Shile Zhang', 'Jiantao Zhou', 'Haiwei Wu'] | 2023-05-23 | null | null | null | null | ['synthetic-image-detection'] | ['computer-vision'] | [ 4.98068094e-01 -6.56482130e-02 -4.42753509e-02 -6.45944178e-02
-1.06589878e+00 -6.45773709e-01 1.03999531e+00 -3.02659035e-01
-2.57461369e-01 6.80963933e-01 -1.79139569e-01 -3.49511087e-01
3.64093184e-01 -6.56290829e-01 -7.44896948e-01 -6.72348678e-01
2.83133715e-01 2.52536327e-01 8.72675851e-02 -2.29774803... | [12.446094512939453, 1.0804506540298462] |
fa1184a1-0a9e-4e81-8f7d-3d59adc17b62 | autoencoders-as-cross-modal-teachers-can | 2212.08320 | null | https://arxiv.org/abs/2212.08320v2 | https://arxiv.org/pdf/2212.08320v2.pdf | Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning? | The success of deep learning heavily relies on large-scale data with comprehensive labels, which is more expensive and time-consuming to fetch in 3D compared to 2D images or natural languages. This promotes the potential of utilizing models pretrained with data more than 3D as teachers for cross-modal knowledge transfe... | ['Kaisheng Ma', 'Li Yi', 'Zheng Ge', 'Jianjian Sun', 'Junbo Zhang', 'Linfeng Zhang', 'Zekun Qi', 'Runpei Dong'] | 2022-12-16 | null | null | null | null | ['3d-point-cloud-classification', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-2.06263870e-01 5.42877197e-01 -3.03800195e-01 -4.34047937e-01
-7.93606758e-01 -9.06291425e-01 5.29837787e-01 -1.97834074e-01
-2.32150584e-01 2.46063054e-01 5.61074689e-02 -4.99896228e-01
9.92618352e-02 -1.20540440e+00 -1.41171384e+00 -5.94864309e-01
2.98838347e-01 6.87497735e-01 2.32192740e-01 -2.78763354... | [8.126980781555176, -3.3590643405914307] |
5a539e04-abf2-489f-bb1a-406961216c9b | generating-informative-and-diverse | 1809.05972 | null | http://arxiv.org/abs/1809.05972v5 | http://arxiv.org/pdf/1809.05972v5.pdf | Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization | Responses generated by neural conversational models tend to lack
informativeness and diversity. We present Adversarial Information Maximization
(AIM), an adversarial learning strategy that addresses these two related but
distinct problems. To foster response diversity, we leverage adversarial
training that allows distr... | ['Xiujun Li', 'Chris Brockett', 'Yizhe Zhang', 'Jianfeng Gao', 'Zhe Gan', 'Michel Galley', 'Bill Dolan'] | 2018-09-16 | generating-informative-and-diverse-1 | http://papers.nips.cc/paper/7452-generating-informative-and-diverse-conversational-responses-via-adversarial-information-maximization | http://papers.nips.cc/paper/7452-generating-informative-and-diverse-conversational-responses-via-adversarial-information-maximization.pdf | neurips-2018-12 | ['conversational-response-generation'] | ['natural-language-processing'] | [ 3.06746989e-01 2.65209526e-01 -1.29668266e-02 -8.23479354e-01
-1.41402447e+00 -8.85631919e-01 9.15135682e-01 -2.51990050e-01
-4.17831033e-01 9.80121672e-01 7.58707225e-01 1.15934327e-01
1.71247527e-01 -8.51226449e-01 -4.60154802e-01 -3.62747431e-01
5.97383939e-02 6.32539213e-01 -1.81564063e-01 -6.10446036... | [12.741774559020996, 8.151371002197266] |
4344736f-0d7a-4130-8e8b-0f3cfd2efc8c | video-highlights-detection-and-summarization | null | null | https://aclanthology.org/W17-4501 | https://aclanthology.org/W17-4501.pdf | Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowdsourced Time-Sync Comments | With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection. Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection. However, this task is non-trivial: (1) time-sync comm... | ['Chaomei Chen', 'Qing Ping'] | 2017-09-01 | null | null | null | ws-2017-9 | ['highlight-detection'] | ['computer-vision'] | [ 3.14665250e-02 -5.13218224e-01 -4.10650790e-01 1.27346188e-01
-8.96465719e-01 -6.94862366e-01 5.75325012e-01 7.59401798e-01
-2.91619986e-01 5.81355453e-01 7.23691583e-01 4.07578051e-01
3.38344783e-01 -3.26882571e-01 -3.80737305e-01 -3.20983797e-01
-2.08938941e-01 -1.34341255e-01 6.64095044e-01 -1.31268814... | [10.150444030761719, 0.4545128345489502] |
f86237a8-c98e-4def-9b35-b39efefea691 | fine-grained-hand-gesture-recognition-in | 2109.02917 | null | https://arxiv.org/abs/2109.02917v1 | https://arxiv.org/pdf/2109.02917v1.pdf | Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene | This paper contributes a new high-quality dataset for hand gesture recognition in hand hygiene systems, named "MFH". Generally, current datasets are not focused on: (i) fine-grained actions; and (ii) data mismatch between different viewpoints, which are available under realistic settings. To address the aforementioned ... | ['Quang D. Tran', 'An T. Duong', 'Duy Nguyen', 'Vi C. Pham', 'Tuong Do', 'Huy Q. Vo'] | 2021-09-07 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 1.48424149e-01 -4.79804099e-01 -5.26017308e-01 -2.88879663e-01
-9.43319798e-01 -3.78480047e-01 4.44741368e-01 -4.63804752e-01
-3.94892186e-01 7.50124335e-01 3.86399537e-01 1.42441079e-01
-1.49568081e-01 -4.10976440e-01 -3.56415361e-01 -1.12822104e+00
2.03010663e-01 5.13712168e-01 2.19094783e-01 3.16639155... | [6.674374103546143, -0.23655816912651062] |
dde5c71b-a8ec-4c23-a8f5-ec3bdc0ea4f5 | data-fusion-on-motion-and-magnetic-sensors | 1711.07328 | null | http://arxiv.org/abs/1711.07328v1 | http://arxiv.org/pdf/1711.07328v1.pdf | Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living | Several types of sensors have been available in off-the-shelf mobile devices,
including motion, magnetic, vision, acoustic, and location sensors. This paper
focuses on the fusion of the data acquired from motion and magnetic sensors,
i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of
Activi... | ['Francisco Flórez-Revuelta', 'Ivan Miguel Pires', 'Susanna Spinsante', 'Nuno Pombo', 'Nuno M. Garcia'] | 2017-10-31 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.03241645e-01 -3.13758999e-01 -2.05981925e-01 -2.32823014e-01
1.39846774e-02 2.49056801e-01 2.91627228e-01 1.06145978e-01
-5.86652339e-01 7.48184800e-01 3.54225904e-01 2.73435544e-02
-3.38953108e-01 -7.23895371e-01 -2.39310190e-01 -6.99154258e-01
1.94514945e-01 4.55675237e-02 -2.99012065e-01 4.72581275... | [7.289506912231445, 0.6319808959960938] |
85d0b1a7-37dd-4c17-8341-bb49a1b71559 | is-multi-modal-vision-supervision-beneficial | 2302.05016 | null | https://arxiv.org/abs/2302.05016v2 | https://arxiv.org/pdf/2302.05016v2.pdf | Is Multimodal Vision Supervision Beneficial to Language? | Vision (image and video) - Language (VL) pre-training is the recent popular paradigm that achieved state-of-the-art results on multi-modal tasks like image-retrieval, video-retrieval, visual question answering etc. These models are trained in an unsupervised way and greatly benefit from the complementary modality super... | ['Vasudev Lal', 'Avinash Madasu'] | 2023-02-10 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [ 4.04739715e-02 4.14712839e-02 -2.80270368e-01 -4.82740372e-01
-4.86927241e-01 -3.79221439e-01 1.35171187e+00 -2.48195119e-02
-5.44523835e-01 5.58542788e-01 2.94072211e-01 -5.44135749e-01
3.32185954e-01 -5.27160287e-01 -1.10973072e+00 -3.86130571e-01
3.15893918e-01 5.10996580e-01 3.74681987e-02 -3.49064171... | [10.765305519104004, 1.7296489477157593] |
787198ee-8420-4cdc-80c5-e9d2682bfa6f | relate-physically-plausible-multi-object | 2007.01272 | null | https://arxiv.org/abs/2007.01272v2 | https://arxiv.org/pdf/2007.01272v2.pdf | RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces | We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects. Similar to other generative approaches, RELATE is trained end-to-end on raw, unlabeled data. RELATE combines an object-centric GAN formulation with a model that explicitly accounts for correlations... | ['Aron Monszpart', 'Sebastien Ehrhardt', 'Niloy Mitra', 'Martin Engelcke', 'Ingmar Posner', 'Andrea Vedaldi', 'Oliver Groth'] | 2020-07-02 | null | http://proceedings.neurips.cc/paper/2020/hash/806beafe154032a5b818e97b4420ad98-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/806beafe154032a5b818e97b4420ad98-Paper.pdf | neurips-2020-12 | ['scene-generation'] | ['computer-vision'] | [ 1.62914142e-01 2.37580433e-01 3.62718225e-01 -2.89867222e-01
-7.76601315e-01 -7.44773984e-01 9.61122394e-01 -3.78322661e-01
2.81693846e-01 8.30572069e-01 3.93332750e-01 1.07539922e-01
-3.63550670e-02 -7.77789533e-01 -1.18559015e+00 -4.66227442e-01
2.46792007e-02 8.97427976e-01 7.35923424e-02 -6.84333593... | [9.21086597442627, -3.003009080886841] |
40d44154-049c-4361-81ec-fa080bd2fca7 | robust-probabilistic-time-series-forecasting | 2202.11910 | null | https://arxiv.org/abs/2202.11910v1 | https://arxiv.org/pdf/2202.11910v1.pdf | Robust Probabilistic Time Series Forecasting | Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could be prone to input perturbations, and the notion of such perturbations, together with that of robustness, has not even been completely estab... | ['Yuyang Wang', 'Ernest K. Ryu', 'Youngsuk Park', 'Taeho Yoon'] | 2022-02-24 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [ 1.23467371e-01 6.59950599e-02 4.66548741e-01 -2.91415304e-01
-8.92392278e-01 -1.03022742e+00 8.02461326e-01 -2.39289571e-02
2.21837536e-01 6.67886794e-01 3.47527862e-01 -5.21130323e-01
-3.41264009e-01 -9.51141000e-01 -8.50477993e-01 -1.16908371e+00
-3.17409813e-01 2.20467206e-02 2.06940070e-01 -3.16093355... | [6.862733364105225, 3.350553274154663] |
c2266741-0eb2-4ac0-8c63-80124da13ad5 | adaptive-quantum-state-tomography-with-neural | 1812.06693 | null | http://arxiv.org/abs/1812.06693v1 | http://arxiv.org/pdf/1812.06693v1.pdf | Adaptive Quantum State Tomography with Neural Networks | Quantum State Tomography is the task of determining an unknown quantum state
by making measurements on identical copies of the state. Current algorithms are
costly both on the experimental front -- requiring vast numbers of measurements
-- as well as in terms of the computational time to analyze those measurements.
In ... | ['Yihui Quek', 'Stanislav Fort', 'Hui Khoon Ng'] | 2018-12-17 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 5.30850828e-01 3.38685326e-02 2.60505259e-01 -2.19192713e-01
-1.26176214e+00 -5.32201111e-01 3.03848058e-01 9.60378535e-03
-9.55271602e-01 1.02917135e+00 -2.87304491e-01 -7.35951364e-01
-3.33466679e-01 -1.18030894e+00 -7.59717584e-01 -9.94782150e-01
-4.68002893e-02 1.07116532e+00 9.61853191e-02 -2.67244637... | [5.62322998046875, 4.89316987991333] |
4ba94737-b6d8-4ee7-acbe-d3fa74b81b3d | efficient-convolutional-neural-networks-for-4 | 2001.04537 | null | https://arxiv.org/abs/2001.04537v3 | https://arxiv.org/pdf/2001.04537v3.pdf | Deep convolutional neural networks for multi-planar lung nodule detection: improvement in small nodule identification | Objective: In clinical practice, small lung nodules can be easily overlooked by radiologists. The paper aims to provide an efficient and accurate detection system for small lung nodules while keeping good performance for large nodules. Methods: We propose a multi-planar detection system using convolutional neural netwo... | ['Raymond N. J. Veldhuis', 'Xiaonan Cui', 'Xueping Jing', 'Sunyi Zheng', 'Peter M. A. van Ooijen', 'Matthijs Oudkerk', 'Ludo J. Cornelissen'] | 2020-01-13 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 3.96624953e-02 4.61457282e-01 -3.12944263e-01 2.10742354e-01
-9.46863532e-01 -4.39689308e-01 1.47459686e-01 -1.13765113e-01
-3.98640633e-01 1.54801711e-01 -2.04319119e-01 -6.26880407e-01
-5.98790310e-02 -9.68629897e-01 -4.43389475e-01 -6.49842322e-01
-9.47702006e-02 8.11897635e-01 9.62056577e-01 3.33836555... | [15.434067726135254, -2.118642807006836] |
e246eeaf-e3e3-421e-b748-1767f9b398c1 | space-invariant-projection-in-streaming | 2303.06293 | null | https://arxiv.org/abs/2303.06293v1 | https://arxiv.org/pdf/2303.06293v1.pdf | Space-Invariant Projection in Streaming Network Embedding | Newly arriving nodes in dynamics networks would gradually make the node embedding space drifted and the retraining of node embedding and downstream models indispensable. An exact threshold size of these new nodes, below which the node embedding space will be predicatively maintained, however, is rarely considered in ei... | ['Jichang Zhao', 'Huiwen Wang', 'Yanwen Zhang'] | 2023-03-11 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-4.80143055e-02 3.83370101e-01 -1.63612336e-01 5.52481301e-02
1.52526543e-01 -6.46553099e-01 5.49785912e-01 -8.85709096e-03
-4.22096193e-01 6.38669133e-01 -1.13403931e-01 -4.94294614e-01
-3.78027081e-01 -9.39956725e-01 -4.12872046e-01 -1.07968855e+00
-7.37156868e-01 3.33454520e-01 4.53238010e-01 -3.64844710... | [7.180582523345947, 6.073592662811279] |
c65a5f3d-a92e-4643-9c34-c4d0cfb167fb | deepfh-segmentations-for-superpixel-based | 2108.03503 | null | https://arxiv.org/abs/2108.03503v1 | https://arxiv.org/pdf/2108.03503v1.pdf | DeepFH Segmentations for Superpixel-based Object Proposal Refinement | Class-agnostic object proposal generation is an important first step in many object detection pipelines. However, object proposals of modern systems are rather inaccurate in terms of segmentation and only roughly adhere to object boundaries. Since typical refinement steps are usually not applicable to thousands of prop... | ['Simone Frintrop', 'Christian Wilms'] | 2021-08-07 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 5.38927242e-02 3.78878206e-01 -5.94146885e-02 -6.57719076e-01
-8.56023788e-01 -4.49463904e-01 5.46034098e-01 2.97620565e-01
-5.99814296e-01 6.18361712e-01 -2.70076334e-01 1.89244598e-01
3.36187899e-01 -7.81348228e-01 -8.00775111e-01 -2.46089160e-01
4.01221842e-01 8.92194033e-01 1.19789267e+00 -8.04370921... | [9.415509223937988, 0.5669470429420471] |
ee42c4f5-1ae7-400e-8fa8-da2ecc8a0cf6 | efficient-speech-translation-with-dynamic | 2210.16264 | null | https://arxiv.org/abs/2210.16264v2 | https://arxiv.org/pdf/2210.16264v2.pdf | Efficient Speech Translation with Dynamic Latent Perceivers | Transformers have been the dominant architecture for Speech Translation in recent years, achieving significant improvements in translation quality. Since speech signals are longer than their textual counterparts, and due to the quadratic complexity of the Transformer, a down-sampling step is essential for its adoption ... | ['Marta R. Costa-jussà', 'José A. R. Fonollosa', 'Gerard I. Gállego', 'Ioannis Tsiamas'] | 2022-10-28 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.96080202e-01 3.67540658e-01 -1.90801948e-01 -4.28129733e-01
-1.28850770e+00 -8.37830245e-01 5.46840250e-01 -1.75581545e-01
-1.63733929e-01 3.04404795e-01 6.02921605e-01 -8.10076416e-01
4.57163990e-01 -5.98965764e-01 -7.64458001e-01 -3.54546726e-01
2.70155281e-01 5.36864340e-01 -1.38977692e-01 -1.46494567... | [14.53664779663086, 7.144913673400879] |
e75da0e5-a3d0-4f50-bffb-c7798e246526 | sepit-approaching-a-single-channel-speech | 2205.11801 | null | https://arxiv.org/abs/2205.11801v4 | https://arxiv.org/pdf/2205.11801v4.pdf | SepIt: Approaching a Single Channel Speech Separation Bound | We present an upper bound for the Single Channel Speech Separation task, which is based on an assumption regarding the nature of short segments of speech. Using the bound, we are able to show that while the recent methods have made significant progress for a few speakers, there is room for improvement for five and ten ... | ['Lior Wolf', 'Eliya Nachmani', 'Shahar Lutati'] | 2022-05-24 | null | null | null | null | ['audio-source-separation', 'speech-separation', 'multi-speaker-source-separation'] | ['audio', 'speech', 'speech'] | [ 1.51777223e-01 2.75203526e-01 -1.60541430e-01 -4.02749658e-01
-1.22800839e+00 -5.41593194e-01 4.23608333e-01 -1.59297600e-01
-3.02500963e-01 6.10251784e-01 1.95778877e-01 -7.29559839e-01
-6.93811625e-02 -1.27020935e-02 -3.95530999e-01 -5.33022821e-01
-4.23812181e-01 5.76070607e-01 1.99039623e-01 -1.80222124... | [14.674079895019531, 6.215672016143799] |
a0908b59-fd03-49b0-8e5e-9438d2fe8295 | stacked-dense-u-nets-with-dual-transformers | 1812.01936 | null | http://arxiv.org/abs/1812.01936v1 | http://arxiv.org/pdf/1812.01936v1.pdf | Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment | Face Analysis Project on MXNet | ['Stefanos Zafeiriou', 'Niannan Xue', 'Jia Guo', 'Jiankang Deng'] | 2018-12-05 | null | null | null | null | ['robust-face-alignment', 'robust-face-recognition'] | ['computer-vision', 'computer-vision'] | [-3.57896090e-01 2.76142210e-02 6.74940765e-01 -7.16409683e-01
6.43501878e-01 -1.34319037e-01 4.40783858e-01 -1.29846811e+00
-4.78241593e-02 4.37579215e-01 -2.92879701e-01 -6.16958737e-01
5.86471893e-02 -1.01015711e+00 -5.85514866e-02 -5.25837362e-01
-5.61892271e-01 2.29362249e-01 -2.22789705e-01 9.11648124... | [13.35816764831543, 0.7489392161369324] |
e2588e2b-1407-480e-977f-859828e25329 | joint-face-completion-and-super-resolution | 2003.00255 | null | https://arxiv.org/abs/2003.00255v2 | https://arxiv.org/pdf/2003.00255v2.pdf | Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning | Previous research on face restoration often focused on repairing a specific type of low-quality facial images such as low-resolution (LR) or occluded facial images. However, in the real world, both the above-mentioned forms of image degradation often coexist. Therefore, it is important to design a model that can repair... | ['Zhilei Liu', 'Baoyuan Wu', 'Le Li', 'Cuicui Zhang', 'Yunpeng Wu'] | 2020-02-29 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 2.91496247e-01 1.04515053e-01 3.53684157e-01 -3.92416090e-01
-7.70222187e-01 -1.15602642e-01 2.25734472e-01 -9.97281671e-01
3.02851498e-01 7.80813158e-01 2.94551462e-01 1.09021649e-01
9.32151079e-02 -1.11590755e+00 -9.35038090e-01 -7.78844595e-01
2.46493921e-01 -1.53970551e-02 -1.74108699e-01 -3.10621828... | [12.812390327453613, -0.039651162922382355] |
18d34dc9-390c-4cce-9686-825d28838b08 | global-inference-for-bridging-anaphora | null | null | https://aclanthology.org/N13-1111 | https://aclanthology.org/N13-1111.pdf | Global Inference for Bridging Anaphora Resolution | null | ['Yufang Hou', 'Katja Markert', 'Michael Strube'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['bridging-anaphora-resolution'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.362463474273682, 3.77134370803833] |
c81267aa-efb4-4a50-b095-9ce2d97faee1 | infinitenature-zero-learning-perpetual-view | 2207.11148 | null | https://arxiv.org/abs/2207.11148v1 | https://arxiv.org/pdf/2207.11148v1.pdf | InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images | We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view ... | ['Angjoo Kanazawa', 'Noah Snavely', 'Qianqian Wang', 'Zhengqi Li'] | 2022-07-22 | null | null | null | null | ['perpetual-view-generation'] | ['computer-vision'] | [ 4.41942424e-01 1.99591517e-01 1.68298259e-01 -1.47104368e-01
-7.79055834e-01 -1.15995145e+00 8.65070939e-01 -6.85823143e-01
6.00908883e-02 7.07783878e-01 2.24423394e-01 -5.65133430e-02
3.82394820e-01 -7.25290000e-01 -1.18574917e+00 -4.62460071e-01
1.56380638e-01 5.19223869e-01 3.44370782e-01 4.18173559... | [9.462883949279785, -2.552820920944214] |
aa445478-749b-41ea-b83b-7e8bdbdf4ac5 | shadownav-crater-based-localization-for | 2301.04630 | null | https://arxiv.org/abs/2301.04630v1 | https://arxiv.org/pdf/2301.04630v1.pdf | ShadowNav: Crater-Based Localization for Nighttime and Permanently Shadowed Region Lunar Navigation | There has been an increase in interest in missions that drive significantly longer distances per day than what has currently been performed. Further, some of these proposed missions require autonomous driving and absolute localization in darkness. For example, the Endurance A mission proposes to drive 1200km of its tot... | ['Deegan Atha', 'Larry Matthies', 'John Elliott', 'Shreyansh Daftry', 'Hiro Ono', 'R. Michael Swan', 'Abhishek Cauligi'] | 2023-01-11 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [-5.57703227e-02 -2.84666151e-01 1.52862687e-02 -3.81550133e-01
-6.09424412e-01 -9.33764756e-01 6.73798800e-01 -2.22795665e-01
-7.10113943e-01 7.00399280e-01 -4.12542105e-01 -3.84840518e-01
-1.04397699e-01 -7.53862977e-01 -5.36172330e-01 -3.86231780e-01
-3.82822186e-01 6.96148753e-01 4.42178339e-01 -7.30171382... | [7.326161861419678, -1.8995832204818726] |
75fc5fa2-1624-4269-bc94-85402f73f76a | stranding-risk-for-underactuated-vessels-in | 2307.01917 | null | https://arxiv.org/abs/2307.01917v1 | https://arxiv.org/pdf/2307.01917v1.pdf | Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers | Low-propulsion vessels can take advantage of powerful ocean currents to navigate towards a destination. Recent results demonstrated that vessels can reach their destination with high probability despite forecast errors. However, these results do not consider the critical aspect of safety of such vessels: because of the... | ['Claire J. Tomlin', 'Pierre F. J. Lermusiaux', 'Manan Doshi', 'Hanna Krasowski', 'Marius Wiggert', 'Andreas Doering'] | 2023-07-04 | null | null | null | null | ['navigate'] | ['reasoning'] | [-3.52977961e-01 3.46678376e-01 2.19153240e-01 1.13967188e-01
-6.69652104e-01 -1.14850903e+00 4.47085649e-01 1.89461857e-01
-5.31282604e-01 9.51837182e-01 7.59268329e-02 -8.77512038e-01
-4.60348457e-01 -1.02077889e+00 -8.28634620e-01 -6.74686134e-01
-8.60478103e-01 1.53649807e-01 4.14225161e-01 -7.56044567... | [4.963461875915527, 1.8788238763809204] |
480f5e4d-d98a-4b96-9b75-8d03aaf281dd | lic-gan-language-information-conditioned | 2306.01937 | null | https://arxiv.org/abs/2306.01937v1 | https://arxiv.org/pdf/2306.01937v1.pdf | LIC-GAN: Language Information Conditioned Graph Generative GAN Model | Deep generative models for Natural Language data offer a new angle on the problem of graph synthesis: by optimizing differentiable models that directly generate graphs, it is possible to side-step expensive search procedures in the discrete and vast space of possible graphs. We introduce LIC-GAN, an implicit, likelihoo... | ['Abishek Sridhar', 'Arnhav Datar', 'Robert Lo'] | 2023-06-02 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 7.99940005e-02 7.05617905e-01 4.12976630e-02 -2.20574901e-01
-1.06096971e+00 -9.43643153e-01 8.27223063e-01 -1.30167902e-01
-1.40046244e-02 8.53295445e-01 9.42918956e-02 -4.36494261e-01
5.45381233e-02 -1.26659369e+00 -9.23756719e-01 -3.98067653e-01
-2.34404996e-01 8.42889428e-01 1.99564011e-03 -3.82300764... | [7.000943183898926, 6.080301284790039] |
23fb9427-93a1-4498-942b-7e77d747e197 | 3ddepthnet-point-cloud-guided-depth | 2003.09175 | null | https://arxiv.org/abs/2003.09175v1 | https://arxiv.org/pdf/2003.09175v1.pdf | 3dDepthNet: Point Cloud Guided Depth Completion Network for Sparse Depth and Single Color Image | In this paper, we propose an end-to-end deep learning network named 3dDepthNet, which produces an accurate dense depth image from a single pair of sparse LiDAR depth and color image for robotics and autonomous driving tasks. Based on the dimensional nature of depth images, our network offers a novel 3D-to-2D coarse-to-... | ['Zhe Zhang', 'Feng Zheng', 'Rui Xiang', 'Huapeng Su'] | 2020-03-20 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-1.36931539e-01 3.04798275e-01 -4.12173457e-02 -5.74159741e-01
-6.15086794e-01 -1.31667048e-01 5.03514469e-01 -4.06615168e-01
-4.93463725e-01 4.25383180e-01 1.19711794e-01 -1.45943463e-01
1.51742831e-01 -8.70062649e-01 -1.06050229e+00 -3.09898764e-01
-1.48583755e-01 6.25112057e-01 1.77800450e-02 -9.12720188... | [8.591405868530273, -2.663264274597168] |
536f5def-ff8b-473d-be67-9623266ceaf1 | hierarchical-imitation-and-reinforcement | 1803.00590 | null | http://arxiv.org/abs/1803.00590v2 | http://arxiv.org/pdf/1803.00590v2.pdf | Hierarchical Imitation and Reinforcement Learning | We study how to effectively leverage expert feedback to learn sequential
decision-making policies. We focus on problems with sparse rewards and long
time horizons, which typically pose significant challenges in reinforcement
learning. We propose an algorithmic framework, called hierarchical guidance,
that leverages the... | ['Hal Daumé III', 'Miroslav Dudík', 'Alekh Agarwal', 'Yisong Yue', 'Nan Jiang', 'Hoang M. Le'] | 2018-03-01 | hierarchical-imitation-and-reinforcement-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2290 | http://proceedings.mlr.press/v80/le18a/le18a.pdf | icml-2018-7 | ['montezumas-revenge'] | ['playing-games'] | [ 8.76116231e-02 2.25946128e-01 -7.22474396e-01 -3.89470495e-02
-1.18176150e+00 -1.09136832e+00 4.80501920e-01 2.85705123e-02
-5.33926785e-01 1.24746180e+00 1.69290811e-01 -8.13787103e-01
-3.64273816e-01 -6.21078610e-01 -8.32686782e-01 -4.04727906e-01
-4.32903081e-01 6.15211189e-01 1.38535574e-01 -1.29285023... | [4.0418806076049805, 1.907841444015503] |
c29041b9-c65d-4ca1-9f88-77c4a1ca625a | gans-and-closures-micro-macro-consistency-in | 2208.10715 | null | https://arxiv.org/abs/2208.10715v3 | https://arxiv.org/pdf/2208.10715v3.pdf | GANs and Closures: Micro-Macro Consistency in Multiscale Modeling | Sampling the phase space of molecular systems -- and, more generally, of complex systems effectively modeled by stochastic differential equations -- is a crucial modeling step in many fields, from protein folding to materials discovery. These problems are often multiscale in nature: they can be described in terms of lo... | ['Ioannis G. Kevrekidis', 'Andrew L. Ferguson', 'Juan M. Bello-Rivas', 'Ellis R. Crabtree'] | 2022-08-23 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 3.42999905e-01 1.31639794e-01 -1.22665659e-01 -1.50652915e-01
-8.24560821e-01 -6.48884237e-01 8.96046817e-01 -4.36409824e-02
-3.29726785e-01 1.35303271e+00 5.51693216e-02 -2.50035644e-01
-8.99589993e-03 -1.04859686e+00 -8.67697597e-01 -1.41329730e+00
-1.93417892e-01 8.86388063e-01 1.32845774e-01 -5.72780848... | [5.124663352966309, 5.2260637283325195] |
f41125fb-1350-4897-b83d-bc0aa4111bcd | human-activity-recognition-in-an-open-world | 2212.12141 | null | https://arxiv.org/abs/2212.12141v1 | https://arxiv.org/pdf/2212.12141v1.pdf | Human Activity Recognition in an Open World | Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples. Novelty manifests in HAR as unseen samples, activities, objects, environments, and sensor changes, among other w... | ['Walter J. Scheirer', 'Eric Robertson', 'Adam Kaufman', 'Christopher Funk', 'Ameya Shringi', 'Dawei Du', 'Jin Huang', 'Samuel Grieggs', 'Derek S. Prijatelj'] | 2022-12-23 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 1.33234322e-01 -3.43939066e-01 4.03126180e-01 -1.88497126e-01
-5.67858040e-01 -8.17609668e-01 5.64272225e-01 2.53226578e-01
-5.88965535e-01 7.31965363e-01 1.60864696e-01 2.68479079e-01
-3.12861025e-01 -3.23755980e-01 -9.31422651e-01 -6.02204561e-01
-6.78059697e-01 2.32210711e-01 5.05081296e-01 2.04216242... | [7.968994617462158, 0.8929967284202576] |
819b1fda-7342-40ca-8ccd-ae23e29c8f8a | multiple-instance-based-video-anomaly | 2007.01548 | null | https://arxiv.org/abs/2007.01548v2 | https://arxiv.org/pdf/2007.01548v2.pdf | Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding | In this paper, we propose a weakly supervised deep temporal encoding-decoding solution for anomaly detection in surveillance videos using multiple instance learning. The proposed approach uses both abnormal and normal video clips during the training phase which is developed in the multiple instance framework where we t... | ['Alireza Bab-Hadiashar', 'Ammar Mansoor Kamoona', 'Reza Hoseinnezhad', 'Amirali Khodadadian Gosta'] | 2020-07-03 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 2.46581405e-01 -9.31959301e-02 2.09644586e-02 -3.98998052e-01
-6.13700449e-01 -2.02630281e-01 4.43931937e-01 2.18282387e-01
-5.02597451e-01 4.49144661e-01 -1.74633697e-01 -3.22286636e-02
-2.70589203e-01 -3.44035178e-01 -1.16566074e+00 -9.35310006e-01
-8.40267658e-01 6.96849599e-02 5.52161992e-01 6.13210574... | [7.882320880889893, 1.504918098449707] |
0b5a4f48-98d4-4000-ac9e-1b526f01b270 | metaformer-a-unified-meta-framework-for-fine | 2203.02751 | null | https://arxiv.org/abs/2203.02751v1 | https://arxiv.org/pdf/2203.02751v1.pdf | MetaFormer: A Unified Meta Framework for Fine-Grained Recognition | Fine-Grained Visual Classification(FGVC) is the task that requires recognizing the objects belonging to multiple subordinate categories of a super-category. Recent state-of-the-art methods usually design sophisticated learning pipelines to tackle this task. However, visual information alone is often not sufficient to a... | ['Zehuan Yuan', 'Jia Sun', 'Bin Wen', 'Yi Jiang', 'Qishuai Diao'] | 2022-03-05 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [-2.30113447e-01 -5.39223373e-01 -1.53716087e-01 -3.86469394e-01
-9.08504188e-01 -7.17190623e-01 9.11852479e-01 -1.36270881e-01
-4.00617868e-01 4.46152091e-01 1.14822149e-01 -2.69872975e-02
7.24136755e-02 -5.27876556e-01 -7.21035123e-01 -6.65040553e-01
3.86469185e-01 1.01366989e-01 2.67938852e-01 1.25430614... | [9.644185066223145, 2.0591914653778076] |
7665ab3a-97d4-418d-b90e-966a596474cc | multi-objective-coordination-graphs-for-the | 2207.00368 | null | https://arxiv.org/abs/2207.00368v1 | https://arxiv.org/pdf/2207.00368v1.pdf | Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models | Many real-world problems contain multiple objectives and agents, where a trade-off exists between objectives. Key to solving such problems is to exploit sparse dependency structures that exist between agents. For example, in wind farm control a trade-off exists between maximising power and minimising stress on the syst... | ['Patrick Mannion', 'Enda Howley', 'Diederik M. Roijers', 'Timothy Verstraeten', 'Conor F. Hayes'] | 2022-07-01 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 7.28686824e-02 -5.17646745e-02 -9.11121741e-02 -5.12715466e-02
-1.63022965e-01 -7.35632539e-01 1.56402707e-01 5.22149920e-01
-4.94266003e-01 1.13987482e+00 -2.71390975e-01 -4.06062961e-01
-1.13088274e+00 -1.02935624e+00 -6.30841553e-01 -1.04672754e+00
-7.81414986e-01 7.30591118e-01 -6.13936316e-03 -3.04526716... | [4.7905755043029785, 2.3731307983398438] |
e583ddd7-4254-4b02-a10a-01cf9005e4c2 | a-hybrid-parametric-deep-learning-approach | 1908.10133 | null | https://arxiv.org/abs/1908.10133v1 | https://arxiv.org/pdf/1908.10133v1.pdf | A hybrid parametric-deep learning approach for sound event localization and detection | This work describes and discusses an algorithm submitted to the Sound Event Localization and Detection Task of DCASE2019 Challenge. The proposed methodology relies on parametric spatial audio analysis for source localization and detection, combined with a deep learning-based monophonic event classifier. The evaluation ... | ['Xavier Serra', 'Andres Perez-Lopez', 'Eduardo Fonseca'] | 2019-08-27 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 1.52093126e-02 -3.11759681e-01 6.02437079e-01 6.27352968e-02
-1.56104314e+00 -5.62975705e-01 6.25905991e-01 7.91226208e-01
-5.61966836e-01 5.92162013e-01 6.34712994e-01 3.03142786e-01
-1.39125764e-01 -3.44690323e-01 -6.12759650e-01 -6.55949295e-01
-4.13874835e-01 -1.01892417e-02 5.55004299e-01 1.70543000... | [15.152026176452637, 5.250824451446533] |
585f37b6-fce1-4be1-ac18-02033d5c01fa | talking-head-generation-driven-by-speech | 2204.12756 | null | https://arxiv.org/abs/2204.12756v1 | https://arxiv.org/pdf/2204.12756v1.pdf | Talking Head Generation Driven by Speech-Related Facial Action Units and Audio- Based on Multimodal Representation Fusion | Talking head generation is to synthesize a lip-synchronized talking head video by inputting an arbitrary face image and corresponding audio clips. Existing methods ignore not only the interaction and relationship of cross-modal information, but also the local driving information of the mouth muscles. In this study, we ... | ['Longbiao Wang', 'Jiaxing Liu', 'Zhilei Liu', 'Sen Chen'] | 2022-04-27 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 1.29468322e-01 5.69624268e-02 -2.29576766e-01 -4.22444224e-01
-1.12336981e+00 -1.69848546e-01 6.91816032e-01 -7.25936592e-01
-2.27782428e-02 4.77293909e-01 7.67818451e-01 3.23189408e-01
3.23148519e-01 -4.11351830e-01 -7.44602084e-01 -9.20713842e-01
2.97590166e-01 -1.64209321e-01 -2.10595597e-02 -1.43328458... | [13.309487342834473, -0.27983227372169495] |
85168829-7164-4119-bf07-977ea69ce226 | better-speech-synthesis-through-scaling | 2305.07243 | null | https://arxiv.org/abs/2305.07243v2 | https://arxiv.org/pdf/2305.07243v2.pdf | Better speech synthesis through scaling | In recent years, the field of image generation has been revolutionized by the application of autoregressive transformers and DDPMs. These approaches model the process of image generation as a step-wise probabilistic processes and leverage large amounts of compute and data to learn the image distribution. This methodolo... | ['James Betker'] | 2023-05-12 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 1.58007458e-01 2.59536624e-01 1.35055274e-01 -2.94749349e-01
-9.01832581e-01 -5.99157274e-01 1.17538917e+00 -6.45271182e-01
1.55655593e-02 6.92164004e-01 4.51075315e-01 -3.59942555e-01
4.99480277e-01 -7.27876246e-01 -7.65040040e-01 -7.71862686e-01
3.37566227e-01 5.67176521e-01 8.71373862e-02 -1.57016858... | [11.300065994262695, -0.07476253807544708] |
0189857e-2e93-4227-bcfd-2a2697015d23 | entropy-based-training-methods-for-scalable | 2306.04952 | null | https://arxiv.org/abs/2306.04952v1 | https://arxiv.org/pdf/2306.04952v1.pdf | Entropy-based Training Methods for Scalable Neural Implicit Sampler | Efficiently sampling from un-normalized target distributions is a fundamental problem in scientific computing and machine learning. Traditional approaches like Markov Chain Monte Carlo (MCMC) guarantee asymptotically unbiased samples from such distributions but suffer from computational inefficiency, particularly when ... | ['Zhihua Zhang', 'Boya Zhang', 'Weijian Luo'] | 2023-06-08 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 2.76276231e-01 -3.16408277e-01 -4.21179622e-01 -2.10676357e-01
-1.17762983e+00 -3.17867547e-01 7.91878760e-01 -6.84992373e-02
-4.11560535e-01 1.05374062e+00 1.60841737e-02 -4.61224258e-01
1.24765001e-01 -9.99360025e-01 -7.56075740e-01 -1.01412058e+00
1.34709284e-01 6.80251479e-01 2.46378705e-01 4.82703716... | [7.015018463134766, 3.950286865234375] |
4018152b-1709-4ef1-b559-73d783f2a1c8 | towards-automatic-learning-of-procedures-from | 1703.09788 | null | http://arxiv.org/abs/1703.09788v3 | http://arxiv.org/pdf/1703.09788v3.pdf | Towards Automatic Learning of Procedures from Web Instructional Videos | The potential for agents, whether embodied or software, to learn by observing
other agents performing procedures involving objects and actions is rich.
Current research on automatic procedure learning heavily relies on action
labels or video subtitles, even during the evaluation phase, which makes them
infeasible in re... | ['Chenliang Xu', 'Luowei Zhou', 'Jason J. Corso'] | 2017-03-28 | null | null | null | null | ['procedure-learning', 'dense-video-captioning'] | ['computer-vision', 'computer-vision'] | [ 6.50981605e-01 4.13355052e-01 -3.92458171e-01 -3.47685575e-01
-7.92817891e-01 -1.04567397e+00 4.96934354e-01 1.63300801e-02
-4.48681891e-01 5.04472375e-01 5.11293054e-01 -3.39046180e-01
5.78009307e-01 -4.04862761e-01 -1.37460828e+00 -5.37534416e-01
-6.52485015e-03 1.69756711e-01 2.55167931e-01 4.08463806... | [9.00365924835205, 0.6334124803543091] |
711baa82-15af-4a6d-8d5c-7c07e865d5bd | adaptive-dimension-reduction-and-variational | 2209.08527 | null | https://arxiv.org/abs/2209.08527v1 | https://arxiv.org/pdf/2209.08527v1.pdf | Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification | Transductive Few-Shot learning has gained increased attention nowadays considering the cost of data annotations along with the increased accuracy provided by unlabelled samples in the domain of few shot. Especially in Few-Shot Classification (FSC), recent works explore the feature distributions aiming at maximizing lik... | ['Vincent Gripon', 'Stéphane Pateux', 'Yuqing Hu'] | 2022-09-18 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 3.15498978e-01 -5.19714728e-02 -1.62634656e-01 -3.19683313e-01
-1.02674603e+00 -7.39428475e-02 6.81262314e-01 4.55032766e-01
-7.27497876e-01 9.18396473e-01 7.31683001e-02 4.54378754e-01
-4.71958607e-01 -7.45899916e-01 -6.13416016e-01 -1.03312087e+00
7.54533410e-02 7.00841606e-01 3.17175746e-01 -8.47463086... | [9.792594909667969, 2.916146755218506] |
69edba9d-9da0-4346-9087-5d88a95851e8 | 190600494 | 1906.00494 | null | https://arxiv.org/abs/1906.00494v2 | https://arxiv.org/pdf/1906.00494v2.pdf | Graphon Estimation from Partially Observed Network Data | We consider estimating the edge-probability matrix of a network generated from a graphon model when the full network is not observed---only some overlapping subgraphs are. We extend the neighbourhood smoothing (NBS) algorithm of Zhang et al. (2017) to this missing-data set-up and show experimentally that, for a wide ra... | ['Soumendu Sundar Mukherjee', 'Sayak Chakrabarti'] | 2019-06-02 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 3.55847597e-01 5.97805798e-01 -2.27797359e-01 -7.87311718e-02
-3.88838857e-01 -5.33801854e-01 7.40583539e-01 1.55420661e-01
-2.95136143e-02 7.19671786e-01 3.37433994e-01 -5.45949459e-01
-4.43844974e-01 -7.98355877e-01 -1.02134216e+00 -4.46724266e-01
-4.89543229e-01 5.88096142e-01 3.62755090e-01 -1.09135002... | [6.937926769256592, 5.146551609039307] |
13669e26-62e2-4fa4-b8a4-4fba6aafda33 | large-deviation-principles-for-block-and-step | 2101.07025 | null | https://arxiv.org/abs/2101.07025v2 | https://arxiv.org/pdf/2101.07025v2.pdf | Large Deviation Principles for Block and Step Graphon Random Graph Models | Borgs, Chayes, Gaudio, Petti and Sen [arXiv:2007.14508] proved a large deviation principle for block model random graphs with rational block ratios. We strengthen their result by allowing any block ratios (and also establish a simpler formula for the rate function). We apply the new result to derive a large deviation p... | ['Oleg Pikhurko', 'Jan Grebík'] | 2021-01-18 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 2.22433463e-01 6.34107411e-01 -5.72707117e-01 6.34851679e-02
-5.57532907e-01 -6.10406101e-01 4.34790403e-01 -9.16427821e-02
1.24970458e-01 1.23499727e+00 -1.13850012e-01 -6.92638814e-01
-4.37683672e-01 -1.26904714e+00 -8.26364636e-01 -9.00215447e-01
-2.79689431e-01 5.04926920e-01 5.16345918e-01 -1.77749351... | [6.782261371612549, 5.119195461273193] |
a13c98a7-8512-4b7e-9737-52d27b2c64d4 | fusedream-training-free-text-to-image | 2112.01573 | null | https://arxiv.org/abs/2112.01573v1 | https://arxiv.org/pdf/2112.01573v1.pdf | FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization | Generating images from natural language instructions is an intriguing yet highly challenging task. We approach text-to-image generation by combining the power of the retrained CLIP representation with an off-the-shelf image generator (GANs), optimizing in the latent space of GAN to find images that achieve maximum CLIP... | ['Qiang Liu', 'Hao Su', 'Shujian Zhang', 'Lemeng Wu', 'Chengyue Gong', 'Xingchao Liu'] | 2021-12-02 | null | null | null | null | ['zero-shot-text-to-image-generation'] | ['natural-language-processing'] | [ 4.97108251e-01 1.96708396e-01 1.05259478e-01 -9.82012898e-02
-1.00503623e+00 -8.56440067e-01 8.46910179e-01 -6.22079730e-01
-1.68912873e-01 8.66227746e-01 4.85821553e-02 -1.70192987e-01
3.47339064e-01 -8.04691970e-01 -1.09734905e+00 -8.75515461e-01
6.27521336e-01 3.80569756e-01 -3.27765673e-01 -3.17566276... | [11.575328826904297, -0.37975093722343445] |
576eb5fd-ccc0-4479-a575-b269c4cd8f12 | restricted-black-box-adversarial-attack | 2204.12347 | null | https://arxiv.org/abs/2204.12347v1 | https://arxiv.org/pdf/2204.12347v1.pdf | Restricted Black-box Adversarial Attack Against DeepFake Face Swapping | DeepFake face swapping presents a significant threat to online security and social media, which can replace the source face in an arbitrary photo/video with the target face of an entirely different person. In order to prevent this fraud, some researchers have begun to study the adversarial methods against DeepFake or f... | ['Xiaohua Xie', 'JianHuang Lai', 'YuAn Wang', 'Junhao Dong'] | 2022-04-26 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [ 3.46568525e-01 2.26396292e-01 2.44504049e-01 -2.02422842e-01
-5.58440685e-01 -1.04705811e+00 5.39525747e-01 -1.02774346e+00
1.15428641e-01 5.48006117e-01 -2.15313971e-01 -2.59387553e-01
1.52940542e-01 -9.76025760e-01 -1.14184427e+00 -8.83306921e-01
1.57326594e-01 -2.29183096e-03 -1.51713639e-01 -2.49195337... | [12.702727317810059, 0.9156668782234192] |
d9c529ee-aedb-428b-b6a9-d8fc8f9f8639 | a-comparison-of-stereo-matching-cost-between | 1905.09147 | null | https://arxiv.org/abs/1905.09147v1 | https://arxiv.org/pdf/1905.09147v1.pdf | A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images | Stereo dense image matching can be categorized to low-level feature based matching and deep feature based matching according to their matching cost metrics. Census has been proofed to be one of the most efficient low-level feature based matching methods, while fast Convolutional Neural Network (fst-CNN), as a deep feat... | ['Xu Huang', 'Shuang Song', 'Xiaohu Lu', 'Rongjun Qin', 'Bihe Chen'] | 2019-05-22 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [-1.53065249e-01 -5.20056367e-01 1.33161649e-01 -4.03483003e-01
-3.41247112e-01 -1.86905116e-01 5.59155703e-01 7.41690844e-02
-5.70760369e-01 6.07426226e-01 3.29087436e-01 -6.33767322e-02
-1.42903149e-01 -1.45491898e+00 -5.25718451e-01 -3.44268441e-01
-1.77938938e-02 3.34657431e-01 2.92931557e-01 -4.19032007... | [8.89329719543457, -2.2839701175689697] |
34cd7f63-edf1-4e2c-87ae-8d4e699eec33 | related-terms-search-based-on-wordnet | 0907.2209 | null | http://arxiv.org/abs/0907.2209v2 | http://arxiv.org/pdf/0907.2209v2.pdf | Related terms search based on WordNet / Wiktionary and its application in Ontology Matching | A set of ontology matching algorithms (for finding correspondences between
concepts) is based on a thesaurus that provides the source data for the
semantic distance calculations. In this wiki era, new resources may spring up
and improve this kind of semantic search. In the paper a solution of this task
based on Russian... | ['Feiyu Lin', 'A. A. Krizhanovsky'] | 2009-07-13 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [-2.35928953e-01 3.43941808e-01 4.09719616e-01 -5.12247682e-01
-1.21804431e-01 -2.95264035e-01 7.70209193e-01 7.50797868e-01
-9.33164895e-01 6.81691587e-01 3.76015663e-01 -1.87088937e-01
-1.01546776e+00 -1.19094980e+00 1.51751429e-01 -9.57277194e-02
1.72850952e-01 1.16783130e+00 3.53516132e-01 -1.02908039... | [9.276726722717285, 8.148526191711426] |
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