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db4dec29-c984-4fee-906d-37fecea605cc | robust-unsupervised-graph-representation | 2201.08557 | null | https://arxiv.org/abs/2201.08557v2 | https://arxiv.org/pdf/2201.08557v2.pdf | Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective | Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rendering them infeasible when label information is not available. A straightforward direction is to employ the widely used Infomax technique fro... | ['Qinghua Zheng', 'Jun Zhou', 'Ziqi Liu', 'Jundong Li', 'Minnan Luo', 'Jihong Wang'] | 2022-01-21 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 4.71166402e-01 5.02012491e-01 -3.16289306e-01 5.52653382e-03
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-1.93227410e-01 -3.02513450e-01 -2.81859022e-02 -3.03586364... | [6.1593146324157715, 7.290466785430908] |
a5b0c5e2-4883-4f8b-a191-4b88e32fc89d | how-to-train-good-word-embeddings-for | null | null | https://aclanthology.org/W16-2922 | https://aclanthology.org/W16-2922.pdf | How to Train good Word Embeddings for Biomedical NLP | null | ['Gamal Crichton', 'Billy Chiu', 'Anna Korhonen', 'Sampo Pyysalo'] | 2016-08-01 | null | null | null | ws-2016-8 | ['learning-word-embeddings'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.43160343170166, 3.6355607509613037] |
a09e669e-44b7-40ec-b23b-54d7d34ae860 | feature-reduction-for-machine-learning-on | 2101.05546 | null | https://arxiv.org/abs/2101.05546v1 | https://arxiv.org/pdf/2101.05546v1.pdf | Feature reduction for machine learning on molecular features: The GeneScore | We present the GeneScore, a concept of feature reduction for Machine Learning analysis of biomedical data. Using expert knowledge, the GeneScore integrates different molecular data types into a single score. We show that the GeneScore is superior to a binary matrix in the classification of cancer entities from SNV, Ind... | ['Sylvia Nürnberg', 'Frank Ueckert', 'Theresa Grooss', 'Anastasia Steshina', 'Alexander Denker'] | 2021-01-14 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 2.93036312e-01 -8.12916011e-02 -3.78562212e-01 -4.06610221e-01
-6.12379253e-01 -4.99657184e-01 2.90396750e-01 9.57600534e-01
-3.50151628e-01 9.50452566e-01 1.17025621e-01 -5.41615844e-01
-7.08390355e-01 -8.35084856e-01 -1.22991078e-01 -8.18408012e-01
-1.20176151e-01 4.51145023e-01 -1.01075031e-01 -1.61361307... | [6.228966236114502, 5.629906177520752] |
2c43d9ca-282e-4305-9d93-3ac27b8115b7 | top-down-beats-bottom-up-in-3d-instance | 2302.02871 | null | https://arxiv.org/abs/2302.02871v3 | https://arxiv.org/pdf/2302.02871v3.pdf | Top-Down Beats Bottom-Up in 3D Instance Segmentation | Most 3D instance segmentation methods exploit a bottom-up strategy, typically including resource-exhaustive post-processing. For point grouping, bottom-up methods rely on prior assumptions about the objects in the form of hyperparameters, which are domain-specific and need to be carefully tuned. On the contrary, we add... | ['Anton Konushin', 'Anna Vorontsova', 'Danila Rukhovich', 'Maksim Kolodiazhnyi'] | 2023-02-06 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 0.03941243 0.20798756 -0.1640212 -0.5384459 -0.8457157 -0.7651859
0.6854952 0.02019189 -0.37431064 0.05815756 -0.19156608 -0.6183239
0.22254954 -0.7372163 -0.9383805 -0.2921008 0.05501155 0.9402181
0.848351 -0.1790176 0.38968366 0.43219736 -1.4942499 -0.07035264
0.70893484 1.2157217 0.053... | [8.093801498413086, -3.096825122833252] |
7b4ca608-345d-4a31-a086-91de41744b88 | stochastic-talking-face-generation-using | 2011.10727 | null | https://arxiv.org/abs/2011.10727v1 | https://arxiv.org/pdf/2011.10727v1.pdf | Stochastic Talking Face Generation Using Latent Distribution Matching | The ability to envisage the visual of a talking face based just on hearing a voice is a unique human capability. There have been a number of works that have solved for this ability recently. We differ from these approaches by enabling a variety of talking face generations based on single audio input. Indeed, just havin... | ['Rajesh M Hegde', 'Vinay P Namboodiri', 'Ashish Sardana', 'Ravindra Yadav'] | 2020-11-21 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 3.13216448e-01 3.78890246e-01 2.08378151e-01 -3.83218378e-01
-1.25635552e+00 -6.38145268e-01 9.10342336e-01 -9.19690430e-01
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2.25345328e-01 5.25215507e-01 -1.34459749e-01 -9.34644639... | [13.154438972473145, -0.38568711280822754] |
af169a1e-8880-449f-a257-aa0ae08f9b19 | on-the-susceptibility-and-robustness-of-time | 2301.03703 | null | https://arxiv.org/abs/2301.03703v1 | https://arxiv.org/pdf/2301.03703v1.pdf | On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense | Under adversarial attacks, time series regression and classification are vulnerable. Adversarial defense, on the other hand, can make the models more resilient. It is important to evaluate how vulnerable different time series models are to attacks and how well they recover using defense. The sensitivity to various atta... | ['Bidhan Bashyal', 'Asadullah Hill Galib'] | 2023-01-09 | null | null | null | null | ['adversarial-defense', 'time-series-regression'] | ['adversarial', 'time-series'] | [-1.65027767e-01 -4.62637961e-01 2.27279946e-01 -1.29417581e-02
-6.44625366e-01 -1.29441512e+00 7.07265615e-01 -3.22355270e-01
-4.37436886e-02 5.20685315e-01 8.99235457e-02 -7.00555027e-01
-1.67867959e-01 -8.76006901e-01 -5.18667579e-01 -7.27260113e-01
-6.32867754e-01 -1.13203198e-01 3.14861685e-02 -6.84084654... | [5.652348041534424, 7.742506504058838] |
a48cfd94-e1e8-4271-afdd-c7ac7803721a | alpcah-sample-wise-heteroscedastic-pca-with | 2307.02745 | null | https://arxiv.org/abs/2307.02745v1 | https://arxiv.org/pdf/2307.02745v1.pdf | ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value Regularization | Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction that is useful for various data science problems. However, many applications involve heterogeneous data that varies in quality due to noise characteristics associated with different sources of the data. Methods that deal with... | ['Laura Balzano', 'Jeffrey A. Fessler', 'Javier Salazar Cavazos'] | 2023-07-06 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-3.06177497e-01 -5.58283925e-01 2.28934169e-01 -1.19786881e-01
-7.01003671e-01 -5.94921589e-01 4.22745287e-01 -4.13661689e-01
-9.19482186e-02 5.27385533e-01 6.31976426e-01 6.84191585e-02
-7.21337080e-01 -4.99250025e-01 -5.52962005e-01 -1.29728353e+00
2.75722314e-02 4.39500719e-01 -2.56700516e-01 1.82731107... | [7.701168060302734, 4.22667121887207] |
da517ede-0b10-41e4-a868-28026e7b25f4 | food-recognition-and-recipe-analysis | 1801.07239 | null | http://arxiv.org/abs/1801.07239v1 | http://arxiv.org/pdf/1801.07239v1.pdf | Food recognition and recipe analysis: integrating visual content, context and external knowledge | The central role of food in our individual and social life, combined with
recent technological advances, has motivated a growing interest in applications
that help to better monitor dietary habits as well as the exploration and
retrieval of food-related information. We review how visual content, context
and external kn... | ['Weiqing Min', 'Luis Herranz', 'Shuqiang Jiang'] | 2018-01-22 | null | null | null | null | ['food-recognition', 'food-recommendation'] | ['computer-vision', 'miscellaneous'] | [-3.24024081e-01 -4.78229791e-01 -6.73378229e-01 -3.55380654e-01
2.09339499e-01 -6.23374760e-01 3.82005535e-02 1.52609110e+00
-1.62577525e-01 9.96742919e-02 9.00410831e-01 -6.07423894e-02
1.37952104e-01 -1.16396904e+00 -7.88111389e-02 -3.65349233e-01
-2.54488409e-01 -3.87884647e-01 1.05783559e-01 -4.91399825... | [11.546428680419922, 4.478516101837158] |
635be74a-6998-43ef-840c-03013f3c2ed1 | low-resource-white-box-semantic-segmentation | 2306.07809 | null | https://arxiv.org/abs/2306.07809v1 | https://arxiv.org/pdf/2306.07809v1.pdf | Low-Resource White-Box Semantic Segmentation of Supporting Towers on 3D Point Clouds via Signature Shape Identification | Research in 3D semantic segmentation has been increasing performance metrics, like the IoU, by scaling model complexity and computational resources, leaving behind researchers and practitioners that (1) cannot access the necessary resources and (2) do need transparency on the model decision mechanisms. In this paper, w... | ['Patrizio Frosini', 'Manuel Silva', 'Alex Coronati', 'Giovanni Bocchi', 'Alessandra Micheletti', 'Cláudia Soares', 'Diogo Lavado'] | 2023-06-13 | null | null | null | null | ['3d-semantic-segmentation'] | ['computer-vision'] | [ 1.06215410e-01 2.83301950e-01 -1.31002754e-01 -5.32802165e-01
-6.89872682e-01 -9.72517848e-01 1.77704412e-02 1.42258182e-01
-2.18978226e-01 1.60183564e-01 -4.98877794e-01 -9.73043263e-01
6.72165230e-02 -1.03144467e+00 -8.44499648e-01 -1.99577555e-01
-1.76081091e-01 1.12417042e+00 5.64104855e-01 1.00428285... | [7.964091777801514, -3.2657992839813232] |
f566bc21-b6bf-44e7-9ee8-8c442451ead9 | building-dynamic-knowledge-graphs-from-text-2 | 1910.09532 | null | https://arxiv.org/abs/1910.09532v3 | https://arxiv.org/pdf/1910.09532v3.pdf | Building Dynamic Knowledge Graphs from Text-based Games | We are interested in learning how to update Knowledge Graphs (KG) from text. In this preliminary work, we propose a novel Sequence-to-Sequence (Seq2Seq) architecture to generate elementary KG operations. Furthermore, we introduce a new dataset for KG extraction built upon text-based game transitions (over 300k data poi... | ['Marc-Alexandre Côté', 'Mikuláš Zelinka', 'Xingdi Yuan', 'Romain Laroche', 'Adam Trischler'] | 2019-10-21 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 1.00035384e-01 1.66375667e-01 -2.75135607e-01 -3.03422272e-01
-4.67661113e-01 -7.20545709e-01 5.06926715e-01 3.69636625e-01
-7.57125854e-01 1.10891140e+00 2.59693414e-01 -6.01818681e-01
-1.85859129e-01 -1.20099723e+00 -9.80085313e-01 5.82271582e-03
-5.53154409e-01 6.74578607e-01 6.27991736e-01 -5.06996572... | [9.254733085632324, 8.096877098083496] |
48aa36a5-7378-47df-9c29-301b6bebb945 | dcmt-a-direct-entire-space-causal-multi-task | 2302.06141 | null | https://arxiv.org/abs/2302.06141v1 | https://arxiv.org/pdf/2302.06141v1.pdf | DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation | In recommendation scenarios, there are two long-standing challenges, i.e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks. To cope with these issues, existing works emphasize on leveraging Multi-Task ... | ['Yan Wang', 'Guanfeng Liu', 'Fei Wu', 'Chaochao Chen', 'Jun Zhou', 'Tiehua Zhang', 'Lu Yu', 'Longfei Li', 'Xinxing Yang', 'Mingjie Zhong', 'Feng Zhu'] | 2023-02-13 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 1.43476740e-01 -4.22721714e-01 -8.78804982e-01 -3.44536304e-01
-9.41505253e-01 -3.31084073e-01 4.27697510e-01 -1.88878357e-01
-1.19640924e-01 1.02086759e+00 1.74110994e-01 -7.69320369e-01
-4.13920611e-01 -7.64715075e-01 -9.04344857e-01 -4.77646351e-01
2.84468025e-01 6.78375661e-02 -4.12305705e-02 5.24429344... | [9.727709770202637, 5.396203517913818] |
439057d1-df74-4000-b0aa-98ebbd5a2fd7 | mitosis-domain-generalization-in | 2204.03742 | null | https://arxiv.org/abs/2204.03742v1 | https://arxiv.org/pdf/2204.03742v1.pdf | Mitosis domain generalization in histopathology images -- The MIDOG challenge | The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitotic figures by pathologists is known to be subject to a strong inter-rater bias, which limits the prognostic value. State-of-the-art deep lear... | ['Katharina Breininger', 'Mitko Veta', 'Xiyue Wang', 'Sen yang', 'April Khademi', 'Salar Razavi', 'Jingxin Liu', 'Xi Long', 'YuBo Wang', 'Jingtang Liang', 'Viktor H. Koelzer', 'Maxime W. Lafarge', 'Satoshi Kondo', 'Thomas Wittenberg', 'Jakob Dexl', 'Nasir Rajpoot', 'Mostafa Jahanifar', 'Saima Ben Hadj', 'Rutger H. J. F... | 2022-04-06 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 2.07171842e-01 2.30079889e-02 -2.49736801e-01 -1.55705109e-01
-1.23438048e+00 -6.19666457e-01 5.83008230e-01 7.73534894e-01
-8.12096596e-01 8.66358280e-01 2.95934007e-02 -3.62485409e-01
-1.84100389e-01 -6.67361498e-01 -4.80581433e-01 -1.22589922e+00
2.14291230e-01 1.02057815e+00 3.43964577e-01 1.17207490... | [15.126108169555664, -3.0650148391723633] |
d8e2407b-bcef-4925-94e5-6264bc1211c8 | forward-compatible-few-shot-class-incremental | 2203.06953 | null | https://arxiv.org/abs/2203.06953v1 | https://arxiv.org/pdf/2203.06953v1.pdf | Forward Compatible Few-Shot Class-Incremental Learning | Novel classes frequently arise in our dynamically changing world, e.g., new users in the authentication system, and a machine learning model should recognize new classes without forgetting old ones. This scenario becomes more challenging when new class instances are insufficient, which is called few-shot class-incremen... | ['De-Chuan Zhan', 'ShiLiang Pu', 'Liang Ma', 'Han-Jia Ye', 'Fu-Yun Wang', 'Da-Wei Zhou'] | 2022-03-14 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhou_Forward_Compatible_Few-Shot_Class-Incremental_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhou_Forward_Compatible_Few-Shot_Class-Incremental_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['few-shot-class-incremental-learning'] | ['methodology'] | [-2.21197516e-01 -2.34905839e-01 -5.92122912e-01 -3.78868699e-01
-3.57122183e-01 -4.72341061e-01 5.60189307e-01 2.84854621e-01
-5.16445041e-01 8.55505168e-01 2.79827439e-03 -3.40572625e-01
-1.16597831e-01 -1.12892914e+00 -4.45377856e-01 -6.29067838e-01
-1.83169991e-01 5.47151029e-01 4.88177210e-01 -1.52408704... | [9.843206405639648, 3.3978559970855713] |
0d95698b-9a00-4429-91e1-25647540a628 | action-recognition-in-video-sequences-using | null | null | https://ieeexplore.ieee.org/abstract/document/8121994 | https://ieeexplore.ieee.org/document/8121994 | Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features | Recurrent neural network (RNN) and long short-term memory (LSTM) have achieved great success in processing sequential multimedia data and yielded the state-of-the-art results in speech recognition, digital signal processing, video processing, and text data analysis. In this paper, we propose a novel action recognition ... | ['Sung Wook Baik', 'Muhammad Sajjad', 'Khan Muhammad', 'Jamil Ahmad', 'Amin Ullah'] | 2017-11-28 | null | null | null | ieee-access-2017-11 | ['action-recognition-in-videos'] | ['computer-vision'] | [ 4.17272687e-01 -6.68518484e-01 -2.57907957e-01 -2.34102592e-01
-3.86729240e-01 1.02532722e-01 4.80362415e-01 -3.21802527e-01
-7.24247873e-01 5.48043907e-01 5.36836743e-01 -1.66712075e-01
1.34751797e-01 -5.81825495e-01 -7.40113258e-01 -7.18937039e-01
-2.67659634e-01 -2.63072014e-01 6.29086077e-01 2.96268091... | [8.44186782836914, 0.3907858729362488] |
1a7acfcb-b3d7-4324-a771-51af3988ee42 | in-n-out-towards-good-initialization-for | 2106.13953 | null | https://arxiv.org/abs/2106.13953v3 | https://arxiv.org/pdf/2106.13953v3.pdf | In-N-Out: Towards Good Initialization for Inpainting and Outpainting | In computer vision, recovering spatial information by filling in masked regions, e.g., inpainting, has been widely investigated for its usability and wide applicability to other various applications: image inpainting, image extrapolation, and environment map estimation. Most of them are studied separately depending on ... | ['Sung-Eui Yoon', 'Woobin Im', 'Changho Jo'] | 2021-06-26 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 4.98257220e-01 5.32425344e-02 -1.14925377e-01 -2.91037291e-01
-8.67483735e-01 -2.73106843e-01 4.07914221e-01 -3.18513811e-01
-3.92875403e-01 5.03049433e-01 1.85967073e-01 -3.20608526e-01
3.56258929e-01 -3.84748131e-01 -1.22726429e+00 -7.20358729e-01
2.39104107e-01 6.66568950e-02 5.18966734e-01 -1.82574049... | [11.090998649597168, -0.8745765089988708] |
7df981a2-7bfc-454d-ab2e-0e159304275b | disco-distilled-student-models-co-training | 2305.12074 | null | https://arxiv.org/abs/2305.12074v1 | https://arxiv.org/pdf/2305.12074v1.pdf | DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining | Many text mining models are constructed by fine-tuning a large deep pre-trained language model (PLM) in downstream tasks. However, a significant challenge is maintaining performance when we use a lightweight model with limited labeled samples. We present DisCo, a semi-supervised learning (SSL) framework for fine-tuning... | ['Zheng Wang', 'Ting Deng', 'Weiyi Yang', 'Chenghua Lin', 'JianXin Li', 'Qianren Mao', 'Weifeng Jiang'] | 2023-05-20 | null | null | null | null | ['semi-supervised-text-classification-1', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.65069574e-01 6.35252833e-01 -6.14782155e-01 -5.23134410e-01
-1.18646324e+00 -6.68121338e-01 7.52261162e-01 4.92413282e-01
-4.49801981e-01 9.20672119e-01 7.21053421e-01 -3.89296114e-01
1.83050662e-01 -8.49187016e-01 -9.19402003e-01 -2.44564697e-01
2.93641090e-01 9.23611224e-01 1.14393428e-01 -2.98539490... | [11.899808883666992, 8.99242115020752] |
522cecca-ad41-4ef4-9fd8-c29fba7ca728 | reference-limited-compositional-zero-shot | 2208.10046 | null | https://arxiv.org/abs/2208.10046v2 | https://arxiv.org/pdf/2208.10046v2.pdf | Reference-Limited Compositional Zero-Shot Learning | Compositional zero-shot learning (CZSL) refers to recognizing unseen compositions of known visual primitives, which is an essential ability for artificial intelligence systems to learn and understand the world. While considerable progress has been made on existing benchmarks, we suspect whether popular CZSL methods can... | ['Donglin Wang', 'Qiyao Wei', 'Siteng Huang'] | 2022-08-22 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 6.46532118e-01 -2.39940181e-01 -1.97133899e-01 1.33520458e-02
-5.60311437e-01 -3.76206934e-01 7.37073779e-01 -1.15131773e-01
7.65088350e-02 3.06063086e-01 3.35733622e-01 1.09159306e-01
1.87404200e-01 -9.01446640e-01 -9.68067169e-01 -8.58690321e-01
1.91414401e-01 6.44855738e-01 7.29616880e-01 -2.66857475... | [10.238746643066406, 2.258432388305664] |
827bafa3-e62c-4c24-a53e-b068c6d53234 | video-violence-recognition-and-localization | 2202.02212 | null | https://arxiv.org/abs/2202.02212v4 | https://arxiv.org/pdf/2202.02212v4.pdf | Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model | The significant growth of surveillance camera networks necessitates scalable AI solutions to efficiently analyze the large amount of video data produced by these networks. As a typical analysis performed on surveillance footage, video violence detection has recently received considerable attention. The majority of rese... | ['Ehsan Nazerfard', 'Hamid Mohammadi'] | 2022-02-04 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 2.08768100e-01 3.48616153e-01 -2.99642235e-01 -2.20574021e-01
-3.55583847e-01 -1.85784519e-01 4.42019641e-01 -2.75772680e-02
-5.45021713e-01 5.65466106e-01 2.15654522e-01 1.16793767e-01
-7.64580145e-02 -5.52906454e-01 -6.48540676e-01 -6.87387466e-01
-5.72974123e-02 9.09412950e-02 4.88239944e-01 -8.49998593... | [8.00833797454834, 0.8244011402130127] |
c902686e-05a7-4524-8f5f-94fbf57604cd | 190406197 | 1904.06197 | null | https://arxiv.org/abs/1904.06197v2 | https://arxiv.org/pdf/1904.06197v2.pdf | Simulation of hyperelastic materials in real-time using Deep Learning | The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel computing, adaptive meshing, and model order reduction. In this paper ... | ['Stéphane Cotin', 'Pablo Márquez-Neila', 'Andrea Mendizabal'] | 2019-04-10 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [ 8.00615847e-02 -2.80112207e-01 3.17919701e-01 1.13649599e-01
-2.38293499e-01 -3.04141995e-02 2.26073861e-01 1.15552463e-01
-1.10522367e-01 9.34364498e-01 -1.48988232e-01 -1.14202693e-01
-3.64791542e-01 -1.27421308e+00 -9.22381461e-01 -6.01637602e-01
-2.47188285e-01 8.22433114e-01 1.49982035e-01 -6.14752352... | [6.320991516113281, 3.3872530460357666] |
32fd29c5-ee46-4f0a-99e0-51d6d248eb5e | objective-assessment-of-spatial-audio-quality | 2212.01451 | null | https://arxiv.org/abs/2212.01451v1 | https://arxiv.org/pdf/2212.01451v1.pdf | Objective Assessment of Spatial Audio Quality using Directional Loudness Maps | This work introduces a feature extracted from stereophonic/binaural audio signals aiming to represent a measure of perceived quality degradation in processed spatial auditory scenes. The feature extraction technique is based on a simplified stereo signal model considering auditory events positioned towards a given dire... | ['Jürgen Herre', 'Pablo M. Delgado'] | 2022-12-02 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 5.70299685e-01 -3.27966273e-01 6.81750953e-01 -2.79973030e-01
-1.19180751e+00 -5.24074256e-01 3.59532386e-01 6.14639878e-01
-2.83339322e-01 5.02347350e-01 7.68033385e-01 4.62595187e-02
-7.29618013e-01 -5.45053184e-01 -1.69661954e-01 -6.96224153e-01
-2.00574532e-01 4.56243344e-02 6.58265412e-01 -3.35528612... | [15.339091300964355, 5.614477634429932] |
24da084e-b47c-4591-be91-a2ad3adeb65d | meta-neural-network-for-realtime-and-passive | 1909.07122 | null | https://arxiv.org/abs/1909.07122v1 | https://arxiv.org/pdf/1909.07122v1.pdf | Meta-neural-network for Realtime and Passive Deep-learning-based Object Recognition | Deep-learning recently show great success across disciplines yet conventionally require time-consuming computer processing or bulky-sized diffractive elements. Here we theoretically propose and experimentally demonstrate a purely-passive "meta-neural-network" with compactness and high-resolution for real-time recognizi... | ['Xue-Feng Zhu', 'Yujiang Ding', 'Jingkai Weng', 'Bin Liang', 'Jing Yang', 'Jianchun Cheng', 'Chengbo Hu'] | 2019-09-16 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 5.41662574e-01 5.66487491e-01 6.35237992e-01 -6.76097050e-02
-5.76639056e-01 -3.39588314e-01 4.55333769e-01 -4.38270867e-01
-4.37855572e-01 4.29383993e-01 -2.27603674e-01 -2.81173080e-01
-4.56129909e-01 -1.13355362e+00 -8.91666055e-01 -1.69750643e+00
5.35727339e-03 2.57404804e-01 1.74952224e-01 -1.39637187... | [8.191153526306152, 2.4575695991516113] |
4782a326-0755-455c-944d-4594c37ae53a | neural-modular-control-for-embodied-question | 1810.11181 | null | https://arxiv.org/abs/1810.11181v2 | https://arxiv.org/pdf/1810.11181v2.pdf | Neural Modular Control for Embodied Question Answering | We present a modular approach for learning policies for navigation over long planning horizons from language input. Our hierarchical policy operates at multiple timescales, where the higher-level master policy proposes subgoals to be executed by specialized sub-policies. Our choice of subgoals is compositional and sema... | ['Stefan Lee', 'Dhruv Batra', 'Georgia Gkioxari', 'Devi Parikh', 'Abhishek Das'] | 2018-10-26 | null | null | null | null | ['embodied-question-answering'] | ['computer-vision'] | [-9.77998227e-03 3.31665277e-01 -7.47483410e-03 -4.14727539e-01
-8.53808403e-01 -1.02124858e+00 6.56891406e-01 3.41966808e-01
-6.12022340e-01 1.01315010e+00 4.28133398e-01 -8.40636015e-01
-2.23222226e-01 -9.11879003e-01 -1.14302266e+00 -4.15438741e-01
-3.42039078e-01 7.00135469e-01 4.30249512e-01 -4.48304564... | [4.226853370666504, 1.253157377243042] |
ddc34d61-a7c3-46c4-8222-7b2291223a6c | noise-blind-image-deblurring | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Jin_Noise-Blind_Image_Deblurring_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Jin_Noise-Blind_Image_Deblurring_CVPR_2017_paper.pdf | Noise-Blind Image Deblurring | We present a novel approach to noise-blind deblurring, the problem of deblurring an image with known blur, but unknown noise level. We introduce an efficient and robust solution based on a Bayesian framework using a smooth generalization of the 0-1 loss. A novel bound allows the calculation of very high-dimensional ... | ['Paolo Favaro', 'Stefan Roth', 'Meiguang Jin'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['blind-image-deblurring'] | ['computer-vision'] | [ 2.12485030e-01 -4.56120819e-01 3.28941166e-01 6.08956395e-03
-1.04433000e+00 -3.45599294e-01 5.21800339e-01 -6.03718817e-01
-4.85675573e-01 7.30728805e-01 5.02171159e-01 -2.10946992e-01
-2.74826974e-01 -3.43962908e-01 -6.56011999e-01 -1.02694440e+00
4.29715291e-02 3.69577259e-02 1.11129120e-01 1.76694989... | [11.664325714111328, -2.642866849899292] |
544aba5d-cd86-4582-87ef-1d3109d21990 | single-anchor-uwb-localization-using-channel | 2211.04246 | null | https://arxiv.org/abs/2211.04246v1 | https://arxiv.org/pdf/2211.04246v1.pdf | Single-anchor UWB Localization using Channel Impulse Response Distributions | Ultra-wideband (UWB) devices are widely used in indoor localization scenarios. Single-anchor UWB localization shows advantages because of its simple system setup compared to conventional two-way ranging (TWR) and trilateration localization methods. In this work, we focus on single-anchor UWB localization methods that l... | ['Andreas Burg', 'Alexios Balatsoukas-Stimming', 'Sitian Li'] | 2022-11-08 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-1.82943732e-01 -5.62264025e-01 8.34225118e-02 -3.31671029e-01
-1.38768935e+00 -5.19511700e-01 3.48003715e-01 1.62367791e-01
-4.29049075e-01 9.61211145e-01 -7.78844506e-02 -3.74067128e-01
-4.37283337e-01 -6.67558789e-01 -4.20250744e-01 -8.84234488e-01
-4.17468488e-01 9.89334732e-02 -1.38641670e-01 1.74843237... | [6.3155598640441895, 1.048790693283081] |
9a3dd177-4c61-4664-89dc-c8283625f513 | android-malware-detection-using-autoencoder | 1901.07315 | null | http://arxiv.org/abs/1901.07315v1 | http://arxiv.org/pdf/1901.07315v1.pdf | Android Malware Detection Using Autoencoder | Smartphones have become an intrinsic part of human's life. The smartphone
unifies diverse advanced characteristics. It enables users to store various
data such as photos, health data, credential bank data, and personal
information. The Android operating system is the prevalent mobile operating
system and, in the meanti... | ['Abdelmonim Naway', 'Yuancheng Li'] | 2019-01-14 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [-2.29543261e-02 -3.46259296e-01 -5.91886878e-01 1.09632201e-01
8.14393014e-02 -3.08655977e-01 7.54086077e-01 1.09021841e-02
-3.74218315e-01 5.53158700e-01 -1.48245737e-01 -6.00044549e-01
1.35443166e-01 -6.68549478e-01 -4.79064316e-01 -6.28326416e-01
1.78873479e-01 -4.96433526e-02 1.72490761e-01 -2.27103010... | [14.426189422607422, 9.682908058166504] |
08db6033-c2a6-45d0-be5d-cb939bffb281 | the-nni-query-by-example-system-for-mediaeval | null | null | http://www.npu-aslp.org/lxie/papers/2014QUESST-NNI.pdf | http://www.npu-aslp.org/lxie/papers/2014QUESST-NNI.pdf | The NNI Query-by-Example System for MediaEval 2014 | In this paper we describe the system proposed by NNI (NWPU-NTU-I2R) team for the QUESST task within the Mediaeval 2014 evaluation. To solve the problem, we used both dynamic time warping (DTW) and symbolic search (SS) based approaches. The DTW system performs template matching using subsequence DTW algorithm and poster... | ['Haizhou Li', 'Eng Siong Chng', 'Bin Ma', 'Su Jun Leow', 'Lei Wang', 'Hang Lv', 'JIA YU', 'Hongjie Chen', 'Cheung-Chi Leung', 'Lei Xie', 'Xiong Xiao', 'HaiHua Xu', 'Peng Yang'] | 2014-10-16 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 5.31822324e-01 -5.06446123e-01 -4.55056787e-01 -2.24359959e-01
-1.21378112e+00 -1.05567002e+00 6.45679533e-01 -1.09545738e-01
-7.99174249e-01 5.76468349e-01 2.50005722e-01 -4.25042123e-01
-3.68781269e-01 -5.83199978e-01 -4.70516741e-01 -2.06966221e-01
-1.00363925e-01 5.31068563e-01 5.18740714e-01 -2.92989463... | [14.301230430603027, 6.448639869689941] |
56149a83-5c92-4192-a9f0-5108ebae38e1 | evolutionary-deep-nets-for-non-intrusive-load | 2303.03538 | null | https://arxiv.org/abs/2303.03538v1 | https://arxiv.org/pdf/2303.03538v1.pdf | Evolutionary Deep Nets for Non-Intrusive Load Monitoring | Non-Intrusive Load Monitoring (NILM) is an energy efficiency technique to track electricity consumption of an individual appliance in a household by one aggregated single, such as building level meter readings. The goal of NILM is to disaggregate the appliance from the aggregated singles by computational method. In thi... | ['Kenneth A. Loparo', 'Jinsong Wang'] | 2023-03-06 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-5.47226295e-02 -4.31222767e-02 5.36635071e-02 -6.53885186e-01
-4.07607317e-01 -7.72928447e-02 4.20240641e-01 -2.64222831e-01
7.34109357e-02 9.02086496e-01 2.16739908e-01 -5.93062155e-02
-8.52527469e-02 -1.18552220e+00 -2.12711811e-01 -9.90820527e-01
-2.95444112e-02 3.91582757e-01 -5.94422281e-01 1.44105712... | [16.061491012573242, 7.576128959655762] |
930932f3-6953-490c-99d2-6e404a20badb | scalable-discovery-of-time-series-shapelets | 1503.03238 | null | http://arxiv.org/abs/1503.03238v1 | http://arxiv.org/pdf/1503.03238v1.pdf | Scalable Discovery of Time-Series Shapelets | Time-series classification is an important problem for the data mining
community due to the wide range of application domains involving time-series
data. A recent paradigm, called shapelets, represents patterns that are highly
predictive for the target variable. Shapelets are discovered by measuring the
prediction accu... | ['Lars Schmidt-Thieme', 'Josif Grabocka', 'Martin Wistuba'] | 2015-03-11 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-2.13995501e-02 -4.01689261e-01 -3.80128205e-01 -1.76433846e-01
-5.13302505e-01 -6.50574505e-01 3.45712125e-01 6.37299895e-01
8.19280818e-02 4.76942390e-01 -1.07900761e-01 -2.61175603e-01
-7.80796885e-01 -9.43616390e-01 -2.92505771e-01 -6.36083543e-01
-7.73977757e-01 5.52551031e-01 4.15643334e-01 6.78885803... | [7.292686462402344, 3.346958637237549] |
ad03a8b2-7379-4ff5-8e70-18de1a020efb | moore-model-based-offline-to-online | 2201.10070 | null | https://arxiv.org/abs/2201.10070v1 | https://arxiv.org/pdf/2201.10070v1.pdf | MOORe: Model-based Offline-to-Online Reinforcement Learning | With the success of offline reinforcement learning (RL), offline trained RL policies have the potential to be further improved when deployed online. A smooth transfer of the policy matters in safe real-world deployment. Besides, fast adaptation of the policy plays a vital role in practical online performance improvemen... | ['Chongjie Zhang', 'Bin Wang', 'Chao Wang', 'Yihuan Mao'] | 2022-01-25 | null | null | null | null | ['d4rl'] | ['robots'] | [-4.85744804e-01 -1.11260287e-01 -7.89734840e-01 -1.19130298e-01
-7.96613634e-01 -7.43163049e-01 3.96004736e-01 -3.23226303e-02
-5.11766255e-01 9.83085752e-01 -8.35183114e-02 -9.53836441e-01
-1.88998058e-01 -4.34625894e-01 -8.45936179e-01 -4.86392021e-01
-5.04929543e-01 5.54815292e-01 2.08265096e-01 -3.39668185... | [4.089633464813232, 2.2403101921081543] |
f2ba79db-44e1-4911-99fe-0b18ac993d33 | reducing-hallucinations-in-neural-machine | 2211.09878 | null | https://arxiv.org/abs/2211.09878v2 | https://arxiv.org/pdf/2211.09878v2.pdf | Reducing Hallucinations in Neural Machine Translation with Feature Attribution | Neural conditional language generation models achieve the state-of-the-art in Neural Machine Translation (NMT) but are highly dependent on the quality of parallel training dataset. When trained on low-quality datasets, these models are prone to various error types, including hallucinations, i.e. outputs that are fluent... | ['Lucia Specia', 'Marina Fomicheva', 'Joël Tang'] | 2022-11-17 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 5.29011428e-01 5.19452691e-01 1.10284097e-01 -1.46311060e-01
-1.08848882e+00 -6.35406673e-01 9.09752190e-01 8.33351612e-02
-2.84906387e-01 8.37160051e-01 4.11457688e-01 -2.89069235e-01
4.85172868e-01 -6.41014874e-01 -1.01328921e+00 -3.26910645e-01
4.89214629e-01 7.58043945e-01 -4.76238549e-01 -3.94672543... | [11.703813552856445, 9.913617134094238] |
ffd465c6-6f04-4a56-ba42-1ed5bd877490 | detection-of-word-adversarial-examples-in | 2203.01677 | null | https://arxiv.org/abs/2203.01677v1 | https://arxiv.org/pdf/2203.01677v1.pdf | Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation | Word-level adversarial attacks have shown success in NLP models, drastically decreasing the performance of transformer-based models in recent years. As a countermeasure, adversarial defense has been explored, but relatively few efforts have been made to detect adversarial examples. However, detecting adversarial exampl... | ['Nojun Kwak', 'Jiho Jang', 'Jangho Kim', 'KiYoon Yoo'] | 2022-03-03 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 9.59863290e-02 -1.18179008e-01 -2.33352810e-01 -2.09984571e-01
-1.28802145e+00 -1.09653592e+00 1.11090827e+00 3.45452368e-01
-3.26311618e-01 6.40486896e-01 2.62652874e-01 -6.30187094e-01
3.23038489e-01 -9.19306040e-01 -4.58957523e-01 -5.93882382e-01
1.26510397e-01 3.50126177e-01 5.40420189e-02 -3.42353225... | [6.033651351928711, 8.070252418518066] |
68e63160-b0c7-4b05-b6f7-456a37b202e1 | on-the-importance-of-distinguishing-word | null | null | https://aclanthology.org/N19-1222 | https://aclanthology.org/N19-1222.pdf | On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping | Meaning conflation deficiency is one of the main limiting factors of word representations which, given their widespread use at the core of many NLP systems, can lead to inaccurate semantic understanding of the input text and inevitably hamper the performance. Sense representations target this problem. However, their po... | ['Mohammad Taher Pilehvar'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['reverse-dictionary'] | ['natural-language-processing'] | [ 6.67357981e-01 2.50524729e-01 -2.82154530e-01 -2.30470911e-01
-3.96359622e-01 -6.93470418e-01 7.53171742e-01 5.18930197e-01
-6.70061588e-01 6.72762632e-01 5.00103652e-01 -6.91925943e-01
-9.17549655e-02 -7.93146312e-01 -1.88851982e-01 -2.58078933e-01
6.30422592e-01 5.78702033e-01 -1.93466693e-02 -6.18829548... | [10.408653259277344, 8.91614818572998] |
71bad3c0-04a1-4770-af22-53b2a8f4370a | study-of-list-based-omp-and-an-enhanced-model | 2105.03774 | null | https://arxiv.org/abs/2105.03774v1 | https://arxiv.org/pdf/2105.03774v1.pdf | Study of List-Based OMP and an Enhanced Model for Direction Finding with Non-Uniform Arrays | This paper proposes an enhanced coarray transformation model (EDCTM) and a mixed greedy maximum likelihood algorithm called List-Based Maximum Likelihood Orthogonal Matching Pursuit (LBML-OMP) for direction-of-arrival estimation with non-uniform linear arrays (NLAs). The proposed EDCTM approach obtains improved estimat... | ['R. C. de Lamare', 'W. S. Leite'] | 2021-05-08 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 1.00279093e-01 -4.95744824e-01 8.21372047e-02 -1.17160967e-02
-1.04198086e+00 -2.67209679e-01 2.49021232e-01 1.34557709e-01
-4.16372567e-02 5.04031837e-01 5.46743453e-01 -4.02016908e-01
-6.32911623e-01 -4.68296915e-01 -4.21557248e-01 -8.29634726e-01
-8.34543943e-01 1.78305164e-01 -2.53180891e-01 1.46723613... | [6.480166912078857, 1.354430913925171] |
870a2c10-2339-43ae-8816-f68fe7832ce8 | global-and-local-relative-position-embedding | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4920_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700171.pdf | Global-and-Local Relative Position Embedding for Unsupervised Video Summarization | In order to summarize a content video properly, it is important to grasp the sequential structure of video as well as the long-term dependency between frames. The necessity of them is more obvious, especially for unsupervised learning. One possible solution is to utilize a well-known technique in the field of natural l... | ['Sanghyun Woo', 'Yunjae Jung', 'In So Kweon', 'Donghyeon Cho'] | null | null | null | null | eccv-2020-8 | ['unsupervised-video-summarization'] | ['computer-vision'] | [-1.79301221e-02 -1.37542263e-01 -3.15894693e-01 -3.21397424e-01
-5.33410490e-01 -5.57555020e-01 4.94925767e-01 2.70633101e-01
-6.32902682e-01 4.87867117e-01 5.44963837e-01 -8.23602974e-02
-1.30444899e-01 -5.57203889e-01 -5.51044822e-01 -5.30265808e-01
-3.86534631e-01 -7.88163394e-02 3.06358278e-01 -4.14344370... | [10.153538703918457, 0.5615969300270081] |
411428cf-192a-4443-b2cc-520d4617c063 | nondiscriminatory-treatment-a-straightforward | 2101.10913 | null | https://arxiv.org/abs/2101.10913v1 | https://arxiv.org/pdf/2101.10913v1.pdf | Nondiscriminatory Treatment: a straightforward framework for multi-human parsing | Multi-human parsing aims to segment every body part of every human instance. Nearly all state-of-the-art methods follow the "detection first" or "segmentation first" pipelines. Different from them, we present an end-to-end and box-free pipeline from a new and more human-intuitive perspective. In training time, we direc... | ['Yueming Zhang', 'Tong Zhang', 'Guoshan Zhang', 'Min Yan'] | 2021-01-26 | null | null | null | null | ['multi-human-parsing', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 4.49770510e-01 7.02015400e-01 -2.59270109e-02 -8.36408436e-01
-6.84431076e-01 -6.22524738e-01 4.96435970e-01 1.09914966e-01
-3.78271192e-01 3.10342729e-01 1.86296850e-02 -1.29621774e-01
4.83724058e-01 -7.27603972e-01 -8.44804883e-01 -4.38660979e-01
3.76013935e-01 9.34551299e-01 5.69201291e-01 -1.72280651... | [8.584108352661133, -0.02253217250108719] |
ae717c30-08cf-4a8f-b579-86005a054868 | dynamic-texture-analysis-for-detecting-fake | 2007.15271 | null | https://arxiv.org/abs/2007.15271v1 | https://arxiv.org/pdf/2007.15271v1.pdf | Dynamic texture analysis for detecting fake faces in video sequences | The creation of manipulated multimedia content involving human characters has reached in the last years unprecedented realism, calling for automated techniques to expose synthetically generated faces in images and videos. This work explores the analysis of spatio-temporal texture dynamics of the video signal, with the ... | ['Giulia Boato', 'Mattia Bonomi', 'Cecilia Pasquini'] | 2020-07-30 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 3.63457680e-01 -1.64448619e-01 6.79029245e-03 -5.89218996e-02
-3.13284159e-01 -6.74925745e-01 1.05352581e+00 1.08137224e-02
-2.56270975e-01 4.11631376e-01 -6.26626089e-02 9.14219320e-02
-1.19800568e-01 -5.25808632e-01 -4.81408328e-01 -9.52215433e-01
-7.11835742e-01 2.30548456e-01 4.06895638e-01 -2.12856591... | [12.514843940734863, 1.1449313163757324] |
dce9490d-1e4f-4a29-ba05-3f32de1b1f7d | uctransnet-rethinking-the-skip-connections-in | 2109.04335 | null | https://arxiv.org/abs/2109.04335v3 | https://arxiv.org/pdf/2109.04335v3.pdf | UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer | Most recent semantic segmentation methods adopt a U-Net framework with an encoder-decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to model the global multi-scale context: 1) Not each skip connection setting is effective due to the issue of incompatible feature sets of encode... | ['Osmar R. Zaiane', 'Jiaqi Wang', 'Peng Cao', 'Haonan Wang'] | 2021-09-09 | null | null | null | null | ['unet-segmentation'] | ['computer-vision'] | [ 2.90416449e-01 2.14923665e-01 -1.46658659e-01 -2.81927198e-01
-9.71231043e-01 -2.44103417e-01 6.93581030e-02 -2.07966119e-01
-2.34412283e-01 5.06055474e-01 2.16216698e-01 -3.92106295e-01
1.31899104e-01 -8.78180981e-01 -8.51092458e-01 -6.07543349e-01
3.51491362e-01 4.67067398e-02 6.30435526e-01 -3.23963106... | [14.605147361755371, -2.6272103786468506] |
aa30057a-4448-4c3f-b23d-d570ca2e9226 | instructeval-systematic-evaluation-of | 2307.00259 | null | https://arxiv.org/abs/2307.00259v1 | https://arxiv.org/pdf/2307.00259v1.pdf | InstructEval: Systematic Evaluation of Instruction Selection Methods | In-context learning (ICL) performs tasks by prompting a large language model (LLM) using an instruction and a small set of annotated examples called demonstrations. Recent work has shown that the precise details of the inputs used in the prompt significantly impacts ICL, which has incentivized instruction selection alg... | ['Karthik Narasimhan', 'Ameet Deshpande', 'Mengzhou Xia', 'Chris Pan', 'Anirudh Ajith'] | 2023-07-01 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [ 3.78584832e-01 -2.61492729e-01 -8.22792709e-01 -5.96440077e-01
-1.11274421e+00 -9.44089711e-01 8.49172711e-01 1.48257822e-01
-4.92759854e-01 6.51876330e-01 3.15234989e-01 -1.11029959e+00
6.31880164e-02 -8.86040106e-02 -8.44388366e-01 -1.52021110e-01
-9.71094146e-02 3.78841788e-01 3.07558537e-01 -1.66455567... | [10.617579460144043, 8.288359642028809] |
b62ae0da-41f6-463b-8e21-d27ad8c26145 | simultaneous-face-hallucination-and | 2104.06534 | null | https://arxiv.org/abs/2104.06534v2 | https://arxiv.org/pdf/2104.06534v2.pdf | Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN | Existing thermal-to-visible face verification approaches expect the thermal and visible face images to be of similar resolution. This is unlikely in real-world long-range surveillance systems, since humans are distant from the cameras. To address this issue, we introduce the task of thermal-to-visible face verification... | ['Vishal M. Patel', 'Shuowen Hu', 'Rakhil Immidisetti'] | 2021-04-13 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 3.46194506e-01 -2.69018617e-02 3.25067878e-01 -7.56037593e-01
-8.71371567e-01 -5.80931425e-01 7.29016542e-01 -1.20070684e+00
2.58834176e-02 4.83431011e-01 6.93591982e-02 -1.20366439e-01
2.09738731e-01 -4.61528540e-01 -7.78777003e-01 -8.27418566e-01
3.81967783e-01 2.45888501e-01 -2.59665340e-01 -9.67298672... | [12.891942024230957, 0.029022470116615295] |
5e57157d-f2ed-4e35-a6a6-6ef8905568ef | nus-hlt-report-for-activitynet-challenge-2021 | null | null | http://research.google.com/ava/2021/S3_NUS_Report_AVA_ActiveSpeaker_2021.pdf | http://research.google.com/ava/2021/S3_NUS_Report_AVA_ActiveSpeaker_2021.pdf | NUS-HLT Report for ActivityNet Challenge 2021 AVA (Speaker) | Active speaker detection (ASD) seeks to detect who is speaking in a visual scene of one or more speakers. The successful ASD depends on accurate interpretation of short-term and long-term audio and visual information, as well as audiovisual interaction. Unlike the prior work where systems makedecision instantaneously u... | ['Haizhou Li', 'Mike Zheng Shou', 'Xinyuan Qian', 'Rohan Kumar Das', 'Zexu Pan', 'Ruijie Tao'] | 2021-06-01 | null | null | null | the-activitynet-large-scale-activity | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [-1.84456989e-01 -9.84339491e-02 2.44308598e-02 -6.74266279e-01
-1.30503857e+00 -6.82567537e-01 8.49098265e-01 -3.14946100e-02
-2.32956007e-01 2.99404353e-01 6.36900485e-01 -1.91121072e-01
1.77457586e-01 -1.09308943e-01 -5.90162039e-01 -7.21370876e-01
-2.36573666e-01 9.69610587e-02 2.92709619e-01 9.63659808... | [14.415695190429688, 5.14167594909668] |
9add3d0d-0a37-4c33-9454-6d3685f21b4f | adversarial-networks-and-machine-learning-for | 2301.11964 | null | https://arxiv.org/abs/2301.11964v2 | https://arxiv.org/pdf/2301.11964v2.pdf | Adversarial Networks and Machine Learning for File Classification | Correctly identifying the type of file under examination is a critical part of a forensic investigation. The file type alone suggests the embedded content, such as a picture, video, manuscript, spreadsheet, etc. In cases where a system owner might desire to keep their files inaccessible or file type concealed, we propo... | ['Josh Angichiodo', 'Ken St. Germain'] | 2023-01-27 | null | null | null | null | ['type'] | ['speech'] | [ 2.20379844e-01 -5.45271486e-02 1.90202724e-02 -2.96248317e-01
-9.60043609e-01 -9.73264575e-01 3.77514124e-01 1.90860838e-01
-8.67312253e-02 9.13555026e-01 -3.14799458e-01 -7.65409648e-01
1.54368550e-01 -1.01244819e+00 -1.04788899e+00 -6.03066862e-01
-2.54038945e-02 5.40311515e-01 4.91957255e-02 2.18061998... | [12.368803977966309, 1.0194900035858154] |
8248cdc9-3210-43dd-906a-93384acd0b9c | open-source-large-language-models-outperform | 2307.02179 | null | https://arxiv.org/abs/2307.02179v1 | https://arxiv.org/pdf/2307.02179v1.pdf | Open-Source Large Language Models Outperform Crowd Workers and Approach ChatGPT in Text-Annotation Tasks | This study examines the performance of open-source Large Language Models (LLMs) in text annotation tasks and compares it with proprietary models like ChatGPT and human-based services such as MTurk. While prior research demonstrated the high performance of ChatGPT across numerous NLP tasks, open-source LLMs like HugginC... | ['Fabrizio Gilardi', 'Maria Korobeynikova', 'Juan Diego Bermeo', 'Shirin Dehghani', 'Zeynab Samei', 'Maël Kubli', 'Meysam Alizadeh'] | 2023-07-05 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [-4.41906869e-01 1.38063207e-01 -1.31476775e-01 -2.73190737e-01
-1.20261395e+00 -6.55926406e-01 8.93004715e-01 1.95821926e-01
-6.80470645e-01 6.66903019e-01 4.88474220e-01 -1.85324535e-01
2.09664211e-01 4.62871753e-02 -7.08024427e-02 -2.43061632e-01
1.29170775e-01 8.99307430e-01 6.16808712e-01 -1.18389107... | [9.913901329040527, 8.779406547546387] |
fe97889d-d30a-4d73-8e6a-4dac5e778e86 | a-graph-to-graphs-framework-for | 2003.12725 | null | https://arxiv.org/abs/2003.12725v3 | https://arxiv.org/pdf/2003.12725v3.pdf | A Graph to Graphs Framework for Retrosynthesis Prediction | A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods rely on matching the target molecule with a large set of reaction templates, which are very computationally expensive and also suffer from t... | ['Ming Zhang', 'Jian Tang', 'Hongyu Guo', 'Chence Shi', 'Minkai Xu'] | 2020-03-28 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/4152-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/4152-Paper.pdf | icml-2020-1 | ['retrosynthesis'] | ['medical'] | [ 6.00448370e-01 1.13925777e-01 -5.02378166e-01 1.83192283e-01
-8.13804865e-01 -9.79265928e-01 8.05767417e-01 3.17192435e-01
1.54287875e-01 1.00953233e+00 -6.94327876e-02 -5.56072891e-01
1.96127400e-01 -1.02747631e+00 -8.70508611e-01 -9.93105710e-01
3.23357642e-01 8.45024288e-01 4.58807021e-01 -4.01268035... | [4.53211784362793, 6.104106903076172] |
3921771a-58fd-43d4-82d3-8204fd5ba1ad | ddm-net-end-to-end-learning-of-keypoint | 2212.04575 | null | https://arxiv.org/abs/2212.04575v2 | https://arxiv.org/pdf/2212.04575v2.pdf | DDM-NET: End-to-end learning of keypoint feature Detection, Description and Matching for 3D localization | In this paper, we propose an end-to-end framework that jointly learns keypoint detection, descriptor representation and cross-frame matching for the task of image-based 3D localization. Prior art has tackled each of these components individually, purportedly aiming to alleviate difficulties in effectively train a holis... | ['Gang Hua', 'Haoxiang Li', 'Enrique Dunn', 'Li Guan', 'Xiangyu Xu'] | 2022-12-08 | null | null | null | null | ['keypoint-detection'] | ['computer-vision'] | [-6.20516092e-02 -1.42509431e-01 -2.94690430e-01 -3.21940809e-01
-1.37816846e+00 -7.04272032e-01 8.39089692e-01 1.98498085e-01
-6.30663276e-01 1.14459090e-01 1.25786997e-02 8.13611299e-02
-4.78365123e-02 -3.49421322e-01 -1.01390827e+00 -3.54917079e-01
-1.39196321e-01 5.04257083e-01 3.06251198e-01 -2.68060248... | [7.978281021118164, -2.1625473499298096] |
0137b61c-ea2c-4187-b31d-fa5e347f0dfa | zooming-slow-mo-fast-and-accurate-one-stage | 2002.11616 | null | https://arxiv.org/abs/2002.11616v1 | https://arxiv.org/pdf/2002.11616v1.pdf | Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution | In this paper, we explore the space-time video super-resolution task, which aims to generate a high-resolution (HR) slow-motion video from a low frame rate (LFR), low-resolution (LR) video. A simple solution is to split it into two sub-tasks: video frame interpolation (VFI) and video super-resolution (VSR). However, te... | ['Yun Fu', 'Yulun Zhang', 'Yapeng Tian', 'Jan P. Allebach', 'Chenliang Xu', 'Xiaoyu Xiang'] | 2020-02-26 | zooming-slow-mo-fast-and-accurate-one-stage-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Xiang_Zooming_Slow-Mo_Fast_and_Accurate_One-Stage_Space-Time_Video_Super-Resolution_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xiang_Zooming_Slow-Mo_Fast_and_Accurate_One-Stage_Space-Time_Video_Super-Resolution_CVPR_2020_paper.pdf | cvpr-2020-6 | ['space-time-video-super-resolution'] | ['computer-vision'] | [ 3.98106456e-01 -2.72246599e-01 -3.20668340e-01 -3.25106233e-01
-9.51960981e-01 -8.52646958e-03 4.47555035e-01 -8.99455070e-01
-2.63038963e-01 9.17339981e-01 3.34016025e-01 -7.78784137e-03
8.73491317e-02 -7.49661028e-01 -9.50817227e-01 -6.32574975e-01
1.92116320e-01 -1.70322120e-01 4.72145587e-01 -9.32520255... | [11.016128540039062, -1.860333800315857] |
3e2f38e9-5f46-414e-9a27-c861e1e60526 | image-shadow-removal-using-end-to-end-deep | null | null | https://www.mdpi.com/2076-3417/9/5/1009 | https://www.mdpi.com/2076-3417/9/5/1009/pdf-vor | Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks | Image degradation caused by shadows is likely to cause technological issues in image segmentation and target recognition. In view of the existing shadow removal methods, there are problems such as small and trivial shadow processing, the scarcity of end-to-end automatic methods, the neglecting of light, and high-level ... | ['Jinjiang Li', 'Meng Han', 'Hui Fan'] | 2019-03-11 | null | null | null | null | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 5.10713577e-01 -3.82915735e-01 1.22308500e-01 -3.00694197e-01
-2.27807716e-01 7.31867924e-02 -4.71538864e-02 -4.71959203e-01
-3.83820415e-01 7.94773400e-01 1.08180523e-01 -3.30507278e-01
4.28546727e-01 -6.43155813e-01 -4.29388195e-01 -1.05969608e+00
2.74477959e-01 -1.81678116e-01 9.69966173e-01 -2.02279106... | [10.837363243103027, -3.9728007316589355] |
5147a27e-2be6-4bc7-b00a-5197d6990f69 | single-image-deraining-via-scale-space | 2006.05049 | null | https://arxiv.org/abs/2006.05049v2 | https://arxiv.org/pdf/2006.05049v2.pdf | Single Image Deraining via Scale-space Invariant Attention Neural Network | Image enhancement from degradation of rainy artifacts plays a critical role in outdoor visual computing systems. In this paper, we tackle the notion of scale that deals with visual changes in appearance of rain steaks with respect to the camera. Specifically, we revisit multi-scale representation by scale-space theory,... | ['Xian-Ming Liu', 'Deming Zhai', 'Junjun Jiang', 'Bo Pang'] | 2020-06-09 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 2.25237355e-01 -6.00173712e-01 4.60735440e-01 -5.48690557e-01
-2.42043108e-01 -2.72611767e-01 4.02281396e-02 -2.80738622e-01
-4.22846168e-01 7.77666330e-01 4.72470783e-02 1.42540321e-01
-5.15388208e-04 -7.38300025e-01 -6.46455824e-01 -9.30665433e-01
-1.24238923e-01 -7.66009033e-01 3.91824335e-01 -5.45734406... | [10.907129287719727, -3.183201551437378] |
d8727a9b-9e14-48cf-82de-1fd25264255d | toward-a-task-of-feedback-comment-generation | null | null | https://aclanthology.org/D19-1316 | https://aclanthology.org/D19-1316.pdf | Toward a Task of Feedback Comment Generation for Writing Learning | In this paper, we introduce a novel task called feedback comment generation {---} a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning for non-native learners of English. There has been almost no work on this task nor corpus annotated with feedback comments. We... | ['Ryo Nagata'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['comment-generation'] | ['natural-language-processing'] | [ 3.34224761e-01 6.45072699e-01 1.10932207e-02 -5.67592084e-01
-1.32300639e+00 -5.37951708e-01 7.48331130e-01 4.01592851e-01
-6.02947474e-01 1.26285911e+00 6.19940698e-01 -8.49835515e-01
3.05109173e-01 -4.20407504e-01 -6.70184612e-01 -2.04409435e-01
3.51869017e-01 2.70299911e-01 3.22930992e-01 -4.63259876... | [11.734583854675293, 9.03400707244873] |
5cc35e3a-426b-496b-94c7-ecedb34e25c0 | breaking-shortcut-exploring-fully | 2105.05838 | null | https://arxiv.org/abs/2105.05838v2 | https://arxiv.org/pdf/2105.05838v2.pdf | Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning | Previous cycle-consistency correspondence learning methods usually leverage image patches for training. In this paper, we present a fully convolutional method, which is simpler and more coherent to the inference process. While directly applying fully convolutional training results in model collapse, we study the underl... | ['Han Hu', 'Philip H. S. Torr', 'Zheng Zhang', 'Yue Cao', 'Zhenda Xie', 'Zhenyu Jiang', 'Yansong Tang'] | 2021-05-12 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [ 2.92493790e-01 -3.49834189e-02 -5.73378503e-01 -5.00162005e-01
-2.97894329e-01 -6.23659313e-01 6.31220818e-01 -5.53611405e-02
-1.66427553e-01 5.35016954e-01 -3.60227078e-02 -3.02013576e-01
2.34867200e-01 -5.16267180e-01 -9.69650924e-01 -7.53571689e-01
1.61748856e-01 1.53653741e-01 3.97510231e-01 -1.39143905... | [9.124653816223145, -0.1137237399816513] |
323d5879-544e-441d-9b63-4b2aa9537960 | 3d-multi-object-tracking-using-graph-neural | 2203.10926 | null | https://arxiv.org/abs/2203.10926v2 | https://arxiv.org/pdf/2203.10926v2.pdf | 3D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge Modality Attention | Online 3D multi-object tracking (MOT) has witnessed significant research interest in recent years, largely driven by demand from the autonomous systems community. However, 3D offline MOT is relatively less explored. Labeling 3D trajectory scene data at a large scale while not relying on high-cost human experts is still... | ['Abhinav Valada', 'Martin Buchner'] | 2022-03-21 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [ 1.51523456e-01 -1.45557508e-01 -1.52518004e-01 -1.05916448e-01
-3.81999373e-01 -6.98827922e-01 6.17629647e-01 1.72084898e-01
-3.15918267e-01 3.65291953e-01 -1.51469126e-01 -2.46490732e-01
-1.97713107e-01 -7.64185607e-01 -9.91142154e-01 -4.21563804e-01
-2.75509298e-01 5.16640127e-01 6.57296479e-01 3.79259028... | [6.439594268798828, -2.129971981048584] |
40dd74ad-9f77-45b5-a3e8-8c1ad7a0f223 | using-a-supervised-method-without-supervision | 2011.07954 | null | https://arxiv.org/abs/2011.07954v4 | https://arxiv.org/pdf/2011.07954v4.pdf | Using a Supervised Method without supervision for foreground segmentation | Neural networks are a powerful framework for foreground segmentation in video acquired by static cameras, segmenting moving objects from the background in a robust way in various challenging scenarios. The premier methods are those based on supervision requiring a final training stage on a database of tens to hundreds ... | ['Michael Werman', 'Levi Kassel'] | 2020-10-26 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 8.11542451e-01 -2.71369182e-02 -8.30376819e-02 -4.19437259e-01
-3.87981504e-01 -3.41237813e-01 4.85494018e-01 -1.81478098e-01
-8.11859965e-01 7.55706787e-01 -3.53591561e-01 -1.13330103e-01
2.14964762e-01 -6.17308319e-01 -8.16877902e-01 -9.92922306e-01
1.17722608e-01 7.59393632e-01 1.04591072e+00 1.39043361... | [9.01090145111084, -0.4129634499549866] |
7347a3de-84dd-4211-8e86-1eb987e1f5c7 | self-supervised-auxiliary-learning-with-meta | 2007.08294 | null | https://arxiv.org/abs/2007.08294v5 | https://arxiv.org/pdf/2007.08294v5.pdf | Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs | Graph neural networks have shown superior performance in a wide range of applications providing a powerful representation of graph-structured data. Recent works show that the representation can be further improved by auxiliary tasks. However, the auxiliary tasks for heterogeneous graphs, which contain rich semantic inf... | ['Jung-Woo Ha', 'Kyung-Min Kim', 'Jinyoung Park', 'Dasol Hwang', 'Hyunwoo J. Kim', 'Sunyoung Kwon'] | 2020-07-16 | null | http://proceedings.neurips.cc/paper/2020/hash/74de5f915765ea59816e770a8e686f38-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/74de5f915765ea59816e770a8e686f38-Paper.pdf | neurips-2020-12 | ['auxiliary-learning'] | ['methodology'] | [ 3.12850177e-01 6.13891840e-01 -6.63535714e-01 -3.30515593e-01
-2.75196612e-01 -2.59541124e-01 5.55040359e-01 4.33385700e-01
-6.06115833e-02 8.05255175e-01 6.64499700e-02 -4.08520728e-01
-1.59257159e-01 -1.13747406e+00 -6.15635812e-01 -5.56512773e-01
-1.65456310e-01 6.11855745e-01 4.15496558e-01 -4.30767924... | [7.353494167327881, 6.358311653137207] |
14581a94-0b91-4fe1-bd51-a71a50a748f0 | local-to-global-panorama-inpainting-for | 2303.10344 | null | https://arxiv.org/abs/2303.10344v1 | https://arxiv.org/pdf/2303.10344v1.pdf | Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction | Predicting panoramic indoor lighting from a single perspective image is a fundamental but highly ill-posed problem in computer vision and graphics. To achieve locale-aware and robust prediction, this problem can be decomposed into three sub-tasks: depth-based image warping, panorama inpainting and high-dynamic-range (H... | ['Yanwen Guo', 'Yan Zhang', 'Zhenyu Chen', 'Jie Guo', 'Shan Yang', 'Zhen He', 'Jiayang Bai'] | 2023-03-18 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 4.65433031e-01 -2.87499219e-01 8.51480812e-02 -2.26615340e-01
-5.97777009e-01 -2.90823877e-01 4.21626896e-01 -6.90388381e-01
2.54403859e-01 8.15748513e-01 4.62803066e-01 5.10543250e-02
-2.50471365e-02 -1.03537393e+00 -1.09850395e+00 -7.51115203e-01
7.19459295e-01 -1.42776161e-01 1.13218427e-01 -2.90421516... | [10.325384140014648, -2.20694637298584] |
5e11fdc3-f0a9-41b6-af94-d92ec2e9e585 | data-augmentation-using-random-image-cropping-1 | 2111.08270 | null | https://arxiv.org/abs/2111.08270v1 | https://arxiv.org/pdf/2111.08270v1.pdf | Data Augmentation using Random Image Cropping for High-resolution Virtual Try-On (VITON-CROP) | Image-based virtual try-on provides the capacity to transfer a clothing item onto a photo of a given person, which is usually accomplished by warping the item to a given human pose and adjusting the warped item to the person. However, the results of real-world synthetic images (e.g., selfies) from the previous method i... | ['Jaegul Choo', 'Seunghwan Choi', 'Sunghyun Park', 'Taewon Kang'] | 2021-11-16 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 3.50223809e-01 -9.79976635e-03 1.89658418e-01 -1.89647257e-01
-1.93806469e-01 -6.58415377e-01 4.37183738e-01 -6.80613577e-01
1.11926533e-02 7.27852762e-01 7.20247701e-02 3.04611564e-01
5.89081049e-01 -6.41234636e-01 -1.00970018e+00 -3.07429105e-01
5.48558950e-01 1.66951060e-01 2.57165045e-01 -5.03176451... | [11.899657249450684, -0.8627941608428955] |
e605abce-6aee-4517-b7a5-6602d104b2c4 | chatgpt-edss-empathetic-dialogue-speech | 2305.13724 | null | https://arxiv.org/abs/2305.13724v1 | https://arxiv.org/pdf/2305.13724v1.pdf | ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from ChatGPT-derived Context Word Embeddings | We propose ChatGPT-EDSS, an empathetic dialogue speech synthesis (EDSS) method using ChatGPT for extracting dialogue context. ChatGPT is a chatbot that can deeply understand the content and purpose of an input prompt and appropriately respond to the user's request. We focus on ChatGPT's reading comprehension and introd... | ['Hiroshi Saruwatari', 'Kentaro Tachibana', 'Eiji Iimori', 'Shinnosuke Takamichi', 'Yuki Saito'] | 2023-05-23 | null | null | null | null | ['chatbot', 'reading-comprehension', 'chatbot', 'speech-synthesis'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'speech'] | [-1.34499997e-01 5.88646114e-01 2.02929586e-01 -5.65316379e-01
-7.96607614e-01 -5.45204341e-01 5.62231421e-01 -1.29819617e-01
-4.54102531e-02 5.26324332e-01 1.09349537e+00 -3.84994805e-01
5.49914300e-01 -4.09416407e-01 9.81769711e-02 -4.63315606e-01
5.10406673e-01 6.84539258e-01 -3.04690421e-01 -8.43728781... | [12.89903736114502, 7.789802551269531] |
d64c9848-e958-404e-88ad-dfc0f13d9158 | document-intelligence-metrics-for-visually | 2205.11215 | null | https://arxiv.org/abs/2205.11215v1 | https://arxiv.org/pdf/2205.11215v1.pdf | Document Intelligence Metrics for Visually Rich Document Evaluation | The processing of Visually-Rich Documents (VRDs) is highly important in information extraction tasks associated with Document Intelligence. We introduce DI-Metrics, a Python library devoted to VRD model evaluation comprising text-based, geometric-based and hierarchical metrics for information extraction tasks. We apply... | ['Adam Karwan', 'Krzysztof Wilkosz', 'Zhuoyu Han', 'Swapnil Gupta', 'Jonathan Degange'] | 2022-05-23 | null | null | null | null | ['document-ai'] | ['natural-language-processing'] | [-6.48483559e-02 5.97570688e-02 9.91874561e-02 -1.47184670e-01
-7.82927096e-01 -1.09382355e+00 1.15678799e+00 8.50159883e-01
-1.07315667e-01 3.01342398e-01 4.12509590e-01 -4.20443207e-01
-4.20229822e-01 -1.05007780e+00 -1.56602219e-01 8.66526067e-02
-1.94963321e-01 3.76037717e-01 2.82852560e-01 -4.16132249... | [11.672042846679688, 2.6363253593444824] |
8b8203aa-16e3-434d-966a-902d51269b5d | lenia-biology-of-artificial-life | 1812.05433 | null | https://arxiv.org/abs/1812.05433v3 | https://arxiv.org/pdf/1812.05433v3.pdf | Lenia - Biology of Artificial Life | We report a new system of artificial life called Lenia (from Latin lenis "smooth"), a two-dimensional cellular automaton with continuous space-time-state and generalized local rule. Computer simulations show that Lenia supports a great diversity of complex autonomous patterns or "lifeforms" bearing resemblance to real-... | ['Bert Wang-Chak Chan'] | 2018-12-13 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-4.81954902e-01 -2.03445539e-01 2.19414547e-01 4.50942546e-01
9.22320604e-01 -1.04476011e+00 9.86150324e-01 -2.06121847e-01
-1.34892121e-01 1.05892015e+00 -1.43466353e-01 -1.74514666e-01
-2.26268992e-01 -1.07298672e+00 -3.29546750e-01 -1.16283429e+00
-6.43761218e-01 6.09478891e-01 5.43226004e-01 -7.94965148... | [5.580287933349609, 4.140581130981445] |
220e99e8-85a3-4951-b934-81016e560877 | context-aware-relative-object-queries-to | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Choudhuri_Context-Aware_Relative_Object_Queries_To_Unify_Video_Instance_and_Panoptic_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Choudhuri_Context-Aware_Relative_Object_Queries_To_Unify_Video_Instance_and_Panoptic_CVPR_2023_paper.pdf | Context-Aware Relative Object Queries To Unify Video Instance and Panoptic Segmentation | Object queries have emerged as a powerful abstraction to generically represent object proposals. However, their use for temporal tasks like video segmentation poses two questions: 1) How to process frames sequentially and propagate object queries seamlessly across frames. Using independent object queries per frame ... | ['Alexander G. Schwing', 'Girish Chowdhary', 'Anwesa Choudhuri'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['panoptic-segmentation', 'video-instance-segmentation', 'video-semantic-segmentation', 'multi-object-tracking', 'multi-object-tracking-and-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-2.39627697e-02 -6.66263878e-01 -3.26269448e-01 -2.65148491e-01
-6.30178869e-01 -8.49763751e-01 4.76163149e-01 1.82404265e-01
-6.77376866e-01 4.51517671e-01 -2.28641063e-01 1.78853527e-01
3.80508602e-02 -4.45553720e-01 -6.16846800e-01 -5.33667922e-01
-1.04133829e-01 5.18177927e-01 1.61559486e+00 -2.48802692... | [9.074602127075195, -0.14852751791477203] |
f446ee99-e3be-47f2-b291-17ed4fe62860 | faster-voxelpose-real-time-3d-human-pose | 2207.10955 | null | https://arxiv.org/abs/2207.10955v1 | https://arxiv.org/pdf/2207.10955v1.pdf | Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection | While the voxel-based methods have achieved promising results for multi-person 3D pose estimation from multi-cameras, they suffer from heavy computation burdens, especially for large scenes. We present Faster VoxelPose to address the challenge by re-projecting the feature volume to the three two-dimensional coordinate ... | ['Yizhou Wang', 'Rujie Wu', 'Chunyu Wang', 'Wentao Zhu', 'Hang Ye'] | 2022-07-22 | null | null | null | null | ['3d-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.88344496e-01 -3.00947487e-01 1.40366480e-01 -3.79652798e-01
-7.92632222e-01 -4.61792469e-01 4.00149703e-01 -3.02637797e-02
-5.75527430e-01 3.11162710e-01 2.62800097e-01 3.71618092e-01
3.24218273e-01 -7.24373817e-01 -6.56187534e-01 -2.29752257e-01
1.38783187e-01 1.12044728e+00 2.18042478e-01 1.69720441... | [7.02118444442749, -1.0028704404830933] |
aad7d170-49f4-4362-a333-1cd579a0f8b4 | ogb-lsc-a-large-scale-challenge-for-machine | 2103.09430 | null | https://arxiv.org/abs/2103.09430v3 | https://arxiv.org/pdf/2103.09430v3.pdf | OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs | Enabling effective and efficient machine learning (ML) over large-scale graph data (e.g., graphs with billions of edges) can have a great impact on both industrial and scientific applications. However, existing efforts to advance large-scale graph ML have been largely limited by the lack of a suitable public benchmark.... | ['Jure Leskovec', 'Yuxiao Dong', 'Maho Nakata', 'Hongyu Ren', 'Matthias Fey', 'Weihua Hu'] | 2021-03-17 | null | null | null | null | ['graph-regression'] | ['graphs'] | [-2.42136672e-01 2.52687067e-01 -7.17691123e-01 -9.11539271e-02
-8.35978329e-01 -4.66093004e-01 4.32863504e-01 5.34166873e-01
-8.25173780e-03 8.88378203e-01 -1.02528809e-02 -5.64513564e-01
-3.06744725e-01 -9.69472885e-01 -8.36833119e-01 -2.11696699e-01
-7.77193248e-01 7.15020001e-01 2.67592400e-01 -2.71994293... | [7.012141227722168, 6.144567012786865] |
ebeed233-6985-46e2-972d-d87238d5fd2a | conditional-generative-data-free-knowledge | 2112.15358 | null | https://arxiv.org/abs/2112.15358v4 | https://arxiv.org/pdf/2112.15358v4.pdf | Conditional Generative Data-free Knowledge Distillation | Knowledge distillation has made remarkable achievements in model compression. However, most existing methods require the original training data, which is usually unavailable due to privacy and security issues. In this paper, we propose a conditional generative data-free knowledge distillation (CGDD) framework for train... | ['Libo Zhou', 'Yang Yang', 'Xinyi Yu', 'Linlin Ou', 'Ling Yan'] | 2021-12-31 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 4.71519113e-01 2.61747122e-01 -3.18891406e-01 -4.31553423e-01
-5.18852055e-01 -4.16350543e-01 4.33599085e-01 -2.43427485e-01
-6.82703912e-01 1.06897247e+00 -1.42721951e-01 -2.79934436e-01
2.55993642e-02 -1.10279524e+00 -9.99079347e-01 -1.06463504e+00
3.06716800e-01 2.69249290e-01 1.25643713e-02 1.72810495... | [9.459946632385254, 3.3428030014038086] |
78d0ee74-1187-4a24-9611-d940dfbe5ade | frequency-and-spatial-domain-based-saliency | 2010.04022 | null | https://arxiv.org/abs/2010.04022v1 | https://arxiv.org/pdf/2010.04022v1.pdf | Frequency and Spatial domain based Saliency for Pigmented Skin Lesion Segmentation | Skin lesion segmentation can be rather a challenging task owing to the presence of artifacts, low contrast between lesion and boundary, color variegation, fuzzy skin lesion borders and heterogeneous background in dermoscopy images. In this paper, we propose a simple yet effective saliency-based approach derived in the ... | ['Zanobya N. Khan'] | 2020-10-08 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 8.26849043e-01 -2.15569302e-01 1.59306116e-02 -8.28475058e-02
-6.83648825e-01 -4.20827538e-01 4.74241585e-01 4.60765749e-01
-3.37990880e-01 7.48657048e-01 1.82103753e-01 5.41645242e-03
-3.01635891e-01 -4.83768582e-01 -2.59676367e-01 -7.28438795e-01
3.24804604e-01 -4.25944209e-01 8.89674485e-01 -1.95425954... | [15.55118179321289, -2.9936695098876953] |
8df6c22b-fcdc-4915-83f8-9120b684a98e | emovie-a-mandarin-emotion-speech-dataset-with | 2106.09317 | null | https://arxiv.org/abs/2106.09317v1 | https://arxiv.org/pdf/2106.09317v1.pdf | EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model | Recently, there has been an increasing interest in neural speech synthesis. While the deep neural network achieves the state-of-the-art result in text-to-speech (TTS) tasks, how to generate a more emotional and more expressive speech is becoming a new challenge to researchers due to the scarcity of high-quality emotion... | ['Zhou Zhao', 'Ming Lei', 'Rongjie Huang', 'Feiyang Chen', 'Jinglin Liu', 'Yi Ren', 'Chenye Cui'] | 2021-06-17 | null | null | null | null | ['emotional-speech-synthesis'] | ['speech'] | [ 1.85159333e-02 2.36492693e-01 2.78379738e-01 -6.38047099e-01
-8.77069533e-01 -2.09328607e-01 3.17014545e-01 -4.85893577e-01
-2.53313690e-01 7.88317382e-01 3.96042466e-01 -9.17728394e-02
5.10926008e-01 -3.10689539e-01 -4.96650159e-01 -6.80611312e-01
2.96382397e-01 6.17387183e-02 -2.66478807e-01 -2.23892897... | [14.246064186096191, 6.158961772918701] |
645503b4-cf59-4964-9a8d-114fe41ce9c2 | model-based-convolutional-de-aliasing-network | 1908.02054 | null | https://arxiv.org/abs/1908.02054v1 | https://arxiv.org/pdf/1908.02054v1.pdf | Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging | Parallel imaging has been an essential technique to accelerate MR imaging. Nevertheless, the acceleration rate is still limited due to the ill-condition and challenges associated with the undersampled reconstruction. In this paper, we propose a model-based convolutional de-aliasing network with adaptive parameter learn... | ['Shan-Shan Wang', 'Yanxia Chen', 'Taohui Xiao', 'Qiegen Liu', 'Cheng Li'] | 2019-08-06 | null | null | null | null | ['de-aliasing'] | ['computer-vision'] | [ 2.39829093e-01 -3.55160564e-01 6.72053695e-02 -4.73226100e-01
-6.92578435e-01 6.31913170e-02 1.82732970e-01 -6.65890649e-02
-7.44644105e-01 5.09102464e-01 2.29048625e-01 -2.39958555e-01
-5.19805133e-01 -2.26123691e-01 -5.12659788e-01 -7.95605302e-01
-6.15972221e-01 4.06115443e-01 2.32300863e-01 3.45837511... | [13.534424781799316, -2.3922982215881348] |
2a7a5331-292c-432b-983b-96ed1fafa048 | on-random-walk-based-graph-sampling | null | null | https://ieeexplore.ieee.org/document/7113345 | https://ronghuali.github.io/PaperFiles/On%20random%20walk%20based%20graph%20sampling.pdf | On Random Walk Based Graph Sampling | Random walk based graph sampling has been recognized as a fundamental technique to collect uniform node samples from a large graph. In this paper, we first present a comprehensive analysis of the drawbacks of three widely-used random walk based graph sampling algorithms, called re-weighted random walk (RW) algorithm, M... | ['Rong-Hua Li', 'Jeffrey Xu Yu', 'Tan Ji', 'Rui Mao', 'Lu Qin'] | 2020-05-13 | null | null | null | 2020-5 | ['graph-sampling'] | ['graphs'] | [ 1.04809918e-01 1.80853948e-01 -3.95936847e-01 -4.55189012e-02
-5.47599614e-01 -2.64953703e-01 5.53954840e-01 2.04947934e-01
-4.25770074e-01 1.06631291e+00 6.73222123e-03 -6.39223993e-01
-4.27175283e-01 -1.35527611e+00 -9.93516445e-02 -6.84769452e-01
-2.13915572e-01 6.03426814e-01 1.07282948e+00 7.55478293... | [7.0001726150512695, 5.282647132873535] |
a915e799-1a9e-403b-aa17-b7a7b956d7d2 | yolo-drone-airborne-real-time-detection-of | 2304.06925 | null | https://arxiv.org/abs/2304.06925v1 | https://arxiv.org/pdf/2304.06925v1.pdf | YOLO-Drone:Airborne real-time detection of dense small objects from high-altitude perspective | Unmanned Aerial Vehicles (UAVs), specifically drones equipped with remote sensing object detection technology, have rapidly gained a broad spectrum of applications and emerged as one of the primary research focuses in the field of computer vision. Although UAV remote sensing systems have the ability to detect various o... | ['Zhengnan Jiang', 'Hanzheng Hu', 'Feng Xiong', 'Jiahui Xiong', 'Li Zhu'] | 2023-04-14 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 2.06406981e-01 -5.78235269e-01 3.49576265e-01 1.91492751e-01
-1.60848394e-01 -6.42575264e-01 4.11222458e-01 -3.16561937e-01
-6.71593130e-01 6.92206621e-01 -9.87071753e-01 3.82274836e-02
-7.10491464e-02 -9.45998490e-01 -6.10457361e-01 -8.95140409e-01
-1.14718117e-01 -1.98336318e-01 6.29519463e-01 -3.43696713... | [8.414885520935059, -0.9942310452461243] |
abf993c6-6c2b-4228-8365-593d5eac0ab5 | compartmental-and-cellular-automaton-seirs | 2112.02661 | null | https://arxiv.org/abs/2112.02661v1 | https://arxiv.org/pdf/2112.02661v1.pdf | Compartmental and cellular automaton $SEIRS$ epidemiology models for the COVID-19 pandemic with the effects of temporal immunity and vaccination | We consider the $SEIRS$ epidemiology model with such features of the COVID-19 outbreak as: abundance of unidentified infected individuals, limited time of immunity and a possibility of vaccination. Within a compartmental realization of this model, we found the disease-free and the endemic stationary states. They exist ... | ['Taras Patsahan', 'Jaroslav Ilnytskyi'] | 2021-12-05 | null | null | null | null | ['epidemiology'] | ['medical'] | [-7.12568983e-02 1.73411816e-01 7.68541172e-02 2.67394364e-01
2.56705582e-01 -3.68688017e-01 9.03714359e-01 4.44918603e-01
-6.93888426e-01 9.25269604e-01 -1.90260157e-01 -3.78038496e-01
-6.31981254e-01 -9.70561862e-01 -4.31452841e-01 -1.07705069e+00
-8.33451092e-01 8.47059488e-01 2.75937945e-01 -5.69110751... | [5.927765369415283, 4.3777947425842285] |
61e9b752-5d61-4ac2-8d1c-28cd9e8f3bf5 | multi-view-inference-for-relation-extraction | 2104.13579 | null | https://arxiv.org/abs/2104.13579v1 | https://arxiv.org/pdf/2104.13579v1.pdf | Multi-view Inference for Relation Extraction with Uncertain Knowledge | Knowledge graphs (KGs) are widely used to facilitate relation extraction (RE) tasks. While most previous RE methods focus on leveraging deterministic KGs, uncertain KGs, which assign a confidence score for each relation instance, can provide prior probability distributions of relational facts as valuable external knowl... | ['Shikun Zhang', 'Canming Huang', 'Wei Ye', 'Bo Li'] | 2021-04-28 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-4.02626783e-01 6.40946567e-01 -9.41425085e-01 -4.52348053e-01
-5.91283858e-01 -6.01908565e-01 6.83286905e-01 3.39576334e-01
2.46495735e-02 1.01366544e+00 3.86623889e-01 -3.02188903e-01
-3.41210604e-01 -1.33684635e+00 -8.08011591e-01 -1.15101635e-01
2.32097115e-02 5.92223048e-01 4.84726816e-01 -4.28833179... | [9.168194770812988, 8.322436332702637] |
50a0beaa-6125-4654-a4da-5a120e76a741 | conceptfusion-open-set-multimodal-3d-mapping | 2302.07241 | null | https://arxiv.org/abs/2302.07241v2 | https://arxiv.org/pdf/2302.07241v2.pdf | ConceptFusion: Open-set Multimodal 3D Mapping | Building 3D maps of the environment is central to robot navigation, planning, and interaction with objects in a scene. Most existing approaches that integrate semantic concepts with 3D maps largely remain confined to the closed-set setting: they can only reason about a finite set of concepts, pre-defined at training ti... | ['Antonio Torralba', 'Florian Shkurti', 'Liam Paull', 'Madhava Krishna', 'Celso Miguel de Melo', 'Joshua B. Tenenbaum', 'Ayush Tewari', 'Nikhil Keetha', 'Soroush Saryazdi', 'Ganesh Iyer', 'Shuang Li', 'Tao Chen', 'Mohd Omama', 'Qiao Gu', 'Alihusein Kuwajerwala', 'Krishna Murthy Jatavallabhula'] | 2023-02-14 | null | null | null | null | ['robot-navigation'] | ['robots'] | [-3.49521521e-03 2.17467979e-01 -1.70307949e-01 -5.27984262e-01
-1.04340672e+00 -8.84874821e-01 7.42633283e-01 1.78747565e-01
-2.30802149e-01 5.47880113e-01 3.07189971e-01 -4.86949861e-01
-1.48242086e-01 -7.92222977e-01 -8.76447082e-01 -2.15423316e-01
9.35342070e-03 7.45070338e-01 2.50604093e-01 -5.31581461... | [4.764964580535889, 0.315258264541626] |
ec259496-c797-48e4-b8a4-677ae49e92eb | wear-a-multimodal-dataset-for-wearable-and | 2304.05088 | null | https://arxiv.org/abs/2304.05088v2 | https://arxiv.org/pdf/2304.05088v2.pdf | WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition | Though research has shown the complementarity of camera- and inertial-based data, datasets which offer both modalities remain scarce. In this paper, we introduce WEAR, an outdoor sports dataset for both vision- and inertial-based human activity recognition (HAR). The dataset comprises data from 18 participants performi... | ['Hilde Kuehne', 'Kristof Van Laerhoven', 'Michael Moeller', 'Marius Bock'] | 2023-04-11 | null | null | null | null | ['egocentric-activity-recognition', 'action-localization', 'human-activity-recognition', 'wearable-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'time-series', 'time-series'] | [ 1.69974759e-01 -4.01590824e-01 -2.54963726e-01 -1.25633016e-01
-7.20241249e-01 -6.07687175e-01 1.02771723e+00 -1.14152946e-01
-4.61116612e-01 5.99507391e-01 7.35708654e-01 1.34304211e-01
-1.75776437e-01 -2.21073300e-01 -6.34089291e-01 -4.24136847e-01
-1.92417413e-01 6.82691485e-02 1.65412411e-01 -2.55569905... | [7.7265825271606445, 0.5440412759780884] |
6ac668c3-095e-47c0-81d4-a7bf4f740556 | occlusion-aware-video-object-inpainting | 2108.06765 | null | https://arxiv.org/abs/2108.06765v1 | https://arxiv.org/pdf/2108.06765v1.pdf | Occlusion-Aware Video Object Inpainting | Conventional video inpainting is neither object-oriented nor occlusion-aware, making it liable to obvious artifacts when large occluded object regions are inpainted. This paper presents occlusion-aware video object inpainting, which recovers both the complete shape and appearance for occluded objects in videos given th... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Lei Ke'] | 2021-08-15 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ke_Occlusion-Aware_Video_Object_Inpainting_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ke_Occlusion-Aware_Video_Object_Inpainting_ICCV_2021_paper.pdf | iccv-2021-1 | ['texture-synthesis', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [ 9.88002867e-02 -1.49715438e-01 -3.49487782e-01 2.36221906e-02
-8.06236148e-01 -6.80714607e-01 2.65860468e-01 -6.65633619e-01
1.87525243e-01 9.06106532e-01 3.76706719e-01 2.26891354e-01
3.91758651e-01 -4.32793617e-01 -1.15328395e+00 -5.55688262e-01
6.13635108e-02 2.45320216e-01 4.85935241e-01 2.16613963... | [10.801901817321777, -1.3492519855499268] |
80b03680-7136-4dcf-a854-a002f565a28c | a-unified-technique-for-entropy-enhancement | null | null | https://www.sciencedirect.com/science/article/pii/S0010482522002165?dgcid=author | https://www.sciencedirect.com/science/article/pii/S0010482522002165?dgcid=author | A Unified Technique for Entropy Enhancement Based Diabetic Retinopathy Detection Using Hybrid Neural Network | In this paper, a unified technique for entropy enhancement-based diabetic retinopathy detection using a hybrid neural network is proposed for diagnosing diabetic retinopathy. Medical images play crucial roles in the diagnosis, but two images representing two different stages of a disease look alike. It, consequently, m... | ['R. Noor', 'M. Arif', 'A. Ullah', 'M. Imran', 'Fatima'] | 2020-07-01 | null | null | null | journal-2020-7 | ['diabetic-retinopathy-detection'] | ['medical'] | [ 3.41063082e-01 -2.87038743e-01 1.20532222e-01 -4.01490003e-01
-4.05531347e-01 7.36170486e-02 1.86175451e-01 1.51183203e-01
-5.70185840e-01 6.94863617e-01 3.47503662e-01 -1.98468715e-01
-6.07360959e-01 -7.86364675e-01 1.23069607e-01 -1.04861224e+00
-1.00957125e-03 -1.64252385e-01 6.85674399e-02 -2.20351201... | [15.817502975463867, -3.946977138519287] |
46a68eb8-5e02-4c5d-864a-74b9bea77ac7 | end-to-end-multi-modal-multi-task-vehicle | 1801.06734 | null | http://arxiv.org/abs/1801.06734v2 | http://arxiv.org/pdf/1801.06734v2.pdf | End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception | Convolutional Neural Networks (CNN) have been successfully applied to
autonomous driving tasks, many in an end-to-end manner. Previous end-to-end
steering control methods take an image or an image sequence as the input and
directly predict the steering angle with CNN. Although single task learning on
steering angles ha... | ['Jiebo Luo', 'Jerry Yu', 'Yixuan Zhang', 'Zhengyuan Yang', 'Junjie Cai'] | 2018-01-20 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [ 1.02230839e-01 -2.42425531e-01 -3.06652337e-01 -1.02178335e+00
-5.23017287e-01 -3.22287261e-01 5.40733039e-01 -6.18747771e-01
-5.14591038e-01 4.58313942e-01 -2.94454664e-01 -5.37392437e-01
1.13719650e-01 -7.92180181e-01 -9.70608234e-01 -5.78669965e-01
3.08115989e-01 2.07963154e-01 3.40184957e-01 -6.75609171... | [8.04005241394043, -1.39430832862854] |
a7eb7330-55ef-47d0-bcc9-fa2d6e3b4d9e | m-2-3dlanenet-multi-modal-3d-lane-detection | 2209.05996 | null | https://arxiv.org/abs/2209.05996v2 | https://arxiv.org/pdf/2209.05996v2.pdf | M^2-3DLaneNet: Multi-Modal 3D Lane Detection | Estimating accurate lane lines in 3D space remains challenging due to their sparse and slim nature. In this work, we propose the M^2-3DLaneNet, a Multi-Modal framework for effective 3D lane detection. Aiming at integrating complementary information from multi-sensors, M^2-3DLaneNet first extracts multi-modal features w... | ['Zhen Li', 'Shuguang Cui', 'Tang Kun', 'Shuqi Mei', 'Chao Zheng', 'Chaoda Zheng', 'Xu Yan', 'Yueru Luo'] | 2022-09-13 | null | null | null | null | ['3d-lane-detection', 'lane-detection'] | ['computer-vision', 'computer-vision'] | [-3.58285271e-02 -3.33598495e-01 -1.12365082e-01 -4.65718538e-01
-1.04282117e+00 -5.00871778e-01 4.29784626e-01 -2.75559962e-01
-2.31284276e-01 4.34498042e-01 3.33367229e-01 -1.34013623e-01
-1.07294299e-01 -9.32020366e-01 -9.12159145e-01 -4.61725652e-01
2.34603509e-01 1.48426205e-01 5.18111706e-01 -5.88683248... | [8.03225326538086, -1.8401490449905396] |
a466c063-01c0-414c-8192-797858cd5e4d | baller2vec-a-look-ahead-multi-entity | 2104.11980 | null | https://arxiv.org/abs/2104.11980v2 | https://arxiv.org/pdf/2104.11980v2.pdf | baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents | In many multi-agent spatiotemporal systems, agents operate under the influence of shared, unobserved variables (e.g., the play a team is executing in a game of basketball). As a result, the trajectories of the agents are often statistically dependent at any given time step; however, almost universally, multi-agent mode... | ['Anh Nguyen', 'Michael A. Alcorn'] | 2021-04-24 | null | https://openreview.net/forum?id=p2XgjS3Qp4X | https://openreview.net/pdf?id=p2XgjS3Qp4X | neurips-2021-12 | ['trajectory-modeling'] | ['time-series'] | [-8.50169063e-01 -2.61967033e-01 1.19240163e-02 8.67502764e-02
-5.51346004e-01 -7.26104021e-01 9.03592825e-01 2.37654403e-01
-7.58305073e-01 6.50578439e-01 5.38946748e-01 3.06058601e-02
-5.35492674e-02 -7.58520544e-01 -9.47596133e-01 -6.07549250e-01
-3.95416617e-01 1.12002409e+00 1.60189852e-01 -4.38246489... | [5.853166103363037, 0.7291544675827026] |
3760f5a4-c513-454f-8f0e-aa8d2b83ae31 | lanesnns-spiking-neural-networks-for-lane | 2208.02253 | null | https://arxiv.org/abs/2208.02253v1 | https://arxiv.org/pdf/2208.02253v1.pdf | LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor | Autonomous Driving (AD) related features represent important elements for the next generation of mobile robots and autonomous vehicles focused on increasingly intelligent, autonomous, and interconnected systems. The applications involving the use of these features must provide, by definition, real-time decisions, and t... | ['Muhammad Shafique', 'Guido Masera', 'Maurizio Martina', 'Alberto Marchisio', 'Alberto Viale'] | 2022-08-03 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [ 1.77815378e-01 -2.00316206e-01 1.52376369e-01 -4.68424857e-01
-1.34197965e-01 -2.48392463e-01 4.77180302e-01 -4.19188216e-02
-1.12823808e+00 6.78489864e-01 -6.53736353e-01 -1.78440303e-01
-1.29632965e-01 -9.34175372e-01 -9.65613425e-01 -7.26179302e-01
-1.12241365e-01 7.29981586e-02 8.42401743e-01 2.42561270... | [8.19087028503418, 2.4010822772979736] |
87f7eea6-e073-4832-9a7f-7e198e4626cc | a-model-based-active-testing-approach-to | 1401.3850 | null | http://arxiv.org/abs/1401.3850v1 | http://arxiv.org/pdf/1401.3850v1.pdf | A Model-Based Active Testing Approach to Sequential Diagnosis | Model-based diagnostic reasoning often leads to a large number of diagnostic
hypotheses. The set of diagnoses can be reduced by taking into account extra
observations (passive monitoring), measuring additional variables (probing) or
executing additional tests (sequential diagnosis/test sequencing). In this
paper we com... | ['Alexander Feldman', 'Arjan van Gemund', 'Gregory Provan'] | 2014-01-16 | null | null | null | null | ['sequential-diagnosis'] | ['medical'] | [ 4.74919140e-01 7.61829078e-01 -1.89998552e-01 -1.67792261e-01
-6.67305946e-01 -7.14322507e-01 4.17135835e-01 2.69893050e-01
2.75438368e-01 8.40165436e-01 -6.11348808e-01 -8.43723357e-01
-6.30312443e-01 -1.22223163e+00 -4.85525668e-01 -5.03825366e-01
-3.18481296e-01 1.12806201e+00 8.43978524e-01 9.40387473... | [5.389865875244141, 2.7074971199035645] |
d00cd722-c8c7-4d4f-bf31-a200d7c22c3c | real-time-covid-19-diagnosis-from-x-ray | 2106.01435 | null | https://arxiv.org/abs/2106.01435v1 | https://arxiv.org/pdf/2106.01435v1.pdf | Real-Time COVID-19 Diagnosis from X-Ray Images Using Deep CNN and Extreme Learning Machines Stabilized by Chimp Optimization Algorithm | Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the ... | ['Tarik A. Rashid', 'Sarkhel H. Taher Karim', 'Gholam-Reza Parvizi', 'Mokhtar Mohammadi', 'Mohammad Khishe', 'Hu Tianqing'] | 2021-05-14 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-9.17725265e-02 -8.78148153e-02 1.94476992e-01 -2.04216525e-01
-5.15042722e-01 7.85099044e-02 1.12477586e-01 3.72475684e-01
-1.07786763e+00 6.06027007e-01 -5.69728494e-01 -4.12200093e-01
-5.83417356e-01 -6.72838986e-01 -3.50510240e-01 -1.08025587e+00
-3.54228020e-02 7.08591163e-01 9.93234441e-02 1.02620348... | [14.951973915100098, -2.518519639968872] |
02cc0d32-49a6-4b98-8e47-1971e0f35a12 | a-fast-and-accurate-physics-informed-neural | 2009.11990 | null | https://arxiv.org/abs/2009.11990v2 | https://arxiv.org/pdf/2009.11990v2.pdf | A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder | Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-domin... | ['Youngsoo Choi', 'David Widemann', 'Youngkyu Kim', 'Tarek Zohdi'] | 2020-09-25 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-1.27803847e-01 -2.01860338e-01 1.58468008e-01 1.75217673e-01
-3.43782604e-01 3.38068642e-02 5.31518102e-01 -3.04281980e-01
-2.80036211e-01 8.83424401e-01 -7.93271586e-02 -4.50952321e-01
-4.27415401e-01 -9.21362638e-01 -7.55826354e-01 -1.04594183e+00
-2.01833695e-01 8.33904445e-01 -1.00731671e-01 -4.22429651... | [6.489818572998047, 3.496568202972412] |
a49675f8-4a45-4656-8510-5f4b1905979b | tensor-networks-for-multi-modal-non-euclidean | 2103.14998 | null | https://arxiv.org/abs/2103.14998v1 | https://arxiv.org/pdf/2103.14998v1.pdf | Tensor Networks for Multi-Modal Non-Euclidean Data | Modern data sources are typically of large scale and multi-modal natures, and acquired on irregular domains, which poses serious challenges to traditional deep learning models. These issues are partially mitigated by either extending existing deep learning algorithms to irregular domains through graphs, or by employing... | ['Danilo P. Mandic', 'Kriton Konstantinidis', 'Yao Lei Xu'] | 2021-03-27 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-8.33436996e-02 -1.15254313e-01 -1.31303236e-01 1.01940721e-01
-5.51096678e-01 -3.70672077e-01 6.40559852e-01 1.09702855e-01
8.70666206e-02 5.89521050e-01 5.14489770e-01 -1.99527174e-01
-7.26592541e-01 -7.95963109e-01 -5.04470050e-01 -6.38651252e-01
-4.83408213e-01 2.20390484e-01 -4.17089574e-02 -1.84598744... | [6.882254600524902, 5.846897602081299] |
5acfd885-902f-45ea-b47d-d213bbd217ca | improved-cross-lingual-transfer-learning-for | 2306.00789 | null | https://arxiv.org/abs/2306.00789v1 | https://arxiv.org/pdf/2306.00789v1.pdf | Improved Cross-Lingual Transfer Learning For Automatic Speech Translation | Research in multilingual speech-to-text translation is topical. Having a single model that supports multiple translation tasks is desirable. The goal of this work it to improve cross-lingual transfer learning in multilingual speech-to-text translation via semantic knowledge distillation. We show that by initializing th... | ['James Glass', 'Victoria Mingote', 'Pablo Gimeno', 'Luis Vicente', 'Antoine Laurent', 'Nauman Dawalatabad', 'Sameer Khurana'] | 2023-06-01 | null | null | null | null | ['speech-to-text-translation', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.36530548e-01 1.20489292e-01 -4.22420621e-01 -3.15599024e-01
-2.03953099e+00 -8.01020503e-01 7.58542240e-01 -3.66954237e-01
-3.37922722e-01 1.05943871e+00 3.72658163e-01 -8.85704339e-01
5.80192685e-01 -2.35500753e-01 -1.28461945e+00 -4.06536371e-01
5.84691823e-01 1.01997161e+00 -1.63112640e-01 -4.46460217... | [14.438764572143555, 7.291724681854248] |
7a5dcde4-62a5-45b6-ae52-ca4a7aca34d5 | slide-single-image-3d-photography-with-soft | 2109.01068 | null | https://arxiv.org/abs/2109.01068v1 | https://arxiv.org/pdf/2109.01068v1.pdf | SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting | Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches combine monocular depth networks with inpainting networks to achieve compelling results. A drawback of these techniques is the use of hard depth layering, making them unable to model intricate appearance details s... | ['Ce Liu', 'Brian Curless', 'David Salesin', 'William T. Freeman', 'Dominik Kaeser', 'Michael Krainin', 'Richard Tucker', 'Abhishek Kar', 'Kyle Sargent', 'Huiwen Chang', 'Varun Jampani'] | 2021-09-02 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jampani_SLIDE_Single_Image_3D_Photography_With_Soft_Layering_and_Depth-Aware_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jampani_SLIDE_Single_Image_3D_Photography_With_Soft_Layering_and_Depth-Aware_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-matting'] | ['computer-vision'] | [ 2.68548399e-01 2.94118375e-01 -6.47676829e-03 -5.46519339e-01
-5.54998875e-01 -5.81282020e-01 5.48722148e-01 -5.42602301e-01
-1.14177644e-01 3.61268580e-01 1.99766174e-01 -2.42307216e-01
3.94646615e-01 -4.65866268e-01 -9.35696542e-01 -4.88489389e-01
3.84719133e-01 1.68124691e-01 3.48853886e-01 -5.09070493... | [9.260276794433594, -3.0565261840820312] |
10cd6d92-3b06-4c9d-b07d-aeebb42c63f8 | almost-no-label-no-cry | null | null | http://papers.nips.cc/paper/5453-almost-no-label-no-cry | http://papers.nips.cc/paper/5453-almost-no-label-no-cry.pdf | (Almost) No Label No Cry | In Learning with Label Proportions (LLP), the objective is to learn a supervised classifier when, instead of labels, only label proportions for bags of observations are known. This setting has broad practical relevance, in particular for privacy preserving data processing. We first show that the mean operator, a statis... | ['Giorgio Patrini', 'Tiberio Caetano', 'Paul Rivera', 'Richard Nock'] | 2014-12-01 | null | null | null | neurips-2014-12 | ['style-generalization'] | ['computer-vision'] | [ 2.57488072e-01 4.38916266e-01 -3.17472875e-01 -6.32619619e-01
-1.30572259e+00 -1.12193036e+00 2.82452703e-01 6.08807147e-01
-6.75060928e-01 7.59014666e-01 -1.84575886e-01 -2.72693813e-01
-2.95225829e-01 -5.17428219e-01 -9.83189523e-01 -1.13452816e+00
-1.19362846e-01 5.84411502e-01 -2.28536859e-01 4.63268697... | [8.293940544128418, 4.240356922149658] |
d678feb0-36fc-41c8-8a47-bcf31f0ef798 | solitary-pulmonary-nodules-prediction-for | 2305.10466 | null | https://arxiv.org/abs/2305.10466v1 | https://arxiv.org/pdf/2305.10466v1.pdf | Solitary pulmonary nodules prediction for lung cancer patients using nomogram and machine learning | Lung cancer(LC) is a type of malignant neoplasm that originates in the bronchial mucosa or glands.As a clinically common nodule,solitary pulmonary nodules(SPNs) have a significantly higher probability of malignancy when they are larger than 8 mm in diameter.But there is also a risk of lung cancer when the diameter is l... | ['Gongjin Song', 'Hailan Zhang'] | 2023-05-17 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [-2.82677621e-01 3.04918528e-01 -6.76199913e-01 2.66989201e-01
-3.88289958e-01 -1.40415519e-01 2.28446558e-01 2.62187183e-01
-2.45210141e-01 4.87021983e-01 8.10802355e-02 -8.23065996e-01
-1.64962515e-01 -9.87293303e-01 -1.20980456e-01 -6.66274071e-01
-3.31658348e-02 9.85927284e-01 6.08307779e-01 2.30920091... | [15.376104354858398, -2.3576602935791016] |
3063c7d4-6a65-4bf9-a3f4-979ad7a4f307 | leaps-end-to-end-one-step-person-search-with | 2303.11859 | null | https://arxiv.org/abs/2303.11859v1 | https://arxiv.org/pdf/2303.11859v1.pdf | LEAPS: End-to-End One-Step Person Search With Learnable Proposals | We propose an end-to-end one-step person search approach with learnable proposals, named LEAPS. Given a set of sparse and learnable proposals, LEAPS employs a dynamic person search head to directly perform person detection and corresponding re-id feature generation without non-maximum suppression post-processing. The d... | ['Yanwei Pang', 'Fahad Khan', 'Jin Xie', 'Rao Muhammad Anwer', 'Jiale Cao', 'Zhiqiang Dong'] | 2023-03-21 | null | null | null | null | ['person-search', 'human-detection'] | ['computer-vision', 'computer-vision'] | [-1.05034411e-01 -4.61989902e-02 -1.33032754e-01 -4.75755990e-01
-9.29412127e-01 -3.55853558e-01 6.49674535e-01 -8.56310204e-02
-9.17763948e-01 5.93651772e-01 5.90672374e-01 3.33795816e-01
-3.00062299e-01 -7.40201652e-01 -5.28657913e-01 -6.02140367e-01
-1.23834282e-01 1.09504032e+00 4.50768262e-01 -2.70363957... | [14.871386528015137, 0.7978830337524414] |
d461097a-6776-42e9-8b9b-38a56b46f6fd | casa-nlu-context-aware-self-attentive-natural | 1909.08705 | null | https://arxiv.org/abs/1909.08705v1 | https://arxiv.org/pdf/1909.08705v1.pdf | CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots | Natural Language Understanding (NLU) is a core component of dialog systems. It typically involves two tasks - intent classification (IC) and slot labeling (SL), which are then followed by a dialogue management (DM) component. Such NLU systems cater to utterances in isolation, thus pushing the problem of context managem... | ['Mona Diab', 'Garima Lalwani', 'Peng Zhang', 'Arshit Gupta'] | 2019-09-18 | casa-nlu-context-aware-self-attentive-natural-1 | https://aclanthology.org/D19-1127 | https://aclanthology.org/D19-1127.pdf | ijcnlp-2019-11 | ['dialogue-management'] | ['natural-language-processing'] | [ 4.61105496e-01 3.42538327e-01 -3.22989464e-01 -6.24362886e-01
-6.20587170e-01 -7.27093458e-01 1.02637148e+00 3.13812256e-01
-3.63393366e-01 7.68974483e-01 9.00650263e-01 -5.19714117e-01
4.41187024e-01 -4.42146182e-01 -1.75331607e-01 -1.27705336e-01
1.07978016e-01 8.52672338e-01 3.70768845e-01 -6.58055186... | [12.707925796508789, 7.745953559875488] |
cfa95833-c870-4b79-ac9e-dc1fc29bf0d3 | self-supervised-multi-task-procedure-learning | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2830_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620545.pdf | Self-Supervised Multi-Task Procedure Learning from Instructional Videos | We address the problem of unsupervised procedure learning from instructional videos of multiple tasks using Deep Neural Networks (DNNs). Unlike existing works, we assume that training videos come from multiple tasks without key-step annotations or grammars, and the goals are to classify a test video to the underlying t... | ['Ehsan Elhamifar', 'Dat Huynh'] | null | null | null | null | eccv-2020-8 | ['procedure-learning'] | ['computer-vision'] | [ 6.92004144e-01 2.86421161e-02 -5.85803270e-01 -5.27531564e-01
-1.00280845e+00 -6.47214472e-01 2.99270719e-01 2.90428042e-01
-6.11976087e-01 5.83116353e-01 1.74673721e-01 -4.38595470e-03
-2.49628812e-01 -6.17265880e-01 -1.38427377e+00 -8.13623905e-01
-1.79372221e-01 1.13740817e-01 2.85621762e-01 5.13976574... | [8.714776039123535, 0.7132071256637573] |
2c6e50e1-398e-468d-9064-f25fe3612607 | two-branch-multi-scale-deep-neural-network | 2211.16786 | null | https://arxiv.org/abs/2211.16786v1 | https://arxiv.org/pdf/2211.16786v1.pdf | Two-branch Multi-scale Deep Neural Network for Generalized Document Recapture Attack Detection | The image recapture attack is an effective image manipulation method to erase certain forensic traces, and when targeting on personal document images, it poses a great threat to the security of e-commerce and other web applications. Considering the current learning-based methods suffer from serious overfitting problem,... | ['Haoliang Li', 'Shiqi Wang', 'Chenqi Kong', 'Jiaxing Li'] | 2022-11-30 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 2.38891497e-01 -7.50987172e-01 -1.26249745e-01 1.00052021e-01
-8.51496577e-01 -3.79094601e-01 4.03345346e-01 -1.64431408e-01
-3.22046846e-01 4.79455978e-01 -5.82204610e-02 -5.82158506e-01
-2.35005602e-01 -7.18444109e-01 -8.70139301e-01 -6.21654391e-01
-6.30289689e-02 -3.78083944e-01 2.30600774e-01 -2.06007436... | [12.392779350280762, 0.986628532409668] |
5b5adb8d-afb0-4a15-87a6-2927cc75fe69 | micro-stripes-analyses-for-iris-presentation | 2010.14850 | null | https://arxiv.org/abs/2010.14850v2 | https://arxiv.org/pdf/2010.14850v2.pdf | Micro Stripes Analyses for Iris Presentation Attack Detection | Iris recognition systems are vulnerable to the presentation attacks, such as textured contact lenses or printed images. In this paper, we propose a lightweight framework to detect iris presentation attacks by extracting multiple micro-stripes of expanded normalized iris textures. In this procedure, a standard iris segm... | ['Arjan Kuijper', 'Florian Kirchbuchner', 'Naser Damer', 'Meiling Fang'] | 2020-10-28 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 6.77760899e-01 -4.28217277e-02 -4.05945629e-01 -3.07835877e-01
-4.23861623e-01 -4.90277231e-01 5.14667153e-01 -1.23827443e-01
-1.88374758e-01 1.62164748e-01 1.18605420e-02 -4.95833397e-01
-2.12377161e-01 -3.66548210e-01 -4.28422749e-01 -7.59882569e-01
3.68747413e-02 1.14066459e-01 3.20261456e-02 1.45801440... | [3.7397847175598145, -3.634079933166504] |
a54ce186-e0ec-4710-964d-9387022b7a85 | ranking-based-siamese-visual-tracking | 2205.11761 | null | https://arxiv.org/abs/2205.11761v1 | https://arxiv.org/pdf/2205.11761v1.pdf | Ranking-Based Siamese Visual Tracking | Current Siamese-based trackers mainly formulate the visual tracking into two independent subtasks, including classification and localization. They learn the classification subnetwork by processing each sample separately and neglect the relationship among positive and negative samples. Moreover, such tracking paradigm t... | ['Qiang Ling', 'Feng Tang'] | 2022-05-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tang_Ranking-Based_Siamese_Visual_Tracking_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tang_Ranking-Based_Siamese_Visual_Tracking_CVPR_2022_paper.pdf | cvpr-2022-1 | ['visual-tracking'] | ['computer-vision'] | [-3.26503068e-01 -2.18615666e-01 -4.70342129e-01 -2.51239568e-01
-5.50916255e-01 -5.19692659e-01 5.48824549e-01 -2.00476591e-02
-4.38083977e-01 7.29992330e-01 -3.08422089e-01 1.50067639e-02
-7.77499080e-02 -4.41831708e-01 -6.94974184e-01 -9.36619282e-01
-7.65419006e-02 3.33096415e-01 8.34029078e-01 2.83509731... | [6.317736625671387, -2.134039878845215] |
881b0e0f-3d6c-4801-b7f1-16e62d4c2d95 | unsupervised-video-object-segmentation-with-2 | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2189_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590477.pdf | Unsupervised Video Object Segmentation with Joint Hotspot Tracking | Object tracking is a well-studied problem in computer vision while identifying salient spots of objects in a video is a less explored direction in the literature. Video eye gaze estimation methods aim to tackle a related task but salient spots in those methods are not bounded by objects and tend to produce very scatter... | ['Radomír Měch', 'You He', 'Zhe Lin', 'Jianming Zhang', 'Huchuan Lu', 'Lu Zhang'] | null | null | null | null | eccv-2020-8 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 3.70325238e-01 9.46971588e-03 -4.93899703e-01 -1.43371135e-01
-2.07839489e-01 -2.24321127e-01 1.56631276e-01 -3.35781902e-01
-5.16758263e-01 3.95846665e-01 -1.36021003e-01 6.21830299e-02
-1.26751676e-01 -6.53394908e-02 -9.03700173e-01 -8.32352400e-01
8.15738142e-02 1.77414417e-01 8.42288077e-01 1.78218111... | [9.298672676086426, -0.22327828407287598] |
ffde47c2-ffd0-4b7a-ae9b-1483912bc78f | split-kalmannet-a-robust-model-based-deep | 2210.09636 | null | https://arxiv.org/abs/2210.09636v1 | https://arxiv.org/pdf/2210.09636v1.pdf | Split-KalmanNet: A Robust Model-Based Deep Learning Approach for SLAM | Simultaneous localization and mapping (SLAM) is a method that constructs a map of an unknown environment and localizes the position of a moving agent on the map simultaneously. Extended Kalman filter (EKF) has been widely adopted as a low complexity solution for online SLAM, which relies on a motion and measurement mod... | ['Namyoon Lee', 'Yonina C. Eldar', 'Nir Shlezinger', 'Jeonghun Park', 'Geon Choi'] | 2022-10-18 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-2.78940946e-01 -4.26092029e-01 5.77516295e-02 -1.53765216e-01
-4.24870133e-01 -2.85940766e-01 6.94171131e-01 -1.49894714e-01
-7.68323302e-01 7.47901618e-01 -1.92609485e-02 -2.08374068e-01
-3.70877922e-01 -5.09522080e-01 -8.77203584e-01 -7.49499977e-01
-3.60296339e-01 4.11911070e-01 7.69782588e-02 -2.38165453... | [7.49202823638916, -2.0712409019470215] |
4625e940-ac91-404e-b392-4bda34324174 | recurrent-convolutional-neural-networks-for-2 | null | null | https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9745/9552 | https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9745/9552 | Recurrent Convolutional Neural Networks for Text Classification | Text classification is a foundational task in many NLP applications. Traditional text classifiers often rely on many human-designed features, such as dictionaries, knowledge bases and special tree kernels. In contrast to traditional methods, we introduce a recurrent convolutional neural network for text classification ... | ['Jun Zhao', 'Kang Liu', 'Liheng Xu', 'Siwei Lai'] | 2015-01-01 | null | null | null | proceedings-of-the-twenty-ninth-aaai | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.58832088e-01 -4.48624879e-01 -5.35597742e-01 -3.97765547e-01
-4.50530499e-01 -2.23423302e-01 8.33230615e-01 6.71356022e-01
-8.17023933e-01 5.11221230e-01 3.51019859e-01 -5.98295629e-01
9.33514759e-02 -9.66588676e-01 -8.82000253e-02 -5.21264017e-01
1.69603720e-01 1.42794494e-02 1.74093917e-01 -3.22131157... | [10.705069541931152, 7.670263290405273] |
c155334f-a793-4fff-b6ad-475e8fae4f83 | belittling-the-source-trustworthiness | 1809.00494 | null | http://arxiv.org/abs/1809.00494v1 | http://arxiv.org/pdf/1809.00494v1.pdf | Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web | With the growth of the internet, the number of fake-news online has been
proliferating every year. The consequences of such phenomena are manifold,
ranging from lousy decision-making process to bullying and violence episodes.
Therefore, fact-checking algorithms became a valuable asset. To this aim, an
important step to... | ['Jens Lehmann', 'Piyush Chawla', 'Diego Esteves', 'Aniketh Janardhan Reddy'] | 2018-09-03 | belittling-the-source-trustworthiness-1 | https://aclanthology.org/W18-5508 | https://aclanthology.org/W18-5508.pdf | ws-2018-11 | ['web-credibility', 'subjectivity-analysis'] | ['methodology', 'natural-language-processing'] | [-4.05828267e-01 4.49842364e-02 -5.79348087e-01 -2.00260088e-01
-9.60096657e-01 -8.46504927e-01 7.24033952e-01 7.33650446e-01
-2.42693573e-01 9.17229712e-01 -4.71881106e-02 -2.70210296e-01
8.05452242e-02 -8.10083032e-01 -6.76075995e-01 -4.86476779e-01
2.59891581e-02 2.62409329e-01 7.11750805e-01 -3.15509975... | [8.128194808959961, 10.215356826782227] |
79e2e95f-9299-47c6-9261-562056adedcf | representing-focus-in-ltag | null | null | https://aclanthology.org/W12-4609 | https://aclanthology.org/W12-4609.pdf | Representing Focus in LTAG | null | ['Kata Balogh'] | 2012-09-01 | representing-focus-in-ltag-1 | https://aclanthology.org/W12-4609 | https://aclanthology.org/W12-4609.pdf | ws-2012-9 | ['dialogue-management'] | ['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... | [-7.336844444274902, 3.683255195617676] |
723f9b98-0ce4-494d-bdc9-06cef388eefb | emulating-the-dynamics-of-complex-systems | 2306.16335 | null | https://arxiv.org/abs/2306.16335v1 | https://arxiv.org/pdf/2306.16335v1.pdf | Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX) | In this study, we propose a novel surrogate modelling approach to efficiently and accurately approximate the response of complex dynamical systems driven by time-varying exogenous excitations over extended time periods. Our approach, that we name \emph{manifold nonlinear autoregressive modelling with exogenous input} (... | ['Bruno Sudret', 'Stefano Marelli', 'Styfen Schär'] | 2023-06-28 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-2.78190672e-02 1.44537417e-02 3.54275733e-01 5.13391793e-01
-4.18717772e-01 -6.70602262e-01 5.50244868e-01 -1.44155219e-01
-7.37856477e-02 6.73971891e-01 -1.60706446e-01 -3.85512471e-01
-7.10760236e-01 -4.79097456e-01 -8.52252126e-01 -9.60941136e-01
-1.35002777e-01 5.67326069e-01 -2.54914910e-01 -5.56807399... | [6.479795455932617, 3.454958915710449] |
9863cf2a-2db1-4e8a-9ea9-960f927fee3f | self-supervised-learning-for-cardiac-mr-image | 1907.02757 | null | https://arxiv.org/abs/1907.02757v1 | https://arxiv.org/pdf/1907.02757v1.pdf | Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction | In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, whi... | ['Florian Guitton', 'Giacomo Tarroni', 'Yike Guo', 'Wenjia Bai', 'Steffen E. Petersen', 'Jinming Duan', 'Daniel Rueckert', 'Chen Chen', 'Paul M. Matthews'] | 2019-07-05 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 4.36735690e-01 3.07106674e-01 -2.68876344e-01 -7.90427983e-01
-6.59737885e-01 -4.73589987e-01 2.97140867e-01 5.61919570e-01
-8.05100381e-01 6.94687068e-01 -1.37885004e-01 -3.14283408e-02
-1.36848658e-01 -5.74961662e-01 -5.18805027e-01 -7.99701869e-01
-8.76523703e-02 4.01480466e-01 3.12500030e-01 1.28116950... | [14.714964866638184, -2.2536938190460205] |
811e57ba-e8dc-402b-8939-cf493589e4c6 | delving-globally-into-texture-and-structure | 2209.08217 | null | https://arxiv.org/abs/2209.08217v1 | https://arxiv.org/pdf/2209.08217v1.pdf | Delving Globally into Texture and Structure for Image Inpainting | Image inpainting has achieved remarkable progress and inspired abundant methods, where the critical bottleneck is identified as how to fulfill the high-frequency structure and low-frequency texture information on the masked regions with semantics. To this end, deep models exhibit powerful superiority to capture them, y... | ['Yong Rui', 'Meng Wang', 'Yang Wang', 'Haipeng Liu'] | 2022-09-17 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 1.32393926e-01 2.17283860e-01 -2.39693463e-01 -1.67435750e-01
-8.30406845e-01 -1.22630484e-01 3.03140461e-01 -1.61289185e-01
2.34550610e-01 5.48012197e-01 4.24187124e-01 4.39582437e-01
-5.14147896e-03 -1.03977132e+00 -1.10094476e+00 -8.76872659e-01
3.26754838e-01 2.41002262e-01 2.89627701e-01 -4.91185129... | [11.260812759399414, -1.2752388715744019] |
0b95804f-5532-4b04-a169-6fc48d681f28 | uav-trajectory-planning-for-data-collection | null | null | https://ieeexplore.ieee.org/document/8842600 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8842600 | UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices | The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; ot... | ['Ali Ghrayeb', 'Tri Minh Nguyen', 'Chadi M. Assi', 'Sanaa Sharafeddine', 'Moataz Samir'] | 2019-09-17 | null | null | null | ieee-transactions-on-wireless-communications-1 | ['trajectory-planning'] | ['robots'] | [ 2.83965886e-01 2.47338280e-01 -1.64300308e-01 2.31570508e-02
-3.10080796e-01 -8.22420239e-01 -1.37923220e-02 1.45408258e-01
-2.63796002e-01 9.94752526e-01 -5.32005250e-01 -4.94664252e-01
-9.31520879e-01 -1.07438290e+00 -3.99567693e-01 -1.05528462e+00
-3.23730826e-01 5.41458070e-01 2.34594211e-01 -4.84678000... | [5.894326686859131, 1.5370090007781982] |
f71f8964-9740-4ef0-a13f-a4b27da67811 | a-max-affine-spline-perspective-of-recurrent | null | null | https://openreview.net/forum?id=BJej72AqF7 | https://openreview.net/pdf?id=BJej72AqF7 | A MAX-AFFINE SPLINE PERSPECTIVE OF RECURRENT NEURAL NETWORKS | We develop a framework for understanding and improving recurrent neural net-works (RNNs) using max-affine spline operators (MASO). We prove that RNNs using piecewise affine and convex nonlinearities can be written as a simple piecewise affine spline operator. The resulting representation provides several new perspectiv... | ['Richard Baraniuk', 'Zichao Wang', 'Randall Balestriero'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['l2-regularization'] | ['methodology'] | [ 4.13004696e-01 4.67077494e-01 -1.70843899e-01 -2.94715703e-01
-6.65965021e-01 -6.42524600e-01 4.46159959e-01 -5.44967830e-01
-3.52075577e-01 4.53190714e-01 3.91401619e-01 -3.56088668e-01
-1.32573098e-01 -4.29414868e-01 -1.13816249e+00 -1.00212693e+00
1.11676835e-01 3.19558829e-01 -1.82004794e-01 -3.61478299... | [7.820025444030762, 3.543745756149292] |
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