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6b45c83f-cf81-4084-b3c8-0d897ebe9268 | relational-features-in-fine-grained-opinion | null | null | https://aclanthology.org/J13-3002 | https://aclanthology.org/J13-3002.pdf | Relational Features in Fine-Grained Opinion Analysis | null | ['ro', 'Richard Johansson', 'Aless Moschitti'] | 2013-01-01 | null | null | null | cl-2013-1 | ['fine-grained-opinion-analysis'] | ['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
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2009735107421875, 3.7909207344055176] |
ad5c893c-bdc4-4f9d-aa04-5ec11b70458a | improved-financial-forecasting-via-quantum | 2306.12965 | null | https://arxiv.org/abs/2306.12965v1 | https://arxiv.org/pdf/2306.12965v1.pdf | Improved Financial Forecasting via Quantum Machine Learning | Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn pre... | ['Samurai Brito', 'André J. Ferreira-Martins', 'Iordanis Kerenidis', 'Natansh Mathur', 'Skander Kazdaghli', 'Sohum Thakkar'] | 2023-05-31 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 1.90490093e-02 -7.58804334e-03 -3.17283154e-01 -2.74984151e-01
-7.98556387e-01 -4.75690931e-01 5.50282359e-01 1.96527168e-01
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7.20408978e-03 6.09656036e-01 -1.59742739e-02 -5.72589457... | [5.581636428833008, 4.970762729644775] |
cf0e5f7b-f116-4133-951b-787c7bb36881 | everything-at-once-multi-modal-fusion | 2112.04446 | null | https://arxiv.org/abs/2112.04446v2 | https://arxiv.org/pdf/2112.04446v2.pdf | Everything at Once -- Multi-modal Fusion Transformer for Video Retrieval | Multi-modal learning from video data has seen increased attention recently as it allows to train semantically meaningful embeddings without human annotation enabling tasks like zero-shot retrieval and classification. In this work, we present a multi-modal, modality agnostic fusion transformer approach that learns to ex... | ['Hilde Kuehne', 'James Glass', 'David Harwath', 'Rogerio Feris', 'Brian Kingsbury', 'Samuel Thomas', 'Andrew Rouditchenko', 'Brian Chen', 'Nina Shvetsova'] | 2021-12-08 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 2.87416697e-01 -1.51242554e-01 -1.92126170e-01 -2.82566279e-01
-1.37963247e+00 -8.19684625e-01 7.60677278e-01 3.99001002e-01
-7.50500858e-01 5.01035631e-01 3.92226994e-01 2.20747009e-01
-2.81442285e-01 -5.01151562e-01 -9.10330951e-01 -6.39259458e-01
-1.46937311e-01 4.82357413e-01 5.26892245e-01 4.87032980... | [10.24343204498291, 1.122999906539917] |
2f0bc557-856a-4a28-9b24-bf06d58787a9 | interactive-combinatorial-bandits-balancing | 2207.03091 | null | https://arxiv.org/abs/2207.03091v2 | https://arxiv.org/pdf/2207.03091v2.pdf | Online SuBmodular + SuPermodular (BP) Maximization with Bandit Feedback | We investigate non-modular function maximization in an online setting with $m$ users. The optimizer maintains a set $S_q$ for each user $q \in \{1, \ldots, m\}$. At round $i$, a user with unknown utility $h_q$ arrives; the optimizer selects a new item to add to $S_q$, and receives a noisy marginal gain. The goal is to ... | ['Jeff Bilmes', 'Maryam Fazel', 'Lillian J Ratliff', 'Omid Sadeghi', 'Adhyyan Narang'] | 2022-07-07 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.74252898e-01 2.79781729e-01 -5.50514460e-01 -4.17987794e-01
-8.94877076e-01 -7.47467041e-01 -6.64353371e-01 5.99421300e-02
-5.33316016e-01 8.07553411e-01 -1.92804009e-01 -3.73855621e-01
-8.00302744e-01 -9.14941847e-01 -9.88496423e-01 -7.70442307e-01
-6.51338518e-01 4.24346507e-01 -4.68615323e-01 -3.77068847... | [4.873674392700195, 3.622356653213501] |
483de7c6-f9e4-4c9f-b4aa-af6dd88a57e3 | continuous-sign-language-recognition-via | 2207.00928 | null | https://arxiv.org/abs/2207.00928v1 | https://arxiv.org/pdf/2207.00928v1.pdf | Continuous Sign Language Recognition via Temporal Super-Resolution Network | Aiming at the problem that the spatial-temporal hierarchical continuous sign language recognition model based on deep learning has a large amount of computation, which limits the real-time application of the model, this paper proposes a temporal super-resolution network(TSRNet). The data is reconstructed into a dense f... | ['Quan Gan', 'Fei Yuan', 'Jing Li', 'Qidan Zhu'] | 2022-07-03 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 4.52395201e-01 -3.18923920e-01 -1.24808662e-01 -1.50832206e-01
-1.10756302e+00 2.19484121e-01 4.17545617e-01 -7.81680822e-01
-7.99111426e-01 5.18766046e-01 2.77739614e-01 2.79973984e-01
-1.21802554e-01 -7.24080503e-01 -3.94000381e-01 -1.11825538e+00
1.42666250e-01 -2.28872478e-01 2.78647393e-01 -1.82069018... | [9.21805477142334, -6.464583396911621] |
a92447fc-988a-496c-be09-811c69807c5c | unsupervised-neural-machine-translation-1 | 1810.12703 | null | http://arxiv.org/abs/1810.12703v1 | http://arxiv.org/pdf/1810.12703v1.pdf | Unsupervised Neural Machine Translation Initialized by Unsupervised Statistical Machine Translation | Recent work achieved remarkable results in training neural machine
translation (NMT) systems in a fully unsupervised way, with new and dedicated
architectures that rely on monolingual corpora only. In this work, we propose
to define unsupervised NMT (UNMT) as NMT trained with the supervision of
synthetic bilingual data... | ['Atsushi Fujita', 'Benjamin Marie'] | 2018-10-30 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 5.44077396e-01 4.09347892e-01 -4.70274836e-01 -5.09997010e-01
-1.16610348e+00 -5.28351605e-01 1.13841069e+00 -4.36244756e-01
-5.27759016e-01 1.11166191e+00 1.93491995e-01 -9.14058745e-01
6.98667228e-01 -4.58712041e-01 -1.13418591e+00 -3.24065149e-01
5.40943325e-01 1.32074511e+00 -4.20863986e-01 -6.07907176... | [11.558069229125977, 10.332286834716797] |
f50c3784-cdfb-4a98-9705-73d546518343 | nutribullets-hybrid-multi-document-health | 2104.03465 | null | https://arxiv.org/abs/2104.03465v1 | https://arxiv.org/pdf/2104.03465v1.pdf | Nutribullets Hybrid: Multi-document Health Summarization | We present a method for generating comparative summaries that highlights similarities and contradictions in input documents. The key challenge in creating such summaries is the lack of large parallel training data required for training typical summarization systems. To this end, we introduce a hybrid generation approac... | ['Regina Barzilay', 'Tao Lei', 'Lili Yu', 'Darsh J Shah'] | 2021-04-08 | null | null | null | null | ['text-infilling'] | ['natural-language-processing'] | [ 7.31404483e-01 9.70894098e-01 -2.83686191e-01 -2.61942148e-01
-1.25958335e+00 -5.90140522e-01 7.90204883e-01 1.12450314e+00
-1.61394924e-01 1.05986500e+00 1.11259711e+00 -1.39661238e-01
-1.01448938e-01 -7.79007792e-01 -5.43592930e-01 -2.48880032e-02
1.73749000e-01 9.15244341e-01 1.26791432e-01 -5.08206606... | [12.352731704711914, 9.336812973022461] |
9cc0cd68-2da9-4914-aa63-26b5bc0421bc | instance-shadow-detection | 1911.07034 | null | https://arxiv.org/abs/1911.07034v2 | https://arxiv.org/pdf/1911.07034v2.pdf | Instance Shadow Detection | Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks. Second, ... | ['Xiao-Wei Hu', 'Chi-Wing Fu', 'Pheng-Ann Heng', 'Tianyu Wang', 'Qiong Wang'] | 2019-11-16 | instance-shadow-detection-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Instance_Shadow_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Instance_Shadow_Detection_CVPR_2020_paper.pdf | cvpr-2020-6 | ['shadow-detection'] | ['computer-vision'] | [ 7.12175190e-01 1.22111514e-01 2.09439173e-01 -8.76038909e-01
-5.61551690e-01 -3.88339281e-01 6.26244366e-01 -4.46670294e-01
-2.29841582e-02 7.40955949e-01 3.90350297e-02 1.29130810e-01
1.26387671e-01 -5.94274580e-01 -7.68853366e-01 -6.70175076e-01
3.02335799e-01 5.00668526e-01 7.10661769e-01 3.79679948... | [10.852727890014648, -4.107909679412842] |
e1fa0ea8-5b1d-428c-bfec-c5f960e0f42c | weakly-supervised-3d-multi-person-pose | 2211.16951 | null | https://arxiv.org/abs/2211.16951v1 | https://arxiv.org/pdf/2211.16951v1.pdf | Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR | Depth estimation is usually ill-posed and ambiguous for monocular camera-based 3D multi-person pose estimation. Since LiDAR can capture accurate depth information in long-range scenes, it can benefit both the global localization of individuals and the 3D pose estimation by providing rich geometry features. Motivated by... | ['Yuexin Ma', 'Jingyi Yu', 'Jingya Wang', 'Lan Xu', 'Juze Zhang', 'Yiming Ren', 'Yiteng Xu', 'Peishan Cong'] | 2022-11-30 | null | null | null | null | ['3d-pose-estimation', '3d-multi-person-pose-estimation', 'multi-person-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.11679453e-01 -5.45102596e-01 -1.94629401e-01 -4.68476832e-01
-4.43745285e-01 -4.71403062e-01 1.95917577e-01 -3.51948619e-01
-5.38656354e-01 6.96964145e-01 2.20358044e-01 4.59486842e-01
-7.62799904e-02 -6.90174162e-01 -4.08323109e-01 -5.53790331e-01
1.57815039e-01 7.64039159e-01 2.04939499e-01 -6.97953850... | [7.038650035858154, -1.0043952465057373] |
8cf11527-7bf2-4c5c-b804-bbd742c69f5e | global-local-bidirectional-reasoning-for | 2003.12971 | null | https://arxiv.org/abs/2003.12971v1 | https://arxiv.org/pdf/2003.12971v1.pdf | Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds | Local and global patterns of an object are closely related. Although each part of an object is incomplete, the underlying attributes about the object are shared among all parts, which makes reasoning the whole object from a single part possible. We hypothesize that a powerful representation of a 3D object should model ... | ['Jie zhou', 'Yongming Rao', 'Jiwen Lu'] | 2020-03-29 | global-local-bidirectional-reasoning-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Rao_Global-Local_Bidirectional_Reasoning_for_Unsupervised_Representation_Learning_of_3D_Point_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Rao_Global-Local_Bidirectional_Reasoning_for_Unsupervised_Representation_Learning_of_3D_Point_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-object-classification'] | ['computer-vision'] | [-0.13834001 0.50113213 -0.2596282 -0.5263697 -0.46187413 -0.706431
0.7510849 0.48690104 0.293899 0.13003351 -0.06048245 -0.05789443
-0.21074972 -0.94525766 -1.0433865 -0.5212901 -0.06582409 1.0326111
0.6386391 -0.14219336 0.26267 1.0236537 -1.7777061 0.39682662
0.49090657 1.2801071 0.216... | [7.9995012283325195, -3.2598462104797363] |
641fbe11-5211-4c54-bf5c-c8f83908fdf7 | coqa-a-conversational-question-answering | 1808.07042 | null | http://arxiv.org/abs/1808.07042v2 | http://arxiv.org/pdf/1808.07042v2.pdf | CoQA: A Conversational Question Answering Challenge | Humans gather information by engaging in conversations involving a series of
interconnected questions and answers. For machines to assist in information
gathering, it is therefore essential to enable them to answer conversational
questions. We introduce CoQA, a novel dataset for building Conversational
Question Answeri... | ['Siva Reddy', 'Danqi Chen', 'Christopher D. Manning'] | 2018-08-21 | coqa-a-conversational-question-answering-1 | https://aclanthology.org/Q19-1016 | https://aclanthology.org/Q19-1016.pdf | tacl-2019-3 | ['generative-question-answering'] | ['natural-language-processing'] | [ 3.92808728e-02 5.36431491e-01 2.15988442e-01 -4.79119003e-01
-1.20652092e+00 -1.00368667e+00 8.34486485e-01 3.39888602e-01
-2.66396672e-01 9.79792774e-01 1.14010775e+00 -5.69480002e-01
-1.05204113e-01 -6.00277781e-01 -5.02877772e-01 -1.89462706e-01
1.41956687e-01 9.76308644e-01 3.91991973e-01 -8.70898843... | [12.073373794555664, 8.012422561645508] |
32b96701-ed2a-4aa9-9af7-4c20d4cac93e | sentiment-analysis-itas-complicated | null | null | https://aclanthology.org/N18-1171 | https://aclanthology.org/N18-1171.pdf | Sentiment Analysis: It's Complicated! | Sentiment analysis is used as a proxy to measure human emotion, where the objective is to categorize text according to some predefined notion of sentiment. Sentiment analysis datasets are typically constructed with gold-standard sentiment labels, assigned based on the results of manual annotations. When working with su... | ['Rohit Verma', 'Robert Belfer', 'Bh', 'Lal', 'Jeremy Georges-Filteau', 'Kian Kenyon-Dean', 'Scott Fujimoto', 'Shruti eri', 'Nirmal Kanagasabai', 'Eisha Ahmed', 'Derek Ruths', 'Auguste e', 'Roman Sarrazingendron', 'Christopher Glasz', 'Barleen Kaur'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [ 2.83848435e-01 2.06940070e-01 1.70429293e-02 -8.24204385e-01
-7.30576515e-01 -1.02096820e+00 5.40267646e-01 9.32896376e-01
-6.99020982e-01 4.97733563e-01 4.87206906e-01 -5.26957631e-01
3.19719426e-02 -7.76692569e-01 -3.88773382e-01 -5.75372219e-01
6.33124828e-01 4.19488996e-01 -8.90306681e-02 -6.30525470... | [11.065223693847656, 6.914811611175537] |
f96e61b0-15d4-4d57-a26a-c842e906a2c3 | engineering-education-in-the-age-of | 2102.07900 | null | https://arxiv.org/abs/2102.07900v1 | https://arxiv.org/pdf/2102.07900v1.pdf | Engineering Education in the Age of Autonomous Machines | In the past few years, we have observed a huge supply-demand gap for autonomous driving engineers. The core problem is that autonomous driving is not one single technology but rather a complex system integrating many technologies, and no one single academic department can provide comprehensive education in this field. ... | ['Hironori Kasahara', 'Jean-Luc Gaudiot', 'Shaoshan Liu'] | 2021-02-16 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-3.54985416e-01 2.72745788e-02 -1.68658540e-01 -2.14505777e-01
-4.44368482e-01 -6.03913903e-01 4.83464599e-02 -1.49173945e-01
-3.74230631e-02 5.27413309e-01 -4.65902746e-01 -9.91520464e-01
-2.85545141e-02 -7.59314656e-01 -7.24807620e-01 -2.48147205e-01
2.50843227e-01 1.00253582e-01 5.14606118e-01 -8.53371263... | [5.613361835479736, 1.0261492729187012] |
a2b2a3ca-70a5-4104-a2bf-d8ff9c1b5349 | proceedings-38th-international-conference-on | 2208.02685 | null | https://arxiv.org/abs/2208.02685v1 | https://arxiv.org/pdf/2208.02685v1.pdf | Proceedings 38th International Conference on Logic Programming | ICLP is the premier international event for presenting research in logic programming. Contributions to ICLP 2022 were sought in all areas of logic programming, including but not limited to: Foundations: Semantics, Formalisms, Nonmonotonic reasoning, Knowledge representation. Languages issues: Concurrency, Objects, Coor... | ['Tuncay Tekle', 'Martin Gebser', 'Veronica Dahl', 'Carmine Dodaro', 'Jose F. Morales', 'Yuliya Lierler'] | 2022-08-04 | null | null | null | null | ['data-integration', 'probabilistic-programming', 'automated-theorem-proving', 'automated-theorem-proving'] | ['knowledge-base', 'methodology', 'miscellaneous', 'reasoning'] | [-5.09687901e-01 3.78067344e-01 -2.08871275e-01 -4.69289422e-01
3.39967608e-01 -1.02106881e+00 5.19024670e-01 9.69981432e-01
2.09362566e-01 1.22281528e+00 -8.01486745e-02 -5.64293385e-01
-8.79004359e-01 -1.08875477e+00 -2.63309747e-01 -8.89859628e-03
-4.85328078e-01 1.27414417e+00 8.10610175e-01 -3.25239182... | [8.670280456542969, 6.73847770690918] |
d1377fd0-880a-47af-b24c-059e0a7c46da | video-transformer-for-deepfake-detection-with | 2108.05307 | null | https://arxiv.org/abs/2108.05307v1 | https://arxiv.org/pdf/2108.05307v1.pdf | Video Transformer for Deepfake Detection with Incremental Learning | Face forgery by deepfake is widely spread over the internet and this raises severe societal concerns. In this paper, we propose a novel video transformer with incremental learning for detecting deepfake videos. To better align the input face images, we use a 3D face reconstruction method to generate UV texture from a s... | ['Hang Dai', 'Sohail A. Khan'] | 2021-08-11 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 4.08769101e-02 -3.84192437e-01 -3.17905933e-01 -3.02887499e-01
-6.29247129e-01 -5.74578881e-01 3.34228337e-01 -9.62875128e-01
3.55356447e-02 2.56803215e-01 1.80733442e-01 -2.54721958e-02
6.31710812e-02 -6.10560119e-01 -7.78297842e-01 -8.81758511e-01
1.43273130e-01 -1.97238833e-01 2.94265356e-02 8.19839835... | [12.887310028076172, 1.0403811931610107] |
f9be96b6-c859-4e3b-bd4b-8f1866c03f56 | dependency-based-embeddings-for-sentence | null | null | https://aclanthology.org/N16-1175 | https://aclanthology.org/N16-1175.pdf | Dependency Based Embeddings for Sentence Classification Tasks | null | ['Man', 'ros', 'Suresh har', 'Alex Komninos'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['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
-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... | [-7.294086933135986, 3.6589674949645996] |
2c0d3568-556d-4974-84e3-7e1a5f8845c3 | are-these-birds-similar-learning-branched | null | null | https://ieeexplore.ieee.org/document/8960960 | http://artelab.dista.uninsubria.it/res/research/papers/2019/2019-IVCNZ-Nawaz-Birds.pdf | Are These Birds Similar: Learning Branched Networks for Fine-grained Representations | Fine-grained image classification is a challenging task due to the presence of hierarchical coarse-to-fine-grained distribution in the dataset. Generally, parts are used to discriminate various objects in fine-grained datasets, however, not all parts are beneficial and indispensable. In recent years, natural language d... | ['Ignazio Gallo', 'Nicola Landro', 'Moreno Caraffini', 'Alessandro Calefati', 'Shah Nawaz'] | 2020-01-16 | null | null | null | null | ['multimodal-text-and-image-classification'] | ['methodology'] | [-1.97964236e-01 -4.08876300e-01 -6.71944678e-01 -7.67452776e-01
-9.09691513e-01 -6.67542517e-01 8.23252857e-01 9.43588391e-02
-2.35930458e-01 7.33765006e-01 6.39846683e-01 3.48469406e-01
-1.76420137e-01 -8.05207431e-01 -5.43922544e-01 -7.05201447e-01
1.76192775e-01 4.81761307e-01 8.15105662e-02 2.57452000... | [9.632585525512695, 2.090695858001709] |
24ee5096-4aee-4066-87fa-8298c41d0613 | texturepose-supervising-human-mesh-estimation-1 | 1910.11322 | null | https://arxiv.org/abs/1910.11322v1 | https://arxiv.org/pdf/1910.11322v1.pdf | TexturePose: Supervising Human Mesh Estimation with Texture Consistency | This work addresses the problem of model-based human pose estimation. Recent approaches have made significant progress towards regressing the parameters of parametric human body models directly from images. Because of the absence of images with 3D shape ground truth, relevant approaches rely on 2D annotations or sophis... | ['Nikos Kolotouros', 'Georgios Pavlakos', 'Kostas Daniilidis'] | 2019-10-24 | texturepose-supervising-human-mesh-estimation | http://openaccess.thecvf.com/content_ICCV_2019/html/Pavlakos_TexturePose_Supervising_Human_Mesh_Estimation_With_Texture_Consistency_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Pavlakos_TexturePose_Supervising_Human_Mesh_Estimation_With_Texture_Consistency_ICCV_2019_paper.pdf | iccv-2019-10 | ['weakly-supervised-3d-human-pose-estimation'] | ['computer-vision'] | [-1.73563808e-02 8.97298604e-02 -2.07114458e-01 -4.12649184e-01
-4.86420214e-01 -4.94658172e-01 4.96540904e-01 -3.84969622e-01
-3.76178473e-01 5.20423770e-01 3.40141803e-01 2.86339670e-01
2.66541839e-01 -3.72676075e-01 -9.30949807e-01 -6.07180178e-01
1.23532951e-01 5.53880155e-01 2.95684576e-01 -2.95137197... | [7.155539512634277, -1.1022436618804932] |
0f56fac5-f1e6-46a3-9c8e-0cb56a55ee29 | clothing-and-people-a-social-signal | 1704.02231 | null | http://arxiv.org/abs/1704.02231v1 | http://arxiv.org/pdf/1704.02231v1.pdf | Clothing and People - A Social Signal Processing Perspective | In our society and century, clothing is not anymore used only as a means for
body protection. Our paper builds upon the evidence, studied within the social
sciences, that clothing brings a clear communicative message in terms of social
signals, influencing the impression and behaviour of others towards a person.
In fac... | ['Petia Radeva', 'Federico Parezzan', 'Marco Cristani', 'Mariella Dimiccoli', 'Maedeh Aghaei'] | 2017-04-07 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 2.59288520e-01 2.85983860e-01 7.49485642e-02 -5.19111872e-01
-2.39871303e-03 -5.96550643e-01 8.35536599e-01 6.97777212e-01
-6.81832731e-01 4.42692727e-01 5.28924823e-01 1.35618910e-01
-3.39620590e-01 -8.12559068e-01 -5.45458734e-01 -6.41000628e-01
-1.81931213e-01 2.65675426e-01 1.90867379e-01 -4.51758802... | [11.478618621826172, 0.30778729915618896] |
66ce3863-cf65-4998-857e-0de880768e59 | large-language-models-struggle-to-learn-long | 2211.08411 | null | https://arxiv.org/abs/2211.08411v1 | https://arxiv.org/pdf/2211.08411v1.pdf | Large Language Models Struggle to Learn Long-Tail Knowledge | The internet contains a wealth of knowledge -- from the birthdays of historical figures to tutorials on how to code -- all of which may be learned by language models. However, there is a huge variability in the number of times a given piece of information appears on the web. In this paper, we study the relationship bet... | ['Colin Raffel', 'Eric Wallace', 'Adam Roberts', 'Haikang Deng', 'Nikhil Kandpal'] | 2022-11-15 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [-3.12167406e-01 -4.66843173e-02 -2.17996672e-01 -2.33246848e-01
-1.24892783e+00 -1.04835606e+00 6.74542665e-01 7.48186231e-01
-6.77427173e-01 7.77315795e-01 3.26332629e-01 -7.78286934e-01
-5.17289162e-01 -1.08539307e+00 -1.04375994e+00 -2.13613990e-03
5.14862649e-02 7.52222598e-01 5.91923296e-01 -5.95671773... | [11.021828651428223, 8.00523567199707] |
c095ce22-c931-46d4-8a04-b08bee76eaa1 | loss-attitude-aware-energy-management-for | 2301.07789 | null | https://arxiv.org/abs/2301.07789v1 | https://arxiv.org/pdf/2301.07789v1.pdf | Loss Attitude Aware Energy Management for Signal Detection | This work considers a Bayesian signal processing problem where increasing the power of the probing signal may cause risks or undesired consequences. We employ a market based approach to solve energy management problems for signal detection while balancing multiple objectives. In particular, the optimal amount of resour... | ['Pramod K. Varshney', 'Makan Fardad', 'Tianyun Zhang', 'Chen Quan', 'Baocheng Geng'] | 2023-01-18 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 1.36615574e-01 3.27192783e-01 -2.40668803e-01 -4.06205565e-01
-4.74899650e-01 -3.03677082e-01 -1.30837172e-01 1.62421465e-01
-1.01698422e+00 7.22624779e-01 -8.72483552e-02 -2.93002367e-01
-8.49633962e-02 -9.71011519e-01 -9.58996490e-02 -6.74970567e-01
-9.60491076e-02 -6.68354258e-02 -1.98553175e-01 1.51207700... | [4.306277751922607, 2.9803271293640137] |
07ba32c0-d3af-44c2-b863-94515de4444f | minimizing-safety-interference-for-safe-and | 2107.07316 | null | https://arxiv.org/abs/2107.07316v1 | https://arxiv.org/pdf/2107.07316v1.pdf | Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning | Despite recent advances in reinforcement learning (RL), its application in safety critical domains like autonomous vehicles is still challenging. Although punishing RL agents for risky situations can help to learn safe policies, it may also lead to highly conservative behavior. In this paper, we propose a distributiona... | ['Christoph Stiller', 'Johannes Fischer', 'Marvin Busch', 'Tizian Engelgeh', 'Danial Kamran'] | 2021-07-15 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-6.28373027e-02 6.12780392e-01 -2.95342058e-01 -2.74817407e-01
-8.49327803e-01 -6.15787685e-01 4.77423817e-01 1.07106932e-01
-8.75445843e-01 1.25336421e+00 -2.51330763e-01 -6.97173893e-01
-2.29912296e-01 -9.49317217e-01 -9.12936568e-01 -1.07370043e+00
-3.63621682e-01 5.36091685e-01 5.24915397e-01 -5.20199418... | [4.871094703674316, 1.7690609693527222] |
12210fac-f8c2-4b61-baa1-00d5d99a5c4e | morphological-classification-of-astronomical | 2105.02958 | null | https://arxiv.org/abs/2105.02958v1 | https://arxiv.org/pdf/2105.02958v1.pdf | Morphological classification of astronomical images with limited labelling | The task of morphological classification is complex for simple parameterization, but important for research in the galaxy evolution field. Future galaxy surveys (e.g. EUCLID) will collect data about more than a $10^9$ galaxies. To obtain morphological information one needs to involve people to mark up galaxy images, wh... | ['Sergey Gerasimov', 'Alex Meshcheryakov', 'Andrey Soroka'] | 2021-04-27 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [-3.32681447e-01 3.92523319e-01 6.08922124e-01 -3.47992420e-01
-4.60880995e-01 -7.59112120e-01 5.59395611e-01 -3.56610212e-03
-7.66862988e-01 7.86275685e-01 -3.80697787e-01 -4.09040242e-01
1.16767786e-01 -1.30135083e+00 -8.30826342e-01 -9.67149079e-01
-2.29332209e-01 1.11055517e+00 6.04456007e-01 -1.51745498... | [7.945366859436035, 2.966765880584717] |
a74ddd6b-74ab-4bcc-bbc8-e5420fd66cba | rvt-robotic-view-transformer-for-3d-object | 2306.14896 | null | https://arxiv.org/abs/2306.14896v1 | https://arxiv.org/pdf/2306.14896v1.pdf | RVT: Robotic View Transformer for 3D Object Manipulation | For 3D object manipulation, methods that build an explicit 3D representation perform better than those relying only on camera images. But using explicit 3D representations like voxels comes at large computing cost, adversely affecting scalability. In this work, we propose RVT, a multi-view transformer for 3D manipulati... | ['Dieter Fox', 'Yu-Wei Chao', 'Valts Blukis', 'Yijie Guo', 'Jie Xu', 'Ankit Goyal'] | 2023-06-26 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [-3.43432724e-01 -1.44483671e-01 2.03839620e-03 -2.67364025e-01
-7.57188380e-01 -7.32240736e-01 4.61694270e-01 -2.80813187e-01
-2.57225007e-01 3.11895460e-01 6.93066567e-02 -3.01021755e-01
1.49298921e-01 -4.14106220e-01 -1.24852729e+00 -2.55334705e-01
-5.82636148e-02 5.44672847e-01 3.08006853e-01 -2.01228321... | [4.770730018615723, 0.46780747175216675] |
0be72a91-d681-4169-9970-15f6ba558cce | the-statcan-dialogue-dataset-retrieving-data | 2304.01412 | null | https://arxiv.org/abs/2304.01412v2 | https://arxiv.org/pdf/2304.01412v2.pdf | The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents | We introduce the StatCan Dialogue Dataset consisting of 19,379 conversation turns between agents working at Statistics Canada and online users looking for published data tables. The conversations stem from genuine intents, are held in English or French, and lead to agents retrieving one of over 5000 complex data tables... | ['Harm de Vries', 'Siva Reddy', 'Xing Han Lu'] | 2023-04-03 | null | null | null | null | ['dialogue-generation', 'table-retrieval', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 2.01562002e-01 4.03987259e-01 -2.40653940e-02 -3.75833571e-01
-1.50950372e+00 -1.09455061e+00 1.27156079e+00 2.92194188e-01
-5.13400257e-01 1.01469243e+00 9.27478850e-01 -4.72057790e-01
9.48534831e-02 -6.01102233e-01 -4.77427542e-01 -1.21157564e-01
1.67001486e-02 1.21663308e+00 2.70571679e-01 -4.74168986... | [12.423127174377441, 7.997835636138916] |
7b4d3e7d-2213-4f34-babf-0bbd83ea38f3 | vision-language-navigation-with-self | 1911.07883 | null | https://arxiv.org/abs/1911.07883v4 | https://arxiv.org/pdf/1911.07883v4.pdf | Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks | Vision-Language Navigation (VLN) is a task where agents learn to navigate following natural language instructions. The key to this task is to perceive both the visual scene and natural language sequentially. Conventional approaches exploit the vision and language features in cross-modal grounding. However, the VLN task... | ['Xiaojun Chang', 'Fengda Zhu', 'Xiaodan Liang', 'Yi Zhu'] | 2019-11-18 | vision-language-navigation-with-self-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Vision-Language_Navigation_With_Self-Supervised_Auxiliary_Reasoning_Tasks_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhu_Vision-Language_Navigation_With_Self-Supervised_Auxiliary_Reasoning_Tasks_CVPR_2020_paper.pdf | cvpr-2020-6 | ['vision-language-navigation'] | ['computer-vision'] | [ 3.84058766e-02 5.30189872e-02 -1.32822543e-01 -4.34713125e-01
-3.47174734e-01 -5.58504224e-01 1.04928434e+00 1.22109711e-01
-6.44726992e-01 5.17474055e-01 3.68861407e-01 -2.95125633e-01
3.91649380e-02 -6.59888923e-01 -8.93048823e-01 -6.52096331e-01
4.00307067e-02 6.01993263e-01 4.57291186e-01 -6.37761176... | [4.478273391723633, 0.492919921875] |
b839c710-aa65-406a-ab36-e37f7bfab555 | joint-topology-preserving-and-feature | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Cheng_Joint_Topology-Preserving_and_Feature-Refinement_Network_for_Curvilinear_Structure_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Cheng_Joint_Topology-Preserving_and_Feature-Refinement_Network_for_Curvilinear_Structure_Segmentation_ICCV_2021_paper.pdf | Joint Topology-Preserving and Feature-Refinement Network for Curvilinear Structure Segmentation | Curvilinear structure segmentation (CSS) is under semantic segmentation, whose applications include crack detection, aerial road extraction, and biomedical image segmentation. In general, geometric topology and pixel-wise features are two critical aspects of CSS. However, most semantic segmentation methods only foc... | ['Jun Guo', 'Yajing Xu', 'Xuhong Guo', 'Kaili Zhao', 'Mingfei Cheng'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['boundary-detection'] | ['computer-vision'] | [ 5.54696977e-01 1.51829943e-01 -2.59157866e-01 -3.67796898e-01
-3.34574342e-01 -5.51994503e-01 8.43414590e-02 4.40511107e-01
-5.16598113e-02 3.65722954e-01 -7.33640492e-02 -1.66572496e-01
-1.80935666e-01 -8.54448676e-01 -5.93018711e-01 -3.86209041e-01
-1.92433875e-02 4.33125049e-02 9.39346313e-01 -3.12947154... | [9.357362747192383, -0.1574830412864685] |
9ae543c7-543b-4544-857b-b365d6ed7e03 | deep-contextual-recurrent-residual-networks | 1704.03594 | null | http://arxiv.org/abs/1704.03594v1 | http://arxiv.org/pdf/1704.03594v1.pdf | Deep Contextual Recurrent Residual Networks for Scene Labeling | Designed as extremely deep architectures, deep residual networks which
provide a rich visual representation and offer robust convergence behaviors
have recently achieved exceptional performance in numerous computer vision
problems. Being directly applied to a scene labeling problem, however, they
were limited to captur... | ['Khoa Luu', 'T. Hoang Ngan Le', 'Marios Savvides', 'Dipan Pal', 'Chi Nhan Duong', 'Ligong Han'] | 2017-04-12 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 2.14353025e-01 -1.82454228e-01 -1.78370133e-01 -6.35174394e-01
-3.72701824e-01 -2.41218284e-01 6.09936476e-01 -1.29292086e-01
-5.50902665e-01 5.50074577e-01 2.60777652e-01 -2.54675388e-01
1.65452272e-01 -6.37422383e-01 -6.87131226e-01 -4.66312200e-01
3.42401743e-01 1.31626114e-01 3.15706789e-01 -3.53834629... | [9.569232940673828, 0.38557109236717224] |
f1c5e153-bee1-4014-92b6-fe87d8fbd9aa | variational-image-restoration-network | 2008.10796 | null | https://arxiv.org/abs/2008.10796v3 | https://arxiv.org/pdf/2008.10796v3.pdf | Deep Variational Network Toward Blind Image Restoration | Blind image restoration (IR) is a common yet challenging problem in computer vision. Classical model-based methods and recent deep learning (DL)-based methods represent two different methodologies for this problem, each with their own merits and drawbacks. In this paper, we propose a novel blind image restoration metho... | ['Kwan-Yen K. Wong', 'Qian Zhao', 'Lei Zhang', 'Hongwei Yong', 'Zongsheng Yue', 'Deyu Meng'] | 2020-08-25 | null | null | null | null | ['image-deblocking'] | ['computer-vision'] | [ 2.66463488e-01 -4.41515446e-01 3.19679715e-02 -6.46541119e-02
-7.11206973e-01 -1.11985452e-01 6.57193303e-01 -5.56535661e-01
-2.15115607e-01 5.27084589e-01 2.90041149e-01 -4.45004180e-02
-2.79074520e-01 -4.25404459e-01 -4.79853839e-01 -1.20076108e+00
5.55996239e-01 2.49802228e-02 -9.70795974e-02 -6.77639320... | [11.521479606628418, -2.4591922760009766] |
166fb127-e370-4676-80d2-2f5c03bd3ba0 | capsule-networks-for-brain-tumor | 1811.00597 | null | http://arxiv.org/abs/1811.00597v1 | http://arxiv.org/pdf/1811.00597v1.pdf | Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries | According to official statistics, cancer is considered as the second leading
cause of human fatalities. Among different types of cancer, brain tumor is seen
as one of the deadliest forms due to its aggressive nature, heterogeneous
characteristics, and low relative survival rate. Determining the type of brain
tumor has ... | ['Parnian Afshar', 'Konstantinos N. Plataniotis', 'Arash Mohammadi'] | 2018-11-01 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-6.69151098e-02 9.29299593e-02 -1.77872583e-01 -5.43603525e-02
-3.42335373e-01 -2.83105731e-01 6.56862855e-01 2.57819921e-01
-6.04888916e-01 6.54647171e-01 7.20032379e-02 -3.26166868e-01
8.92806277e-02 -7.28232086e-01 -2.39231095e-01 -9.44573045e-01
2.93256715e-02 3.46552402e-01 4.29806441e-01 -1.39001131... | [14.800202369689941, -2.5410823822021484] |
dc9e6c3b-44d0-4e5b-a241-975dce03c068 | audio-spectral-enhancement-leveraging | 2108.03703 | null | https://arxiv.org/abs/2108.03703v1 | https://arxiv.org/pdf/2108.03703v1.pdf | Audio Spectral Enhancement: Leveraging Autoencoders for Low Latency Reconstruction of Long, Lossy Audio Sequences | With active research in audio compression techniques yielding substantial breakthroughs, spectral reconstruction of low-quality audio waves remains a less indulged topic. In this paper, we propose a novel approach for reconstructing higher frequencies from considerably longer sequences of low-quality MP3 audio waves. O... | ['Harshavardhan Abichandani', 'Darshan Deshpande'] | 2021-08-08 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 4.85365748e-01 -1.17345728e-01 -2.45833565e-02 -2.95157917e-02
-1.21576333e+00 -5.19115984e-01 -6.38412759e-02 6.55289069e-02
-9.36913565e-02 6.88287556e-01 6.25272453e-01 -6.58790767e-02
-1.33701682e-01 -5.95487416e-01 -8.07237864e-01 -5.36032557e-01
-3.26356560e-01 -3.00280869e-01 1.26171425e-01 -1.63662121... | [15.534723281860352, 5.804286003112793] |
dcb0c426-bd85-40d6-b68c-85dfd89b1f4b | investigating-and-exploiting-image-resolution | 2006.14715 | null | https://arxiv.org/abs/2006.14715v1 | https://arxiv.org/pdf/2006.14715v1.pdf | Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification | Skin cancer is among the most common cancer types. Dermoscopic image analysis improves the diagnostic accuracy for detection of malignant melanoma and other pigmented skin lesions when compared to unaided visual inspection. Hence, computer-based methods to support medical experts in the diagnostic procedure are of grea... | ['Georg Dorffner', 'Isabella Ellinger', 'Rupert Ecker', 'Gerald Schaefer', 'Chunliang Wang', 'Amirreza Mahbod'] | 2020-06-25 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.21388912e-01 6.67896634e-03 -1.94831118e-01 -4.94139455e-03
-4.27292496e-01 -3.53258729e-01 4.35159355e-01 1.52358413e-01
-7.61621058e-01 6.58882201e-01 -1.26943126e-01 -4.07932192e-01
-1.37231067e-01 -8.34817648e-01 -4.18122649e-01 -7.99337626e-01
3.52920443e-01 -2.80398399e-01 3.30768973e-01 -8.62599760... | [15.67648983001709, -2.9951796531677246] |
2a897b09-923c-45ca-9887-ae7bcdbdee5f | deliberated-domain-bridging-for-domain | 2209.07695 | null | https://arxiv.org/abs/2209.07695v3 | https://arxiv.org/pdf/2209.07695v3.pdf | Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation | In unsupervised domain adaptation (UDA), directly adapting from the source to the target domain usually suffers significant discrepancies and leads to insufficient alignment. Thus, many UDA works attempt to vanish the domain gap gradually and softly via various intermediate spaces, dubbed domain bridging (DB). However,... | ['Yi Jin', 'Kai Chen', 'Miao Zheng', 'Huaian Chen', 'Xin Jin', 'Zhixiang Wei', 'Lin Chen'] | 2022-09-16 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.18816125e-01 3.07638794e-01 -1.57726452e-01 -5.16652822e-01
-8.13855410e-01 -5.52600741e-01 6.14710093e-01 8.07151422e-02
-3.67864907e-01 8.01546335e-01 1.25856632e-02 -1.99079573e-01
-2.89526419e-03 -7.64677048e-01 -7.84870982e-01 -7.71300256e-01
7.18188763e-01 8.56509089e-01 5.19604921e-01 -2.92387456... | [9.632830619812012, 1.3774003982543945] |
746728e7-7921-4426-9dfa-c3c192bf1162 | face-sketch-synthesis-style-similaritya-new | 1804.02975 | null | http://arxiv.org/abs/1804.02975v1 | http://arxiv.org/pdf/1804.02975v1.pdf | Face Sketch Synthesis Style Similarity:A New Structure Co-occurrence Texture Measure | Existing face sketch synthesis (FSS) similarity measures are sensitive to
slight image degradation (e.g., noise, blur). However, human perception of the
similarity of two sketches will consider both structure and texture as
essential factors and is not sensitive to slight ("pixel-level") mismatches.
Consequently, the u... | ['Paul L. Rosin', 'Ming-Ming Cheng', 'Yu-Huan Wu', 'Shengchuan Zhang', 'Deng-Ping Fan', 'Bo Ren', 'Rongrong Ji'] | 2018-04-09 | null | null | null | null | ['face-sketch-synthesis'] | ['computer-vision'] | [ 2.11000815e-01 -6.05691075e-01 -5.60211614e-02 -4.07484263e-01
-4.98724669e-01 -2.95135617e-01 6.94607019e-01 -9.50770900e-02
1.79605708e-02 5.61437964e-01 1.23375610e-01 1.28107101e-01
-9.12964866e-02 -8.32175136e-01 -1.64486974e-01 -5.04394710e-01
3.18517983e-01 -1.80031255e-01 4.47856992e-01 -3.09868753... | [12.759565353393555, 0.21592658758163452] |
2f0b1fac-99aa-4e2b-a5a7-f874c196f7ca | putting-people-in-their-place-monocular | 2112.08274 | null | https://arxiv.org/abs/2112.08274v3 | https://arxiv.org/pdf/2112.08274v3.pdf | Putting People in their Place: Monocular Regression of 3D People in Depth | Given an image with multiple people, our goal is to directly regress the pose and shape of all the people as well as their relative depth. Inferring the depth of a person in an image, however, is fundamentally ambiguous without knowing their height. This is particularly problematic when the scene contains people of ver... | ['Michael J. Black', 'Tao Mei', 'Yili Fu', 'Qian Bao', 'Wu Liu', 'Yu Sun'] | 2021-12-15 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Sun_Putting_People_in_Their_Place_Monocular_Regression_of_3D_People_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Sun_Putting_People_in_Their_Place_Monocular_Regression_of_3D_People_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-depth-estimation'] | ['computer-vision'] | [-1.86918914e-01 2.66043186e-01 -4.18068133e-02 -4.79466826e-01
-1.56569660e-01 -6.62217379e-01 1.90128744e-01 3.56346332e-02
-1.90397039e-01 2.45185331e-01 1.72069445e-01 1.36984408e-01
3.23273152e-01 -8.82861316e-01 -7.82547116e-01 -4.17167604e-01
4.11787242e-01 9.32348430e-01 1.91796198e-01 -9.48539376... | [7.086113929748535, -1.1091279983520508] |
5cd38144-7e27-497a-b535-0d03fb256520 | learning-nonautonomous-systems-via-dynamic | 2306.15618 | null | https://arxiv.org/abs/2306.15618v1 | https://arxiv.org/pdf/2306.15618v1.pdf | Learning Nonautonomous Systems via Dynamic Mode Decomposition | We present a data-driven learning approach for unknown nonautonomous dynamical systems with time-dependent inputs based on dynamic mode decomposition (DMD). To circumvent the difficulty of approximating the time-dependent Koopman operators for nonautonomous systems, a modified system derived from local parameterization... | ['Daniel M. Tartakovsky', 'Hannah Lu'] | 2023-06-27 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-2.86426932e-01 8.87930542e-02 -1.56514591e-03 1.47725970e-01
-6.57375574e-01 -4.22149807e-01 7.30221331e-01 -4.11210477e-01
-2.57974744e-01 1.04489601e+00 -1.79121718e-01 -2.14180395e-01
-5.79023778e-01 -3.74277562e-01 -9.26918089e-01 -1.15405881e+00
-2.97738701e-01 6.66056871e-01 -3.44026893e-01 -3.87238920... | [6.48972225189209, 3.447875738143921] |
d9991ec9-c0b8-4324-aa3f-efcf94e4cd59 | entred-benchmarking-relation-extraction-with | 2305.13551 | null | https://arxiv.org/abs/2305.13551v2 | https://arxiv.org/pdf/2305.13551v2.pdf | How Fragile is Relation Extraction under Entity Replacements? | Relation extraction (RE) aims to extract the relations between entity names from the textual context. In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context. However, existing work has found that the RE mod... | ['Muhao Chen', 'Manjuan Duan', 'Jing Tang', 'Wenxuan Zhou', 'Yuxuan Liang', 'Yujun Cai', 'Fei Wang', 'Bryan Hooi', 'Yiwei Wang'] | 2023-05-22 | null | null | null | null | ['causal-inference', 'causal-inference', 'relation-extraction'] | ['knowledge-base', 'miscellaneous', 'natural-language-processing'] | [-1.06753506e-01 5.36714256e-01 -4.51388389e-01 -4.20071304e-01
-4.72088635e-01 -5.79060972e-01 6.09568357e-01 1.26243696e-01
-4.37042236e-01 7.18932509e-01 2.81523973e-01 -3.78998458e-01
-7.27285147e-02 -8.04821491e-01 -7.10725605e-01 -3.28146219e-02
-8.82150978e-02 4.82763052e-01 3.66011411e-01 -3.92734259... | [9.442658424377441, 8.678647994995117] |
b4355538-667f-4080-b553-941d5c44d5fa | fice-text-conditioned-fashion-image-editing | 2301.02110 | null | https://arxiv.org/abs/2301.02110v1 | https://arxiv.org/pdf/2301.02110v1.pdf | FICE: Text-Conditioned Fashion Image Editing With Guided GAN Inversion | Fashion-image editing represents a challenging computer vision task, where the goal is to incorporate selected apparel into a given input image. Most existing techniques, known as Virtual Try-On methods, deal with this task by first selecting an example image of the desired apparel and then transferring the clothing on... | ['Simon Dobrišek', 'Vitomir Štruc', 'Clinton Fookes', 'Martin Pernuš'] | 2023-01-05 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 7.19375432e-01 -8.19316581e-02 -1.45230204e-01 -4.36772853e-01
-5.67586958e-01 -6.99337244e-01 8.47285986e-01 -3.16739470e-01
-2.23440498e-01 5.93872845e-01 9.83874053e-02 9.25031006e-02
5.51731139e-02 -7.19512224e-01 -1.05649269e+00 -4.42080528e-01
7.11381435e-01 5.21152437e-01 -1.92586228e-01 -4.64481175... | [11.664528846740723, -0.5637024641036987] |
a16a647b-b116-4b36-991b-57b81365f14f | fine-grained-post-training-for-improving | null | null | https://aclanthology.org/2021.naacl-main.122 | https://aclanthology.org/2021.naacl-main.122.pdf | Fine-grained Post-training for Improving Retrieval-based Dialogue Systems | Retrieval-based dialogue systems display an outstanding performance when pre-trained language models are used, which includes bidirectional encoder representations from transformers (BERT). During the multi-turn response selection, BERT focuses on training the relationship between the context with multiple utterances a... | ['Jungyun Seo', 'Youngjoong Ko', 'Byoungjae Kim', 'Taesuk Hong', 'Janghoon Han'] | 2021-05-24 | null | null | null | naacl-2021-4 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 3.59499335e-01 1.69205844e-01 -1.69081792e-01 -8.75183940e-01
-9.44661438e-01 -3.05079222e-01 8.37863803e-01 1.83119386e-01
-2.88086563e-01 6.32772446e-01 7.06607282e-01 -2.39975184e-01
-2.28443053e-02 -6.60653770e-01 -2.55328119e-01 -3.93575549e-01
3.36784095e-01 6.06626451e-01 2.07429528e-01 -9.23685312... | [12.571048736572266, 7.830224514007568] |
48d87f47-b17d-46e1-8bfe-4e19810d458b | dipnet-efficiency-distillation-and-iterative | 2304.07018 | null | https://arxiv.org/abs/2304.07018v1 | https://arxiv.org/pdf/2304.07018v1.pdf | DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution | Efficient deep learning-based approaches have achieved remarkable performance in single image super-resolution. However, recent studies on efficient super-resolution have mainly focused on reducing the number of parameters and floating-point operations through various network designs. Although these methods can decreas... | ['Shuaicheng Liu', 'Haoqiang Fan', 'Qi Wu', 'Ting Jiang', 'Youwei Li', 'Xinpeng Li', 'Lei Yu'] | 2023-04-14 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 1.04067937e-01 -2.47780636e-01 -4.61152613e-01 -4.28859800e-01
-8.53876472e-01 -1.38841018e-01 1.61064208e-01 -3.15965325e-01
-3.67602110e-01 7.98200607e-01 2.14570150e-01 -1.63063675e-01
-3.04538250e-01 -8.76835406e-01 -7.48746753e-01 -6.27957344e-01
5.77718914e-02 -4.81398143e-02 3.61417562e-01 -1.39395088... | [10.935439109802246, -1.7189263105392456] |
b6cb9d33-4a5c-43c0-ba3c-963924b2b890 | text-ranking-and-classification-using-data | 2109.11577 | null | https://arxiv.org/abs/2109.11577v2 | https://arxiv.org/pdf/2109.11577v2.pdf | Text Ranking and Classification using Data Compression | A well-known but rarely used approach to text categorization uses conditional entropy estimates computed using data compression tools. Text affinity scores derived from compressed sizes can be used for classification and ranking tasks, but their success depends on the compression tools used. We use the Zstandard compre... | ['Igor L. Markov', 'Nitya Kasturi'] | 2021-09-23 | null | https://openreview.net/forum?id=mCyM2CWFZX5 | https://openreview.net/pdf?id=mCyM2CWFZX5 | neurips-workshop-icbinb-2021-12 | ['text-categorization'] | ['natural-language-processing'] | [ 1.20220803e-01 -2.99020201e-01 -6.19689584e-01 -2.64538199e-01
-8.38352561e-01 -8.20748389e-01 9.15617704e-01 7.28841007e-01
-1.11527312e+00 5.07418036e-01 3.55986089e-01 -5.12933612e-01
-3.58144045e-01 -7.64588416e-01 -6.47878945e-02 -4.55439925e-01
6.74599633e-02 1.01838815e+00 4.20434058e-01 -1.53976217... | [10.596752166748047, 8.427888870239258] |
6f08b18e-5ca0-46f6-83bd-32e57ab5d27c | federated-learning-for-semantic-parsing-task | 2305.17221 | null | https://arxiv.org/abs/2305.17221v1 | https://arxiv.org/pdf/2305.17221v1.pdf | Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms | This paper studies a new task of federated learning (FL) for semantic parsing, where multiple clients collaboratively train one global model without sharing their semantic parsing data. By leveraging data from multiple clients, the FL paradigm can be especially beneficial for clients that have little training data to d... | ['Huan Sun', 'Yu Su', 'Wei-Han Lee', 'Changchang Liu', 'Tianshu Zhang'] | 2023-05-26 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [-7.39302412e-02 3.50089282e-01 -3.47233385e-01 -8.42460275e-01
-1.26875722e+00 -5.00608861e-01 -2.15313374e-03 -2.07979679e-01
-4.81195867e-01 5.38876414e-01 2.60052472e-01 -3.13892335e-01
1.56432409e-02 -6.65438771e-01 -8.72992218e-01 -6.06423080e-01
2.73740321e-01 8.65242660e-01 4.22194391e-01 8.64419565... | [5.814409255981445, 6.302374839782715] |
0b63885b-eca6-478e-9ddb-fc55a8526c63 | object-centric-anomaly-detection-by-attribute | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Saleh_Object-Centric_Anomaly_Detection_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Saleh_Object-Centric_Anomaly_Detection_2013_CVPR_paper.pdf | Object-Centric Anomaly Detection by Attribute-Based Reasoning | When describing images, humans tend not to talk about the obvious, but rather mention what they find interesting. We argue that abnormalities and deviations from typicalities are among the most important components that form what is worth mentioning. In this paper we introduce the abnormality detection as a recognition... | ['Ahmed Elgammal', 'Ali Farhadi', 'Babak Saleh'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['image-categorization'] | ['computer-vision'] | [ 3.38377655e-01 1.23952843e-01 2.30589025e-02 -7.52193809e-01
-2.52574116e-01 -4.49296504e-01 5.89396179e-01 8.22937727e-01
9.32392403e-02 2.72460580e-01 2.12588772e-01 -1.98740825e-01
-3.14829528e-01 -5.93699038e-01 -6.31523967e-01 -4.63944167e-01
-1.11520410e-01 4.50970769e-01 3.23887080e-01 7.01475714... | [10.051623344421387, 1.4001904726028442] |
38ae41fd-fe8f-4f52-9006-17f050697e37 | web-image-search-engine-based-on-lsh-index | 2108.13301 | null | https://arxiv.org/abs/2108.13301v1 | https://arxiv.org/pdf/2108.13301v1.pdf | Web image search engine based on LSH index and CNN Resnet50 | To implement a good Content Based Image Retrieval (CBIR) system, it is essential to adopt efficient search methods. One way to achieve this results is by exploiting approximate search techniques. In fact, when we deal with very large collections of data, using an exact search method makes the system very slow. In this ... | ['Stefano Poleggi', 'Alice Nannini', 'Marco Parola'] | 2021-08-20 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-2.85389066e-01 -5.25324166e-01 2.72947829e-02 -2.56416708e-01
-7.20872164e-01 -3.71549815e-01 7.63855994e-01 5.95664859e-01
-1.05573487e+00 3.33615154e-01 1.16372310e-01 -4.64336425e-02
-3.57744873e-01 -1.19689262e+00 -7.29071975e-01 -5.45127988e-01
-3.16356957e-01 5.00866115e-01 7.02495873e-01 -4.76681888... | [10.553587913513184, 0.4427817761898041] |
f45d94d2-9869-401e-9a94-ee9d76f71dbe | old-swedish-part-of-speech-tagging-between | null | null | https://aclanthology.org/W16-2104 | https://aclanthology.org/W16-2104.pdf | Old Swedish Part-of-Speech Tagging between Variation and External Knowledge | null | ['Gerlof Bouma', 'Yvonne Adesam'] | 2016-08-01 | null | null | null | ws-2016-8 | ['morphological-tagging'] | ['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... | [-7.253198623657227, 3.805964708328247] |
4b63d9ad-991c-4198-8b1c-b63c0d777245 | 3d-concept-grounding-on-neural-fields | 2207.06403 | null | https://arxiv.org/abs/2207.06403v1 | https://arxiv.org/pdf/2207.06403v1.pdf | 3D Concept Grounding on Neural Fields | In this paper, we address the challenging problem of 3D concept grounding (i.e. segmenting and learning visual concepts) by looking at RGBD images and reasoning about paired questions and answers. Existing visual reasoning approaches typically utilize supervised methods to extract 2D segmentation masks on which concept... | ['Chuang Gan', 'Joshua B. Tenenbaum', 'Chunru Lin', 'Yilun Du', 'Yining Hong'] | 2022-07-13 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.28274092e-01 4.62626964e-01 -2.70925164e-01 -6.41303599e-01
-5.78724384e-01 -9.37736094e-01 7.68688500e-01 6.32283866e-01
-1.73035562e-01 8.20398480e-02 -1.12107269e-01 -4.15464789e-01
-1.71988040e-01 -1.08365679e+00 -9.60016370e-01 -4.17629868e-01
1.31581038e-01 6.65866137e-01 1.12978458e-01 2.56563425... | [8.04708480834961, -3.2346935272216797] |
d8f8baa5-42e2-4606-8266-22162f1294d4 | hierarchical-spatial-aware-siamese-network | 1711.09539 | null | http://arxiv.org/abs/1711.09539v2 | http://arxiv.org/pdf/1711.09539v2.pdf | Hierarchical Spatial-aware Siamese Network for Thermal Infrared Object Tracking | Most thermal infrared (TIR) tracking methods are discriminative, treating the
tracking problem as a classification task. However, the objective of the
classifier (label prediction) is not coupled to the objective of the tracker
(location estimation). The classification task focuses on the between-class
difference of th... | ['Nana Fan', 'Hongzhi Wang', 'Qiao Liu', 'Zhenyu He', 'Xin Li'] | 2017-11-27 | null | null | null | null | ['thermal-infrared-object-tracking'] | ['computer-vision'] | [ 9.35713351e-02 -4.84240770e-01 -9.72992256e-02 -2.41264924e-01
-7.68855691e-01 -5.54451585e-01 5.21914423e-01 -4.92865503e-01
-4.89962488e-01 1.02500573e-01 -1.83391467e-01 5.11680404e-03
5.19756973e-02 -2.97418743e-01 -6.04609191e-01 -1.13423777e+00
2.83239394e-01 2.04780445e-01 7.17532396e-01 2.34268889... | [6.33281135559082, -2.192270040512085] |
4d2db566-1bbe-4b41-97cd-835608dde280 | utility-based-evaluation-metrics-for-models | null | null | https://aclanthology.org/W15-1108 | https://aclanthology.org/W15-1108.pdf | Utility-based evaluation metrics for models of language acquisition: A look at speech segmentation | null | ['Lawrence Phillips', 'Lisa Pearl'] | 2015-06-01 | null | null | null | ws-2015-6 | ['scene-labeling'] | ['computer-vision'] | [-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... | [-7.341408729553223, 3.7981784343719482] |
7a35483d-b750-4611-b3ef-56c33bba3e3f | a-job-assignment-heuristic-for-lifelong-multi | 2003.07108 | null | http://arxiv.org/abs/2003.07108v2 | http://arxiv.org/pdf/2003.07108v2.pdf | A Job-Assignment Heuristic for Lifelong Multi-Agent Path Finding Problem with Multiple Delivery Locations | In this paper we proposed multiple job-assignment heuristics to generate
low-total-cost solutions and determine the best performing method amongst them. | ['Polat Faruk', 'Semiz Fatih'] | 2020-04-25 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-1.09989643e-01 9.16506350e-02 -2.52648443e-01 -3.81923735e-01
-3.80747199e-01 -3.93217683e-01 -1.11827835e-01 4.21671383e-02
-3.28008771e-01 1.61871719e+00 -3.94792259e-01 -2.45096564e-01
-8.61123502e-01 -7.26511359e-01 -1.14123404e-01 -6.77130997e-01
-1.02752440e-01 1.65083647e+00 2.17419818e-01 -2.07201466... | [5.015581130981445, 2.120194911956787] |
c0c6a6fb-77f5-4ce8-b926-f4f829a164cb | cross-modality-fusion-transformer-for | 2111.00273 | null | https://arxiv.org/abs/2111.00273v4 | https://arxiv.org/pdf/2111.00273v4.pdf | Cross-Modality Fusion Transformer for Multispectral Object Detection | Multispectral image pairs can provide the combined information, making object detection applications more reliable and robust in the open world. To fully exploit the different modalities, we present a simple yet effective cross-modality feature fusion approach, named Cross-Modality Fusion Transformer (CFT) in this pape... | ['Wang Zhaokui', 'Han Dapeng', 'Fang Qingyun'] | 2021-10-30 | null | null | null | null | ['multispectral-object-detection'] | ['computer-vision'] | [ 2.32887924e-01 -6.36353672e-01 -8.19399208e-02 -2.76878119e-01
-1.10680759e+00 -6.57336771e-01 6.93243206e-01 -2.09208414e-01
-2.32338816e-01 2.12258235e-01 6.65930510e-02 -1.40551987e-04
-1.09113298e-01 -7.85440207e-01 -5.77094436e-01 -9.01071489e-01
3.65225405e-01 -3.98983240e-01 2.75526494e-01 -7.30400681... | [10.103190422058105, -1.6004008054733276] |
5f934725-9969-4b86-ade1-cee4ce7c3f43 | semidefinite-programming-based | 1310.2273 | null | http://arxiv.org/abs/1310.2273v2 | http://arxiv.org/pdf/1310.2273v2.pdf | Semidefinite Programming Based Preconditioning for More Robust Near-Separable Nonnegative Matrix Factorization | Nonnegative matrix factorization (NMF) under the separability assumption can
provably be solved efficiently, even in the presence of noise, and has been
shown to be a powerful technique in document classification and hyperspectral
unmixing. This problem is referred to as near-separable NMF and requires that
there exist... | ['Nicolas Gillis', 'Stephen A. Vavasis'] | 2013-10-08 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 8.00402761e-01 -2.85867989e-01 -1.78636581e-01 -3.69175188e-02
-7.67809451e-01 -8.05180013e-01 2.60628641e-01 -3.25049728e-01
-2.01475978e-01 6.00162387e-01 2.59415150e-01 -3.57474029e-01
-5.38625419e-01 -5.33067822e-01 -5.15503168e-01 -1.26134753e+00
4.39351536e-02 5.63880444e-01 -7.12212861e-01 -9.80165526... | [10.04980182647705, -1.9789663553237915] |
00e1c27a-2930-44c0-93d5-e5fb911237d5 | learning-to-disentangle-interleaved | null | null | https://aclanthology.org/N18-1164 | https://aclanthology.org/N18-1164.pdf | Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking | An enormous amount of conversation occurs online every day, such as on chat platforms where multiple conversations may take place concurrently. Interleaved conversations lead to difficulties in not only following discussions but also retrieving relevant information from simultaneous messages. Conversation disentangleme... | ['Yan-Ying Chen', 'Jyun-Yu Jiang', 'Francine Chen', 'Wei Wang'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['conversation-disentanglement'] | ['natural-language-processing'] | [ 3.55673224e-01 -7.70867104e-03 8.49145055e-02 -6.36144876e-01
-1.19872582e+00 -6.82678461e-01 1.14343131e+00 1.59845814e-01
-1.93225041e-01 7.45109379e-01 1.12656510e+00 -1.03148520e-01
-3.77523601e-02 -4.52552885e-01 -1.06808744e-01 -5.41290224e-01
-2.44895324e-01 7.10038185e-01 -3.34019333e-01 -4.28028017... | [12.638164520263672, 7.7619500160217285] |
13c9ae6e-9de5-4dcd-8c76-b5baa18004d4 | artificial-intelligence-solution-for | 2203.05563 | null | https://arxiv.org/abs/2203.05563v1 | https://arxiv.org/pdf/2203.05563v1.pdf | Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients | Glioblastomas are the most common malignant brain tumors in adults. Approximately 200000 people die each year from Glioblastoma in the world. Glioblastoma patients have a median survival of 12 months with optimal therapy and about 4 months without treatment. Glioblastomas appear as heterogeneous necrotic masses with ir... | ['Vikram Goddla'] | 2022-03-09 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 4.03203100e-01 2.48068303e-01 -3.63687962e-01 -1.63964540e-01
-6.61008239e-01 -3.95543486e-01 2.26959571e-01 2.40913495e-01
-6.84357285e-01 8.93964589e-01 4.43469226e-01 -6.53921008e-01
2.13693708e-01 -5.87824583e-01 1.27350107e-01 -1.19166839e+00
1.84378382e-02 7.82670557e-01 5.02907149e-02 2.97527254... | [14.784815788269043, -2.5181639194488525] |
2c1c4910-2d87-4448-90e0-7ff3c3e5c9aa | deep-clustering-with-incomplete-noisy | 2305.19391 | null | https://arxiv.org/abs/2305.19391v1 | https://arxiv.org/pdf/2305.19391v1.pdf | Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach | The recent integration of deep learning and pairwise similarity annotation-based constrained clustering -- i.e., $\textit{deep constrained clustering}$ (DCC) -- has proven effective for incorporating weak supervision into massive data clustering: Less than 1% of pair similarity annotations can often substantially enhan... | ['Xiao Fu', 'Shahana Ibrahim', 'Tri Nguyen'] | 2023-05-30 | null | null | null | null | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [ 1.49426848e-01 1.44513041e-01 9.07143131e-02 -6.16240084e-01
-1.25528550e+00 -7.59284914e-01 1.12262361e-01 3.39643598e-01
-4.75557894e-01 6.46988034e-01 -1.78056553e-01 -1.70207441e-01
-6.01231039e-01 -2.30156034e-01 -9.03599501e-01 -1.08429027e+00
-3.28426361e-01 4.86160159e-01 -2.65116766e-02 2.20974609... | [9.145593643188477, 3.2785425186157227] |
ec7755d9-eab2-45e0-96d7-4c999de9e448 | learning-the-night-sky-with-deep-generative | 2302.02030 | null | https://arxiv.org/abs/2302.02030v1 | https://arxiv.org/pdf/2302.02030v1.pdf | Learning the Night Sky with Deep Generative Priors | Recovering sharper images from blurred observations, referred to as deconvolution, is an ill-posed problem where classical approaches often produce unsatisfactory results. In ground-based astronomy, combining multiple exposures to achieve images with higher signal-to-noise ratios is complicated by the variation of poin... | ['Yashil Sukurdeep', 'Tamas Budavari', 'Daniel Hall', 'Fausto Navarro'] | 2023-02-03 | null | null | null | null | ['deblurring', 'astronomy'] | ['computer-vision', 'miscellaneous'] | [ 3.81127536e-01 -4.74082261e-01 6.83386803e-01 -3.38524282e-01
-9.49885070e-01 -6.77678823e-01 5.89272082e-01 -4.08910930e-01
-3.62520486e-01 8.13605607e-01 4.64696854e-01 4.66758311e-02
-6.67035818e-01 -5.65423608e-01 -8.13040853e-01 -1.02180016e+00
1.92644417e-01 1.38081685e-01 5.52840196e-02 -9.01933014... | [11.499863624572754, -2.6683311462402344] |
9886a50f-405e-40fc-bba5-5bdca2e2e2d0 | m2rnet-multi-modal-and-multi-scale-refined | 2109.07922 | null | https://arxiv.org/abs/2109.07922v1 | https://arxiv.org/pdf/2109.07922v1.pdf | M2RNet: Multi-modal and Multi-scale Refined Network for RGB-D Salient Object Detection | Salient object detection is a fundamental topic in computer vision. Previous methods based on RGB-D often suffer from the incompatibility of multi-modal feature fusion and the insufficiency of multi-scale feature aggregation. To tackle these two dilemmas, we propose a novel multi-modal and multi-scale refined network (... | ['Hongpeng Wang', 'Xiuli Shao', 'Ruixun Zhang', 'Jinchao Zhu', 'Xian Fang'] | 2021-09-16 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [-1.26656681e-01 8.13027695e-02 -9.10604279e-03 -2.77511656e-01
-7.48921156e-01 4.96179648e-02 4.19820160e-01 -4.17379141e-02
-4.44089830e-01 5.77949584e-01 2.44993538e-01 1.85795635e-01
-1.99615926e-01 -7.05464125e-01 -4.54339564e-01 -7.97130644e-01
1.54581144e-01 -1.61600009e-01 9.46603358e-01 -2.60583609... | [9.691781997680664, -0.7652511596679688] |
3ccd59df-4e15-4c27-ad16-e88fc5edf5d8 | a-transformer-based-audio-captioning-model | 2007.00222 | null | https://arxiv.org/abs/2007.00222v2 | https://arxiv.org/pdf/2007.00222v2.pdf | A Transformer-based Audio Captioning Model with Keyword Estimation | One of the problems with automated audio captioning (AAC) is the indeterminacy in word selection corresponding to the audio event/scene. Since one acoustic event/scene can be described with several words, it results in a combinatorial explosion of possible captions and difficulty in training. To solve this problem, we ... | ['Shoichiro Saito', 'Masahiro Yasuda', 'Yuma Koizumi', 'Ryo Masumura', 'Kyosuke Nishida'] | 2020-07-01 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 6.49836719e-01 -1.52630314e-01 2.94729829e-01 -1.15744218e-01
-1.82893527e+00 -7.52128184e-01 1.51445717e-01 1.76595002e-02
-1.66856110e-01 6.09564483e-01 5.63639522e-01 -1.06465437e-01
7.80514404e-02 -1.56916067e-01 -9.04358149e-01 -4.37840939e-01
1.38204083e-01 6.14829540e-01 2.13107929e-01 2.04979271... | [15.280731201171875, 4.920375347137451] |
a62c6e62-62cb-4dbf-8c07-a0d3398b8f2e | high-dynamic-range-image-forensics-using-cnn | 1902.10938 | null | http://arxiv.org/abs/1902.10938v1 | http://arxiv.org/pdf/1902.10938v1.pdf | High dynamic range image forensics using cnn | High dynamic range (HDR) imaging has recently drawn much attention in
multimedia community. In this paper, we proposed a HDR image forensics method
based on convolutional neural network (CNN).To our best knowledge, this is the
first time to apply deep learning method on HDR image forensics. The proposed
algorithm uses ... | ['Xiaofeng Zhu', 'Yongqing Huo'] | 2019-02-28 | null | null | null | null | ['tone-mapping', 'image-forensics', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.37798771e-01 -4.09387112e-01 1.85201198e-01 -4.32710834e-02
-6.00622416e-01 -3.14426333e-01 6.48643017e-01 -3.74505639e-01
-3.65412384e-01 7.10156024e-01 3.31399515e-02 -3.54686379e-01
1.89835243e-02 -1.10596657e+00 -7.17563450e-01 -5.66139221e-01
-3.54082622e-02 -2.77117975e-02 2.48959407e-01 -4.47483569... | [11.003253936767578, -2.171233654022217] |
820e88b0-2782-4f74-8497-657da744c492 | implementation-of-neural-network-and-feature | 1802.06288 | null | http://arxiv.org/abs/1802.06288v1 | http://arxiv.org/pdf/1802.06288v1.pdf | Implementation of Neural Network and feature extraction to classify ECG signals | This paper presents a suitable and efficient implementation of a feature
extraction algorithm (Pan Tompkins algorithm) on electrocardiography (ECG)
signals, for detection and classification of four cardiac diseases: Sleep
Apnea, Arrhythmia, Supraventricular Arrhythmia and Long Term Atrial
Fibrillation (AF) and differen... | ['Amogh Raut', 'Dhruv Tyagi', 'Soumya Saxena', 'R Karthik', 'Rajesh Kumar M'] | 2018-02-17 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 3.77442002e-01 -2.54164219e-01 -4.00818922e-02 -1.89883053e-01
-1.54017955e-02 -3.60360771e-01 -8.03998411e-02 3.18996161e-01
-4.98291731e-01 1.16925335e+00 -1.61832884e-01 -6.74086034e-01
-5.79845130e-01 -2.95621544e-01 5.19314766e-01 -5.83881497e-01
-6.07599556e-01 3.45972925e-01 -2.83013344e-01 1.74643770... | [14.196540832519531, 3.213219404220581] |
ba3d5298-398d-4d62-9bfa-a50515be146e | deep-learning-approaches-to-lexical | 2305.12000 | null | https://arxiv.org/abs/2305.12000v1 | https://arxiv.org/pdf/2305.12000v1.pdf | Deep Learning Approaches to Lexical Simplification: A Survey | Lexical Simplification (LS) is the task of replacing complex for simpler words in a sentence whilst preserving the sentence's original meaning. LS is the lexical component of Text Simplification (TS) with the aim of making texts more accessible to various target populations. A past survey (Paetzold and Specia, 2017) ha... | ['Marcos Zampieri', 'Matthew Shardlow', 'Tharindu Ranasinghe', 'Kai North'] | 2023-05-19 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [ 1.52504995e-01 2.48732910e-01 -2.92766273e-01 -2.35016897e-01
-8.17429662e-01 -2.75071055e-01 7.28328526e-01 4.54763830e-01
-6.88132644e-01 8.08339536e-01 1.03946722e+00 -1.64252385e-01
2.08675101e-01 -4.35172558e-01 -5.24283171e-01 -1.96652383e-01
2.97035635e-01 5.14289856e-01 -5.66423237e-01 -5.43498337... | [11.06657886505127, 10.324089050292969] |
60ee634b-5e2f-474e-8898-f861cda5d979 | clickbait-sensational-headline-generation | 1909.03582 | null | https://arxiv.org/abs/1909.03582v1 | https://arxiv.org/pdf/1909.03582v1.pdf | Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning | Sensational headlines are headlines that capture people's attention and generate reader interest. Conventional abstractive headline generation methods, unlike human writers, do not optimize for maximal reader attention. In this paper, we propose a model that generates sensational headlines without labeled data. We firs... | ['Chien-Sheng Wu', 'Pascale Fung', 'Peng Xu', 'Andrea Madotto'] | 2019-09-09 | clickbait-sensational-headline-generation-1 | https://aclanthology.org/D19-1303 | https://aclanthology.org/D19-1303.pdf | ijcnlp-2019-11 | ['headline-generation'] | ['natural-language-processing'] | [ 9.68793109e-02 5.00566363e-01 -2.67295152e-01 -2.92888612e-01
-1.55648959e+00 -5.00937700e-01 6.93715811e-01 1.83234408e-01
-4.46530670e-01 1.05075359e+00 9.89169300e-01 -1.18529215e-01
4.96369898e-01 -6.22654498e-01 -7.52994001e-01 -2.90948778e-01
3.86317343e-01 2.86081254e-01 -6.43653423e-02 -3.49060327... | [12.192848205566406, 9.121403694152832] |
30117710-8d02-4734-b0a7-95426eb5378f | efficient-rgb-d-semantic-segmentation-for | 2011.06961 | null | https://arxiv.org/abs/2011.06961v3 | https://arxiv.org/pdf/2011.06961v3.pdf | Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis | Analyzing scenes thoroughly is crucial for mobile robots acting in different environments. Semantic segmentation can enhance various subsequent tasks, such as (semantically assisted) person perception, (semantic) free space detection, (semantic) mapping, and (semantic) navigation. In this paper, we propose an efficient... | ['Horst-Michael Gross', 'Tim Wengefeld', 'Benjamin Lewandowski', 'Mona Köhler', 'Daniel Seichter'] | 2020-11-13 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 2.19575092e-01 1.19833559e-01 4.91077960e-01 -5.14974535e-01
-2.67343462e-01 -5.39687455e-01 5.75603127e-01 1.55310392e-01
-9.25826609e-01 4.55658734e-01 -2.24854991e-01 -4.54618305e-01
-1.24164373e-01 -8.58792603e-01 -7.54275560e-01 -4.45386916e-01
-4.68068868e-02 7.75562942e-01 5.92345774e-01 -4.95855540... | [8.400199890136719, -2.184539794921875] |
b121a515-76a7-47b3-9374-ef790d3b225d | time-series-alignment-with-global-invariances | 2002.03848 | null | https://arxiv.org/abs/2002.03848v2 | https://arxiv.org/pdf/2002.03848v2.pdf | Time Series Alignment with Global Invariances | Multivariate time series are ubiquitous objects in signal processing. Measuring a distance or similarity between two such objects is of prime interest in a variety of applications, including machine learning, but can be very difficult as soon as the temporal dynamics and the representation of the time series, {\em i.e.... | ['Nicolas Courty', 'Laetitia Chapel', 'Romain Tavenard', 'Rémi Flamary', 'Yann Soullard', 'Titouan Vayer'] | 2020-02-10 | null | null | null | null | ['time-series-alignment'] | ['time-series'] | [ 1.70695037e-01 -4.46897209e-01 2.77450234e-01 -2.75937378e-01
-4.46257323e-01 -8.80324841e-01 7.78781235e-01 4.56005543e-01
-5.85885406e-01 4.68465149e-01 -1.51540637e-01 5.25905415e-02
-7.19561398e-01 -4.77255464e-01 -5.16033947e-01 -9.49717879e-01
-6.09921932e-01 2.16298819e-01 8.48254934e-03 -1.83932200... | [7.454353332519531, 3.389874219894409] |
67941c07-e89f-48e6-88ba-04b85f5ab3fb | interventional-video-grounding-with-dual | 2106.11013 | null | https://arxiv.org/abs/2106.11013v2 | https://arxiv.org/pdf/2106.11013v2.pdf | Interventional Video Grounding with Dual Contrastive Learning | Video grounding aims to localize a moment from an untrimmed video for a given textual query. Existing approaches focus more on the alignment of visual and language stimuli with various likelihood-based matching or regression strategies, i.e., P(Y|X). Consequently, these models may suffer from spurious correlations betw... | ['Wei Lu', 'Hao Zhang', 'Sicong Leng', 'Jun Liu', 'Yao Xiao', 'Rui Qiao', 'Guoshun Nan'] | 2021-06-21 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Nan_Interventional_Video_Grounding_With_Dual_Contrastive_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Nan_Interventional_Video_Grounding_With_Dual_Contrastive_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-grounding'] | ['computer-vision'] | [ 1.65345550e-01 -1.79472536e-01 -7.70583689e-01 -3.33413273e-01
-8.64045262e-01 -4.77336168e-01 6.86595023e-01 -1.29778489e-01
-5.38807400e-02 5.29009461e-01 6.00574553e-01 -1.97012201e-02
-1.26994520e-01 -3.68036866e-01 -1.12930512e+00 -6.01089776e-01
-3.10521200e-02 -2.76332527e-01 -1.52499467e-01 5.20784676... | [10.068399429321289, 0.7955071926116943] |
986fdc58-04ab-4a48-ac86-18dd9684372e | content4all-open-research-sign-language | 2105.02351 | null | https://arxiv.org/abs/2105.02351v1 | https://arxiv.org/pdf/2105.02351v1.pdf | Content4All Open Research Sign Language Translation Datasets | Computational sign language research lacks the large-scale datasets that enables the creation of useful reallife applications. To date, most research has been limited to prototype systems on small domains of discourse, e.g. weather forecasts. To address this issue and to push the field forward, we release six datasets ... | ['Richard Bowden', 'Robin Nachtrab-Ribback', 'Giacomo Inches', 'Marco Giovanelli', 'Guillaume Rochette', 'Ben Saunders', 'Necati Cihan Camgoz'] | 2021-05-05 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 2.36399144e-01 3.53968859e-01 -5.27506769e-01 -4.91403013e-01
-6.93417430e-01 -7.58688569e-01 7.19560027e-01 -1.77243769e-01
-4.90326583e-01 8.42088878e-01 1.22877419e+00 -4.00638074e-01
2.50881016e-01 -4.14576769e-01 -4.51082110e-01 2.28216778e-02
9.99323577e-02 3.01356375e-01 3.33900213e-01 -3.77547950... | [9.153910636901855, -6.471294403076172] |
5651f9d8-6404-4a54-85aa-25ba6c0a8faf | accented-text-to-speech-synthesis-with-a | 2211.03316 | null | https://arxiv.org/abs/2211.03316v1 | https://arxiv.org/pdf/2211.03316v1.pdf | Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder | Accent plays a significant role in speech communication, influencing understanding capabilities and also conveying a person's identity. This paper introduces a novel and efficient framework for accented Text-to-Speech (TTS) synthesis based on a Conditional Variational Autoencoder. It has the ability to synthesize a sel... | ['Dorien Herremans', 'Berrak Sisman', 'Ambuj Mehrish', 'Jan Melechovsky'] | 2022-11-07 | null | null | null | null | ['text-to-speech-synthesis'] | ['speech'] | [-1.64755702e-01 1.92169398e-01 -1.19815491e-01 -6.81946397e-01
-5.61684132e-01 -4.22435820e-01 6.79647863e-01 -3.88811052e-01
-2.08205044e-01 1.01184309e+00 6.70582592e-01 -3.87945503e-01
3.45345736e-01 -6.10041082e-01 -3.50387961e-01 -8.89533997e-01
4.06609029e-01 3.93643647e-01 -9.08430591e-02 -6.36793435... | [14.769609451293945, 6.594698429107666] |
ff4f4ec2-5c6e-4aa0-8162-5e0159f6c4ad | deep-single-image-camera-calibration-by | 2303.17166 | null | https://arxiv.org/abs/2303.17166v1 | https://arxiv.org/pdf/2303.17166v1.pdf | Deep Single Image Camera Calibration by Heatmap Regression to Recover Fisheye Images Under ManhattanWorld AssumptionWithout Ambiguity | In orthogonal world coordinates, a Manhattan world lying along cuboid buildings is widely useful for various computer vision tasks. However, the Manhattan world has much room for improvement because the origin of pan angles from an image is arbitrary, that is, four-fold rotational symmetric ambiguity of pan angles. To ... | ['Takayoshi Yamashita', 'Yasunori Ishii', 'Satoshi Sato', 'Nobuhiko Wakai'] | 2023-03-30 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [-1.31858364e-01 -1.77700907e-01 -2.63386220e-01 -4.70831633e-01
-3.94810051e-01 -5.92448890e-01 3.18399370e-01 -5.95905721e-01
-3.97403359e-01 3.06239456e-01 -1.00641385e-01 -2.43819699e-01
-4.82538119e-02 -8.05707157e-01 -8.66983235e-01 -6.33124590e-01
4.63822573e-01 3.65634292e-01 2.87448943e-01 -3.30536574... | [8.033538818359375, -2.28489351272583] |
3c730c26-b705-4812-82c8-492f446e0169 | recycle-your-wav2vec2-codebook-a-speech | null | null | https://aclanthology.org/2022.coling-1.626 | https://aclanthology.org/2022.coling-1.626.pdf | Recycle Your Wav2Vec2 Codebook: A Speech Perceiver for Keyword Spotting | Speech information in a pretrained wav2vec2.0 model is usually leveraged through its encoder, which has at least 95M parameters, being not so suitable for small footprint Keyword Spotting. In this work, we show an efficient way of profiting from wav2vec2.0’s linguistic knowledge, by recycling the phonetic information e... | ['Mireia Farrús', 'Jordi Luque', 'Guillermo Cámbara'] | null | null | null | null | coling-2022-10 | ['small-footprint-keyword-spotting', 'keyword-spotting'] | ['speech', 'speech'] | [ 1.05223954e-01 4.52637106e-01 -2.67383866e-02 -1.42063335e-01
-9.26240444e-01 -8.55875552e-01 4.07112151e-01 2.68402189e-01
-8.30096483e-01 2.18198583e-01 6.50767207e-01 -7.84611702e-01
3.41853559e-01 -6.32811844e-01 -9.76212919e-01 -5.71759999e-01
1.94155630e-02 4.65320557e-01 -3.35139632e-02 1.01678237... | [14.262812614440918, 6.747035503387451] |
46bfc932-07ea-43ee-9e6f-3ea898bfb3af | contextual-pyramid-attention-network-for | 2004.07018 | null | https://arxiv.org/abs/2004.07018v1 | https://arxiv.org/pdf/2004.07018v1.pdf | Contextual Pyramid Attention Network for Building Segmentation in Aerial Imagery | Building extraction from aerial images has several applications in problems such as urban planning, change detection, and disaster management. With the increasing availability of data, Convolutional Neural Networks (CNNs) for semantic segmentation of remote sensing imagery has improved significantly in recent years. Ho... | ['Clint Sebastian', 'Raffaele Imbriaco', 'Peter H. N. de With', 'Egor Bondarev'] | 2020-04-15 | null | null | null | null | ['segmentation-of-remote-sensing-imagery'] | ['miscellaneous'] | [ 4.80606288e-01 -2.45346174e-01 1.10704660e-01 -5.42054057e-01
-6.56176805e-01 -6.85626924e-01 4.00800318e-01 3.60763371e-01
-6.05301797e-01 4.58376169e-01 1.89430520e-01 -3.64180773e-01
-2.60724604e-01 -1.33552039e+00 -7.87670672e-01 -5.37227690e-01
-2.78986156e-01 4.35588323e-02 4.74178612e-01 -2.32837707... | [9.327454566955566, -1.2885466814041138] |
966faf70-d237-4b1d-8212-454ec65c5d03 | commonsense-knowledge-from-scene-graphs-for | 2210.14162 | null | https://arxiv.org/abs/2210.14162v1 | https://arxiv.org/pdf/2210.14162v1.pdf | Commonsense Knowledge from Scene Graphs for Textual Environments | Text-based games are becoming commonly used in reinforcement learning as real-world simulation environments. They are usually imperfect information games, and their interactions are only in the textual modality. To challenge these games, it is effective to complement the missing information by providing knowledge outsi... | ['Michiaki Tatsubori', 'Daiki Kimura', 'Tsunehiko Tanaka'] | 2022-10-19 | null | null | null | null | ['text-based-games', 'common-sense-reasoning'] | ['playing-games', 'reasoning'] | [-2.39132605e-02 -3.75476554e-02 1.74600244e-01 3.09779290e-02
1.98706407e-02 -5.38679719e-01 8.32809508e-01 2.89025158e-01
-7.09408879e-01 8.78842950e-01 3.18185031e-01 -3.51871580e-01
-2.23446175e-01 -1.27077508e+00 -5.17180860e-01 -1.88827530e-01
2.02493742e-01 4.20141578e-01 6.79093778e-01 -9.84024704... | [10.714873313903809, 1.7242745161056519] |
be3fc162-4083-4c04-8b5a-c2731f4486d3 | learnda-learnable-knowledge-guided-data | 2106.01649 | null | https://arxiv.org/abs/2106.01649v1 | https://arxiv.org/pdf/2106.01649v1.pdf | LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification | Modern models for event causality identification (ECI) are mainly based on supervised learning, which are prone to the data lacking problem. Unfortunately, the existing NLP-related augmentation methods cannot directly produce the available data required for this task. To solve the data lacking problem, we introduce a n... | ['Yuguang Chen', 'Weihua Peng', 'Jun Zhao', 'Kang Liu', 'Yubo Chen', 'Pengfei Cao', 'Xinyu Zuo'] | 2021-06-03 | null | https://aclanthology.org/2021.acl-long.276 | https://aclanthology.org/2021.acl-long.276.pdf | acl-2021-5 | ['event-causality-identification'] | ['natural-language-processing'] | [ 3.82319063e-01 4.60901678e-01 -3.65144700e-01 -2.75591344e-01
-8.59169900e-01 -5.96084177e-01 9.13245499e-01 2.31443435e-01
-2.96892136e-01 1.28483343e+00 6.26572728e-01 -4.00377065e-01
-3.00987381e-02 -9.30018663e-01 -7.96614587e-01 -3.08187276e-01
-9.13863331e-02 5.05326092e-01 3.33455771e-01 -2.22728804... | [9.181829452514648, 9.11620807647705] |
915fe2b8-a7d8-4c53-9a7a-11ec665d5e8b | perceiving-the-invisible-proposal-free-amodal | 2205.14637 | null | https://arxiv.org/abs/2205.14637v1 | https://arxiv.org/pdf/2205.14637v1.pdf | Perceiving the Invisible: Proposal-Free Amodal Panoptic Segmentation | Amodal panoptic segmentation aims to connect the perception of the world to its cognitive understanding. It entails simultaneously predicting the semantic labels of visible scene regions and the entire shape of traffic participant instances, including regions that may be occluded. In this work, we formulate a proposal-... | ['Abhinav Valada', 'Rohit Mohan'] | 2022-05-29 | null | null | null | null | ['amodal-panoptic-segmentation'] | ['computer-vision'] | [ 3.40945244e-01 2.97675729e-01 -2.91570187e-01 -6.15477979e-01
-7.56288230e-01 -5.80528975e-01 6.70146286e-01 -9.90700498e-02
7.66727701e-02 5.00112832e-01 2.12513760e-01 -2.94777006e-01
6.43630549e-02 -5.64840734e-01 -8.30439866e-01 -4.99437392e-01
3.01522374e-01 6.69628978e-01 6.09209061e-01 9.53312665... | [9.556949615478516, 0.3472689986228943] |
75b3bf1d-4998-4f9c-a4cf-654db345d353 | exploring-simple-3d-multi-object-tracking-for | 2108.10312 | null | https://arxiv.org/abs/2108.10312v1 | https://arxiv.org/pdf/2108.10312v1.pdf | Exploring Simple 3D Multi-Object Tracking for Autonomous Driving | 3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles. Existing methods are predominantly based on the tracking-by-detection pipeline and inevitably require a heuristic matching step for the detection association. In this paper, we present SimTrack to simplify the hand-crafted tra... | ['Alan Yuille', 'Xiaodong Yang', 'Chenxu Luo'] | 2021-08-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Luo_Exploring_Simple_3D_Multi-Object_Tracking_for_Autonomous_Driving_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Luo_Exploring_Simple_3D_Multi-Object_Tracking_for_Autonomous_Driving_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-multi-object-tracking'] | ['computer-vision'] | [-1.43901050e-01 -3.97960007e-01 -3.03038538e-01 -4.98823583e-01
-7.09837139e-01 -6.86479568e-01 7.24990487e-01 4.22920696e-02
-6.99673355e-01 4.28833932e-01 -5.24303675e-01 -4.11419511e-01
2.99868011e-03 -5.89037776e-01 -1.08661592e+00 -5.64479649e-01
1.16671897e-01 9.65434372e-01 1.13232005e+00 1.72745794... | [6.672203540802002, -2.219566822052002] |
fe44b236-5983-4c36-8969-dbb0ddc8f32f | sub-lexical-dialogue-act-classification-in-a | null | null | https://aclanthology.org/W13-3915 | https://aclanthology.org/W13-3915.pdf | Sub-lexical Dialogue Act Classification in a Spoken Dialogue System Support for the Elderly with Cognitive Disabilities | null | ['Yoshihiro Fujita', 'Shinichi Onaka', 'Minoru Kamata', 'Ken Sadohara', 'Hiroaki Kojima', 'Takenobu Inoue', 'Misato Nihei', 'Takuya Narita'] | 2013-08-01 | null | null | null | ws-2013-8 | ['dialogue-act-classification'] | ['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... | [-7.353734016418457, 3.7106878757476807] |
b8a05338-2c80-4863-a527-d75c2dcee129 | towards-a-generic-multimodal-architecture-for | 2108.04343 | null | https://arxiv.org/abs/2108.04343v1 | https://arxiv.org/pdf/2108.04343v1.pdf | Towards a Generic Multimodal Architecture for Batch and Streaming Big Data Integration | Big Data are rapidly produced from various heterogeneous data sources. They are of different types (text, image, video or audio) and have different levels of reliability and completeness. One of the most interesting architectures that deal with the large amount of emerging data at high velocity is called the lambda arc... | ['Dalila Chiadmi', 'Maryem Rhanoui', 'Siham Yousfi'] | 2021-08-09 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-4.43191439e-01 1.55381730e-03 -2.33361460e-02 -3.30513686e-01
-4.53869462e-01 -3.75892401e-01 5.41971803e-01 9.49088454e-01
-2.81504869e-01 4.21617568e-01 2.42766306e-01 5.84479868e-02
-4.48914856e-01 -1.27863777e+00 -3.59278351e-01 -4.70585883e-01
-2.63194859e-01 7.82183468e-01 7.65644431e-01 -5.11359155... | [8.452091217041016, 0.19973288476467133] |
8e2afee3-3cc1-48af-9ec2-5e965ce20394 | diversify-your-vision-datasets-with-automatic | 2305.16289 | null | https://arxiv.org/abs/2305.16289v1 | https://arxiv.org/pdf/2305.16289v1.pdf | Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentation | Many fine-grained classification tasks, like rare animal identification, have limited training data and consequently classifiers trained on these datasets often fail to generalize to variations in the domain like changes in weather or location. As such, we explore how natural language descriptions of the domains seen i... | ['Trevor Darrell', 'Joseph E. Gonzalez', 'Jiezhi Yang', 'Han Zhang', 'Alyssa Umino', 'Lisa Dunlap'] | 2023-05-25 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 6.22722507e-01 6.98316172e-02 2.87290573e-01 -7.21449912e-01
-3.52788240e-01 -1.02982116e+00 8.85908961e-01 2.67938554e-01
-5.92504323e-01 7.70142257e-01 3.37072536e-02 -2.26318121e-01
2.57195175e-01 -6.72710180e-01 -1.01605248e+00 -1.64587989e-01
2.44546339e-01 6.12988234e-01 5.26245013e-02 -1.77083522... | [9.96374225616455, 1.7049845457077026] |
3e02c187-f28f-446a-9058-661add5d1ef2 | diving-deep-into-clickbaits-who-use-them-to | 1703.09400 | null | http://arxiv.org/abs/1703.09400v1 | http://arxiv.org/pdf/1703.09400v1.pdf | Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects? | The use of alluring headlines (clickbait) to tempt the readers has become a
growing practice nowadays. For the sake of existence in the highly competitive
media industry, most of the on-line media including the mainstream ones, have
started following this practice. Although the wide-spread practice of clickbait
makes t... | ['Naeemul Hassan', 'Md Main Uddin Rony', 'Mohammad Yousuf'] | 2017-03-28 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [-4.55518156e-01 -5.88359125e-03 -3.83582383e-01 -2.03658622e-02
-7.75094748e-01 -4.71587509e-01 8.97578597e-01 6.24737263e-01
-6.53745234e-01 4.51972663e-01 5.66873968e-01 -6.06634915e-01
-1.22508638e-01 -7.17816591e-01 -4.51256007e-01 -2.56570783e-02
3.28240812e-01 1.50231853e-01 8.55874538e-01 -1.92628488... | [7.75368070602417, 9.78248119354248] |
e15aec1a-e61a-4f82-b462-bcaae86f119e | disentangled-recurrent-wasserstein-1 | 2101.07496 | null | https://arxiv.org/abs/2101.07496v1 | https://arxiv.org/pdf/2101.07496v1.pdf | Disentangled Recurrent Wasserstein Autoencoder | Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as images in an unsupervised learning framework. However, only a few works have explored unsupervised disentangled sequential representation lea... | ['Xuan Zhang', 'Li Erran Li', 'Ligong Han', 'Martin Renqiang Min', 'Jun Han'] | 2021-01-19 | disentangled-recurrent-wasserstein | https://openreview.net/forum?id=O7ms4LFdsX | https://openreview.net/pdf?id=O7ms4LFdsX | iclr-2021-1 | ['unconditional-video-generation'] | ['computer-vision'] | [ 3.20880294e-01 1.52904674e-01 -3.82013083e-01 -1.66585401e-01
-4.88345146e-01 -6.63336337e-01 8.56067598e-01 -7.44679332e-01
-2.82050576e-03 8.00282657e-01 6.57666266e-01 -2.01601058e-01
-1.86998665e-01 -7.51018763e-01 -8.25662553e-01 -1.03753173e+00
-6.47201389e-03 4.21712250e-01 -3.23542565e-01 -8.67907181... | [10.918984413146973, 0.45350998640060425] |
99b51221-0cd5-46d5-b6a4-34dd830bc249 | dhsegment-a-generic-deep-learning-approach | 1804.10371 | null | https://arxiv.org/abs/1804.10371v2 | https://arxiv.org/pdf/1804.10371v2.pdf | dhSegment: A generic deep-learning approach for document segmentation | In recent years there have been multiple successful attempts tackling document processing problems separately by designing task specific hand-tuned strategies. We argue that the diversity of historical document processing tasks prohibits to solve them one at a time and shows a need for designing generic approaches in o... | ['Benoit Seguin', 'Sofia Ares Oliveira', 'Frederic Kaplan'] | 2018-04-27 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 4.58950251e-01 -2.04011753e-01 2.86266029e-01 -3.47108006e-01
-1.00155008e+00 -8.96485925e-01 8.82061720e-01 3.04014564e-01
-7.57153094e-01 4.41985369e-01 7.79527202e-02 -5.19430518e-01
-1.96796149e-01 -5.52069426e-01 -6.81803048e-01 -4.64819998e-01
-1.55912116e-01 1.60021320e-01 2.28976801e-01 -2.40623340... | [11.66201114654541, 2.6945865154266357] |
b480ff78-3a70-49de-bb12-fd1b75b88715 | can-large-language-models-reason-about | 2207.08143 | null | https://arxiv.org/abs/2207.08143v3 | https://arxiv.org/pdf/2207.08143v3.pdf | Can large language models reason about medical questions? | Although large language models (LLMs) often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring strong reasoning skills and expert domain knowledge. We set out to investigate whether GPT-3.5 (Codex and InstructGPT) can be applied to answer and reason about difficult real-wo... | ['Ole Winther', 'Christoffer Egeberg Hother', 'Valentin Liévin'] | 2022-07-17 | null | null | null | null | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 2.93454409e-01 7.95346677e-01 -9.43022594e-02 -2.37239510e-01
-1.46201515e+00 -6.16001725e-01 3.69392514e-01 7.59113550e-01
-5.27254522e-01 8.30426753e-01 3.68718147e-01 -9.28165197e-01
-5.98136961e-01 -5.61691046e-01 -7.69113123e-01 3.33749317e-02
4.55519140e-01 1.01148999e+00 2.39426538e-01 -3.01738709... | [8.985782623291016, 8.410384178161621] |
df7f6327-e13f-4768-8f14-71624b1e8900 | deep-occlusion-aware-instance-segmentation | 2103.12340 | null | https://arxiv.org/abs/2103.12340v1 | https://arxiv.org/pdf/2103.12340v1.pdf | Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers | Segmenting highly-overlapping objects is challenging, because typically no distinction is made between real object contours and occlusion boundaries. Unlike previous two-stage instance segmentation methods, we model image formation as composition of two overlapping layers, and propose Bilayer Convolutional Network (BCN... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Lei Ke'] | 2021-03-23 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ke_Deep_Occlusion-Aware_Instance_Segmentation_With_Overlapping_BiLayers_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ke_Deep_Occlusion-Aware_Instance_Segmentation_With_Overlapping_BiLayers_CVPR_2021_paper.pdf | cvpr-2021-1 | ['amodal-instance-segmentation', 'real-time-instance-segmentation', 'occlusion-handling'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.91477656e-01 4.06382054e-01 -2.91299939e-01 -3.24407697e-01
-4.86442715e-01 -6.27146602e-01 3.44606996e-01 -1.51541933e-01
-3.00645232e-01 3.51481825e-01 -2.17552036e-01 -2.42955089e-01
4.07355666e-01 -6.40779257e-01 -6.88630044e-01 -6.15349889e-01
8.13800693e-02 3.67584676e-01 9.21924889e-01 2.26788223... | [9.420957565307617, 0.06944147497415543] |
de0e5f0d-15ba-438a-8389-607d3b6a5cbb | dense-but-efficient-videoqa-for-intricate | 2210.10300 | null | https://arxiv.org/abs/2210.10300v1 | https://arxiv.org/pdf/2210.10300v1.pdf | Dense but Efficient VideoQA for Intricate Compositional Reasoning | It is well known that most of the conventional video question answering (VideoQA) datasets consist of easy questions requiring simple reasoning processes. However, long videos inevitably contain complex and compositional semantic structures along with the spatio-temporal axis, which requires a model to understand the c... | ['Eun-Sol Kim', 'Wooyoung Kang', 'Jihyeon Lee'] | 2022-10-19 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [-1.62622020e-01 -2.96449780e-01 3.80545110e-02 -6.42176330e-01
-7.37915397e-01 -6.02629542e-01 4.35282826e-01 -5.61284065e-01
-1.47483110e-01 3.68211299e-01 8.16074073e-01 -4.40257080e-02
-9.56235602e-02 -5.31742513e-01 -8.49128246e-01 -5.64380348e-01
9.15123597e-02 1.07462674e-01 3.17509621e-01 -3.01716298... | [10.407377243041992, 1.0591470003128052] |
76cebbc2-cab6-4b7a-b65c-a7184c15b3c0 | the-open-images-dataset-v4-unified-image | 1811.00982 | null | https://arxiv.org/abs/1811.00982v2 | https://arxiv.org/pdf/1811.00982v2.pdf | The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale | We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined lis... | ['Tom Duerig', 'Jordi Pont-Tuset', 'Matteo Malloci', 'Ivan Krasin', 'Alexander Kolesnikov', 'Vittorio Ferrari', 'Shahab Kamali', 'Jasper Uijlings', 'Hassan Rom', 'Alina Kuznetsova', 'Neil Alldrin', 'Stefan Popov'] | 2018-11-02 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 6.93843216e-02 2.04693392e-01 -3.58833015e-01 -2.91852921e-01
-3.61761391e-01 -9.97473121e-01 6.19398057e-01 1.87113732e-01
-3.91017854e-01 2.93908924e-01 2.58508511e-03 -2.40914747e-01
-1.52342394e-01 -5.21224141e-01 -7.14584172e-01 -4.51398909e-01
-3.86914343e-01 4.06590641e-01 6.21066630e-01 -3.60995978... | [9.98962688446045, 1.5758353471755981] |
788d0aa6-a78a-471a-a18b-f60ee1a70683 | lake-ice-monitoring-with-webcams-and-crowd | 2002.07875 | null | https://arxiv.org/abs/2002.07875v2 | https://arxiv.org/pdf/2002.07875v2.pdf | Lake Ice Monitoring with Webcams and Crowd-Sourced Images | Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way... | ['Laura Leal-Taixe', 'Emmanuel Baltsavias', 'Manu Tom', 'Konrad Schindler', 'Rajanie Prabha', 'Mathias Rothermel'] | 2020-02-18 | null | null | null | null | ['webcam-rgb-image-classification', 'lake-ice-detection', 'lake-detection', 'change-detection-for-remote-sensing-images', 'lake-ice-detection', 'segmentation-of-remote-sensing-imagery', 'remote-sensing-image-classification', 'the-semantic-segmentation-of-remote-sensing'] | ['computer-code', 'computer-vision', 'computer-vision', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous'] | [ 8.71278625e-03 -3.46492529e-01 3.50989223e-01 -6.04254961e-01
-6.13637686e-01 -1.03600073e+00 4.65838879e-01 -1.69990629e-01
-4.92178231e-01 4.57403183e-01 -2.11652771e-01 -3.59030217e-01
5.72577000e-01 -8.23001027e-01 -9.09013331e-01 -7.37634897e-01
-2.62335837e-01 3.73237729e-01 3.06418955e-01 -4.29409385... | [9.50558853149414, -1.5695388317108154] |
280b5082-76e9-4289-8bce-da129655b743 | sliding-mode-theory-under-feedback | 2109.05464 | null | https://arxiv.org/abs/2109.05464v1 | https://arxiv.org/pdf/2109.05464v1.pdf | Sliding-mode theory under feedback constraints and the problem of epidemic control | One of the most important branches of nonlinear control theory is the so-called sliding-mode. Its aim is the design of a (nonlinear) feedback law that brings and maintains the state trajectory of a dynamic system on a given sliding surface. Here, dynamics becomes completely independent of the model parameters and can b... | ['Gianluigi Pillonetto', 'Mauro Bisiacco'] | 2021-09-12 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 4.33258384e-01 3.31531167e-01 -4.52398062e-01 3.62723291e-01
1.83291420e-01 -6.43524826e-01 5.82920730e-01 3.12384337e-01
-3.38117152e-01 9.04592037e-01 -4.91905332e-01 -3.73123795e-01
-6.05592430e-01 -7.35786915e-01 -8.51210058e-01 -1.02895510e+00
-3.89966816e-01 3.79094809e-01 5.99713504e-01 -8.36227953... | [5.519758224487305, 2.8589084148406982] |
fab2acca-c398-4e96-b266-15fec57fd581 | llql-logistic-likelihood-q-learning-for | 2307.02345 | null | https://arxiv.org/abs/2307.02345v1 | https://arxiv.org/pdf/2307.02345v1.pdf | LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning | Currently, research on Reinforcement learning (RL) can be broadly classified into two categories: online RL and offline RL. Both in online and offline RL, the primary focus of research on the Bellman error lies in the optimization techniques and performance improvement, rather than exploring the inherent structural pro... | ['Yu Guang Wang', 'Bingxin Zhou', 'Outongyi Lv'] | 2023-07-05 | null | null | null | null | ['q-learning', 'reinforcement-learning-1', 'offline-rl'] | ['methodology', 'methodology', 'playing-games'] | [-4.17016238e-01 1.10812038e-01 -4.51045960e-01 -2.12497532e-01
-6.98400378e-01 -5.29862940e-01 3.11909206e-02 1.45443141e-01
-8.19191158e-01 1.05706835e+00 -2.54079372e-01 -5.17024338e-01
-4.47665453e-01 -4.54923958e-01 -7.49541163e-01 -8.36500168e-01
-3.51373255e-01 9.93818194e-02 -1.53798848e-01 -6.56233802... | [4.219833850860596, 2.55479097366333] |
f627518f-a27a-43dd-8abf-5e5af50e985b | aist-an-interpretable-attention-based-deep | 2012.08713 | null | https://arxiv.org/abs/2012.08713v2 | https://arxiv.org/pdf/2012.08713v2.pdf | AIST: An Interpretable Attention-based Deep Learning Model for Crime Prediction | Accuracy and interpretability are two essential properties for a crime prediction model. Because of the adverse effects that the crimes can have on human life, economy and safety, we need a model that can predict future occurrence of crime as accurately as possible so that early steps can be taken to avoid the crime. O... | ['Tanzima Hashem', 'Yeasir Rayhan'] | 2020-12-16 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 4.25440297e-02 -8.97617415e-02 -2.86135137e-01 -5.69241881e-01
2.06554011e-01 -2.59655058e-01 5.05595565e-01 5.44572413e-01
-1.35840744e-01 5.09847343e-01 6.54394209e-01 -4.72752810e-01
-7.38887548e-01 -1.20007086e+00 -2.44660303e-01 -3.32657576e-01
-3.89132440e-01 2.94028699e-01 1.68974131e-01 -1.46808073... | [6.713070869445801, 1.9809898138046265] |
ef5d4dd8-99ec-4c81-9574-9e6a7cfe9443 | effectively-leveraging-multi-modal-features | 2203.13281 | null | https://arxiv.org/abs/2203.13281v1 | https://arxiv.org/pdf/2203.13281v1.pdf | Effectively leveraging Multi-modal Features for Movie Genre Classification | Movie genre classification has been widely studied in recent years due to its various applications in video editing, summarization, and recommendation. Prior work has typically addressed this task by predicting genres based solely on the visual content. As a result, predictions from these methods often perform poorly f... | ['Huayan Wang', 'Jiayi Liu', 'Xin Miao', 'Bryan A. Plummer', 'Yiwen Gu', 'Zhongping Zhang'] | 2022-03-24 | null | null | null | null | ['boundary-detection', 'genre-classification'] | ['computer-vision', 'computer-vision'] | [ 3.24919522e-01 -4.86112088e-01 -3.25867563e-01 -1.95920125e-01
-9.33818758e-01 -5.44297576e-01 5.36679924e-01 2.74741530e-01
-2.19580129e-01 4.22133476e-01 3.39544475e-01 1.46983191e-01
1.35989547e-01 -5.16166747e-01 -5.09421408e-01 -4.47120696e-01
-9.31572616e-02 -1.18709520e-01 4.15555298e-01 -1.38127608... | [10.153129577636719, 0.524202287197113] |
ce905218-f6ce-400b-b0ec-3927d976ca51 | discriminative-deep-dyna-q-robust-planning | 1808.09442 | null | http://arxiv.org/abs/1808.09442v2 | http://arxiv.org/pdf/1808.09442v2.pdf | Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning | This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving
the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed
framework that extends the Dyna-Q algorithm to integrate planning for
task-completion dialogue policy learning. To obviate DDQ's high dependency on
the quality of simul... | ['Yun-Nung Chen', 'Shang-Yu Su', 'Jingjing Liu', 'Jianfeng Gao', 'Xiujun Li'] | 2018-08-28 | discriminative-deep-dyna-q-robust-planning-1 | https://aclanthology.org/D18-1416 | https://aclanthology.org/D18-1416.pdf | emnlp-2018-10 | ['task-completion-dialogue-policy-learning'] | ['natural-language-processing'] | [-4.10014123e-01 3.17221642e-01 1.45302325e-01 -1.80876240e-01
-8.00467908e-01 -7.35831857e-01 1.01474404e+00 -1.29935026e-01
-8.71191084e-01 7.11534679e-01 4.58453536e-01 -3.21213186e-01
-1.04277998e-01 -5.57388425e-01 -5.43845475e-01 -4.15110141e-01
-3.50761324e-01 4.73341614e-01 2.20524907e-01 -5.05242527... | [4.083180904388428, 1.7200183868408203] |
5ec0f125-28d2-4c9d-a813-d722ef12492d | deep-autoencoding-gaussian-mixture-model-for | null | null | https://openreview.net/forum?id=BJJLHbb0- | https://openreview.net/pdf?id=BJJLHbb0- | Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection | Unsupervised anomaly detection on multi- or high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruit... | ['Cristian Lumezanu', 'Wei Cheng', 'Qi Song', 'Daeki Cho', 'Bo Zong', 'Martin Renqiang Min', 'Haifeng Chen'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['unsupervised-anomaly-detection-with-specified-5', 'unsupervised-anomaly-detection-with-specified-4', 'unsupervised-anomaly-detection-with-specified-7', 'unsupervised-anomaly-detection-with-specified-6', 'unsupervised-anomaly-detection-with-specified'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-3.87643963e-01 -2.52496656e-02 1.98568881e-01 -2.10553274e-01
-6.33863747e-01 7.19411895e-02 5.23281455e-01 5.85593507e-02
-4.82625872e-01 2.64748991e-01 -6.54027238e-02 -1.87175021e-01
-8.69554132e-02 -6.57075047e-01 -5.74224830e-01 -1.11681294e+00
6.82364777e-02 6.66459143e-01 -1.78471372e-01 4.35890973... | [7.638636112213135, 2.359224557876587] |
99cb5e8a-abaa-4dc7-bcae-fdad81cdbda5 | towards-explainable-collaborative-filtering | 2304.13937 | null | https://arxiv.org/abs/2304.13937v1 | https://arxiv.org/pdf/2304.13937v1.pdf | Towards Explainable Collaborative Filtering with Taste Clusters Learning | Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN. However, the explainability o... | ['Xing Xie', 'Yunjun Gao', 'Lu Chen', 'Mingqi Wu', 'Xiting Wang', 'Jing Yao', 'Jianxun Lian', 'Yuntao Du'] | 2023-04-27 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.96378157e-01 -2.52854247e-02 -6.36908650e-01 -7.33766079e-01
-3.73387218e-01 -4.48082000e-01 1.96384862e-01 2.03939602e-01
1.72471300e-01 3.28713983e-01 8.63132954e-01 -3.23178858e-01
-6.22684777e-01 -6.44343317e-01 -2.74296850e-01 -4.13635969e-01
-1.26831785e-01 3.36919308e-01 -2.99637735e-01 7.56166950... | [9.895395278930664, 5.637152194976807] |
e264a585-452f-4834-b8fb-e0e5bbeaae2c | outline-then-details-syntactically-guided | 2305.00909 | null | https://arxiv.org/abs/2305.00909v3 | https://arxiv.org/pdf/2305.00909v3.pdf | Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation | For a complicated algorithm, its implementation by a human programmer usually starts with outlining a rough control flow followed by iterative enrichments, eventually yielding carefully generated syntactic structures and variables in a hierarchy. However, state-of-the-art large language models generate codes in a singl... | ['Zhangyang Wang', 'Dejia Xu', 'Yihan Xi', 'Kevin Wang', 'Ajay Kumar Jaiswal', 'S P Sharan', 'Wenqing Zheng'] | 2023-04-28 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 5.46072237e-02 2.26241961e-01 -3.55164498e-01 -5.02023935e-01
-9.32837009e-01 -8.12999725e-01 5.11779845e-01 2.69642293e-01
2.61361510e-01 2.23236352e-01 6.55106723e-01 -8.24548721e-01
3.97291273e-01 -8.95514965e-01 -1.00108325e+00 1.18662966e-02
2.10145861e-01 3.04131240e-01 8.82631913e-03 -2.04065159... | [7.726959228515625, 7.862443447113037] |
d5d3e75e-9e73-4f10-8e8d-c472a372edc8 | robustsense-defending-adversarial-attack-for | 2204.01560 | null | https://arxiv.org/abs/2204.01560v2 | https://arxiv.org/pdf/2204.01560v2.pdf | SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition | Deep neural networks have empowered accurate device-free human activity recognition, which has wide applications. Deep models can extract robust features from various sensors and generalize well even in challenging situations such as data-insufficient cases. However, these systems could be vulnerable to input perturbat... | ['Lihua Xie', 'Han Zou', 'Jianfei Yang'] | 2022-04-04 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 4.83989418e-01 -6.79410324e-02 -7.69532397e-02 -3.45251933e-02
-5.40586531e-01 -6.12444341e-01 4.78695899e-01 -2.94981033e-01
-2.84948021e-01 8.68918955e-01 6.83497265e-02 -4.12706465e-01
2.82629002e-02 -9.39546645e-01 -8.82345796e-01 -8.79144073e-01
-1.73190340e-01 -3.59874934e-01 1.96606722e-02 -7.56779015... | [13.607733726501465, 5.654835224151611] |
7ea950c3-6cb7-47eb-a7bc-ee4924971fe1 | exploring-overcomplete-representations-for | 2010.10661 | null | https://arxiv.org/abs/2010.10661v1 | https://arxiv.org/pdf/2010.10661v1.pdf | Exploring Overcomplete Representations for Single Image Deraining using CNNs | Removal of rain streaks from a single image is an extremely challenging problem since the rainy images often contain rain streaks of different size, shape, direction and density. Most recent methods for deraining use a deep network following a generic "encoder-decoder" architecture which captures low-level features acr... | ['Vishal M. Patel', 'Jeya Maria Jose Valanarasu', 'Rajeev Yasarla'] | 2020-10-20 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [-1.20178767e-01 -1.74719021e-01 6.67461395e-01 -4.77097869e-01
-1.88792408e-01 -1.17649272e-01 3.65502909e-02 -3.11953366e-01
-3.20601672e-01 8.93724799e-01 1.94485307e-01 1.68253258e-02
1.85784727e-01 -9.62248921e-01 -9.57466066e-01 -9.51966763e-01
-2.32203588e-01 -1.05682403e-01 4.30863500e-01 -2.55782545... | [10.927396774291992, -3.2249531745910645] |
50f354f9-e869-414d-82ee-a0491ac6a6b1 | a-first-look-at-dataset-bias-in-license-plate | 2208.10657 | null | https://arxiv.org/abs/2208.10657v2 | https://arxiv.org/pdf/2208.10657v2.pdf | A First Look at Dataset Bias in License Plate Recognition | Public datasets have played a key role in advancing the state of the art in License Plate Recognition (LPR). Although dataset bias has been recognized as a severe problem in the computer vision community, it has been largely overlooked in the LPR literature. LPR models are usually trained and evaluated separately on ea... | ['David Menotti', 'Eduardo Luz', 'Valter Estevam', 'Marcelo Santos', 'Rayson Laroca'] | 2022-08-23 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 2.39036173e-01 -5.08705676e-01 -1.51127130e-01 -4.02490407e-01
-9.58825946e-01 -9.01471674e-01 7.94159055e-01 4.95973118e-02
-5.03027797e-01 5.91237664e-01 -1.73759565e-01 -2.38715738e-01
-2.14600265e-01 -6.05107605e-01 -7.72356331e-01 -8.66776109e-01
1.56897426e-01 4.04700398e-01 3.21554273e-01 1.84622854... | [9.839425086975098, -4.9075422286987305] |
e3e41dcb-e834-41c6-ba07-d456ab948317 | learning-reasoning-patterns-for-relational | null | null | https://aclanthology.org/2022.findings-acl.129 | https://aclanthology.org/2022.findings-acl.129.pdf | Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph | Relational triple extraction is a critical task for constructing knowledge graphs. Existing methods focused on learning text patterns from explicit relational mentions. However, they usually suffered from ignoring relational reasoning patterns, thus failed to extract the implicitly implied triples. Fortunately, the gra... | ['Yongfeng Huang', 'Yunqi Zhang', 'Yubo Chen'] | null | null | null | null | findings-acl-2022-5 | ['relational-reasoning'] | ['natural-language-processing'] | [ 3.90158370e-02 7.99499393e-01 -6.35637224e-01 -6.11207902e-01
-2.89624780e-01 -5.40954590e-01 4.74222988e-01 4.43802893e-01
5.34314036e-01 3.98672342e-01 3.60831112e-01 -7.74416566e-01
-3.82323563e-01 -1.62166727e+00 -1.06953800e+00 7.43646771e-02
-1.18733473e-01 4.03882295e-01 2.39894569e-01 -3.83182853... | [9.10258674621582, 7.915329456329346] |
fb7dff59-1b31-4f3c-8a57-231a0afc9268 | semi-supervised-stance-detection-of-tweets | 2201.00614 | null | https://arxiv.org/abs/2201.00614v2 | https://arxiv.org/pdf/2201.00614v2.pdf | Semi-supervised Stance Detection of Tweets Via Distant Network Supervision | Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection. Today's best neural stance detectors need large volumes of training data, which is difficult to curate given the fast-changing landscape of soci... | ['Tanmoy Chakraborty', 'Soumen Chakrabarti', 'Samiya Caur', 'Subhabrata Dutta'] | 2022-01-03 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [-9.91404951e-02 4.12458688e-01 -6.72095358e-01 -6.10915124e-01
-8.58911693e-01 -7.92197287e-01 1.12815726e+00 5.06629109e-01
-4.76117641e-01 8.20801675e-01 8.89911234e-01 -3.58886003e-01
4.73903000e-01 -9.79047418e-01 -3.99642825e-01 -4.11118865e-01
3.61840948e-02 5.22107601e-01 2.54220795e-02 -7.35925019... | [8.733528137207031, 10.199392318725586] |
f6198630-5f5d-418e-b25f-92e018f77e44 | directional-sparse-filtering-using-weighted | 2102.00196 | null | https://arxiv.org/abs/2102.00196v3 | https://arxiv.org/pdf/2102.00196v3.pdf | Directional Sparse Filtering using Weighted Lehmer Mean for Blind Separation of Unbalanced Speech Mixtures | In blind source separation of speech signals, the inherent imbalance in the source spectrum poses a challenge for methods that rely on single-source dominance for the estimation of the mixing matrix. We propose an algorithm based on the directional sparse filtering (DSF) framework that utilizes the Lehmer mean with lea... | ['Andy W. H. Khong', 'Ching-Hui Ooi', 'Anh H. T. Nguyen', 'Karn Watcharasupat'] | 2021-01-30 | directional-sparse-filtering-using-weighted-1 | https://arxiv.org/abs/2102.00196 | https://arxiv.org/abs/2102.00196 | null | ['audio-source-separation', 'multi-speaker-source-separation'] | ['audio', 'speech'] | [ 4.60703135e-01 -5.30445516e-01 1.43102422e-01 -2.59496957e-01
-1.40437639e+00 -6.41450346e-01 4.25330579e-01 -3.37694079e-01
1.40284777e-01 4.49842006e-01 8.48532319e-01 -6.98740557e-02
-5.44655740e-01 7.06166029e-02 -3.92884135e-01 -9.58679557e-01
-2.24152908e-01 -7.89151415e-02 -3.73789705e-02 -8.88838433... | [15.172135353088379, 5.729889392852783] |
c779b828-292f-4f65-a472-2fbb1668bb81 | dualposenet-category-level-6d-object-pose-and | 2103.06526 | null | https://arxiv.org/abs/2103.06526v3 | https://arxiv.org/pdf/2103.06526v3.pdf | DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency | Category-level 6D object pose and size estimation is to predict full pose configurations of rotation, translation, and size for object instances observed in single, arbitrary views of cluttered scenes. In this paper, we propose a new method of Dual Pose Network with refined learning of pose consistency for this task, s... | ['Yuanqing Li', 'Kui Jia', 'Songcen Xu', 'Zhihao LI', 'Zewei Wei', 'Jiehong Lin'] | 2021-03-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Lin_DualPoseNet_Category-Level_6D_Object_Pose_and_Size_Estimation_Using_Dual_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Lin_DualPoseNet_Category-Level_6D_Object_Pose_and_Size_Estimation_Using_Dual_ICCV_2021_paper.pdf | iccv-2021-1 | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [-9.81022939e-02 2.96049803e-01 7.16815367e-02 -6.79585636e-01
-8.77837300e-01 -5.51191270e-01 4.23251152e-01 -2.34498858e-01
-7.00639561e-02 2.79383212e-01 -5.56799443e-03 2.13647261e-01
1.55419856e-01 -5.80090761e-01 -1.40796578e+00 -5.47812045e-01
1.33437291e-01 9.09927607e-01 4.73271906e-01 1.62110224... | [7.641714096069336, -2.716809034347534] |
43c38974-9c63-4060-9e09-89a0258ac3ec | interaction-aware-motion-planning-for | 2212.11819 | null | https://arxiv.org/abs/2212.11819v2 | https://arxiv.org/pdf/2212.11819v2.pdf | Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainties in Multi-Vehicle Scenarios | This paper proposes an interaction-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. An interaction-aware motion-prediction model is used to predict the behaviors of surrounding vehicles. The multi-modal prediction uncertainties, containing both the maneuver and tra... | ['Erik Frisk', 'Björn Olofsson', 'Jian Zhou'] | 2022-12-22 | null | null | null | null | ['motion-prediction', 'motion-planning'] | ['computer-vision', 'robots'] | [-1.02523334e-01 4.16329086e-01 -1.23330556e-01 -2.95914441e-01
-4.45510864e-01 -2.38698110e-01 7.69111991e-01 -1.04950167e-01
-1.95766881e-01 6.40218735e-01 -4.01932597e-02 -5.17746508e-01
-5.18041611e-01 -8.29736114e-01 -4.82640356e-01 -8.92452180e-01
-1.89748317e-01 2.92422146e-01 6.04095340e-01 -3.18132281... | [5.513972282409668, 1.5954967737197876] |
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