paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3e170fa0-369b-4504-969a-e7d9c95323d2 | classification-of-cross-cultural-news-events | 2301.05543 | null | https://arxiv.org/abs/2301.05543v1 | https://arxiv.org/pdf/2301.05543v1.pdf | Classification of Cross-cultural News Events | We present a methodology to support the analysis of culture from text such as news events and demonstrate its usefulness on categorizing news events from different categories (society, business, health, recreation, science, shopping, sports, arts, computers, games and home) across different geographical locations (diff... | ['Dunja Mladenic', 'Abdul Sittar'] | 2023-01-13 | null | null | null | null | ['culture'] | ['speech'] | [-0.42266774 -0.31742427 -0.17943846 -0.13492309 -0.2720359 -0.98806655
1.097493 0.80389726 -0.70294535 0.74112004 1.0192717 -0.05202051
-0.17227992 -1.0002959 -0.22186185 -0.50230294 0.04447618 0.5035684
0.24821445 -0.5535126 0.7468938 0.21453299 -1.6576037 0.59402645
0.58983123 0.7067299 0.1... | [9.329093933105469, 9.68499755859375] |
11add1be-ec95-4a6e-aeb4-ab0fa37ae174 | resetox-re-learning-attention-weights-for | 2305.11761 | null | https://arxiv.org/abs/2305.11761v1 | https://arxiv.org/pdf/2305.11761v1.pdf | ReSeTOX: Re-learning attention weights for toxicity mitigation in machine translation | Our proposed method, ReSeTOX (REdo SEarch if TOXic), addresses the issue of Neural Machine Translation (NMT) generating translation outputs that contain toxic words not present in the input. The objective is to mitigate the introduction of toxic language without the need for re-training. In the case of identified added... | ['Marta R. Costa-jussà', 'Carlos Escolano', 'Javier García Gilabert'] | 2023-05-19 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 6.63756728e-01 1.04923993e-01 -1.38167620e-01 9.77284238e-02
-1.13930225e+00 -7.14743555e-01 4.97620642e-01 1.59326360e-01
-5.94013095e-01 1.31610882e+00 2.64675081e-01 -6.98681951e-01
2.44864494e-01 -7.12468266e-01 -1.11040211e+00 -7.11732447e-01
5.40950775e-01 6.28393829e-01 -3.47523749e-01 -3.59306961... | [11.656037330627441, 9.973502159118652] |
1afa3802-2f07-41b5-a060-93ac552f23c9 | machine-learning-assisted-quantum-state | 2003.03441 | null | https://arxiv.org/abs/2003.03441v1 | https://arxiv.org/pdf/2003.03441v1.pdf | Machine learning assisted quantum state estimation | We build a general quantum state tomography framework that makes use of machine learning techniques to reconstruct quantum states from a given set of coincidence measurements. For a wide range of pure and mixed input states we demonstrate via simulations that our method produces functionally equivalent reconstructed st... | ['Sanjaya Lohani', 'Brian T. Kirby', 'Ryan T. Glasser', 'Onur Danaci', 'Michael Brodsky'] | 2020-03-06 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 5.57896912e-01 -2.20377937e-01 1.34696037e-01 -3.86873245e-01
-1.25055754e+00 -4.28700805e-01 8.12102854e-01 -3.97394896e-02
-6.13188207e-01 1.09996605e+00 -1.35298863e-01 -5.19391537e-01
-2.85785496e-02 -9.51746881e-01 -6.25673532e-01 -7.48086452e-01
-4.47420888e-02 8.77295732e-01 2.31790151e-02 -3.76068890... | [5.608392715454102, 4.885988712310791] |
bf916c23-86f8-478b-8b25-347b40566b3c | a-simple-transformer-based-model-for-ego4d | 2211.08704 | null | https://arxiv.org/abs/2211.08704v1 | https://arxiv.org/pdf/2211.08704v1.pdf | A Simple Transformer-Based Model for Ego4D Natural Language Queries Challenge | This report describes Badgers@UW-Madison, our submission to the Ego4D Natural Language Queries (NLQ) Challenge. Our solution inherits the point-based event representation from our prior work on temporal action localization, and develops a Transformer-based model for video grounding. Further, our solution integrates sev... | ['Yin Li', 'Fangzhou Mu', 'Sicheng Mo'] | 2022-11-16 | null | null | null | null | ['video-grounding', 'action-localization'] | ['computer-vision', 'computer-vision'] | [-4.40448105e-01 -4.76452224e-02 -5.79882979e-01 -5.73557764e-02
-1.06697381e+00 -7.12035835e-01 8.29428792e-01 5.10799736e-02
-6.59939408e-01 6.11120641e-01 8.22653532e-01 -1.54924065e-01
-3.01271398e-02 -5.22407234e-01 -7.85174906e-01 -6.09197691e-02
-5.47069311e-01 1.72227532e-01 5.19689739e-01 -3.12002033... | [8.446380615234375, 0.3987608253955841] |
0a4a04b4-79b6-4483-964c-7447a31f9e23 | learning-to-adapt-to-online-streams-with | 2303.01630 | null | https://arxiv.org/abs/2303.01630v1 | https://arxiv.org/pdf/2303.01630v1.pdf | Learning to Adapt to Online Streams with Distribution Shifts | Test-time adaptation (TTA) is a technique used to reduce distribution gaps between the training and testing sets by leveraging unlabeled test data during inference. In this work, we expand TTA to a more practical scenario, where the test data comes in the form of online streams that experience distribution shifts over ... | ['James Z. Wang', 'Yandong Li', 'Yimu Pan', 'Chenyan Wu'] | 2023-03-02 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 2.46977463e-01 -3.96965623e-01 -3.94215286e-01 -6.38443470e-01
-7.54861653e-01 -7.42521048e-01 3.60101134e-01 -7.12152645e-02
-4.33045655e-01 7.18892217e-01 -3.69088709e-01 -5.49306154e-01
-6.16877340e-02 -6.19346082e-01 -1.01588559e+00 -5.28851211e-01
-2.21986920e-01 8.95042062e-01 6.21265769e-01 5.67286164... | [9.447734832763672, 3.1335561275482178] |
f86b643d-2393-4f9f-b787-e48a31f03d92 | irt2-inductive-linking-and-ranking-in | 2301.00716 | null | https://arxiv.org/abs/2301.00716v1 | https://arxiv.org/pdf/2301.00716v1.pdf | IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale | We address the challenge of building domain-specific knowledge models for industrial use cases, where labelled data and taxonomic information is initially scarce. Our focus is on inductive link prediction models as a basis for practical tools that support knowledge engineers with exploring text collections and discover... | ['Maurice Falk', 'Adrian Ulges', 'Felix Hamann'] | 2023-01-02 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [ 1.54390678e-01 6.72193110e-01 -7.84267247e-01 5.97520284e-02
-6.07551455e-01 -5.39125800e-01 6.36140049e-01 6.14247501e-01
-2.57078260e-01 1.01395893e+00 3.64975572e-01 -4.57020760e-01
-8.52380037e-01 -1.13851547e+00 -1.05131376e+00 5.93964197e-02
-4.34014201e-01 1.33560479e+00 3.52841675e-01 -4.13901448... | [9.119913101196289, 8.127791404724121] |
c4a40746-7c02-4031-9895-77f7bd962b8c | off-grid-direction-of-arrival-estimation | 2112.05487 | null | https://arxiv.org/abs/2112.05487v3 | https://arxiv.org/pdf/2112.05487v3.pdf | Off-Grid Direction-of-Arrival Estimation Using Second-Order Taylor Approximation | The problem of off-grid direction-of-arrival (DOA) estimation is investigated. We develop a grid-based method to jointly estimate the closest spatial frequency (the sine of DOA) grids, and the gaps between the estimated grids and the corresponding frequencies. By using a second-order Taylor approximation, the data mode... | ['Abdelhak M. Zoubir', 'Hing Cheung So', 'Huiping Huang'] | 2021-12-10 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-1.77350715e-01 -3.51524502e-01 -7.92571530e-02 2.00668260e-01
-9.65045333e-01 -2.85265028e-01 2.56284177e-01 -1.77089691e-01
1.14174731e-01 8.74294102e-01 5.19728839e-01 -3.90307233e-02
-7.17203915e-01 -6.71687663e-01 -3.92735124e-01 -1.10116136e+00
-6.17342472e-01 -1.09735347e-01 -1.90118060e-01 -3.04981656... | [6.4856414794921875, 1.3569008111953735] |
8739340d-81df-40f0-bafc-ece07aa75f73 | random-forests-and-vgg-net-an-algorithm-for | 1703.05148 | null | http://arxiv.org/abs/1703.05148v1 | http://arxiv.org/pdf/1703.05148v1.pdf | Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge | This manuscript briefly describes an algorithm developed for the ISIC 2017
Skin Lesion Classification Competition. In this task, participants are asked to
complete two independent binary image classification tasks that involve three
unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis).
In the fi... | ['Yixin Luo', 'Yanzhi Song', 'Songtao Guo'] | 2017-03-15 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 8.27448905e-01 -1.51167020e-01 -2.87378162e-01 -2.77033538e-01
-4.94247496e-01 -5.29523194e-01 8.26207459e-01 4.34920102e-01
-8.41053307e-01 9.06662107e-01 -1.57183692e-01 -5.69508493e-01
1.42729878e-02 -5.19917309e-01 -1.53482899e-01 -8.68838012e-01
2.89514303e-01 1.10033914e-01 2.03582376e-01 1.00884877... | [15.684576988220215, -2.9989824295043945] |
3c79914a-ac85-48d0-942e-42c8375fca56 | ethics-and-deep-learning | 2305.15239 | null | https://arxiv.org/abs/2305.15239v2 | https://arxiv.org/pdf/2305.15239v2.pdf | Deep Learning and Ethics | This article appears as chapter 21 of Prince (2023, Understanding Deep Learning); a complete draft of the textbook is available here: http://udlbook.com. This chapter considers potential harms arising from the design and use of AI systems. These include algorithmic bias, lack of explainability, data privacy violations,... | ['Simon J. D. Prince', 'Travis LaCroix'] | 2023-05-24 | null | null | null | null | ['ethics', 'philosophy'] | ['miscellaneous', 'miscellaneous'] | [-7.74972066e-02 8.01391840e-01 -3.60217124e-01 -4.97721940e-01
-4.40950066e-01 -4.96045053e-01 6.84705675e-01 2.37199903e-01
-7.13965237e-01 8.35543394e-01 4.60896224e-01 -8.19791615e-01
-3.33927572e-01 -4.73878354e-01 -6.76464319e-01 -4.10087764e-01
4.71052289e-01 -1.66683942e-01 -6.51961207e-01 -7.45481849... | [8.956751823425293, 6.131832599639893] |
d69416ae-d280-467e-b010-ebcd5e1b8239 | simultaneously-short-and-long-term-temporal | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lao_Simultaneously_Short-_and_Long-Term_Temporal_Modeling_for_Semi-Supervised_Video_Semantic_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lao_Simultaneously_Short-_and_Long-Term_Temporal_Modeling_for_Semi-Supervised_Video_Semantic_CVPR_2023_paper.pdf | Simultaneously Short- and Long-Term Temporal Modeling for Semi-Supervised Video Semantic Segmentation | In order to tackle video semantic segmentation task at a lower cost, e.g., only one frame annotated per video, lots of efforts have been devoted to investigate the utilization of those unlabeled frames by either assigning pseudo labels or performing feature enhancement. In this work, we propose a novel feature enha... | ['Wei Chu', 'Jingdong Chen', 'Jian Wang', 'Yingying Zhang', 'Xin Guo', 'Weixiang Hong', 'Jiangwei Lao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-semantic-segmentation', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 2.59117812e-01 3.75253260e-02 -3.64217669e-01 -4.59975243e-01
-6.43283010e-01 -4.57964748e-01 3.87570441e-01 -1.47565708e-01
-6.18999600e-01 5.54944515e-01 -8.68221819e-02 5.46458252e-02
1.02162562e-01 -3.79316181e-01 -7.01955318e-01 -7.04514742e-01
7.04883039e-02 -6.67650774e-02 7.37510502e-01 2.69228578... | [9.183842658996582, 0.08443867415189743] |
37e7a5e9-7876-486a-afe9-d7a1c337284f | dual-accuracy-quality-driven-neural-network | 2212.06370 | null | https://arxiv.org/abs/2212.06370v2 | https://arxiv.org/pdf/2212.06370v2.pdf | Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation | Accurate uncertainty quantification is necessary to enhance the reliability of deep learning models in real-world applications. In the case of regression tasks, prediction intervals (PIs) should be provided along with the deterministic predictions of deep learning models. Such PIs are useful or "high-quality" as long a... | ['John W. Sheppard', 'Giorgio Morales'] | 2022-12-13 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 7.49805719e-02 3.74779731e-01 -4.29148108e-01 -6.45985663e-01
-8.34286332e-01 -3.57679188e-01 1.90388978e-01 4.04667675e-01
-8.99577886e-02 1.04195392e+00 -3.15265894e-01 -5.45251071e-01
-2.54645824e-01 -1.17798293e+00 -1.24016368e+00 -6.95232153e-01
-1.78857967e-01 4.18644667e-01 1.17050439e-01 7.02532232... | [7.681352615356445, 3.958826780319214] |
a27e80ec-22e4-4d20-af6f-47dca96151f4 | a-data-driven-rutting-depth-short-time | 2305.06707 | null | https://arxiv.org/abs/2305.06707v1 | https://arxiv.org/pdf/2305.06707v1.pdf | A data-driven rutting depth short-time prediction model with metaheuristic optimization for asphalt pavements based on RIOHTrack | Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements' ... | ['Jinde Cao', 'Wei Huang', 'Nadezhda Gorbacheva', 'Sergey Gorbachev', 'Xinli Shi', 'Iakov Korovin', 'Zhuoxuan Li'] | 2023-05-11 | null | null | null | null | ['community-detection', 'metaheuristic-optimization'] | ['graphs', 'methodology'] | [-2.07781971e-01 -1.19975777e-02 2.76432544e-01 4.50633699e-03
-1.23134069e-01 -4.50668521e-02 1.55571988e-02 6.97113946e-02
-1.45152435e-01 1.00392115e+00 -5.41663349e-01 -2.28896901e-01
-9.89335239e-01 -1.47713757e+00 -4.86710608e-01 -1.10431564e+00
-6.08071983e-01 9.01014626e-01 2.97784984e-01 -6.86909735... | [6.214677810668945, 3.235653877258301] |
10c5a0cc-0a32-479a-bfd6-336a897be73f | monotonic-value-function-factorisation-for | 2003.08839 | null | https://arxiv.org/abs/2003.08839v2 | https://arxiv.org/pdf/2003.08839v2.pdf | Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning | In many real-world settings, a team of agents must coordinate its behaviour while acting in a decentralised fashion. At the same time, it is often possible to train the agents in a centralised fashion where global state information is available and communication constraints are lifted. Learning joint action-values cond... | ['Tabish Rashid', 'Shimon Whiteson', 'Jakob Foerster', 'Mikayel Samvelyan', 'Gregory Farquhar', 'Christian Schroeder de Witt'] | 2020-03-19 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.51009631e-01 6.74215183e-02 -4.04609442e-01 -8.89216363e-02
-6.40009224e-01 -7.08133280e-01 1.09752834e+00 2.81275988e-01
-9.79987741e-01 1.06302905e+00 3.60078961e-01 -9.64513272e-02
-4.95489359e-01 -5.37525356e-01 -7.31316328e-01 -9.17723060e-01
-5.71442008e-01 1.06392205e+00 2.16373190e-01 -4.93832171... | [3.790670156478882, 1.9906933307647705] |
69379e36-ded1-4f66-a874-1507bcb0921c | submarine-cable-network-design-for-regional | 2201.05802 | null | https://arxiv.org/abs/2201.05802v1 | https://arxiv.org/pdf/2201.05802v1.pdf | Submarine Cable Network Design for Regional Connectivity | This paper optimizes path planning for a trunkand-branch topology network in an irregular 2-dimensional manifold embedded in 3-dimensional Euclidean space with application to submarine cable network planning. We go beyond our earlier focus on the costs of cable construction (including labor, equipment and materials) to... | ['Moshe Zukerman', 'Bill Moran', 'Zengfu Wang', 'Tianjiao Wang'] | 2022-01-15 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 1.59181327e-01 5.14251113e-01 -1.30038381e-01 1.43272907e-01
-2.89244801e-01 -1.22341645e+00 -1.26413211e-01 1.67423323e-01
-5.05609453e-01 8.63463461e-01 -2.91063279e-01 -8.90087128e-01
-8.57418120e-01 -8.93887579e-01 -6.12836242e-01 -8.05395842e-01
-8.49432766e-01 6.23376667e-01 2.08647788e-01 -5.18317521... | [4.987887859344482, 2.0078437328338623] |
afdcbfde-d4b6-4c8f-a918-5b617f761b1e | this-is-not-the-texture-you-are-looking-for | 2012.11905 | null | https://arxiv.org/abs/2012.11905v3 | https://arxiv.org/pdf/2012.11905v3.pdf | GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning | With the ongoing rise of machine learning, the need for methods for explaining decisions made by artificial intelligence systems is becoming a more and more important topic. Especially for image classification tasks, many state-of-the-art tools to explain such classifiers rely on visual highlighting of important areas ... | ['Elisabeth André', 'Alexander Heimerl', 'Katharina Weitz', 'Tobias Huber', 'Silvan Mertes'] | 2020-12-22 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 6.98384881e-01 9.56184089e-01 -1.74949482e-01 -2.65941381e-01
-1.10350937e-01 -1.90551117e-01 9.11059380e-01 4.23467427e-01
-2.06946552e-01 9.61094856e-01 2.58336782e-01 -5.45348108e-01
1.21492811e-01 -7.70814776e-01 -7.65854478e-01 -3.15301836e-01
2.61627465e-01 3.90805215e-01 -3.00660640e-01 -4.11764503... | [8.837448120117188, 5.504513740539551] |
0608de78-44bc-4c98-a56e-1ac7c40ac4c3 | learning-by-inertia-self-supervised-monocular | 1905.01634 | null | https://arxiv.org/abs/1905.01634v1 | https://arxiv.org/pdf/1905.01634v1.pdf | Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles | In this paper, we present iDVO (inertia-embedded deep visual odometry), a self-supervised learning based monocular visual odometry (VO) for road vehicles. When modelling the geometric consistency within adjacent frames, most deep VO methods ignore the temporal continuity of the camera pose, which results in a very seve... | ['Qi. Wang', 'Chengze Wang', 'Yuan Yuan'] | 2019-05-05 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-6.78005338e-01 -1.81671396e-01 -5.06371975e-01 -2.28561819e-01
1.20933466e-01 -1.31496802e-01 6.97862267e-01 -7.19877899e-01
-2.85720468e-01 4.34864879e-01 1.36506647e-01 -1.76898271e-01
2.48953313e-01 -5.19606590e-01 -9.62775648e-01 -7.27117419e-01
6.64730594e-02 3.49508405e-01 3.55900139e-01 -1.96764499... | [8.200479507446289, -2.127206563949585] |
b852c726-0dc4-4b8a-884c-47277e01b676 | color-mismatches-in-stereoscopic-video-real | 2303.06657 | null | https://arxiv.org/abs/2303.06657v2 | https://arxiv.org/pdf/2303.06657v2.pdf | Color Mismatches in Stereoscopic Video: Real-World Dataset and Deep Correction Method | We propose a real-world dataset of stereoscopic videos for color-mismatch correction. It includes real-world distortions achieved using a beam splitter. Our dataset is larger than any other for this task. We compared eight color-mismatch-correction methods on artificial and real-world datasets and showed that local met... | ['Dmitriy Vatolin', 'Maxim Velikanov', 'Nikita Alutis', 'Egor Chistov'] | 2023-03-12 | null | null | null | null | ['color-mismatch-correction'] | ['computer-vision'] | [ 3.06215972e-01 -6.80356622e-01 1.63410783e-01 -2.74865508e-01
-5.52891612e-01 -4.28506792e-01 4.52405185e-01 -7.27123678e-01
-4.10274476e-01 7.90025592e-01 4.09809828e-01 -1.18893363e-01
1.85720429e-01 -4.79624927e-01 -8.20765018e-01 -7.43956387e-01
5.42404577e-02 -1.03654392e-01 6.96733713e-01 -4.44857538... | [10.83622932434082, -2.2830145359039307] |
9b3ff13d-d45a-4e20-84e9-6c64547db122 | unsupervised-steganalysis-based-on-artificial | 1703.00796 | null | http://arxiv.org/abs/1703.00796v1 | http://arxiv.org/pdf/1703.00796v1.pdf | Unsupervised Steganalysis Based on Artificial Training Sets | In this paper, an unsupervised steganalysis method that combines artificial
training setsand supervised classification is proposed. We provide a formal
framework for unsupervisedclassification of stego and cover images in the
typical situation of targeted steganalysis (i.e.,for a known algorithm and
approximate embeddi... | ['David Megías', 'Daniel Lerch-Hostalot'] | 2017-03-02 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 9.19540584e-01 3.08134794e-01 -3.56337219e-01 5.76463044e-02
-5.03105283e-01 -2.39717618e-01 8.13680291e-01 2.05053780e-02
-3.95848870e-01 6.64351344e-01 -3.59017611e-01 -5.06988347e-01
5.07224984e-02 -8.60713005e-01 -7.18878031e-01 -1.12940478e+00
-3.01886380e-01 6.39667511e-02 3.69638950e-01 -4.20826554... | [4.322463035583496, 8.043136596679688] |
3a819b5b-080c-43a4-8459-5f1f82fa59d2 | diverse-image-to-image-translation-via | 1808.00948 | null | http://arxiv.org/abs/1808.00948v1 | http://arxiv.org/pdf/1808.00948v1.pdf | Diverse Image-to-Image Translation via Disentangled Representations | Image-to-image translation aims to learn the mapping between two visual
domains. There are two main challenges for many applications: 1) the lack of
aligned training pairs and 2) multiple possible outputs from a single input
image. In this work, we present an approach based on disentangled
representation for producing ... | ['Ming-Hsuan Yang', 'Jia-Bin Huang', 'Hung-Yu Tseng', 'Hsin-Ying Lee', 'Maneesh Kumar Singh'] | 2018-08-02 | diverse-image-to-image-translation-via-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Hsin-Ying_Lee_Diverse_Image-to-Image_Translation_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Hsin-Ying_Lee_Diverse_Image-to-Image_Translation_ECCV_2018_paper.pdf | eccv-2018-9 | ['multimodal-unsupervised-image-to-image', 'synthetic-to-real-translation'] | ['computer-vision', 'computer-vision'] | [ 6.47212327e-01 -1.43807992e-01 -1.13296643e-01 -4.37226802e-01
-8.77424300e-01 -7.88917422e-01 7.72504508e-01 -3.09134990e-01
-4.33925033e-01 7.99297035e-01 1.25498369e-01 2.73857415e-01
-1.49926636e-02 -4.57002789e-01 -9.04710114e-01 -6.92290485e-01
3.60282987e-01 4.29534316e-01 -1.54235318e-01 -1.64038137... | [11.723296165466309, -0.3617647886276245] |
5dab8395-bfe9-4a1f-8baf-894a66f52693 | using-ballistocardiography-for-sleep-stage | 2202.01038 | null | https://arxiv.org/abs/2202.01038v2 | https://arxiv.org/pdf/2202.01038v2.pdf | Using Ballistocardiography for Sleep Stage Classification | A practical way of detecting sleep stages has become more necessary as we begin to learn about the vast effects that sleep has on people's lives. The current methods of sleep stage detection are expensive, invasive to a person's sleep, and not practical in a modern home setting. While the method of detecting sleep stag... | ['Jiebei Liu', 'Mehdi Boukhechba', 'Krista Nelson', 'Peter Morris'] | 2022-02-02 | null | null | null | null | ['sleep-stage-detection', 'heart-rate-variability'] | ['medical', 'medical'] | [ 1.47549212e-01 -2.82877594e-01 -6.79321364e-02 -3.25100869e-01
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-1.98309142e-02 -1.32743776e-01 7.22791031e-02 -1.45398546... | [13.580391883850098, 3.379685401916504] |
f336e740-ad85-4049-ac25-02554e20b164 | neuricam-video-super-resolution-and | 2207.12496 | null | https://arxiv.org/abs/2207.12496v2 | https://arxiv.org/pdf/2207.12496v2.pdf | NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras | We present NeuriCam, a novel deep learning-based system to achieve video capture from low-power dual-mode IoT camera systems. Our idea is to design a dual-mode camera system where the first mode is low-power (1.1 mW) but only outputs grey-scale, low resolution, and noisy video and the second mode consumes much higher p... | ['Collin Pernu', 'Shyamnath Gollakota', 'Michael Taylor', 'Joshua Smith', 'Ali Saffari', 'Bandhav Veluri'] | 2022-07-25 | null | null | null | null | ['colorization', 'video-super-resolution', 'key-frame-based-video-super-resolution-k-15', 'total-energy'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [ 1.47430584e-01 -2.54856735e-01 -4.74535450e-02 5.74492365e-02
-6.52115285e-01 -6.22462153e-01 -1.71919446e-03 -5.36558688e-01
-5.52494466e-01 3.65755171e-01 -1.22715682e-01 -3.95262063e-01
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1.28000468e-01 -4.46025848e-01 5.69217086e-01 3.38367708... | [10.753118515014648, -1.902133822441101] |
f988a0e9-d021-49d4-b63a-60b3f070c1fb | masked-autoencoders-for-point-cloud-self | 2203.06604 | null | https://arxiv.org/abs/2203.06604v2 | https://arxiv.org/pdf/2203.06604v2.pdf | Masked Autoencoders for Point Cloud Self-supervised Learning | As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. Inspired by this, we propose a neat scheme of masked autoencoders for point cloud self-supervised learning, addressing the challenges posed by point cloud's properties, incl... | ['Li Yuan', 'Yonghong Tian', 'Wei Liu', 'Francis E. H. Tay', 'Wenxiao Wang', 'Yatian Pang'] | 2022-03-13 | null | null | null | null | ['3d-part-segmentation', 'few-shot-3d-point-cloud-classification', 'point-cloud-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.24084030e-02 3.43763649e-01 -1.13282613e-01 -2.88311094e-01
-9.32722032e-01 -3.45709652e-01 6.81714475e-01 -1.23331331e-01
-5.35428673e-02 4.69204903e-01 -1.45655990e-01 8.27566981e-02
9.61885303e-02 -1.17859960e+00 -1.41043627e+00 -9.69308019e-01
-1.30380392e-01 7.21419632e-01 4.02275294e-01 -1.22134894... | [8.048383712768555, -3.3919057846069336] |
d9259ac7-8fde-4e06-be9d-4575774c410b | enhancing-social-network-hate-detection-using | null | null | https://www.sciencedirect.com/science/article/pii/S1566253523002038 | https://www.sciencedirect.com/science/article/pii/S1566253523002038/pdfft?md5=088fd26f0b8960763d5c7de8b3958527&pid=1-s2.0-S1566253523002038-main.pdf | Enhancing social network hate detection using back translation and GPT-3 augmentations during training and test-time | Social media platforms have become an essential means of communication, but they also serve as a breeding ground for hateful content. Detecting hate speech accurately is challenging due to factors such as slang and implicit hate speech. In response to these challenges, this paper presents a novel ensemble approach util... | ['Lior Rokach', 'Shvat Messica', 'Ofir Arbili', 'Or Katz', 'Dan Presil', 'Seffi Cohen'] | 2023-06-17 | null | null | null | information-fusion-2023-6 | ['hate-speech-detection'] | ['natural-language-processing'] | [-1.72334164e-02 -2.59967417e-01 -4.68146205e-02 -2.30138265e-02
-6.37433589e-01 -7.47739553e-01 6.85744762e-01 2.12834895e-01
-1.61357611e-01 4.65893716e-01 2.45837763e-01 -1.08398654e-01
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1.24854846e-02 -1.13848172e-01 -1.84524357e-01 -3.17919582... | [8.731955528259277, 10.538505554199219] |
ac07064c-1c0e-468d-ae5d-1af9d55bd7a0 | deep-multi-survey-classification-of-variable | 1810.09440 | null | http://arxiv.org/abs/1810.09440v1 | http://arxiv.org/pdf/1810.09440v1.pdf | Deep multi-survey classification of variable stars | During the last decade, a considerable amount of effort has been made to
classify variable stars using different machine learning techniques. Typically,
light curves are represented as vectors of statistical descriptors or features
that are used to train various algorithms. These features demand big
computational power... | ['Karim Pichara', 'Carlos Aguirre', 'Ignacio Becker'] | 2018-10-21 | null | null | null | null | ['classification-of-variable-stars'] | ['miscellaneous'] | [-3.51951629e-01 -6.82049274e-01 -5.90997841e-03 -6.19950533e-01
-1.61392406e-01 -9.57038522e-01 8.66137147e-01 -7.93738477e-03
-4.03053045e-01 5.02526283e-01 -4.13769096e-01 -4.44593966e-01
-4.59741391e-02 -8.53221238e-01 -5.23347557e-01 -8.10331047e-01
2.27426335e-01 6.19089723e-01 4.55576330e-01 -3.15325946... | [7.660707473754883, 3.085437774658203] |
fd7c71c9-0c11-44fc-b442-2bbaa59f010c | chatface-chat-guided-real-face-editing-via | 2305.14742 | null | https://arxiv.org/abs/2305.14742v2 | https://arxiv.org/pdf/2305.14742v2.pdf | ChatFace: Chat-Guided Real Face Editing via Diffusion Latent Space Manipulation | Editing real facial images is a crucial task in computer vision with significant demand in various real-world applications. While GAN-based methods have showed potential in manipulating images especially when combined with CLIP, these methods are limited in their ability to reconstruct real images due to challenging GA... | ['Li Yuan', 'Yuesheng Zhu', 'Jiaxi Cui', 'Munan Ning', 'Qin Guo', 'Dongxu Yue'] | 2023-05-24 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 3.93697828e-01 1.06352866e-01 1.22872807e-01 -4.04903084e-01
-3.85814101e-01 -3.43598306e-01 6.81566536e-01 -9.23483968e-01
-8.05041268e-02 4.58716065e-01 1.85250476e-01 1.44293413e-01
5.92921376e-02 -8.64850819e-01 -5.46683609e-01 -8.52754772e-01
5.45991302e-01 3.64994526e-01 -1.41881183e-01 -2.76663095... | [12.537205696105957, -0.3402900993824005] |
db360c41-570d-4056-bd8a-7bbf03430c7d | seeing-wake-words-audio-visual-keyword | 2009.01225 | null | https://arxiv.org/abs/2009.01225v1 | https://arxiv.org/pdf/2009.01225v1.pdf | Seeing wake words: Audio-visual Keyword Spotting | The goal of this work is to automatically determine whether and when a word of interest is spoken by a talking face, with or without the audio. We propose a zero-shot method suitable for in the wild videos. Our key contributions are: (1) a novel convolutional architecture, KWS-Net, that uses a similarity map intermedia... | ['Triantafyllos Afouras', 'Andrew Zisserman', 'Themos Stafylakis', 'Samuel Albanie', 'Liliane Momeni'] | 2020-09-02 | null | null | null | null | ['visual-keyword-spotting'] | ['computer-vision'] | [ 4.97400045e-01 -8.25353190e-02 -1.07530773e-01 -1.64540574e-01
-1.00664508e+00 -4.84796464e-01 6.61576509e-01 -1.92773744e-01
-5.26719511e-01 3.32527578e-01 5.66196382e-01 -1.26754805e-01
2.55540282e-01 -1.68522522e-01 -7.82745361e-01 -5.77862024e-01
9.92234051e-02 2.15288609e-01 4.44126457e-01 -2.08255127... | [14.38314151763916, 5.082785129547119] |
10c13a85-e02e-4172-97bd-6aa292c208fe | trajectory-space-factorization-for-deep-video | 1908.08289 | null | https://arxiv.org/abs/1908.08289v1 | https://arxiv.org/pdf/1908.08289v1.pdf | Trajectory Space Factorization for Deep Video-Based 3D Human Pose Estimation | Existing deep learning approaches on 3d human pose estimation for videos are either based on Recurrent or Convolutional Neural Networks (RNNs or CNNs). However, RNN-based frameworks can only tackle sequences with limited frames because sequential models are sensitive to bad frames and tend to drift over long sequences.... | ['Jiahao Lin', 'Gim Hee Lee'] | 2019-08-22 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-2.42612481e-01 -5.06006420e-01 -2.72560924e-01 -8.37760195e-02
-5.77683091e-01 -4.26371276e-01 1.83539882e-01 -3.93366098e-01
-6.13126159e-01 3.12988698e-01 3.79122198e-01 -1.05760314e-01
2.04196498e-01 -4.38243777e-01 -1.00691509e+00 -5.17628908e-01
-2.25447983e-01 1.75852478e-01 1.25397876e-01 -3.12195629... | [7.25264835357666, -0.5400376915931702] |
50759bfa-f6c7-483a-a720-312c9a4ba76e | june-germany-an-agent-based-epidemiology | 2303.05742 | null | https://arxiv.org/abs/2303.05742v1 | https://arxiv.org/pdf/2303.05742v1.pdf | JUNE-Germany: An Agent-Based Epidemiology Simulation including Multiple Virus Strains, Vaccinations and Testing Campaigns | The June software package is an open-source framework for the detailed simulation of epidemics based on social interactions in a virtual population reflecting age, gender, ethnicity, and socio-economic indicators in England. In this paper, we present a new version of the framework specifically adapted for Germany, whic... | ['Matthias Schott', 'Friedemann Neuhaus', 'Andrew Iskauskas', 'Lucas Heger', 'Kerem Akdogan'] | 2023-03-10 | null | null | null | null | ['epidemiology'] | ['medical'] | [-4.87478942e-01 1.09199680e-01 5.88340051e-02 7.48068243e-02
2.03267068e-01 -2.69424498e-01 9.62230325e-01 6.51768744e-01
-7.24610627e-01 1.02579165e+00 2.49144092e-01 -6.05217934e-01
-3.88520658e-01 -1.22493148e+00 -3.90560068e-02 -4.12774265e-01
-3.17761958e-01 9.37328279e-01 4.06744123e-01 -6.68248296... | [5.96627950668335, 4.392341613769531] |
fa0f32ff-b434-4e4b-9d59-eebed2cf4bbb | a-dynamic-programming-algorithm-for-tree | null | null | https://aclanthology.info/papers/N15-1049/n15-1049 | https://www.aclweb.org/anthology/N15-1049 | A Dynamic Programming Algorithm for Tree Trimming-based Text Summarization | null | ['Shin-ichi Minato', 'Tsutomu Hirao', 'Norihito Yasuda', 'Masaaki Nagata', 'Masaaki Nishino'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['extractive-document-summarization'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5392241477966309, 15.869229316711426] |
65e17575-3d78-4325-b087-391e6d9b2dd2 | weakly-supervised-action-selection-learning | 2105.02439 | null | https://arxiv.org/abs/2105.02439v1 | https://arxiv.org/pdf/2105.02439v1.pdf | Weakly Supervised Action Selection Learning in Video | Localizing actions in video is a core task in computer vision. The weakly supervised temporal localization problem investigates whether this task can be adequately solved with only video-level labels, significantly reducing the amount of expensive and error-prone annotation that is required. A common approach is to tra... | ['Guangwei Yu', 'Maksims Volkovs', 'Satya Krishna Gorti', 'Junwei Ma'] | 2021-05-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ma_Weakly_Supervised_Action_Selection_Learning_in_Video_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ma_Weakly_Supervised_Action_Selection_Learning_in_Video_CVPR_2021_paper.pdf | cvpr-2021-1 | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 4.34659421e-01 -1.47804916e-01 -8.23070288e-01 -4.18615967e-01
-8.03149104e-01 -5.85463881e-01 5.74654639e-01 -9.83631052e-03
-6.06229901e-01 6.30967677e-01 2.36984029e-01 -4.33929935e-02
4.67025906e-01 -2.58552760e-01 -8.87035668e-01 -8.62007678e-01
-1.59449294e-01 -9.49453488e-02 6.65250182e-01 3.74814510... | [8.465780258178711, 0.5607909560203552] |
e035fce2-3e8a-4c08-8586-5191ad871f4a | guaranteed-quantization-error-computation-for | 2304.13812 | null | https://arxiv.org/abs/2304.13812v1 | https://arxiv.org/pdf/2304.13812v1.pdf | Guaranteed Quantization Error Computation for Neural Network Model Compression | Neural network model compression techniques can address the computation issue of deep neural networks on embedded devices in industrial systems. The guaranteed output error computation problem for neural network compression with quantization is addressed in this paper. A merged neural network is built from a feedforwar... | ['Weiming Xiang', 'Zihao Mo', 'Wesley Cooke'] | 2023-04-26 | null | null | null | null | ['neural-network-compression', 'model-compression', 'neural-network-compression'] | ['methodology', 'methodology', 'miscellaneous'] | [ 7.82442451e-01 6.65844262e-01 -1.83182850e-01 -1.62575006e-01
1.67187378e-02 3.27659920e-02 -6.32284135e-02 -1.62948012e-01
-1.66334629e-01 7.07227051e-01 -7.71550357e-01 -5.02338469e-01
-4.55737233e-01 -7.07610250e-01 -9.69320297e-01 -6.73468053e-01
4.12475970e-03 -4.80896384e-02 -2.55896300e-01 -9.37081352... | [8.347882270812988, 2.9952657222747803] |
92e7e175-c752-46ba-8b7c-91d7e1634d0c | does-the-geometry-of-word-embeddings-help | null | null | https://aclanthology.org/W17-2628 | https://aclanthology.org/W17-2628.pdf | Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations | We investigate the pertinence of methods from algebraic topology for text data analysis. These methods enable the development of mathematically-principled isometric-invariant mappings from a set of vectors to a document embedding, which is stable with respect to the geometry of the document in the selected metric space... | ['Ravich', 'Paul Michel', 'Abhilasha er', 'Shruti Rijhwani'] | 2017-08-01 | null | null | null | ws-2017-8 | ['document-embedding'] | ['methodology'] | [-2.92678863e-01 -1.19342752e-01 -5.30850217e-02 -4.20615375e-01
-1.63534582e-01 -8.19221199e-01 1.08061421e+00 4.10019040e-01
-3.80661488e-01 3.41210008e-01 5.65408528e-01 -3.85412067e-01
-7.88074672e-01 -5.94182312e-01 5.88769428e-02 -8.21072102e-01
-9.91490334e-02 6.78457379e-01 -1.48916155e-01 -4.68580335... | [10.218193054199219, 7.516526699066162] |
e27846ff-1167-4b08-98f1-7a04faa03121 | psvt-end-to-end-multi-person-3d-pose-and | 2303.09187 | null | https://arxiv.org/abs/2303.09187v1 | https://arxiv.org/pdf/2303.09187v1.pdf | PSVT: End-to-End Multi-person 3D Pose and Shape Estimation with Progressive Video Transformers | Existing methods of multi-person video 3D human Pose and Shape Estimation (PSE) typically adopt a two-stage strategy, which first detects human instances in each frame and then performs single-person PSE with temporal model. However, the global spatio-temporal context among spatial instances can not be captured. In thi... | ['Jingdong Wang', 'Dongmei Fu', 'Chang Xu', 'Errui Ding', 'Junyu Han', 'Haocheng Feng', 'Jian Wang', 'Yang Qiansheng', 'Zhongwei Qiu'] | 2023-03-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qiu_PSVT_End-to-End_Multi-Person_3D_Pose_and_Shape_Estimation_With_Progressive_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qiu_PSVT_End-to-End_Multi-Person_3D_Pose_and_Shape_Estimation_With_Progressive_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-4.63873707e-02 -3.92907888e-01 8.86403918e-02 -4.97469991e-01
-7.48194695e-01 -2.37105295e-01 4.43011940e-01 -2.73146629e-01
-4.05186385e-01 3.36405605e-01 4.68428403e-01 5.24901032e-01
2.83712428e-02 -4.93911415e-01 -6.77793741e-01 -3.31095338e-01
2.47830778e-01 6.25709713e-01 7.08887815e-01 -2.71124654... | [7.180736064910889, -0.7010944485664368] |
1a91d85b-438f-49fd-88e0-74296a06e7b9 | zero-shot-video-object-segmentation-via-1 | 2001.06807 | null | https://arxiv.org/abs/2001.06807v1 | https://arxiv.org/pdf/2001.06807v1.pdf | Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks | This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS). The suggested AGNN recasts this task as a process of iterative information fusion over video graphs. Specifically, AGNN builds a fully connected graph to efficiently represent frames as nodes, and relations ... | ['Xiankai Lu', 'David Crandall', 'Jianbing Shen', 'Wenguan Wang', 'Ling Shao'] | 2020-01-19 | zero-shot-video-object-segmentation-via | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Zero-Shot_Video_Object_Segmentation_via_Attentive_Graph_Neural_Networks_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Zero-Shot_Video_Object_Segmentation_via_Attentive_Graph_Neural_Networks_ICCV_2019_paper.pdf | iccv-2019-10 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 2.33038738e-01 2.25832015e-01 -3.86699528e-01 -2.85180420e-01
-2.82750785e-01 -1.48885563e-01 4.92283285e-01 -1.21789016e-01
-2.65876353e-02 3.11491370e-01 -1.39143512e-01 -4.43884917e-02
-1.37611583e-01 -7.12594151e-01 -1.06944311e+00 -5.44027686e-01
-3.64627361e-01 3.18377674e-01 8.05177391e-01 -2.17803493... | [9.288053512573242, -0.13676093518733978] |
337ab378-cc5e-4b0a-b343-dbce5ee1ddb5 | crrn-multi-scale-guided-concurrent-reflection | 1805.11802 | null | http://arxiv.org/abs/1805.11802v1 | http://arxiv.org/pdf/1805.11802v1.pdf | CRRN: Multi-Scale Guided Concurrent Reflection Removal Network | Removing the undesired reflections from images taken through the glass is of
broad application to various computer vision tasks. Non-learning based methods
utilize different handcrafted priors such as the separable sparse gradients
caused by different levels of blurs, which often fail due to their limited
description c... | ['Ah-Hwee Tan', 'Ling-Yu Duan', 'Alex C. Kot', 'Renjie Wan', 'Boxin Shi'] | 2018-05-30 | crrn-multi-scale-guided-concurrent-reflection-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Wan_CRRN_Multi-Scale_Guided_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wan_CRRN_Multi-Scale_Guided_CVPR_2018_paper.pdf | cvpr-2018-6 | ['reflection-removal'] | ['computer-vision'] | [ 4.89475280e-01 -5.07232666e-01 3.48642945e-01 -4.18145567e-01
-7.55789280e-01 -1.30716367e-02 5.12765288e-01 -7.89936900e-01
-3.73147547e-01 5.03532648e-01 2.50043780e-01 2.16574267e-01
3.55814514e-03 -4.60197300e-01 -5.66340208e-01 -1.06263483e+00
3.00979674e-01 -2.35059634e-01 3.19900364e-01 -2.20030710... | [10.59076976776123, -2.746812343597412] |
3f2d9e98-1ca7-46d8-9cc6-3da9f1348205 | learning-discriminative-shrinkage-deep | 2111.13876 | null | https://arxiv.org/abs/2111.13876v3 | https://arxiv.org/pdf/2111.13876v3.pdf | Learning Discriminative Shrinkage Deep Networks for Image Deconvolution | Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms and data terms of the latent clear images. However, explicitly designing these two terms is quite challenging and usually leads to complex ... | ['Ming-Hsuan Yang', 'Shao-Yi Chien', 'Jinshan Pan', 'Pin-Hung Kuo'] | 2021-11-27 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 8.07560757e-02 -2.39363998e-01 2.24773526e-01 -5.48469007e-01
-6.74202204e-01 -1.97757006e-01 4.57709998e-01 -5.97043574e-01
-5.58255792e-01 7.94718325e-01 2.02227011e-01 -1.96671292e-01
-1.42494187e-01 -4.92428541e-01 -6.89379930e-01 -1.02078569e+00
2.90253311e-01 9.35114920e-02 -5.78730367e-02 -7.11565837... | [11.546626091003418, -2.530163526535034] |
0a464271-7151-42f4-87ff-e59cb77d18cf | making-a-bird-ai-expert-work-for-you-and-me | 2112.02747 | null | https://arxiv.org/abs/2112.02747v1 | https://arxiv.org/pdf/2112.02747v1.pdf | Making a Bird AI Expert Work for You and Me | As powerful as fine-grained visual classification (FGVC) is, responding your query with a bird name of "Whip-poor-will" or "Mallard" probably does not make much sense. This however commonly accepted in the literature, underlines a fundamental question interfacing AI and human -- what constitutes transferable knowledge ... | ['Jun Guo', 'Yi-Zhe Song', 'Zhanyu Ma', 'Ruoyi Du', 'Kaiyue Pang', 'Dongliang Chang'] | 2021-12-06 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 2.49392372e-02 5.49031720e-02 3.04211497e-01 -2.46095866e-01
-2.57562816e-01 -1.09561992e+00 4.53687370e-01 -2.44875759e-01
-8.88068676e-01 6.84142172e-01 -1.71156198e-01 -3.93623650e-01
-3.44982862e-01 -6.05790854e-01 -6.99536741e-01 -6.81799173e-01
1.01824902e-01 6.12130880e-01 2.05277622e-01 -3.20460290... | [10.074553489685059, 2.22320818901062] |
e70d05e6-6964-4f62-8a85-8131ef898591 | error-bounded-foreground-and-background | 1908.09539 | null | https://arxiv.org/abs/1908.09539v1 | https://arxiv.org/pdf/1908.09539v1.pdf | Error Bounded Foreground and Background Modeling for Moving Object Detection in Satellite Videos | Detecting moving objects from ground-based videos is commonly achieved by using background subtraction techniques. Low-rank matrix decomposition inspires a set of state-of-the-art approaches for this task. It is integrated with structured sparsity regularization to achieve background subtraction in the developed method... | ['Junpeng Zhang', 'Xiuping Jia', 'Jiankun Hu'] | 2019-08-26 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 6.46423101e-01 -4.94683743e-01 1.06329136e-01 5.81772206e-03
-5.90020418e-01 -2.92386204e-01 5.61534524e-01 -4.38187867e-01
-2.94653654e-01 8.64440203e-01 8.16698968e-02 -9.19938013e-02
-9.27456692e-02 -4.30944145e-01 -4.96887863e-01 -1.24544919e+00
-6.35430589e-02 2.10176110e-02 5.62845469e-01 -1.53665006... | [9.005847930908203, -0.7878136038780212] |
88f30e06-c805-486d-8688-85214d3a5af4 | the-power-of-log-sum-exp-sequential-density | 2105.13636 | null | https://arxiv.org/abs/2105.13636v2 | https://arxiv.org/pdf/2105.13636v2.pdf | The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization | We propose a model for multiclass classification of time series to make a prediction as early and as accurate as possible. The matrix sequential probability ratio test (MSPRT) is known to be asymptotically optimal for this setting, but contains a critical assumption that hinders broad real-world applications; the MSPRT... | ['Akinori F. Ebihara', 'Taiki Miyagawa'] | 2021-05-28 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 9.58533734e-02 -3.18621874e-01 -5.75251758e-01 -4.40450042e-01
-1.11019278e+00 -4.48226333e-01 3.88279796e-01 2.01450437e-01
-3.22416991e-01 5.99828720e-01 -2.17757389e-01 -5.56246698e-01
-1.54436618e-01 -5.03064632e-01 -7.94314146e-01 -8.80677998e-01
-3.02243292e-01 6.79914594e-01 2.59043038e-01 1.54090777... | [8.483939170837402, 3.838719606399536] |
f3b7e997-0e18-4e6d-9f02-20fdf7c80a28 | evaluating-resilience-of-encrypted-traffic | 2105.14564 | null | https://arxiv.org/abs/2105.14564v1 | https://arxiv.org/pdf/2105.14564v1.pdf | Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks | Machine learning and deep learning algorithms can be used to classify encrypted Internet traffic. Classification of encrypted traffic can become more challenging in the presence of adversarial attacks that target the learning algorithms. In this paper, we focus on investigating the effectiveness of different evasion at... | ['Ashraf Matrawy', 'Danish Sattar', 'Ramy Maarouf'] | 2021-05-30 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-1.56378195e-01 -3.40096921e-01 -1.47196770e-01 -7.74798542e-02
-7.92599618e-02 -9.75581825e-01 7.03994930e-01 -1.38521433e-01
-2.78842866e-01 7.62138546e-01 -1.48112491e-01 -1.05391157e+00
-1.58732593e-01 -1.09349012e+00 -6.64327919e-01 -6.86333895e-01
-1.87624618e-01 2.09582776e-01 1.40712261e-01 -2.56658107... | [5.49380350112915, 7.56704568862915] |
b58c6db9-cb01-4f3a-a790-fa16cbdc934a | skin-lesion-classification-using-deep-neural | 1911.07817 | null | https://arxiv.org/abs/1911.07817v1 | https://arxiv.org/pdf/1911.07817v1.pdf | Skin Lesion Classification Using Deep Neural Network | This paper reports the methods and techniques we have developed for classify dermoscopic images (task 1) of the ISIC 2019 challenge dataset for skin lesion classification, our approach aims to use ensemble deep neural network with some powerful techniques to deal with unbalance data sets as its the main problem for thi... | ['Alla Eddine Guissous'] | 2019-11-18 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 3.11729282e-01 3.98153327e-02 -2.06373706e-01 -1.29832685e-01
-3.25833231e-01 -3.31114471e-01 6.08976245e-01 -1.72952741e-01
-3.06447208e-01 6.43341899e-01 -1.05233319e-01 -6.60063386e-01
-3.51248413e-01 -5.53437710e-01 -2.20887467e-01 -7.56707966e-01
-7.27201328e-02 -9.69119091e-03 -1.86272673e-02 -5.72189927... | [15.69808292388916, -2.987238883972168] |
13940183-644b-49ac-850a-d704d1b0216b | sign-language-recognition-system-using | 2201.01486 | null | https://arxiv.org/abs/2201.01486v2 | https://arxiv.org/pdf/2201.01486v2.pdf | Sign Language Recognition System using TensorFlow Object Detection API | Communication is defined as the act of sharing or exchanging information, ideas or feelings. To establish communication between two people, both of them are required to have knowledge and understanding of a common language. But in the case of deaf and dumb people, the means of communication are different. Deaf is the i... | ['Sudhakar Singh', 'Richa Mishra', 'Amisha Gangwar', 'Sharvani Srivastava'] | 2022-01-05 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-2.70546407e-01 -3.92706841e-01 1.02619408e-02 -6.32467568e-01
-1.59995124e-01 -5.60779214e-01 4.84832108e-01 -6.13339603e-01
-6.54952109e-01 6.25721872e-01 3.46919864e-01 -4.86038804e-01
6.29904717e-02 -8.12872469e-01 -1.61933303e-01 -4.43389773e-01
3.38844389e-01 4.35590237e-01 1.97131678e-01 -5.18796802... | [9.055892944335938, -6.359498500823975] |
d3f9f020-4212-4e7a-84b9-66975579337c | semi-targeted-model-poisoning-attack-on | 2203.11633 | null | https://arxiv.org/abs/2203.11633v2 | https://arxiv.org/pdf/2203.11633v2.pdf | Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis | Model poisoning attacks on federated learning (FL) intrude in the entire system via compromising an edge model, resulting in malfunctioning of machine learning models. Such compromised models are tampered with to perform adversary-desired behaviors. In particular, we considered a semi-targeted situation where the sourc... | ['Jun Sakuma', 'Hideya Ochiai', 'Yuwei Sun'] | 2022-03-22 | null | null | null | null | ['neural-network-security'] | ['miscellaneous'] | [ 3.61114651e-01 -1.16735034e-01 7.34662712e-02 -2.99837254e-02
-8.76579165e-01 -1.11976910e+00 5.26046038e-01 2.40995869e-01
-6.13185763e-01 6.05216861e-01 -4.92625207e-01 -3.77984852e-01
-5.17323911e-02 -8.62061143e-01 -8.37045133e-01 -1.30219376e+00
-1.72570825e-01 2.85176426e-01 2.77563572e-01 8.04264173... | [5.723337650299072, 7.3460164070129395] |
b6fdf66e-4bfb-4d8c-b7a3-5024c8daa224 | domain-class-correlation-decomposition-for | 2106.15206 | null | https://arxiv.org/abs/2106.15206v1 | https://arxiv.org/pdf/2106.15206v1.pdf | Domain-Class Correlation Decomposition for Generalizable Person Re-Identification | Domain generalization in person re-identification is a highly important meaningful and practical task in which a model trained with data from several source domains is expected to generalize well to unseen target domains. Domain adversarial learning is a promising domain generalization method that aims to remove domain... | ['Xinmei Tian', 'Kaiwen Yang'] | 2021-06-29 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 3.45955104e-01 -1.00247532e-01 -9.21095163e-02 -4.01068419e-01
-6.54459953e-01 -5.85844517e-01 5.00165224e-01 -2.08479464e-01
-2.29349360e-01 9.87478197e-01 1.23421259e-01 2.79311180e-01
-1.80865228e-01 -8.60534310e-01 -6.57893062e-01 -7.65533328e-01
9.75244939e-02 5.91389775e-01 -1.62522018e-01 -3.11755866... | [14.719287872314453, 1.0898947715759277] |
2389ebce-f48d-4de4-838e-67c7d74c6478 | look-read-and-enrich-learning-from-scientific | 1909.09070 | null | https://arxiv.org/abs/1909.09070v1 | https://arxiv.org/pdf/1909.09070v1.pdf | Look, Read and Enrich. Learning from Scientific Figures and their Captions | Compared to natural images, understanding scientific figures is particularly hard for machines. However, there is a valuable source of information in scientific literature that until now has remained untapped: the correspondence between a figure and its caption. In this paper we investigate what can be learnt by lookin... | ['Jose Manuel Gomez-Perez', 'Raul Ortega'] | 2019-09-19 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [ 2.94756025e-01 4.35051054e-01 6.42484650e-02 -4.57134753e-01
-9.07449365e-01 -1.10289657e+00 9.09140527e-01 6.72197700e-01
-2.73548514e-01 6.16554737e-01 3.03919226e-01 -5.83108604e-01
1.56161979e-01 -6.09769404e-01 -1.34747970e+00 -1.12273417e-01
7.95008689e-02 5.13930976e-01 4.38794866e-02 7.99718425... | [10.859111785888672, 1.6835113763809204] |
d1c3ca10-5a3b-45db-9e11-eb04adb57495 | multi-modal-domain-adaptation-for-fine | 2001.09691 | null | https://arxiv.org/abs/2001.09691v2 | https://arxiv.org/pdf/2001.09691v2.pdf | Multi-Modal Domain Adaptation for Fine-Grained Action Recognition | Fine-grained action recognition datasets exhibit environmental bias, where multiple video sequences are captured from a limited number of environments. Training a model in one environment and deploying in another results in a drop in performance due to an unavoidable domain shift. Unsupervised Domain Adaptation (UDA) a... | ['Jonathan Munro', 'Dima Damen'] | 2020-01-27 | multi-modal-domain-adaptation-for-fine-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Munro_Multi-Modal_Domain_Adaptation_for_Fine-Grained_Action_Recognition_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Munro_Multi-Modal_Domain_Adaptation_for_Fine-Grained_Action_Recognition_CVPR_2020_paper.pdf | cvpr-2020-6 | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 6.62362695e-01 -1.47328541e-01 -1.49686471e-01 -2.03294054e-01
-8.32288742e-01 -7.58687973e-01 9.23102915e-01 -4.88315493e-01
-5.45762718e-01 7.27588117e-01 3.09188068e-01 1.96820065e-01
2.07590282e-01 -5.55693090e-01 -1.09762502e+00 -7.54950166e-01
1.06943481e-01 3.57036054e-01 4.54063773e-01 -1.47166088... | [8.315997123718262, 0.7219056487083435] |
5c7596e6-ba15-4115-a5fc-a7630ae6afa8 | inductive-entity-representations-from-text | 2010.03496 | null | https://arxiv.org/abs/2010.03496v3 | https://arxiv.org/pdf/2010.03496v3.pdf | Inductive Entity Representations from Text via Link Prediction | Knowledge Graphs (KG) are of vital importance for multiple applications on the web, including information retrieval, recommender systems, and metadata annotation. Regardless of whether they are built manually by domain experts or with automatic pipelines, KGs are often incomplete. Recent work has begun to explore the u... | ['Paul Groth', 'Michael Cochez', 'Daniel Daza'] | 2020-10-07 | null | null | null | null | ['inductive-link-prediction', 'inductive-knowledge-graph-completion'] | ['graphs', 'knowledge-base'] | [ 3.46674882e-02 5.88525832e-01 -6.46522284e-01 -1.56541064e-01
-7.71723926e-01 -7.50953078e-01 8.61874521e-01 7.20668018e-01
-5.16323328e-01 8.62816811e-01 3.75889450e-01 -3.88452888e-01
-5.42093754e-01 -9.64944839e-01 -1.07726264e+00 -3.32496241e-02
-2.87976623e-01 8.85542333e-01 4.16346043e-01 -3.48792374... | [9.118700981140137, 8.111726760864258] |
80a5a049-f8df-484b-9540-bd67a36e616c | building-efficient-cnn-architecture-for | 1804.01259 | null | http://arxiv.org/abs/1804.01259v2 | http://arxiv.org/pdf/1804.01259v2.pdf | Building Efficient CNN Architecture for Offline Handwritten Chinese Character Recognition | Deep convolutional networks based methods have brought great breakthrough in
images classification, which provides an end-to-end solution for handwritten
Chinese character recognition(HCCR) problem through learning discriminative
features automatically. Nevertheless, state-of-the-art CNNs appear to incur
huge computati... | ['Nanjun Teng', 'Zhiyuan Li', 'Min Jin', 'Huaxiang Lu'] | 2018-04-04 | null | null | null | null | ['offline-handwritten-chinese-character', 'offline-handwritten-chinese-character'] | ['computer-vision', 'natural-language-processing'] | [ 3.98002230e-02 -4.62193668e-01 5.06905951e-02 -6.25355899e-01
-6.32999718e-01 -3.26086670e-01 7.96603560e-02 -4.61324491e-02
-8.60652268e-01 4.08632636e-01 -4.71563607e-01 -3.76102686e-01
5.72715700e-02 -8.47151101e-01 -7.27851152e-01 -7.86533833e-01
2.08220840e-01 4.40396480e-02 2.48077407e-01 8.30419734... | [11.767833709716797, 2.5770952701568604] |
085eb006-b062-48b0-ac62-a2a1617ad22d | lite-hdseg-lidar-semantic-segmentation-using | 2103.08852 | null | https://arxiv.org/abs/2103.08852v1 | https://arxiv.org/pdf/2103.08852v1.pdf | Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions | Autonomous driving vehicles and robotic systems rely on accurate perception of their surroundings. Scene understanding is one of the crucial components of perception modules. Among all available sensors, LiDARs are one of the essential sensing modalities of autonomous driving systems due to their active sensing nature ... | ['Liu Bingbing', 'Ehsan Taghavi', 'Ran Cheng', 'Ryan Razani'] | 2021-03-16 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 2.76052713e-01 -6.49370477e-02 -4.85709496e-02 -9.43777978e-01
-7.03748822e-01 -1.35369912e-01 4.09282416e-01 -1.64765492e-01
-6.56479299e-01 2.39485577e-01 -4.75226730e-01 -2.77435929e-01
-2.77798492e-02 -1.15286016e+00 -9.59862769e-01 -4.40363944e-01
3.44727844e-01 5.83448529e-01 9.03429031e-01 -2.80652195... | [8.238816261291504, -2.6237387657165527] |
37d839b9-9f5e-4c11-847f-149e039621d7 | language-models-are-few-shot-learners | 2005.14165 | null | https://arxiv.org/abs/2005.14165v4 | https://arxiv.org/pdf/2005.14165v4.pdf | Language Models are Few-Shot Learners | Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples... | ['Scott Gray', 'Christopher Hesse', 'Rewon Child', 'Gretchen Krueger', 'Ariel Herbert-Voss', 'Arvind Neelakantan', 'Sandhini Agarwal', 'Mark Chen', 'Tom B. Brown', 'Pranav Shyam', 'Nick Ryder', 'Mateusz Litwin', 'Jeffrey Wu', 'Ilya Sutskever', 'Eric Sigler', 'Clemens Winter', 'Benjamin Chess', 'Amanda Askell', 'Alec Ra... | 2020-05-28 | null | http://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf | neurips-2020-12 | ['multi-task-language-understanding', 'unsupervised-machine-translation'] | ['methodology', 'natural-language-processing'] | [ 2.74567068e-01 1.14149459e-01 -7.35276714e-02 -4.51432645e-01
-1.43723369e+00 -6.54035866e-01 6.88199699e-01 -3.18594575e-02
-5.72976947e-01 9.86091316e-01 3.90646428e-01 -6.04604721e-01
9.35501233e-02 -7.43201435e-01 -7.08051503e-01 -3.33050221e-01
4.84785080e-01 9.73098218e-01 -4.37013619e-02 -6.43152237... | [11.044540405273438, 8.35805892944336] |
7fb4623d-d731-4077-ac56-0c1465bfb325 | highlight-specular-reflection-separation | 2207.03543 | null | https://arxiv.org/abs/2207.03543v1 | https://arxiv.org/pdf/2207.03543v1.pdf | Highlight Specular Reflection Separation based on Tensor Low-rank and Sparse Decomposition Using Polarimetric Cues | This paper is concerned with specular reflection removal based on tensor low-rank decomposition framework with the help of polarization information. Our method is motivated by the observation that the specular highlight of an image is sparsely distributed while the remaining diffuse reflection can be well approximated ... | ['Hong Zhang', 'Moein Shakeri'] | 2022-07-07 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 4.65480745e-01 -5.34416199e-01 2.34439105e-01 1.71159893e-01
-5.47658801e-01 -7.68531919e-01 3.35762054e-01 -8.59465897e-01
9.04156566e-02 6.70047104e-01 3.09145719e-01 9.38650146e-02
-7.95950666e-02 -6.57442272e-01 -4.61227864e-01 -1.35334682e+00
3.14685017e-01 1.02192014e-01 -1.60321649e-02 -3.43938380... | [10.215747833251953, -2.8657689094543457] |
06b81ac8-4f51-4335-a61c-01bb91f4cde0 | gradient-guided-unsupervised-text-style | 2202.00469 | null | https://arxiv.org/abs/2202.00469v1 | https://arxiv.org/pdf/2202.00469v1.pdf | Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning | Text style transfer is a challenging text generation problem, which aims at altering the style of a given sentence to a target one while keeping its content unchanged. Since there is a natural scarcity of parallel datasets, recent works mainly focus on solving the problem in an unsupervised manner. However, previous gr... | ['Wei Wei', 'Ziao Li', 'Chenghao Fan'] | 2022-01-23 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 7.45282650e-01 -8.32550600e-02 -3.37049440e-02 -3.38775843e-01
-5.90583146e-01 -4.35324043e-01 7.13563681e-01 -7.75141492e-02
-1.72859192e-01 1.00874162e+00 2.16695756e-01 -4.08358648e-02
1.26977727e-01 -7.73150206e-01 -6.57247126e-01 -7.05021679e-01
5.81479788e-01 3.14674646e-01 1.89218938e-01 -4.07783777... | [11.680246353149414, 9.468636512756348] |
51a85541-af10-42f3-8c46-2b09c130984a | tweet-fid-an-annotated-dataset-for-multiple | 2205.10726 | null | https://arxiv.org/abs/2205.10726v2 | https://arxiv.org/pdf/2205.10726v2.pdf | TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks | Foodborne illness is a serious but preventable public health problem -- with delays in detecting the associated outbreaks resulting in productivity loss, expensive recalls, public safety hazards, and even loss of life. While social media is a promising source for identifying unreported foodborne illnesses, there is a d... | ['Elke Rundensteiner', 'Hao Feng', 'Thomas Hartvigsen', 'Dandan Tao', 'Dongyu Zhang', 'Ruofan Hu'] | 2022-05-22 | null | https://aclanthology.org/2022.lrec-1.668 | https://aclanthology.org/2022.lrec-1.668.pdf | lrec-2022-6 | ['slot-filling'] | ['natural-language-processing'] | [ 2.16301814e-01 3.45577672e-02 -2.85640836e-01 -3.27846438e-01
-8.67337644e-01 -5.07265925e-01 4.62701201e-01 1.53711975e+00
-5.59978843e-01 5.86060643e-01 5.02130449e-01 -2.81436056e-01
-5.77908196e-02 -8.61933410e-01 -8.28663409e-01 -3.38270247e-01
-4.92282510e-01 9.52939034e-01 -2.68866211e-01 -1.69252697... | [8.50288200378418, 9.36020565032959] |
b7f44a81-6389-48a3-904d-e50676702cca | era-entity-relationship-aware-video | 2109.02625 | null | https://arxiv.org/abs/2109.02625v1 | https://arxiv.org/pdf/2109.02625v1.pdf | ERA: Entity Relationship Aware Video Summarization with Wasserstein GAN | Video summarization aims to simplify large scale video browsing by generating concise, short summaries that diver from but well represent the original video. Due to the scarcity of video annotations, recent progress for video summarization concentrates on unsupervised methods, among which the GAN based methods are most... | ['Claudio T. Silva', 'Jianzhe Lin', 'Guande Wu'] | 2021-09-06 | null | null | null | null | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 5.12172639e-01 2.04678267e-01 -2.41692707e-01 -1.55223176e-01
-8.76741767e-01 -3.07169139e-01 4.37009335e-01 -1.23972371e-01
-1.79213896e-01 9.97824073e-01 4.90992546e-01 2.13735148e-01
1.27745077e-01 -7.03306735e-01 -8.51493180e-01 -9.33767319e-01
1.17613040e-01 1.38200507e-01 4.32115495e-01 -6.41996637... | [10.417654037475586, 0.4241519868373871] |
3079fce5-dbdb-45fd-88b7-d382ae73784c | a-deep-generative-approach-to-native-language | null | null | https://aclanthology.org/2020.coling-main.159 | https://aclanthology.org/2020.coling-main.159.pdf | A Deep Generative Approach to Native Language Identification | Native language identification (NLI) {--} identifying the native language (L1) of a person based on his/her writing in the second language (L2) {--} is useful for a variety of purposes, including marketing, security, and educational applications. From a traditional machine learning perspective,NLI is usually framed as ... | ['Walter Daelemans', 'Ilia Markov', 'Ehsan Lotfi'] | 2020-12-01 | null | null | null | coling-2020-8 | ['native-language-identification'] | ['natural-language-processing'] | [ 3.01660359e-01 -1.73484117e-01 -2.89216071e-01 -3.00741583e-01
-1.05568898e+00 -5.86960256e-01 9.74076450e-01 1.21564083e-01
-4.23922956e-01 5.77217102e-01 1.09804548e-01 -5.32961607e-01
-4.81993034e-02 -5.11827707e-01 -3.66669595e-01 -6.19539738e-01
4.01175559e-01 9.34220314e-01 -3.21301103e-01 2.26213798... | [10.400660514831543, 10.524559020996094] |
c93fd1b6-3455-46d1-b49d-0940c8d730b3 | unsupervised-learning-of-the-total-variation | 2206.04406 | null | https://arxiv.org/abs/2206.04406v1 | https://arxiv.org/pdf/2206.04406v1.pdf | Unsupervised Learning of the Total Variation Flow | The total variation (TV) flow generates a scale-space representation of an image based on the TV functional. This gradient flow observes desirable features for images such as sharp edges and enables spectral, scale, and texture analysis. The standard numerical approach for TV flow requires solving multiple non-smooth o... | ['Carola-Bibiane Schönlieb', 'Yury Korolev', 'Sören Dittmer', 'Tamara G. Grossmann'] | 2022-06-09 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.79458612e-01 -1.13988398e-02 7.72055835e-02 -1.91058353e-01
-5.19913554e-01 -3.97831500e-01 5.80798924e-01 3.50987613e-02
-4.19401199e-01 7.65572846e-01 -1.05727129e-01 -2.34927446e-01
-2.21642807e-01 -8.18690836e-01 -6.29152834e-01 -8.06902766e-01
-2.75575578e-01 2.52139926e-01 3.13501030e-01 -8.19664076... | [11.655491828918457, -2.4985930919647217] |
cc12fb10-c846-4f14-9e0d-11d917210720 | coconet-a-collaborative-convolutional-network | 1901.09886 | null | https://arxiv.org/abs/1901.09886v4 | https://arxiv.org/pdf/1901.09886v4.pdf | CoCoNet: A Collaborative Convolutional Network | We present an end-to-end deep network for fine-grained visual categorization called Collaborative Convolutional Network (CoCoNet). The network uses a collaborative layer after the convolutional layers to represent an image as an optimal weighted collaboration of features learned from training samples as a whole rather ... | ['Umapada Pal', 'Steven Mills', 'Brendan McCane', 'Tapabrata Chakraborti'] | 2019-01-28 | null | null | null | null | ['fine-grained-visual-recognition', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision'] | [-1.12002477e-01 -3.52465451e-01 -8.64940733e-02 -7.88327098e-01
-2.63488322e-01 -8.79838169e-01 8.23589265e-01 -7.86699355e-02
-8.13238919e-01 4.07638341e-01 4.22081202e-01 -5.07435389e-02
-4.31130826e-01 -6.71102822e-01 -5.96189499e-01 -4.66067612e-01
-6.41595304e-01 4.55132276e-01 2.30111018e-01 1.26004796... | [9.762425422668457, 2.2152695655822754] |
15116980-89ba-42ba-9586-9965f93f8e5a | growing-regression-forests-by-classification | 1312.6430 | null | http://arxiv.org/abs/1312.6430v2 | http://arxiv.org/pdf/1312.6430v2.pdf | Growing Regression Forests by Classification: Applications to Object Pose Estimation | In this work, we propose a novel node splitting method for regression trees
and incorporate it into the regression forest framework. Unlike traditional
binary splitting, where the splitting rule is selected from a predefined set of
binary splitting rules via trial-and-error, the proposed node splitting method
first fin... | ['Kota Hara', 'Rama Chellappa'] | 2013-12-22 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [ 2.95261770e-01 2.46782571e-01 -4.20379847e-01 -5.43394387e-01
-6.22632146e-01 -2.32026860e-01 3.05805951e-01 1.42207220e-01
-5.70979357e-01 7.67150640e-01 -2.57168382e-01 -5.72436571e-01
-2.07160875e-01 -9.59058285e-01 -3.25046688e-01 -1.00099015e+00
9.65552330e-02 6.57040477e-01 4.03292120e-01 3.83352935... | [8.871759414672852, 3.793179512023926] |
daa7dc85-8c35-4a6d-b1c2-8fc1edd5e2c3 | cqsumdp-a-chatgpt-annotated-resource-for | 2305.06147 | null | https://arxiv.org/abs/2305.06147v1 | https://arxiv.org/pdf/2305.06147v1.pdf | CQSumDP: A ChatGPT-Annotated Resource for Query-Focused Abstractive Summarization Based on Debatepedia | Debatepedia is a publicly available dataset consisting of arguments and counter-arguments on controversial topics that has been widely used for the single-document query-focused abstractive summarization task in recent years. However, it has been recently found that this dataset is limited by noise and even most querie... | ['Jimmy Huang', 'Enamul Hoque', 'Israt Jahan', 'Mizanur Rahman', 'Md Tahmid Rahman Laskar'] | 2023-03-31 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.56751382e-01 6.16911292e-01 -3.08020622e-01 -2.47997120e-02
-1.64532685e+00 -9.54588413e-01 1.16252697e+00 7.71009564e-01
-3.86332422e-01 1.33561516e+00 1.08236170e+00 -3.35959285e-01
-1.37881711e-01 -5.63103914e-01 -6.05590999e-01 -3.47853988e-01
3.03496063e-01 7.89174736e-01 2.40500584e-01 -4.27491903... | [12.29647445678711, 9.53061580657959] |
49aba34a-8b70-402d-a469-ae867b74eee2 | leveraging-shape-completion-for-3d-siamese | 1903.01784 | null | http://arxiv.org/abs/1903.01784v2 | http://arxiv.org/pdf/1903.01784v2.pdf | Leveraging Shape Completion for 3D Siamese Tracking | Point clouds are challenging to process due to their sparsity, therefore
autonomous vehicles rely more on appearance attributes than pure geometric
features. However, 3D LIDAR perception can provide crucial information for
urban navigation in challenging light or weather conditions. In this paper, we
investigate the ve... | ['Bernard Ghanem', 'Silvio Giancola', 'Jesus Zarzar'] | 2019-03-05 | leveraging-shape-completion-for-3d-siamese-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Giancola_Leveraging_Shape_Completion_for_3D_Siamese_Tracking_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Giancola_Leveraging_Shape_Completion_for_3D_Siamese_Tracking_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-object-tracking'] | ['computer-vision'] | [-8.61168280e-02 -1.95493251e-01 -4.22093213e-01 -3.77071261e-01
-7.62818456e-01 -8.29190731e-01 8.49621713e-01 -1.92945153e-01
-3.37612361e-01 2.64858246e-01 -1.35774657e-01 -3.39122862e-01
1.04146838e-01 -5.01447082e-01 -7.72527158e-01 -6.23508751e-01
-1.56189531e-01 6.93886280e-01 2.61042058e-01 2.98640013... | [6.644569396972656, -2.3615593910217285] |
a3fcc9d1-04f4-4ae6-a5cd-06f8595d96bc | jpeg-steganalysis-based-on-steganographic | 2302.02276 | null | https://arxiv.org/abs/2302.02276v1 | https://arxiv.org/pdf/2302.02276v1.pdf | JPEG Steganalysis Based on Steganographic Feature Enhancement and Graph Attention Learning | The purpose of image steganalysis is to determine whether the carrier image contains hidden information or not. Since JEPG is the most commonly used image format over social networks, steganalysis in JPEG images is also the most urgently needed to be explored. However, in order to detect whether secret information is h... | ['Hanzhou Wu', 'Zhiguang Yang', 'Qiyun Liu'] | 2023-02-05 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 7.69665718e-01 1.57179058e-01 3.35868485e-02 2.28620157e-01
-1.00209653e-01 4.04890515e-02 4.74895447e-01 -2.70534486e-01
-3.15471500e-01 3.04178655e-01 2.95461044e-02 -3.30191135e-01
1.41022325e-01 -1.03772557e+00 -7.21442819e-01 -1.03328824e+00
-1.54295117e-01 -3.35398048e-01 3.72289419e-01 -5.19290626... | [4.294129371643066, 8.056547164916992] |
079fb356-30ee-472c-8047-550d22f30c49 | automatic-sound-event-detection-and | 2301.02214 | null | https://arxiv.org/abs/2301.02214v2 | https://arxiv.org/pdf/2301.02214v2.pdf | Automatic Sound Event Detection and Classification of Great Ape Calls Using Neural Networks | We present a novel approach to automatically detect and classify great ape calls from continuous raw audio recordings collected during field research. Our method leverages deep pretrained and sequential neural networks, including wav2vec 2.0 and LSTM, and is validated on three data sets from three different great ape l... | ['Steven Moran', 'Adriano R. Lameira', 'Isaac Schamberg', 'Adrian Soldati', 'Zifan Jiang'] | 2023-01-05 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [ 4.09862131e-01 -4.27586287e-01 2.69856244e-01 -4.61782724e-01
-7.51629889e-01 -6.19878113e-01 3.74468654e-01 1.47987708e-01
-1.04166031e+00 5.75438380e-01 1.97793931e-01 -1.41073868e-01
2.23141506e-01 -6.85132682e-01 -1.14116542e-01 -2.07697153e-01
-8.47805679e-01 5.08439839e-01 4.26751286e-01 1.93717517... | [15.225688934326172, 5.270585060119629] |
a46dc8fe-a50d-4b6d-9a26-8a47c40d6886 | speech-denoising-using-only-single-noisy | 2111.00242 | null | https://arxiv.org/abs/2111.00242v4 | https://arxiv.org/pdf/2111.00242v4.pdf | Self-Supervised Speech Denoising Using Only Noisy Audio Signals | In traditional speech denoising tasks, clean audio signals are often used as the training target, but absolutely clean signals are collected from expensive recording equipment or in studios with the strict environments. To overcome this drawback, we propose an end-to-end self-supervised speech denoising training scheme... | ['Lotfi Senhadji', 'Lei LI', 'Huazhong Shu', 'Guanyu Yang', 'Jiasong Wu', 'Qingchun Li'] | 2021-10-30 | null | null | null | null | ['audio-denoising', 'speech-denoising'] | ['audio', 'speech'] | [ 2.54058897e-01 -2.21111387e-01 4.80855405e-01 -4.81714427e-01
-1.32219160e+00 -2.86104947e-01 2.15845376e-01 -1.28107131e-01
-4.22620952e-01 5.12070656e-01 1.76295161e-01 -1.01093709e-01
1.38688564e-01 -7.26917922e-01 -5.05629718e-01 -9.78657126e-01
8.84395689e-02 -2.44327128e-01 2.14849010e-01 -1.66006908... | [15.012343406677246, 5.942212104797363] |
82745c42-2af3-4d29-a209-51e95a2226ab | quantifying-natural-and-artificial | 1412.6703 | null | http://arxiv.org/abs/1412.6703v2 | http://arxiv.org/pdf/1412.6703v2.pdf | Quantifying Natural and Artificial Intelligence in Robots and Natural Systems with an Algorithmic Behavioural Test | One of the most important aims of the fields of robotics, artificial
intelligence and artificial life is the design and construction of systems and
machines as versatile and as reliable as living organisms at performing high
level human-like tasks. But how are we to evaluate artificial systems if we are
not certain how... | ['Hector Zenil'] | 2014-12-20 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 6.82351217e-02 3.70727807e-01 3.89817685e-01 1.13392428e-01
8.08975041e-01 -5.71712673e-01 1.03574264e+00 -1.21309139e-01
-3.75868410e-01 8.69997263e-01 -2.33528644e-01 -1.05241187e-01
-3.24515879e-01 -9.42501664e-01 -9.77199152e-02 -7.37917185e-01
-2.81846881e-01 3.75108361e-01 3.88451606e-01 -9.38662648... | [5.58473014831543, 4.163905620574951] |
1d71a1db-bebd-4ab2-afd9-d494da813f5e | signing-at-scale-learning-to-co-articulate | 2203.15354 | null | https://arxiv.org/abs/2203.15354v1 | https://arxiv.org/pdf/2203.15354v1.pdf | Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production | Sign languages are visual languages, with vocabularies as rich as their spoken language counterparts. However, current deep-learning based Sign Language Production (SLP) models produce under-articulated skeleton pose sequences from constrained vocabularies and this limits applicability. To be understandable and accepte... | ['Richard Bowden', 'Necati Cihan Camgoz', 'Ben Saunders'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Saunders_Signing_at_Scale_Learning_to_Co-Articulate_Signs_for_Large-Scale_Photo-Realistic_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Saunders_Signing_at_Scale_Learning_to_Co-Articulate_Signs_for_Large-Scale_Photo-Realistic_CVPR_2022_paper.pdf | cvpr-2022-1 | ['sign-language-production'] | ['natural-language-processing'] | [ 3.63652259e-01 2.12537035e-01 -2.14731619e-01 -1.67699605e-01
-9.70174372e-01 -5.78547657e-01 8.32542241e-01 -1.07512164e+00
-2.60795534e-01 6.59135401e-01 7.90157318e-01 1.06351085e-01
1.97361305e-01 -2.84978300e-01 -8.48144054e-01 -5.41481078e-01
8.28162134e-02 5.29973149e-01 1.00190565e-01 -4.52421963... | [9.204911231994629, -6.528158664703369] |
1650a335-f7e6-44a0-ac10-ff97bf7f45e4 | graph-neural-network-for-spatiotemporal-data | 2306.00012 | null | https://arxiv.org/abs/2306.00012v1 | https://arxiv.org/pdf/2306.00012v1.pdf | Graph Neural Network for spatiotemporal data: methods and applications | In the era of big data, there has been a surge in the availability of data containing rich spatial and temporal information, offering valuable insights into dynamic systems and processes for applications such as weather forecasting, natural disaster management, intelligent transport systems, and precision agriculture. ... | ['Liang Zhao', 'Xiaoyun Gong', 'Minxing Zhang', 'Zhenke Liu', 'Dazhou Yu', 'Yun Li'] | 2023-05-30 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-1.09843798e-02 -2.62520492e-01 -3.56087446e-01 -1.64660200e-01
3.17569196e-01 -6.98590279e-01 4.14915085e-01 4.46724951e-01
5.64449728e-02 5.76520920e-01 -8.66159275e-02 -7.68872201e-01
-7.75209308e-01 -1.26177573e+00 -4.57958519e-01 -4.90241349e-01
-7.81389236e-01 6.01641871e-02 3.64323318e-01 -5.65544367... | [6.787037372589111, 2.7024269104003906] |
49367560-a41b-4b96-9f83-417b6ef9a3f6 | practical-hidden-voice-attacks-against-speech | 1904.05734 | null | http://arxiv.org/abs/1904.05734v1 | http://arxiv.org/pdf/1904.05734v1.pdf | Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems | Voice Processing Systems (VPSes), now widely deployed, have been made
significantly more accurate through the application of recent advances in
machine learning. However, adversarial machine learning has similarly advanced
and has been used to demonstrate that VPSes are vulnerable to the injection of
hidden commands - ... | ['Patrick Traynor', 'Hadi Abdullah', 'Kevin R. B. Butler', 'Joseph Wilson', 'Christian Peeters', 'Washington Garcia'] | 2019-03-18 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 3.15169990e-01 -1.19757667e-01 3.05030107e-01 1.84039935e-01
-1.11367476e+00 -1.21333539e+00 5.39472163e-01 -2.76901692e-01
-1.56316429e-01 3.59129071e-01 -1.77301377e-01 -7.18104005e-01
3.14558327e-01 -4.69278365e-01 -9.29117501e-01 -7.81502128e-01
-5.76842606e-01 -1.04978584e-01 2.16991231e-01 -2.23839238... | [13.935384750366211, 5.776515007019043] |
33da2562-c0e3-48ee-acbf-f03707a32728 | better-generalized-few-shot-learning-even | 2211.16095 | null | https://arxiv.org/abs/2211.16095v2 | https://arxiv.org/pdf/2211.16095v2.pdf | Better Generalized Few-Shot Learning Even Without Base Data | This paper introduces and studies zero-base generalized few-shot learning (zero-base GFSL), which is an extreme yet practical version of few-shot learning problem. Motivated by the cases where base data is not available due to privacy or ethical issues, the goal of zero-base GFSL is to newly incorporate the knowledge o... | ['Seong-Woong Kim', 'Dong-Wan Choi'] | 2022-11-29 | null | null | null | null | ['generalized-few-shot-learning'] | ['methodology'] | [ 8.76144618e-02 7.55905360e-03 -2.60030955e-01 -2.02138275e-01
-6.59069479e-01 -8.34180489e-02 4.68917251e-01 1.72452286e-01
-7.04334736e-01 9.42739308e-01 4.65927087e-02 1.22420691e-01
-1.98797077e-01 -1.11473298e+00 -5.10734260e-01 -8.87768149e-01
2.33919278e-01 2.22307339e-01 4.75308806e-01 -3.31831336... | [9.938302993774414, 3.180414915084839] |
ef80dcdf-2e57-43c9-bc0d-b50c15ca8fec | automatic-generation-of-alternative-starting | 1411.4023 | null | http://arxiv.org/abs/1411.4023v1 | http://arxiv.org/pdf/1411.4023v1.pdf | Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games | Simple board games, like Tic-Tac-Toe and CONNECT-4, play an important role
not only in the development of mathematical and logical skills, but also in the
emotional and social development. In this paper, we address the problem of
generating targeted starting positions for such games. This can facilitate new
approaches ... | ['Sumit Gulwani', 'Krishnendu Chatterjee', 'Umair Z. Ahmed'] | 2014-11-14 | null | null | null | null | ['board-games'] | ['playing-games'] | [-3.42361242e-01 4.90811437e-01 2.83093750e-01 1.95862681e-01
-4.21437532e-01 -5.56017458e-01 1.11578867e-01 1.41709879e-01
-2.06312060e-01 9.09278154e-01 -5.60843468e-01 -6.13431096e-01
-5.58118045e-01 -1.17806304e+00 -3.48753542e-01 -2.47208163e-01
-3.20387632e-01 5.92418015e-01 5.09752870e-01 -9.41123486... | [3.459728717803955, 1.4746270179748535] |
0d74b945-1af6-4fc5-a8f9-ad1c1f8fecbd | a-perspectival-mirror-of-the-elephant | 2303.16281 | null | https://arxiv.org/abs/2303.16281v2 | https://arxiv.org/pdf/2303.16281v2.pdf | A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Wikipedia, and YouTube | Contrary to Google Search's mission of delivering information from "many angles so you can form your own understanding of the world," we find that Google and its most prominent returned results - Wikipedia and YouTube - simply reflect a narrow set of cultural stereotypes tied to the search language for complex topics l... | ['Michael D. Smith', 'Michael J. Puett', 'Queenie Luo'] | 2023-03-28 | null | null | null | null | ['culture'] | ['speech'] | [-3.19408119e-01 -1.61341354e-02 -7.47887492e-01 2.17179894e-01
-6.39307737e-01 -9.35990155e-01 1.00699317e+00 1.75084427e-01
-4.85723197e-01 3.78279507e-01 1.11982334e+00 -1.06442666e+00
-2.61495300e-02 -3.46082896e-01 -2.24995002e-01 -3.42453301e-01
5.61507225e-01 -1.98739842e-02 -6.49486110e-02 -7.58365035... | [8.878748893737793, 9.939428329467773] |
c0108558-f280-4ef8-a9f1-f7b3076a5cc0 | selective-query-guided-debiasing-network-for | 2210.08714 | null | https://arxiv.org/abs/2210.08714v2 | https://arxiv.org/pdf/2210.08714v2.pdf | Selective Query-guided Debiasing for Video Corpus Moment Retrieval | Video moment retrieval (VMR) aims to localize target moments in untrimmed videos pertinent to a given textual query. Existing retrieval systems tend to rely on retrieval bias as a shortcut and thus, fail to sufficiently learn multi-modal interactions between query and video. This retrieval bias stems from learning freq... | ['Chang D. Yoo', 'Hee Suk Yoon', 'Junyeong Kim', 'Dahyun Kim', 'Eunseop Yoon', 'Ji Woo Hong', 'Sunjae Yoon'] | 2022-10-17 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-4.53701466e-02 -5.75596273e-01 -7.76936769e-01 -4.64967787e-02
-7.37821460e-01 -5.39317012e-01 8.19838941e-01 -1.23373330e-01
-2.83476803e-02 3.10889333e-01 6.66520894e-01 3.43105868e-02
-4.92997378e-01 -4.88374680e-01 -8.31729412e-01 -6.62909508e-01
-1.62418798e-01 1.74282178e-01 2.25856602e-01 -2.04333156... | [10.215118408203125, 0.7899797558784485] |
7466e0f8-20e7-4c4f-a463-e0eb2701c04c | a-jet-tagging-algorithm-of-graph-network-with | 2210.13869 | null | https://arxiv.org/abs/2210.13869v3 | https://arxiv.org/pdf/2210.13869v3.pdf | A jet tagging algorithm of graph network with HaarPooling message passing | Recently methods of graph neural networks (GNNs) have been applied to solving the problems in high energy physics (HEP) and have shown its great potential for quark-gluon tagging with graph representation of jet events. In this paper, we introduce an approach of GNNs combined with a HaarPooling operation to analyze the... | ['Wei Li', 'Feiyi Liu', 'Fei Ma'] | 2022-10-25 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-8.29376519e-01 8.47915411e-02 1.43447176e-01 -2.02664837e-01
-1.47963434e-01 -4.35912222e-01 6.55789733e-01 5.86772561e-01
-5.67293465e-01 8.29944253e-01 -9.92310569e-02 -3.80948871e-01
-4.58309442e-01 -1.37660885e+00 -6.80866838e-01 -8.50781977e-01
-7.99540102e-01 1.01920438e+00 4.70280975e-01 -4.43415672... | [15.703714370727539, 2.9178473949432373] |
422c59cc-a25c-4a79-9599-e0b04d559d11 | parallax-estimation-for-push-frame-satellite | 2102.02301 | null | https://arxiv.org/abs/2102.02301v1 | https://arxiv.org/pdf/2102.02301v1.pdf | Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products | Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images. This new acquisition mode allows for modern image restoration techniques, including multi-frame super-resolution. As the satellite moves during the acquisition of the burst, elevation changes in the scene tran... | ['Gabriele Facciolo', 'Thibaud Ehret', 'Jérémy Anger'] | 2021-02-03 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 3.70562494e-01 -3.22985172e-01 2.31733248e-01 -1.43523544e-01
-4.25281018e-01 -5.81391275e-01 6.04285955e-01 -3.43510091e-01
-3.18281353e-01 8.90216589e-01 -1.29297659e-01 5.62676862e-02
-2.61962950e-01 -8.23891938e-01 -5.48070490e-01 -1.04094064e+00
-2.13182062e-01 4.36210096e-01 4.69507903e-01 -5.75148880... | [9.884976387023926, -2.3990890979766846] |
7b21fd13-a5bf-42f6-8080-010535a40318 | on-device-evaluation-toolkit-for-machine | 2306.14574 | null | https://arxiv.org/abs/2306.14574v1 | https://arxiv.org/pdf/2306.14574v1.pdf | On-Device Evaluation Toolkit for Machine Learning on Heterogeneous Low-Power System-on-Chip | Network delays, throughput bottlenecks and privacy issues push Artificial Intelligence of Things (AIoT) designers towards evaluating the feasibility of moving model training and execution (inference) as near as possible to the terminals. Meanwhile, results from the TinyML community demonstrate that, in some cases, it i... | ['Emmanuel Baccelli', 'Kaspar Schleiser', 'Koen Zandberg', 'Zhaolan Huang'] | 2023-06-26 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-2.71086216e-01 9.04681385e-02 -2.27999449e-01 -2.70738304e-01
-8.99909064e-02 -5.31147540e-01 3.94066930e-01 -1.57452315e-01
-4.11001951e-01 4.27829802e-01 -4.68502373e-01 -8.81652832e-01
2.41325665e-02 -9.61427808e-01 -4.28905249e-01 -3.17097813e-01
-1.50591269e-01 6.03024602e-01 2.08369181e-01 1.42275140... | [8.05953598022461, 2.606752872467041] |
a7f5d83e-644b-4784-9d6c-2d36d9803606 | depthwise-separable-temporal-convolutional | null | null | https://ieeexplore.ieee.org/document/9320343 | https://ieeexplore.ieee.org/document/9320343 | Depthwise Separable Temporal Convolutional Network for Action Segmentation | Fine-grained temporal action segmentation in long,
untrimmed RGB videos is a key topic in visual human-
machine interaction. Recent temporal convolution based
approaches either use encoder-decoder(ED) architecture or
dilations with doubling factor in consecutive convolution
layers to segment actions in videos. How... | ['Heiko Neumann', 'Wolfgang Mader', 'Christian Jarvers', 'Basavaraj Hampiholi'] | 2021-01-19 | null | null | null | 2020-international-conference-on-3d-vision | ['action-segmentation'] | ['computer-vision'] | [ 2.15366542e-01 -1.97692692e-01 -2.88782090e-01 -4.12907124e-01
-4.43338096e-01 -5.21717429e-01 4.77930158e-01 -7.87223041e-01
-6.52607143e-01 6.01802766e-01 4.43070531e-01 -2.05245137e-01
2.56032676e-01 -4.44004476e-01 -8.21664274e-01 -7.10232973e-01
-3.08699429e-01 -4.14357632e-02 8.99378002e-01 -2.21367367... | [8.855517387390137, 0.15690116584300995] |
b3b6e7b7-0a0b-4252-8276-a419c887414d | sparse-subspace-clustering-in-diverse | 2206.07602 | null | https://arxiv.org/abs/2206.07602v2 | https://arxiv.org/pdf/2206.07602v2.pdf | Sparse Subspace Clustering in Diverse Multiplex Network Model | The paper considers the DIverse MultiPLEx (DIMPLE) network model, introduced in Pensky and Wang (2021), where all layers of the network have the same collection of nodes and are equipped with the Stochastic Block Models. In addition, all layers can be partitioned into groups with the same community structures, although... | ['Marianna Pensky', 'Majid Noroozi'] | 2022-06-15 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.03628645e-02 -4.53216285e-02 -2.22263902e-01 2.12201238e-01
3.18060726e-01 -7.30887413e-01 6.83572650e-01 9.00203548e-03
-5.26952520e-02 5.30255914e-01 2.10999191e-01 -2.76543111e-01
-6.58674777e-01 -8.17050755e-01 -3.46340746e-01 -1.02021086e+00
-3.39156389e-01 4.60232466e-01 5.18562794e-01 2.18326598... | [7.069379806518555, 5.300108432769775] |
87d7cf1d-1362-420f-b42c-bdae9d91cd4a | voxel-level-importance-maps-for-interpretable | 2108.05388 | null | https://arxiv.org/abs/2108.05388v1 | https://arxiv.org/pdf/2108.05388v1.pdf | Voxel-level Importance Maps for Interpretable Brain Age Estimation | Brain aging, and more specifically the difference between the chronological and the biological age of a person, may be a promising biomarker for identifying neurodegenerative diseases. For this purpose accurate prediction is important but the localisation of the areas that play a significant role in the prediction is a... | ['Daniel Rueckert', 'Alexander Hammers', 'Vasileios Baltatzis', 'Kyriaki-Margarita Bintsi'] | 2021-08-11 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 1.78117618e-01 2.95243740e-01 3.11848551e-01 -3.91718477e-01
-2.21093982e-01 8.02021995e-02 4.15337741e-01 5.24612904e-01
-9.00121212e-01 8.36271524e-01 7.06567228e-01 -2.81172812e-01
-3.29820216e-01 -5.46942115e-01 -6.40766799e-01 -7.63137162e-01
-5.86376548e-01 5.06415784e-01 1.24732584e-01 1.84289850... | [14.120383262634277, -1.664013147354126] |
67d27c6b-0582-493d-99a6-12201fd2c508 | tandem-tracking-and-dense-mapping-in-real | 2111.07418 | null | https://arxiv.org/abs/2111.07418v1 | https://arxiv.org/pdf/2111.07418v1.pdf | TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo | In this paper, we present TANDEM a real-time monocular tracking and dense mapping framework. For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding window of keyframes. To increase the robustness, we propose a novel tracking front-end that performs dense direct image alignment using depth... | ['Daniel Cremers', 'Niclas Zeller', 'Nan Yang', 'Lukas Koestler'] | 2021-11-14 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.06314877e-01 -1.47026286e-01 7.41305649e-02 -3.24908465e-01
-6.49324536e-01 -5.10548532e-01 6.10227764e-01 -2.05597967e-01
-3.26016456e-01 5.75660586e-01 -1.77779980e-02 1.25735313e-01
3.66243452e-01 -6.71817422e-01 -8.62266302e-01 -4.76156086e-01
4.87660438e-01 7.59358525e-01 6.00249767e-01 1.32889524... | [7.925031661987305, -2.3076629638671875] |
14cb2198-239b-4da0-89a6-c79bbf3419ae | longformer-the-long-document-transformer | 2004.05150 | null | https://arxiv.org/abs/2004.05150v2 | https://arxiv.org/pdf/2004.05150v2.pdf | Longformer: The Long-Document Transformer | Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of ... | ['Iz Beltagy', 'Matthew E. Peters', 'Arman Cohan'] | 2020-04-10 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 4.18330282e-01 1.68609649e-01 7.63562992e-02 -3.19659144e-01
-1.36504972e+00 -7.58933485e-01 8.27078044e-01 -2.04631928e-02
-6.20700836e-01 6.80673659e-01 9.87183690e-01 -4.23221141e-01
3.89943063e-01 -5.05739093e-01 -1.02796769e+00 -4.47308779e-01
2.07693577e-01 8.17166030e-01 1.07672170e-01 -3.06049407... | [11.895686149597168, 9.034870147705078] |
3a4aa586-844b-4204-af63-ac2a165c086d | guided-interactive-video-object-segmentation | 2104.10386 | null | https://arxiv.org/abs/2104.10386v1 | https://arxiv.org/pdf/2104.10386v1.pdf | Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps | We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagatio... | ['Chang-Su Kim', 'Yeong Jun Koh', 'Yuk Heo'] | 2021-04-21 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Heo_Guided_Interactive_Video_Object_Segmentation_Using_Reliability-Based_Attention_Maps_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Heo_Guided_Interactive_Video_Object_Segmentation_Using_Reliability-Based_Attention_Maps_CVPR_2021_paper.pdf | cvpr-2021-1 | ['interactive-video-object-segmentation'] | ['computer-vision'] | [-2.06246570e-01 4.12960583e-03 -2.31236100e-01 -5.20318449e-01
-8.09808314e-01 -3.98058385e-01 -2.46801272e-01 1.06673457e-01
-4.22325641e-01 4.62995440e-01 1.92248188e-02 -1.15057498e-01
1.37151927e-01 -6.32106125e-01 -5.36788404e-01 -5.15353322e-01
-9.07861441e-02 1.55516267e-01 9.02080894e-01 1.81557357... | [9.265617370605469, -0.1471736580133438] |
f675bf15-a4d1-4fe2-a3cb-e54cdf2314c0 | using-open-ended-stressor-responses-to | 2211.07932 | null | https://arxiv.org/abs/2211.07932v1 | https://arxiv.org/pdf/2211.07932v1.pdf | Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics | Stressors are related to depression, but this relationship is complex. We investigate the relationship between open-ended text responses about stressors and depressive symptoms across gender and racial/ethnic groups. First, we use topic models and other NLP tools to find thematic and vocabulary differences when reporti... | ['Philip Resnik', 'Mark Dredze', 'Carlos Aguirre'] | 2022-11-15 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-3.67780447e-01 3.30178104e-02 -7.57938564e-01 -6.85995400e-01
-6.07853889e-01 -3.84086490e-01 3.64241242e-01 9.90788400e-01
-5.36610961e-01 5.52999020e-01 1.50861907e+00 -6.07785210e-02
-3.35085988e-01 -8.84095430e-01 7.03047216e-02 1.53106064e-01
2.38022000e-01 9.12382901e-02 -6.59371972e-01 -3.41998458... | [9.251423835754395, 10.190064430236816] |
f19c6542-0c6b-4052-ae02-020d10a9b38c | sequential-decision-making-for-active-object | 2110.11524 | null | https://arxiv.org/abs/2110.11524v4 | https://arxiv.org/pdf/2110.11524v4.pdf | Sequential Voting with Relational Box Fields for Active Object Detection | A key component of understanding hand-object interactions is the ability to identify the active object -- the object that is being manipulated by the human hand. In order to accurately localize the active object, any method must reason using information encoded by each image pixel, such as whether it belongs to the han... | ['Kris M. Kitani', 'Xingyu Liu', 'Qichen Fu'] | 2021-10-21 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Fu_Sequential_Voting_With_Relational_Box_Fields_for_Active_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Fu_Sequential_Voting_With_Relational_Box_Fields_for_Active_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['active-object-detection'] | ['computer-vision'] | [ 4.69832301e-01 -4.01876904e-02 -3.12628984e-01 -9.33016241e-02
-7.94898391e-01 -7.85190582e-01 2.73287833e-01 3.29728186e-01
-5.76312900e-01 6.80858791e-01 4.87495326e-02 -1.71035096e-01
7.57002644e-03 -7.97908485e-01 -1.08759344e+00 -1.12831569e+00
1.25544921e-01 4.91856158e-01 7.13091791e-01 2.07697034... | [9.260706901550293, 0.9267863631248474] |
e32478d2-8d7c-4c8d-a642-d93140d01dd2 | single-image-deraining-a-comprehensive | 1903.08558 | null | http://arxiv.org/abs/1903.08558v1 | http://arxiv.org/pdf/1903.08558v1.pdf | Single Image Deraining: A Comprehensive Benchmark Analysis | We present a comprehensive study and evaluation of existing single image
deraining algorithms, using a new large-scale benchmark consisting of both
synthetic and real-world rainy images.This dataset highlights diverse data
sources and image contents, and is divided into three subsets (rain streak,
rain drop, rain and m... | ['Roberto Cesar-Junior', 'Siyuan Li', 'Xiaojie Guo', 'Iago Breno Araujo', 'Zhangyang Wang', 'Wenqi Ren', 'Xiaochun Cao', 'Roberto Hirata Junior', 'Eric K. Tokuda', 'Jiawan Zhang'] | 2019-03-20 | single-image-deraining-a-comprehensive-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Li_Single_Image_Deraining_A_Comprehensive_Benchmark_Analysis_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Single_Image_Deraining_A_Comprehensive_Benchmark_Analysis_CVPR_2019_paper.pdf | cvpr-2019-6 | ['single-image-deraining'] | ['computer-vision'] | [ 2.47689620e-01 -4.87196267e-01 3.04509401e-01 -6.49823666e-01
-7.79399216e-01 -3.37329298e-01 5.19999385e-01 -3.49390775e-01
-2.80139297e-01 9.94590938e-01 5.56769744e-02 -2.15920553e-01
1.67397514e-01 -5.08817434e-01 -4.77453023e-01 -1.26554060e+00
-3.92528653e-01 2.45463490e-01 1.60255730e-01 -5.43315113... | [10.94969367980957, -3.2746517658233643] |
3cf8e78f-88b9-41c5-a750-800d64c5cdbd | exploring-the-versatility-of-zero-shot-clip | 2306.01111 | null | https://arxiv.org/abs/2306.01111v1 | https://arxiv.org/pdf/2306.01111v1.pdf | Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification | Interstitial lung diseases (ILD) present diagnostic challenges due to their varied manifestations and overlapping imaging features. To address this, we propose a machine learning approach that utilizes CLIP, a multimodal (image and text) self-supervised model, for ILD classification. We extensively integrate zero-shot ... | ['Curtis Langlotz', 'Rishi Raj', 'Neha Simha', 'Haiwei Henry Guo', 'Malgorzata Polacin', 'Maayane Attias', 'Christian Bluethgen', 'Cara Van Uden'] | 2023-06-01 | null | null | null | null | ['lung-disease-classification'] | ['medical'] | [ 7.27906168e-01 7.89845660e-02 -4.26436931e-01 -4.87762064e-01
-1.48330557e+00 -6.35179341e-01 4.34161395e-01 5.18687069e-01
-2.97635287e-01 6.59172535e-01 3.53389114e-01 -2.46663779e-01
-3.91271502e-01 -2.50220805e-01 -5.70318103e-01 -7.78045297e-01
-5.11043146e-02 8.18874717e-01 1.36130467e-01 5.50598681... | [15.093027114868164, -2.0241730213165283] |
ac4e00d7-ffe4-4200-896c-c2602a0a5558 | cascading-and-direct-approaches-to | 2303.08809 | null | https://arxiv.org/abs/2303.08809v2 | https://arxiv.org/pdf/2303.08809v2.pdf | Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken Sentences | Past work on unsupervised parsing is constrained to written form. In this paper, we present the first study on unsupervised spoken constituency parsing given unlabeled spoken sentences and unpaired textual data. The goal is to determine the spoken sentences' hierarchical syntactic structure in the form of constituency ... | ['Hung-Yi Lee', 'Cheng-I Lai', 'Yuan Tseng'] | 2023-03-15 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 6.98758245e-01 7.61666358e-01 -1.43164784e-01 -1.13647413e+00
-1.34183621e+00 -8.59797359e-01 1.98243141e-01 3.90175998e-01
-4.30936486e-01 6.24362469e-01 7.77245224e-01 -8.53375435e-01
5.50554276e-01 -6.58564150e-01 -5.90978920e-01 -3.90647262e-01
-7.91509002e-02 5.15141845e-01 6.69199973e-02 -1.06308252... | [10.423152923583984, 9.637972831726074] |
d11cd9ac-0a85-4a36-9850-40e692317bc1 | segmentation-guided-deep-hdr-deghosting | 2207.01229 | null | https://arxiv.org/abs/2207.01229v1 | https://arxiv.org/pdf/2207.01229v1.pdf | Segmentation Guided Deep HDR Deghosting | We present a motion segmentation guided convolutional neural network (CNN) approach for high dynamic range (HDR) image deghosting. First, we segment the moving regions in the input sequence using a CNN. Then, we merge static and moving regions separately with different fusion networks and combine fused features to gene... | ['R. Venkatesh Babu', 'Susmit Agrawal', 'K. Ram Prabhakar'] | 2022-07-04 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 4.08959925e-01 -2.87860483e-01 -1.03312314e-01 -2.31286302e-01
-6.35909736e-01 -3.74453008e-01 4.21022296e-01 -4.05479461e-01
-3.84725034e-01 6.43913329e-01 2.48225778e-01 -1.03438303e-01
3.29623699e-01 -8.70798767e-01 -8.30848396e-01 -7.75630534e-01
-3.23516279e-02 -3.49477753e-02 7.71522820e-01 -4.02448356... | [10.924922943115234, -2.2066643238067627] |
48980dc2-d80a-4445-9e67-0c9d32c9536d | cross-domain-toxic-spans-detection | 2306.09642 | null | https://arxiv.org/abs/2306.09642v1 | https://arxiv.org/pdf/2306.09642v1.pdf | Cross-Domain Toxic Spans Detection | Given the dynamic nature of toxic language use, automated methods for detecting toxic spans are likely to encounter distributional shift. To explore this phenomenon, we evaluate three approaches for detecting toxic spans under cross-domain conditions: lexicon-based, rationale extraction, and fine-tuned language models.... | ['Ilia Markov', 'Piek Vossen', 'Wondimagegnhue Tufa', 'Baran Barbarestani', 'Stefan F. Schouten'] | 2023-06-16 | null | null | null | null | ['toxic-spans-detection'] | ['natural-language-processing'] | [-1.65654421e-01 -1.76292196e-01 -4.23141837e-01 -5.93236014e-02
-1.15395010e+00 -1.00077355e+00 6.08450234e-01 6.38518810e-01
-4.84416306e-01 9.41668451e-01 6.39954984e-01 -5.85048974e-01
-1.37558237e-01 -7.63767898e-01 -3.32870632e-01 -1.90592200e-01
2.52217263e-01 3.78639311e-01 4.85068420e-04 -2.90251702... | [8.953405380249023, 10.414466857910156] |
ad55633a-34d5-4660-9112-720b43d85c7a | dialoguegcn-a-graph-convolutional-neural | 1908.11540 | null | https://arxiv.org/abs/1908.11540v1 | https://arxiv.org/pdf/1908.11540v1.pdf | DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation | Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based a... | ['Alexander Gelbukh', 'Niyati Chhaya', 'Soujanya Poria', 'Navonil Majumder', 'Deepanway Ghosal'] | 2019-08-30 | dialoguegcn-a-graph-convolutional-neural-1 | https://aclanthology.org/D19-1015 | https://aclanthology.org/D19-1015.pdf | ijcnlp-2019-11 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 6.45023510e-02 2.38313228e-01 6.78651109e-02 -4.86129880e-01
-2.82097310e-01 -4.60278004e-01 4.16418910e-01 3.17631125e-01
-3.30803275e-01 6.19342506e-01 7.41806686e-01 -3.20539474e-01
3.71359169e-01 -3.92617047e-01 7.51813278e-02 -2.54320592e-01
-2.00106770e-01 8.92933682e-02 -3.32458884e-01 -5.82118809... | [12.94887924194336, 6.240513801574707] |
867647d8-737c-4c03-9281-67d85ad9de5d | exploring-contextual-relationships-for | 2207.04693 | null | https://arxiv.org/abs/2207.04693v2 | https://arxiv.org/pdf/2207.04693v2.pdf | Exploring Contextual Relationships for Cervical Abnormal Cell Detection | Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, ... | ['Jianxin Wang', 'Jianfeng Liu', 'Yun Du', 'Liyan Liao', 'Hulin Kuang', 'Qing Liu', 'Shuo Feng', 'Yixiong Liang'] | 2022-07-11 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.89353004e-01 -8.21990445e-02 -1.92364991e-01 -8.18828046e-02
-1.03608155e+00 -4.07993793e-01 5.74319124e-01 4.61356640e-01
-2.73769170e-01 5.81690371e-01 1.08352430e-01 -4.53417122e-01
3.90122116e-01 -8.45815122e-01 -6.39456332e-01 -1.12164187e+00
2.40662754e-01 7.10135102e-02 2.97531337e-01 4.98564616... | [15.036355018615723, -3.0635459423065186] |
db2d458f-ba7c-44e3-b955-e78b2ae19118 | pesto-a-post-user-fusion-network-for-rumour | null | null | https://openreview.net/forum?id=vVNYde75l4m | https://openreview.net/pdf?id=vVNYde75l4m | PESTO: A Post-User Fusion Network for Rumour Detection on Social Media | Rumour detection on social media is an important topic due to the challenges of misinformation propagation and slow verification of misleading information.
Most previous work focus on the response posts on social media, ignoring the useful characteristics of involved users and their relations.
In this paper, we propose... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['rumour-detection'] | ['natural-language-processing'] | [-1.69949085e-01 2.88818516e-02 -2.10430712e-01 -2.29210794e-01
1.63582806e-02 -1.32068828e-01 1.06627476e+00 3.53506207e-01
9.29546282e-02 4.21631992e-01 7.44399965e-01 -2.58714259e-01
-5.70956804e-02 -9.78250504e-01 -3.43404979e-01 -1.14630073e-01
-4.57740426e-01 2.84747869e-01 5.69341838e-01 -9.52497005... | [8.177431106567383, 10.152320861816406] |
9764903d-d49d-40a8-8c09-89ec319540ad | multi-grained-vision-language-pre-training | 2111.08276 | null | https://arxiv.org/abs/2111.08276v3 | https://arxiv.org/pdf/2111.08276v3.pdf | Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts | Most existing methods in vision language pre-training rely on object-centric features extracted through object detection and make fine-grained alignments between the extracted features and texts. It is challenging for these methods to learn relations among multiple objects. To this end, we propose a new method called X... | ['Hang Li', 'Xinsong Zhang', 'Yan Zeng'] | 2021-11-16 | null | null | null | null | ['referring-expression-segmentation', 'open-vocabulary-attribute-detection'] | ['computer-vision', 'computer-vision'] | [ 2.25289688e-02 -2.64378011e-01 -2.54738480e-01 -5.66756189e-01
-9.72651660e-01 -6.88639164e-01 1.10791910e+00 9.38619748e-02
-7.63048530e-01 1.00790307e-01 3.24968725e-01 -1.80186540e-01
1.65957183e-01 -3.27737689e-01 -9.19082344e-01 -4.67264056e-01
4.95716125e-01 6.56595886e-01 4.11988765e-01 1.41566411... | [10.541574478149414, 1.534245491027832] |
252a1c58-ba55-4151-b2da-d787e3714094 | implicit-subspace-prior-learning-for-dual | 2010.05508 | null | https://arxiv.org/abs/2010.05508v1 | https://arxiv.org/pdf/2010.05508v1.pdf | Implicit Subspace Prior Learning for Dual-Blind Face Restoration | Face restoration is an inherently ill-posed problem, where additional prior constraints are typically considered crucial for mitigating such pathology. However, real-world image prior are often hard to simulate with precise mathematical models, which inevitably limits the performance and generalization ability of exist... | ['Wen Gao', 'Siwei Ma', 'Peiran Ren', 'Shanshe Wang', 'Zhanning Gao', 'Pan Wang', 'Lingbo Yang'] | 2020-10-12 | null | null | null | null | ['blind-face-restoration'] | ['computer-vision'] | [ 6.03931129e-01 -4.57087696e-01 4.70035709e-02 -2.59586424e-01
-7.83444345e-01 -3.65203857e-01 5.06871343e-01 -5.96896231e-01
-4.19580601e-02 5.10365069e-01 5.98017216e-01 -3.14023614e-01
-2.36470684e-01 -2.83154309e-01 -8.96354377e-01 -9.82936502e-01
3.03867310e-01 -1.74306199e-01 -4.00548875e-01 -3.44540834... | [12.793700218200684, -0.21835441887378693] |
ae1fbf17-66e4-4492-8d87-e7e7b4b498d6 | propall-probabilistic-partial-label-learning | 2208.09931 | null | https://arxiv.org/abs/2208.09931v1 | https://arxiv.org/pdf/2208.09931v1.pdf | ProPaLL: Probabilistic Partial Label Learning | Partial label learning is a type of weakly supervised learning, where each training instance corresponds to a set of candidate labels, among which only one is true. In this paper, we introduce ProPaLL, a novel probabilistic approach to this problem, which has at least three advantages compared to the existing approache... | ['Bartosz Zieliński', 'Jacek Tabor', 'Łukasz Struski'] | 2022-08-21 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 8.74199048e-02 2.17503011e-01 -6.60628736e-01 -7.72171021e-01
-8.44139040e-01 -4.80950624e-01 7.44602323e-01 2.45779410e-01
-3.86393905e-01 9.95524943e-01 -8.62066522e-02 -1.80778503e-01
-2.27085829e-01 -6.95198119e-01 -5.87359488e-01 -8.76001239e-01
7.18674213e-02 8.57842207e-01 4.81254816e-01 3.64958823... | [9.499072074890137, 3.9843647480010986] |
c8f07375-129f-4a7b-9d81-d2540accc2e1 | classifying-temporal-relations-by | null | null | https://aclanthology.org/P17-2001 | https://aclanthology.org/P17-2001.pdf | Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths | Temporal relation classification is becoming an active research field. Lots of methods have been proposed, while most of them focus on extracting features from external resources. Less attention has been paid to a significant advance in a closely related task: relation extraction. In this work, we borrow a state-of-the... | ['Yusuke Miyao', 'Fei Cheng'] | 2017-07-01 | null | null | null | acl-2017-7 | ['temporal-relation-classification'] | ['natural-language-processing'] | [-7.87196755e-02 5.62570930e-01 -6.07780695e-01 -5.69088101e-01
-6.93788469e-01 -3.76615226e-01 7.29083002e-01 6.84202254e-01
-6.70457125e-01 1.08823252e+00 3.98273200e-01 -4.17888045e-01
5.93091361e-02 -1.06561053e+00 -6.54462576e-01 -3.18750203e-01
-3.49780977e-01 4.76011932e-01 6.15319490e-01 -4.56791639... | [9.329765319824219, 8.863997459411621] |
1739ff6a-4940-4d81-b1ad-fd5a037ebd75 | why-are-nlp-models-fumbling-at-elementary-1 | 2205.15683 | null | https://arxiv.org/abs/2205.15683v1 | https://arxiv.org/pdf/2205.15683v1.pdf | Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers | From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP). It is a challenging and unique task that demands blending surface level text pattern recognition with mathematical reasoning. In spite of extensive research, we... | ['Savitha Sam Abraham', 'Deepak P', 'Marco Fisichella', 'Sairam Gurajada', 'Sowmya S Sundaram'] | 2022-05-31 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 2.26318136e-01 -3.47561985e-02 -4.12216969e-02 -3.44196826e-01
-4.43239063e-01 -6.47831321e-01 7.30624914e-01 6.12365603e-01
-6.06032789e-01 4.28216249e-01 2.29723111e-01 -6.25492096e-01
-5.73370159e-01 -8.85929465e-01 -2.96275228e-01 -3.12110275e-01
1.77561760e-01 3.72726381e-01 -2.01578394e-01 -3.08270425... | [9.393394470214844, 7.259620666503906] |
a1739bf4-ad06-47ea-a3fc-3ac4f570e527 | bdcn-semantic-embedding-self-explanatory | null | null | https://aclanthology.org/2021.ccl-1.105 | https://aclanthology.org/2021.ccl-1.105.pdf | BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network | “Building an interpretable AI diagnosis system for breast cancer is an important embodiment ofAI assisted medicine. Traditional breast cancer diagnosis methods based on machine learning areeasy to explain but the accuracy is very low. Deep neural network greatly improves the accuracy of diagnosis but the black box mode... | ['He Jianrong', 'Zhong Keting', 'Chen Dehua'] | null | null | null | null | ccl-2021-8 | ['text-categorization'] | ['natural-language-processing'] | [-3.05714637e-01 6.60003841e-01 -5.49653828e-01 -4.49457586e-01
-7.98454285e-02 -2.56705761e-01 3.43846291e-01 6.75513968e-02
1.59029700e-02 4.29377407e-01 3.77834588e-01 -5.44941604e-01
-4.30390656e-01 -9.32917655e-01 -4.91987526e-01 -8.83463085e-01
2.05437362e-01 6.86302304e-01 -2.12649807e-01 -1.12425998... | [15.26753044128418, -2.7174618244171143] |
54da8cfe-16c9-4f25-81c5-6617986297d4 | onionnet-sharing-features-in-cascaded-deep | 1608.02728 | null | http://arxiv.org/abs/1608.02728v1 | http://arxiv.org/pdf/1608.02728v1.pdf | OnionNet: Sharing Features in Cascaded Deep Classifiers | The focus of our work is speeding up evaluation of deep neural networks in
retrieval scenarios, where conventional architectures may spend too much time
on negative examples. We propose to replace a monolithic network with our novel
cascade of feature-sharing deep classifiers, called OnionNet, where subsequent
stages m... | ['Nikos Komodakis', 'Martin Simonovsky'] | 2016-08-09 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [-1.45687442e-02 -1.98445797e-01 1.71642173e-02 -5.00224590e-01
-6.47516727e-01 -6.80067122e-01 5.59827626e-01 2.20986292e-01
-6.41305685e-01 5.28588295e-01 -4.43472236e-01 -2.48400480e-01
1.28578067e-01 -8.66952837e-01 -9.84763682e-01 -3.36212754e-01
-1.17159911e-01 3.10389757e-01 2.36568972e-01 -2.89489955... | [9.353074073791504, 2.0582103729248047] |
58c00135-85da-45e0-955b-5a01751bdf0f | inter-case-predictive-process-monitoring-a | 2307.00080 | null | https://arxiv.org/abs/2307.00080v1 | https://arxiv.org/pdf/2307.00080v1.pdf | Inter-case Predictive Process Monitoring: A candidate for Quantum Machine Learning? | Regardless of the domain, forecasting the future behaviour of a running process instance is a question of interest for decision makers, especially when multiple instances interact. Fostered by the recent advances in machine learning research, several methods have been proposed to predict the next activity, outcome or r... | ['Carl Corea', 'Patrick Delfmann', 'David Fitzek', 'Stefan Hill'] | 2023-06-30 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 3.68644685e-01 4.48781811e-02 1.47433892e-01 -2.99764991e-01
-8.06311488e-01 -2.96945751e-01 9.37816978e-01 6.60194874e-01
-2.89699405e-01 5.01967371e-01 -3.43014091e-01 -2.94843495e-01
-4.01214451e-01 -9.36527610e-01 -4.02993470e-01 -7.77084351e-01
-1.31180882e-01 8.75667989e-01 2.48074472e-01 -2.76252106... | [8.568169593811035, 5.909644603729248] |
6f1f9570-7df5-40e4-8dee-df5fc5780073 | material-recognition-for-automated-progress | 2006.16344 | null | https://arxiv.org/abs/2006.16344v2 | https://arxiv.org/pdf/2006.16344v2.pdf | Material Recognition for Automated Progress Monitoring using Deep Learning Methods | Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation. Construction monitoring has not been an exception; as a part of construction monitoring systems, material classification and recognition have drawn the a... | ['Mohammad Tayarani Darbandy', 'Navid Ghassemi', 'Hadi Mahami', 'Roohallah Alizadehsani', 'Saeid Nahavandi', 'Darius Nahavandi', 'Afshin Shoeibi', 'Sadiq Hussain', 'Farnad Nasirzadeh', 'Abbas Khosravi'] | 2020-06-29 | null | null | null | null | ['material-classification', 'material-recognition'] | ['computer-vision', 'computer-vision'] | [ 6.99836165e-02 -2.81442970e-01 1.36582971e-01 -3.61767203e-01
-4.48846817e-01 -5.51174916e-02 2.31527224e-01 -2.17889939e-02
-6.11703508e-02 7.31772900e-01 -3.19146067e-01 4.45787348e-02
-2.18783692e-01 -1.13346100e+00 -5.75424075e-01 -1.01725793e+00
-6.27365243e-03 4.04178292e-01 8.42690468e-02 -2.34547377... | [7.4279584884643555, 1.7866448163986206] |
e8519cd9-6054-4c19-97eb-b50891ce3f9b | ds-net-dynamic-spatiotemporal-network-for | 2012.04886 | null | https://arxiv.org/abs/2012.04886v3 | https://arxiv.org/pdf/2012.04886v3.pdf | DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection | As moving objects always draw more attention of human eyes, the temporal motive information is always exploited complementarily with spatial information to detect salient objects in videos. Although efficient tools such as optical flow have been proposed to extract temporal motive information, it often encounters diffi... | ['Jiaxiang Wang', 'Jing Liu', 'Weikang Wang', 'Yuting Su'] | 2020-12-09 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 1.03639029e-01 -2.75109798e-01 -3.00063133e-01 -2.11690307e-01
-3.54870856e-01 -3.59301902e-02 4.45506990e-01 -1.55434802e-01
-4.18790519e-01 6.27883971e-01 4.71767783e-01 6.27604946e-02
-2.11229920e-02 -4.55131501e-01 -5.81573725e-01 -6.62734210e-01
-8.34438577e-02 -4.24629062e-01 9.87534523e-01 -3.52888495... | [9.651511192321777, -0.36167773604393005] |
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