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
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2a91a47c-6e51-4149-ae02-0ad8f4b2fc3f | memory-maps-for-video-object-detection-and | 2303.03508 | null | https://arxiv.org/abs/2303.03508v1 | https://arxiv.org/pdf/2303.03508v1.pdf | Memory Maps for Video Object Detection and Tracking on UAVs | This paper introduces a novel approach to video object detection detection and tracking on Unmanned Aerial Vehicles (UAVs). By incorporating metadata, the proposed approach creates a memory map of object locations in actual world coordinates, providing a more robust and interpretable representation of object locations ... | ['Andreas Zell', 'Yitong Quan', 'Benjamin Kiefer'] | 2023-03-06 | null | null | null | null | ['video-anomaly-detection', 'video-object-detection'] | ['computer-vision', 'computer-vision'] | [ 1.14436649e-01 -7.54405439e-01 4.33139317e-02 -2.57614106e-02
1.24836743e-01 -9.60537791e-01 7.28857398e-01 2.60696024e-01
-6.08664453e-01 5.75126886e-01 -3.13594699e-01 -1.44119158e-01
-2.72857130e-01 -6.49583995e-01 -6.38978660e-01 -5.80069125e-01
-5.35309374e-01 -1.60114929e-01 8.59310687e-01 2.18334690... | [6.951349258422852, -1.8705164194107056] |
86bc35c5-2624-4db2-b649-926f2dff6481 | inspired-toward-sociable-recommendation | 2009.14306 | null | https://arxiv.org/abs/2009.14306v2 | https://arxiv.org/pdf/2009.14306v2.pdf | INSPIRED: Toward Sociable Recommendation Dialog Systems | In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner. However, this is a challenge when developing a sociable recommendation dialog system, due to the lack of dialog dataset annotated with such sociable strategies. Therefore, we present INSPIRED, a new datas... | ['Dongyeop Kang', 'Weiyan Shi', 'Shirley Anugrah Hayati', 'Zhou Yu', 'Qingxiaoyang Zhu'] | 2020-09-29 | null | https://aclanthology.org/2020.emnlp-main.654 | https://aclanthology.org/2020.emnlp-main.654.pdf | emnlp-2020-11 | ['movie-recommendation'] | ['miscellaneous'] | [-4.74442482e-01 5.38675070e-01 -4.86379653e-01 -9.05447245e-01
2.02474028e-01 -7.87204444e-01 7.80642152e-01 5.12285829e-02
-4.22607183e-01 8.39853048e-01 1.06730914e+00 8.22102949e-02
-7.61265680e-02 -5.68067670e-01 6.23734072e-02 -3.70997041e-02
4.53846812e-01 6.78138673e-01 2.42070854e-01 -7.97349811... | [12.460420608520508, 7.647930145263672] |
ecff6006-d6ec-4e26-8927-01709fce9d5f | tsa-net-tube-self-attention-network-for | 2201.03746 | null | https://arxiv.org/abs/2201.03746v1 | https://arxiv.org/pdf/2201.03746v1.pdf | TSA-Net: Tube Self-Attention Network for Action Quality Assessment | In recent years, assessing action quality from videos has attracted growing attention in computer vision community and human computer interaction. Most existing approaches usually tackle this problem by directly migrating the model from action recognition tasks, which ignores the intrinsic differences within the featur... | ['Lihua Zhang', 'Chixiao Chen', 'Peng Zhai', 'Dingkang Yang', 'Shunli Wang'] | 2022-01-11 | null | null | null | null | ['action-quality-assessment', 'action-assessment'] | ['computer-vision', 'computer-vision'] | [ 3.30171943e-01 -1.75196141e-01 -2.04680085e-01 -1.83241248e-01
-7.03460157e-01 1.56213701e-01 3.57684016e-01 -3.16074491e-01
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-1.67028412e-01 -7.59858966e-01 -4.06864196e-01 -6.72876835e-01
1.79108769e-01 -1.92342907e-01 7.06604004e-01 -1.48361757... | [8.378626823425293, 0.7033786773681641] |
3a167890-7073-4cfa-b927-29ec8cbd98b6 | hierarchical-ranking-for-answer-selection | 2102.00677 | null | https://arxiv.org/abs/2102.00677v1 | https://arxiv.org/pdf/2102.00677v1.pdf | Hierarchical Ranking for Answer Selection | Answer selection is a task to choose the positive answers from a pool of candidate answers for a given question. In this paper, we propose a novel strategy for answer selection, called hierarchical ranking. We introduce three levels of ranking: point-level ranking, pair-level ranking, and list-level ranking. They formu... | ['Tiegang Gao', 'Renhong Cheng', 'Mengting Hu', 'Hang Gao'] | 2021-02-01 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-2.70231552e-02 -9.42168534e-02 -2.17018202e-01 -5.59601128e-01
-2.02467394e+00 -5.76190412e-01 6.48725569e-01 1.46385193e-01
-5.07795691e-01 8.64242971e-01 4.74292874e-01 -1.67691901e-01
-6.76922798e-01 -5.60675323e-01 -3.46186459e-01 -6.57880545e-01
2.06170484e-01 9.15376604e-01 8.41254115e-01 -5.57956398... | [11.280601501464844, 7.985755920410156] |
359f047c-adcd-4fbe-94ef-bae62d6490f1 | 1st-place-solution-for-the-uvo-challenge-on-1 | 2110.11661 | null | https://arxiv.org/abs/2110.11661v2 | https://arxiv.org/pdf/2110.11661v2.pdf | UVO Challenge on Video-based Open-World Segmentation 2021: 1st Place Solution | In this report, we introduce our (pretty straightforard) two-step "detect-then-match" video instance segmentation method. The first step performs instance segmentation for each frame to get a large number of instance mask proposals. The second step is to do inter-frame instance mask matching with the help of optical fl... | ['Vincent Lepetit', 'Yang Xiao', 'Wen Guo', 'Yuming Du'] | 2021-10-22 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.53279826e-01 1.01447217e-02 -4.19421196e-01 -3.80011737e-01
-8.91808391e-01 -7.58689940e-01 4.00287569e-01 -7.44245574e-02
-5.13934791e-01 5.24695635e-01 -9.95729789e-02 -2.03252614e-01
3.54812354e-01 -4.87680405e-01 -6.26105011e-01 -1.88100651e-01
-9.47614536e-02 4.09734875e-01 1.03475606e+00 -1.27392530... | [9.134201049804688, -0.20467641949653625] |
339e8f0c-bb0e-43d5-9a65-690db2a6df39 | open-relation-and-event-type-discovery-with | 2212.00178 | null | https://arxiv.org/abs/2212.00178v1 | https://arxiv.org/pdf/2212.00178v1.pdf | Open Relation and Event Type Discovery with Type Abstraction | Conventional closed-world information extraction (IE) approaches rely on human ontologies to define the scope for extraction. As a result, such approaches fall short when applied to new domains. This calls for systems that can automatically infer new types from given corpora, a task which we refer to as type discovery.... | ['Jiawei Han', 'Heng Ji', 'Sha Li'] | 2022-11-30 | null | null | null | null | ['event-extraction', 'type'] | ['natural-language-processing', 'speech'] | [ 5.61596155e-02 4.95693892e-01 -4.70251948e-01 -4.74468708e-01
-4.77248311e-01 -8.23339045e-01 9.38277960e-01 4.91717517e-01
-2.74748504e-01 1.02338290e+00 2.58816928e-01 -4.19200957e-01
-2.64549395e-03 -1.13918948e+00 -7.87824571e-01 -3.46626967e-01
5.51775619e-02 4.75406379e-01 2.73074538e-01 1.77703835... | [9.302626609802246, 8.717145919799805] |
0429a52e-c376-452e-9a7e-894e2025ab55 | variational-autoencoders-with-implicit-priors | null | null | https://openreview.net/forum?id=ryeHw1vjiQ | https://openreview.net/pdf?id=ryeHw1vjiQ | Variational Autoencoders with implicit priors for short-duration text-independent speaker verification | In this work, we exploited different strategies to provide prior knowledge to commonly used generative modeling approaches aiming to obtain speaker-dependent low dimensional representations from short-duration segments of speech data, making use of available information of speaker identities. Namely, convolutional vari... | ['Anonymous'] | 2018-10-22 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 9.63614136e-02 2.25784644e-01 6.85783327e-02 -7.24994838e-01
-1.08857155e+00 -6.05980396e-01 9.41475213e-01 -2.78128535e-01
-5.46534359e-01 5.27544320e-01 5.76058984e-01 -7.39012063e-02
1.53555155e-01 -3.74174327e-01 -9.14902210e-01 -8.91097188e-01
4.22737390e-01 4.52274263e-01 -4.86464024e-01 2.37426236... | [14.901813507080078, 6.495072364807129] |
9b0924ed-87c8-4e2d-ae34-7f47778cf6d4 | learning-chess-blindfolded-evaluating | 2102.13249 | null | https://arxiv.org/abs/2102.13249v2 | https://arxiv.org/pdf/2102.13249v2.pdf | Chess as a Testbed for Language Model State Tracking | Transformer language models have made tremendous strides in natural language understanding tasks. However, the complexity of natural language makes it challenging to ascertain how accurately these models are tracking the world state underlying the text. Motivated by this issue, we consider the task of language modeling... | ['Kevin Gimpel', 'Karen Livescu', 'Sam Wiseman', 'Shubham Toshniwal'] | 2021-02-26 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [ 1.07733242e-01 1.32988706e-01 -4.86743957e-01 -9.45012420e-02
-6.84014857e-01 -9.76880014e-01 8.09676588e-01 3.75462532e-01
-4.14268285e-01 6.18447185e-01 1.38653979e-01 -9.58677828e-01
1.52574569e-01 -9.46260095e-01 -7.79891074e-01 -4.26502377e-02
-9.85730886e-02 6.25861228e-01 6.96419656e-01 -5.27182102... | [9.088247299194336, 7.353518009185791] |
4fd644a0-974e-4f89-b8ac-b2d8824da723 | conversational-intent-understanding-for | 1901.04899 | null | http://arxiv.org/abs/1901.04899v1 | http://arxiv.org/pdf/1901.04899v1.pdf | Conversational Intent Understanding for Passengers in Autonomous Vehicles | Understanding passenger intents and extracting relevant slots are important
building blocks towards developing a contextual dialogue system responsible for
handling certain vehicle-passenger interactions in autonomous vehicles (AV).
When the passengers give instructions to AMIE (Automated-vehicle Multimodal
In-cabin Ex... | ['Nachman Lama', 'Esme Asli Arslan', 'Sahay Saurav', 'Kumar Shachi H', 'Okur Eda'] | 2018-12-14 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 2.34599262e-01 6.74289227e-01 -1.97285652e-01 -9.47575808e-01
-9.88037586e-01 -5.70023179e-01 9.09263074e-01 -6.21120222e-02
-4.03213978e-01 6.77030444e-01 5.36283374e-01 -8.77165675e-01
1.98822975e-01 -5.14424205e-01 -2.97692657e-01 -3.09159338e-01
1.67564258e-01 7.91897058e-01 2.31539294e-01 -1.01082301... | [12.694375038146973, 7.722817420959473] |
3aa3dd23-a5e9-496a-aa45-98b4a2ed483c | tieval-an-evaluation-framework-for-temporal | 2301.04643 | null | https://arxiv.org/abs/2301.04643v2 | https://arxiv.org/pdf/2301.04643v2.pdf | tieval: An Evaluation Framework for Temporal Information Extraction Systems | Temporal information extraction (TIE) has attracted a great deal of interest over the last two decades, leading to the development of a significant number of datasets. Despite its benefits, having access to a large volume of corpora makes it difficult when it comes to benchmark TIE systems. On the one hand, different d... | ['Ricardo Campos', 'Alípio Jorge', 'Hugo Sousa'] | 2023-01-11 | null | null | null | null | ['temporal-relation-extraction', 'event-extraction', 'temporal-relation-classification', 'temporal-information-extraction', 'temporal-tagging'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-9.40225199e-02 -9.52489004e-02 -2.34884962e-01 -3.43571454e-01
-4.21404123e-01 -8.72623563e-01 8.26073527e-01 7.97132373e-01
-7.26104915e-01 6.25751257e-01 -3.40593196e-02 -3.88417095e-01
-4.74994481e-01 -6.01679265e-01 -1.85472831e-01 -4.25249040e-01
6.03542924e-02 3.58599395e-01 7.09679842e-01 -2.23349646... | [9.443304061889648, 9.234559059143066] |
2af25376-53a8-4476-9b38-f0118176c073 | ischemic-stroke-lesion-segmentation-in-ct | 1811.01085 | null | http://arxiv.org/abs/1811.01085v1 | http://arxiv.org/pdf/1811.01085v1.pdf | Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss | We present a fully convolutional neural network for segmenting ischemic
stroke lesions in CT perfusion images for the ISLES 2018 challenge. Treatment
of stroke is time sensitive and current standards for lesion identification
require manual segmentation, a time consuming and challenging process.
Automatic segmentation ... | ['S. Mazdak Abulnaga', 'Jonathan Rubin'] | 2018-11-02 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [ 2.52946645e-01 -1.01065002e-02 -3.82884920e-01 -3.92808676e-01
-1.23386753e+00 -7.61901855e-01 2.50024498e-01 2.06554666e-01
-8.35335314e-01 5.76963544e-01 4.87806231e-01 -5.04209340e-01
3.11852656e-02 -5.29878199e-01 -5.51966906e-01 -5.28595328e-01
-3.19235444e-01 7.43910551e-01 5.88009596e-01 8.19927976... | [14.284843444824219, -2.1074516773223877] |
9cc43b46-76eb-4810-aa16-fbe0b7faa944 | reducing-bias-and-increasing-utility-by | 2101.07235 | null | https://arxiv.org/abs/2101.07235v2 | https://arxiv.org/pdf/2101.07235v2.pdf | Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary | We introduce FELICIA (FEderated LearnIng with a CentralIzed Adversary) a generative mechanism enabling collaborative learning. In particular, we show how a data owner with limited and biased data could benefit from other data owners while keeping data from all the sources private. This is a common scenario in medical i... | ['Raymond T Ng', 'Juan Lavista Ferres', 'Christopher West', 'Anthony Ortiz', 'Caleb Robinson', 'Sumit Mukherjee', 'Jean-Francois Rajotte'] | 2021-01-18 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 3.56416970e-01 7.56260216e-01 -7.75754601e-02 -2.41289452e-01
-1.12460649e+00 -7.70971239e-01 5.45238495e-01 -7.42161274e-02
-5.38402557e-01 1.07673335e+00 1.75997857e-02 -2.40507036e-01
-4.02252143e-03 -1.12449384e+00 -8.88093114e-01 -1.10865462e+00
-1.84934527e-01 4.12025273e-01 -4.18435007e-01 4.12745923... | [6.0956573486328125, 6.848149299621582] |
13a0850a-618c-472e-a166-8d2be8513547 | model-patching-closing-the-subgroup | 2008.06775 | null | https://arxiv.org/abs/2008.06775v1 | https://arxiv.org/pdf/2008.06775v1.pdf | Model Patching: Closing the Subgroup Performance Gap with Data Augmentation | Classifiers in machine learning are often brittle when deployed. Particularly concerning are models with inconsistent performance on specific subgroups of a class, e.g., exhibiting disparities in skin cancer classification in the presence or absence of a spurious bandage. To mitigate these performance differences, we i... | ['Christopher Ré', 'Albert Gu', 'Yixuan Li', 'Karan Goel'] | 2020-08-15 | null | https://openreview.net/forum?id=9YlaeLfuhJF | https://openreview.net/pdf?id=9YlaeLfuhJF | iclr-2021-1 | ['skin-cancer-classification'] | ['medical'] | [ 1.02967036e+00 6.66731656e-01 -6.63131535e-01 -4.47934806e-01
-1.05871773e+00 -6.17445469e-01 8.60920846e-01 2.56438851e-01
-1.55452579e-01 5.49233854e-01 3.42284620e-01 -3.04267406e-01
-1.04223341e-01 -6.18891120e-01 -9.51489806e-01 -9.75564182e-01
1.37817189e-01 3.54650728e-02 2.64311880e-01 -7.59766772... | [15.131891250610352, -2.5793685913085938] |
1efe5d87-ed96-42ba-8820-0931b6f95573 | improved-pronunciation-prediction-accuracy | null | null | https://aclanthology.org/2021.sigmorphon-1.24 | https://aclanthology.org/2021.sigmorphon-1.24.pdf | Improved pronunciation prediction accuracy using morphology | Pronunciation lexicons and prediction models are a key component in several speech synthesis and recognition systems. We know that morphologically related words typically follow a fixed pattern of pronunciation which can be described by language-specific paradigms. In this work we explore how deep recurrent neural netw... | ['Antoine Bruguier', 'Neha Chaudhari', 'Saumya Sahai', 'Dravyansh Sharma'] | null | null | null | null | acl-sigmorphon-2021-8 | ['morphological-inflection'] | ['natural-language-processing'] | [ 2.96527654e-01 -1.10231541e-01 -2.84879118e-01 -2.24389464e-01
-6.77257299e-01 -9.12258387e-01 4.67293203e-01 2.57656068e-01
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7.73282303e-03 -7.52540767e-01 -5.92932522e-01 -3.90180349e-01
2.20246702e-01 5.57282686e-01 -1.94806293e-01 -4.60526049... | [10.804110527038574, 9.781210899353027] |
ba3ca476-3bad-4268-b88b-cda63798746a | social-learning-under-platform-influence | 2202.12453 | null | https://arxiv.org/abs/2202.12453v1 | https://arxiv.org/pdf/2202.12453v1.pdf | Social Learning under Platform Influence: Consensus and Persistent Disagreement | Individuals increasingly rely on social networking platforms to form opinions. However, these platforms typically aim to maximize engagement, which may not align with social good. In this paper, we introduce an opinion dynamics model where agents are connected in a social network, and update their opinions based on the... | ['Jerry Anunrojwong', 'Bar Light', 'Nicole Immorlica', 'Ozan Candogan'] | 2022-02-25 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [-4.67368424e-01 5.95266283e-01 -3.32166195e-01 2.09958404e-01
7.61432424e-02 -1.04133821e+00 5.09615183e-01 1.97321489e-01
-1.13134779e-01 1.12849355e+00 1.64511740e-01 -2.85470515e-01
-6.86100051e-02 -1.05487573e+00 -4.82050776e-01 -9.74545300e-01
9.39328596e-02 5.23706675e-01 2.45759021e-02 -6.75286114... | [6.865180015563965, 5.330889701843262] |
5822555f-35f3-4206-8622-685bf5a50ed4 | a-multi-cascaded-deep-model-for-bilingual-sms | 1911.13066 | null | https://arxiv.org/abs/1911.13066v1 | https://arxiv.org/pdf/1911.13066v1.pdf | A Multi-cascaded Deep Model for Bilingual SMS Classification | Most studies on text classification are focused on the English language. However, short texts such as SMS are influenced by regional languages. This makes the automatic text classification task challenging due to the multilingual, informal, and noisy nature of language in the text. In this work, we propose a novel mult... | ['Asim Karim', 'Muhammad Haroon Shakeel', 'Imdadullah Khan'] | 2019-11-29 | null | null | null | null | ['multilingual-text-classification', 'lexical-normalization'] | ['miscellaneous', 'natural-language-processing'] | [-2.00617891e-02 -3.67312849e-01 -5.11437654e-01 -6.84545040e-01
-9.01578128e-01 -5.98145068e-01 7.22329617e-01 2.86215752e-01
-8.71424139e-01 7.38094866e-01 4.15898353e-01 -8.71000290e-01
6.38366282e-01 -5.10468483e-01 -5.86941063e-01 -2.76644230e-01
6.19941235e-01 6.44299209e-01 -2.47906059e-01 -5.59831738... | [10.80510425567627, 9.891663551330566] |
12fd67e0-2cac-4309-bc5a-7c45e3cdab4d | karasinger-score-free-singing-voice-synthesis | 2110.04005 | null | https://arxiv.org/abs/2110.04005v1 | https://arxiv.org/pdf/2110.04005v1.pdf | KaraSinger: Score-Free Singing Voice Synthesis with VQ-VAE using Mel-spectrograms | In this paper, we propose a novel neural network model called KaraSinger for a less-studied singing voice synthesis (SVS) task named score-free SVS, in which the prosody and melody are spontaneously decided by machine. KaraSinger comprises a vector-quantized variational autoencoder (VQ-VAE) that compresses the Mel-spec... | ['Yi-Hsuan Yang', 'Jen-Yu Liu', 'Chien-Feng Liao'] | 2021-10-08 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 1.03426866e-01 -8.20719525e-02 2.05941331e-02 -6.63536936e-02
-9.88869429e-01 -6.41689301e-01 1.59407362e-01 -4.38241482e-01
-7.88631812e-02 3.58254552e-01 4.74429250e-01 -1.53252646e-01
-3.24058942e-02 -2.75292784e-01 -8.31267834e-01 -6.49468601e-01
4.04994609e-03 1.06745139e-01 6.38671499e-03 -2.01780662... | [15.526739120483398, 6.142667770385742] |
a44860e2-45d1-411c-980c-4c13eb42a5a0 | alzheimers-disease-diagnostics-by-a-deeply | 1607.00556 | null | http://arxiv.org/abs/1607.00556v1 | http://arxiv.org/pdf/1607.00556v1.pdf | Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network | Early diagnosis, playing an important role in preventing progress and
treating the Alzheimer's disease (AD), is based on classification of features
extracted from brain images. The features have to accurately capture main
AD-related variations of anatomical brain structures, such as, e.g., ventricles
size, hippocampus ... | ['Ayman El-Baz', "Georgy Gimel'farb", 'Ehsan Hosseini-Asl'] | 2016-07-02 | null | null | null | null | ['skull-stripping'] | ['medical'] | [-2.99342424e-01 1.76295012e-01 3.26826304e-01 -9.08150852e-01
-3.21524501e-01 -1.86870415e-02 3.44994396e-01 1.72694139e-02
-4.82464731e-01 5.98965168e-01 5.66218495e-02 -6.51577860e-02
-2.87570983e-01 -7.91439772e-01 -4.94160384e-01 -4.94485676e-01
-7.49393880e-01 9.17286456e-01 4.98292059e-01 -7.92813674... | [14.185647964477539, -1.7849012613296509] |
a84330d7-3306-4780-aa23-6cb25f4c9783 | robustness-of-sam-segment-anything-under | 2306.07713 | null | https://arxiv.org/abs/2306.07713v1 | https://arxiv.org/pdf/2306.07713v1.pdf | Robustness of SAM: Segment Anything Under Corruptions and Beyond | Segment anything model (SAM), as the name suggests, is claimed to be capable of cutting out any object. SAM is a vision foundation model which demonstrates impressive zero-shot transfer performance with the guidance of a prompt. However, there is currently a lack of comprehensive evaluation of its robustness performanc... | ['Choong Seon Hong', 'Chenshuang Zhang', 'Shehbaz Tariq', 'Donghun Kim', 'Taegoo Kang', 'Chaoning Zhang', 'Yu Qiao'] | 2023-06-13 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 4.77720261e-01 -6.52713999e-02 7.55428150e-02 -6.26839921e-02
-8.20636749e-01 -9.50613797e-01 9.22317863e-01 -5.01215994e-01
2.19782647e-02 4.57539558e-01 3.72249395e-01 -3.59969050e-01
2.23136291e-01 -6.21686041e-01 -9.28015888e-01 -5.54014564e-01
3.73932660e-01 -1.31342590e-01 2.42716298e-01 -3.19539130... | [8.168388366699219, -1.228469729423523] |
bdd21aa2-f14f-4610-8de0-a97a1c580398 | contextual-information-integration-for-stance | 2211.01874 | null | https://arxiv.org/abs/2211.01874v2 | https://arxiv.org/pdf/2211.01874v2.pdf | Contextual information integration for stance detection via cross-attention | Stance detection deals with identifying an author's stance towards a target. Most existing stance detection models are limited because they do not consider relevant contextual information which allows for inferring the stance correctly. Complementary context can be found in knowledge bases but integrating the context i... | ['Iryna Gurevych', 'Andreas Waldis', 'Tilman Beck'] | 2022-11-03 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 1.64977670e-01 3.12134880e-03 -9.58339572e-01 -1.94180578e-01
-1.04320145e+00 -1.09296131e+00 1.05831826e+00 6.16986990e-01
-5.91813862e-01 8.50314319e-01 7.00045228e-01 -2.85481632e-01
2.23009199e-01 -9.03605878e-01 -6.94055676e-01 -2.42048547e-01
3.33589882e-01 6.52616680e-01 7.08913386e-01 -5.71630239... | [9.016279220581055, 9.910894393920898] |
3b556306-673a-402e-9a51-9bae66155cad | spatial-scale-aligned-network-for-fine | 2001.01211 | null | https://arxiv.org/abs/2001.01211v1 | https://arxiv.org/pdf/2001.01211v1.pdf | Spatial-Scale Aligned Network for Fine-Grained Recognition | Existing approaches for fine-grained visual recognition focus on learning marginal region-based representations while neglecting the spatial and scale misalignments, leading to inferior performance. In this paper, we propose the spatial-scale aligned network (SSANET) and implicitly address misalignments during the reco... | ['Hai-Hua Xu', 'Yu-Wing Tai', 'Lizhao Gao', 'Junling Liu', 'Chong Sun'] | 2020-01-05 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 1.62908241e-01 -3.89913946e-01 -4.68331546e-01 -5.25403261e-01
-8.11760128e-01 -5.10407567e-01 6.12693608e-01 -2.74089783e-01
-3.95144165e-01 2.58316576e-01 3.06577981e-01 5.46525344e-02
-1.68814555e-01 -5.67867041e-01 -6.44306540e-01 -8.76576841e-01
-3.93201448e-02 9.87491980e-02 5.54320872e-01 6.69737309... | [9.659908294677734, 1.9686601161956787] |
933eae00-d4e9-47cc-aac9-7e4a04ffd90f | on-the-capability-of-neural-networks-to | 2007.08032 | null | https://arxiv.org/abs/2007.08032v3 | https://arxiv.org/pdf/2007.08032v3.pdf | When and how CNNs generalize to out-of-distribution category-viewpoint combinations | Object recognition and viewpoint estimation lie at the heart of visual understanding. Recent works suggest that convolutional neural networks (CNNs) fail to generalize to out-of-distribution (OOD) category-viewpoint combinations, ie. combinations not seen during training. In this paper, we investigate when and how such... | ['Frédo Durand', 'Spandan Madan', 'Xavier Boix', 'Tomotake Sasaki', 'Timothy Henry', 'Helen Ho', 'Nishchal Bhandari', 'Jamell Dozier', 'Hanspeter Pfister'] | 2020-07-15 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 8.20733309e-02 2.17256676e-02 -4.07031272e-03 -5.83226204e-01
1.36865139e-01 -1.00949836e+00 8.41120899e-01 -1.71019867e-01
-2.32398346e-01 1.10585958e-01 9.11381096e-02 -2.25724250e-01
-2.39654914e-01 -6.66337609e-01 -8.83396924e-01 -5.97452462e-01
-1.03997834e-01 4.07815099e-01 2.83399552e-01 -1.59556925... | [9.581777572631836, 2.077199697494507] |
f2f1faff-814a-4b1a-8bba-31f6b493a2a2 | explaining-neural-network-predictions-on | 2104.04488 | null | https://arxiv.org/abs/2104.04488v2 | https://arxiv.org/pdf/2104.04488v2.pdf | Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks | Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. However, for models with text ... | ['Yangfeng Ji', 'Sachindra Joshi', 'Chulaka Gunasekara', 'Hui Wan', 'Jatin Ganhotra', 'Song Feng', 'Hanjie Chen'] | 2021-04-09 | null | https://aclanthology.org/2021.naacl-main.306 | https://aclanthology.org/2021.naacl-main.306.pdf | naacl-2021-4 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 4.41990644e-01 1.77803695e-01 -2.33453900e-01 -7.71714091e-01
-2.69657075e-01 -3.27376783e-01 9.75195348e-01 2.04583511e-01
-8.73731673e-02 5.39677501e-01 3.91116768e-01 -5.81541896e-01
-3.21852714e-01 -4.07436341e-01 -7.69985437e-01 -3.39828968e-01
3.16972911e-01 4.13710088e-01 -2.43736342e-01 1.31944865... | [9.253952980041504, 6.286302089691162] |
e25ad364-4e47-4bd6-b15f-86bca6a1db6a | identity-preserving-realistic-talking-face | 2005.12318 | null | https://arxiv.org/abs/2005.12318v1 | https://arxiv.org/pdf/2005.12318v1.pdf | Identity-Preserving Realistic Talking Face Generation | Speech-driven facial animation is useful for a variety of applications such as telepresence, chatbots, etc. The necessary attributes of having a realistic face animation are 1) audio-visual synchronization (2) identity preservation of the target individual (3) plausible mouth movements (4) presence of natural eye blink... | ['Brojeshwar Bhowmick', 'Sandika Biswas', 'Sanjana Sinha'] | 2020-05-25 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization', 'talking-face-generation'] | ['audio', 'computer-vision', 'computer-vision'] | [-4.52459753e-02 2.22217381e-01 -3.55853094e-03 -1.75351679e-01
-5.93612731e-01 -2.32007742e-01 5.85913479e-01 -5.32284677e-01
9.06496048e-02 6.15420938e-01 4.13282454e-01 3.11690480e-01
3.17329526e-01 -2.81320333e-01 -5.50055325e-01 -8.23764503e-01
-6.73645549e-03 -6.44712001e-02 2.87235808e-02 -4.18388903... | [13.217726707458496, -0.41062334179878235] |
a6a4f16e-6fe2-469e-86cc-4f8c845ec0b6 | embedding-space-augmentation-for-weakly | 2210.17013 | null | https://arxiv.org/abs/2210.17013v1 | https://arxiv.org/pdf/2210.17013v1.pdf | Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images | Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelines for WSI-level analysis, the WSIs are often divided into patches and deep features for patches (i.e., patch embeddings) are extracted prio... | ['Faisal Mahmood', 'Nasir Rajpoot', 'Guillaume Jaume', 'Imaad Zaffar'] | 2022-10-31 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 6.01949334e-01 3.58446568e-01 -3.30846310e-01 -1.15810283e-01
-1.72118258e+00 -5.27602553e-01 4.16801900e-01 1.48985133e-01
-2.49006122e-01 7.15256810e-01 2.25685641e-01 -1.58483371e-01
3.24193180e-01 -8.73778045e-01 -1.06812334e+00 -1.12716293e+00
3.84435773e-01 3.60320926e-01 1.55941054e-01 2.67082483... | [15.076680183410645, -2.9110348224639893] |
a1d43c32-5a79-4100-a594-5677b2ef415e | pre-training-and-fine-tuning-neural-topic-1 | null | null | https://aclanthology.org/2022.acl-long.413 | https://aclanthology.org/2022.acl-long.413.pdf | Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge | Recent years have witnessed growing interests in incorporating external knowledge such as pre-trained word embeddings (PWEs) or pre-trained language models (PLMs) into neural topic modeling. However, we found that employing PWEs and PLMs for topic modeling only achieved limited performance improvements but with huge co... | ['Yunbo Cao', 'Qian-Wen Zhang', 'Deyu Zhou', 'Boyu Wang', 'Xuemeng Hu', 'Linhai Zhang'] | null | null | null | null | acl-2022-5 | ['topic-models'] | ['natural-language-processing'] | [-1.30689442e-01 3.81725281e-01 -3.36550266e-01 -3.89748454e-01
-5.39033651e-01 2.42688376e-02 9.42046463e-01 7.47555345e-02
-5.71271956e-01 6.10140800e-01 3.63297433e-01 -2.30648220e-01
3.99182774e-02 -1.22829986e+00 -5.73258758e-01 -4.95042741e-01
6.33499119e-04 6.12213016e-01 5.43287516e-01 1.48983166... | [10.46237564086914, 6.939294815063477] |
86341710-795d-477a-942c-942ec53d7fb2 | global-local-transformer-for-brain-age | 2109.01663 | null | https://arxiv.org/abs/2109.01663v1 | https://arxiv.org/pdf/2109.01663v1.pdf | Global-Local Transformer for Brain Age Estimation | Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information from the whole input image, ignoring the local fine-grained details. In this paper, we propose a global-local transformer, which consists o... | ['Yangming Ou', 'P. Ellen Grant', 'Sheng He'] | 2021-09-03 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-3.16067457e-01 1.89605784e-02 8.58342573e-02 -6.35095119e-01
-6.22046947e-01 1.08091183e-01 3.60439599e-01 3.08187723e-01
-8.33282053e-01 7.74850845e-01 3.81819934e-01 1.91504896e-01
-1.48096427e-01 -8.07717621e-01 -5.30462086e-01 -9.33139980e-01
-3.26282918e-01 2.63444066e-01 2.15589851e-01 1.93900928... | [14.093441009521484, -1.5288227796554565] |
8c6281b0-50ba-43f6-9346-155c95326959 | understanding-how-people-rate-their | 2206.00167 | null | https://arxiv.org/abs/2206.00167v1 | https://arxiv.org/pdf/2206.00167v1.pdf | Understanding How People Rate Their Conversations | User ratings play a significant role in spoken dialogue systems. Typically, such ratings tend to be averaged across all users and then utilized as feedback to improve the system or personalize its behavior. While this method can be useful to understand broad, general issues with the system and its behavior, it does not... | ['Dilek Hakkani-Tur', 'Julia Hirschberg', 'Pankaj Rajan', 'Nicole Chartier', 'Alexandros Papangelis'] | 2022-06-01 | null | null | null | null | ['spoken-dialogue-systems'] | ['speech'] | [-2.57745147e-01 4.22969818e-01 -4.07831520e-02 -9.12452340e-01
-3.87013517e-02 -5.11983693e-01 5.99130630e-01 2.63971299e-01
-5.32706618e-01 4.89231765e-01 6.97250009e-01 -1.79667056e-01
6.89339787e-02 -5.20765364e-01 2.01816902e-01 -3.40291739e-01
1.94215178e-01 5.16747713e-01 -3.35434347e-01 -4.70891893... | [12.82366943359375, 7.893664836883545] |
f4127ed8-5581-4e95-b8ea-f79298a251b0 | discovering-picturesque-highlights-from | 1601.04406 | null | http://arxiv.org/abs/1601.04406v1 | http://arxiv.org/pdf/1601.04406v1.pdf | Discovering Picturesque Highlights from Egocentric Vacation Videos | We present an approach for identifying picturesque highlights from large
amounts of egocentric video data. Given a set of egocentric videos captured
over the course of a vacation, our method analyzes the videos and looks for
images that have good picturesque and artistic properties. We introduce novel
techniques to aut... | ['Vinay Bettadapura', 'Daniel Castro', 'Irfan Essa'] | 2016-01-18 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 7.16997236e-02 -3.47766101e-01 -1.53788805e-01 -2.17722550e-01
-6.55409932e-01 -8.98286462e-01 5.51914394e-01 1.01607807e-01
-1.44471060e-02 2.85256296e-01 8.34966838e-01 2.57382631e-01
-7.10188374e-02 -6.42653048e-01 -7.12056637e-01 -3.57451230e-01
-5.15002549e-01 -2.42438570e-01 3.34460884e-02 -3.02940726... | [10.299599647521973, 0.5022799968719482] |
c20de731-d330-4f16-b100-8b450af53659 | real-masks-and-fake-faces-on-the-masked-face | 2103.01546 | null | https://arxiv.org/abs/2103.01546v2 | https://arxiv.org/pdf/2103.01546v2.pdf | Real Masks and Spoof Faces: On the Masked Face Presentation Attack Detection | Face masks have become one of the main methods for reducing the transmission of COVID-19. This makes face recognition (FR) a challenging task because masks hide several discriminative features of faces. Moreover, face presentation attack detection (PAD) is crucial to ensure the security of FR systems. In contrast to th... | ['Arjan Kuijper', 'Florian Kirchbuchner', 'Naser Damer', 'Meiling Fang'] | 2021-03-02 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 4.44686711e-01 -1.29233524e-01 3.33771557e-01 -2.08117113e-01
-1.61172554e-01 -7.87319481e-01 3.59944910e-01 -4.53030437e-01
-1.09637842e-01 5.02356589e-01 -1.21782824e-01 -1.79428741e-01
5.51321395e-02 -4.98006403e-01 -6.05815232e-01 -7.56336808e-01
-6.90865517e-01 -3.86984855e-01 2.18085006e-01 -1.12687632... | [13.061750411987305, 1.0637691020965576] |
03f926fb-5284-434a-b1d2-efbbe1fd25fa | phoneme-aware-and-channel-wise-attentive | 2106.13514 | null | https://arxiv.org/abs/2106.13514v1 | https://arxiv.org/pdf/2106.13514v1.pdf | Phoneme-aware and Channel-wise Attentive Learning for Text DependentSpeaker Verification | This paper proposes a multi-task learning network with phoneme-aware and channel-wise attentive learning strategies for text-dependent Speaker Verification (SV). In the proposed structure, the frame-level multi-task learning along with the segment-level adversarial learning is adopted for speaker embedding extraction. ... | ['Qingyang Hong', 'Lin Li', 'Zheng Li', 'Yan Liu'] | 2021-06-25 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 5.43729886e-02 -2.73258358e-01 -1.18613087e-01 -4.71387714e-01
-1.21901226e+00 -3.92899871e-01 4.58990842e-01 -1.95697978e-01
-4.19786721e-01 4.37475204e-01 6.40007317e-01 -2.18880430e-01
3.34349602e-01 -2.11080864e-01 -7.07193971e-01 -1.20111132e+00
-2.05693677e-01 -4.45790321e-01 -8.98358505e-03 -1.54895365... | [14.369524955749512, 6.080367088317871] |
997f8042-cf67-4859-9c20-b807101c8a63 | knowledge-grounded-conversational-symptom | 2101.09773 | null | https://arxiv.org/abs/2101.09773v1 | https://arxiv.org/pdf/2101.09773v1.pdf | Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks | In this work, we propose a novel goal-oriented dialog task, automatic symptom detection. We build a system that can interact with patients through dialog to detect and collect clinical symptoms automatically, which can save a doctor's time interviewing the patient. Given a set of explicit symptoms provided by the patie... | ['James Glass', 'Shang-Wen Li', 'Hongyin Luo'] | 2021-01-24 | null | https://aclanthology.org/2020.clinicalnlp-1.16 | https://aclanthology.org/2020.clinicalnlp-1.16.pdf | emnlp-clinicalnlp-2020-11 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [ 1.48810908e-01 9.84363139e-01 -4.17967737e-01 -8.16784918e-01
-8.96156013e-01 -4.79941517e-01 -8.87654051e-02 6.52224064e-01
-3.40681970e-01 6.97232842e-01 4.51389551e-01 -4.87040102e-01
-4.85013165e-02 -8.29630077e-01 1.54524118e-01 -3.43089461e-01
1.12196498e-01 1.12430060e+00 3.12580615e-01 -1.49440721... | [12.392280578613281, 8.417745590209961] |
636ecc72-1fc3-46c7-af20-d01128f3a152 | solving-quantitative-reasoning-problems-with | 2206.14858 | null | https://arxiv.org/abs/2206.14858v2 | https://arxiv.org/pdf/2206.14858v2.pdf | Solving Quantitative Reasoning Problems with Language Models | Language models have achieved remarkable performance on a wide range of tasks that require natural language understanding. Nevertheless, state-of-the-art models have generally struggled with tasks that require quantitative reasoning, such as solving mathematics, science, and engineering problems at the college level. T... | ['Vedant Misra', 'Guy Gur-Ari', 'Behnam Neyshabur', 'Yuhuai Wu', 'Theo Gutman-Solo', 'Imanol Schlag', 'Cem Anil', 'Ambrose Slone', 'Vinay Ramasesh', 'Henryk Michalewski', 'Ethan Dyer', 'David Dohan', 'Anders Andreassen', 'Aitor Lewkowycz'] | 2022-06-29 | null | null | null | null | ['math-word-problem-solving', 'multi-task-language-understanding', 'arithmetic-reasoning', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'methodology', 'reasoning', 'reasoning', 'time-series'] | [-4.51947033e-01 2.80891240e-01 -2.26435393e-01 -2.72948951e-01
-9.08521533e-01 -8.34543765e-01 5.04064918e-01 6.16830826e-01
-4.19253558e-01 6.80169582e-01 -1.87248796e-01 -1.13374662e+00
-7.40741491e-02 -1.13055968e+00 -7.87057161e-01 2.37430975e-01
1.18746869e-01 4.85263377e-01 7.46148825e-02 -4.37798053... | [9.622613906860352, 7.311420917510986] |
b8b461dc-19d1-4705-9e6d-e6feeb49f8db | asr-based-features-for-emotion-recognition-a | 1805.09197 | null | http://arxiv.org/abs/1805.09197v3 | http://arxiv.org/pdf/1805.09197v3.pdf | ASR-based Features for Emotion Recognition: A Transfer Learning Approach | During the last decade, the applications of signal processing have
drastically improved with deep learning. However areas of affecting computing
such as emotional speech synthesis or emotion recognition from spoken language
remains challenging. In this paper, we investigate the use of a neural
Automatic Speech Recognit... | ['Noé Tits', 'Thierry Dutoit', 'Kevin El Haddad'] | 2018-05-23 | asr-based-features-for-emotion-recognition-a-1 | https://aclanthology.org/W18-3307 | https://aclanthology.org/W18-3307.pdf | ws-2018-7 | ['emotional-speech-synthesis'] | ['speech'] | [ 3.24629396e-02 1.19789734e-01 2.10388109e-01 -9.16234493e-01
-5.79596877e-01 -2.83569664e-01 4.96758521e-01 -9.19168144e-02
-2.41936579e-01 6.03275299e-01 4.42861527e-01 2.27258489e-01
5.18612675e-02 -3.17293882e-01 -2.51744300e-01 -5.84963202e-01
-2.98376232e-01 -2.05344304e-01 -5.51600635e-01 -5.84478557... | [13.628520965576172, 5.720970153808594] |
cd3a7a5c-8b45-44a3-83d5-c80b1d8ae5cc | non-local-intrinsic-decomposition-with-near | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Cheng_Non-Local_Intrinsic_Decomposition_With_Near-Infrared_Priors_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Cheng_Non-Local_Intrinsic_Decomposition_With_Near-Infrared_Priors_ICCV_2019_paper.pdf | Non-Local Intrinsic Decomposition With Near-Infrared Priors | Intrinsic image decomposition is a highly under-constrained problem that has been extensively studied by computer vision researchers. Previous methods impose additional constraints by exploiting either empirical or data-driven priors. In this paper, we revisit intrinsic image decomposition with the aid of near-infrared... | [' Imari Sato', ' Shaodi You', ' Yinqiang Zheng', 'Ziang Cheng'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 9.87867653e-01 -4.35774811e-02 3.10539067e-01 -4.31013435e-01
-6.26925051e-01 -5.65561771e-01 5.28429389e-01 -4.22822446e-01
-3.40486228e-01 5.55602431e-01 1.90290883e-01 -3.85048464e-02
-1.30463526e-01 -6.39065087e-01 -5.84469259e-01 -1.18693149e+00
4.93169367e-01 -3.22636813e-01 -1.59696206e-01 -8.64243060... | [10.088234901428223, -2.8284285068511963] |
a9e4948b-05ab-46ac-9d9a-0808ee4e160a | sendd-sparse-efficient-neural-depth-and | 2305.06477 | null | https://arxiv.org/abs/2305.06477v1 | https://arxiv.org/pdf/2305.06477v1.pdf | SENDD: Sparse Efficient Neural Depth and Deformation for Tissue Tracking | Deformable tracking and real-time estimation of 3D tissue motion is essential to enable automation and image guidance applications in robotically assisted surgery. Our model, Sparse Efficient Neural Depth and Deformation (SENDD), extends prior 2D tracking work to estimate flow in 3D space. SENDD introduces novel contri... | ['Septimiu E. Salcudean', 'Simon DiMaio', 'Omid Mohareri', 'Adam Schmidt'] | 2023-05-10 | null | null | null | null | ['motion-estimation'] | ['computer-vision'] | [-2.14829430e-01 2.01414928e-01 -4.82584596e-01 2.28386119e-01
-7.14238405e-01 -8.78841400e-01 2.76084542e-01 8.56743231e-02
-5.58012664e-01 3.56811613e-01 4.05672491e-01 -3.41013789e-01
1.33914530e-01 -4.56789792e-01 -4.35641766e-01 -4.38013643e-01
-5.45427263e-01 4.40893054e-01 4.73363131e-01 1.95233807... | [14.036113739013672, -3.0984606742858887] |
500ae70c-3f49-4f1f-b48b-5517b241f189 | retraining-a-graph-based-recommender-with | 2305.03624 | null | https://arxiv.org/abs/2305.03624v1 | https://arxiv.org/pdf/2305.03624v1.pdf | Retraining A Graph-based Recommender with Interests Disentanglement | In a practical recommender system, new interactions are continuously observed. Some interactions are expected, because they largely follow users' long-term preferences. Some other interactions are indications of recent trends in user preference changes or marketing positions of new items. Accordingly, the recommender n... | ['Jie Zhang', 'Aixin Sun', 'Yitong Ji'] | 2023-05-05 | null | null | null | null | ['incremental-learning', 'marketing'] | ['methodology', 'miscellaneous'] | [-9.05294344e-02 -7.90934637e-02 -4.39820647e-01 -4.89503860e-01
-9.97975916e-02 -6.69173121e-01 4.83562201e-01 6.58859983e-02
-3.33559543e-01 6.09259307e-01 5.77245653e-01 -8.34966525e-02
-6.05248988e-01 -8.12176347e-01 -6.43103957e-01 -6.08431280e-01
-3.92044127e-01 5.23369610e-01 4.05479334e-02 -4.25769061... | [10.232356071472168, 5.55294942855835] |
f2397fbb-93c7-4ef2-8cb7-16efab16f58c | chinese-zero-pronoun-resolution-with-deep | null | null | https://aclanthology.org/D17-1135 | https://aclanthology.org/D17-1135.pdf | Chinese Zero Pronoun Resolution with Deep Memory Network | Existing approaches for Chinese zero pronoun resolution typically utilize only syntactical and lexical features while ignoring semantic information. The fundamental reason is that zero pronouns have no descriptive information, which brings difficulty in explicitly capturing their semantic similarities with antecedents.... | ['Wei-Nan Zhang', 'Ting Liu', 'Qingyu Yin', 'Yu Zhang'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['chinese-zero-pronoun-resolution'] | ['natural-language-processing'] | [-5.10900728e-02 -8.34658518e-02 -5.72859645e-01 -4.07422394e-01
-8.34429145e-01 -5.74746311e-01 5.58547318e-01 3.41006219e-01
-6.44949615e-01 8.30884337e-01 6.81495428e-01 -1.25355005e-01
-8.05724226e-03 -1.09579813e+00 -5.09897053e-01 -4.24585432e-01
2.88623571e-01 4.63084012e-01 7.16287345e-02 -5.09701073... | [10.223319053649902, 9.231841087341309] |
9349e9e3-c8bc-40d9-86bb-e583758ee017 | online-gesture-recognition-using-transformer | 2305.03407 | null | https://arxiv.org/abs/2305.03407v1 | https://arxiv.org/pdf/2305.03407v1.pdf | Online Gesture Recognition using Transformer and Natural Language Processing | The Transformer architecture is shown to provide a powerful machine transduction framework for online handwritten gestures corresponding to glyph strokes of natural language sentences. The attention mechanism is successfully used to create latent representations of an end-to-end encoder-decoder model, solving multi-lev... | ['M. Ramo', 'O. Akinremi', 'F. Balado', 'G. C. M. Silvestre'] | 2023-05-05 | null | null | null | null | ['gesture-recognition', 'handwriting-recognition'] | ['computer-vision', 'computer-vision'] | [ 7.84467876e-01 4.51162606e-01 -7.62272999e-02 -5.16749263e-01
-9.03161943e-01 -7.93266773e-01 7.62629926e-01 -5.41262984e-01
-5.52544475e-01 4.10704285e-01 1.80235311e-01 -4.44088072e-01
-5.72494641e-02 -4.57214594e-01 -6.97457671e-01 -7.83680260e-01
-1.81953743e-01 7.20797956e-01 1.18730359e-01 -1.66533068... | [9.231735229492188, -6.5418291091918945] |
2ddb1cf5-32cb-4e61-882b-abb4fd4ffd81 | toward-an-intelligent-tutoring-system-for | 2210.13635 | null | https://arxiv.org/abs/2210.13635v1 | https://arxiv.org/pdf/2210.13635v1.pdf | Toward an Intelligent Tutoring System for Argument Mining in Legal Texts | We propose an adaptive environment (CABINET) to support caselaw analysis (identifying key argument elements) based on a novel cognitive computing framework that carefully matches various machine learning (ML) capabilities to the proficiency of a user. CABINET supports law students in their learning as well as professio... | ['Karim Benyekhlef', 'Kevin D. Ashley', 'Vern R. Walker', 'Jaromir Savelka', 'Hannes Westermann'] | 2022-10-24 | null | null | null | null | ['argument-mining'] | ['natural-language-processing'] | [ 1.73268721e-01 4.06880528e-01 -2.84970757e-02 -4.65516932e-02
-8.27728689e-01 -8.07660818e-01 7.19086885e-01 9.18928564e-01
-3.82788301e-01 8.34293425e-01 -2.72473127e-01 -1.18396461e+00
-6.40228510e-01 -1.02996528e+00 -4.58160818e-01 -3.40024024e-01
6.07508540e-01 5.56666970e-01 4.66805905e-01 -4.02274281... | [9.733309745788574, 9.048041343688965] |
aae4e2d4-5e95-481e-a7c3-af1d593310a0 | realtabformer-generating-realistic-relational | 2302.02041 | null | https://arxiv.org/abs/2302.02041v1 | https://arxiv.org/pdf/2302.02041v1.pdf | REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers | Tabular data is a common form of organizing data. Multiple models are available to generate synthetic tabular datasets where observations are independent, but few have the ability to produce relational datasets. Modeling relational data is challenging as it requires modeling both a "parent" table and its relationships ... | ['Olivier Dupriez', 'Aivin V. Solatorio'] | 2023-02-04 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 2.11763501e-01 6.75691009e-01 -2.70248771e-01 -7.62930274e-01
-1.33982289e+00 -6.76887333e-01 5.59241593e-01 1.97811142e-01
3.75648022e-01 9.96581554e-01 2.67987520e-01 -6.00181162e-01
-3.45314387e-03 -1.23270988e+00 -1.48740125e+00 -3.82959068e-01
-3.58085483e-02 1.32663131e+00 1.68035477e-01 -2.26249725... | [9.761838912963867, 7.879315376281738] |
502b42c8-320f-453a-b5d1-28fdedfa6678 | bayesian-experimental-design-for-symbolic | 2211.15860 | null | https://arxiv.org/abs/2211.15860v1 | https://arxiv.org/pdf/2211.15860v1.pdf | Bayesian Experimental Design for Symbolic Discovery | This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive models taking general functional forms. We apply constrained first-order methods to optimize an appropriate selection criterion, using Hamiltoni... | ['Nimrod Megiddo', 'Lior Horesh', 'Joao Goncalves', 'Sanjeeb Dash', 'Cristina Cornelio', 'Kenneth L. Clarkson'] | 2022-11-29 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 5.26035666e-01 -2.69904137e-01 -5.69110692e-01 -3.67873669e-01
-4.46777254e-01 -3.81933779e-01 7.16943860e-01 -1.64947599e-01
-4.86910164e-01 1.34131086e+00 -3.94438863e-01 -9.21308935e-01
-7.06560791e-01 -6.17883325e-01 -6.08876824e-01 -7.79033840e-01
-2.14768350e-01 7.40380764e-01 -7.31175626e-03 3.57538849... | [6.663104057312012, 3.939760684967041] |
e80ba69f-9625-4ea1-98ae-f6faf3d7121a | what-makes-visual-place-recognition-easy-or | 2106.12671 | null | https://arxiv.org/abs/2106.12671v1 | https://arxiv.org/pdf/2106.12671v1.pdf | What makes visual place recognition easy or hard? | Visual place recognition is a fundamental capability for the localization of mobile robots. It places image retrieval in the practical context of physical agents operating in a physical world. It is an active field of research and many different approaches have been proposed and evaluated in many different experiments.... | ['Peer Neubert', 'Stefan Schubert'] | 2021-06-23 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-8.79114680e-03 -2.37336367e-01 -2.80969441e-01 -3.29381585e-01
-2.06579253e-01 -7.78372109e-01 8.28056216e-01 1.75601259e-01
-5.16556501e-01 6.35856926e-01 -2.10077360e-01 -4.62804168e-01
-2.42323399e-01 -5.13762712e-01 -5.13572812e-01 -7.53540158e-01
-4.38775867e-01 5.49329579e-01 5.20608127e-01 -2.48492584... | [7.46663761138916, -1.936576008796692] |
a9d8dd2f-efa0-4ec2-8f25-81d29db3a212 | machine-learning-models-and-facial-regions | 2202.08913 | null | https://arxiv.org/abs/2202.08913v1 | https://arxiv.org/pdf/2202.08913v1.pdf | Machine learning models and facial regions videos for estimating heart rate: a review on Patents, Datasets and Literature | Estimating heart rate is important for monitoring users in various situations. Estimates based on facial videos are increasingly being researched because it makes it possible to monitor cardiac information in a non-invasive way and because the devices are simpler, requiring only cameras that capture the user's face. Fr... | ['Erick Giovani Sperandio Nascimento', 'Ingrid Winkler', 'Lian Filipe Santana Nascimento', 'Paulo Henrique Miranda Sá', 'José Vinícius Dantas Paranhos', 'Yasmin da Silva Bonfim', 'Victor Rocha Santos', 'Lucas Lemos Ortega', 'Tiago Palma Pagano'] | 2022-02-17 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 1.23243131e-01 1.50879934e-01 -8.43487442e-01 3.76759768e-02
-1.85222372e-01 -4.36386466e-01 -2.43028596e-01 -1.09217726e-01
-2.73017257e-01 6.13564372e-01 -1.73934363e-02 -3.89872879e-01
-2.22804341e-02 -4.41344887e-01 -1.97296247e-01 -6.32383049e-01
-1.11094750e-01 -4.54329282e-01 -4.59199101e-01 3.18458408... | [13.856539726257324, 2.833507537841797] |
83ef974c-2c72-4afc-81cf-a82509c361f8 | best-of-both-worlds-robust-accented-speech | 2103.05834 | null | https://arxiv.org/abs/2103.05834v1 | https://arxiv.org/pdf/2103.05834v1.pdf | Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning | Training deep neural networks for automatic speech recognition (ASR) requires large amounts of transcribed speech. This becomes a bottleneck for training robust models for accented speech which typically contains high variability in pronunciation and other semantics, since obtaining large amounts of annotated accented ... | ['Duen Horng Chau', 'Sundararajan Srinivasan', 'Monica Sunkara', 'Sravan Bodapati', 'Nilaksh Das'] | 2021-03-10 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [ 2.08941057e-01 3.34621519e-01 1.19586341e-01 -6.68251574e-01
-1.23254681e+00 -9.30311620e-01 3.75264376e-01 -1.77428901e-01
-7.35583484e-01 6.44177854e-01 3.36975694e-01 -5.78247488e-01
7.66058624e-01 -3.07712406e-01 -7.80803978e-01 -4.17713702e-01
3.76214892e-01 8.61528099e-01 -1.68876126e-01 -4.11760658... | [14.42362117767334, 6.700058937072754] |
76a47ead-b2e9-46c7-8dfe-8a1b228020f2 | model-based-versus-model-free-feeding-control | 2306.09915 | null | https://arxiv.org/abs/2306.09915v1 | https://arxiv.org/pdf/2306.09915v1.pdf | Model-based versus model-free feeding control and water quality monitoring for fish growth tracking in aquaculture systems | The high concentration level of the environmental factors, such as a high ammonia concentration and pH level, affect the water quality, affecting fish's survival and mass death. Therefore, there is a critical need to develop control strategies to determine optimal, efficient, and reliable feeding and water quality moni... | ['Taous-Meriem Laleg-Kirati', "Ibrahima N'Doye", 'Fahad Aljehani'] | 2023-06-14 | null | null | null | null | ['q-learning'] | ['methodology'] | [-3.79974097e-01 -2.30432674e-01 4.11977544e-02 2.39599898e-01
2.03016013e-01 -4.38591093e-01 -1.59845725e-01 5.22359371e-01
-4.56816256e-01 7.58910716e-01 -6.16914928e-02 1.19078293e-01
-5.23808241e-01 -9.21286047e-01 -7.83137143e-01 -1.38601184e+00
-4.19718862e-01 -2.51010228e-02 -9.33112055e-02 -1.69837087... | [4.6811604499816895, 2.1591053009033203] |
1bd21129-4747-4865-a95c-540543025791 | from-scratch-to-sketch-deep-decoupled | 2208.04833 | null | https://arxiv.org/abs/2208.04833v1 | https://arxiv.org/pdf/2208.04833v1.pdf | From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching Agent | We present an automated learning framework for a robotic sketching agent that is capable of learning stroke-based rendering and motor control simultaneously. We formulate the robotic sketching problem as a deep decoupled hierarchical reinforcement learning; two policies for stroke-based rendering and motor control are ... | ['Byoung-Tak Zhang', 'Minsu Lee', 'Minji Kim', 'Ganghun Lee'] | 2022-08-09 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-1.89393312e-01 1.09384269e-01 -2.12739289e-01 1.16257787e-01
-1.51958734e-01 -7.97302961e-01 8.25404704e-01 -9.14293110e-01
-1.90076753e-01 6.17930353e-01 -3.70505929e-01 -1.75046816e-01
-4.73805845e-01 -6.87645197e-01 -8.06212246e-01 -6.55004025e-01
-1.36651173e-01 7.40707159e-01 1.22326128e-01 -2.85113543... | [4.727212429046631, 0.588460385799408] |
1864c07e-71df-4eb6-86df-7b64f6885991 | self-supervised-learning-via-inter-modal | 2307.03008 | null | https://arxiv.org/abs/2307.03008v1 | https://arxiv.org/pdf/2307.03008v1.pdf | Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation | Deep learning has become a valuable tool for the automation of certain medical image segmentation tasks, significantly relieving the workload of medical specialists. Some of these tasks require segmentation to be performed on a subset of the input dimensions, the most common case being 3D-to-2D. However, the performanc... | ['Hrvoje Bogunović', 'Ursula Schmidt-Erfurth', 'Julia Mai', 'Dmitrii Lachinov', 'Guilherme Aresta', 'José Morano'] | 2023-07-06 | null | null | null | null | ['self-supervised-learning', 'medical-image-segmentation', 'transfer-learning'] | ['computer-vision', 'medical', 'miscellaneous'] | [ 2.45058745e-01 3.12959701e-01 -2.23361000e-01 -4.85507786e-01
-6.63201392e-01 -3.58754396e-01 2.36180544e-01 -1.63782369e-02
-6.85703337e-01 8.71482074e-01 -5.00509106e-02 -2.77338624e-01
-2.24371538e-01 -4.65137750e-01 -4.51790333e-01 -6.97755873e-01
5.35421148e-02 8.31740975e-01 2.70474762e-01 1.68456957... | [14.410202980041504, -2.4842326641082764] |
43a7769a-1fd2-4afd-b2ed-84fe3b389ff3 | new-approach-for-solar-tracking-systems-based | 1809.07048 | null | http://arxiv.org/abs/1809.07048v1 | http://arxiv.org/pdf/1809.07048v1.pdf | New approach for solar tracking systems based on computer vision, low cost hardware and deep learning | In this work, a new approach for Sun tracking systems is presented. Due to
the current system limitations regarding costs and operational problems, a new
approach based on low cost, computer vision open hardware and deep learning has
been developed. The preliminary tests carried out successfully in Plataforma
solar de ... | ['Ginés García', 'Jesús Fernández-Reche', 'Jose A. Carballo', 'Manuel Berenguel', 'Javier Bonilla'] | 2018-09-19 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [-2.14257032e-01 -4.29265201e-01 9.86921489e-02 2.12222159e-01
5.14109969e-01 -6.30191505e-01 7.18252659e-01 -1.02566108e-01
7.91822821e-02 7.67696381e-01 -3.83104950e-01 -4.36259210e-01
-1.10173084e-01 -8.55559051e-01 -1.89431578e-01 -1.01654363e+00
1.22087978e-01 1.25976220e-01 3.03321719e-01 -3.16002220... | [9.637843132019043, -1.6985565423965454] |
ec7db6ed-a162-4ddf-93a6-3370ad85a24b | direct-superpoints-matching-for-fast-and | 2307.01362 | null | https://arxiv.org/abs/2307.01362v1 | https://arxiv.org/pdf/2307.01362v1.pdf | Direct Superpoints Matching for Fast and Robust Point Cloud Registration | Although deep neural networks endow the downsampled superpoints with discriminative feature representations, directly matching them is usually not used alone in state-of-the-art methods, mainly for two reasons. First, the correspondences are inevitably noisy, so RANSAC-like refinement is usually adopted. Such ad hoc po... | ['Huaizu Jiang', 'Hanumant Singh', 'Yiming Xie', 'Aniket Gupta'] | 2023-07-03 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-2.08677158e-01 -3.88791859e-01 -8.60046595e-02 -4.94673222e-01
-8.61833096e-01 -3.90731871e-01 5.60506165e-01 9.80124101e-02
-4.22264814e-01 2.61929989e-01 -1.25797868e-01 1.01451799e-01
-7.98662975e-02 -5.95575154e-01 -9.30829585e-01 -6.51895642e-01
3.05285990e-01 6.16664767e-01 2.95969516e-01 -2.79820323... | [7.6676716804504395, -3.0945184230804443] |
5301b032-0d99-48bc-bb5d-cf31490c29a3 | rmpe-regional-multi-person-pose-estimation | 1612.00137 | null | http://arxiv.org/abs/1612.00137v5 | http://arxiv.org/pdf/1612.00137v5.pdf | RMPE: Regional Multi-person Pose Estimation | Multi-person pose estimation in the wild is challenging. Although
state-of-the-art human detectors have demonstrated good performance, small
errors in localization and recognition are inevitable. These errors can cause
failures for a single-person pose estimator (SPPE), especially for methods that
solely depend on huma... | ['Yu-Wing Tai', 'Hao-Shu Fang', 'Shuqin Xie', 'Cewu Lu'] | 2016-12-01 | rmpe-regional-multi-person-pose-estimation-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Fang_RMPE_Regional_Multi-Person_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Fang_RMPE_Regional_Multi-Person_ICCV_2017_paper.pdf | iccv-2017-10 | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 1.74595322e-02 5.93229011e-02 4.83305126e-01 -2.58092135e-01
-9.55082834e-01 -3.98312956e-01 5.31493962e-01 -4.05177921e-02
-8.64347577e-01 7.86613286e-01 -1.44294295e-02 4.47433889e-01
3.14496845e-01 -4.95275944e-01 -7.95148075e-01 -2.22602561e-01
-1.32849747e-02 1.00759304e+00 6.66290402e-01 -1.50618151... | [7.137802600860596, -0.8205591440200806] |
ffd39386-dbff-40c6-898e-eb00c3defdda | good-great-excellent-global-inference-of | null | null | https://aclanthology.org/Q13-1023 | https://aclanthology.org/Q13-1023.pdf | Good, Great, Excellent: Global Inference of Semantic Intensities | Adjectives like good, great, and excellent are similar in meaning, but differ in intensity. Intensity order information is very useful for language learners as well as in several NLP tasks, but is missing in most lexical resources (dictionaries, WordNet, and thesauri). In this paper, we present a primarily unsupervised... | ['Gerard de Melo', 'Mohit Bansal'] | 2013-01-01 | null | null | null | tacl-2013-1 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-1.57063767e-01 1.31566331e-01 -5.56135535e-01 -6.30236447e-01
-9.25598562e-01 -1.11252809e+00 4.00405824e-01 9.75257933e-01
-9.78220463e-01 7.70872116e-01 4.62513298e-01 -1.43065378e-01
-4.75263685e-01 -8.93088043e-01 -2.58863300e-01 -3.01372617e-01
5.55200428e-02 7.56495953e-01 1.45319253e-01 -5.39884746... | [10.324832916259766, 9.193887710571289] |
f8a3f840-633f-4afd-8451-f27d0f0da64e | towards-stable-co-saliency-detection-and | 2209.12138 | null | https://arxiv.org/abs/2209.12138v2 | https://arxiv.org/pdf/2209.12138v2.pdf | Towards Stable Co-saliency Detection and Object Co-segmentation | In this paper, we present a novel model for simultaneous stable co-saliency detection (CoSOD) and object co-segmentation (CoSEG). To detect co-saliency (segmentation) accurately, the core problem is to well model inter-image relations between an image group. Some methods design sophisticated modules, such as recurrent ... | ['Shouhong Ding', 'Mofei Song', 'Senyun Kuang', 'Lv Tang', 'Bo Li'] | 2022-09-25 | null | null | null | null | ['saliency-detection'] | ['computer-vision'] | [ 1.74397245e-01 -1.64396197e-01 -6.59395978e-02 -7.17618987e-02
-4.11719441e-01 -3.07888210e-01 4.47047323e-01 -1.82688087e-01
-3.00213426e-01 3.73251557e-01 1.20775327e-01 -1.02360792e-01
-1.16509549e-01 -4.72408444e-01 -8.98872018e-01 -4.11388516e-01
2.88676023e-01 5.46112061e-02 9.45745707e-01 -2.63352871... | [9.741229057312012, -0.2849617302417755] |
c7ce82fe-fda3-45b6-bb79-1e7faa83272f | hyperspectral-image-super-resolution-via-dual | 2304.04589 | null | https://arxiv.org/abs/2304.04589v8 | https://arxiv.org/pdf/2304.04589v8.pdf | Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution | Since the number of incident energies is limited, it is difficult to directly acquire hyperspectral images (HSI) with high spatial resolution. Considering the high dimensionality and correlation of HSI, super-resolution (SR) of HSI remains a challenge in the absence of auxiliary high-resolution images. Furthermore, it ... | ['Yuan Liyin', 'Qian Chen', 'Xiubao Sui', 'Chuncheng Zhang', 'YuAn Liu', 'Tingting Liu'] | 2023-04-10 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 6.52153730e-01 -4.71588969e-01 1.30894005e-01 -1.75150767e-01
-6.34268999e-01 -9.60262939e-02 2.01862678e-01 -2.77116001e-01
-1.51446477e-01 8.19272757e-01 2.41826385e-01 2.32136071e-01
-6.43232763e-01 -1.08476019e+00 -3.85899454e-01 -1.12954712e+00
1.01245426e-01 -5.65211773e-01 1.79691806e-01 -4.12149161... | [10.21865177154541, -1.8928794860839844] |
9ec9bb01-e9d3-47fe-b05a-94fccbd0e881 | code-structure-guided-transformer-for-source | 2104.09340 | null | https://arxiv.org/abs/2104.09340v2 | https://arxiv.org/pdf/2104.09340v2.pdf | Code Structure Guided Transformer for Source Code Summarization | Code summaries help developers comprehend programs and reduce their time to infer the program functionalities during software maintenance. Recent efforts resort to deep learning techniques such as sequence-to-sequence models for generating accurate code summaries, among which Transformer-based approaches have achieved ... | ['Michael R. Lyu', 'Xin Xia', 'Lun Yiu Nie', 'Jichuan Zeng', 'Yulan He', 'Cuiyun Gao', 'Shuzheng Gao'] | 2021-04-19 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 8.62157270e-02 2.66820770e-02 -2.64626861e-01 -3.74766022e-01
-9.57247853e-01 -3.74306351e-01 2.70629287e-01 4.39000219e-01
1.03370570e-01 3.23127806e-01 4.39800262e-01 -4.42451268e-01
3.68708342e-01 -7.05905497e-01 -1.03105009e+00 -2.84462810e-01
1.23849593e-01 -1.93218365e-01 2.36430019e-01 -6.11741096... | [7.569018840789795, 7.963604927062988] |
ea0b8da3-4e9f-42d6-a396-b8809d67c253 | minvis-a-minimal-video-instance-segmentation | 2208.02245 | null | https://arxiv.org/abs/2208.02245v1 | https://arxiv.org/pdf/2208.02245v1.pdf | MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training | We propose MinVIS, a minimal video instance segmentation (VIS) framework that achieves state-of-the-art VIS performance with neither video-based architectures nor training procedures. By only training a query-based image instance segmentation model, MinVIS outperforms the previous best result on the challenging Occlude... | ['Anima Anandkumar', 'Zhiding Yu', 'De-An Huang'] | 2022-08-03 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 9.16414186e-02 8.89045298e-02 -6.47312939e-01 -1.61555856e-01
-1.18518257e+00 -7.69097924e-01 3.22685778e-01 -4.04984616e-02
-6.73229575e-01 4.38385874e-01 -2.59355426e-01 -1.07403882e-01
2.73670107e-01 -5.72518826e-01 -1.24366260e+00 -5.11908114e-01
1.60515774e-02 6.76296949e-01 9.39268291e-01 4.41695094... | [9.115948677062988, -0.053552862256765366] |
a9267d88-8a3d-47b4-bc94-23dc3cb8ee62 | gm-tcnet-gated-multi-scale-temporal | 2210.15834 | null | https://arxiv.org/abs/2210.15834v1 | https://arxiv.org/pdf/2210.15834v1.pdf | GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition | In human-computer interaction, Speech Emotion Recognition (SER) plays an essential role in understanding the user's intent and improving the interactive experience. While similar sentimental speeches own diverse speaker characteristics but share common antecedents and consequences, an essential challenge for SER is how... | ['Kun-Hong Liu', 'Li-Yan Chen', 'Chang-Li Wu', 'Yan Luo', 'Yong Xu', 'Xuan-Ze Wang', 'Xin-Cheng Wen', 'Jia-Xin Ye'] | 2022-10-28 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-9.68828723e-02 -3.33451897e-01 6.87416494e-02 -5.77117920e-01
-3.33157063e-01 -3.14311415e-01 2.52847105e-01 -1.52335867e-01
-8.52563232e-02 4.50995833e-01 5.46498299e-01 -8.72392431e-02
1.77147403e-01 -4.51339781e-01 -2.63484001e-01 -5.61809778e-01
-4.94517267e-01 -5.64642966e-01 2.08695270e-02 -4.79456723... | [13.417219161987305, 5.7325053215026855] |
464b163e-67ad-4b20-802e-8cf953584d7e | micro-expression-detection-in-long-videos | 1903.10765 | null | http://arxiv.org/abs/1903.10765v1 | http://arxiv.org/pdf/1903.10765v1.pdf | Micro-expression detection in long videos using optical flow and recurrent neural networks | Facial micro-expressions are subtle and involuntary expressions that can
reveal concealed emotions. Micro-expressions are an invaluable source of
information in application domains such as lie detection, mental health,
sentiment analysis and more. One of the biggest challenges in this field of
research is the small amo... | ['Michiel Verburg', 'Vlado Menkovski'] | 2019-03-26 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 2.92725861e-01 1.49775803e-01 -2.50474393e-01 -6.06866121e-01
-3.85618418e-01 -2.18596324e-01 3.31646413e-01 -1.52311064e-02
-5.00129759e-01 7.86173940e-01 1.64680585e-01 2.64970839e-01
1.59535766e-01 -3.71935666e-01 -1.81147307e-01 -8.32467020e-01
-2.25137800e-01 -4.21284765e-01 -1.32892296e-01 -2.32637450... | [13.61422348022461, 1.8958269357681274] |
c1ea68f0-0f39-4af4-b452-90e48e4077b3 | a-large-scale-homography-benchmark | 2302.09997 | null | https://arxiv.org/abs/2302.09997v1 | https://arxiv.org/pdf/2302.09997v1.pdf | A Large Scale Homography Benchmark | We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D. The applications of the Pi3D dataset are diverse, e.g. training or evaluating monocular depth, surface normal estimatio... | ['Jiri Matas', 'Wolfgang Förstner', 'Michal Polic', 'Dmytro Mishkin', 'Daniel Barath'] | 2023-02-20 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.67623475e-01 -2.28669927e-01 4.98973532e-03 -2.21150130e-01
-1.04302061e+00 -7.11299837e-01 7.76372254e-01 -3.40531170e-01
2.52623092e-02 1.29616246e-01 2.76143074e-01 2.67292142e-01
-1.51802883e-01 -7.15796649e-01 -1.27570152e+00 -4.00824159e-01
-1.47199556e-01 9.20143187e-01 3.62336129e-01 -1.49608016... | [8.057168006896973, -2.322669506072998] |
da578cd3-60d9-4617-b37d-565e013626b7 | online-functional-connectivity-analysis-of | 2303.03279 | null | https://arxiv.org/abs/2303.03279v1 | https://arxiv.org/pdf/2303.03279v1.pdf | Online functional connectivity analysis of large all-to-all networks | The analysis of EEG/MEG functional connectivity has become an important tool in neural research. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, i.e., after the conclusion of the experiment.... | ['Johannes Vorwerk', 'Jens Haueisen', 'Daniel Baumgarten', 'Matti Hämäläinen', 'Jinlong Dong', 'Lorenz Esch'] | 2023-03-06 | null | null | null | null | ['connectivity-estimation', 'eeg', 'eeg'] | ['graphs', 'methodology', 'time-series'] | [-1.45023525e-01 -1.86645702e-01 3.90079618e-01 -2.27880284e-01
-1.91265315e-01 -5.51386952e-01 1.20844625e-01 4.63666946e-01
-6.29488051e-01 7.97448933e-01 -2.09134206e-01 -2.49649659e-01
-5.85591793e-01 -7.07741916e-01 -6.32174730e-01 -5.28994024e-01
-9.23176706e-01 2.88030505e-01 1.85133398e-01 1.53046648... | [13.030318260192871, 3.395862102508545] |
71c2c9ef-4a2d-4c98-b1ce-f0d42dfed29c | afdp-an-automated-function-description | 1910.06965 | null | http://arxiv.org/abs/1910.06965v1 | http://arxiv.org/pdf/1910.06965v1.pdf | AFDP: An Automated Function Description Prediction Approach to Improve Accuracy of Protein Function Predictions | With the rapid growth in high-throughput biological sequencing technologies
and subsequently the amount of produced omics data, it is essential to develop
automated methods to annotate the functionality of unknown genes and proteins.
There are developed tools such as AHRD applying known proteins characterization
to ann... | [] | 2019-10-15 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 2.42378071e-01 -3.13389078e-02 2.57166117e-01 -3.90381157e-01
-2.02684030e-01 -9.52667654e-01 1.35289565e-01 7.51422584e-01
-2.27681935e-01 1.45921004e+00 -1.59226045e-01 -2.22510442e-01
-2.83800751e-01 -4.93937105e-01 -6.47988558e-01 -5.60005605e-01
8.06108266e-02 8.33233535e-01 7.18710542e-01 -3.34065139... | [4.754537105560303, 5.439800262451172] |
97afa044-a444-4df0-a4fe-86709dc94402 | your-diffusion-model-is-secretly-a-zero-shot | 2303.16203 | null | https://arxiv.org/abs/2303.16203v2 | https://arxiv.org/pdf/2303.16203v2.pdf | Your Diffusion Model is Secretly a Zero-Shot Classifier | The recent wave of large-scale text-to-image diffusion models has dramatically increased our text-based image generation abilities. These models can generate realistic images for a staggering variety of prompts and exhibit impressive compositional generalization abilities. Almost all use cases thus far have solely focu... | ['Deepak Pathak', 'Ellis Brown', 'Shivam Duggal', 'Mihir Prabhudesai', 'Alexander C. Li'] | 2023-03-28 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 2.79778540e-01 2.86154449e-01 -5.09676516e-01 -4.41959500e-01
-9.30469453e-01 -4.38870162e-01 1.32689035e+00 -1.72772989e-01
-1.00442700e-01 4.86339629e-01 7.75069952e-01 -2.38389388e-01
1.68654576e-01 -8.91688883e-01 -5.90809703e-01 -5.34026980e-01
4.02117938e-01 6.51267529e-01 4.91146557e-02 -2.45445341... | [11.250561714172363, -0.015052932314574718] |
ae29fab4-e576-4c9d-9b02-edde459185fe | solving-the-false-positives-problem-in-fraud | 1710.07709 | null | http://arxiv.org/abs/1710.07709v1 | http://arxiv.org/pdf/1710.07709v1.pdf | Solving the "false positives" problem in fraud prediction | In this paper, we present an automated feature engineering based approach to
dramatically reduce false positives in fraud prediction. False positives plague
the fraud prediction industry. It is estimated that only 1 in 5 declared as
fraud are actually fraud and roughly 1 in every 6 customers have had a valid
transactio... | ['Santiago Moral Rubio', 'Kalyan Veeramachaneni', 'Roy Wedge', 'Sergio Iglesias Perez', 'James Max Kanter'] | 2017-10-20 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-1.38863340e-01 3.36558856e-02 3.15709342e-03 -7.42903173e-01
-7.82982409e-01 -4.38325614e-01 2.64859229e-01 5.02250910e-01
-6.22481525e-01 8.45820963e-01 -3.06450650e-02 -3.90660554e-01
9.64374095e-02 -1.18879950e+00 -6.84907556e-01 -7.11151678e-03
-3.25529546e-01 6.37595236e-01 6.35894537e-02 -2.43767992... | [7.679744243621826, 5.4446611404418945] |
adb7d2ad-f02d-4120-95ca-8eca90af446c | deep-denoising-entity-pre-training-for-neural | 2111.07393 | null | https://arxiv.org/abs/2111.07393v1 | https://arxiv.org/pdf/2111.07393v1.pdf | DEEP: DEnoising Entity Pre-training for Neural Machine Translation | It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. Earlier named entity translation methods mainly focus on phonetic transliteration, which ignores the sentence context for translation and is limited in domain and language ... | ['Graham Neubig', 'Kyunghyun Cho', 'Hiroaki Hayashi', 'Junjie Hu'] | 2021-11-14 | null | https://aclanthology.org/2022.acl-long.123 | https://aclanthology.org/2022.acl-long.123.pdf | acl-2022-5 | ['transliteration'] | ['natural-language-processing'] | [ 1.48790345e-01 1.30490866e-02 -4.36410040e-01 -4.50799763e-01
-1.68979931e+00 -8.31794679e-01 5.43765068e-01 -2.43422911e-01
-4.50434029e-01 1.11741376e+00 5.94037116e-01 -6.93416834e-01
7.56065607e-01 -7.11711884e-01 -1.08042407e+00 -2.44765371e-01
6.82594836e-01 6.71375573e-01 -4.88735825e-01 -3.96663517... | [11.638959884643555, 10.267261505126953] |
5a7c03e6-5323-4709-8c88-c38f963199d0 | neural-architecture-search-for-visual-anomaly | 2304.08975 | null | https://arxiv.org/abs/2304.08975v2 | https://arxiv.org/pdf/2304.08975v2.pdf | Neural Architecture Search for Visual Anomaly Segmentation | This paper presents the first application of neural architecture search to the complex task of segmenting visual anomalies. Measurement of anomaly segmentation performance is challenging due to imbalanced anomaly pixels, varying region areas, and various types of anomalies. First, the region-weighted Average Precision ... | ['Joaquin Vanschoren', 'Tommie Kerssies'] | 2023-04-18 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 4.37112510e-01 -1.73571065e-01 -1.67312294e-01 -1.62375867e-01
-7.18395531e-01 -6.45906746e-01 1.68692261e-01 6.77053928e-01
-2.02720352e-02 2.13482782e-01 -7.74295747e-01 -5.03761768e-01
-3.78390312e-01 -5.49260437e-01 -6.91357493e-01 -6.98698699e-01
-1.04557373e-01 4.10754353e-01 1.51575774e-01 1.65913373... | [7.629028797149658, 2.015986919403076] |
2c4086dd-109e-49e8-a29d-c62c46bd7b8e | prescriptive-business-process-monitoring-for | 2008.08693 | null | https://arxiv.org/abs/2008.08693v1 | https://arxiv.org/pdf/2008.08693v1.pdf | Prescriptive Business Process Monitoring for Recommending Next Best Actions | Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producin... | ['Martin Matzner', 'Sven Weinzierl', 'Sebastian Dunzer', 'Sandra Zilker'] | 2020-08-19 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 4.34917927e-01 2.98213750e-01 9.37177986e-03 -2.63124853e-01
-7.89895207e-02 -1.45028144e-01 9.89355981e-01 7.33019650e-01
-2.80357510e-01 5.95327139e-01 3.21881443e-01 -6.31511927e-01
-8.16265941e-01 -1.32461178e+00 -5.00684321e-01 -4.18779522e-01
-2.11482763e-01 8.06813598e-01 1.10919893e-01 2.23372340... | [8.606925964355469, 5.955133438110352] |
9ffe7572-9ee7-4ac9-a736-8be13a69947e | on-the-relation-between-prediction-and | 2112.05248 | null | https://arxiv.org/abs/2112.05248v1 | https://arxiv.org/pdf/2112.05248v1.pdf | On the Relation between Prediction and Imputation Accuracy under Missing Covariates | Missing covariates in regression or classification problems can prohibit the direct use of advanced tools for further analysis. Recent research has realized an increasing trend towards the usage of modern Machine Learning algorithms for imputation. It originates from their capability of showing favourable prediction ac... | ['Markus Pauly', 'Justus Tulowietzki', 'Burim Ramosaj'] | 2021-12-09 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 3.89391154e-01 -2.08617952e-02 -7.42949784e-01 -7.35160887e-01
-9.45439339e-01 -1.12478189e-01 4.22866106e-01 4.06926513e-01
-5.41627586e-01 1.34061885e+00 3.58367443e-01 -5.88671744e-01
-5.34436822e-01 -7.02632368e-01 -6.35839164e-01 -5.03828108e-01
-1.69761494e-01 4.72523332e-01 -5.63237727e-01 3.04853886... | [7.826644420623779, 4.975827217102051] |
4a111909-c8d6-4d2d-984a-846ae4f47f6d | text-detection-recognition-in-the-wild-for | 2205.08565 | null | https://arxiv.org/abs/2205.08565v2 | https://arxiv.org/pdf/2205.08565v2.pdf | Text Detection & Recognition in the Wild for Robot Localization | Signage is everywhere and a robot should be able to take advantage of signs to help it localize (including Visual Place Recognition (VPR)) and map. Robust text detection & recognition in the wild is challenging due to such factors as pose, irregular text, illumination, and occlusion. We propose an end-to-end scene text... | ['John Zelek', 'Zobeir Raisi'] | 2022-05-17 | null | null | null | null | ['text-spotting', 'visual-place-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.05335546e-01 -6.11307442e-01 1.54459462e-01 -5.46324909e-01
-5.93556821e-01 -5.93720555e-01 8.47627342e-01 -7.36465156e-02
-5.26317835e-01 2.13225469e-01 1.92680016e-01 -1.24975659e-01
3.08335423e-01 -1.83571890e-01 -7.08950520e-01 -2.83827752e-01
2.25999266e-01 6.97469771e-01 6.72913015e-01 -9.38515067... | [11.945646286010742, 2.2488279342651367] |
224e954b-a461-43f0-97a8-065fe6a43b3b | robust-decision-focused-learning-for-reward | 2304.03365 | null | https://arxiv.org/abs/2304.03365v1 | https://arxiv.org/pdf/2304.03365v1.pdf | Robust Decision-Focused Learning for Reward Transfer | Decision-focused (DF) model-based reinforcement learning has recently been introduced as a powerful algorithm which can focus on learning the MDP dynamics which are most relevant for obtaining high rewards. While this approach increases the performance of agents by focusing the learning towards optimizing for the rewar... | ['Finale Doshi-Velez', 'Omer Gottesman', 'Sonali Parbhoo', 'Abhishek Sharma'] | 2023-04-06 | null | null | null | null | ['model-based-reinforcement-learning'] | ['reasoning'] | [-2.54640758e-01 2.95264453e-01 -3.21972400e-01 2.25000843e-01
-9.02742624e-01 -5.53065538e-01 6.28940582e-01 3.84924501e-01
-5.99900007e-01 1.08512425e+00 -1.35889873e-02 -3.80933851e-01
-5.83237052e-01 -7.21125305e-01 -7.73587406e-01 -7.94700325e-01
-5.66171706e-01 5.48896134e-01 -1.56318128e-01 -2.32327297... | [4.157845497131348, 2.37015438079834] |
617501be-4788-4a52-821e-5c7ee02613d1 | a-compact-sequence-encoding-scheme-for-online | 2012.00873 | null | https://arxiv.org/abs/2012.00873v1 | https://arxiv.org/pdf/2012.00873v1.pdf | A compact sequence encoding scheme for online human activity recognition in HRI applications | Human activity recognition and analysis has always been one of the most active areas of pattern recognition and machine intelligence, with applications in various fields, including but not limited to exertion games, surveillance, sports analytics and healthcare. Especially in Human-Robot Interaction, human activity und... | ['Stefanos Kollias', 'Kostas Karpouzis', 'Georgios Tsatiris'] | 2020-12-01 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 4.45297241e-01 2.22511161e-02 -1.92981780e-01 -2.13512406e-01
-1.94315121e-01 -1.51948184e-01 4.34724659e-01 1.85632586e-01
-6.83980882e-01 5.29343009e-01 1.31931126e-01 -1.02672994e-01
-2.64134973e-01 -6.84792995e-01 -4.29811060e-01 -4.43929166e-01
-2.37206802e-01 4.13331032e-01 4.59304959e-01 -2.52410084... | [7.7573466300964355, 0.39160603284835815] |
55247958-ad8d-4047-a467-4b14bc114d7d | improving-sign-language-translation-with | 2105.12397 | null | https://arxiv.org/abs/2105.12397v1 | https://arxiv.org/pdf/2105.12397v1.pdf | Improving Sign Language Translation with Monolingual Data by Sign Back-Translation | Despite existing pioneering works on sign language translation (SLT), there is a non-trivial obstacle, i.e., the limited quantity of parallel sign-text data. To tackle this parallel data bottleneck, we propose a sign back-translation (SignBT) approach, which incorporates massive spoken language texts into SLT training.... | ['Houqiang Li', 'Junfu Pu', 'Weizhen Qi', 'Wengang Zhou', 'Hao Zhou'] | 2021-05-26 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhou_Improving_Sign_Language_Translation_With_Monolingual_Data_by_Sign_Back-Translation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhou_Improving_Sign_Language_Translation_With_Monolingual_Data_by_Sign_Back-Translation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['sign-language-recognition', 'sign-language-translation'] | ['computer-vision', 'computer-vision'] | [ 4.72781450e-01 -1.86709389e-01 -2.36775026e-01 -6.35562897e-01
-1.27585351e+00 -5.13338566e-01 5.53573310e-01 -7.71460474e-01
-3.63396972e-01 5.75407505e-01 6.73817992e-01 -1.98941007e-01
5.23874760e-01 -2.13368878e-01 -7.19004214e-01 -5.77014804e-01
4.89085793e-01 7.56725669e-01 7.92759806e-02 -2.52839416... | [9.207501411437988, -6.529805660247803] |
c2511266-2eab-4f58-a06f-fc34379d7d0f | robust-real-time-tracking-of-multiple-objects | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Possegger_Robust_Real-Time_Tracking_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Possegger_Robust_Real-Time_Tracking_2013_CVPR_paper.pdf | Robust Real-Time Tracking of Multiple Objects by Volumetric Mass Densities | Combining foreground images from multiple views by projecting them onto a common ground-plane has been recently applied within many multi-object tracking approaches. These planar projections introduce severe artifacts and constrain most approaches to objects moving on a common 2D ground-plane. To overcome these limitat... | ['Thomas Mauthner', 'Horst Bischof', 'Peter M. Roth', 'Horst Possegger', 'Sabine Sternig'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['3d-object-tracking'] | ['computer-vision'] | [ 8.86443704e-02 -4.06282604e-01 1.68916471e-02 9.17801186e-02
-8.98684204e-01 -7.86069632e-01 7.33064830e-01 2.29795098e-01
-3.56174827e-01 4.66315389e-01 -3.06142181e-01 2.78298277e-02
1.94966704e-01 -5.68135798e-01 -8.11757088e-01 -7.45091498e-01
-1.80107027e-01 8.92259419e-01 1.16452265e+00 4.93714869... | [6.65265417098999, -2.1780431270599365] |
7a974a8b-9ca6-445c-b2a4-297365893203 | qa-is-the-new-kr-question-answer-pairs-as | 2207.00630 | null | https://arxiv.org/abs/2207.00630v1 | https://arxiv.org/pdf/2207.00630v1.pdf | QA Is the New KR: Question-Answer Pairs as Knowledge Bases | In this position paper, we propose a new approach to generating a type of knowledge base (KB) from text, based on question generation and entity linking. We argue that the proposed type of KB has many of the key advantages of a traditional symbolic KB: in particular, it consists of small modular components, which can b... | ['John Wieting', 'Pat Verga', 'Alessandro Presta', 'Nitish Gupta', 'Michiel de Jong', 'William W. Cohen', 'Wenhu Chen'] | 2022-07-01 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-2.68754750e-01 1.07036805e+00 -2.85328209e-01 -3.24223101e-01
-1.11846828e+00 -6.05237007e-01 5.84685683e-01 7.66592562e-01
-2.46782139e-01 1.21795428e+00 2.06524432e-01 -5.53180814e-01
-4.97788221e-01 -1.28431571e+00 -6.66206121e-01 3.24309647e-01
2.47104853e-01 8.69245291e-01 1.10109746e+00 -9.40542161... | [10.322897911071777, 7.937964916229248] |
1db8b398-7ab3-46e5-84c4-a3a66a928d92 | learning-music-audio-representations-via-weak | 2112.04214 | null | https://arxiv.org/abs/2112.04214v2 | https://arxiv.org/pdf/2112.04214v2.pdf | Learning music audio representations via weak language supervision | Audio representations for music information retrieval are typically learned via supervised learning in a task-specific fashion. Although effective at producing state-of-the-art results, this scheme lacks flexibility with respect to the range of applications a model can have and requires extensively annotated datasets. ... | ['Gyorgy Fazekas', 'Elio Quinton', 'Emmanouil Benetos', 'Ilaria Manco'] | 2021-12-08 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 5.06144047e-01 1.14457399e-01 -2.05203503e-01 -2.55068362e-01
-1.60221660e+00 -8.36041510e-01 7.28669524e-01 9.56022069e-02
-3.91337276e-01 3.86034727e-01 5.02088547e-01 1.86155841e-01
-2.89814800e-01 -3.90386939e-01 -9.14676428e-01 -5.08997202e-01
-1.38157338e-01 4.51242715e-01 -1.43952593e-01 -3.27016681... | [15.539666175842285, 5.104557991027832] |
115e64b0-0cee-4a98-836f-5c9915df8b0a | multi-label-learning-based-deep-transfer | 1805.01282 | null | http://arxiv.org/abs/1805.01282v1 | http://arxiv.org/pdf/1805.01282v1.pdf | Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification | Deep Neural Network (DNN) has recently achieved outstanding performance in a
variety of computer vision tasks, including facial attribute classification.
The great success of classifying facial attributes with DNN often relies on a
massive amount of labelled data. However, in real-world applications, labelled
data are ... | ['Si Chen', 'Ni Zhuang', 'Chunhua Shen', 'Yan Yan', 'Hanzi Wang'] | 2018-05-03 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [ 3.30271631e-01 5.06116338e-02 -1.69390246e-01 -7.26187646e-01
-3.21611643e-01 -6.75572827e-02 4.69320208e-01 -4.78421431e-03
-3.04898858e-01 6.02264524e-01 -1.54359251e-01 1.06404416e-01
-1.64979398e-01 -9.18368161e-01 -4.51500416e-01 -1.00023687e+00
4.98114601e-02 5.04205346e-01 -7.62492046e-02 -1.40194356... | [13.508076667785645, 0.8130905032157898] |
f108787d-f613-4949-91d6-8efb14ea6226 | efficient-and-scalable-recommendation-via | 2207.05959 | null | https://arxiv.org/abs/2207.05959v2 | https://arxiv.org/pdf/2207.05959v2.pdf | Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation | Collaborative filtering (CF) is widely searched in recommendation with various types of solutions. Recent success of Graph Convolution Networks (GCN) in CF demonstrates the effectiveness of modeling high-order relationships through graphs, while repetitive graph convolution and iterative batch optimization limit their ... | ['Tommy W. S. Chow', 'Jianghong Ma', 'Tianjun Wei'] | 2022-07-13 | null | null | null | null | ['graph-sampling', 'graph-partitioning'] | ['graphs', 'graphs'] | [-3.53724957e-02 -1.84071973e-01 -3.33263665e-01 -1.91638514e-01
2.57320285e-01 -3.68024588e-01 2.51519144e-01 4.87559468e-01
-3.31140272e-02 3.66406083e-01 3.41176301e-01 -2.46065140e-01
-7.50707746e-01 -1.10152042e+00 -6.22246861e-01 -4.11136776e-01
-3.87498796e-01 2.59504408e-01 1.25105843e-01 -3.38449568... | [10.190051078796387, 5.626008033752441] |
e475cbd0-2009-436b-b9ec-caaa3a0b2dc1 | rethinking-table-parsing-using-graph-neural | 1905.13391 | null | https://arxiv.org/abs/1905.13391v2 | https://arxiv.org/pdf/1905.13391v2.pdf | Rethinking Table Recognition using Graph Neural Networks | Document structure analysis, such as zone segmentation and table recognition, is a complex problem in document processing and is an active area of research. The recent success of deep learning in solving various computer vision and machine learning problems has not been reflected in document structure analysis since co... | ['Faisal Shafait', 'Shah Rukh Qasim', 'Hassan Mahmood'] | 2019-05-31 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 2.97137231e-01 8.76121148e-02 -1.03987828e-01 -3.01717728e-01
-4.37918156e-01 -7.88377047e-01 5.52010059e-01 4.92167681e-01
-1.50573909e-01 3.35020304e-01 2.93018907e-01 -6.79872155e-01
-1.17768407e-01 -1.19140434e+00 -8.09418261e-01 -2.87006766e-01
-6.73180968e-02 6.29358649e-01 1.88199326e-01 -4.33614582... | [11.668123245239258, 2.924440622329712] |
14e726f8-f297-4984-b95c-88d7fd1d216b | adversarial-self-supervised-learning-for-semi | 2007.05934 | null | https://arxiv.org/abs/2007.05934v1 | https://arxiv.org/pdf/2007.05934v1.pdf | Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition | We consider the problem of semi-supervised 3D action recognition which has been rarely explored before. Its major challenge lies in how to effectively learn motion representations from unlabeled data. Self-supervised learning (SSL) has been proved very effective at learning representations from unlabeled data in the im... | ['Jiashi Feng', 'Xuecheng Nie', 'Liang Wang', 'Chenyang Si', 'Tieniu Tan', 'Wei Wang'] | 2020-07-12 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/123_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520035.pdf | eccv-2020-8 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 4.62794751e-01 -6.02646172e-03 -7.50280261e-01 -4.24662352e-01
-6.87486887e-01 -3.50169003e-01 6.27390087e-01 -7.28968084e-01
-2.00434074e-01 8.01105499e-01 5.17182112e-01 -1.53603449e-01
6.50840998e-02 -2.12898403e-01 -6.44218147e-01 -9.62224722e-01
4.79225479e-02 3.19891751e-01 1.32856384e-01 1.39387503... | [8.138470649719238, 0.657090961933136] |
e7a9a292-9fc8-4656-90a7-631c069a42f6 | kgplm-knowledge-guided-language-model-pre | 2012.03551 | null | https://arxiv.org/abs/2012.03551v1 | https://arxiv.org/pdf/2012.03551v1.pdf | KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning | Recent studies on pre-trained language models have demonstrated their ability to capture factual knowledge and applications in knowledge-aware downstream tasks. In this work, we present a language model pre-training framework guided by factual knowledge completion and verification, and use the generative and discrimina... | ['Qun Liu', 'Jinghui Xiao', 'Xin Jiang', 'Bin He'] | 2020-12-07 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 7.48119205e-02 3.26486647e-01 2.23250855e-02 -4.96033013e-01
-1.53686714e+00 -7.67309904e-01 7.40393937e-01 7.81468004e-02
-3.90656978e-01 8.69158745e-01 4.24075365e-01 -7.11881518e-01
-6.27832934e-02 -8.66673350e-01 -9.71677184e-01 -3.43127996e-01
3.26335043e-01 6.72300994e-01 3.39291751e-01 -6.16560638... | [11.162311553955078, 8.054126739501953] |
8a0abeab-9260-471b-8d69-a17f2dd51293 | metric-learning-improves-the-ability-of | 2302.14616 | null | https://arxiv.org/abs/2302.14616v1 | https://arxiv.org/pdf/2302.14616v1.pdf | Metric Learning Improves the Ability of Combinatorial Coverage Metrics to Anticipate Classification Error | Machine learning models are increasingly used in practice. However, many machine learning methods are sensitive to test or operational data that is dissimilar to training data. Out-of-distribution (OOD) data is known to increase the probability of error and research into metrics that identify what dissimilarities in da... | ['Laura Freeman', 'Tyler Cody'] | 2023-02-28 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.38968551e-01 -1.20996982e-01 -4.95897114e-01 -7.24026740e-01
-1.06874204e+00 -6.75098181e-01 2.92872578e-01 6.21546090e-01
-2.54333377e-01 6.65898561e-01 9.00151283e-02 -4.58043993e-01
-6.27584934e-01 -8.22137773e-01 -5.65100014e-01 -3.41194898e-01
9.25499573e-02 5.19231558e-01 -7.74883479e-02 4.02074128... | [8.873300552368164, 4.605966091156006] |
520c511b-1f17-4b58-a2af-3c9ad9605275 | multi-view-vision-to-geometry-knowledge | 2207.03128 | null | https://arxiv.org/abs/2207.03128v4 | https://arxiv.org/pdf/2207.03128v4.pdf | PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition | As two fundamental representation modalities of 3D objects, 3D point clouds and multi-view 2D images record shape information from different domains of geometric structures and visual appearances. In the current deep learning era, remarkable progress in processing such two data modalities has been achieved through resp... | ['Yue Qian', 'Junhui Hou', 'Qijian Zhang'] | 2022-07-07 | null | null | null | null | ['3d-shape-retrieval', '3d-shape-recognition'] | ['computer-vision', 'computer-vision'] | [-1.83273152e-01 -1.21470638e-01 -3.05473562e-02 -5.03056109e-01
-6.89524174e-01 -8.15647542e-01 8.16598415e-01 -3.14395353e-02
1.19565642e-02 -9.14864391e-02 -9.32758749e-02 -2.61890322e-01
1.79643761e-02 -9.87374783e-01 -1.05404139e+00 -4.88975734e-01
2.60609269e-01 7.70744979e-01 7.72174820e-02 -1.45315558... | [8.186348915100098, -3.416020631790161] |
28f3ad2f-1614-4a3b-b49b-e02a1863e98a | on-the-cross-modal-transfer-from-natural | 2204.08653 | null | https://arxiv.org/abs/2204.08653v1 | https://arxiv.org/pdf/2204.08653v1.pdf | On The Cross-Modal Transfer from Natural Language to Code through Adapter Modules | Pre-trained neural Language Models (PTLM), such as CodeBERT, are recently used in software engineering as models pre-trained on large source code corpora. Their knowledge is transferred to downstream tasks (e.g. code clone detection) via fine-tuning. In natural language processing (NLP), other alternatives for transfer... | ['Fatemeh H. Fard', 'Ramansh Grover', 'Divyam Goel'] | 2022-04-19 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [ 1.40820220e-02 2.98635632e-01 -5.58475368e-02 -2.03901425e-01
-4.89301503e-01 -7.33885169e-01 3.91977429e-01 6.56492040e-02
-4.88671780e-01 2.41048500e-01 6.94936067e-02 -7.89382637e-01
2.48129033e-02 -6.43859804e-01 -1.15020955e+00 -2.32998848e-01
-2.25842193e-01 1.55631810e-01 3.49608302e-01 -1.44370437... | [7.660797119140625, 7.8557353019714355] |
a81f459f-cd7a-44cc-abfa-a166877e6244 | getting-more-data-schoolkids-as-annotators | null | null | https://aclanthology.org/L12-1495 | https://aclanthology.org/L12-1495.pdf | Getting more data -- Schoolkids as annotators | We present a new way to get more morphologically and syntactically annotated data. We have developed an annotation editor tailored to school children to involve them in text annotation. Using this editor, they practice morphology and dependency-based syntax in the same way as they normally do at (Czech) schools, withou... | ["Barbora Hladk{\\'a}", 'Jirka Hana'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['text-annotation'] | ['natural-language-processing'] | [-2.34861076e-01 8.48187268e-01 -5.88974059e-02 -6.39393747e-01
-2.56889522e-01 -7.52815127e-01 2.52480537e-01 7.28675008e-01
-7.80369937e-01 7.62571037e-01 3.14020216e-01 -3.24620783e-01
7.22981766e-02 -7.79688358e-01 -5.21767419e-03 -1.30034715e-01
5.22823393e-01 8.31946254e-01 8.00192177e-01 -4.61748213... | [10.394126892089844, 10.120427131652832] |
0c54777b-a765-4d8a-b843-1dafc384685e | non-uniform-speaker-disentanglement-for | 2306.01861 | null | https://arxiv.org/abs/2306.01861v2 | https://arxiv.org/pdf/2306.01861v2.pdf | Non-uniform Speaker Disentanglement For Depression Detection From Raw Speech Signals | While speech-based depression detection methods that use speaker-identity features, such as speaker embeddings, are popular, they often compromise patient privacy. To address this issue, we propose a speaker disentanglement method that utilizes a non-uniform mechanism of adversarial SID loss maximization. This is achie... | ['Abeer Alwan', 'Vijay Ravi', 'Jinhan Wang'] | 2023-06-02 | null | null | null | null | ['disentanglement', 'speaker-identification'] | ['methodology', 'speech'] | [ 3.33241045e-01 3.71606916e-01 -1.61001489e-01 -6.18115962e-01
-1.30504537e+00 -6.78214669e-01 3.41033906e-01 3.25425953e-01
-5.76587915e-01 6.22685373e-01 4.08733577e-01 -4.99352008e-01
1.75865427e-01 -4.77062404e-01 -2.32540473e-01 -7.53236592e-01
-1.63521498e-01 1.03807254e-02 -5.19722462e-01 2.17754859... | [14.004011154174805, 5.885194301605225] |
df22bf3b-1959-4b6b-a6ab-2de8547a127b | interaction-part-mining-a-mid-level-approach | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Zhou_Interaction_Part_Mining_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhou_Interaction_Part_Mining_2015_CVPR_paper.pdf | Interaction Part Mining: A Mid-Level Approach for Fine-Grained Action Recognition | Modeling human-object interactions and manipulating motions lies in the heart of fine-grained action recognition. Previous methods heavily rely on explicit detection of the object being interacted, which requires intensive human labour on object annotation. To bypass this constraint and achieve better classification pe... | ['Yang Zhou', 'Richang Hong', 'Bingbing Ni', 'Qi Tian', 'Meng Wang'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 3.91440511e-01 -1.33069336e-01 -3.61567646e-01 -1.94173366e-01
-4.67639208e-01 -2.21677899e-01 4.40216005e-01 1.20287485e-01
-2.57500052e-01 3.77043873e-01 3.55323732e-01 3.34233642e-01
-1.69155329e-01 -7.81697333e-01 -6.38012052e-01 -6.87807322e-01
-2.33597890e-01 4.52003419e-01 9.82532203e-01 1.18994400... | [8.175671577453613, 0.46758249402046204] |
4f98f28a-baeb-406b-9756-41547d651496 | mbore-multi-objective-bayesian-optimisation | 2203.16912 | null | https://arxiv.org/abs/2203.16912v1 | https://arxiv.org/pdf/2203.16912v1.pdf | MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation | Optimisation problems often have multiple conflicting objectives that can be computationally and/or financially expensive. Mono-surrogate Bayesian optimisation (BO) is a popular model-based approach for optimising such black-box functions. It combines objective values via scalarisation and builds a Gaussian process (GP... | ['Alma A. M. Rahat', 'Tinkle Chugh', 'George De Ath'] | 2022-03-31 | null | null | null | null | ['density-ratio-estimation', 'bayesian-optimisation'] | ['methodology', 'methodology'] | [ 1.23255059e-01 -2.15731487e-01 -1.20512143e-01 -3.27562571e-01
-1.42590868e+00 -4.43406403e-01 8.03838670e-01 3.68384570e-01
-7.24869549e-01 1.00182700e+00 -4.95697968e-02 -2.15779126e-01
-8.73601019e-01 -7.30354071e-01 -6.27570093e-01 -1.16933286e+00
-2.87960142e-01 1.11628318e+00 3.08261842e-01 -1.68116391... | [6.305703163146973, 3.8288803100585938] |
871560f3-31a7-4a8a-927e-f0a77d111666 | evaluating-temporal-observation-based-causal | 2302.00064 | null | https://arxiv.org/abs/2302.00064v2 | https://arxiv.org/pdf/2302.00064v2.pdf | Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour | Autonomous robots are required to reason about the behaviour of dynamic agents in their environment. The creation of models to describe these relationships is typically accomplished through the application of causal discovery techniques. However, as it stands observational causal discovery techniques struggle to adequa... | ['Lars Kunze', 'Rhys Howard'] | 2023-01-31 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.63519782e-01 3.24635029e-01 -3.43826145e-01 -4.69559669e-01
-8.36294293e-02 -2.45719954e-01 1.38300657e+00 3.28639716e-01
-1.84092373e-01 1.09285867e+00 5.33076942e-01 -5.73394537e-01
-7.64129639e-01 -7.78746426e-01 -7.63479233e-01 -5.50304651e-01
-7.79320061e-01 6.00782275e-01 2.71205187e-01 -1.36694610... | [7.8574347496032715, 5.328951358795166] |
9dd6bad4-b6e9-45bf-888b-101874672619 | crossing-the-gap-domain-generalization-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ren_Crossing_the_Gap_Domain_Generalization_for_Image_Captioning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ren_Crossing_the_Gap_Domain_Generalization_for_Image_Captioning_CVPR_2023_paper.pdf | Crossing the Gap: Domain Generalization for Image Captioning | Existing image captioning methods are under the assumption that the training and testing data are from the same domain or that the data from the target domain (i.e., the domain that testing data lie in) are accessible. However, this assumption is invalid in real-world applications where the data from the target dom... | ['Wanli Ouyang', 'Yongdong Zhang', 'Hao Du', 'Tong He', 'Yan Lu', 'Shancheng Fang', 'Zhendong Mao', 'Yuchen Ren'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 5.04838407e-01 1.41834691e-01 -4.38749552e-01 -4.69340146e-01
-7.49300599e-01 -7.02356458e-01 6.50990725e-01 -2.59263456e-01
-2.64717966e-01 9.73448098e-01 1.10435456e-01 -2.50732422e-01
2.09336400e-01 -6.28279626e-01 -1.12006664e+00 -6.50613844e-01
4.58640844e-01 6.09896302e-01 1.21853583e-01 -1.67261772... | [10.337489128112793, 2.882642984390259] |
efd9486b-9cef-4cfe-9b8e-a06eda52e936 | view-inter-prediction-gan-unsupervised | 1811.02744 | null | http://arxiv.org/abs/1811.02744v1 | http://arxiv.org/pdf/1811.02744v1.pdf | View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions | In this paper we present a novel unsupervised representation learning
approach for 3D shapes, which is an important research challenge as it avoids
the manual effort required for collecting supervised data. Our method trains an
RNN-based neural network architecture to solve multiple view inter-prediction
tasks for each... | ['Yu-Shen Liu', 'Matthias Zwicker', 'Zhizhong Han', 'Mingyang Shang'] | 2018-11-07 | null | null | null | null | ['3d-point-cloud-linear-classification'] | ['computer-vision'] | [ 7.74932653e-03 2.61353225e-01 -9.73798987e-03 -5.33547640e-01
-9.13098931e-01 -7.07968473e-01 5.31437874e-01 -3.33690733e-01
3.76954943e-01 6.06892966e-02 4.42049801e-01 -5.82059892e-03
1.28026903e-01 -1.03490281e+00 -9.53129709e-01 -7.42693365e-01
3.84981871e-01 8.94631207e-01 -4.73086052e-02 9.35532227... | [8.338624954223633, -3.460123062133789] |
31eebbd3-6f0c-4300-8d44-0217cd5f710a | a-muze-net-music-generation-by-composing-the | 2111.12986 | null | https://arxiv.org/abs/2111.12986v1 | https://arxiv.org/pdf/2111.12986v1.pdf | A-Muze-Net: Music Generation by Composing the Harmony based on the Generated Melody | We present a method for the generation of Midi files of piano music. The method models the right and left hands using two networks, where the left hand is conditioned on the right hand. This way, the melody is generated before the harmony. The Midi is represented in a way that is invariant to the musical scale, and the... | ['Lior Wolf', 'Eliya Nachmani', 'Or Goren'] | 2021-11-25 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.82116050e-01 3.43996704e-01 1.45753203e-02 1.84472710e-01
-4.33575749e-01 -8.79530311e-01 3.43446016e-01 -1.27813384e-01
-2.57134378e-01 6.07086003e-01 5.04547298e-01 7.40788206e-02
-5.36933020e-02 -8.21201444e-01 -7.53539920e-01 -8.00723255e-01
-9.36002359e-02 5.88645756e-01 1.07503742e-01 -4.28900599... | [16.002500534057617, 5.493551731109619] |
add560c5-985f-4eb4-9e62-642c30cffabb | semantic-graph-convolutional-network-for | 1910.09183 | null | https://arxiv.org/abs/1910.09183v1 | https://arxiv.org/pdf/1910.09183v1.pdf | Semantic Graph Convolutional Network for Implicit Discourse Relation Classification | Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives communicating these relations. Modeling the semantic interactions between the two arguments of a relation has proven useful for detecting implic... | ['Jie zhou', 'Wei Cheng', 'Ruiying Geng', 'Yingxue Zhang', 'Ping Jian', 'Fandong Meng'] | 2019-10-21 | null | null | null | null | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 4.76228356e-01 1.14081407e+00 -3.42934400e-01 -3.67542088e-01
-2.39650577e-01 -6.63076162e-01 8.75332057e-01 6.30625069e-01
-7.41815194e-02 5.61860740e-01 7.32235134e-01 -8.41801465e-01
-1.44479182e-02 -1.10192692e+00 -5.57966709e-01 -1.91937968e-01
-1.59005776e-01 5.34540594e-01 5.54511189e-01 -6.89364374... | [10.71588134765625, 9.254990577697754] |
fb9d9d25-6c90-40bd-9408-737a1eddaf08 | reconstructing-humpty-dumpty-multi-feature | 2212.06023 | null | https://arxiv.org/abs/2212.06023v1 | https://arxiv.org/pdf/2212.06023v1.pdf | Reconstructing Humpty Dumpty: Multi-feature Graph Autoencoder for Open Set Action Recognition | Most action recognition datasets and algorithms assume a closed world, where all test samples are instances of the known classes. In open set problems, test samples may be drawn from either known or unknown classes. Existing open set action recognition methods are typically based on extending closed set methods by addi... | ['Christopher Funk', 'Anthony Hoogs', 'Ameya Shringi', 'Dawei Du'] | 2022-12-12 | null | null | null | null | ['open-set-action-recognition'] | ['computer-vision'] | [ 3.99839103e-01 9.99231860e-02 -2.26610646e-01 -2.04447180e-01
-5.69919348e-01 -4.67742860e-01 2.54072875e-01 -1.46923244e-01
-1.32730246e-01 8.14078093e-01 4.04363215e-01 2.86522955e-01
-3.96506906e-01 -6.68174386e-01 -1.01627994e+00 -8.17317069e-01
-2.73785204e-01 6.15465939e-01 3.84601772e-01 -5.69975078... | [8.464584350585938, 0.89312344789505] |
7a6c417f-66fa-4c17-a9b4-9eecb48f25e2 | a-convolution-recurrent-autoencoder-for | 1904.12413 | null | http://arxiv.org/abs/1904.12413v1 | http://arxiv.org/pdf/1904.12413v1.pdf | A convolution recurrent autoencoder for spatio-temporal missing data imputation | When sensors collect spatio-temporal data in a large geographical area, the
existence of missing data cannot be escaped. Missing data negatively impacts
the performance of data analysis and machine learning algorithms. In this
paper, we study deep autoencoders for missing data imputation in
spatio-temporal problems. We... | ['Amelia Regan', 'Reza Asadi'] | 2019-04-29 | null | null | null | null | ['multivariate-time-series-imputation'] | ['time-series'] | [-1.71867311e-01 -4.48736668e-01 -5.00237465e-01 -7.12669730e-01
-3.58238965e-01 9.24933404e-02 2.99036264e-01 -2.09863752e-01
-3.51313502e-01 1.13498640e+00 9.13018286e-01 -5.35386920e-01
-4.29322779e-01 -1.02843750e+00 -9.23320770e-01 -6.01094306e-01
-3.47343981e-01 1.57798558e-01 -3.27109665e-01 -1.05227210... | [6.7580060958862305, 2.662545919418335] |
bc4aa5ed-1aa7-416b-b95c-2db01e4eb88d | contextual-mask-auto-encoder-for-dense | 2208.07670 | null | https://arxiv.org/abs/2208.07670v3 | https://arxiv.org/pdf/2208.07670v3.pdf | ConTextual Masked Auto-Encoder for Dense Passage Retrieval | Dense passage retrieval aims to retrieve the relevant passages of a query from a large corpus based on dense representations (i.e., vectors) of the query and the passages. Recent studies have explored improving pre-trained language models to boost dense retrieval performance. This paper proposes CoT-MAE (ConTextual Mas... | ['Songlin Hu', 'Zhongyuan Wang', 'Zijia Lin', 'Meng Lin', 'Guangyuan Ma', 'Xing Wu'] | 2022-08-16 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-1.60383880e-01 -2.52294868e-01 -3.76454264e-01 -3.01829249e-01
-1.70617819e+00 -5.31847715e-01 8.20817769e-01 3.43612939e-01
-3.45565796e-01 6.40619397e-01 1.09338820e+00 3.51643488e-02
1.46157995e-01 -9.10589516e-01 -9.20625746e-01 -4.52364147e-01
-6.36456162e-02 5.72650611e-01 -1.17847547e-01 -4.43781912... | [11.42757797241211, 7.7344651222229] |
6977f633-ff73-4abf-884a-2f212cf0a080 | multi-domain-pose-network-for-multi-person | 1810.08338 | null | http://arxiv.org/abs/1810.08338v1 | http://arxiv.org/pdf/1810.08338v1.pdf | Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking | Multi-person human pose estimation and tracking in the wild is important and
challenging. For training a powerful model, large-scale training data are
crucial. While there are several datasets for human pose estimation, the best
practice for training on multi-dataset has not been investigated. In this
paper, we present... | ['Yongchen Lu', 'Linfu Wen', 'Tang Tang', 'Hengkai Guo', 'Guozhong Luo', 'Riwei Chen'] | 2018-10-19 | null | null | null | null | ['multi-person-pose-estimation-and-tracking'] | ['computer-vision'] | [-2.67468482e-01 -3.79954726e-02 -2.25919560e-01 -3.83139729e-01
-1.05300152e+00 -4.77698326e-01 2.32181370e-01 -4.05281037e-01
-6.55984342e-01 8.64213765e-01 4.33412075e-01 3.63295138e-01
3.38531137e-01 -2.53491372e-01 -8.81591558e-01 -3.29462796e-01
4.21105623e-02 9.27459955e-01 5.31795204e-01 -3.09086978... | [7.04201602935791, -0.8608893752098083] |
58b8ad78-0b63-47c7-bfca-80e309c898b1 | learnable-spatio-temporal-map-embeddings-for | 2211.07635 | null | https://arxiv.org/abs/2211.07635v1 | https://arxiv.org/pdf/2211.07635v1.pdf | Learnable Spatio-Temporal Map Embeddings for Deep Inertial Localization | Indoor localization systems often fuse inertial odometry with map information via hand-defined methods to reduce odometry drift, but such methods are sensitive to noise and struggle to generalize across odometry sources. To address the robustness problem in map utilization, we propose a data-driven prior on possible us... | ['Kris Kitani', 'Vivek Roy', 'Karnik Ram', 'Dennis Melamed'] | 2022-11-14 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-1.66433156e-01 6.44877702e-02 -1.90875009e-01 -5.02252877e-01
-8.37899745e-01 -5.73995352e-01 5.80252051e-01 2.88704842e-01
-6.64523423e-01 1.08966887e+00 5.71249068e-01 -2.33605042e-01
-3.69981825e-01 -1.08389354e+00 -1.06601930e+00 -3.22501242e-01
-3.60565752e-01 4.51807290e-01 1.64854512e-01 -3.11880589... | [6.463015079498291, 0.7126482725143433] |
bdfdb8c9-0f49-4f4e-955e-53647c4f82ca | entropy-difference-based-stereo-error | 1711.10412 | null | http://arxiv.org/abs/1711.10412v1 | http://arxiv.org/pdf/1711.10412v1.pdf | Entropy-difference based stereo error detection | Stereo depth estimation is error-prone; hence, effective error detection
methods are desirable. Most such existing methods depend on characteristics of
the stereo matching cost curve, making them unduly dependent on functional
details of the matching algorithm. As a remedy, we propose a novel error
detection approach b... | ['Ram Mohana Reddy Guddeti', 'Subhayan Mukherjee', 'Irene Cheng', 'Anup Basu'] | 2017-11-28 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 3.38966072e-01 -1.32025659e-01 -1.24029517e-02 -4.92500603e-01
-4.69880670e-01 -1.92431986e-01 2.66897082e-01 3.56690168e-01
-4.80624944e-01 7.39833415e-01 1.58142820e-01 -2.54382901e-02
1.03035986e-01 -8.89447510e-01 -4.41125125e-01 -5.10103822e-01
1.70609400e-01 -9.63552073e-02 6.10974967e-01 1.86399743... | [9.075057029724121, -2.4789514541625977] |
e2709cb5-cf42-4f03-ae38-05be4e15df1f | 2d-shapley-a-framework-for-fragmented-data | 2306.10473 | null | https://arxiv.org/abs/2306.10473v1 | https://arxiv.org/pdf/2306.10473v1.pdf | 2D-Shapley: A Framework for Fragmented Data Valuation | Data valuation -- quantifying the contribution of individual data sources to certain predictive behaviors of a model -- is of great importance to enhancing the transparency of machine learning and designing incentive systems for data sharing. Existing work has focused on evaluating data sources with the shared feature ... | ['Ruoxi Jia', 'Xi Chen', 'Xiangyu Chang', 'Hoang Anh Just', 'Zhihong Liu'] | 2023-06-18 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 2.50777423e-01 5.04883766e-01 -1.00679982e+00 -5.65695286e-01
-7.05699265e-01 -6.52102172e-01 4.65984762e-01 2.40792632e-01
-3.76495957e-01 1.08798969e+00 5.63100219e-01 -2.57696301e-01
-5.07364571e-01 -8.86606276e-01 -5.50114751e-01 -5.49649239e-01
-2.47221589e-01 1.33764192e-01 -2.95880437e-01 1.21974833... | [8.686422348022461, 5.404581546783447] |
49c1e2b2-f08b-4ffe-801a-5ba2a3c6034c | medical-scientific-table-to-text-generation | 2205.12368 | null | https://arxiv.org/abs/2205.12368v2 | https://arxiv.org/pdf/2205.12368v2.pdf | Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint | Structured (tabular) data in the preclinical and clinical domains contains valuable information about individuals and an efficient table-to-text summarization system can drastically reduce manual efforts to condense this data into reports. However, in practice, the problem is heavily impeded by the data paucity, data s... | ['Yike Guo', 'Vibhor Gupta', 'Bingyuan Chen', 'Tong Li', 'Julia Ive', 'Jingqing Zhang', 'Heng-Yi Wu'] | 2022-05-24 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 6.48806632e-01 5.09819269e-01 -1.89455450e-01 -3.21946979e-01
-1.15336680e+00 -6.03009880e-01 5.42415679e-01 9.91953611e-01
-2.89141625e-01 1.54885304e+00 5.06286502e-01 -2.04584792e-01
-1.47167176e-01 -6.19461954e-01 -6.54932976e-01 -1.83870614e-01
1.61340371e-01 6.54140711e-01 -1.45087183e-01 -2.87108719... | [12.233797073364258, 9.520767211914062] |
6070d4fe-8a67-4d8a-8377-4b8ca8e3c33a | locally-weighted-mean-phase-angle-lwmpa-based | 2109.08774 | null | https://arxiv.org/abs/2109.08774v1 | https://arxiv.org/pdf/2109.08774v1.pdf | Locally Weighted Mean Phase Angle (LWMPA) Based Tone Mapping Quality Index (TMQI-3) | High Dynamic Range (HDR) images are the ones that contain a greater range of luminosity as compared to the standard images. HDR images have a higher detail and clarity of structure, objects, and color, which the standard images lack. HDR images are useful in capturing scenes that pose high brightness, darker areas, and... | ['Sarwan Ali', 'Abdul Haseeb', 'Inaam Ul Hassan'] | 2021-09-17 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 3.18553686e-01 -4.79921073e-01 2.80190289e-01 -8.92874300e-02
-5.15894055e-01 -6.16793871e-01 4.57560241e-01 -1.75101623e-01
-2.11650342e-01 6.62083328e-01 2.12377924e-02 -2.16235578e-01
6.14207312e-02 -9.36916471e-01 -2.98840970e-01 -6.91772044e-01
1.55109763e-01 -1.75503418e-01 4.99808520e-01 -5.10077953... | [10.913724899291992, -2.39638352394104] |
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