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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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
84520eeb-8eae-409a-a5b3-ddbdcf4f0cf4 | os-msl-one-stage-multimodal-sequential-link | 2207.01241 | null | https://arxiv.org/abs/2207.01241v1 | https://arxiv.org/pdf/2207.01241v1.pdf | OS-MSL: One Stage Multimodal Sequential Link Framework for Scene Segmentation and Classification | Scene segmentation and classification (SSC) serve as a critical step towards the field of video structuring analysis. Intuitively, jointly learning of these two tasks can promote each other by sharing common information. However, scene segmentation concerns more on the local difference between adjacent shots while clas... | ['Bo Ren', 'Deqiang Jiang', 'Xinghua Jiang', 'Zhuoxuan Jiang', 'Di Yin', 'Lingfeng Qiao', 'Ye Liu'] | 2022-07-04 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 3.31669152e-01 -9.93738472e-02 -5.05795598e-01 -4.74982142e-01
-5.97649992e-01 -3.56963813e-01 6.75331235e-01 2.37992063e-01
-2.55024731e-01 4.27341491e-01 5.35791993e-01 3.03238630e-02
-8.76217522e-03 -6.11330390e-01 -5.07491171e-01 -6.57932460e-01
1.54598266e-01 5.92488758e-02 8.11179996e-01 -7.92349577... | [9.493596076965332, 0.6137098670005798] |
4fa06af9-3252-4948-91f5-8c41c57f08b1 | large-scale-evolution-of-convolutional-neural | 1703.05422 | null | http://arxiv.org/abs/1703.05422v1 | http://arxiv.org/pdf/1703.05422v1.pdf | Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing | This work presents a new algorithm called evolutionary exploration of
augmenting convolutional topologies (EXACT), which is capable of evolving the
structure of convolutional neural networks (CNNs). EXACT is in part modeled
after the neuroevolution of augmenting topologies (NEAT) algorithm, with
notable exceptions to a... | ['Travis Desell'] | 2017-03-15 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-9.60047841e-02 3.64620358e-01 4.79623824e-01 -2.15845510e-01
5.87917030e-01 -5.98529577e-01 5.78473985e-01 -1.99029908e-01
-1.02701592e+00 9.74476755e-01 -2.55076557e-01 -4.77129906e-01
-1.57183677e-01 -7.93281555e-01 -9.32716668e-01 -7.09187031e-01
-4.02333260e-01 5.98095894e-01 4.19880450e-01 -6.44424200... | [8.281147956848145, 3.243543863296509] |
cea1cd7b-fb2c-4ae8-bbf8-a9083744e449 | learning-6-dof-fine-grained-grasp-detection | 2301.11564 | null | https://arxiv.org/abs/2301.11564v1 | https://arxiv.org/pdf/2301.11564v1.pdf | Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding | Robotic grasping is a fundamental ability for a robot to interact with the environment. Current methods focus on how to obtain a stable and reliable grasping pose in object wise, while little work has been studied on part (shape)-wise grasping which is related to fine-grained grasping and robotic affordance. Parts can ... | ['Yue Zhang', 'Yu Zheng', 'Yi Ren', 'Penglei Sun', 'Yaoxian Song'] | 2023-01-27 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-2.29820967e-01 -2.22210675e-01 -1.94286227e-01 -5.56324601e-01
-4.40836966e-01 -8.24828923e-01 2.99518228e-01 -3.83900315e-01
1.76526740e-01 -9.44305584e-02 1.23373397e-01 5.91451637e-02
-4.08970952e-01 -8.37309599e-01 -1.20758498e+00 -5.90047896e-01
-2.20398858e-01 5.66413701e-01 2.84722596e-01 -3.63452822... | [5.737176895141602, -0.8964018225669861] |
3054a947-647d-4916-bdad-ddf753c95c6d | multi-level-cross-modal-feature-alignment-via | 2306.06066 | null | https://arxiv.org/abs/2306.06066v1 | https://arxiv.org/pdf/2306.06066v1.pdf | Multi-level Cross-modal Feature Alignment via Contrastive Learning towards Zero-shot Classification of Remote Sensing Image Scenes | Zero-shot classification of image scenes which can recognize the image scenes that are not seen in the training stage holds great promise of lowering the dependence on large numbers of labeled samples. To address the zero-shot image scene classification, the cross-modal feature alignment methods have been proposed in r... | ['Zhigang Han', 'Wei Yang', 'Zheng Li', 'Suqiang Ma', 'Chun Liu'] | 2023-05-31 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 4.34920281e-01 -4.95372593e-01 -1.02274515e-01 -4.02056009e-01
-7.88589656e-01 -1.76100716e-01 5.38925290e-01 2.46397197e-01
-1.69888377e-01 2.59982646e-01 -6.79645687e-02 3.79124552e-01
-4.78193551e-01 -8.79356802e-01 -3.66255015e-01 -1.13886619e+00
1.06546655e-01 1.13090068e-01 3.27104062e-01 -6.16835877... | [9.793902397155762, 1.9657878875732422] |
e7b7c65d-9f1b-4d15-bd2b-fd30609756a2 | predicting-real-time-scientific-experiments | 2204.11718 | null | https://arxiv.org/abs/2204.11718v1 | https://arxiv.org/pdf/2204.11718v1.pdf | Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement Learning | Life and physical sciences have always been quick to adopt the latest advances in machine learning to accelerate scientific discovery. Examples of this are cell segmentation or cancer detection. Nevertheless, these exceptional results are based on mining previously created datasets to discover patterns or trends. Recen... | ['Juan Manuel Parrilla-Gutierrez'] | 2022-04-25 | null | null | null | null | ['scientific-results-extraction'] | ['natural-language-processing'] | [ 1.93698362e-01 9.71326381e-02 8.22768286e-02 9.94035453e-02
-1.02901213e-01 -5.76817572e-01 7.99255192e-01 7.95147941e-02
-3.75904739e-01 8.25406194e-01 -6.40038013e-01 -4.29829061e-01
-2.16846183e-01 -8.90842140e-01 -1.07069850e+00 -9.81296360e-01
-2.28556439e-01 5.96443832e-01 3.59767109e-01 -1.81535050... | [7.821029186248779, 3.070791721343994] |
468b8497-bfb2-46f0-b48e-eafd68db3b43 | corpus-creation-and-analysis-for-named-entity | null | null | https://aclanthology.org/P19-2025 | https://aclanthology.org/P19-2025.pdf | Corpus Creation and Analysis for Named Entity Recognition in Telugu-English Code-Mixed Social Media Data | Named Entity Recognition(NER) is one of the important tasks in Natural Language Processing(NLP) and also is a subtask of Information Extraction. In this paper we present our work on NER in Telugu-English code-mixed social media data. Code-Mixing, a progeny of multilingualism is a way in which multilingual people expres... | ['Vamshi Krishna Srirangam', 'Manish Shrivastava', 'Appidi Abhinav Reddy', 'Vinay Singh'] | 2019-07-01 | null | null | null | acl-2019-7 | ['entity-extraction'] | ['natural-language-processing'] | [-4.80156451e-01 1.58994600e-01 1.15757458e-01 -4.05582935e-01
-7.97500253e-01 -7.71468759e-01 7.49067545e-01 6.19713366e-01
-7.96086133e-01 1.25112188e+00 5.08538961e-01 -5.54912090e-01
3.61059248e-01 -6.71016932e-01 -4.31898355e-01 -2.76400924e-01
-5.07154278e-02 3.23644876e-01 2.45676652e-01 -2.71099478... | [9.802860260009766, 9.89149284362793] |
fc41f0ba-53ad-44ba-bee2-2b61649478ae | dehazenet-an-end-to-end-system-for-single | 1601.07661 | null | http://arxiv.org/abs/1601.07661v2 | http://arxiv.org/pdf/1601.07661v2.pdf | DehazeNet: An End-to-End System for Single Image Haze Removal | Single image haze removal is a challenging ill-posed problem. Existing
methods use various constraints/priors to get plausible dehazing solutions. The
key to achieve haze removal is to estimate a medium transmission map for an
input hazy image. In this paper, we propose a trainable end-to-end system
called DehazeNet, f... | ['DaCheng Tao', 'Chunmei Qing', 'Xiangmin Xu', 'Kui Jia', 'Bolun Cai'] | 2016-01-28 | null | null | null | null | ['single-image-haze-removal'] | ['computer-vision'] | [ 2.83887655e-01 -2.93997109e-01 6.85624003e-01 -3.30509663e-01
-4.41195935e-01 5.86525188e-04 3.07697892e-01 -4.04841214e-01
-2.88426816e-01 4.56042886e-01 2.90907267e-02 -1.94476932e-01
-1.32067099e-01 -1.07312107e+00 -8.53251338e-01 -1.19804442e+00
-1.30705044e-01 -2.29202986e-01 3.34503382e-01 -5.18117607... | [10.939643859863281, -3.2091450691223145] |
29b7775d-18c9-44b4-8b25-911348d8286f | alp-data-augmentation-using-lexicalized-pcfgs | 2112.11916 | null | https://arxiv.org/abs/2112.11916v1 | https://arxiv.org/pdf/2112.11916v1.pdf | ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification | Data augmentation has been an important ingredient for boosting performances of learned models. Prior data augmentation methods for few-shot text classification have led to great performance boosts. However, they have not been designed to capture the intricate compositional structure of natural language. As a result, t... | ['Yo-Sub Han', 'Jeong-Won Cha', 'Seong Joon Oh', 'Daecheol Woo', 'Hazel Kim'] | 2021-12-16 | null | null | null | null | ['few-shot-text-classification', 'semi-supervised-text-classification-1'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.21292633e-01 4.51471925e-01 -4.62526441e-01 -6.96379364e-01
-8.25599074e-01 -2.70817757e-01 8.10363948e-01 3.15418184e-01
-3.23603123e-01 8.75021279e-01 4.99196857e-01 -4.08280581e-01
3.01771462e-01 -8.73426020e-01 -3.17932814e-01 -6.59851193e-01
4.05547768e-01 6.80024564e-01 3.22963148e-02 -6.99778438... | [10.949853897094727, 8.229547500610352] |
a018c791-af65-4363-84dd-2687d7cc87f9 | virel-unsupervised-visual-relations-discovery | 2207.00590 | null | https://arxiv.org/abs/2207.00590v1 | https://arxiv.org/pdf/2207.00590v1.pdf | ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy | Visual relations form the basis of understanding our compositional world, as relationships between visual objects capture key information in a scene. It is then advantageous to learn relations automatically from the data, as learning with predefined labels cannot capture all possible relations. However, current relatio... | ['Jure Leskovec', 'Tailin Wu', 'Daniel Zeng'] | 2022-07-04 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [ 3.12783599e-01 5.60455024e-01 -3.36180270e-01 -4.52770561e-01
-1.47908390e-01 -8.51314425e-01 8.23097110e-01 5.35130322e-01
1.93703160e-01 2.74939746e-01 3.81823599e-01 -5.19163013e-01
-3.64811659e-01 -8.53036106e-01 -7.68016934e-01 -2.93841392e-01
-3.30242008e-01 7.48269975e-01 4.01522249e-01 -5.95017662... | [10.41714859008789, 1.6806588172912598] |
ff47498e-3cd5-4029-875a-9444f0d15324 | learning-foresightful-dense-visual-affordance | 2303.11057 | null | https://arxiv.org/abs/2303.11057v2 | https://arxiv.org/pdf/2303.11057v2.pdf | Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation | Understanding and manipulating deformable objects (e.g., ropes and fabrics) is an essential yet challenging task with broad applications. Difficulties come from complex states and dynamics, diverse configurations and high-dimensional action space of deformable objects. Besides, the manipulation tasks usually require mu... | ['Hao Dong', 'Chuanruo Ning', 'Ruihai Wu'] | 2023-03-20 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-1.73749745e-01 -2.20775455e-01 -5.84489226e-01 -9.87644568e-02
-2.34990656e-01 -7.22882986e-01 2.32875302e-01 -4.78362799e-01
-1.76611245e-01 8.75324190e-01 1.92702964e-01 4.01417352e-02
-4.69927073e-01 -3.59851897e-01 -8.45210671e-01 -7.69580901e-01
-1.97102770e-01 5.40058792e-01 1.75254792e-01 -2.89930522... | [4.785405158996582, 0.6302896738052368] |
9e5b06e5-75f3-4d3f-b9b9-92d102c9c966 | unsupervised-single-image-deraining-with-self | 1811.08575 | null | http://arxiv.org/abs/1811.08575v1 | http://arxiv.org/pdf/1811.08575v1.pdf | Unsupervised Single Image Deraining with Self-supervised Constraints | Most existing single image deraining methods require learning supervised
models from a large set of paired synthetic training data, which limits their
generality, scalability and practicality in real-world multimedia applications.
Besides, due to lack of labeled-supervised constraints, directly applying
existing unsupe... | ['Jianxin Lin', 'Zhibo Chen', 'Xin Jin', 'Zhikai Chen', 'Wei Zhou'] | 2018-11-21 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 4.20892000e-01 -1.18304223e-01 3.37941319e-01 -4.48307097e-01
-7.46735871e-01 -3.36491913e-01 4.25987840e-01 -6.15850210e-01
-2.19665408e-01 9.95177865e-01 -1.78449541e-01 -2.77417183e-01
3.10434729e-01 -9.36941743e-01 -8.11428845e-01 -1.35439587e+00
2.74347365e-01 -1.40641168e-01 -1.02478296e-01 -1.36098295... | [10.933990478515625, -3.170112371444702] |
2fa04522-cb10-4253-b046-bb577208113b | cross-domain-ner-using-cross-domain-language | null | null | https://aclanthology.org/P19-1236 | https://aclanthology.org/P19-1236.pdf | Cross-Domain NER using Cross-Domain Language Modeling | Due to limitation of labeled resources, cross-domain named entity recognition (NER) has been a challenging task. Most existing work considers a supervised setting, making use of labeled data for both the source and target domains. A disadvantage of such methods is that they cannot train for domains without NER data. To... | ['Yue Zhang', 'Chen Jia', 'Xiaobo Liang'] | 2019-07-01 | null | null | null | acl-2019-7 | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [ 2.26049691e-01 3.22669856e-02 -4.36861962e-01 -6.06868088e-01
-8.04726064e-01 -1.08868217e+00 5.99695385e-01 -1.02686144e-01
-8.10281694e-01 1.13839996e+00 -1.03831850e-02 -1.09026648e-01
2.15588212e-01 -7.11076260e-01 -5.69486976e-01 -2.75090456e-01
4.12962765e-01 8.15916359e-01 5.53162932e-01 -3.50989640... | [9.84460735321045, 9.538708686828613] |
de44a022-1734-490e-8c99-cc7b8bd3c5af | nbc-softmax-darkweb-author-fingerprinting-and | 2212.08184 | null | https://arxiv.org/abs/2212.08184v1 | https://arxiv.org/pdf/2212.08184v1.pdf | NBC-Softmax : Darkweb Author fingerprinting and migration tracking | Metric learning aims to learn distances from the data, which enhances the performance of similarity-based algorithms. An author style detection task is a metric learning problem, where learning style features with small intra-class variations and larger inter-class differences is of great importance to achieve better p... | ['Marius Portmann', 'Shekhar S. Chandra', 'Gayan K. Kulatilleke'] | 2022-12-15 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-1.64124906e-01 -5.59950650e-01 -3.27446461e-01 -7.68262684e-01
-6.75954580e-01 -4.49944705e-01 5.18586397e-01 -6.19668420e-03
-5.53786874e-01 5.86251855e-01 -7.21750334e-02 6.73799217e-02
-1.85337976e-01 -6.12094283e-01 -2.88150728e-01 -5.31894147e-01
-1.56322733e-01 2.67024279e-01 2.10089698e-01 -1.76123619... | [9.489439964294434, 3.27461314201355] |
5068df03-747f-4a0d-9a8f-3c762dbe73f4 | unsupervised-representation-learning-for-gaze | 1911.06939 | null | https://arxiv.org/abs/1911.06939v4 | https://arxiv.org/pdf/1911.06939v4.pdf | Unsupervised Representation Learning for Gaze Estimation | Although automatic gaze estimation is very important to a large variety of application areas, it is difficult to train accurate and robust gaze models, in great part due to the difficulty in collecting large and diverse data (annotating 3D gaze is expensive and existing datasets use different setups). To address this i... | ['Jean-Marc Odobez', 'Yu Yu'] | 2019-11-16 | unsupervised-representation-learning-for-gaze-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Unsupervised_Representation_Learning_for_Gaze_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_Unsupervised_Representation_Learning_for_Gaze_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['gaze-redirection', 'head-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.88389763e-01 2.22223356e-01 -1.18090948e-02 -5.64590335e-01
-9.92150530e-02 -2.36662447e-01 5.02559185e-01 -5.06677389e-01
-3.78549814e-01 4.95293230e-01 9.84103829e-02 3.71110067e-02
-1.36527255e-01 -1.24156617e-01 -7.78758764e-01 -8.36119950e-01
2.98085481e-01 4.86048348e-02 1.97103918e-01 -2.53387332... | [14.111705780029297, 0.05675153061747551] |
03ec8d2e-fe09-40f0-9af0-bea69440e55b | cross-lingual-transfer-of-cognitive | 2302.12695 | null | https://arxiv.org/abs/2302.12695v2 | https://arxiv.org/pdf/2302.12695v2.pdf | Cross-Lingual Transfer of Cognitive Processing Complexity | When humans read a text, their eye movements are influenced by the structural complexity of the input sentences. This cognitive phenomenon holds across languages and recent studies indicate that multilingual language models utilize structural similarities between languages to facilitate cross-lingual transfer. We use s... | ['Lisa Beinborn', 'Nora Hollenstein', 'Charlotte Pouw'] | 2023-02-24 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-3.42751980e-01 -3.42464745e-01 -1.96894333e-01 -3.60217571e-01
-4.69662428e-01 -8.58137608e-01 8.89859140e-01 5.09580731e-01
-1.00906837e+00 3.28719795e-01 4.74077761e-01 -7.98112929e-01
-1.22048780e-01 -3.67152900e-01 -4.70111370e-01 -2.04943009e-02
2.20275268e-01 3.32389891e-01 1.51115015e-01 -4.96630996... | [10.80453109741211, 9.92039680480957] |
5d3683da-66be-46ce-8257-c4fd46a0e74d | p2m-detrack-processing-in-pixel-in-memory-for | 2205.14285 | null | https://arxiv.org/abs/2205.14285v1 | https://arxiv.org/pdf/2205.14285v1.pdf | P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking | Today's high resolution, high frame rate cameras in autonomous vehicles generate a large volume of data that needs to be transferred and processed by a downstream processor or machine learning (ML) accelerator to enable intelligent computing tasks, such as multi-object detection and tracking. The massive amount of data... | ['Peter A. Beerel', 'Akhilesh R. Jaiswal', 'Ajey P. Jacob', 'Wael Abd-Almageed', 'Andrew Schmidt', 'Ravi T. Lakkireddy', 'Shunlin Lu', 'Mulin Tian', 'Zixu Wang', 'Zeyu Liu', 'Joe Mathai', 'Zihan Yin', 'Souvik Kundu', 'Gourav Datta'] | 2022-05-28 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 5.09704828e-01 -1.63487177e-02 -4.56152521e-02 -2.85421550e-01
-5.85621893e-01 -4.11030233e-01 4.42142367e-01 1.26887545e-01
-9.30497408e-01 1.23150043e-01 -6.43481016e-01 -4.85479116e-01
5.22903621e-01 -7.44662941e-01 -1.05384302e+00 -6.89830065e-01
3.36011261e-01 -1.83466107e-01 6.22848690e-01 2.36649841... | [8.298600196838379, 2.407804012298584] |
3085ae14-8faf-4d26-a3f2-412a8a2e06b5 | arigan-synthetic-arabidopsis-plants-using | 1709.00938 | null | http://arxiv.org/abs/1709.00938v1 | http://arxiv.org/pdf/1709.00938v1.pdf | ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network | In recent years, there has been an increasing interest in image-based plant
phenotyping, applying state-of-the-art machine learning approaches to tackle
challenging problems, such as leaf segmentation (a multi-instance problem) and
counting. Most of these algorithms need labelled data to learn a model for the
task at h... | ['Sotirios A. Tsaftaris', 'Hanno Scharr', 'Mario Valerio Giuffrida'] | 2017-09-04 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 8.63938749e-01 3.87176365e-01 2.72553325e-01 1.91529244e-02
-4.30552065e-01 -1.11927772e+00 5.03033280e-01 -3.85420211e-02
-1.08051542e-02 7.55041063e-01 -7.84257770e-01 -7.20565200e-01
1.15282319e-01 -1.36282837e+00 -1.05629528e+00 -8.73044491e-01
2.33122244e-01 7.94394791e-01 8.64110067e-02 -2.03164481... | [9.125652313232422, -1.5299009084701538] |
a6f46d92-4767-45c2-9460-cbd5c4c42089 | varitex-variational-neural-face-textures | 2104.05988 | null | https://arxiv.org/abs/2104.05988v3 | https://arxiv.org/pdf/2104.05988v3.pdf | VariTex: Variational Neural Face Textures | Deep generative models can synthesize photorealistic images of human faces with novel identities. However, a key challenge to the wide applicability of such techniques is to provide independent control over semantically meaningful parameters: appearance, head pose, face shape, and facial expressions. In this paper, we ... | ['Otmar Hilliges', 'Thabo Beeler', 'Gengyan Li', 'Abhimitra Meka', 'Marcel C. Bühler'] | 2021-04-13 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Buhler_VariTex_Variational_Neural_Face_Textures_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Buhler_VariTex_Variational_Neural_Face_Textures_ICCV_2021_paper.pdf | iccv-2021-1 | ['face-model'] | ['computer-vision'] | [ 1.99728951e-01 4.81642485e-01 1.61306322e-01 -6.66240692e-01
-7.06821978e-01 -6.82728350e-01 8.40177059e-01 -7.19893456e-01
1.16172470e-02 6.10250592e-01 4.78898019e-01 4.25776899e-01
2.90754348e-01 -5.84719777e-01 -1.01993454e+00 -7.85145283e-01
3.28151733e-01 5.25274038e-01 -3.42054218e-01 -1.72342002... | [12.717199325561523, -0.29717379808425903] |
bf903e0c-c1f0-476f-9985-ca4e800157b8 | parameter-sensitivity-of-deep-feature-based | 2208.10743 | null | https://arxiv.org/abs/2208.10743v1 | https://arxiv.org/pdf/2208.10743v1.pdf | Parameter Sensitivity of Deep-Feature based Evaluation Metrics for Audio Textures | Standard evaluation metrics such as the Inception score and Fr\'echet Audio Distance provide a general audio quality distance metric between the synthesized audio and reference clean audio. However, the sensitivity of these metrics to variations in the statistical parameters that define an audio texture is not well stu... | ['Lonce Wyse', 'Zhuoyao Li', 'Purnima Kamath', 'Zequn Gong', 'Yize Wei', 'Chitralekha Gupta'] | 2022-08-23 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 2.29821101e-01 -5.67366421e-01 4.59355593e-01 -2.74438530e-01
-1.44046462e+00 -8.56345057e-01 4.97225761e-01 5.86996675e-01
-1.93073407e-01 3.86574209e-01 5.04140258e-01 6.49430379e-02
-6.16509378e-01 -5.34942687e-01 -2.31815204e-01 -8.33307683e-01
-4.95664895e-01 -5.12751043e-02 5.02346992e-01 -3.13464075... | [15.450896263122559, 5.5339508056640625] |
542ad8cc-bf39-4ba9-b591-79454afbc265 | d-d-learning-human-dynamics-from-dynamic | 2209.08790 | null | https://arxiv.org/abs/2209.08790v1 | https://arxiv.org/pdf/2209.08790v1.pdf | D&D: Learning Human Dynamics from Dynamic Camera | 3D human pose estimation from a monocular video has recently seen significant improvements. However, most state-of-the-art methods are kinematics-based, which are prone to physically implausible motions with pronounced artifacts. Current dynamics-based methods can predict physically plausible motion but are restricted ... | ['Cewu Lu', 'Gang Yu', 'Gang Liu', 'Chao Xu', 'Siyuan Bian', 'Jiefeng Li'] | 2022-09-19 | null | null | null | null | ['3d-human-pose-estimation', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [-2.74273425e-01 5.30823395e-02 -2.55025983e-01 1.44871697e-01
-3.99388015e-01 -4.31728274e-01 5.95312893e-01 -6.00114465e-01
-2.32340157e-01 7.58724570e-01 3.87444019e-01 4.09423606e-03
2.00934038e-01 -4.48656112e-01 -1.19834149e+00 -5.49032032e-01
-5.84859513e-02 6.21695876e-01 2.91033566e-01 -4.11552846... | [7.108145713806152, -0.5382227301597595] |
d70a963c-cc48-4636-ac16-5f9626bcdb92 | named-entity-and-relation-extraction-with | 2212.01612 | null | https://arxiv.org/abs/2212.01612v1 | https://arxiv.org/pdf/2212.01612v1.pdf | Named Entity and Relation Extraction with Multi-Modal Retrieval | Multi-modal named entity recognition (NER) and relation extraction (RE) aim to leverage relevant image information to improve the performance of NER and RE. Most existing efforts largely focused on directly extracting potentially useful information from images (such as pixel-level features, identified objects, and asso... | ['Wei Lu', 'Kewei Tu', 'Pengjun Xie', 'Yong Jiang', 'Jiong Cai', 'Xinyu Wang'] | 2022-12-03 | null | null | null | null | ['multi-modal-named-entity-recognition'] | ['natural-language-processing'] | [ 1.73987731e-01 -7.48479292e-02 -7.70085454e-02 -2.03727946e-01
-1.43112957e+00 -6.50232971e-01 7.46189058e-01 1.37148172e-01
-5.93821943e-01 5.74710011e-01 2.66195029e-01 2.83310339e-02
2.25690622e-02 -7.11829603e-01 -6.76429212e-01 -5.15431166e-01
4.93748039e-01 2.30076835e-01 5.89335740e-01 -3.05739902... | [10.795315742492676, 1.4694178104400635] |
69cca4a6-04bc-4b28-8792-2bffcf9fd6bb | sketching-image-gist-human-mimetic | 2007.08760 | null | https://arxiv.org/abs/2007.08760v1 | https://arxiv.org/pdf/2007.08760v1.pdf | Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph Generation | Scene graph aims to faithfully reveal humans' perception of image content. When humans analyze a scene, they usually prefer to describe image gist first, namely major objects and key relations in a scene graph. This humans' inherent perceptive habit implies that there exists a hierarchical structure about humans' prefe... | ['Xilin Chen', 'Wenbin Wang', 'Ruiping Wang', 'Shiguang Shan'] | 2020-07-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1834_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580222.pdf | eccv-2020-8 | ['scene-parsing'] | ['computer-vision'] | [ 5.56350768e-01 4.85265464e-01 -8.27980638e-02 -6.55124784e-01
-1.27846420e-01 -4.90900576e-02 4.92068201e-01 3.91095221e-01
-2.02429146e-01 1.83665663e-01 5.27186036e-01 -2.71919161e-01
-1.64167471e-02 -1.07786059e+00 -8.13635647e-01 -3.49846661e-01
6.48180246e-02 1.48777172e-01 5.79525948e-01 -3.59059989... | [10.311270713806152, 1.4774476289749146] |
c59f98b9-d9b3-4274-9987-d36207dc1c69 | analysis-of-mesh-based-motion-compensation-in | 2302.01594 | null | https://arxiv.org/abs/2302.01594v1 | https://arxiv.org/pdf/2302.01594v1.pdf | Analysis of mesh-based motion compensation in wavelet lifting of dynamical 3-D+t CT data | Factorized in the lifting structure, the wavelet transform can easily be extended by arbitrary compensation methods. Thereby, the transform can be adapted to displacements in the signal without losing the ability of perfect reconstruction. This leads to an improvement of scalability. In temporal direction of dynamic me... | ['André Kaup', 'Jürgen Seiler', 'Thomas Richter', 'Wolfgang Schnurrer'] | 2023-02-03 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 5.41549444e-01 2.85826087e-01 -2.40462422e-02 -4.30365205e-02
-6.34814441e-01 -1.95860520e-01 2.94037730e-01 -3.86203676e-02
-3.06792587e-01 8.01616251e-01 4.49313164e-01 5.52272648e-02
-8.29913765e-02 -8.95917594e-01 -5.91563702e-01 -6.09771848e-01
-4.98298585e-01 3.32405090e-01 7.91355252e-01 -4.39104795... | [11.559479713439941, -2.3497023582458496] |
03209bf0-b5bc-41c6-b0d4-bea6ca379f0b | from-heuristics-to-language-models-a-journey | null | null | https://ceur-ws.org/Vol-3320/paper6.pdf | https://ceur-ws.org/Vol-3320/paper6.pdf | From Heuristics to Language Models: A Journey Through the Universe of Semantic Table Interpretation with DAGOBAH | This paper presents DAGOBAH SL 2022, a semantic table interpretation system that has been continuously improved over the last four years when participating in the SemTab challenge. This year, we have improved the lookup coverage using external resources and we have integrated language models for better understanding th... | ['Jixiong Liu and Raphaël Troncy', 'Thomas Labbé', 'Yoan Chabot', 'Viet-Phi Huynh'] | 2022-10-25 | null | null | null | semtab-iswc-2022-10 | ['column-type-annotation', 'cell-entity-annotation'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.07197289e-02 5.73639631e-01 -3.60644341e-01 -6.58397138e-01
-1.07624280e+00 -5.66357315e-01 5.68040073e-01 7.75718272e-01
-4.79813039e-01 1.06104589e+00 5.94883204e-01 -6.31357074e-01
-5.32375909e-02 -9.13982749e-01 -7.94406295e-01 3.39516133e-01
8.64822492e-02 1.26911747e+00 4.14008826e-01 -7.13117301... | [9.558639526367188, 7.931718826293945] |
0d9ebb87-eae4-4a50-a0fb-e28edb48bd8e | learning-how-to-interact-with-a-complex | 2204.10374 | null | https://arxiv.org/abs/2204.10374v1 | https://arxiv.org/pdf/2204.10374v1.pdf | Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning | Hierarchical Reinforcement Learning (HRL) allows interactive agents to decompose complex problems into a hierarchy of sub-tasks. Higher-level tasks can invoke the solutions of lower-level tasks as if they were primitive actions. In this work, we study the utility of hierarchical decompositions for learning an appropria... | ['Doina Precup', 'Philippe Hamel', 'Tyler Jackson', 'Zafarali Ahmed', 'Daniel Toyama', 'Anita Gergely', 'Amelia Glaese', 'Gheorghe Comanici'] | 2022-04-21 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 8.14598352e-02 3.96738231e-01 1.00961290e-02 -3.87020111e-02
-5.98238349e-01 -8.47218812e-01 4.93268162e-01 -3.76993239e-01
-4.12156135e-01 6.69676244e-01 -4.12972234e-02 -4.54323769e-01
-2.60517150e-01 -7.27357090e-01 -7.66383708e-01 -6.99157298e-01
-1.63895980e-01 7.61937082e-01 4.64760512e-01 -4.65909034... | [4.109830379486084, 1.4150550365447998] |
8f7d9151-d0a3-4c3e-b60d-b89bf185dded | structured-context-transformer-for-generic | 2206.02985 | null | https://arxiv.org/abs/2206.02985v1 | https://arxiv.org/pdf/2206.02985v1.pdf | Structured Context Transformer for Generic Event Boundary Detection | Generic Event Boundary Detection (GEBD) aims to detect moments where humans naturally perceive as event boundaries. In this paper, we present Structured Context Transformer (or SC-Transformer) to solve the GEBD task, which can be trained in an end-to-end fashion. Specifically, we use the backbone convolutional neural n... | ['Longyin Wen', 'Tiejian Luo', 'Libo Zhang', 'YuFei Wang', 'Dexiang Hong', 'Xinyao Wang', 'CongCong Li'] | 2022-06-07 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 2.27696270e-01 -2.95253485e-01 1.92071378e-01 -3.58823568e-01
-6.14403844e-01 -3.23694855e-01 4.14041936e-01 3.28594655e-01
-6.15949571e-01 3.18373799e-01 1.53052971e-01 9.69215017e-03
9.18819010e-02 -4.97428507e-01 -6.60118401e-01 -6.42385364e-01
-2.04284742e-01 -1.36511445e-01 5.81311345e-01 2.03977615... | [8.660748481750488, 0.42408740520477295] |
456d2fee-ec23-49ee-b9bf-8d60b7c01e08 | translations-as-additional-contexts-for | 1806.05516 | null | http://arxiv.org/abs/1806.05516v1 | http://arxiv.org/pdf/1806.05516v1.pdf | Translations as Additional Contexts for Sentence Classification | In sentence classification tasks, additional contexts, such as the
neighboring sentences, may improve the accuracy of the classifier. However,
such contexts are domain-dependent and thus cannot be used for another
classification task with an inappropriate domain. In contrast, we propose the
use of translated sentences ... | ['Seung-won Hwang', 'Kyungjae Lee', 'Jinyeong Yeo', 'Reinald Kim Amplayo'] | 2018-06-14 | null | null | null | null | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 4.46582615e-01 -1.07777961e-01 -1.48372516e-01 -7.68770218e-01
-9.59773481e-01 -7.02492476e-01 5.13622224e-01 2.96284378e-01
-5.84938109e-01 1.04788971e+00 3.12682509e-01 -5.06005704e-01
2.44985193e-01 -5.06965160e-01 -3.80869597e-01 -5.61515093e-01
4.24791932e-01 1.51372656e-01 4.97475117e-01 -5.63612044... | [10.95880126953125, 9.070988655090332] |
8baf7bbd-5272-4f24-b379-425d3f9a77f6 | perturbation-analysis-of-randomized-svd-and | 2203.10262 | null | https://arxiv.org/abs/2203.10262v2 | https://arxiv.org/pdf/2203.10262v2.pdf | Perturbation Analysis of Randomized SVD and its Applications to High-dimensional Statistics | Randomized singular value decomposition (RSVD) is a class of computationally efficient algorithms for computing the truncated SVD of large data matrices. Given a $n \times n$ symmetric matrix $\mathbf{M}$, the prototypical RSVD algorithm outputs an approximation of the $k$ leading singular vectors of $\mathbf{M}$ by co... | ['Minh Tang', 'Yichi Zhang'] | 2022-03-19 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 2.72451222e-01 -9.30652302e-03 3.77075762e-01 6.89472705e-02
-9.29916024e-01 -6.04301512e-01 -2.37568513e-01 -1.03570186e-02
-2.99384445e-01 5.50981462e-01 -1.31444007e-01 -6.27528667e-01
-8.88292134e-01 -7.38162041e-01 -7.31987774e-01 -1.11489832e+00
-1.00704288e+00 -6.48904294e-02 -3.31914663e-01 -3.15776616... | [6.677084445953369, 4.715038776397705] |
5e977496-8aec-4f78-b62b-f7bd8a3a3fa8 | automatic-flare-spot-artifact-detection-and | 2103.04384 | null | https://arxiv.org/abs/2103.04384v1 | https://arxiv.org/pdf/2103.04384v1.pdf | Automatic Flare Spot Artifact Detection and Removal in Photographs | Flare spot is one type of flare artifact caused by a number of conditions, frequently provoked by one or more high-luminance sources within or close to the camera field of view. When light rays coming from a high-luminance source reach the front element of a camera, it can produce intra-reflections within camera elemen... | ['Coloma Ballester', 'Patricia Vitoria'] | 2021-03-07 | null | null | null | null | ['flare-removal'] | ['computer-vision'] | [ 8.87781918e-01 -4.39599782e-01 3.23419034e-01 -8.75370055e-02
-6.69943810e-01 -6.49093747e-01 3.33579302e-01 -5.54818548e-02
7.92188570e-02 7.39247620e-01 9.67253894e-02 4.07245338e-01
-9.38643590e-02 -5.63977361e-01 -8.38585913e-01 -6.41708136e-01
3.22865665e-01 -1.84754163e-01 5.48055887e-01 3.53461891... | [10.645915031433105, -2.7268576622009277] |
e48a7579-2efa-4fcb-8245-261f5aebf75f | prediction-of-the-position-of-external | 2106.01100 | null | https://arxiv.org/abs/2106.01100v6 | https://arxiv.org/pdf/2106.01100v6.pdf | Prediction of the Position of External Markers Using a Recurrent Neural Network Trained With Unbiased Online Recurrent Optimization for Safe Lung Cancer Radiotherapy | During lung radiotherapy, the position of infrared reflective objects on the chest can be recorded to estimate the tumor location. However, radiotherapy systems have a latency inherent to robot control limitations that impedes the radiation delivery precision. Prediction with online learning of recurrent neural network... | ['Ritu Bhusal Chhatkuli', 'Kazuyuki Demachi', 'Hiroyuki Takahashi', 'Mitsuru Uesaka', 'Michel Pohl'] | 2021-06-02 | null | null | null | null | ['respiratory-motion-forecasting'] | ['medical'] | [ 1.74044415e-01 3.14176917e-01 -2.94785172e-01 2.27910623e-01
-9.82517719e-01 -2.57518142e-01 1.78361684e-01 1.36462152e-01
-8.50602031e-01 7.56704032e-01 6.20891564e-02 -4.94480431e-01
-3.80112410e-01 -3.40333909e-01 -6.02023959e-01 -1.08637297e+00
-1.41961172e-01 2.61667371e-01 1.68665290e-01 2.29882775... | [13.760318756103516, -2.649731159210205] |
3c5b0d03-d99e-4160-adc5-6c5d7fdcb072 | entropy-driven-mixed-precision-quantization | null | null | https://openreview.net/forum?id=E28hy5isRzC | https://openreview.net/pdf?id=E28hy5isRzC | Entropy-Driven Mixed-Precision Quantization for Deep Network Design | Deploying deep convolutional neural networks on Internet-of-Things (IoT) devices is challenging due to the limited computational resources, such as limited SRAM memory and Flash storage. Previous works re-design a small network for IoT devices, and then compress the network size by mixed-precision quantization. This tw... | ['Xiuyu Sun', 'Hao Li', 'Hesen Chen', 'Ming Lin', 'Junyan Wang', 'Ce Ge', 'Zhenhong Sun'] | 2022-11-28 | null | null | null | conference-on-neural-information-processing-1 | ['face-detection'] | ['computer-vision'] | [-1.30739212e-01 1.87360853e-01 -5.01457810e-01 -4.09848869e-01
-3.34017962e-01 -3.57106924e-01 1.91650525e-01 -2.23803014e-01
-5.47255218e-01 4.40339565e-01 -1.19063139e-01 -5.79607189e-01
-2.86948290e-02 -9.05384541e-01 -7.62165666e-01 -5.88838160e-01
3.66969198e-01 3.89358968e-01 -1.25884816e-01 1.84017539... | [8.580158233642578, 2.992003917694092] |
f29d5120-8d51-4ebe-8e2c-caed3846042c | mitigating-the-hubness-problem-for-zero-shot | 1907.06371 | null | https://arxiv.org/abs/1907.06371v1 | https://arxiv.org/pdf/1907.06371v1.pdf | Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects | The development of advanced 3D sensors has enabled many objects to be captured in the wild at a large scale, and a 3D object recognition system may therefore encounter many objects for which the system has received no training. Zero-Shot Learning (ZSL) approaches can assist such systems in recognizing previously unseen... | ['Lars Petersson', 'Shafin Rahman', 'Ali Cheraghian', 'Dylan Campbell'] | 2019-07-15 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 5.60909696e-02 -7.79480338e-02 2.71928255e-02 -1.94673210e-01
-6.57953441e-01 -2.98687041e-01 7.49947965e-01 6.15361072e-02
-1.86254129e-01 2.58041948e-01 -3.29131424e-01 6.73021004e-02
-1.29648104e-01 -7.94270992e-01 -6.10184073e-01 -6.97716951e-01
-9.10279378e-02 7.09975183e-01 6.63185418e-01 -2.42025293... | [8.014104843139648, -3.216585874557495] |
85fe8ea8-613c-4e76-a220-a764e823fc89 | flat-and-nested-negation-and-uncertainty | null | null | https://openreview.net/forum?id=cA5yHxUxYyz | https://openreview.net/pdf?id=cA5yHxUxYyz | Flat and Nested Negation and Uncertainty Detection with PubMed BERT | Negation and uncertainty detection is an oft-studied challenge in biomedical NLP. Annotation style for the task has not been standardized and as such, the existing datasets not only vary in domain but require various algorithmic designs due to their structural differences. We present a new negation detection dataset in... | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['negation-detection'] | ['natural-language-processing'] | [ 2.23598942e-01 2.68253982e-01 -4.29257929e-01 -7.29586184e-01
-8.17628980e-01 -7.16830909e-01 2.52903074e-01 6.96152985e-01
-6.03966653e-01 1.16424131e+00 3.68715227e-02 -2.86393017e-01
-3.00347321e-02 -4.31907952e-01 -3.38386923e-01 -4.73616749e-01
1.37919888e-01 6.59301102e-01 1.69670701e-01 9.77976918... | [8.560747146606445, 8.760224342346191] |
2bd69e96-e3e6-4bec-a39f-7f45c169dc1f | paradiseo-from-a-modular-framework-for | 2105.00420 | null | https://arxiv.org/abs/2105.00420v1 | https://arxiv.org/pdf/2105.00420v1.pdf | Paradiseo: From a Modular Framework for Evolutionary Computation to the Automated Design of Metaheuristics ---22 Years of Paradiseo--- | The success of metaheuristic optimization methods has led to the development of a large variety of algorithm paradigms. However, no algorithm clearly dominates all its competitors on all problems. Instead, the underlying variety of landscapes of optimization problems calls for a variety of algorithms to solve them effi... | ['Jan Gmys', 'Benjamin Bouvier', 'Alexandre Quemy', 'Juan J. Merelo', 'Marc Schoenauer', 'Sébastien Verel', 'Arnaud Liefooghe', 'Johann Dreo'] | 2021-05-02 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 1.32877558e-01 -4.73724365e-01 -8.05016086e-02 -1.55884698e-01
-4.63410854e-01 -9.87152457e-01 5.12364745e-01 2.28896379e-01
-2.64935106e-01 7.03238010e-01 -4.69758451e-01 -5.88370025e-01
-7.14642406e-01 -1.15449357e+00 -1.03763208e-01 -8.42432261e-01
-3.37119520e-01 6.32359028e-01 2.54761666e-01 -6.18183792... | [5.786349296569824, 3.6255075931549072] |
a345486b-1512-463d-9594-5a1319942a1e | class-balanced-pixel-level-self-labeling-for | 2203.09744 | null | https://arxiv.org/abs/2203.09744v1 | https://arxiv.org/pdf/2203.09744v1.pdf | Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation | Domain adaptive semantic segmentation aims to learn a model with the supervision of source domain data, and produce satisfactory dense predictions on unlabeled target domain. One popular solution to this challenging task is self-training, which selects high-scoring predictions on target samples as pseudo labels for tra... | ['Lei Zhang', 'Xu Jia', 'Yabin Zhang', 'Chenhang He', 'Shuai Li', 'Ruihuang Li'] | 2022-03-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Class-Balanced_Pixel-Level_Self-Labeling_for_Domain_Adaptive_Semantic_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Class-Balanced_Pixel-Level_Self-Labeling_for_Domain_Adaptive_Semantic_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 6.69440031e-01 4.23137963e-01 -5.51256657e-01 -7.33106434e-01
-1.01435912e+00 -6.00572169e-01 2.72075921e-01 -9.17084292e-02
-2.83066571e-01 7.46336579e-01 -8.13945681e-02 1.85365960e-01
2.76068032e-01 -6.03544056e-01 -7.38865137e-01 -1.01456511e+00
7.04602420e-01 7.73293197e-01 5.78290522e-01 2.63748407... | [9.65475845336914, 1.3061491250991821] |
b166a890-6bfe-4445-9a3a-b7f06da80a7b | skeleton-dml-deep-metric-learning-for | 2012.13823 | null | https://arxiv.org/abs/2012.13823v2 | https://arxiv.org/pdf/2012.13823v2.pdf | Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action Recognition | One-shot action recognition allows the recognition of human-performed actions with only a single training example. This can influence human-robot-interaction positively by enabling the robot to react to previously unseen behaviour. We formulate the one-shot action recognition problem as a deep metric learning problem a... | ['Dietrich Paulus', 'Nick Theisen', 'Simon Häring', 'Raphael Memmesheimer'] | 2020-12-26 | null | null | null | null | ['one-shot-3d-action-recognition'] | ['computer-vision'] | [ 6.70068622e-01 3.16800028e-01 -2.61095554e-01 -4.76449519e-01
-8.93066704e-01 8.14057440e-02 8.84550095e-01 -2.44629562e-01
-9.19141233e-01 4.99695569e-01 4.01441574e-01 3.50274861e-01
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53139a63-4ae5-49c5-abfc-78cf8b0d84e4 | klpt-kurdish-language-processing-toolkit | null | null | https://aclanthology.org/2020.nlposs-1.11 | https://aclanthology.org/2020.nlposs-1.11.pdf | KLPT – Kurdish Language Processing Toolkit | Despite the recent advances in applying language-independent approaches to various natural language processing tasks thanks to artificial intelligence, some language-specific tools are still essential to process a language in a viable manner. Kurdish language is a less-resourced language with a remarkable diversity in ... | ['Sina Ahmadi'] | null | null | null | null | emnlp-nlposs-2020-11 | ['transliteration'] | ['natural-language-processing'] | [-9.10389274e-02 -3.23488414e-01 -1.56654865e-01 -4.93944585e-01
-6.41922593e-01 -1.01611948e+00 8.37250948e-01 3.53997678e-01
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1.06968597e-01 5.44242561e-01 -5.32107651e-02 -6.48668289... | [10.36864185333252, 10.074274063110352] |
d1866b80-cc88-44a4-84f3-bcb389ce7009 | automatic-differentiation-for-adjoint-stencil | 1907.02818 | null | https://arxiv.org/abs/1907.02818v1 | https://arxiv.org/pdf/1907.02818v1.pdf | Automatic Differentiation for Adjoint Stencil Loops | Stencil loops are a common motif in computations including convolutional neural networks, structured-mesh solvers for partial differential equations, and image processing. Stencil loops are easy to parallelise, and their fast execution is aided by compilers, libraries, and domain-specific languages. Reverse-mode automa... | ['Gerard Gorman', 'Fabio Luporini', 'Jan Hückelheim', 'Navjot Kukreja', 'Sri Hari Krishna Narayanan', 'Paul Hovland'] | 2019-07-05 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [-1.55047506e-01 -2.96964616e-01 6.48372293e-01 -9.19373631e-02
2.17758063e-02 -6.35721743e-01 5.45615971e-01 1.31593034e-01
-6.01524174e-01 8.20072174e-01 -8.97249058e-02 -7.32089579e-01
1.38593717e-02 -1.08352673e+00 -4.48978931e-01 -6.54390275e-01
-3.91016752e-01 1.95596650e-01 4.86101985e-01 -4.07410830... | [6.516347408294678, 3.3516931533813477] |
13511506-0da0-4aa5-94a5-597d4d583725 | density-propagation-and-improved-bounds-on | null | null | http://papers.nips.cc/paper/4723-density-propagation-and-improved-bounds-on-the-partition-function | http://papers.nips.cc/paper/4723-density-propagation-and-improved-bounds-on-the-partition-function.pdf | Density Propagation and Improved Bounds on the Partition Function | Given a probabilistic graphical model, its density of states is a function that, for any likelihood value, gives the number of configurations with that probability. We introduce a novel message-passing algorithm called Density Propagation (DP) for estimating this function. We show that DP is exact for tree-structured g... | ['Ashish Sabharwal', 'Carla P. Gomes', 'Stefano Ermon', 'Bart Selman'] | 2012-12-01 | null | null | null | neurips-2012-12 | ['tree-decomposition'] | ['graphs'] | [ 1.72883883e-01 3.15031499e-01 -4.58311260e-01 -2.54705876e-01
-8.48580182e-01 -8.78762305e-01 5.66280425e-01 4.02920991e-01
-2.31510531e-02 1.04819357e+00 5.46329767e-02 -7.40460157e-01
-5.22855997e-01 -9.68019664e-01 -8.02707851e-01 -7.80833483e-01
-7.45228708e-01 9.79472399e-01 4.08590525e-01 2.76816368... | [7.160953998565674, 4.8393778800964355] |
c9641e18-b4ac-46ae-a2b0-2ff15225303b | a-survey-on-video-action-recognition-in | 2206.01038 | null | https://arxiv.org/abs/2206.01038v1 | https://arxiv.org/pdf/2206.01038v1.pdf | A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications | To understand human behaviors, action recognition based on videos is a common approach. Compared with image-based action recognition, videos provide much more information. Reducing the ambiguity of actions and in the last decade, many works focused on datasets, novel models and learning approaches have improved video a... | ['Dejing Dou', 'Jun Cheng', 'Feixiang Lu', 'Ning Ding', 'Haoyi Xiong', 'Jian Bian', 'Qingzhong Wang', 'Fei Wu'] | 2022-06-02 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 1.47300795e-01 -3.22847545e-01 -5.68101168e-01 9.82833207e-02
-1.27247810e-01 -6.10400200e-01 2.58006424e-01 4.85307351e-02
-4.48532879e-01 5.25363743e-01 2.75020391e-01 3.11837643e-02
-3.04352641e-01 -8.51474166e-01 -6.49225056e-01 -5.93020797e-01
-2.05415696e-01 1.50701493e-01 5.39973557e-01 -3.94156665... | [7.869900226593018, 0.32400843501091003] |
13a0010d-68b0-495b-855e-38ac6dfa28a2 | re-examining-linear-embeddings-for-high-1 | 2001.11659 | null | https://arxiv.org/abs/2001.11659v2 | https://arxiv.org/pdf/2001.11659v2.pdf | Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization | Bayesian optimization (BO) is a popular approach to optimize expensive-to-evaluate black-box functions. A significant challenge in BO is to scale to high-dimensional parameter spaces while retaining sample efficiency. A solution considered in existing literature is to embed the high-dimensional space in a lower-dimensi... | ['Roberto Calandra', 'Benjamin Letham', 'Akshara Rai', 'Eytan Bakshy'] | 2020-01-31 | null | http://proceedings.neurips.cc/paper/2020/hash/10fb6cfa4c990d2bad5ddef4f70e8ba2-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/10fb6cfa4c990d2bad5ddef4f70e8ba2-Paper.pdf | neurips-2020-12 | ['misconceptions'] | ['miscellaneous'] | [-1.90402627e-01 -4.43542637e-02 -2.84735054e-01 -1.35600805e-01
-6.36924207e-01 -3.38492841e-01 3.23918462e-01 -5.11425324e-02
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-3.34697872e-01 -5.91572344e-01 -7.35778928e-01 -7.91946173e-01
-4.31516945e-01 3.47116292e-01 2.14361832e-01 -1.43433154... | [6.750175952911377, 4.0211639404296875] |
efc34d0f-30d9-41de-8128-ded50cdf7636 | coarse-to-fine-knowledge-selection-for | 2302.11849 | null | https://arxiv.org/abs/2302.11849v1 | https://arxiv.org/pdf/2302.11849v1.pdf | Coarse-to-Fine Knowledge Selection for Document Grounded Dialogs | Multi-document grounded dialogue systems (DGDS) belong to a class of conversational agents that answer users' requests by finding supporting knowledge from a collection of documents. Most previous studies aim to improve the knowledge retrieval model or propose more effective ways to incorporate external knowledge into ... | ['Cam-Tu Nguyen', 'Yongbin Li', 'Haiyang Yu', 'Cheng Fu', 'Haomin Fu', 'Yeqin Zhang'] | 2023-02-23 | null | null | null | null | ['answer-generation'] | ['natural-language-processing'] | [-8.43926445e-02 1.84184343e-01 -2.62837470e-01 -7.62303919e-02
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1.97965994e-01 9.47763026e-01 5.12002051e-01 -6.34258091... | [12.249053001403809, 8.037956237792969] |
684a7882-5d7f-4020-89dc-cb075cff90d4 | fireball-characteristics-derivable-from | 2102.06574 | null | https://arxiv.org/abs/2102.06574v2 | https://arxiv.org/pdf/2102.06574v2.pdf | Fireball characteristics derivable from acoustic data | Near field acoustical signals from fireballs (ranges<200 km), when detected by dense ground networks, may be used to estimate the orientation of the trajectory of a fireball (Pujol et al., 2005) as well as fragmentation locations (Kalenda et al., 2014; Edwards and Hildebrand, 2004). Distinguishing ballistic arrivals (f... | ['Pavel Spurný', 'Denis Vida', 'Peter Brown', 'Luke McFadden'] | 2021-02-12 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [-3.04846130e-02 -1.89498991e-01 5.55722117e-01 3.47550541e-01
-1.19481599e+00 -7.94656575e-01 1.44887149e-01 6.81123361e-02
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-3.77994120e-01 -9.57171500e-01 -6.61156178e-01 -1.00733256e+00
-4.11876112e-01 5.58185935e-01 4.55897689e-01 -1.81740358... | [6.809706211090088, 2.8264431953430176] |
c40abd5e-bebb-4e9e-b0ba-85926bd64f4c | query-understanding-in-the-age-of-large | 2306.16004 | null | https://arxiv.org/abs/2306.16004v1 | https://arxiv.org/pdf/2306.16004v1.pdf | Query Understanding in the Age of Large Language Models | Querying, conversing, and controlling search and information-seeking interfaces using natural language are fast becoming ubiquitous with the rise and adoption of large-language models (LLM). In this position paper, we describe a generic framework for interactive query-rewriting using LLMs. Our proposal aims to unfold n... | ['Vinay Setty', 'Abhijit Anand', 'Venktesh V', 'Avishek Anand'] | 2023-06-28 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 3.48595411e-01 6.08577430e-01 -4.07698154e-01 -6.26170278e-01
-7.84586906e-01 -9.47133482e-01 1.19501936e+00 2.95794189e-01
-5.27448535e-01 1.32781044e-01 7.01275766e-01 -6.15507901e-01
-1.53023735e-01 -4.76259768e-01 -1.45711243e-01 3.49110812e-01
1.12482823e-01 5.43431699e-01 1.01890169e-01 -6.36234105... | [11.891278266906738, 7.862659931182861] |
a4fe36b2-08d3-409c-8968-b9e7c1d3fae1 | pretrained-language-models-are-all-you-need | null | null | https://openreview.net/forum?id=OA-sluxOzcY | https://openreview.net/pdf?id=OA-sluxOzcY | Pretrained Language Models Are All You Need For Text-to-SQL Schema Linking | The use of Exact Match based Schema Linking (EMSL) has become standard in text-to-SQL: many state-of-the-art text-to-SQL models employ EMSL, and their performance drops significantly when the EMSL component is removed. In this work, however, we demonstrate that EMSL reduces robustness, rendering models vulnerable to ... | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['text-to-sql'] | ['computer-code'] | [ 5.59559427e-02 1.83272541e-01 -1.66712910e-01 -2.45617792e-01
-8.52281034e-01 -7.85218000e-01 4.24967051e-01 6.09563410e-01
-4.82104599e-01 3.65229815e-01 5.06411970e-01 -6.07998788e-01
-1.88847049e-03 -9.36742783e-01 -1.12166297e+00 2.50991076e-01
5.48821867e-01 2.43097022e-01 6.62784874e-01 -6.32988751... | [9.690458297729492, 7.890426158905029] |
3a430fc0-0b2c-4d1f-83cc-080a86ad1988 | multiple-object-tracking-with-context | 1411.7935 | null | http://arxiv.org/abs/1411.7935v2 | http://arxiv.org/pdf/1411.7935v2.pdf | Multiple object tracking with context awareness | Multiple people tracking is a key problem for many applications such as
surveillance, animation or car navigation, and a key input for tasks such as
activity recognition. In crowded environments occlusions and false detections
are common, and although there have been substantial advances in recent years,
tracking is st... | ['Laura Leal-Taixé'] | 2014-11-24 | null | null | null | null | ['multiple-people-tracking'] | ['computer-vision'] | [-5.49898902e-03 -4.96971369e-01 -2.12255418e-01 9.65994038e-03
-1.78505138e-01 -5.50045788e-01 6.16431773e-01 4.13295418e-01
-5.92499554e-01 7.13120878e-01 1.15107827e-01 -5.76460399e-02
4.50732969e-02 -5.01293242e-01 -3.49538952e-01 -8.18290949e-01
3.60007621e-02 2.45220661e-01 7.33492315e-01 1.88202769... | [6.530906677246094, -1.9067034721374512] |
a63a091f-806d-46ec-95d3-28da1c8a1191 | a-novel-transformer-network-with-shifted | 2208.01252 | null | https://arxiv.org/abs/2208.01252v1 | https://arxiv.org/pdf/2208.01252v1.pdf | A Novel Transformer Network with Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting | Earth Observatory is a growing research area that can capitalize on the powers of AI for short time forecasting, a Now-casting scenario. In this work, we tackle the challenge of weather forecasting using a video transformer network. Vision transformer architectures have been explored in various applications, with major... | ['Panos Liatsis', 'Hasan Al Marzouqi', 'Alabi Bojesomo'] | 2022-08-02 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 1.67181507e-01 2.95209289e-02 2.97539979e-01 -5.16561627e-01
-4.39453930e-01 -4.78704065e-01 8.69730711e-01 -3.75711352e-01
-4.65811521e-01 6.96253419e-01 3.21922123e-01 -3.48535836e-01
1.00123324e-01 -6.31926298e-01 -8.70624006e-01 -7.52916455e-01
-2.33526990e-01 4.57475940e-03 1.08519286e-01 -8.32750276... | [6.749782562255859, 2.824658155441284] |
62b237f9-6810-4cfb-bd41-ebc5c95c9b7a | online-mutual-foreground-segmentation-for | 1809.02851 | null | http://arxiv.org/abs/1809.02851v2 | http://arxiv.org/pdf/1809.02851v2.pdf | Online Mutual Foreground Segmentation for Multispectral Stereo Videos | The segmentation of video sequences into foreground and background regions is
a low-level process commonly used in video content analysis and smart
surveillance applications. Using a multispectral camera setup can improve this
process by providing more diverse data to help identify objects despite adverse
imaging condi... | ['Guillaume-Alexandre Bilodeau', 'Pierre-Luc St-Charles', 'Robert Bergevin'] | 2018-09-08 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 7.28432536e-01 -6.90822423e-01 2.62346745e-01 -2.95846522e-01
-6.56855226e-01 -8.79163861e-01 6.08130217e-01 2.84255505e-01
-5.61659515e-01 5.11744320e-01 -2.58267969e-01 1.06467418e-01
-1.59334779e-01 -8.16295624e-01 -4.78395581e-01 -9.80052769e-01
3.20799768e-01 2.35004246e-01 8.67877543e-01 4.78713168... | [9.861888885498047, -2.371594190597534] |
51fcc53e-a7cc-4acb-a7aa-275c8483e357 | satisfiability-aided-language-models-using | 2305.09656 | null | https://arxiv.org/abs/2305.09656v2 | https://arxiv.org/pdf/2305.09656v2.pdf | Satisfiability-Aided Language Models Using Declarative Prompting | Prior work has combined chain-of-thought prompting in large language models (LLMs) with programmatic representations to perform effective and transparent reasoning. While such an approach works very well for tasks that only require forward reasoning (e.g., straightforward arithmetic), it is less effective for constrain... | ['Greg Durrett', 'Isil Dillig', 'Qiaochu Chen', 'Xi Ye'] | 2023-05-16 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 1.72674671e-01 7.17778087e-01 -1.20900363e-01 -3.33199829e-01
-1.06699932e+00 -7.97308087e-01 5.54068208e-01 3.07696313e-01
-2.75968798e-02 4.93401974e-01 2.01928541e-01 -1.17434001e+00
-1.17703145e-02 -1.10065269e+00 -9.82332826e-01 2.17041433e-01
1.52346805e-01 7.87213862e-01 4.18240815e-01 -2.65411764... | [9.164511680603027, 7.227541923522949] |
ec50dd5a-d27a-49aa-b9e7-c8b899e9242f | unsupervised-content-based-image-retrieval-at | null | null | https://iopscience.iop.org/article/10.1088/1742-6596/1950/1/012059/meta | https://iopscience.iop.org/article/10.1088/1742-6596/1950/1/012059/pdf | Unsupervised Content based Image Retrieval at Different Precision Level by Combining Multiple Features | Image retrieval is a procedure of finding appropriate images in the image database. There are two types of image retrieval systems in common practice. These are the text-based image retrieval (TBIR) system and content-based image retrieval (CBIR) system. The content based system is proven to be more effective in which ... | ['Mohd Atif Jamil', 'S. M. Zakariya'] | 2021-01-20 | null | null | null | icmai-2021-1 | ['content-based-image-retrieval'] | ['computer-vision'] | [ 8.58914107e-02 -8.55123758e-01 -1.24125093e-01 -8.13878849e-02
-6.39618993e-01 -4.28939134e-01 6.99027061e-01 4.30921972e-01
-6.36570752e-01 3.72601539e-01 3.07269454e-01 1.37539329e-02
-7.94949830e-01 -7.23545015e-01 8.18582550e-02 -6.89581275e-01
2.80570716e-01 1.85431197e-01 3.82623523e-01 -3.79417002... | [10.763493537902832, 0.03575511276721954] |
f384b243-35cc-4fe4-80d9-5ae7ee1bc9b0 | dfcanet-dense-feature-calibration-attention | 2111.00919 | null | https://arxiv.org/abs/2111.00919v1 | https://arxiv.org/pdf/2111.00919v1.pdf | DFCANet: Dense Feature Calibration-Attention Guided Network for Cross Domain Iris Presentation Attack Detection | An iris presentation attack detection (IPAD) is essential for securing personal identity is widely used iris recognition systems. However, the existing IPAD algorithms do not generalize well to unseen and cross-domain scenarios because of capture in unconstrained environments and high visual correlation amongst bonafid... | ['Raghavendra Ramachandra', 'Sumantra Dutta Roy', 'Aman Verma', 'Gaurav Jaswal'] | 2021-11-01 | null | null | null | null | ['cross-domain-iris-presentation-attack'] | ['computer-vision'] | [ 3.92272174e-01 -3.04871172e-01 -3.63027304e-01 -2.17123806e-01
-4.43162650e-01 -6.31805301e-01 3.43141854e-01 -2.38512650e-01
-1.55299917e-01 6.55833840e-01 2.79236078e-01 -3.60742927e-01
-6.18130982e-01 -2.52186209e-01 -5.60758889e-01 -7.75653064e-01
-1.40637487e-01 3.89309600e-02 -5.31049728e-01 1.25195011... | [3.743922472000122, -3.63281512260437] |
b8991af1-34a4-422b-9402-e607b1bc04b7 | perspectives-on-ai-architectures-and-co | 2304.03748 | null | https://arxiv.org/abs/2304.03748v1 | https://arxiv.org/pdf/2304.03748v1.pdf | Perspectives on AI Architectures and Co-design for Earth System Predictability | Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion tha... | ['Philip W. Jones', 'Ivy B. Peng', 'Matthew R. Norman', 'Sarat S. Sreepathi', 'Tushar Krishna', 'James C. Hoe', 'Maya B. Gokhale', 'Simon D. Hammond', 'Mahantesh Halappanavar', 'James A. Ang', 'Maruti K. Mudunuru'] | 2023-04-07 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-3.04676116e-01 -3.88735622e-01 2.28620619e-01 -1.37956932e-01
-3.21656048e-01 -5.80884576e-01 7.20792353e-01 4.00081873e-01
3.50733787e-01 6.50425673e-01 2.21944660e-01 -6.78907037e-01
-3.52759868e-01 -9.65776086e-01 -5.46425045e-01 -8.13983798e-01
-5.81989348e-01 4.86983478e-01 -2.63318289e-02 -7.86305666... | [6.482177734375, 3.2069108486175537] |
8c276c18-ae5e-4410-b751-7758a9cbeab5 | monte-carlo-tree-search-based-variable | 2210.01628 | null | https://arxiv.org/abs/2210.01628v2 | https://arxiv.org/pdf/2210.01628v2.pdf | Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization | Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios. However, BO suffers from the curse of dimensionality, and scaling it to high-dimensional problems is still a challenge. In this paper, we propose a variable selection method MCTS... | ['Chao Qian', 'Xiaobin Huang', 'Ke Xue', 'Lei Song'] | 2022-10-04 | null | null | null | null | ['variable-selection'] | ['methodology'] | [-1.46332040e-01 -6.85663700e-01 -3.93346816e-01 -1.65707842e-01
-7.96561539e-01 -2.89126128e-01 9.83302519e-02 -3.64847749e-01
-3.43219995e-01 1.18973005e+00 -3.16650718e-01 -3.09331149e-01
-3.08467120e-01 -6.87113702e-01 -5.16444683e-01 -1.12636232e+00
-2.81032681e-01 9.12891507e-01 2.11837724e-01 -1.58462599... | [6.5266804695129395, 4.003716468811035] |
95a10a8f-c525-41f7-8a1d-4ec738444437 | using-deep-learning-with-large-aggregated | 2201.01669 | null | https://arxiv.org/abs/2201.01669v3 | https://arxiv.org/pdf/2201.01669v3.pdf | Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough | The Covid-19 pandemic has been one of the most devastating events in recent history, claiming the lives of more than 5 million people worldwide. Even with the worldwide distribution of vaccines, there is an apparent need for affordable, reliable, and accessible screening techniques to serve parts of the World that do n... | ['Aaron Broukhim', 'Wei Chen', 'Christian Canham', 'Praveen Govindan', 'Akanksha Rajput', 'Gunvant Chaudhari', 'Jaclyn Xiao', 'Jennifer Ranjani J.', 'Daniel C. H. Tan', 'Esin Darici Haritaoglu', 'Mert Pilanci', 'Amil Khanzada', 'Laura Gomezjurado', 'Minami Yamaura', 'Nicholas Rasmussen'] | 2022-01-05 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.22115943e-02 -4.87260334e-02 -1.67779759e-01 4.35445160e-02
-3.91648561e-01 -6.78127110e-01 4.24435616e-01 7.03500569e-01
-9.34441268e-01 6.60294056e-01 -2.64216885e-02 -6.36230767e-01
-1.09023631e-01 -6.89041734e-01 -2.97064155e-01 -4.39670742e-01
-3.03737193e-01 5.99954963e-01 -3.19439769e-02 -3.31433445... | [15.548295974731445, -1.6508674621582031] |
6ff5664c-6660-496c-9787-e1e3e813494b | gu-yi-yu-qian-tao-ming-ming-shi-ti-shi-bie | null | null | https://aclanthology.org/2022.ccl-1.37 | https://aclanthology.org/2022.ccl-1.37.pdf | 古汉语嵌套命名实体识别数据集的构建和应用研究(Construction and application of classical Chinese nested named entity recognition data set) | “本文聚焦研究较少的古汉语嵌套命名实体识别任务,以《史记》作为原始语料,针对古文意义丰富而导致的实体分类模糊问题,分别构建了基于字词本义和语境义2个标注标准的古汉语嵌套命名实体数据集,探讨了数据集的实体分类原则和标注格式,并用RoBERTa-classical-chinese+GlobalPointer模型进行对比试验,标准一数据集F1值为80.42%,标准二F1值为77.43%,以此确定了数据集的标注标准。之后对比了六种预训练模型配合GlobalPointer在古汉语嵌套命名实体识别任务上的表现。最终试验结果:RoBERTa-classical-chinese模型F1值为84.71%,表现最好。” | ['Genhui Liu', 'Jinzhu Liu', 'Zhiqiang Xie'] | null | null | null | null | ccl-2022-10 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-1.05992138e+00 -8.14990282e-01 2.08533540e-01 2.48288482e-01
-6.13713920e-01 -8.59294355e-01 3.60294133e-01 7.90150642e-01
-2.95110345e-01 3.93532664e-01 7.95863569e-01 -1.03492007e-01
-1.93099096e-01 -8.90956163e-01 -5.29613435e-01 -9.20150757e-01
-4.87034738e-01 1.38527286e+00 3.31108809e-01 -9.41700161... | [-3.316066265106201, 6.907612323760986] |
958ab2a4-a697-4c94-9776-a3cd130f6ca1 | gaussian-processes-with-context-supported | 1703.08653 | null | http://arxiv.org/abs/1703.08653v3 | http://arxiv.org/pdf/1703.08653v3.pdf | Gaussian Processes with Context-Supported Priors for Active Object Localization | We devise an algorithm using a Bayesian optimization framework in conjunction
with contextual visual data for the efficient localization of objects in still
images. Recent research has demonstrated substantial progress in object
localization and related tasks for computer vision. However, many current
state-of-the-art ... | ['Melanie Mitchell', 'Jordan Witte', 'Anthony D. Rhodes', 'Bruno Jedynak'] | 2017-03-25 | null | null | null | null | ['active-object-localization'] | ['computer-vision'] | [ 1.40057430e-01 -3.05168301e-01 -1.93275083e-02 -7.05250621e-01
-1.35174322e+00 -2.82237679e-01 6.04342282e-01 1.83724642e-01
-8.10976028e-01 6.71935916e-01 -2.74067149e-02 1.82392243e-02
1.72209755e-01 -3.22164476e-01 -8.28306675e-01 -8.23818922e-01
6.37216866e-02 5.67320585e-01 7.51346707e-01 3.26282561... | [7.639462471008301, -2.428853988647461] |
f1fbcefe-699d-42a9-8e27-6a87970a3ab8 | pseudo-label-guided-cross-video-pixel | 2207.09664 | null | https://arxiv.org/abs/2207.09664v1 | https://arxiv.org/pdf/2207.09664v1.pdf | Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations | Surgical scene segmentation is fundamentally crucial for prompting cognitive assistance in robotic surgery. However, pixel-wise annotating surgical video in a frame-by-frame manner is expensive and time consuming. To greatly reduce the labeling burden, in this work, we study semi-supervised scene segmentation from robo... | ['Pheng-Ann Heng', 'Qi Dou', 'Guangyong Chen', 'Yueming Jin', 'Zixu Zhao', 'Yang Yu'] | 2022-07-20 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 4.48001653e-01 4.21832949e-01 -7.05119133e-01 -5.42042017e-01
-9.73703921e-01 -4.44811642e-01 1.37665376e-01 9.52883216e-04
-4.27403390e-01 6.92040443e-01 2.28929207e-01 -3.32801074e-01
-1.08140379e-01 -3.07584643e-01 -8.56904566e-01 -8.63925576e-01
4.09209877e-01 2.79255211e-01 8.86507481e-02 9.69896093... | [14.273147583007812, -2.989182710647583] |
8c2f4378-d656-4375-9da6-1cc90b17ac9b | towards-effective-multi-label-recognition | 2207.05137 | null | https://arxiv.org/abs/2207.05137v1 | https://arxiv.org/pdf/2207.05137v1.pdf | Towards Effective Multi-Label Recognition Attacks via Knowledge Graph Consistency | Many real-world applications of image recognition require multi-label learning, whose goal is to find all labels in an image. Thus, robustness of such systems to adversarial image perturbations is extremely important. However, despite a large body of recent research on adversarial attacks, the scope of the existing wor... | ['Ehsan Elhamifar', 'Hassan Mahmood'] | 2022-07-11 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 9.23065186e-01 1.44995570e-01 -2.97181100e-01 -3.05022538e-01
-8.60281885e-01 -1.17296827e+00 4.38862860e-01 2.46015266e-01
4.10278654e-03 5.13786614e-01 -6.44774020e-01 -3.22606027e-01
3.59399989e-03 -7.57074475e-01 -1.10088789e+00 -7.41528571e-01
1.22292608e-01 4.29159403e-01 4.01461899e-01 -2.44909879... | [5.681197643280029, 7.74666166305542] |
183a9419-3dbf-489e-8f73-f1ec89368844 | unsupervised-learning-of-style-aware-facial | 2306.10006 | null | https://arxiv.org/abs/2306.10006v2 | https://arxiv.org/pdf/2306.10006v2.pdf | Unsupervised Learning of Style-Aware Facial Animation from Real Acting Performances | This paper presents a novel approach for text/speech-driven animation of a photo-realistic head model based on blend-shape geometry, dynamic textures, and neural rendering. Training a VAE for geometry and texture yields a parametric model for accurate capturing and realistic synthesis of facial expressions from a laten... | ['Peter Eisert', 'Anna Hilsmann', 'Wolfgang Paier'] | 2023-06-16 | null | null | null | null | ['neural-rendering'] | ['computer-vision'] | [ 3.74007642e-01 1.44998699e-01 8.73529837e-02 -6.85835958e-01
-7.93091595e-01 -3.67758900e-01 8.67679417e-01 -5.31049311e-01
5.69715686e-02 3.08715373e-01 5.02315760e-01 6.59139007e-02
6.17063701e-01 -7.84009695e-01 -8.69453490e-01 -6.72398746e-01
-3.65554057e-02 7.34004259e-01 -2.56001323e-01 -2.65401900... | [12.792254447937012, -0.45074620842933655] |
76eaead6-af95-49e8-9566-c2680560c60f | online-segment-to-segment-neural-transduction | 1609.08194 | null | http://arxiv.org/abs/1609.08194v1 | http://arxiv.org/pdf/1609.08194v1.pdf | Online Segment to Segment Neural Transduction | We introduce an online neural sequence to sequence model that learns to
alternate between encoding and decoding segments of the input as it is read. By
independently tracking the encoding and decoding representations our algorithm
permits exact polynomial marginalization of the latent segmentation during
training, and ... | ['Lei Yu', 'Jan Buys', 'Phil Blunsom'] | 2016-09-26 | online-segment-to-segment-neural-transduction-1 | https://aclanthology.org/D16-1138 | https://aclanthology.org/D16-1138.pdf | emnlp-2016-11 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 9.33064282e-01 8.77809346e-01 -3.55141640e-01 -3.74396622e-01
-1.23183644e+00 -9.01189506e-01 5.08644342e-01 1.24682248e-01
-5.79111695e-01 7.79073417e-01 5.39491832e-01 -6.77431941e-01
6.42884791e-01 -7.02130198e-01 -1.22632313e+00 -5.43459415e-01
1.37414977e-01 8.44116569e-01 -1.91327155e-01 1.01666056... | [11.515830039978027, 9.059093475341797] |
d50893fe-5787-4373-8b38-bc56b4c1559d | deep-neural-network-techniques-for-monaural | 2212.00369 | null | https://arxiv.org/abs/2212.00369v2 | https://arxiv.org/pdf/2212.00369v2.pdf | Deep neural network techniques for monaural speech enhancement: state of the art analysis | Deep neural networks (DNN) techniques have become pervasive in domains such as natural language processing and computer vision. They have achieved great success in these domains in task such as machine translation and image generation. Due to their success, these data driven techniques have been applied in audio domain... | ['Peter Ochieng'] | 2022-12-01 | null | null | null | null | ['art-analysis', 'speech-separation', 'speaker-separation'] | ['computer-vision', 'speech', 'speech'] | [ 4.03088987e-01 1.69845410e-02 3.29150796e-01 -3.19200158e-01
-7.07944572e-01 -1.91427514e-01 7.79623568e-01 -1.69581160e-01
-4.31761742e-01 3.99831593e-01 8.99746776e-01 -1.46623492e-01
-1.92963690e-01 -4.36227024e-01 -2.15932310e-01 -7.93682992e-01
2.20420584e-01 -1.33851439e-01 -1.63122952e-01 -6.19983912... | [15.118825912475586, 5.901556491851807] |
ba09d6de-3d41-4d17-ba70-caa89cd5d12a | robust-neural-architecture-search | 2304.02845 | null | https://arxiv.org/abs/2304.02845v2 | https://arxiv.org/pdf/2304.02845v2.pdf | Robust Neural Architecture Search | Neural Architectures Search (NAS) becomes more and more popular over these years. However, NAS-generated models tends to suffer greater vulnerability to various malicious attacks. Lots of robust NAS methods leverage adversarial training to enhance the robustness of NAS-generated models, however, they neglected the natu... | ['Weiping Wang', 'Yong liu', 'Jian Li', 'Xunyu Zhu'] | 2023-04-06 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.98305771e-01 -4.16222125e-01 -3.76852453e-02 -2.57845223e-01
-9.04249489e-01 -8.61023128e-01 5.82743645e-01 -7.07202375e-01
-2.02098459e-01 5.38512349e-01 7.17008784e-02 -4.48243499e-01
4.80623432e-02 -7.78117359e-01 -7.20142424e-01 -7.22584188e-01
3.22930783e-01 1.16480842e-01 3.66327494e-01 -4.02364165... | [5.669137477874756, 7.954410076141357] |
5a5f89ea-d5b5-48e0-919b-6c3681b5180b | aligning-multilingual-word-embeddings-for | 1910.03291 | null | https://arxiv.org/abs/1910.03291v1 | https://arxiv.org/pdf/1910.03291v1.pdf | Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task | In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages. We combine two existing objective functions to make images and captions close in a joint embedding space while adapting the alignment of word embeddings between existing... | ['Alireza Mohammadshahi', 'Karl Aberer', 'Remi Lebret'] | 2019-10-08 | aligning-multilingual-word-embeddings-for-2 | https://aclanthology.org/D19-6402 | https://aclanthology.org/D19-6402.pdf | emnlp-ws-2019-11 | ['multilingual-word-embeddings'] | ['methodology'] | [-2.56591700e-02 -2.79211819e-01 -2.97035664e-01 -4.63303328e-01
-1.66042709e+00 -7.64748871e-01 9.40836191e-01 2.40865558e-01
-8.98367405e-01 5.77230334e-01 4.51337606e-01 2.14198232e-02
3.26337308e-01 -1.03429332e-01 -7.98872530e-01 -2.73158073e-01
2.53100783e-01 6.43787444e-01 6.65260628e-02 -1.58333108... | [11.213693618774414, 1.5315526723861694] |
8ced091f-857b-4253-98ea-03ccbade08e2 | a-dataset-and-reranking-method-for-multimodal | null | null | https://aclanthology.org/W18-1814 | https://aclanthology.org/W18-1814.pdf | A Dataset and Reranking Method for Multimodal MT of User-Generated Image Captions | null | ['Julian Hitschler', 'Shigehiko Schamoni', 'Stefan Riezler'] | 2018-03-01 | null | null | null | ws-2018-3 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2270050048828125, 3.682457208633423] |
312edb1f-a1f9-489a-9a77-e00f7ad1a854 | adversarial-distortion-learning-for-medical | 2204.14100 | null | https://arxiv.org/abs/2204.14100v1 | https://arxiv.org/pdf/2204.14100v1.pdf | Adversarial Distortion Learning for Medical Image Denoising | We present a novel adversarial distortion learning (ADL) for denoising two- and three-dimensional (2D/3D) biomedical image data. The proposed ADL consists of two auto-encoders: a denoiser and a discriminator. The denoiser removes noise from input data and the discriminator compares the denoised result to its noise-free... | ['Jussi Tohka', 'Alejandra Sierra', 'Mohammad Khateri', 'Morteza Ghahremani'] | 2022-04-29 | null | null | null | null | ['color-image-denoising', 'medical-image-denoising', 'grayscale-image-denoising'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.36478466e-01 1.49553522e-01 4.32122141e-01 -2.74281502e-01
-5.80925882e-01 -2.58534223e-01 3.95597339e-01 -2.15390041e-01
-6.42769158e-01 6.08512044e-01 8.71021450e-02 4.37147617e-02
3.71938348e-02 -8.20387900e-01 -6.95657015e-01 -1.13245952e+00
2.33970750e-02 2.92647570e-01 3.64226073e-01 -7.20897242... | [13.223081588745117, -2.5087804794311523] |
51c8786f-0d92-4985-a8f4-931b81236415 | language-learning-using-speech-to-image | 1909.03795 | null | https://arxiv.org/abs/1909.03795v1 | https://arxiv.org/pdf/1909.03795v1.pdf | Language learning using Speech to Image retrieval | Humans learn language by interaction with their environment and listening to other humans. It should also be possible for computational models to learn language directly from speech but so far most approaches require text. We improve on existing neural network approaches to create visually grounded embeddings for spoke... | ['Stefan L. Frank', 'Mirjam Ernestus', 'Danny Merkx'] | 2019-09-09 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [ 4.72736299e-01 3.72989357e-01 3.06282938e-01 -4.14356858e-01
-7.37862110e-01 -4.94856507e-01 1.00990808e+00 7.09452108e-02
-6.68233871e-01 4.64388907e-01 7.76266873e-01 -3.15047145e-01
3.95636469e-01 -7.74196208e-01 -8.54967594e-01 -5.59734583e-01
-3.71050611e-02 4.39459294e-01 4.51149344e-02 -3.17385972... | [10.82374095916748, 1.7444604635238647] |
bf7ce675-f56d-4e39-b57d-d2a74ed21d42 | voxlingua107-a-dataset-for-spoken-language-1 | 2011.12998 | null | https://arxiv.org/abs/2011.12998v1 | https://arxiv.org/pdf/2011.12998v1.pdf | VoxLingua107: a Dataset for Spoken Language Recognition | This paper investigates the use of automatically collected web audio data for the task of spoken language recognition. We generate semi-random search phrases from language-specific Wikipedia data that are then used to retrieve videos from YouTube for 107 languages. Speech activity detection and speaker diarization are ... | ['Tanel Alumäe', 'Jörgen Valk'] | 2020-11-25 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 8.68403614e-02 5.54545373e-02 -3.23976696e-01 -5.46728373e-01
-1.62621844e+00 -7.79457510e-01 5.73874652e-01 2.04378031e-02
-7.57535279e-01 6.86852694e-01 7.05216825e-01 4.53516804e-02
4.80706513e-01 -1.35808393e-01 -7.46062815e-01 -6.02076769e-01
5.30953370e-02 5.11841714e-01 3.10978800e-01 2.53583014... | [14.228422164916992, 6.250761985778809] |
0a561cb5-d065-4e15-9b75-08685286e464 | r-2vos-robust-referring-video-object | 2207.01203 | null | https://arxiv.org/abs/2207.01203v1 | https://arxiv.org/pdf/2207.01203v1.pdf | R^2VOS: Robust Referring Video Object Segmentation via Relational Multimodal Cycle Consistency | Referring video object segmentation (R-VOS) aims to segment the object masks in a video given a referring linguistic expression to the object. It is a recently introduced task attracting growing research attention. However, all existing works make a strong assumption: The object depicted by the expression must exist in... | ['Bhiksha Raj', 'Yan Lu', 'Xiao Li', 'Xiaohao Xu', 'Jinglu Wang', 'Xiang Li'] | 2022-07-04 | null | null | null | null | ['referring-expression-segmentation', 'referring-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.46493569e-01 -7.37763718e-02 -2.59380728e-01 -4.17578936e-01
-7.84204483e-01 -5.20712614e-01 1.68090940e-01 -4.29865211e-01
-2.17829257e-01 3.49934727e-01 -1.57124430e-01 -1.25030279e-01
-6.40265271e-02 -4.53224272e-01 -8.79606128e-01 -5.39235950e-01
2.05536380e-01 5.39638065e-02 4.16010588e-01 -1.73434228... | [9.828984260559082, 0.6351532936096191] |
5b35bb31-4973-44dc-9853-f99e7ea0cfd1 | a-comprehensive-benchmark-for-covid-19 | 2209.07805 | null | https://arxiv.org/abs/2209.07805v3 | https://arxiv.org/pdf/2209.07805v3.pdf | A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care | The COVID-19 pandemic has posed a heavy burden to the healthcare system worldwide and caused huge social disruption and economic loss. Many deep learning models have been proposed to conduct clinical predictive tasks such as mortality prediction for COVID-19 patients in intensive care units using Electronic Health Reco... | ['Ewen M. Harrison', 'Liantao Ma', 'Wen Tang', 'Yasha Wang', 'Wenqing Wang', 'Yinghao Zhu', 'Junyi Gao'] | 2022-09-16 | null | null | null | null | ['mortality-prediction', 'length-of-stay-prediction'] | ['medical', 'medical'] | [-2.97008958e-02 -3.72287720e-01 -1.65631741e-01 -3.75660986e-01
-6.83944702e-01 -9.98881608e-02 -1.34029344e-01 7.11843312e-01
-6.04698360e-01 7.77991056e-01 2.70550609e-01 -7.40893364e-01
-3.88828158e-01 -6.12734437e-01 -3.02187681e-01 -4.80717808e-01
-4.98867184e-01 1.13892615e+00 -2.94958383e-01 1.57987267... | [7.965155601501465, 6.194892406463623] |
b71abe62-d732-4976-aeb2-4688d1633b0c | emotion-dynamics-modeling-via-bert | 2104.07252 | null | https://arxiv.org/abs/2104.07252v2 | https://arxiv.org/pdf/2104.07252v2.pdf | Emotion Dynamics Modeling via BERT | Emotion dynamics modeling is a significant task in emotion recognition in conversation. It aims to predict conversational emotions when building empathetic dialogue systems. Existing studies mainly develop models based on Recurrent Neural Networks (RNNs). They cannot benefit from the power of the recently-developed pre... | ['Jianping Shen', 'Haiqin Yang'] | 2021-04-15 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-3.48062277e-01 -9.62826163e-02 -5.38477581e-03 -6.87524021e-01
-5.09929061e-01 -4.30610776e-01 5.26695490e-01 -1.39868215e-01
-2.20683232e-01 6.01948321e-01 7.13165522e-01 -1.96790155e-02
2.20122516e-01 -3.26374978e-01 -1.17793716e-01 -8.99054825e-01
-3.48348394e-02 2.54018873e-01 -4.72182959e-01 -8.24837863... | [13.046117782592773, 6.06048059463501] |
d8dc0f86-7f6f-4dc9-a614-08306bd8e1f0 | acoustic-absement-in-detail-quantifying | 2304.06183 | null | https://arxiv.org/abs/2304.06183v2 | https://arxiv.org/pdf/2304.06183v2.pdf | Acoustic absement in detail: Quantifying acoustic differences across time-series representations of speech data | The speech signal is a consummate example of time-series data. The acoustics of the signal change over time, sometimes dramatically. Yet, the most common type of comparison we perform in phonetics is between instantaneous acoustic measurements, such as formant values. In the present paper, I discuss the concept of abse... | ['Matthew C. Kelley'] | 2023-04-12 | null | null | null | null | ['dynamic-time-warping'] | ['time-series'] | [ 2.70917535e-01 -4.28799301e-01 2.75007755e-01 -5.90962470e-01
-7.51076400e-01 -7.44274974e-01 6.17342174e-01 -8.28970000e-02
-6.24353290e-01 1.18463397e-01 3.40863466e-01 -5.39620101e-01
-1.95397839e-01 -3.85412514e-01 -1.51459515e-01 -7.95465112e-01
-1.73107252e-01 9.96722355e-02 6.85120746e-02 -2.50207365... | [14.678549766540527, 6.078554630279541] |
c1919c5b-1a95-42a7-93c5-aa026db279b1 | unsupervised-complementary-aware-multi | 2112.04701 | null | https://arxiv.org/abs/2112.04701v1 | https://arxiv.org/pdf/2112.04701v1.pdf | Unsupervised Complementary-aware Multi-process Fusion for Visual Place Recognition | A recent approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques simultaneously. However, selecting the optimal set of techniques to use in a specific deployment environment a-priori is a difficult and unresolved challenge. Further... | ['Michael Milford', 'Tobias Fischer', 'Stephen Hausler'] | 2021-12-09 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 2.64666080e-01 -6.86995625e-01 2.41534617e-02 -3.65246236e-01
-1.10928237e+00 -8.60198438e-01 8.06281388e-01 3.83860648e-01
-4.62419242e-01 4.78559971e-01 -1.73066258e-01 -7.43478686e-02
-4.33778256e-01 -4.34419721e-01 -5.44017851e-01 -6.71567500e-01
2.41284631e-02 4.94938374e-01 6.20053291e-01 -3.86917591... | [7.506068229675293, -1.8909430503845215] |
6148db23-f731-4e86-b71e-ba3284b6310b | unsupervised-improvement-of-audio-text-cross | 2305.01864 | null | https://arxiv.org/abs/2305.01864v2 | https://arxiv.org/pdf/2305.01864v2.pdf | Unsupervised Improvement of Audio-Text Cross-Modal Representations | Recent advances in using language models to obtain cross-modal audio-text representations have overcome the limitations of conventional training approaches that use predefined labels. This has allowed the community to make progress in tasks like zero-shot classification, which would otherwise not be possible. However, ... | ['Paris Smaragdis', 'Fabio Ayres', 'Tiago Tavares', 'Junkai Wu', 'Krishna Subramani', 'Cem Subakan', 'Zhepei Wang'] | 2023-05-03 | null | null | null | null | ['acoustic-scene-classification', 'scene-classification'] | ['audio', 'computer-vision'] | [ 6.41396642e-01 -6.20075464e-02 2.60629743e-01 -6.42092764e-01
-1.59384787e+00 -5.27695894e-01 7.15443492e-01 4.38076973e-01
-5.20987868e-01 4.08934206e-01 4.04492408e-01 -1.33147985e-01
5.54360040e-02 -3.75222415e-01 -4.39148396e-01 -4.73516047e-01
-2.16900166e-02 3.13520849e-01 3.58315468e-01 -2.04162113... | [15.219592094421387, 5.092373371124268] |
5937937e-2806-4722-8ad6-36652538a274 | an-empirical-evaluation-of-similarity | 1401.3973 | null | http://arxiv.org/abs/1401.3973v1 | http://arxiv.org/pdf/1401.3973v1.pdf | An Empirical Evaluation of Similarity Measures for Time Series Classification | Time series are ubiquitous, and a measure to assess their similarity is a
core part of many computational systems. In particular, the similarity measure
is the most essential ingredient of time series clustering and classification
systems. Because of this importance, countless approaches to estimate time
series similar... | ['Joan Serrà', 'Josep Lluis Arcos'] | 2014-01-16 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 1.87829718e-01 -7.43132651e-01 -1.62596479e-01 -3.62993568e-01
-5.93169570e-01 -7.19714522e-01 7.38422215e-01 5.64243376e-01
-3.70335877e-01 5.26658118e-01 -1.07834399e-01 -2.13228330e-01
-7.99388409e-01 -5.29954433e-01 9.16527882e-02 -8.21934998e-01
-6.60504580e-01 1.47700265e-01 1.54825628e-01 -2.72965729... | [7.265561580657959, 3.3236923217773438] |
ff3b5bdb-0c70-46c3-a381-378e1074b253 | language-guided-audio-visual-source | 2303.16342 | null | https://arxiv.org/abs/2303.16342v1 | https://arxiv.org/pdf/2303.16342v1.pdf | Language-Guided Audio-Visual Source Separation via Trimodal Consistency | We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as training data. A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual featur... | ['Kate Saenko', 'Bryan Russell', 'Oriol Nieto', 'Justin Salamon', 'Bryan A. Plummer', 'Andrea Burns', 'Arijit Ray', 'Reuben Tan'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tan_Language-Guided_Audio-Visual_Source_Separation_via_Trimodal_Consistency_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tan_Language-Guided_Audio-Visual_Source_Separation_via_Trimodal_Consistency_CVPR_2023_paper.pdf | cvpr-2023-1 | ['audio-source-separation'] | ['audio'] | [ 3.48811090e-01 -2.12995782e-01 -1.91730514e-01 -3.39527220e-01
-1.30281222e+00 -1.05851877e+00 5.59892356e-01 1.14684820e-01
-3.77906531e-01 3.43366861e-01 2.63870835e-01 2.97807902e-01
1.72933303e-02 -7.39136040e-02 -9.65819299e-01 -4.15681243e-01
-1.49308890e-01 2.74300456e-01 4.28425282e-01 3.97947758... | [14.764171600341797, 4.928518772125244] |
17f9a28f-3745-468c-a7e9-ee8a441a9256 | information-theoretic-evaluation-of-privacy | 2106.06046 | null | https://arxiv.org/abs/2106.06046v5 | https://arxiv.org/pdf/2106.06046v5.pdf | Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI | In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic trustworthy AI framework is introduced. A unified approach to "privacy-preserving interpretable and transferable learning" is considered for studying and op... | ['Bernhard Freudenthaler', 'Lukas Fischer', 'Bernhard A. Moser', 'Mohit Kumar'] | 2021-06-06 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 7.67653733e-02 5.89754283e-01 -2.12171480e-01 -8.23654413e-01
-6.17263973e-01 -3.03940743e-01 3.70078743e-01 4.11768287e-01
-5.02705753e-01 8.78564298e-01 -2.26490013e-02 3.94780375e-03
-5.53852081e-01 -4.97055322e-01 -5.45235693e-01 -7.51517951e-01
-1.47007987e-01 3.23876023e-01 -6.41721189e-01 4.04124588... | [6.303143501281738, 6.625558376312256] |
463c9219-1a83-4f1f-9915-1c522976bd8a | coarse-to-fine-classification-via-parametric | 1405.4308 | null | http://arxiv.org/abs/1405.4308v1 | http://arxiv.org/pdf/1405.4308v1.pdf | Coarse-to-Fine Classification via Parametric and Nonparametric Models for Computer-Aided Diagnosis | Classification is one of the core problems in Computer-Aided Diagnosis (CAD),
targeting for early cancer detection using 3D medical imaging interpretation.
High detection sensitivity with desirably low false positive (FP) rate is
critical for a CAD system to be accepted as a valuable or even indispensable
tool in radio... | ['Xiaojing Ye', 'Shipeng Yu', 'Meizhu Liu', 'Le Lu'] | 2014-05-16 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 4.28581864e-01 2.53971100e-01 -4.20818508e-01 -1.15729935e-01
-1.04691970e+00 -2.69225240e-01 3.07479233e-01 4.59265590e-01
-1.01469215e-02 4.84606534e-01 -6.71049729e-02 -7.53542304e-01
-7.96721637e-01 -7.51924694e-01 -3.94921035e-01 -9.46008444e-01
-1.13097243e-01 5.20112157e-01 3.99147063e-01 1.48631819... | [15.133688926696777, -2.3720474243164062] |
cf668266-7b88-4cad-9e26-43a277739074 | disclda-discriminative-learning-for | null | null | http://papers.nips.cc/paper/3599-disclda-discriminative-learning-for-dimensionality-reduction-and-classification | http://papers.nips.cc/paper/3599-disclda-discriminative-learning-for-dimensionality-reduction-and-classification.pdf | DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification | Probabilistic topic models (and their extensions) have become popular as models of latent structures in collections of text documents or images. These models are usually treated as generative models and trained using maximum likelihood estimation, an approach which may be suboptimal in the context of an overall classif... | ['Michael. I. Jordan', 'Simon Lacoste-Julien', 'Fei Sha'] | 2008-12-01 | null | null | null | neurips-2008-12 | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 1.43525422e-01 1.95920482e-01 -4.03071225e-01 -4.25651789e-01
-8.40034902e-01 -5.34832299e-01 1.21539187e+00 -6.93380758e-02
-1.03033185e-01 2.80537963e-01 6.74348474e-01 1.13952674e-01
-2.46974543e-01 -5.55920601e-01 -1.80746302e-01 -1.21958673e+00
2.03705892e-01 8.47694218e-01 -1.15715392e-01 4.74779427... | [10.343573570251465, 6.907923698425293] |
a2b636a6-fbc9-4fab-8c4f-f0b643790cce | multi-stage-object-detection-with-group | 1608.05159 | null | http://arxiv.org/abs/1608.05159v1 | http://arxiv.org/pdf/1608.05159v1.pdf | Multi-stage Object Detection with Group Recursive Learning | Most of existing detection pipelines treat object proposals independently and
predict bounding box locations and classification scores over them separately.
However, the important semantic and spatial layout correlations among proposals
are often ignored, which are actually useful for more accurate object
detection. In... | ['Xiaodan Liang', 'Jianshu Li', 'Jiashi Feng', 'Tingfa Xu', 'Shuicheng Yan', 'Jianan Li'] | 2016-08-18 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 8.53636190e-02 3.76722887e-02 -1.24354273e-01 -6.36807919e-01
-7.78937876e-01 -5.07649183e-01 4.30780560e-01 3.18554133e-01
-7.93137133e-01 2.06672952e-01 -2.06749812e-01 -5.43945469e-02
3.80896926e-01 -6.10805154e-01 -7.99159467e-01 -5.69198132e-01
3.80172320e-02 4.69721019e-01 1.08709049e+00 1.98983356... | [9.32315444946289, 0.6379354000091553] |
bb6171c6-33c8-4498-8fd1-220d5a3b9f22 | pdfnet-pointwise-dense-flow-network-for-urban | 2109.10083 | null | https://arxiv.org/abs/2109.10083v1 | https://arxiv.org/pdf/2109.10083v1.pdf | PDFNet: Pointwise Dense Flow Network for Urban-Scene Segmentation | In recent years, using a deep convolutional neural network (CNN) as a feature encoder (or backbone) is the most commonly observed architectural pattern in several computer vision methods, and semantic segmentation is no exception. The two major drawbacks of this architectural pattern are: (i) the networks often fail to... | ['Venkata Satya Sai Ajay Daliparthi'] | 2021-09-21 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [-6.57724738e-02 -1.47623241e-01 -3.80410969e-01 -5.42046964e-01
-1.13125801e-01 -4.48114127e-01 6.08245850e-01 -2.39886642e-01
-4.70420599e-01 6.36451304e-01 -3.29752624e-01 -3.60068589e-01
-4.83978763e-02 -9.47533190e-01 -7.75838792e-01 -5.65586567e-01
1.07089065e-01 3.28767151e-01 6.42166018e-01 -2.60067016... | [9.476289749145508, 0.2662179470062256] |
e5628cac-0af6-41cb-91f6-d95bea3a4ddd | automatic-construction-of-an-annotated-corpus | null | null | https://aclanthology.org/2022.lrec-1.755 | https://aclanthology.org/2022.lrec-1.755.pdf | Automatic Construction of an Annotated Corpus with Implicit Aspects | Aspect-based sentiment analysis (ABSA) is a task that involves classifying the polarity of aspects of the products or services described in users’ reviews. Most previous work on ABSA has focused on explicit aspects, which appear as explicit words or phrases in the sentences of the review. However, users often express t... | ['Kiyoaki Shirai', 'Aye Aye Mar'] | null | null | null | null | lrec-2022-6 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 1.11539438e-01 2.25388318e-01 -5.04847705e-01 -9.29541588e-01
-5.29713154e-01 -7.33013511e-01 4.63973999e-01 5.67644238e-01
-2.47929841e-01 5.71910858e-01 3.41266274e-01 -2.12125629e-01
4.49685901e-01 -7.82182395e-01 -2.79300570e-01 -6.90964937e-01
5.72469294e-01 3.72228146e-01 1.34258226e-01 -2.87445426... | [11.356721878051758, 6.705973148345947] |
9b698e9a-12f2-4a92-bf93-1cf9e3e16813 | laplacian-convolutional-representation-for | 2212.01529 | null | https://arxiv.org/abs/2212.01529v2 | https://arxiv.org/pdf/2212.01529v2.pdf | Laplacian Convolutional Representation for Traffic Time Series Imputation | Spatiotemporal traffic data imputation is of great significance in intelligent transportation systems and data-driven decision-making processes. To make an accurate reconstruction from partially observed traffic data, we assert the importance of characterizing both global and local trends in traffic time series. In the... | ['Lijun Sun', 'Nicolas Saunier', 'Zhanhong Cheng', 'Xinyu Chen'] | 2022-12-03 | null | null | null | null | ['image-inpainting', 'traffic-data-imputation'] | ['computer-vision', 'time-series'] | [ 1.41185939e-01 -7.23020732e-01 -2.23699376e-01 -2.32312486e-01
-6.84180260e-01 -2.88688868e-01 4.54576552e-01 -5.15638232e-01
-2.25852340e-01 6.45552278e-01 4.01734710e-01 -5.99231839e-01
-4.59782034e-01 -6.76318347e-01 -7.96357214e-01 -8.65496933e-01
1.14401698e-01 -5.38816163e-03 -8.77906978e-02 -2.65504748... | [6.570418834686279, 2.1183528900146484] |
c37cac19-c806-47a8-b41d-649c74f8b00a | museformer-transformer-with-fine-and-coarse | 2210.10349 | null | https://arxiv.org/abs/2210.10349v2 | https://arxiv.org/pdf/2210.10349v2.pdf | Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation | Symbolic music generation aims to generate music scores automatically. A recent trend is to use Transformer or its variants in music generation, which is, however, suboptimal, because the full attention cannot efficiently model the typically long music sequences (e.g., over 10,000 tokens), and the existing models have ... | ['Tie-Yan Liu', 'Tao Qin', 'Shikun Zhang', 'Wei Ye', 'Xu Tan', 'Wei Hu', 'Rui Wang', 'Peiling Lu', 'Botao Yu'] | 2022-10-19 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 2.16667801e-01 -2.20344335e-01 9.51913521e-02 3.42037588e-01
-7.86776066e-01 -4.78368074e-01 2.06070080e-01 9.21257511e-02
1.71782188e-02 7.39280939e-01 6.40647829e-01 1.56856388e-01
-3.91238749e-01 -7.93400824e-01 -5.34279883e-01 -6.88200295e-01
2.05458961e-02 4.33405429e-01 1.16777979e-01 -4.66624498... | [15.974321365356445, 5.536772727966309] |
7056888f-03b5-408f-aaab-eaa13e1d08d1 | learning-from-miscellaneous-other-class-words | 2106.15167 | null | https://arxiv.org/abs/2106.15167v1 | https://arxiv.org/pdf/2106.15167v1.pdf | Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition | Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to identify and classify named entity mentions. Prototypical network shows superior performance on few-shot NER. However, existing prototypical methods fail to differentiate rich semantics in other-class words, which will aggravate overfitti... | ['Juanzi Li', 'Lei Hou', 'Minghui Liu', 'Yixin Cao', 'Bin Xu', 'Shuai Wang', 'Meihan Tong'] | 2021-06-29 | null | https://aclanthology.org/2021.acl-long.487 | https://aclanthology.org/2021.acl-long.487.pdf | acl-2021-5 | ['miscellaneous', 'few-shot-ner'] | ['miscellaneous', 'natural-language-processing'] | [-2.74271160e-01 2.01031044e-01 -3.00920248e-01 -6.16509676e-01
-7.36577451e-01 -6.66949093e-01 5.18067122e-01 1.25127152e-01
-6.07970417e-01 8.15706253e-01 4.57068741e-01 1.33863047e-01
-9.21739116e-02 -9.39351559e-01 -1.80377126e-01 -3.93316984e-01
2.82648563e-01 3.50365996e-01 5.68741858e-01 -3.17710012... | [9.643937110900879, 9.346454620361328] |
401f125b-d05a-408f-be57-77c246f9837c | transferable-knowledge-based-multi | 2107.12618 | null | https://arxiv.org/abs/2107.12618v1 | https://arxiv.org/pdf/2107.12618v1.pdf | Transferable Knowledge-Based Multi-Granularity Aggregation Network for Temporal Action Localization: Submission to ActivityNet Challenge 2021 | This technical report presents an overview of our solution used in the submission to 2021 HACS Temporal Action Localization Challenge on both Supervised Learning Track and Weakly-Supervised Learning Track. Temporal Action Localization (TAL) requires to not only precisely locate the temporal boundaries of action instanc... | ['Yu Qiao', 'Wei Wu', 'Weihao Gan', 'Dongliang Wang', 'Yukun Li', 'Peiqin Zhuang', 'Haisheng Su'] | 2021-07-27 | null | null | null | null | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 4.83662784e-01 -1.94005966e-02 -5.18405735e-01 -2.82543868e-01
-1.12220883e+00 -4.87618357e-01 5.05553603e-01 -3.78491402e-01
-4.13133889e-01 6.70195282e-01 2.79638737e-01 2.47594580e-01
-7.93745443e-02 -2.35441402e-01 -7.72794425e-01 -8.60219717e-01
-3.86470079e-01 6.52434826e-02 7.64828503e-01 2.05701753... | [8.432629585266113, 0.6539883613586426] |
bae0a0ac-3a2a-44af-87b1-1b9c71dadfd0 | lip-sync-matters-a-novel-multimodal-forgery | null | null | https://ieeexplore.ieee.org/document/9980296 | http://www.apsipa.org/proceedings/2022/APSIPA%202022/ThPM2-1/1570840237.pdf | Lip Sync Matters: A Novel Multimodal Forgery Detector | Deepfake technology has advanced a lot, but it is a double-sided sword for the community. One can use it for beneficial purposes, such as restoring vintage content in old movies, or for nefarious purposes, such as creating fake footage to manipulate the public and distribute non-consensual pornography. A lot of work ha... | ['Hsin-Min Wang', 'Yu Tsao', 'Yan-Tsung Peng', 'Sarwar Khan', 'Ammarah Hashmi', 'Sahibzada Adil Shahzad'] | 2022-11-07 | null | null | null | apsipa-asc-2022-2022-11 | ['face-swapping'] | ['computer-vision'] | [-6.57773949e-03 -3.18205774e-01 -3.05033505e-01 1.07341483e-02
-1.03265214e+00 -4.07040417e-01 4.87595350e-01 -2.73911953e-01
-1.20721199e-01 5.74071050e-01 4.94700849e-01 1.15651794e-01
5.25440156e-01 -3.63003612e-01 -6.68433428e-01 -9.35640216e-01
5.68261921e-01 -1.43530279e-01 2.35692620e-01 -4.05310661... | [12.956488609313965, 1.2933027744293213] |
498307bb-1013-44ff-9a28-2c5719b99cd4 | recovery-of-the-fetal-electrocardiogram-for | 1904.09525 | null | https://arxiv.org/abs/1904.09525v2 | https://arxiv.org/pdf/1904.09525v2.pdf | Recovery of the fetal electrocardiogram for morphological analysis from two trans-abdominal channels via optimal shrinkage | We propose a novel algorithm to recover fetal electrocardiogram (ECG) for both the fetal heart rate analysis and morphological analysis of its waveform from two or three trans-abdominal maternal ECG channels. We design an algorithm based on the optimal-shrinkage and the nonlocal Euclidean median under the wave-shape ma... | ['Hau-Tieng Wu', 'Piers Barker', 'Salim Idriss', 'Stephen Miller', 'Pei-Chun Su'] | 2019-04-21 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [ 4.76100743e-01 1.74677655e-01 3.62116784e-01 -2.34527230e-01
-7.59098828e-01 -7.67761350e-01 -2.51005471e-01 2.18722299e-01
7.96494633e-02 5.62830746e-01 -2.60960579e-01 -4.05533493e-01
-5.43734133e-01 -4.91779327e-01 -4.07591999e-01 -9.38372135e-01
-7.02746570e-01 3.63179177e-01 -5.25923312e-01 3.38933975... | [14.185968399047852, 3.1508004665374756] |
d6a84008-44d0-4cf7-b138-9a0344e3613d | uod-universal-one-shot-detection-of | 2306.07615 | null | https://arxiv.org/abs/2306.07615v3 | https://arxiv.org/pdf/2306.07615v3.pdf | UOD: Universal One-shot Detection of Anatomical Landmarks | One-shot medical landmark detection gains much attention and achieves great success for its label-efficient training process. However, existing one-shot learning methods are highly specialized in a single domain and suffer domain preference heavily in the situation of multi-domain unlabeled data. Moreover, one-shot lea... | ['S. Kevin Zhou', 'Zaiyi Liu', 'Qingsong Yao', 'Quan Quan', 'Heqin Zhu'] | 2023-06-13 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 2.19089642e-01 1.51893616e-01 -4.54468250e-01 -4.18375194e-01
-1.39511442e+00 -1.30190194e-01 4.19154704e-01 2.40834996e-01
-5.99787295e-01 5.13172448e-01 7.60350972e-02 1.26134977e-01
-1.75711408e-01 -6.45846725e-01 -1.95851400e-01 -8.01785290e-01
2.23445341e-01 5.59936285e-01 6.77075148e-01 -5.53149693... | [14.605606079101562, -2.0855934619903564] |
1e80334e-2934-4098-b8de-684d3cd4ad0f | dense-sparse-retrieval-using-sparse-language | 2304.00114 | null | https://arxiv.org/abs/2304.00114v1 | https://arxiv.org/pdf/2304.00114v1.pdf | Dense Sparse Retrieval: Using Sparse Language Models for Inference Efficient Dense Retrieval | Vector-based retrieval systems have become a common staple for academic and industrial search applications because they provide a simple and scalable way of extending the search to leverage contextual representations for documents and queries. As these vector-based systems rely on contextual language models, their usag... | ['ChengXiang Zhai', 'Daniel Campos'] | 2023-03-31 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [-2.44712636e-01 -6.77542925e-01 -7.28637278e-01 -2.73501366e-01
-1.23041153e+00 -6.51631176e-01 9.73489165e-01 3.74219030e-01
-5.07192552e-01 5.72387099e-01 3.40293497e-01 -7.58866370e-01
-2.40673140e-01 -7.80951619e-01 -4.01790440e-01 -4.47571307e-01
1.64696485e-01 7.13814020e-01 2.49760389e-01 -3.91317546... | [11.470147132873535, 7.578574180603027] |
8c452a2a-3410-4935-8be0-8e226e02edd8 | dream3d-zero-shot-text-to-3d-synthesis-using | 2212.14704 | null | https://arxiv.org/abs/2212.14704v2 | https://arxiv.org/pdf/2212.14704v2.pdf | Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models | Recent CLIP-guided 3D optimization methods, such as DreamFields and PureCLIPNeRF, have achieved impressive results in zero-shot text-to-3D synthesis. However, due to scratch training and random initialization without prior knowledge, these methods often fail to generate accurate and faithful 3D structures that conform ... | ['Shenghua Gao', 'XiaoHu Qie', 'Ying Shan', 'Yan-Pei Cao', 'Weihao Cheng', 'Xintao Wang', 'Jiale Xu'] | 2022-12-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Dream3D_Zero-Shot_Text-to-3D_Synthesis_Using_3D_Shape_Prior_and_Text-to-Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Dream3D_Zero-Shot_Text-to-3D_Synthesis_Using_3D_Shape_Prior_and_Text-to-Image_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-to-shape-generation', 'text-to-3d'] | ['computer-vision', 'computer-vision'] | [ 5.43132544e-01 1.58796072e-01 4.39486533e-01 -1.52139932e-01
-7.55856574e-01 -7.00701237e-01 9.13163066e-01 -4.60630447e-01
1.75424799e-01 4.66524303e-01 1.37658924e-01 -2.88546383e-01
1.22565448e-01 -9.86004591e-01 -8.44562352e-01 -5.97318530e-01
5.49478114e-01 7.05037594e-01 -2.23375279e-02 -3.91225070... | [9.283080101013184, -3.3041858673095703] |
2d9146be-b3f7-4f2e-97aa-a442048c404a | learning-low-resource-end-to-end-goal | null | null | https://aclanthology.org/2020.acl-main.57 | https://aclanthology.org/2020.acl-main.57.pdf | Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment | Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog Syste... | ['Xiaodan Zhu', 'Hangyu Li', 'Yinpei Dai', 'Chengguang Tang', 'Jian Sun', 'Yongbin Li'] | 2020-07-01 | null | null | null | acl-2020-6 | ['goal-oriented-dialog', 'dialog-learning'] | ['natural-language-processing', 'natural-language-processing'] | [-3.62114131e-01 2.77480811e-01 -2.86612183e-01 -9.18441892e-01
-1.13470995e+00 -5.26305377e-01 8.92120838e-01 -2.68190771e-01
-5.43998897e-01 1.08399153e+00 3.46181422e-01 -4.11291927e-01
1.59569502e-01 -2.94934809e-01 8.88645090e-03 -2.54261315e-01
2.83918202e-01 1.10441244e+00 4.46759164e-01 -9.78083849... | [12.860274314880371, 8.041276931762695] |
77942b41-28ce-47fe-8db4-6c77bb3092f5 | definition-extraction-feature-analysis-from | null | null | https://aclanthology.org/2020.cogalex-1.10 | https://aclanthology.org/2020.cogalex-1.10.pdf | Definition Extraction Feature Analysis: From Canonical to Naturally-Occurring Definitions | Textual definitions constitute a fundamental source of knowledge when seeking the meaning of words, and they are the cornerstone of lexical resources like glossaries, dictionaries, encyclopedia or thesauri. In this paper, we present an in-depth analytical study on the main features relevant to the task of definition ex... | ['Jose Camacho-Collados', 'Luis Espinosa Anke', 'Mireia Roig Mirapeix'] | null | null | null | null | coling-cogalex-2020-12 | ['definition-extraction'] | ['natural-language-processing'] | [ 6.75749257e-02 -2.83644140e-01 -6.45508170e-01 -4.32941131e-02
-3.94975692e-01 -1.23084891e+00 1.27549005e+00 7.42066681e-01
-7.25666940e-01 8.11564863e-01 4.88425374e-01 -4.89778727e-01
-5.16679585e-01 -7.28714764e-01 -1.45219952e-01 -4.37232196e-01
1.62637785e-01 1.64933264e-01 -1.22034997e-01 -5.95482826... | [10.238306045532227, 9.182602882385254] |
c9d3d3cc-bfbe-435a-90d0-652b6f33361c | lisens-a-scalable-architecture-for-video | 1503.04267 | null | http://arxiv.org/abs/1503.04267v1 | http://arxiv.org/pdf/1503.04267v1.pdf | LiSens --- A Scalable Architecture for Video Compressive Sensing | The measurement rate of cameras that take spatially multiplexed measurements
by using spatial light modulators (SLM) is often limited by the switching speed
of the SLMs. This is especially true for single-pixel cameras where the
photodetector operates at a rate that is many orders-of-magnitude greater than
the SLM. We ... | ['Jian Wang', 'Mohit Gupta', 'Aswin C. Sankaranarayanan'] | 2015-03-14 | null | null | null | null | ['video-compressive-sensing'] | ['computer-vision'] | [ 8.18311572e-01 -1.58971116e-01 3.14862058e-02 -1.19378507e-01
-1.92005605e-01 -5.96393585e-01 4.50431466e-01 -7.58876085e-01
-7.88386881e-01 8.75108063e-01 -2.14741170e-01 -3.93925697e-01
1.94499582e-01 -6.55389130e-01 -7.43357599e-01 -7.00118780e-01
3.36761057e-01 -1.64898202e-01 8.58540118e-01 2.85592765... | [9.605571746826172, -2.5944254398345947] |
9b937ee4-9cc3-477f-a5f6-7c31477dff43 | amorphous-fortress-observing-emergent | 2306.13169 | null | https://arxiv.org/abs/2306.13169v1 | https://arxiv.org/pdf/2306.13169v1.pdf | Amorphous Fortress: Observing Emergent Behavior in Multi-Agent FSMs | We introduce a system called Amorphous Fortress -- an abstract, yet spatial, open-ended artificial life simulation. In this environment, the agents are represented as finite-state machines (FSMs) which allow for multi-agent interaction within a constrained space. These agents are created by randomly generating and evol... | ['Julian Togelius', 'Sam Earle', 'Dipika Rajesh', 'M Charity'] | 2023-06-22 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-7.65639246e-02 1.69572517e-01 2.61601597e-01 5.70538044e-02
2.86700100e-01 -7.09800899e-01 1.10190129e+00 -1.99685887e-01
-3.56935501e-01 1.13650358e+00 -1.57424331e-01 -2.12612003e-01
-1.88184872e-01 -1.03804290e+00 -2.43724257e-01 -5.72923899e-01
-9.42526162e-01 1.03924870e+00 6.12200916e-01 -7.00662315... | [3.710787773132324, 1.5218619108200073] |
695d62e5-495b-4a54-a78e-e2e070ba9394 | humanmac-masked-motion-completion-for-human | 2302.03665 | null | https://arxiv.org/abs/2302.03665v2 | https://arxiv.org/pdf/2302.03665v2.pdf | HumanMAC: Masked Motion Completion for Human Motion Prediction | Human motion prediction is a classical problem in computer vision and computer graphics, which has a wide range of practical applications. Previous effects achieve great empirical performance based on an encoding-decoding style. The methods of this style work by first encoding previous motions to latent representations... | ['Tongliang Liu', 'Xiaobo Xia', 'Yiren Pang', 'Yewen Li', 'Jiawei Zhang', 'Ling-Hao Chen'] | 2023-02-07 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [ 4.23230737e-01 -1.82705685e-01 -2.99966037e-01 -1.96938545e-01
-4.15181518e-01 -1.17198907e-01 5.61884820e-01 -4.38807249e-01
-2.34745473e-01 4.78798687e-01 3.56018066e-01 -1.58766210e-01
1.37683108e-01 -6.89469755e-01 -6.23926163e-01 -1.06772840e+00
3.09961796e-01 1.05689235e-01 3.25606555e-01 -8.46638680... | [10.739822387695312, -1.0810681581497192] |
87bdaee8-81d1-4f01-9429-9d46f5eb29fe | remote-sensing-image-classification-with-the | 2104.00704 | null | https://arxiv.org/abs/2104.00704v1 | https://arxiv.org/pdf/2104.00704v1.pdf | Remote Sensing Image Classification with the SEN12MS Dataset | Image classification is one of the main drivers of the rapid developments in deep learning with convolutional neural networks for computer vision. So is the analogous task of scene classification in remote sensing. However, in contrast to the computer vision community that has long been using well-established, large-sc... | ['Yu-Lun Wu', 'Michael Schmitt'] | 2021-04-01 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 5.76970816e-01 -4.70226556e-01 8.89782906e-02 -4.92248356e-01
-6.74097776e-01 -5.51756203e-01 7.73324013e-01 7.07701147e-02
-8.67049456e-01 6.39549255e-01 6.56238869e-02 -7.99801528e-01
-5.32799959e-01 -1.16099894e+00 -5.11835694e-01 -7.99574792e-01
6.03687624e-03 1.22710288e-01 -2.47656003e-01 -4.60008115... | [9.620850563049316, -1.5550668239593506] |
8e29a6a9-bcf6-4ec3-8734-7026aaa29eb7 | defending-against-insertion-based-textual | 2305.02394 | null | https://arxiv.org/abs/2305.02394v1 | https://arxiv.org/pdf/2305.02394v1.pdf | Defending against Insertion-based Textual Backdoor Attacks via Attribution | Textual backdoor attack, as a novel attack model, has been shown to be effective in adding a backdoor to the model during training. Defending against such backdoor attacks has become urgent and important. In this paper, we propose AttDef, an efficient attribution-based pipeline to defend against two insertion-based poi... | ['V. G. Vinod Vydiswaran', 'Chaowei Xiao', 'Wei Ping', 'Zhuofeng Wu', 'Jiazhao Li'] | 2023-05-03 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [-1.64675545e-02 -3.66434932e-01 -3.07083488e-01 9.83945876e-02
-8.85159433e-01 -1.21346033e+00 6.18269622e-01 5.23420572e-01
-3.45100641e-01 5.89642346e-01 -2.89123170e-02 -7.37082958e-01
3.91667873e-01 -1.04846430e+00 -9.62503254e-01 -5.62724710e-01
-9.23091248e-02 3.38732064e-01 4.54839587e-01 -2.90476084... | [5.986425876617432, 7.832669258117676] |
3049fd6d-2d48-452b-a3df-0533cdf9d5a5 | in-game-toxic-language-detection-shared-task | 2211.05995 | null | https://arxiv.org/abs/2211.05995v3 | https://arxiv.org/pdf/2211.05995v3.pdf | In-game Toxic Language Detection: Shared Task and Attention Residuals | In-game toxic language becomes the hot potato in the gaming industry and community. There have been several online game toxicity analysis frameworks and models proposed. However, it is still challenging to detect toxicity due to the nature of in-game chat, which has extremely short length. In this paper, we describe ho... | ['Soyeon Caren Han', 'Feiqi Cao', 'Weixuan Wu', 'Yuanzhe Jia'] | 2022-11-11 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [-2.69648671e-01 -3.16340566e-01 3.64617705e-02 2.95226544e-01
-1.10568249e+00 -9.43785429e-01 -1.34952934e-02 3.69761176e-02
-4.63239551e-01 8.83484900e-01 3.99540335e-01 -3.24050456e-01
-1.13497593e-01 -7.60474920e-01 -5.97415725e-03 -3.67998958e-01
-1.98332980e-01 1.79528490e-01 6.95377886e-01 -4.38150913... | [8.966323852539062, 10.449366569519043] |
5e3036db-6513-4b62-bb8a-9b01809d6f63 | semantic-prediction-which-one-should-come | 2110.02829 | null | https://arxiv.org/abs/2110.02829v1 | https://arxiv.org/pdf/2110.02829v1.pdf | Semantic Prediction: Which One Should Come First, Recognition or Prediction? | The ultimate goal of video prediction is not forecasting future pixel-values given some previous frames. Rather, the end goal of video prediction is to discover valuable internal representations from the vast amount of available unlabeled video data in a self-supervised fashion for downstream tasks. One of the primary ... | ['and Sven Behnke', 'Jan Nogga', 'Hafez Farazi'] | 2021-10-06 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 5.51672816e-01 3.04796666e-01 -3.35343182e-01 -5.82262695e-01
-2.08100885e-01 -3.15134168e-01 7.37937331e-01 -1.10866927e-01
-1.70667931e-01 7.53306627e-01 7.28076935e-01 4.70446944e-02
2.34721288e-01 -6.90179348e-01 -8.54268253e-01 -4.24034685e-01
-2.63394825e-02 2.11532321e-03 5.28651416e-01 1.18267983... | [8.36875057220459, 0.42859703302383423] |
bca36115-97d9-4b1a-9985-dec6aebaf435 | swapped-goal-conditioned-offline | 2302.08865 | null | https://arxiv.org/abs/2302.08865v1 | https://arxiv.org/pdf/2302.08865v1.pdf | Swapped goal-conditioned offline reinforcement learning | Offline goal-conditioned reinforcement learning (GCRL) can be challenging due to overfitting to the given dataset. To generalize agents' skills outside the given dataset, we propose a goal-swapping procedure that generates additional trajectories. To alleviate the problem of noise and extrapolation errors, we present a... | ['Joni-Kristen Kämäräinen', 'Joni Pajarinen', 'Dingding Cai', 'Huiling Wang', 'Wenyan Yang'] | 2023-02-17 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-6.61941245e-02 -1.46386936e-01 -2.95191497e-01 -1.24239521e-02
-9.33434546e-01 -4.24551159e-01 4.13263977e-01 -2.00660095e-01
-6.65051401e-01 1.48660326e+00 -5.33852093e-02 -3.91275913e-01
-3.92018259e-01 -6.49012446e-01 -9.20530975e-01 -7.19408095e-01
-3.71195465e-01 4.20315504e-01 1.60446614e-01 -4.77198541... | [4.139947414398193, 2.058342695236206] |
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