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4cc79f8c-96e6-4a1d-aa2c-581d03d5c796 | dialects-identification-of-armenian-language | null | null | https://aclanthology.org/2022.digitam-1.2 | https://aclanthology.org/2022.digitam-1.2.pdf | Dialects Identification of Armenian Language | The Armenian language has many dialects that differ from each other syntactically, morphologically, and phonetically. In this work, we implement and evaluate models that determine the dialect of a given passage of text. The proposed models are evaluated for the three major variations of the Armenian language: Eastern, ... | ['Karen Avetisyan'] | null | null | null | null | digitam-lrec-2022-6 | ['dialect-identification'] | ['natural-language-processing'] | [-3.47661436e-01 -3.88686657e-01 -1.97557554e-01 -2.45933115e-01
-2.30631858e-01 -5.97270608e-01 9.26551700e-01 6.04402363e-01
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-1.31727427e-01 -8.68042350e-01 -3.36399376e-02 -5.58251321e-01
2.47867316e-01 5.34033418e-01 1.01979733e-01 -6.91811919... | [10.262991905212402, 10.354966163635254] |
2723adbf-62ae-43c0-9506-8a4e75df245f | learning-prompt-enhanced-context-features-for | 2306.14451 | null | https://arxiv.org/abs/2306.14451v1 | https://arxiv.org/pdf/2306.14451v1.pdf | Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection | Video anomaly detection under weak supervision is challenging due to the absence of frame-level annotations during the training phase. Previous work has employed graph convolution networks or self-attention mechanisms to model temporal relations, along with multiple instance learning (MIL)-based classification loss to ... | ['Shengjin Wang', 'Xiaoyu Wu', 'Yujiang Pu'] | 2023-06-26 | null | null | null | null | ['video-anomaly-detection', 'anomaly-detection-in-surveillance-videos', 'anomaly-detection', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 1.84840262e-01 -3.06810707e-01 -1.97027057e-01 -6.12369716e-01
-4.61323947e-01 -1.45161003e-01 5.34508884e-01 4.91324216e-01
-5.34347415e-01 3.32439929e-01 -4.87976000e-02 -9.72090960e-02
-2.26626173e-01 -7.08821416e-01 -4.55078632e-01 -8.03612888e-01
-2.51299232e-01 -1.03155568e-01 5.85135281e-01 -6.27754405... | [7.84399938583374, 1.6116762161254883] |
2ee55854-3c03-4396-a6bf-c42f95dd02e5 | scene-text-recognition-with-image-text | 2305.04524 | null | https://arxiv.org/abs/2305.04524v1 | https://arxiv.org/pdf/2305.04524v1.pdf | Scene Text Recognition with Image-Text Matching-guided Dictionary | Employing a dictionary can efficiently rectify the deviation between the visual prediction and the ground truth in scene text recognition methods. However, the independence of the dictionary on the visual features may lead to incorrect rectification of accurate visual predictions. In this paper, we propose a new dictio... | ['Umapada Pal', 'Yue Lu', 'Xiao Tu', 'Hongjian Zhan', 'Jiajun Wei'] | 2023-05-08 | null | null | null | null | ['scene-text-recognition', 'text-matching'] | ['computer-vision', 'natural-language-processing'] | [ 4.85527635e-01 -4.56903219e-01 -2.90935844e-01 -4.28072214e-01
-3.43310416e-01 -1.25492245e-01 7.98946619e-01 4.20165733e-02
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7.95981169e-01 4.26329553e-01 3.56853008e-01 -2.42278770... | [11.735645294189453, 2.0475828647613525] |
aa06f9be-671e-4e05-8245-6587c2b58e6f | uniform-hypergraph-partitioning-provable | 1602.06516 | null | http://arxiv.org/abs/1602.06516v4 | http://arxiv.org/pdf/1602.06516v4.pdf | Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques | In a series of recent works, we have generalised the consistency results in
the stochastic block model literature to the case of uniform and non-uniform
hypergraphs. The present paper continues the same line of study, where we focus
on partitioning weighted uniform hypergraphs---a problem often encountered in
computer ... | ['Ambedkar Dukkipati', 'Debarghya Ghoshdastidar'] | 2016-02-21 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [ 3.46468449e-01 3.98692727e-01 -2.91371852e-01 2.15575427e-01
-4.19227451e-01 -6.96076572e-01 2.74918526e-01 2.28131384e-01
-1.17807686e-02 5.44065237e-01 9.41454843e-02 -3.56824279e-01
-6.46799147e-01 -9.63593960e-01 -3.70277107e-01 -1.10729694e+00
-3.05052161e-01 9.50233161e-01 4.36521590e-01 1.76497355... | [7.036774158477783, 5.223842144012451] |
8e18aefb-18f2-4827-b85b-b1f2a4521f3b | pangu-coder-program-synthesis-with-function | 2207.11280 | null | https://arxiv.org/abs/2207.11280v1 | https://arxiv.org/pdf/2207.11280v1.pdf | PanGu-Coder: Program Synthesis with Function-Level Language Modeling | We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i.e. the synthesis of programming language solutions given a natural language problem description. We train PanGu-Coder using a two-stage strategy: the first stage employs Causal Language ... | ['Qun Liu', 'Qianxiang Wang', 'Xin Jiang', 'Jiansheng Wei', 'Guangtai Liang', 'Yasheng Wang', 'Ignacio Iacobacci', 'Yuchi Ma', 'Xin Wang', 'Pingyi Zhou', 'Li Yan', 'Hao Yu', 'Lin Li', 'Bo Shen', 'Meng Xiao', 'Qi Zhang', 'Zhongqi Li', 'Yinpeng Guo', 'Guchun Zhang', 'Milan Gritta', 'Gerasimos Lampouras', 'Fenia Christopo... | 2022-07-22 | null | null | null | null | ['program-synthesis', 'text-to-code-generation'] | ['computer-code', 'computer-code'] | [ 3.03948671e-01 4.73602653e-01 -1.11270271e-01 -3.28685939e-01
-9.85682905e-01 -5.50211668e-01 6.71023309e-01 4.86346662e-01
1.19424768e-01 3.19937319e-01 3.84984583e-01 -9.96361554e-01
5.04411638e-01 -9.04174685e-01 -1.12100303e+00 4.97755036e-02
-1.01352252e-01 3.52786869e-01 9.83146206e-02 -2.79229373... | [7.803714275360107, 7.821127891540527] |
29480276-6728-488b-ac49-e87c901ed057 | diverse-text-generation-via-variational | 2204.01227 | null | https://arxiv.org/abs/2204.01227v1 | https://arxiv.org/pdf/2204.01227v1.pdf | Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors | Generating high quality texts with high diversity is important for many NLG applications, but current methods mostly focus on building deterministic models to generate higher quality texts and do not provide many options for promoting diversity. In this work, we present a novel latent structured variable model to gener... | ['Yangfeng Ji', 'LiWei Wang', 'Jianqiao Zhao', 'Wanyu Du'] | 2022-04-04 | null | null | null | null | ['paraphrase-generation', 'text-style-transfoer', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 4.59577084e-01 3.12452435e-01 -1.78380489e-01 -3.24077189e-01
-1.43670046e+00 -3.98856938e-01 1.03860569e+00 -3.16690773e-01
-3.24090215e-04 1.26484168e+00 8.39415133e-01 -3.13984901e-01
2.80938566e-01 -9.53442097e-01 -7.65085340e-01 -6.88399374e-01
6.96311116e-01 9.22526300e-01 -2.31627092e-01 -2.05185726... | [11.877233505249023, 9.126742362976074] |
2d349f62-b173-461c-beb2-380f148eb9a0 | portrait-eyeglasses-and-shadow-removal-by | 2203.10474 | null | https://arxiv.org/abs/2203.10474v1 | https://arxiv.org/pdf/2203.10474v1.pdf | Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data | In portraits, eyeglasses may occlude facial regions and generate cast shadows on faces, which degrades the performance of many techniques like face verification and expression recognition. Portrait eyeglasses removal is critical in handling these problems. However, completely removing the eyeglasses is challenging beca... | ['Feng Xu', 'Zhibo Wang', 'Junfeng Lyu'] | 2022-03-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lyu_Portrait_Eyeglasses_and_Shadow_Removal_by_Leveraging_3D_Synthetic_Data_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lyu_Portrait_Eyeglasses_and_Shadow_Removal_by_Leveraging_3D_Synthetic_Data_CVPR_2022_paper.pdf | cvpr-2022-1 | ['shadow-removal'] | ['computer-vision'] | [ 3.69540393e-01 -1.53635927e-02 3.19813550e-01 -3.96672130e-01
-1.35687426e-01 -4.95861888e-01 4.30045038e-01 -4.77078676e-01
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3.70724946e-01 -4.79137748e-01 -6.84575856e-01 -8.45040023e-01
3.73776376e-01 -1.54440001e-01 1.56560525e-01 -1.91633061... | [12.902070999145508, -0.00472797779366374] |
3cce8e7e-4fa1-4277-a479-edd14bae6ca2 | squeeze-flow-of-micro-droplets-convolutional | 2211.09061 | null | https://arxiv.org/abs/2211.09061v1 | https://arxiv.org/pdf/2211.09061v1.pdf | Squeeze flow of micro-droplets: convolutional neural network with trainable and tunable refinement | We propose a platform based on neural networks to solve the image-to-image translation problem in the context of squeeze flow of micro-droplets. In the first part of this paper, we present the governing partial differential equations to lay out the underlying physics of the problem. We also discuss our developed Python... | ['S. V. Sreenivasan', 'Shrawan Singhal', 'Aryan Mehboudi'] | 2022-11-16 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 7.96081662e-01 -9.10519511e-02 3.25903952e-01 -2.37302721e-01
-3.86528432e-01 -6.18567050e-01 5.02702594e-01 -2.86408365e-02
-4.75665361e-01 5.04801571e-01 -4.98991311e-01 -2.49083519e-01
-7.17460439e-02 -1.15911174e+00 -1.19366312e+00 -9.93867636e-01
1.58144906e-01 2.34187528e-01 9.80761647e-02 -5.76137118... | [11.031299591064453, -1.1332733631134033] |
aac4cb9f-11c9-4d84-8f7a-f74f3580b09d | frequency-domain-learning-for-volumetric | 2302.08595 | null | https://arxiv.org/abs/2302.08595v2 | https://arxiv.org/pdf/2302.08595v2.pdf | Frequency-domain Learning for Volumetric-based 3D Data Perception | Frequency-domain learning draws attention due to its superior tradeoff between inference accuracy and input data size. Frequency-domain learning in 2D computer vision tasks has shown that 2D convolutional neural networks (CNN) have a stationary spectral bias towards low-frequency channels so that high-frequency channel... | ['Fengbo Ren', 'Suya You', 'Zifan Yu'] | 2023-02-16 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 3.57743204e-01 2.98057586e-01 -1.50620684e-01 -1.38410330e-01
-7.04663277e-01 -5.34097850e-01 2.97703505e-01 1.25975952e-01
-4.29104775e-01 1.85047820e-01 -3.64680648e-01 -4.75100696e-01
-2.74447650e-01 -1.02969587e+00 -1.03868020e+00 -6.21058583e-01
-1.98086515e-01 1.58366144e-01 3.71424347e-01 9.56656262... | [8.011353492736816, -3.426616668701172] |
879c21fb-352b-4b63-ae47-5300e1a1e4f8 | post-processing-recommender-systems-with | 2204.11241 | null | https://arxiv.org/abs/2204.11241v1 | https://arxiv.org/pdf/2204.11241v1.pdf | Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations | Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., movie "x" starred by actress "y" recommended to a user because that user watched other movies with "y" as an actress). However, none of these s... | ['Mirko Marras', 'Gianni Fenu', 'Ludovico Boratto', 'Giacomo Balloccu'] | 2022-04-24 | null | null | null | null | ['explainable-models', 'movie-recommendation'] | ['computer-vision', 'miscellaneous'] | [-3.69898707e-01 5.12918651e-01 -7.72878885e-01 -6.79721057e-01
2.15610087e-01 -4.26345319e-01 6.78420722e-01 3.10055673e-01
9.05559808e-02 4.06421334e-01 5.91413200e-01 -3.84135276e-01
-7.25864470e-01 -8.09281886e-01 -7.33767271e-01 -2.50301093e-01
-1.74874678e-01 5.28312147e-01 9.16789025e-02 -4.37680244... | [9.79137134552002, 5.750641822814941] |
a7f934f0-2d8e-40c4-b02d-824aed47d45d | 190408494 | 1904.08494 | null | https://arxiv.org/abs/1904.08494v2 | https://arxiv.org/pdf/1904.08494v2.pdf | Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles | We address the problem of 3D object detection from 2D monocular images in autonomous driving scenarios. We propose to lift the 2D images to 3D representations using learned neural networks and leverage existing networks working directly on 3D data to perform 3D object detection and localization. We show that, with care... | ['Gaurav Sharma', 'Frederic Jurie', 'Siddharth Srivastava'] | 2019-03-27 | null | null | null | null | ['monocular-3d-object-localization', '3d-object-detection-from-monocular-images'] | ['computer-vision', 'computer-vision'] | [ 2.43812293e-01 2.79535472e-01 2.23832294e-01 -2.84903854e-01
-4.43274587e-01 -6.30098403e-01 4.95900661e-01 -2.05399513e-01
-6.40037358e-01 4.82801646e-01 -2.41949752e-01 -6.34323120e-01
-9.64496098e-03 -5.10454953e-01 -1.18371713e+00 -4.74209547e-01
-2.45721024e-02 5.81029713e-01 5.13922095e-01 -3.65310133... | [7.795698642730713, -2.5394504070281982] |
8dd35154-74d6-4da6-a658-3a2f65077516 | perception-framework-through-real-time | 2103.04136 | null | https://arxiv.org/abs/2103.04136v1 | https://arxiv.org/pdf/2103.04136v1.pdf | Perception Framework through Real-Time Semantic Segmentation and Scene Recognition on a Wearable System for the Visually Impaired | As the scene information, including objectness and scene type, are important for people with visual impairment, in this work we present a multi-task efficient perception system for the scene parsing and recognition tasks. Building on the compact ResNet backbone, our designed network architecture has two paths with shar... | ['Rainer Stiefelhagen', 'Jiaming Zhang', 'Kailun Yang', 'Haoye Chen', 'Yingzhi Zhang'] | 2021-03-06 | null | null | null | null | ['scene-parsing', 'scene-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.73464638e-01 -5.72076887e-02 1.24707930e-01 -6.59792542e-01
-3.23905736e-01 -2.61329804e-02 -2.16788296e-02 -6.72783554e-02
-8.92688751e-01 5.05154967e-01 4.64963049e-01 -2.15948254e-01
-1.56757221e-01 -9.34757411e-01 -4.83122379e-01 -4.36083525e-01
1.98283106e-01 7.33692646e-02 4.29575771e-01 -2.32249632... | [8.27701473236084, -1.5362908840179443] |
866df3da-4a70-40dd-a0ad-9a286f87a901 | explainable-slot-type-attentions-to-improve | 2210.10227 | null | https://arxiv.org/abs/2210.10227v1 | https://arxiv.org/pdf/2210.10227v1.pdf | Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling | Joint intent detection and slot filling is a key research topic in natural language understanding (NLU). Existing joint intent and slot filling systems analyze and compute features collectively for all slot types, and importantly, have no way to explain the slot filling model decisions. In this work, we propose a novel... | ['Hongxia Jin', 'Akhila Yerukola', 'Vijay Srinivasan', 'Kalpa Gunaratna'] | 2022-10-19 | null | null | null | null | ['intent-detection', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.23630702e-01 9.99037385e-01 -7.09040821e-01 -7.11121440e-01
-5.73843300e-01 -1.73290148e-01 5.84771752e-01 5.48735857e-01
-3.47693145e-01 9.26115930e-01 3.42124760e-01 -6.61049426e-01
-1.18459165e-01 -8.31201613e-01 -6.75099850e-01 -7.29314163e-02
1.65924668e-01 9.31949198e-01 3.43246683e-02 -2.46227890... | [12.520949363708496, 7.340747833251953] |
d57cb832-9bfb-43c3-8e7e-55ce9acb8b2f | dynamic-pose-robust-facial-expression | 1607.06250 | null | http://arxiv.org/abs/1607.06250v1 | http://arxiv.org/pdf/1607.06250v1.pdf | Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests | Automatic facial expression classification (FER) from videos is a critical
problem for the development of intelligent human-computer interaction systems.
Still, it is a challenging problem that involves capturing high-dimensional
spatio-temporal patterns describing the variation of one's appearance over
time. Such repr... | ['Séverine Dubuisson', 'Kévin Bailly', 'Arnaud Dapogny'] | 2016-07-21 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [ 1.89762533e-01 -5.34071028e-01 1.44995302e-02 -8.26129854e-01
-7.30322123e-01 -4.59463596e-01 7.32166946e-01 -2.56432742e-01
-2.44526431e-01 7.06188679e-01 7.81927854e-02 6.50491714e-01
6.27477095e-02 -5.10102034e-01 -7.49543905e-01 -1.00762439e+00
-2.96407580e-01 3.45838100e-01 3.04035872e-01 -1.51303872... | [13.579751968383789, 1.565171241760254] |
d90f1792-f194-4ac3-b5f2-8b0c4062916e | exclusive-topic-modeling | 2102.03525 | null | https://arxiv.org/abs/2102.03525v1 | https://arxiv.org/pdf/2102.03525v1.pdf | Exclusive Topic Modeling | We propose an Exclusive Topic Modeling (ETM) for unsupervised text classification, which is able to 1) identify the field-specific keywords though less frequently appeared and 2) deliver well-structured topics with exclusive words. In particular, a weighted Lasso penalty is imposed to reduce the dominance of the freque... | ['Ying Chen', 'Hao Lei'] | 2021-02-06 | null | null | null | null | ['unsupervised-text-classification'] | ['natural-language-processing'] | [ 5.80671057e-03 1.93574101e-01 -5.01001954e-01 -3.34261179e-01
-9.70997453e-01 -3.43606502e-01 5.69393933e-01 6.96197748e-01
-4.10584867e-01 6.27523839e-01 1.89020529e-01 -5.29401936e-02
-4.67913687e-01 -6.04691625e-01 -3.79901767e-01 -8.21227491e-01
-4.44296330e-01 6.86081350e-01 2.53358763e-02 3.23076636... | [10.382752418518066, 6.917177677154541] |
822d611c-a7a7-42c2-b94e-ad0851987616 | dynamic-community-detection-into-analyzing-of | 2011.01140 | null | https://arxiv.org/abs/2011.01140v1 | https://arxiv.org/pdf/2011.01140v1.pdf | Dynamic Community Detection into Analyzing of Wildfires Events | The study and comprehension of complex systems are crucial intellectual and scientific challenges of the 21st century. In this scenario, network science has emerged as a mathematical tool to support the study of such systems. Examples include environmental processes such as wildfires, which are known for their consider... | ['Marcos G Quiles', 'Elbert EN Macau', 'Leonardo N Ferreira', 'Moshé Cotacallapa', 'Didier A Vega-Oliveros', 'Alessandra Marli'] | 2020-11-02 | null | null | null | null | ['dynamic-community-detection'] | ['graphs'] | [ 1.92884013e-01 -3.77453417e-01 2.95798667e-02 1.57058284e-01
6.31832421e-01 -8.80859435e-01 7.65830338e-01 6.07316256e-01
-3.07482153e-01 7.20669448e-01 2.60048807e-01 -6.11702144e-01
-5.46091855e-01 -1.27875280e+00 -1.75143719e-01 -7.23330140e-01
-1.03692496e+00 3.42505351e-02 3.76157165e-01 -4.48684096... | [7.308119297027588, 5.193799018859863] |
05041565-b490-4f33-81ee-b24e98a9318c | cogmen-contextualized-gnn-based-multimodal | 2205.02455 | null | https://arxiv.org/abs/2205.02455v1 | https://arxiv.org/pdf/2205.02455v1.pdf | COGMEN: COntextualized GNN based Multimodal Emotion recognitioN | Emotions are an inherent part of human interactions, and consequently, it is imperative to develop AI systems that understand and recognize human emotions. During a conversation involving various people, a person's emotions are influenced by the other speaker's utterances and their own emotional state over the utteranc... | ['Ashutosh Modi', 'Atin Vikram Singh', 'Ayush Jain', 'Ashwani Bhat', 'Abhinav Joshi'] | 2022-05-05 | null | https://aclanthology.org/2022.naacl-main.306 | https://aclanthology.org/2022.naacl-main.306.pdf | naacl-2022-7 | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-1.37507934e-02 1.41959310e-01 1.44695584e-02 -8.91119957e-01
5.71874417e-02 -3.84765267e-01 6.76269054e-01 1.98243320e-01
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-2.80705959e-01 2.88541347e-01 -4.70614642e-01 -8.14199984... | [12.945305824279785, 6.1586174964904785] |
a1d0e170-36c0-4e57-9535-8c303d48ac9f | physics-based-deep-learning | 2109.05237 | null | https://arxiv.org/abs/2109.05237v3 | https://arxiv.org/pdf/2109.05237v3.pdf | Physics-based Deep Learning | This digital book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. As much as possible, all topics come with hands-on code examples in the form of Jupyter notebooks to quickly get started. Beyond standard supervised learning from data, we... | ['Kiwon Um', 'Felix Trost', 'Patrick Schnell', 'Maximilian Mueller', 'Philipp Holl', 'Nils Thuerey'] | 2021-09-11 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-7.84049273e-01 3.52576897e-02 -2.40193829e-01 -3.90919447e-01
-7.14231968e-01 -2.20663443e-01 3.79446864e-01 2.79269740e-02
-3.06733996e-01 1.25162601e+00 -2.17287436e-01 -4.01480973e-01
-3.40677917e-01 -6.77075922e-01 -7.15738297e-01 -7.29377806e-01
-5.69723964e-01 4.95076060e-01 -1.36443749e-01 -3.42990279... | [6.426987648010254, 3.5043442249298096] |
86b40853-42ef-47d3-8f65-5e7a78d5e5dc | appearance-consensus-driven-self-supervised | 2008.01341 | null | https://arxiv.org/abs/2008.01341v1 | https://arxiv.org/pdf/2008.01341v1.pdf | Appearance Consensus Driven Self-Supervised Human Mesh Recovery | We present a self-supervised human mesh recovery framework to infer human pose and shape from monocular images in the absence of any paired supervision. Recent advances have shifted the interest towards directly regressing parameters of a parametric human model by supervising them on large-scale datasets with 2D landma... | ['R. Venkatesh Babu', 'Rahul Mysore Venkatesh', 'Mugalodi Rakesh', 'Jogendra Nath Kundu', 'Varun Jampani'] | 2020-08-04 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2788_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460766.pdf | eccv-2020-8 | ['human-mesh-recovery'] | ['computer-vision'] | [ 3.00280869e-01 1.78698048e-01 1.47067443e-01 -4.09939021e-01
-5.32339931e-01 -4.27006871e-01 5.58249116e-01 -2.85721570e-01
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6.08379804e-02 1.05974662e+00 1.29295409e-01 -4.71717156... | [7.122588634490967, -1.1755578517913818] |
684bc794-6358-4da2-984f-60970a72afe0 | spa-vae-similar-parts-assignment-for | 2203.07825 | null | https://arxiv.org/abs/2203.07825v2 | https://arxiv.org/pdf/2203.07825v2.pdf | SPA-VAE: Similar-Parts-Assignment for Unsupervised 3D Point Cloud Generation | This paper addresses the problem of unsupervised parts-aware point cloud generation with learned parts-based self-similarity. Our SPA-VAE infers a set of latent canonical candidate shapes for any given object, along with a set of rigid body transformations for each such candidate shape to one or more locations within t... | ['Miaomiao Liu', 'Christian Walder', 'Shidi Li'] | 2022-03-15 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 2.38687322e-01 5.60765386e-01 -3.45667712e-02 -4.91408706e-01
-9.46899116e-01 -5.56607485e-01 5.57382882e-01 -1.77338481e-01
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2.34950900e-01 1.29141414e+00 6.95696846e-02 -1.27617754... | [8.722339630126953, -3.5489590167999268] |
7afb77ea-7d4e-41ca-8d4e-ff20a51544d5 | deep-active-ensemble-sampling-for-image | 2210.05770 | null | https://arxiv.org/abs/2210.05770v1 | https://arxiv.org/pdf/2210.05770v1.pdf | Deep Active Ensemble Sampling For Image Classification | Conventional active learning (AL) frameworks aim to reduce the cost of data annotation by actively requesting the labeling for the most informative data points. However, introducing AL to data hungry deep learning algorithms has been a challenge. Some proposed approaches include uncertainty-based techniques, geometric ... | ['Donald A. Adjeroh', 'Gianfranco Doretto', 'Salman Mohamadi'] | 2022-10-11 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-5.61837368e-02 2.91631997e-01 -3.26915205e-01 -6.31129563e-01
-1.46225023e+00 -3.36412370e-01 5.55170536e-01 3.59367371e-01
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-4.25677538e-01 -7.57868052e-01 -8.98605585e-01 -8.89917612e-01
-2.20378518e-01 8.79907131e-01 4.52515543e-01 3.36277783... | [8.982686042785645, 3.87395977973938] |
c0c302bb-1503-48f3-9c22-a6924404476e | sentinel-2-time-series-analysis-with-3d | null | null | https://www.mdpi.com/2220-9964/10/7/483/htm | https://www.mdpi.com/2220-9964/10/7/483/pdf | Sentinel 2 Time Series Analysis with 3D Feature Pyramid Network and Time Domain Class Activation Intervals for Crop Mapping | In this paper, we provide an innovative contribution in the research domain dedicated to crop mapping by exploiting the of Sentinel-2 satellite images time series, with the specific aim to extract information on “where and when” crops are grown. The final goal is to set up a workflow able to reliably identify (classify... | ['Mirco Boschetti', 'Nicola Landro', 'Riccardo La Grassa', 'Ignazio Gallo'] | 2021-10-07 | null | null | null | isprs-international-journal-of-geo-1 | ['unet-segmentation'] | ['computer-vision'] | [ 4.08266366e-01 5.59423566e-02 8.23478475e-02 -1.29786059e-01
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-4.83232737e-01 2.55996615e-01 -6.83222637e-02 -3.50145012... | [9.34663200378418, -1.573038101196289] |
5513cce4-20b8-4a35-9d7f-c44998d9af3b | an-efficient-encoder-decoder-architecture | 2209.15200 | null | https://arxiv.org/abs/2209.15200v5 | https://arxiv.org/pdf/2209.15200v5.pdf | An efficient encoder-decoder architecture with top-down attention for speech separation | Deep neural networks have shown excellent prospects in speech separation tasks. However, obtaining good results while keeping a low model complexity remains challenging in real-world applications. In this paper, we provide a bio-inspired efficient encoder-decoder architecture by mimicking the brain's top-down attention... | ['Xiaolin Hu', 'Runxuan Yang', 'Kai Li'] | 2022-09-30 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 2.21152455e-01 -4.01821919e-02 2.70318776e-01 -2.98686530e-02
-8.58170807e-01 3.44371684e-02 2.91783929e-01 -2.64051259e-01
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4.16363850e-02 1.55965984e-01 3.86428714e-01 -8.04817528... | [14.78361701965332, 5.8300933837890625] |
6a7245c1-63a6-431e-bc1d-825d4542d241 | proxy-graph-matching-with-proximal-matching | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/17179 | https://ojs.aaai.org/index.php/AAAI/article/view/17179/16986 | Proxy Graph Matching with Proximal Matching Networks | Estimating feature point correspondence is a common technique in computer vision. A line of recent data-driven approaches utilizing the graph neural networks improved the matching accuracy by a large margin. However, these learning-based methods require a lot of labeled training data, which are expensive to collect. Mo... | ['Cheng-Lin Liu', 'Xu-Yao Zhang', 'Tie-Qiang Wang', 'Sitong Wu', 'Chuang Wang', 'Haoru Tan'] | 2021-10-16 | null | null | null | aaai-2021-10 | ['graph-matching'] | ['graphs'] | [ 7.87354410e-02 -7.58005232e-02 -2.67757118e-01 -3.28662306e-01
-7.35526979e-01 -1.99118242e-01 6.46686256e-01 -6.40216842e-03
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2.35797450e-01 2.43578121e-01 1.80065379e-01 -3.27661991... | [8.435141563415527, -2.199864625930786] |
d04df750-6725-4c20-aedb-51b7230e4211 | proto-clip-vision-language-prototypical | 2307.03073 | null | https://arxiv.org/abs/2307.03073v2 | https://arxiv.org/pdf/2307.03073v2.pdf | Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning | We propose a novel framework for few-shot learning by leveraging large-scale vision-language models such as CLIP. Motivated by the unimodal prototypical networks for few-shot learning, we introduce PROTO-CLIP that utilizes image prototypes and text prototypes for few-shot learning. Specifically, PROTO-CLIP adapts the i... | ['Yu Xiang', 'Xinya Du', 'Yu-Wei Chao', 'Kamalesh Palanisamy', 'Jishnu Jaykumar P'] | 2023-07-06 | null | null | null | null | ['few-shot-image-classification', 'few-shot-learning'] | ['computer-vision', 'methodology'] | [ 1.53283164e-01 3.06024705e-03 -3.68712813e-01 -5.05467713e-01
-7.02911854e-01 -7.45538920e-02 8.30375135e-01 -7.92703778e-02
-4.18964505e-01 2.49223009e-01 2.86740303e-01 3.00226122e-01
1.90876350e-01 -6.78025603e-01 -9.88667548e-01 -4.30939376e-01
1.13332324e-01 1.58428892e-01 3.77029240e-01 -6.85180910... | [10.064921379089355, 2.4931466579437256] |
5e4cf8f6-4aa3-4115-8753-dfa975337b5c | cross-domain-video-anomaly-detection-without | 2212.07010 | null | https://arxiv.org/abs/2212.07010v1 | https://arxiv.org/pdf/2212.07010v1.pdf | Cross-Domain Video Anomaly Detection without Target Domain Adaptation | Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume that at least few task-relevant target domain training data are available for adaptation from the source to the target domain. However, this requires laborious model-tuning by the end-user who may prefer to have a system that works ``out-of-the-b... | ['Amit K. Roy-Chowdhury', 'Kuan-Chuan Peng', 'Abhishek Aich'] | 2022-12-14 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [ 2.38620758e-01 -2.67526031e-01 -6.51755705e-02 -4.41172540e-01
-7.14003682e-01 -3.70676368e-01 5.24920046e-01 -1.37237579e-01
-1.84321523e-01 4.19250667e-01 -2.14707062e-01 -2.51739323e-01
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-2.32042134e-01 4.44402158e-01 4.40668195e-01 -7.63087645... | [7.848208904266357, 1.601791501045227] |
050e97e9-c5f2-4e38-8e1c-fd95f662e9c2 | ghost-in-the-minecraft-generally-capable | 2305.17144 | null | https://arxiv.org/abs/2305.17144v2 | https://arxiv.org/pdf/2305.17144v2.pdf | Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory | The captivating realm of Minecraft has attracted substantial research interest in recent years, serving as a rich platform for developing intelligent agents capable of functioning in open-world environments. However, the current research landscape predominantly focuses on specific objectives, such as the popular "Obtai... | ['Jifeng Dai', 'Zhaoxiang Zhang', 'Yu Qiao', 'Xiaogang Wang', 'Lewei Lu', 'Bin Li', 'Gao Huang', 'Chenyu Yang', 'Weijie Su', 'Chenxin Tao', 'Hao Tian', 'Yuntao Chen', 'Xizhou Zhu'] | 2023-05-25 | null | null | null | null | ['navigate', 'common-sense-reasoning'] | ['reasoning', 'reasoning'] | [-1.41815901e-01 -7.26986825e-02 -1.52860463e-01 1.87456533e-01
-4.67262834e-01 -6.92139328e-01 6.23436689e-01 -3.51382822e-01
-5.97224593e-01 9.35038745e-01 -9.19347107e-02 -2.06776410e-01
-1.54309005e-01 -7.79084623e-01 -7.60583162e-01 -6.66681588e-01
-3.87611777e-01 6.96993053e-01 1.18352979e-01 -8.13095987... | [4.091130256652832, 1.5072613954544067] |
c253efa8-f8fe-45da-8e40-215d5a0b1b10 | otw-optimal-transport-warping-for-time-series | 2306.00620 | null | https://arxiv.org/abs/2306.00620v1 | https://arxiv.org/pdf/2306.00620v1.pdf | OTW: Optimal Transport Warping for Time Series | Dynamic Time Warping (DTW) has become the pragmatic choice for measuring distance between time series. However, it suffers from unavoidable quadratic time complexity when the optimal alignment matrix needs to be computed exactly. This hinders its use in deep learning architectures, where layers involving DTW computatio... | ['Steven C. H. Hoi', 'Doyen Sahoo', 'Chenghao Liu', 'Fabian Latorre'] | 2023-06-01 | null | null | null | null | ['dynamic-time-warping'] | ['time-series'] | [-1.08116269e-01 -4.97928411e-01 -4.29913476e-02 -2.61200517e-01
-7.53202856e-01 -7.17746973e-01 6.47149563e-01 3.23986501e-01
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-6.44045234e-01 -6.73709035e-01 -3.93337816e-01 -8.91921282e-01
-8.82473648e-01 1.57462180e-01 2.02797636e-01 -2.39346206... | [7.32248592376709, 3.328270673751831] |
8432a602-f578-4090-bc80-e4726bb33d54 | enquire-one-s-parent-and-child-before | 2101.11268 | null | https://arxiv.org/abs/2101.11268v1 | https://arxiv.org/pdf/2101.11268v1.pdf | Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion | Taxonomy is a hierarchically structured knowledge graph that plays a crucial role in machine intelligence. The taxonomy expansion task aims to find a position for a new term in an existing taxonomy to capture the emerging knowledge in the world and keep the taxonomy dynamically updated. Previous taxonomy expansion solu... | ['Bang Liu', 'Yefeng Zheng', 'Xi Chen', 'Ruihui Zhao', 'Suyuchen Wang'] | 2021-01-27 | null | null | null | null | ['taxonomy-expansion'] | ['natural-language-processing'] | [ 3.41458917e-02 2.66591012e-01 -5.85503638e-01 -2.57083595e-01
1.16081394e-01 -5.40389299e-01 3.32881063e-01 6.59332395e-01
-2.13296384e-01 5.72128534e-01 3.04474056e-01 -3.22225571e-01
-5.55021703e-01 -1.03551853e+00 -4.83239740e-02 -4.55048651e-01
-2.03458995e-01 6.73268676e-01 6.33276880e-01 -2.96969563... | [9.19924545288086, 7.981405735015869] |
2d017a8d-7ac0-4c83-8baf-6679d034487c | designing-for-recommending-intermediate | 2010.04880 | null | https://arxiv.org/abs/2010.04880v1 | https://arxiv.org/pdf/2010.04880v1.pdf | Designing for Recommending Intermediate States in A Scientific Workflow Management System | To process a large amount of data sequentially and systematically, proper management of workflow components (i.e., modules, data, configurations, associations among ports and links) in a Scientific Workflow Management System (SWfMS) is inevitable. Managing data with provenance in a SWfMS to support reusability of workf... | ['Sristy Sumana Nath', 'Banani Roy', 'Debasish Chakroborti'] | 2020-10-10 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [-2.08509602e-02 -3.04385751e-01 3.54751021e-01 -4.48768348e-01
5.61163984e-02 -9.65269804e-01 4.44327354e-01 8.63917291e-01
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-6.11005723e-01 -1.06522489e+00 -4.83152837e-01 -6.13285005e-01
-9.18096006e-02 3.68429869e-01 4.49798942e-01 3.93253952... | [9.056949615478516, 7.744824409484863] |
5d19adb2-47b0-4d17-b279-3b58df93bbbd | surpassing-the-human-accuracy-detecting | 2204.11433 | null | https://arxiv.org/abs/2204.11433v1 | https://arxiv.org/pdf/2204.11433v1.pdf | Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning | We explore the potential of CNN-based models for gallbladder cancer (GBC) detection from ultrasound (USG) images as no prior study is known. USG is the most common diagnostic modality for GB diseases due to its low cost and accessibility. However, USG images are challenging to analyze due to low image quality, noise, a... | ['Chetan Arora', 'Pankaj Gupta', 'Pratyaksha Rana', 'Mayank Gupta', 'Soumen Basu'] | 2022-04-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Basu_Surpassing_the_Human_Accuracy_Detecting_Gallbladder_Cancer_From_USG_Images_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Basu_Surpassing_the_Human_Accuracy_Detecting_Gallbladder_Cancer_From_USG_Images_CVPR_2022_paper.pdf | cvpr-2022-1 | ['gallbladder-cancer-detection'] | ['computer-vision'] | [ 4.16057296e-02 2.49527097e-01 -4.77463864e-02 1.54626325e-01
-8.80824387e-01 -1.64850265e-01 1.58994570e-01 6.90338686e-02
-1.98771372e-01 1.27124965e-01 1.38605744e-01 -6.11662805e-01
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-3.60967070e-01 -4.99339662e-02 4.37430561e-01 -2.58664228... | [15.026774406433105, -2.455826759338379] |
df9c6ef9-a34d-4906-857f-0349ff9a4cf9 | em-fusion-dynamic-object-level-slam-with | 1904.11781 | null | https://arxiv.org/abs/1904.11781v2 | https://arxiv.org/pdf/1904.11781v2.pdf | EM-Fusion: Dynamic Object-Level SLAM with Probabilistic Data Association | The majority of approaches for acquiring dense 3D environment maps with RGB-D cameras assumes static environments or rejects moving objects as outliers. The representation and tracking of moving objects, however, has significant potential for applications in robotics or augmented reality. In this paper, we propose a no... | ['Jörg Stückler', 'Michael Strecke'] | 2019-04-26 | em-fusion-dynamic-object-level-slam-with-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Strecke_EM-Fusion_Dynamic_Object-Level_SLAM_With_Probabilistic_Data_Association_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Strecke_EM-Fusion_Dynamic_Object-Level_SLAM_With_Probabilistic_Data_Association_ICCV_2019_paper.pdf | iccv-2019-10 | ['occlusion-handling'] | ['computer-vision'] | [-4.30481136e-03 -2.97868162e-01 -1.56065179e-02 -4.96194661e-01
-6.25415623e-01 -6.85096145e-01 6.58546627e-01 -1.35735320e-02
-4.57068413e-01 5.69713950e-01 -2.21438661e-01 -4.86855209e-03
-3.49821597e-01 -4.38554555e-01 -8.99577975e-01 -4.61082667e-01
-1.72157317e-01 1.15760481e+00 7.16445029e-01 4.76359278... | [7.365808486938477, -2.3350038528442383] |
671bd0ed-4304-46bc-850d-299dfbdeb467 | discourse-planning-with-an-n-gram-model-of | null | null | https://aclanthology.org/D15-1230 | https://aclanthology.org/D15-1230.pdf | Discourse Planning with an N-gram Model of Relations | null | ['Kathleen McKeown', 'Or Biran'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['concept-to-text-generation'] | ['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... | [-7.38763952255249, 3.6979904174804688] |
91ac1634-8444-4580-955d-e27060f04f6b | video-object-segmentation-using-space-time | 1904.00607 | null | https://arxiv.org/abs/1904.00607v2 | https://arxiv.org/pdf/1904.00607v2.pdf | Video Object Segmentation using Space-Time Memory Networks | We propose a novel solution for semi-supervised video object segmentation. By the nature of the problem, available cues (e.g. video frame(s) with object masks) become richer with the intermediate predictions. However, the existing methods are unable to fully exploit this rich source of information. We resolve the issue... | ['Joon-Young Lee', 'Seoung Wug Oh', 'Seon Joo Kim', 'Ning Xu'] | 2019-04-01 | video-object-segmentation-using-space-time-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Oh_Video_Object_Segmentation_Using_Space-Time_Memory_Networks_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Oh_Video_Object_Segmentation_Using_Space-Time_Memory_Networks_ICCV_2019_paper.pdf | iccv-2019-10 | ['interactive-video-object-segmentation', 'one-shot-visual-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.95259318e-01 -1.32027224e-01 -5.08890986e-01 -2.66846001e-01
-5.94535708e-01 -3.85957003e-01 1.86311409e-01 1.00076392e-01
-5.91406107e-01 5.95035136e-01 -3.16126598e-03 1.87452048e-01
2.33104259e-01 -5.68298817e-01 -1.10967898e+00 -4.38661546e-01
-1.22154467e-01 1.56671837e-01 8.86872649e-01 9.91707817... | [9.20274829864502, -0.03192989155650139] |
13ec277e-c787-4e67-b097-8a57e6e828a1 | audio-video-emotion-recognition-in-the-wild | 2002.09023 | null | https://arxiv.org/abs/2002.09023v1 | https://arxiv.org/pdf/2002.09023v1.pdf | Audio-video Emotion Recognition in the Wild using Deep Hybrid Networks | This paper presents an audiovisual-based emotion recognition hybrid network. While most of the previous work focuses either on using deep models or hand-engineered features extracted from images, we explore multiple deep models built on both images and audio signals. Specifically, in addition to convolutional neural ne... | ['Luisa F. Polanía', 'Xin Guo', 'Kenneth E. Barner'] | 2020-02-20 | null | null | null | null | ['video-emotion-recognition'] | ['computer-vision'] | [ 3.20041597e-01 -1.35835679e-02 -3.04045584e-02 -4.59734768e-01
-7.79732764e-01 -5.22839986e-02 5.33449709e-01 -3.14994484e-01
-5.69692016e-01 3.22272778e-01 2.36581951e-01 1.86719477e-01
4.06873912e-01 -4.22586083e-01 -7.51806915e-01 -6.92094386e-01
-1.78596213e-01 -2.65428871e-01 4.45112847e-02 -1.59188852... | [13.35219955444336, 5.138749599456787] |
83fab808-e252-4f4f-92ce-b148ecfe9630 | using-eeg-signals-to-assess-workload-during | 2305.08044 | null | https://arxiv.org/abs/2305.08044v1 | https://arxiv.org/pdf/2305.08044v1.pdf | Using EEG Signals to Assess Workload during Memory Retrieval in a Real-world Scenario | Objective: The Electroencephalogram (EEG) is gaining popularity as a physiological measure for neuroergonomics in human factor studies because it is objective, less prone to bias, and capable of assessing the dynamics of cognitive states. This study investigated the associations between memory workload and EEG during p... | ['Tzyy-Ping Jung', 'Chung-Kuan Cheng', 'Steven Dong', 'Kuan-Jung Chiang'] | 2023-05-14 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 7.49882236e-02 -3.59821171e-01 1.20773226e-01 -2.84785658e-01
6.29138201e-02 -3.05058688e-01 2.74848372e-01 2.71483064e-01
-6.39417052e-01 7.82676101e-01 1.03420354e-01 -2.68748879e-01
-5.26803672e-01 -4.63552773e-01 -3.91516149e-01 -4.71337318e-01
-3.08474153e-01 5.76882577e-03 -8.75819325e-02 7.01961368... | [13.34228515625, 3.288900852203369] |
6212a61d-4d91-4dc0-af3c-8e7c263ae70a | tea-pse-3-0-tencent-ethereal-audio-lab | 2303.07704 | null | https://arxiv.org/abs/2303.07704v1 | https://arxiv.org/pdf/2303.07704v1.pdf | TEA-PSE 3.0: Tencent-Ethereal-Audio-Lab Personalized Speech Enhancement System For ICASSP 2023 DNS Challenge | This paper introduces the Unbeatable Team's submission to the ICASSP 2023 Deep Noise Suppression (DNS) Challenge. We expand our previous work, TEA-PSE, to its upgraded version -- TEA-PSE 3.0. Specifically, TEA-PSE 3.0 incorporates a residual LSTM after squeezed temporal convolution network (S-TCN) to enhance sequence m... | ['Shidong Shang', 'Tao Yu', 'Yannan Wang', 'Weixin Zhu', 'Wei Rao', 'Shulin He', 'Shimin Zhang', 'Jun Chen', 'Yukai Ju'] | 2023-03-14 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 2.40988489e-02 -4.05704290e-01 6.57699183e-02 -2.54081845e-01
-1.02810991e+00 -5.74490368e-01 5.07574081e-01 -7.28501976e-01
-6.51258588e-01 5.13927996e-01 5.69630623e-01 -4.11411464e-01
-2.60440968e-02 2.48576645e-02 -4.93203998e-01 -5.91135383e-01
-1.05222724e-01 -3.16731423e-01 -1.06119793e-02 -4.52447563... | [14.88580322265625, 5.991230487823486] |
4994af7e-f532-4c6f-b385-019fe591e898 | anchorformer-point-cloud-completion-from | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_AnchorFormer_Point_Cloud_Completion_From_Discriminative_Nodes_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_AnchorFormer_Point_Cloud_Completion_From_Discriminative_Nodes_CVPR_2023_paper.pdf | AnchorFormer: Point Cloud Completion From Discriminative Nodes | Point cloud completion aims to recover the completed 3D shape of an object from its partial observation. A common strategy is to encode the observed points to a global feature vector and then predict the complete points through a generative process on this vector. Nevertheless, the results may suffer from the high-... | ['Tao Mei', 'Jiebo Luo', 'Wengang Zhou', 'Ting Yao', 'Zhaofan Qiu', 'Fuchen Long', 'Zhikai Chen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-completion'] | ['computer-vision'] | [ 2.82866904e-03 -2.21839041e-01 7.52739375e-03 -2.85864532e-01
-8.47435832e-01 -5.97378969e-01 4.05822307e-01 -1.16305538e-01
4.13178474e-01 1.87781364e-01 8.31690505e-02 3.90270561e-01
-2.13677332e-01 -8.36760700e-01 -8.05915892e-01 -8.35674882e-01
1.50197238e-01 9.12328184e-01 8.11802745e-02 3.66870873... | [8.393777847290039, -3.5532195568084717] |
15c3920c-7c53-4d03-8c90-f524ded13b1e | pathways-asynchronous-distributed-dataflow | 2203.12533 | null | https://arxiv.org/abs/2203.12533v1 | https://arxiv.org/pdf/2203.12533v1.pdf | Pathways: Asynchronous Distributed Dataflow for ML | We present the design of a new large scale orchestration layer for accelerators. Our system, Pathways, is explicitly designed to enable exploration of new systems and ML research ideas, while retaining state of the art performance for current models. Pathways uses a sharded dataflow graph of asynchronous operators that... | ['Yonghui Wu', 'Chandramohan A. Thekkath', 'Laurent El Shafey', 'Ryan Sepassi', 'Parker Schuh', 'Brennan Saeta', 'Sudip Roy', 'Ruoming Pang', 'Hyeontaek Lim', 'Michael Isard', 'Dan Hurt', 'Steven Hand', 'Sanjay Ghemawat', 'Jeff Dean', 'Aakanksha Chowdhery', 'Paul Barham'] | 2022-03-23 | null | null | null | null | ['2048'] | ['playing-games'] | [-6.36546791e-01 8.51021856e-02 -5.12518525e-01 -3.34742576e-01
1.33323833e-01 -5.78951240e-01 7.98960984e-01 3.55585843e-01
-1.74519420e-01 2.69792050e-01 5.75515807e-01 -9.42604423e-01
1.56449944e-01 -9.55372036e-01 -3.72787803e-01 -4.30148691e-01
-6.65989876e-01 4.98104990e-01 6.62073553e-01 -3.60574603... | [8.454631805419922, 3.3543128967285156] |
5eef94c0-c661-41de-9fb0-d4214762457c | em-network-oracle-guided-self-distillation | 2306.10058 | null | https://arxiv.org/abs/2306.10058v1 | https://arxiv.org/pdf/2306.10058v1.pdf | EM-Network: Oracle Guided Self-distillation for Sequence Learning | We introduce EM-Network, a novel self-distillation approach that effectively leverages target information for supervised sequence-to-sequence (seq2seq) learning. In contrast to conventional methods, it is trained with oracle guidance, which is derived from the target sequence. Since the oracle guidance compactly repres... | ['Nam Soo Kim', 'Seok Min Kim', 'Minchan Kim', 'Hyeonseung Lee', 'Sunghwan Ahn', 'Ji Won Yoon'] | 2023-06-14 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 8.20107758e-01 3.81112367e-01 -3.80373597e-01 -4.44959104e-01
-1.11946845e+00 -4.72223431e-01 5.86940706e-01 -6.88976347e-01
-4.16070908e-01 6.73203707e-01 2.68156111e-01 -1.12802410e+00
5.33059359e-01 -1.11488953e-01 -8.49685967e-01 -7.75312364e-01
1.28022835e-01 6.25426829e-01 9.05717760e-02 -1.83027327... | [14.472229957580566, 7.1801347732543945] |
5410ed75-5185-4636-9c56-86bdfd29a19e | lip-flow-learning-inference-time-priors-for | 2203.07881 | null | https://arxiv.org/abs/2203.07881v1 | https://arxiv.org/pdf/2203.07881v1.pdf | LiP-Flow: Learning Inference-time Priors for Codec Avatars via Normalizing Flows in Latent Space | Neural face avatars that are trained from multi-view data captured in camera domes can produce photo-realistic 3D reconstructions. However, at inference time, they must be driven by limited inputs such as partial views recorded by headset-mounted cameras or a front-facing camera, and sparse facial landmarks. To mitigat... | ['Otmar Hilliges', 'Jason Saragih', 'Shih-En Wei', 'Alexander Richard', 'Stanislav Pidhorskyi', 'Akin Caliskan', 'Shugao Ma', 'Emre Aksan'] | 2022-03-15 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 9.46783870e-02 4.01369929e-01 -3.20048213e-01 -6.68452799e-01
-6.81848347e-01 -4.25635785e-01 6.88538194e-01 -1.12842405e+00
6.18370473e-02 1.92626134e-01 6.62732244e-01 2.14657769e-01
2.47273907e-01 -4.29595053e-01 -9.41614807e-01 -4.12163854e-01
2.82973617e-01 4.33613986e-01 -5.44552207e-01 2.27822527... | [12.816967964172363, -0.3553785979747772] |
dad352ae-7bae-42c5-9b83-1684ecff61d8 | a-deep-forgetful-novelty-seeking-movie | 1909.01811 | null | https://arxiv.org/abs/1909.01811v1 | https://arxiv.org/pdf/1909.01811v1.pdf | A Deep, Forgetful Novelty-Seeking Movie Recommender Model | As more and more people shift their movie watching online, competition between movie viewing websites are getting more and more intense. Therefore, it has become incredibly important to accurately predict a given user's watching list to maximize the chances of keeping the user on the platform. Recent studies have sugge... | ['Ruomu Zou'] | 2019-09-02 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-4.70103115e-01 -6.84620738e-01 -4.14757222e-01 -6.68442011e-01
-7.63733909e-02 -3.92322510e-01 3.30584347e-01 3.14556867e-01
-3.57490510e-01 1.13115571e-01 4.85757798e-01 -1.67677283e-01
-8.04862604e-02 -7.02355802e-01 -3.78255427e-01 -9.32403281e-02
-5.82009852e-02 -2.98145622e-01 2.11681008e-01 -2.43511915... | [10.15149211883545, 5.644640922546387] |
926a90f3-f9f6-47cd-b022-d88cafe30d54 | fedcbo-reaching-group-consensus-in-clustered | 2305.02894 | null | https://arxiv.org/abs/2305.02894v1 | https://arxiv.org/pdf/2305.02894v1.pdf | FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization | Federated learning is an important framework in modern machine learning that seeks to integrate the training of learning models from multiple users, each user having their own local data set, in a way that is sensitive to data privacy and to communication loss constraints. In clustered federated learning, one assumes a... | ['Yuhua Zhu', 'Sixu Li', 'Nicolas Garcia Trillos', 'Jose A. Carrillo'] | 2023-05-04 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-2.63842463e-01 4.76843752e-02 7.78860897e-02 -1.81550145e-01
-6.75431788e-01 -4.69734609e-01 5.11266589e-01 6.27988815e-01
-4.37258750e-01 7.84456968e-01 -1.59017101e-01 -3.67690213e-02
-7.25313008e-01 -8.92074764e-01 -9.52208996e-01 -1.41826642e+00
-4.75563496e-01 8.45346212e-01 -3.08289528e-01 -9.19195116... | [5.872522354125977, 6.174613952636719] |
c63a7d90-fe9c-4722-a49b-5b129b4d96f0 | learning-to-track-for-spatio-temporal-action | 1506.01929 | null | http://arxiv.org/abs/1506.01929v2 | http://arxiv.org/pdf/1506.01929v2.pdf | Learning to track for spatio-temporal action localization | We propose an effective approach for spatio-temporal action localization in
realistic videos. The approach first detects proposals at the frame-level and
scores them with a combination of static and motion CNN features. It then
tracks high-scoring proposals throughout the video using a
tracking-by-detection approach. O... | ['Zaid Harchaoui', 'Cordelia Schmid', 'Philippe Weinzaepfel'] | 2015-06-05 | learning-to-track-for-spatio-temporal-action-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Weinzaepfel_Learning_to_Track_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Weinzaepfel_Learning_to_Track_ICCV_2015_paper.pdf | iccv-2015-12 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [-1.04478680e-01 -5.94994485e-01 -4.93233353e-01 -8.43813792e-02
-1.24456549e+00 -6.92593634e-01 6.06229722e-01 2.80780375e-01
-8.47027481e-01 5.15444994e-01 3.12413514e-01 5.20603716e-01
1.05179362e-01 -5.17748594e-01 -7.62286484e-01 -6.36408925e-01
-5.07912517e-01 1.46470964e-01 1.39403164e+00 1.83261082... | [8.290385246276855, 0.41444993019104004] |
2d432c70-6abd-4c87-9dfd-2270ce36156f | nlm_nih-at-semeval-2017-task-3-from-question | null | null | https://aclanthology.org/S17-2057 | https://aclanthology.org/S17-2057.pdf | NLM\_NIH at SemEval-2017 Task 3: from Question Entailment to Question Similarity for Community Question Answering | This paper describes our participation in SemEval-2017 Task 3 on Community Question Answering (cQA). The Question Similarity subtask (B) aims to rank a set of related questions retrieved by a search engine according to their similarity to the original question. We adapted our feature-based system for Recognizing Questi... | ['Dina Demner-Fushman', 'Asma Ben Abacha'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [-1.12847254e-01 -8.56713355e-02 6.67288065e-01 -1.24786265e-01
-1.70959651e+00 -8.32773805e-01 7.92900801e-01 5.79931796e-01
-7.76885986e-01 5.23328125e-01 4.12656665e-01 -4.71500486e-01
-4.75080490e-01 -5.84693968e-01 -5.90037227e-01 -4.65874486e-02
3.44322890e-01 5.18414319e-01 6.26836896e-01 -6.71380103... | [11.377742767333984, 8.037906646728516] |
9250e1f7-5c0f-48dd-afd1-2b14cfb74c27 | entity-aware-negative-sampling-with-auxiliary | 2210.06242 | null | https://arxiv.org/abs/2210.06242v1 | https://arxiv.org/pdf/2210.06242v1.pdf | Entity Aware Negative Sampling with Auxiliary Loss of False Negative Prediction for Knowledge Graph Embedding | Knowledge graph (KG) embedding is widely used in many downstream applications using KGs. Generally, since KGs contain only ground truth triples, it is necessary to construct arbitrary negative samples for representation learning of KGs. Recently, various methods for sampling high-quality negatives have been studied bec... | ['Sang-hyun Je'] | 2022-10-12 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-2.09480464e-01 3.83448213e-01 -6.41305029e-01 -1.79346338e-01
-5.85120916e-01 -3.71581465e-01 4.66084540e-01 2.17422143e-01
-3.87330681e-01 1.06052423e+00 -1.03167355e-01 1.78153701e-02
-4.59200256e-02 -1.57321930e+00 -8.64427328e-01 -6.18413985e-01
-1.41243180e-02 6.54691100e-01 5.75499594e-01 -1.57117993... | [8.761134147644043, 7.878274917602539] |
562088cf-4b3f-4de8-9699-95b99067af07 | learning-trajectory-aware-transformer-for | 2204.04216 | null | https://arxiv.org/abs/2204.04216v3 | https://arxiv.org/pdf/2204.04216v3.pdf | Learning Trajectory-Aware Transformer for Video Super-Resolution | Video super-resolution (VSR) aims to restore a sequence of high-resolution (HR) frames from their low-resolution (LR) counterparts. Although some progress has been made, there are grand challenges to effectively utilize temporal dependency in entire video sequences. Existing approaches usually align and aggregate video... | ['Xueming Qian', 'Jianlong Fu', 'Huan Yang', 'Chengxu Liu'] | 2022-04-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Learning_Trajectory-Aware_Transformer_for_Video_Super-Resolution_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_Learning_Trajectory-Aware_Transformer_for_Video_Super-Resolution_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-super-resolution'] | ['computer-vision'] | [ 2.93211281e-01 -4.47916031e-01 -3.89901936e-01 -2.22595289e-01
-1.10358834e+00 -3.40880424e-01 5.01929998e-01 -4.84990209e-01
-2.15162918e-01 7.11929202e-01 5.46824455e-01 6.37286678e-02
6.27000292e-04 -5.77370048e-01 -9.06083941e-01 -5.32006621e-01
3.33215483e-02 -2.91842192e-01 6.01616919e-01 -2.44609207... | [11.034467697143555, -1.8888919353485107] |
60098c21-8244-4b67-9a4c-621a1da2c072 | exploring-vanilla-u-net-for-lesion | 2210.07490 | null | https://arxiv.org/abs/2210.07490v1 | https://arxiv.org/pdf/2210.07490v1.pdf | Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans | Tumor lesion segmentation is one of the most important tasks in medical image analysis. In clinical practice, Fluorodeoxyglucose Positron-Emission Tomography~(FDG-PET) is a widely used technique to identify and quantify metabolically active tumors. However, since FDG-PET scans only provide metabolic information, health... | ['Junjun He', 'Jingqi Niu', 'Meng Wei', 'Yuncheng Yang', 'Qian Wu', 'Can Tu', 'Yanzhou Su', 'Zhongying Deng', 'Ziyan Huang', 'Haoyu Wang', 'Jin Ye'] | 2022-10-14 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 8.97276253e-02 -2.50119120e-01 -9.17102277e-01 -3.13748956e-01
-5.90608656e-01 -3.53731275e-01 7.89981261e-02 1.07947655e-01
-5.63380420e-01 8.41449440e-01 -7.15104640e-02 -7.57379353e-01
2.80554503e-01 -9.10584390e-01 -1.80722430e-01 -7.87666917e-01
2.39083227e-02 6.90216839e-01 1.44541904e-01 2.19936028... | [14.679407119750977, -2.479750394821167] |
3a2d5628-acf7-47a9-8a45-2209eb6a7e22 | referee-towards-reference-free-cross-speaker | 2109.03439 | null | https://arxiv.org/abs/2109.03439v1 | https://arxiv.org/pdf/2109.03439v1.pdf | Referee: Towards reference-free cross-speaker style transfer with low-quality data for expressive speech synthesis | Cross-speaker style transfer (CSST) in text-to-speech (TTS) synthesis aims at transferring a speaking style to the synthesised speech in a target speaker's voice. Most previous CSST approaches rely on expensive high-quality data carrying desired speaking style during training and require a reference utterance to obtain... | ['Dong Yu', 'Dan Su', 'Shan Yang', 'Songxiang Liu'] | 2021-09-08 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 5.67763269e-01 2.91038379e-02 4.18840572e-02 -6.83455169e-01
-1.62209713e+00 -6.70817733e-01 6.27599716e-01 -4.10414606e-01
-1.65649131e-02 3.91745180e-01 4.59202498e-01 -1.85207874e-01
4.43557024e-01 -3.98594528e-01 -6.70746267e-01 -7.25528240e-01
6.14325643e-01 4.87413436e-01 -4.07905318e-02 -5.36310792... | [14.971290588378906, 6.550217628479004] |
4537eb4b-16fc-4271-a78d-94e975ae1b31 | comparative-layer-wise-analysis-of-self | 2211.03929 | null | https://arxiv.org/abs/2211.03929v3 | https://arxiv.org/pdf/2211.03929v3.pdf | Comparative layer-wise analysis of self-supervised speech models | Many self-supervised speech models, varying in their pre-training objective, input modality, and pre-training data, have been proposed in the last few years. Despite impressive successes on downstream tasks, we still have a limited understanding of the properties encoded by the models and the differences across models.... | ['Karen Livescu', 'Bowen Shi', 'Ankita Pasad'] | 2022-11-08 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 3.07473183e-01 -1.23534583e-01 -3.63900214e-02 -6.78346217e-01
-8.47991109e-01 -7.87588537e-01 9.37227190e-01 2.09731251e-01
-6.11616135e-01 2.21401066e-01 8.32418203e-01 -5.78156948e-01
-3.64466339e-01 -1.60778210e-01 -4.42615688e-01 -5.72314978e-01
-2.33576685e-01 2.16873616e-01 4.72220741e-02 -1.17772602... | [14.309172630310059, 6.880849838256836] |
2af0a55e-f3c0-4930-a7fc-1354dfb6347e | audit-audio-editing-by-following-instructions | 2304.00830 | null | https://arxiv.org/abs/2304.00830v2 | https://arxiv.org/pdf/2304.00830v2.pdf | AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models | Audio editing is applicable for various purposes, such as adding background sound effects, replacing a musical instrument, and repairing damaged audio. Recently, some diffusion-based methods achieved zero-shot audio editing by using a diffusion and denoising process conditioned on the text description of the output aud... | ['Sheng Zhao', 'Jiang Bian', 'Zhizheng Wu', 'Lei He', 'Xu Tan', 'Zeqian Ju', 'Yuancheng Wang'] | 2023-04-03 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.24031734e-01 -1.97536603e-01 2.75791794e-01 -3.59805375e-02
-9.82030034e-01 -4.60709572e-01 1.73216417e-01 1.67276021e-02
-3.83924007e-01 4.59840178e-01 5.27121842e-01 1.83447003e-01
-8.49967450e-02 -6.29634738e-01 -6.11443579e-01 -4.96901125e-01
8.82634521e-02 2.07119003e-01 3.49281490e-01 -2.62861371... | [15.391290664672852, 5.719313621520996] |
21880dda-ffab-47fa-9c32-bc30a4b8ad2e | ctbl-augmenting-large-language-models-for | 2303.12024 | null | https://arxiv.org/abs/2303.12024v3 | https://arxiv.org/pdf/2303.12024v3.pdf | cTBLS: Augmenting Large Language Models with Conversational Tables | Optimizing accuracy and performance while eliminating hallucinations of open-domain conversational large language models (LLMs) is an open research challenge. A particularly promising direction is to augment and ground LLMs with information from structured sources. This paper introduces Conversational Tables (cTBLS), a... | ['Larry Heck', 'Anirudh S Sundar'] | 2023-03-21 | null | null | null | null | ['response-generation', 'table-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [-1.05007507e-01 6.50325418e-01 -6.16684668e-02 -4.23003644e-01
-1.70155621e+00 -5.54911315e-01 7.86189139e-01 3.67920995e-01
-2.18280256e-01 1.09331799e+00 1.08536100e+00 -1.17981784e-01
1.29953548e-01 -8.19819152e-01 -6.93319798e-01 -3.24475430e-02
3.60198587e-01 1.49224234e+00 -5.32054007e-02 -7.54250765... | [11.76514720916748, 8.37051010131836] |
a81decd1-d9fa-42d3-b76d-008fda410f45 | research-note-on-uncertain-probabilities-and | 2208.10932 | null | https://arxiv.org/abs/2208.10932v1 | https://arxiv.org/pdf/2208.10932v1.pdf | Research Note on Uncertain Probabilities and Abstract Argumentation | The sixth assessment of the international panel on climate change (IPCC) states that "cumulative net CO2 emissions over the last decade (2010-2019) are about the same size as the 11 remaining carbon budget likely to limit warming to 1.5C (medium confidence)." Such reports directly feed the public discourse, but nuances... | ['Murat Sensoy', 'Lance M. Kaplan', 'Massimiliano Giacomin', 'Federico Cerutti', 'Pietro Baroni'] | 2022-08-23 | null | null | null | null | ['abstract-argumentation', 'abstract-argumentation'] | ['natural-language-processing', 'reasoning'] | [ 3.13354820e-01 8.85635376e-01 -4.88531351e-01 -4.44772691e-01
-6.16593540e-01 -9.21352446e-01 1.06597197e+00 6.25069499e-01
-4.59640235e-01 1.05574572e+00 4.19197738e-01 -1.11487281e+00
-4.59690571e-01 -1.09028506e+00 -7.12626219e-01 -6.55746043e-01
2.25998431e-01 3.88533086e-01 3.38494003e-01 -2.02728119... | [8.285189628601074, 5.762246131896973] |
6b8e7c51-d368-498b-b346-6d84529b5094 | using-massive-multilingual-pre-trained | 2210.06068 | null | https://arxiv.org/abs/2210.06068v2 | https://arxiv.org/pdf/2210.06068v2.pdf | Investigating Massive Multilingual Pre-Trained Machine Translation Models for Clinical Domain via Transfer Learning | Massively multilingual pre-trained language models (MMPLMs) are developed in recent years demonstrating superpowers and the pre-knowledge they acquire for downstream tasks. This work investigates whether MMPLMs can be applied to clinical domain machine translation (MT) towards entirely unseen languages via transfer lea... | ['Goran Nenadic', 'Serge Gladkoff', 'Irina Sorokina', 'Gleb Erofeev', 'Lifeng Han'] | 2022-10-12 | null | null | null | null | ['zero-shot-machine-translation'] | ['natural-language-processing'] | [ 3.39058191e-01 4.00308549e-01 -4.64497298e-01 -3.94136280e-01
-1.55682003e+00 -5.15752137e-01 4.17792827e-01 -3.63945439e-02
-8.38291049e-01 1.26145089e+00 2.42690563e-01 -9.67238367e-01
5.77004440e-02 -4.13231730e-01 -8.63583863e-01 -2.25674152e-01
1.29948214e-01 1.27571189e+00 -1.39442265e-01 -4.58985478... | [11.422256469726562, 10.308087348937988] |
349e4d65-fa8f-4077-be62-2da7eb5c7a8a | video-quality-assessment-for-computer | null | null | https://www.researchgate.net/publication/220183765_Video_Quality_Assessment_for_Computer_Graphics_Applications | https://www.researchgate.net/publication/220183765_Video_Quality_Assessment_for_Computer_Graphics_Applications | Video Quality Assessment for Computer Graphics Applications | Numerous current Computer Graphics methods produce video sequences as their outcome. The merit of these methods is often judged by assessing the quality of a set of results through lengthy user studies. We present a full-reference video quality metric geared specifically towards the requirements of Computer Graphics ap... | ['Hans-Peter Seidel', 'Karol Myszkowski', 'Martin Cadik', 'Tunc Ozan Aydin'] | 2010-12-01 | null | null | null | acm-transactions-on-graphics-2010-12 | ['video-quality-assessment', 'video-compression', 'tone-mapping', 'video-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision', 'time-series'] | [ 4.65775877e-01 -5.83313882e-01 1.57889664e-01 -3.66378248e-01
-4.80873168e-01 -3.46290946e-01 6.59133196e-01 -3.60156082e-05
-2.51932502e-01 4.71002162e-01 1.54109478e-01 -4.92853343e-01
3.42381448e-02 -6.37612879e-01 -2.92283952e-01 -1.82537600e-01
-3.73868644e-01 -3.19406718e-01 7.53580153e-01 -3.48900735... | [11.6061372756958, -1.9652308225631714] |
d59df89b-242f-482d-bda1-19e4c251bbe6 | network-traffic-anomaly-detection-method | 2205.03907 | null | https://arxiv.org/abs/2205.03907v1 | https://arxiv.org/pdf/2205.03907v1.pdf | Network Traffic Anomaly Detection Method Based on Multi scale Residual Feature | To address the problem that traditional network traffic anomaly detection algorithms do not suffi-ciently mine potential features in long time domain, an anomaly detection method based on mul-ti-scale residual features of network traffic is proposed. The original traffic is divided into subse-quences of different time ... | ['Kun Wang', 'Yu Fu', 'Xueyuan Duan'] | 2022-05-08 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 4.57482412e-03 -7.01072276e-01 -3.56242694e-02 -1.16536215e-01
1.10670686e-01 -4.85118739e-02 3.09351802e-01 -7.95661062e-02
-2.16334499e-03 5.72929144e-01 1.02917729e-02 -5.46646595e-01
-2.86703736e-01 -9.96282458e-01 -2.36145422e-01 -7.22948611e-01
-4.97496814e-01 1.53153569e-01 6.66486800e-01 -3.73792440... | [7.475388526916504, 2.313858985900879] |
2ebd5587-d5bf-4007-8cd6-041cd2211b04 | generating-sequences-by-learning-to-self | 2211.00053 | null | https://arxiv.org/abs/2211.00053v1 | https://arxiv.org/pdf/2211.00053v1.pdf | Generating Sequences by Learning to Self-Correct | Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content. Language models, whether fine-tuned or prompted with few-shot demonstrations, frequently violate these constraints, and lack a mechanism to itera... | ['Yejin Choi', 'Daniel Khashabi', 'Tianxiao Shen', 'Faeze Brahman', 'Peter West', 'Ximing Lu', 'Sean Welleck'] | 2022-10-31 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 5.41880965e-01 3.96159112e-01 -1.85349897e-01 -2.31006607e-01
-9.14150119e-01 -1.07981110e+00 6.48959219e-01 2.12511811e-02
-1.73041731e-01 1.04597485e+00 -5.36574125e-02 -6.39372766e-01
4.18866605e-01 -8.75949323e-01 -1.20650613e+00 -1.91554353e-01
1.83916196e-01 4.96192455e-01 2.27548152e-01 -4.99376476... | [8.203746795654297, 7.501020431518555] |
8a780367-142e-48bc-b0b6-b121130a9973 | pmhld-patch-map-based-hybrid-learning | null | null | https://ieeexplore.ieee.org/document/9094006 | https://ieeexplore.ieee.org/document/9094006 | PMHLD: Patch Map Based Hybrid Learning DehazeNet for Single Image Haze Removal | Images captured in a hazy environment usually suffer from bad visibility and missing information. Over many years, learning-based and handcrafted prior-based dehazing algorithms have been rigorously developed. However, both algorithms exhibit some weaknesses in terms of haze removal performance. Therefore, in this work... | ['Sy-Yen Kuo', 'Jian-Jiun Ding', 'Hao-Yu Feng', 'Wei-Ting Chen'] | 2020-05-14 | null | null | null | ieee-transaction-on-image-processing-2020-5 | ['single-image-haze-removal', 'single-image-deraining', 'computational-phenotyping'] | ['computer-vision', 'computer-vision', 'medical'] | [ 2.62618631e-01 -2.67308682e-01 5.03923416e-01 7.14529902e-02
-4.29014117e-01 1.58975739e-02 4.11314040e-01 -4.12116826e-01
-1.87066704e-01 7.86327839e-01 -7.99508467e-02 -6.52382001e-02
1.92361511e-02 -1.09125662e+00 -5.38529575e-01 -1.49912488e+00
2.83191025e-01 -2.67632663e-01 6.15543008e-01 -4.21643049... | [10.901021003723145, -3.1564838886260986] |
01ea9f89-65d6-49c8-8456-43157440eef0 | precise-affordance-annotation-for-egocentric | 2206.05424 | null | https://arxiv.org/abs/2206.05424v1 | https://arxiv.org/pdf/2206.05424v1.pdf | Precise Affordance Annotation for Egocentric Action Video Datasets | Object affordance is an important concept in human-object interaction, providing information on action possibilities based on human motor capacity and objects' physical property thus benefiting tasks such as action anticipation and robot imitation learning. However, existing datasets often: 1) mix up affordance with ob... | ['Yoichi Sato', 'Yusuke Goutsu', 'Takuma Yagi', 'Ryosuke Furuta', 'Yifei HUANG', 'Zecheng Yu'] | 2022-06-11 | null | null | null | null | ['affordance-recognition', 'action-anticipation'] | ['computer-vision', 'computer-vision'] | [ 2.25951225e-01 2.01011866e-01 -3.83662701e-01 -3.33375305e-01
-4.99885976e-02 -6.76512659e-01 7.20238388e-01 -5.44269867e-02
-3.69421482e-01 5.32052279e-01 4.67067450e-01 -1.17399298e-01
-1.25836059e-01 -3.61731559e-01 -5.55203438e-01 -3.02631438e-01
-1.34977162e-01 4.80365098e-01 3.93700659e-01 -1.46126002... | [5.1043701171875, -0.002445972990244627] |
d0710a92-9624-4fc9-aa67-50510011690d | conmix-for-source-free-single-and-multi | 2211.03876 | null | https://arxiv.org/abs/2211.03876v1 | https://arxiv.org/pdf/2211.03876v1.pdf | CoNMix for Source-free Single and Multi-target Domain Adaptation | This work introduces the novel task of Source-free Multi-target Domain Adaptation and proposes adaptation framework comprising of \textbf{Co}nsistency with \textbf{N}uclear-Norm Maximization and \textbf{Mix}Up knowledge distillation (\textit{CoNMix}) as a solution to this problem. The main motive of this work is to sol... | ['Anirban Chakraborty', 'Himanshu Patil', 'Rohit Lal', 'Vikash Kumar'] | 2022-11-07 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 3.27853739e-01 -1.98117644e-01 -3.97501856e-01 -6.76842153e-01
-1.08547282e+00 -8.15504253e-01 6.34417772e-01 -2.23664105e-01
-5.83208859e-01 1.06787729e+00 -1.32739032e-02 -8.72724950e-02
-1.76288143e-01 -5.50305724e-01 -7.22525954e-01 -8.14831734e-01
4.37474847e-01 6.10390306e-01 -3.39077823e-02 -1.71952873... | [10.381721496582031, 3.138502359390259] |
5a70b2a9-1fe8-495c-9110-250fc55168e7 | adaptive-period-embedding-for-representing | 1906.09447 | null | https://arxiv.org/abs/1906.09447v1 | https://arxiv.org/pdf/1906.09447v1.pdf | Adaptive Period Embedding for Representing Oriented Objects in Aerial Images | We propose a novel method for representing oriented objects in aerial images named Adaptive Period Embedding (APE). While traditional object detection methods represent object with horizontal bounding boxes, the objects in aerial images are oritented. Calculating the angle of object is an yet challenging task. While al... | ['Jun Du', 'Yixing Zhu', 'Xueqing Wu'] | 2019-06-22 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 3.62259865e-01 -2.17259064e-01 -2.60765463e-01 -1.11213282e-01
2.25921690e-01 -6.68635368e-01 3.62908751e-01 -1.50654810e-02
-4.29422468e-01 2.69480914e-01 -1.28644571e-01 1.89473480e-02
-1.80413052e-01 -9.72063661e-01 -5.13875663e-01 -7.75128901e-01
-2.05996871e-01 7.06282109e-02 8.24379265e-01 -2.16263324... | [8.688843727111816, -0.7501952648162842] |
d67dfe86-3232-404c-abab-2d8f4ce17a69 | end-to-end-learning-of-geometry-and-context | 1703.04309 | null | http://arxiv.org/abs/1703.04309v1 | http://arxiv.org/pdf/1703.04309v1.pdf | End-to-End Learning of Geometry and Context for Deep Stereo Regression | We propose a novel deep learning architecture for regressing disparity from a
rectified pair of stereo images. We leverage knowledge of the problem's
geometry to form a cost volume using deep feature representations. We learn to
incorporate contextual information using 3-D convolutions over this volume.
Disparity value... | ['Abraham Bachrach', 'Hayk Martirosyan', 'Peter Henry', 'Saumitro Dasgupta', 'Alex Kendall', 'Adam Bry', 'Ryan Kennedy'] | 2017-03-13 | end-to-end-learning-of-geometry-and-context-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Kendall_End-To-End_Learning_of_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Kendall_End-To-End_Learning_of_ICCV_2017_paper.pdf | iccv-2017-10 | ['stereo-lidar-fusion'] | ['computer-vision'] | [ 3.49806398e-01 -5.48101589e-03 1.50405660e-01 -7.58760333e-01
-8.33380997e-01 -3.14289182e-01 5.36537886e-01 -2.25278467e-01
-7.40227938e-01 7.30051696e-01 1.57372773e-01 -3.35872740e-01
3.49545479e-01 -7.78387964e-01 -1.04410982e+00 -2.23789960e-01
-1.19027287e-01 1.45102277e-01 1.74631998e-01 -1.30473346... | [8.649645805358887, -2.087611198425293] |
a0e7fff4-92a9-4191-be8a-34c327cea2cf | peer-to-peer-federated-continual-learning-for | 2304.07421 | null | https://arxiv.org/abs/2304.07421v1 | https://arxiv.org/pdf/2304.07421v1.pdf | Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition | Naturalistic driving action recognition (NDAR) has proven to be an effective method for detecting driver distraction and reducing the risk of traffic accidents. However, the intrusive design of in-cabin cameras raises concerns about driver privacy. To address this issue, we propose a novel peer-to-peer (P2P) federated ... | ['Ziran Wang', 'Lu Su', 'Yunsheng Ma', 'Liangqi Yuan'] | 2023-04-14 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [-4.14118946e-01 5.16723134e-02 -3.89463305e-01 -5.71510911e-01
-1.12899494e+00 -5.31367779e-01 5.59018791e-01 -2.05076426e-01
-4.10861582e-01 5.60184062e-01 2.57644176e-01 -3.67451817e-01
-3.27711135e-01 -3.90372574e-01 -7.92049706e-01 -6.79990768e-01
3.25132430e-01 4.75196578e-02 5.44321716e-01 5.96442372... | [5.86002779006958, 6.283297538757324] |
17845561-4be9-4783-bd06-277649011e9e | how-to-learn-and-generalize-from-three | 2306.06335 | null | https://arxiv.org/abs/2306.06335v1 | https://arxiv.org/pdf/2306.06335v1.pdf | How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations | We present a framework and algorithms to learn controlled dynamics models using neural stochastic differential equations (SDEs) -- SDEs whose drift and diffusion terms are both parametrized by neural networks. We construct the drift term to leverage a priori physics knowledge as inductive bias, and we design the diffus... | ['Ufuk Topcu', 'Cyrus Neary', 'Franck Djeumou'] | 2023-06-10 | null | null | null | null | ['model-based-reinforcement-learning'] | ['reasoning'] | [-2.26653188e-01 1.95561111e-01 -4.13068593e-01 -4.89528030e-02
-1.33332953e-01 -7.22527623e-01 4.32515711e-01 1.57985147e-02
-4.65318888e-01 1.07361293e+00 -3.61946613e-01 -4.08633411e-01
-5.02512574e-01 -6.74876273e-01 -9.49087918e-01 -9.32282329e-01
-8.56040657e-01 7.98561215e-01 2.20353186e-01 -6.46138251... | [4.814412593841553, 2.16741943359375] |
57e73e12-3505-4fe6-822d-463a29bb5530 | supergf-unifying-local-and-global-features | 2212.13105 | null | https://arxiv.org/abs/2212.13105v1 | https://arxiv.org/pdf/2212.13105v1.pdf | SuperGF: Unifying Local and Global Features for Visual Localization | Advanced visual localization techniques encompass image retrieval challenges and 6 Degree-of-Freedom (DoF) camera pose estimation, such as hierarchical localization. Thus, they must extract global and local features from input images. Previous methods have achieved this through resource-intensive or accuracy-reducing m... | ['Takayuki Okatani', 'Boshu Lei', 'Ran Yan', 'Wenzheng Song'] | 2022-12-23 | null | null | null | null | ['visual-localization', 'sparse-learning'] | ['computer-vision', 'methodology'] | [-2.71493375e-01 -7.78635740e-01 -3.78356546e-01 -4.68845814e-01
-1.33837509e+00 -7.80622303e-01 6.57784641e-01 1.30622774e-01
-3.22123080e-01 3.10741276e-01 2.96500444e-01 9.57979187e-02
-3.22436243e-01 -4.93091464e-01 -5.67008078e-01 -5.07243574e-01
-4.73502316e-02 2.74014175e-01 1.90563366e-01 2.01675370... | [7.831641674041748, -2.044353485107422] |
8ca170dc-671d-4e0f-833e-5f8cd35ea2db | 3d-self-supervised-methods-for-medical | 2006.03829 | null | https://arxiv.org/abs/2006.03829v3 | https://arxiv.org/pdf/2006.03829v3.pdf | 3D Self-Supervised Methods for Medical Imaging | Self-supervised learning methods have witnessed a recent surge of interest after proving successful in multiple application fields. In this work, we leverage these techniques, and we propose 3D versions for five different self-supervised methods, in the form of proxy tasks. Our methods facilitate neural network feature... | ['Julius Severin', 'Aiham Taleb', 'Winfried Loetzsch', 'Noel Danz', 'Christoph Lippert', 'Benjamin Bergner', 'Thomas Gaertner'] | 2020-06-06 | null | http://proceedings.neurips.cc/paper/2020/hash/d2dc6368837861b42020ee72b0896182-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/d2dc6368837861b42020ee72b0896182-Paper.pdf | neurips-2020-12 | ['diabetic-retinopathy-detection'] | ['medical'] | [ 2.82457292e-01 6.11379862e-01 -3.91008973e-01 -4.62134033e-01
-9.01425242e-01 -4.28187728e-01 4.29443032e-01 8.41820911e-02
-3.64375830e-01 5.04528940e-01 2.31653914e-01 -3.08670700e-01
-2.61175901e-01 -3.36011797e-01 -8.38383615e-01 -6.87291205e-01
-2.57496327e-01 7.90910959e-01 7.94903114e-02 2.40165547... | [14.707958221435547, -2.3294148445129395] |
60c9db0d-a473-4791-81fe-201ab016f6fe | qurg-question-rewriting-guided-context | 2305.06655 | null | https://arxiv.org/abs/2305.06655v2 | https://arxiv.org/pdf/2305.06655v2.pdf | QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing | Context-dependent Text-to-SQL aims to translate multi-turn natural language questions into SQL queries. Despite various methods have exploited context-dependence information implicitly for contextual SQL parsing, there are few attempts to explicitly address the dependencies between current question and question context... | ['Zhao Yan', 'Zhoujun Li', 'Yunbo Cao', 'Qian-Wen Zhang', 'Liqun Yang', 'Jian Yang', 'Dongling Xiao', 'Linzheng Chai'] | 2023-05-11 | null | null | null | null | ['text-to-sql', 'semantic-parsing', 'question-rewriting'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 3.05979431e-01 2.90269196e-01 -9.29235891e-02 -1.09402180e+00
-1.22729051e+00 -9.39494014e-01 4.42154646e-01 4.23526555e-01
-3.68021727e-01 3.24667811e-01 4.76260900e-01 -8.54528010e-01
4.63646725e-02 -1.26887929e+00 -1.05665648e+00 5.45985818e-01
5.28467834e-01 3.94594222e-01 4.63954508e-01 -6.22077882... | [9.958758354187012, 7.8564043045043945] |
313ca3b6-4e7d-4828-91f2-19c037e8d66a | why-deep-models-often-cannot-beat-non-deep | 2306.17702 | null | https://arxiv.org/abs/2306.17702v1 | https://arxiv.org/pdf/2306.17702v1.pdf | Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction? | Molecular property prediction (MPP) is a crucial task in the drug discovery pipeline, which has recently gained considerable attention thanks to advances in deep neural networks. However, recent research has revealed that deep models struggle to beat traditional non-deep ones on MPP. In this study, we benchmark 12 repr... | ['Stan Z. Li', 'Xiao Zhu', 'Lecheng Zhang', 'Jun Xia'] | 2023-06-30 | null | null | null | null | ['drug-discovery', 'property-prediction', 'molecular-property-prediction'] | ['medical', 'medical', 'miscellaneous'] | [ 2.58610517e-01 -1.86274841e-01 -5.38125515e-01 -2.42939517e-01
-2.99795508e-01 -5.08445084e-01 4.57178175e-01 4.99472827e-01
-1.56636268e-01 1.23284745e+00 -1.53902117e-02 -7.21176863e-01
-6.26383185e-01 -8.63011777e-01 -9.53323364e-01 -8.32785010e-01
-4.25389677e-01 3.88053209e-01 6.20391108e-02 -3.51672649... | [5.2082905769348145, 5.756438732147217] |
0cec79d0-ac1d-49c2-89e0-f0ea1a2a5687 | image-augmentation-based-momentum-memory | 2205.09448 | null | https://arxiv.org/abs/2205.09448v1 | https://arxiv.org/pdf/2205.09448v1.pdf | Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes | Many scenes in real life can be abstracted to the sparse reward visual scenes, where it is difficult for an agent to tackle the task under the condition of only accepting images and sparse rewards. We propose to decompose this problem into two sub-problems: the visual representation and the sparse reward. To address th... | ['Guizhong Liu', 'Biao Zhao', 'Zheng Fang'] | 2022-05-19 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.25871909e-02 1.76722750e-01 -4.31795597e-01 -1.47836491e-01
-2.51650810e-01 -1.21291585e-01 8.44015658e-01 -2.57389843e-01
-6.31119788e-01 7.95369804e-01 -5.61696012e-04 1.76952288e-01
-1.40748974e-02 -3.96132320e-01 -7.08228469e-01 -9.09579933e-01
-1.55137092e-01 3.02055568e-01 6.73022345e-02 -3.41114908... | [4.246084690093994, 1.3570404052734375] |
46e56878-3491-4818-8470-8f43f45de1e6 | a-novel-metric-for-evaluating-semantics-1 | null | null | https://openreview.net/forum?id=mVJ-hJVpq3r | https://openreview.net/pdf?id=mVJ-hJVpq3r | A Novel Metric for Evaluating Semantics Preservation | In this paper, we leverage pre-trained language models (PLMs) to precisely evaluate the semantics preservation of edition process on sentences. Our metric, Neighboring Distribution Divergence (NDD), evaluates the disturbance on predicted distribution of neighboring words from mask language model (MLM). NDD is capable o... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['predicate-detection', 'sentence-compression'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.65151531e-01 3.13303769e-01 -3.61041248e-01 -5.24957180e-01
-7.38300860e-01 -7.00539768e-01 7.20538497e-01 8.00054610e-01
-5.73963583e-01 7.58366644e-01 8.76081049e-01 -5.05824327e-01
4.18941677e-02 -5.47818542e-01 -6.45784855e-01 -3.23530734e-01
-1.91090822e-01 4.78839606e-01 4.62604403e-01 -8.06898773... | [10.95804500579834, 8.873529434204102] |
595b67dd-1cbd-4dec-aaaa-2a6e19570eca | exploring-representation-learning-for-small | 2303.10912 | null | https://arxiv.org/abs/2303.10912v1 | https://arxiv.org/pdf/2303.10912v1.pdf | Exploring Representation Learning for Small-Footprint Keyword Spotting | In this paper, we investigate representation learning for low-resource keyword spotting (KWS). The main challenges of KWS are limited labeled data and limited available device resources. To address those challenges, we explore representation learning for KWS by self-supervised contrastive learning and self-training wit... | ['Yujun Wang', 'Peng Gao', 'Quandong Wang', 'Liyong Guo', 'Fan Cui'] | 2023-03-20 | null | null | null | null | ['small-footprint-keyword-spotting', 'keyword-spotting'] | ['speech', 'speech'] | [ 2.40174472e-01 -6.59794137e-02 -4.01797324e-01 -4.73809093e-01
-1.37479758e+00 -1.31045535e-01 3.43249232e-01 -2.69801885e-01
-4.02987421e-01 4.57019329e-01 3.63474071e-01 -2.99029976e-01
2.36315817e-01 -2.46714994e-01 -7.88340509e-01 -4.36284900e-01
2.16652095e-01 2.68734582e-02 1.04067527e-01 -1.36811463... | [14.570502281188965, 6.308271408081055] |
f55faf8e-26c6-4e57-ac94-88121a18d4d9 | quick-dense-retrievers-consume-kale-post | 2304.01016 | null | https://arxiv.org/abs/2304.01016v3 | https://arxiv.org/pdf/2304.01016v3.pdf | Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders | In this paper, we consider the problem of improving the inference latency of language model-based dense retrieval systems by introducing structural compression and model size asymmetry between the context and query encoders. First, we investigate the impact of pre and post-training compression on the MSMARCO, Natural Q... | ['ChengXiang Zhai', 'Alessandro Magnani', 'Daniel Campos'] | 2023-03-31 | null | null | null | null | ['natural-questions', 'triviaqa'] | ['miscellaneous', 'miscellaneous'] | [-1.49989694e-01 5.93707114e-02 -3.06304544e-01 -4.79094684e-02
-1.16211355e+00 -8.16982985e-01 7.83383489e-01 2.26176262e-01
-7.79438317e-01 5.41244507e-01 3.96087557e-01 -6.22740388e-01
-4.64446604e-01 -8.74514341e-01 -8.66084754e-01 -1.55499339e-01
-1.20296955e-01 1.12296975e+00 2.13173240e-01 -2.22707182... | [11.337923049926758, 7.691100597381592] |
09effb8a-083c-4190-9c78-a182dd00e937 | learning-to-incorporate-texture-saliency | 2208.01587 | null | https://arxiv.org/abs/2208.01587v2 | https://arxiv.org/pdf/2208.01587v2.pdf | Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization | Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge is to precisely capture and sufficiently transfer characteristic cartoon styles (e.g., clear edges, smooth color shading, abstract fine str... | ['Yingjie Tian', 'Yuqi Zhang', 'Xiang Gao'] | 2022-08-02 | null | null | null | null | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 8.86813641e-01 2.46475250e-01 -2.58296747e-02 -1.09793998e-01
-5.92922091e-01 -6.93329990e-01 7.21951663e-01 -4.27912503e-01
6.02823123e-02 9.48500931e-01 1.31324798e-01 -2.92373598e-02
2.53165931e-01 -1.08719134e+00 -1.15427816e+00 -8.93741667e-01
3.36910129e-01 1.66866824e-01 2.08315402e-01 -6.25251889... | [11.636970520019531, -0.626628577709198] |
be450b03-b4db-4c36-a3af-c53c7c4420be | video-event-extraction-via-tracking-visual | 2211.01781 | null | https://arxiv.org/abs/2211.01781v2 | https://arxiv.org/pdf/2211.01781v2.pdf | Video Event Extraction via Tracking Visual States of Arguments | Video event extraction aims to detect salient events from a video and identify the arguments for each event as well as their semantic roles. Existing methods focus on capturing the overall visual scene of each frame, ignoring fine-grained argument-level information. Inspired by the definition of events as changes of st... | ['Heng Ji', 'Shih-Fu Chang', 'Jiajie Zhang', 'Xudong Lin', 'Manling Li', 'Guang Yang'] | 2022-11-03 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 5.47082782e-01 -7.37652257e-02 -3.52930099e-01 -2.70118624e-01
-5.13623059e-01 -5.57291269e-01 7.41722822e-01 4.80053157e-01
-4.98123318e-01 5.18315434e-01 7.58952200e-01 2.13772908e-01
2.59882748e-01 -5.24017215e-01 -1.01306653e+00 -7.66627967e-01
-2.83691287e-01 -4.44671303e-01 7.36038625e-01 1.27107222... | [8.469680786132812, 0.5917987823486328] |
6ff892f7-aa11-4ff3-9fea-98353c630810 | automatic-tracking-of-protein-vesicles | 1506.02083 | null | http://arxiv.org/abs/1506.02083v1 | http://arxiv.org/pdf/1506.02083v1.pdf | Automatic tracking of protein vesicles | With the advance of fluorescence imaging technologies, recently cell
biologists are able to record the movement of protein vesicles within a living
cell. Automatic tracking of the movements of these vesicles become key for
qualitative analysis of dynamics of theses vesicles. In this thesis, we
formulate such tracking p... | ['Min Xu'] | 2015-06-05 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-9.49494988e-02 -7.66052425e-01 1.33117497e-01 1.79433361e-01
-6.31174862e-01 -8.91830444e-01 2.05095828e-01 1.43113390e-01
-7.18420565e-01 9.10310268e-01 -4.88612175e-01 7.05855340e-02
-9.97409150e-02 -4.09929931e-01 -7.77530730e-01 -1.16166615e+00
1.94096506e-01 5.53560972e-01 7.06110358e-01 3.19254160... | [6.714853286743164, -1.9927723407745361] |
219e7334-db59-4078-ac11-5a3e58cc0e99 | caila-concept-aware-intra-layer-adapters-for | 2305.16681 | null | https://arxiv.org/abs/2305.16681v1 | https://arxiv.org/pdf/2305.16681v1.pdf | CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot Learning | Compositionality, the ability to combine existing concepts and generalize towards novel compositions, is a key functionality for intelligent entities. Here, we study the problem of Compositional Zero-Shot Learning (CZSL), which aims at recognizing novel attribute-object compositions. Recent approaches build their syste... | ['Ram Nevatia', 'Haidong Zhu', 'Zhaoheng Zheng'] | 2023-05-26 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 2.70816863e-01 1.15083456e-01 -2.07394913e-01 -3.58934999e-01
-8.25258136e-01 -6.06182098e-01 8.60242248e-01 2.18253627e-01
-3.03075105e-01 4.61308599e-01 4.19736892e-01 2.05419436e-02
3.28621149e-01 -6.59073412e-01 -1.09137321e+00 -3.46199691e-01
1.27916172e-01 5.37358463e-01 4.79422092e-01 -2.17159450... | [10.179327011108398, 2.014559268951416] |
4a0b6169-e525-4169-87a4-7cafa2ea1d54 | prompting-large-language-model-for-machine | 2301.07069 | null | https://arxiv.org/abs/2301.07069v2 | https://arxiv.org/pdf/2301.07069v2.pdf | Prompting Large Language Model for Machine Translation: A Case Study | Research on prompting has shown excellent performance with little or even no supervised training across many tasks. However, prompting for machine translation is still under-explored in the literature. We fill this gap by offering a systematic study on prompting strategies for translation, examining various factors for... | ['Alexandra Birch', 'Barry Haddow', 'Biao Zhang'] | 2023-01-17 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 3.69506031e-01 7.97117036e-03 -4.67331797e-01 -4.62787420e-01
-1.43949664e+00 -9.69720960e-01 1.08336508e+00 2.08897799e-01
-5.17685473e-01 9.71377313e-01 7.09724903e-01 -8.38033497e-01
-1.08690172e-01 -2.74898577e-02 -7.41007388e-01 -2.79124588e-01
2.44127899e-01 6.91169202e-01 -1.87594533e-01 -5.80483496... | [11.591409683227539, 10.252449035644531] |
c5d4acb4-65d2-454a-8492-2196e6f27580 | face-presentation-attack-detection-in-learned | 1810.13170 | null | http://arxiv.org/abs/1810.13170v2 | http://arxiv.org/pdf/1810.13170v2.pdf | Face Presentation Attack Detection in Learned Color-liked Space | Face presentation attack detection (PAD) has become a thorny problem for
biometric systems and numerous countermeasures have been proposed to address
it. However, majority of them directly extract feature descriptors and
distinguish fake faces from the real ones in existing color spaces (e.g. RGB,
HSV and YCbCr). Unfor... | ['Xiaoyi Feng', 'Zhaoqiang Xia', 'Lei Li', 'Fabio Roli', 'Xiaoyue Jiang'] | 2018-10-31 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.55851976e-03 -4.64010566e-01 9.34849679e-02 -2.83571899e-01
-4.56003398e-01 -7.21151352e-01 2.94656634e-01 -7.11275160e-01
-1.08412988e-02 7.33720541e-01 -4.56588596e-01 -4.14511561e-01
1.29656553e-01 -8.26859176e-01 -4.43304807e-01 -9.55122292e-01
2.05934793e-01 6.63772374e-02 -9.59646180e-02 -4.03340399... | [12.849611282348633, 1.0021353960037231] |
48c398d4-5993-4712-93ae-8c362efd0be3 | universal-semi-supervised-model-adaptation | 2307.03449 | null | https://arxiv.org/abs/2307.03449v1 | https://arxiv.org/pdf/2307.03449v1.pdf | Universal Semi-supervised Model Adaptation via Collaborative Consistency Training | In this paper, we introduce a realistic and challenging domain adaptation problem called Universal Semi-supervised Model Adaptation (USMA), which i) requires only a pre-trained source model, ii) allows the source and target domain to have different label sets, i.e., they share a common label set and hold their own priv... | ['Guanbin Li', 'Shuguang Cui', 'Xiaoguang Han', 'Yipeng Qin', 'Yushuang Wu', 'Zizheng Yan'] | 2023-07-07 | null | null | null | null | ['domain-adaptation'] | ['methodology'] | [ 2.39527270e-01 1.34068891e-01 -6.12909019e-01 -5.60488343e-01
-7.70671904e-01 -4.04989272e-01 4.27575380e-01 -1.08210206e-01
-2.28327766e-01 7.80979276e-01 -1.88112527e-01 1.49545342e-01
1.43093854e-01 -5.24708807e-01 -7.18318641e-01 -7.41294801e-01
5.66289485e-01 6.21132374e-01 4.03676182e-01 7.53411129... | [10.329150199890137, 3.1033544540405273] |
e2c92e30-3779-489a-9447-55c7de02b903 | transductive-zero-shot-hashing-for-multi | 1911.07192 | null | https://arxiv.org/abs/1911.07192v2 | https://arxiv.org/pdf/1911.07192v2.pdf | Transductive Zero-Shot Hashing for Multilabel Image Retrieval | Hash coding has been widely used in approximate nearest neighbor search for large-scale image retrieval. Given semantic annotations such as class labels and pairwise similarities of the training data, hashing methods can learn and generate effective and compact binary codes. While some newly introduced images may conta... | ['Song Wang', 'Qin Zou', 'Long Chen', 'Ling Cao', 'Zheng Zhang'] | 2019-11-17 | null | null | null | null | ['multi-label-image-retrieval'] | ['computer-vision'] | [ 1.12199455e-01 -3.05991352e-01 -6.15183353e-01 -6.38999879e-01
-1.32981479e+00 -3.30664575e-01 4.54269141e-01 5.19823253e-01
-2.84766495e-01 5.17106652e-01 1.53743222e-01 6.34403408e-01
-2.75427192e-01 -6.64953113e-01 -4.31551874e-01 -9.45536852e-01
3.11963111e-01 7.07203746e-01 3.04181069e-01 2.01950267... | [11.375194549560547, 1.0109940767288208] |
113a68b0-9d5f-4560-a96a-42d76d0596af | pavementscapes-a-large-scale-hierarchical | 2208.00775 | null | https://arxiv.org/abs/2208.00775v1 | https://arxiv.org/pdf/2208.00775v1.pdf | Pavementscapes: a large-scale hierarchical image dataset for asphalt pavement damage segmentation | Pavement damage segmentation has benefited enormously from deep learning. % and large-scale datasets. However, few current public datasets limit the potential exploration of deep learning in the application of pavement damage segmentation. To address this problem, this study has proposed Pavementscapes, a large-scale d... | ['Weiguang Zhang', 'Ju Huyan', 'Tao Ma', 'Zheng Tong'] | 2022-07-24 | null | null | null | null | ['2048'] | ['playing-games'] | [-2.08379373e-01 1.55203685e-01 5.17988861e-01 -2.79313177e-01
-8.22931111e-01 -1.02316447e-01 -1.79300755e-01 2.49404937e-01
-1.62471831e-01 6.51739597e-01 5.45378029e-03 -1.49044201e-01
-1.47630140e-01 -1.61622298e+00 -7.31580079e-01 -1.03384209e+00
-6.47731602e-01 3.28665614e-01 3.13934684e-01 -4.10424262... | [7.412198066711426, 1.2404476404190063] |
6e4cfd0a-7cb9-463c-a4b6-68df83af8901 | camera-adversarial-transfer-for-unsupervised | 1904.01308 | null | https://arxiv.org/abs/1904.01308v2 | https://arxiv.org/pdf/1904.01308v2.pdf | CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification | Unsupervised person re-ID is the task of identifying people on a target data set for which the ID labels are unavailable during training. In this paper, we propose to unify two trends in unsupervised person re-ID: clustering & fine-tuning and adversarial learning. On one side, clustering groups training images into pse... | ['Xavier Alameda-Pineda', 'Stephane Lathuilière', 'Guillaume Delorme', 'Yihong Xu', 'Radu Horaud'] | 2019-04-02 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 1.32148191e-01 -1.19747803e-01 1.82578340e-01 -3.97100180e-01
-3.40064794e-01 -8.65534544e-01 9.96693730e-01 -1.06938146e-01
-7.12872565e-01 6.01678193e-01 2.71607548e-01 3.15033257e-01
3.24301198e-02 -7.32748330e-01 -6.68965340e-01 -6.64745390e-01
4.43291441e-02 9.66815233e-01 5.08331414e-03 -4.08754684... | [14.722857475280762, 1.0470106601715088] |
0f8a97e8-33df-47fe-8b97-9d27451f6f66 | iterative-optimization-of-pseudo-ground-truth | 2208.14683 | null | https://arxiv.org/abs/2208.14683v1 | https://arxiv.org/pdf/2208.14683v1.pdf | Iterative Optimization of Pseudo Ground-Truth Face Image Quality Labels | While recent face recognition (FR) systems achieve excellent results in many deployment scenarios, their performance in challenging real-world settings is still under question. For this reason, face image quality assessment (FIQA) techniques aim to support FR systems, by providing them with sample quality information t... | ['Vitomir Štruc', 'Žiga Babnik'] | 2022-08-31 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 1.68347523e-01 -1.17031559e-01 -8.40295628e-02 -1.00289035e+00
-9.82917130e-01 -2.65561551e-01 5.36203265e-01 -7.19331726e-02
-1.33001477e-01 5.90339601e-01 -5.74377999e-02 2.60378808e-01
-4.43143398e-01 -6.71775699e-01 -4.43832874e-01 -7.66805828e-01
-5.32455444e-02 7.10760891e-01 -7.53259733e-02 -3.74052614... | [13.068641662597656, 0.7640601992607117] |
6a04930a-494c-462a-b1ae-755d817fa358 | recognize-human-activities-from-partially | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Cao_Recognize_Human_Activities_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Cao_Recognize_Human_Activities_2013_CVPR_paper.pdf | Recognize Human Activities from Partially Observed Videos | Recognizing human activities in partially observed videos is a challenging problem and has many practical applications. When the unobserved subsequence is at the end of the video, the problem is reduced to activity prediction from unfinished activity streaming, which has been studied by many researchers. However, in th... | ['Daniel Barrett', 'Yuewei Lin', 'Yu Cao', 'Haonan Yu', 'Aaron Michaux', 'Sven Dickinson', 'Song Wang', 'Jeffrey Mark Siskind', 'Siddharth Narayanaswamy', 'Andrei Barbu'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 6.76018596e-01 -2.96090245e-01 -2.95407832e-01 -3.65249217e-02
-4.77455497e-01 -2.84247071e-01 4.18566644e-01 -2.13028014e-01
-8.55114982e-02 6.29972160e-01 4.59499359e-01 1.79436252e-01
-8.67375061e-02 -3.33446115e-01 -7.46250033e-01 -1.02290320e+00
-3.32943618e-01 1.11496158e-01 6.55503809e-01 5.20305514... | [8.48975944519043, 0.5577136278152466] |
eceb7b48-b1fe-45cb-ade7-2f6ffd49e083 | detecting-robotic-affordances-on-novel | 1909.05770 | null | https://arxiv.org/abs/1909.05770v2 | https://arxiv.org/pdf/1909.05770v2.pdf | Recognizing Object Affordances to Support Scene Reasoning for Manipulation Tasks | Affordance information about a scene provides important clues as to what actions may be executed in pursuit of meeting a specified goal state. Thus, integrating affordance-based reasoning into symbolic action plannning pipelines would enhance the flexibility of robot manipulation. Unfortunately, the top performing affo... | ['Fu-Jen Chu', 'Ruinian Xu', 'Patricio A. Vela', 'Chao Tang'] | 2019-09-12 | null | null | null | null | ['affordance-recognition', 'affordance-detection'] | ['computer-vision', 'computer-vision'] | [ 4.49204385e-01 4.70089316e-01 -3.86643946e-01 -4.26508158e-01
-2.85862744e-01 -7.34108567e-01 8.05625677e-01 1.57513887e-01
-2.53394127e-01 2.85259068e-01 6.02791071e-01 -3.69457990e-01
-2.42039993e-01 -5.36820292e-01 -7.18508363e-01 -2.78943360e-01
-2.13261560e-01 5.61386287e-01 4.07332659e-01 -3.41900200... | [4.826569080352783, 0.3650049567222595] |
16187e73-30ea-4c23-8974-1a911417b28d | conflict-based-cross-view-consistency-for | 2303.01276 | null | https://arxiv.org/abs/2303.01276v3 | https://arxiv.org/pdf/2303.01276v3.pdf | Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation | Semi-supervised semantic segmentation (SSS) has recently gained increasing research interest as it can reduce the requirement for large-scale fully-annotated training data. The current methods often suffer from the confirmation bias from the pseudo-labelling process, which can be alleviated by the co-training framework... | ['Xiangyu Kong', 'Xiaoxia Xing', 'Dong Xu', 'Luping Zhou', 'Zhen Zhao', 'Zicheng Wang'] | 2023-03-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Conflict-Based_Cross-View_Consistency_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Conflict-Based_Cross-View_Consistency_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 3.57187688e-01 4.05702233e-01 -2.53232062e-01 -6.37325466e-01
-8.35641623e-01 -4.27731186e-01 4.07249987e-01 -1.11462228e-01
-1.96208939e-01 6.30641282e-01 6.12738170e-03 -1.20226435e-01
-8.58555064e-02 -5.39817214e-01 -8.95030320e-01 -8.37603033e-01
3.91885072e-01 4.08360541e-01 6.22647822e-01 2.27377806... | [9.4781494140625, 1.315173625946045] |
7eccebb2-0e38-454a-90e3-e551971b7e22 | improve-few-shot-voice-cloning-using-multi | 2203.09708 | null | https://arxiv.org/abs/2203.09708v1 | https://arxiv.org/pdf/2203.09708v1.pdf | Improve few-shot voice cloning using multi-modal learning | Recently, few-shot voice cloning has achieved a significant improvement. However, most models for few-shot voice cloning are single-modal, and multi-modal few-shot voice cloning has been understudied. In this paper, we propose to use multi-modal learning to improve the few-shot voice cloning performance. Inspired by th... | ['Yue Lin', 'Haitong Zhang'] | 2022-03-18 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 1.21952102e-01 -2.51205917e-02 -5.02470672e-01 -9.11233425e-02
-1.31102240e+00 -1.93010181e-01 6.63872719e-01 -4.73092556e-01
4.48663682e-02 4.92109954e-01 5.90789437e-01 -4.26880360e-01
3.02743465e-02 -3.42639208e-01 -2.57719368e-01 -6.15496039e-01
5.85061848e-01 3.27796072e-01 2.54214704e-01 -1.59378335... | [14.830669403076172, 6.676365852355957] |
f141b18f-4af0-4e6a-80b0-f7540e950b22 | sample-efficient-optimisation-with | 2205.13902 | null | https://arxiv.org/abs/2205.13902v2 | https://arxiv.org/pdf/2205.13902v2.pdf | Sample-Efficient Optimisation with Probabilistic Transformer Surrogates | Faced with problems of increasing complexity, recent research in Bayesian Optimisation (BO) has focused on adapting deep probabilistic models as flexible alternatives to Gaussian Processes (GPs). In a similar vein, this paper investigates the feasibility of employing state-of-the-art probabilistic transformers in BO. U... | ['Haitham Bou Ammar', 'Jun Wang', 'Rasul Tutunov', 'Antoine Grosnit', 'Matthieu Zimmer', 'Alexandre Maraval'] | 2022-05-27 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 3.08415622e-01 3.37627113e-01 2.08276451e-01 -5.33354208e-02
-1.04335904e+00 -4.72901464e-01 8.61287296e-01 4.27910328e-01
-6.51434183e-01 9.34580028e-01 -9.55444500e-02 -2.41551608e-01
-7.37463713e-01 -6.78688407e-01 -8.36201012e-01 -1.10962117e+00
-1.80996522e-01 8.75517607e-01 2.83632487e-01 7.17754737... | [6.456062316894531, 3.799975872039795] |
32113c37-ddfc-4655-89f7-cbe3bba4cf38 | the-single-noun-prior-for-image-clustering | 2104.03952 | null | https://arxiv.org/abs/2104.03952v2 | https://arxiv.org/pdf/2104.03952v2.pdf | Dataset Summarization by K Principal Concepts | We propose the new task of K principal concept identification for dataset summarizarion. The objective is to find a set of K concepts that best explain the variation within the dataset. Concepts are high-level human interpretable terms such as "tiger", "kayaking" or "happy". The K concepts are selected from a (potentia... | ['Yedid Hoshen', 'Niv Cohen'] | 2021-04-08 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 2.29703993e-01 -1.23595051e-01 -5.71328402e-01 -2.75027424e-01
-1.13884544e+00 -7.97524869e-01 3.89778107e-01 4.11891013e-01
-1.31107643e-01 4.28205252e-01 4.52040583e-01 -5.69290146e-02
-5.66393614e-01 -4.62962300e-01 -6.25826657e-01 -9.35259998e-01
1.39902636e-01 4.97977823e-01 -3.25456709e-01 -7.54497806... | [9.334650993347168, 3.0593950748443604] |
c80efe67-c7f0-4de2-b3f9-c930b66d0037 | from-graph-generation-to-graph-classification | 2302.07989 | null | https://arxiv.org/abs/2302.07989v1 | https://arxiv.org/pdf/2302.07989v1.pdf | From Graph Generation to Graph Classification | This note describes a new approach to classifying graphs that leverages graph generative models (GGM). Assuming a GGM that defines a joint probability distribution over graphs and their class labels, I derive classification formulas for the probability of a class label given a graph. A new conditional ELBO can be used ... | ['Oliver Schulte'] | 2023-02-15 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 2.22495049e-01 6.80222392e-01 -4.58070576e-01 -7.23214090e-01
-6.40975118e-01 -5.65812647e-01 8.51531029e-01 2.09850773e-01
3.79431069e-01 5.69629848e-01 -9.78316739e-02 -7.73047090e-01
-3.21414083e-01 -1.49652243e+00 -4.51529771e-01 -6.98110044e-01
-4.32078868e-01 1.05851257e+00 -9.57077220e-02 3.06418031... | [6.9996209144592285, 6.184803485870361] |
a418ed31-dbe9-4129-9285-4d25433b9173 | skrl-modular-and-flexible-library-for | 2202.03825 | null | https://arxiv.org/abs/2202.03825v2 | https://arxiv.org/pdf/2202.03825v2.pdf | skrl: Modular and Flexible Library for Reinforcement Learning | skrl is an open-source modular library for reinforcement learning written in Python and designed with a focus on readability, simplicity, and transparency of algorithm implementations. In addition to supporting environments that use the traditional interfaces from OpenAI Gym and DeepMind, it provides the facility to lo... | ['Simon Bøgh', 'Dimitris Chrysostomou', 'Nestor Arana-Arexolaleiba', 'Antonio Serrano-Muñoz'] | 2022-02-08 | null | null | null | null | ['omniverse-isaac-gym', 'isaac-gym-preview'] | ['robots', 'robots'] | [-9.02203977e-01 -1.65497273e-01 -2.12080687e-01 -9.43485126e-02
-2.74228215e-01 -8.04167688e-01 2.30462179e-01 -6.50789291e-02
-5.43137670e-01 8.10284197e-01 -6.95126802e-02 -5.26979148e-01
-6.73033893e-02 -7.47856915e-01 -5.20473838e-01 -6.57477677e-01
-1.79744661e-01 3.73122007e-01 1.89645439e-01 -3.22119504... | [4.14199686050415, 1.2421175241470337] |
3890cb22-7094-4f23-bf5a-7f01ae342271 | a-unifying-framework-for-causal-explanation | 2205.15462 | null | https://arxiv.org/abs/2205.15462v2 | https://arxiv.org/pdf/2205.15462v2.pdf | Causal Explanations for Sequential Decision Making Under Uncertainty | We introduce a novel framework for causal explanations of stochastic, sequential decision-making systems built on the well-studied structural causal model paradigm for causal reasoning. This single framework can identify multiple, semantically distinct explanations for agent actions -- something not previously possible... | ['Shlomo Zilberstein', 'Claudia V. Goldman', 'Saaduddin Mahmud', 'Samer B. Nashed'] | 2022-05-30 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 3.20483387e-01 7.01964557e-01 -2.33898267e-01 -5.29859841e-01
-3.50135654e-01 -3.87738407e-01 1.11408699e+00 3.50015223e-01
-1.85614638e-02 9.59368587e-01 6.63058043e-01 -9.54931796e-01
-7.05107629e-01 -5.60067773e-01 -2.86555320e-01 -6.48551524e-01
-4.42799062e-01 6.73101902e-01 4.90683317e-01 -1.10923640... | [8.22803783416748, 5.835142135620117] |
8eafcd60-7878-4924-a579-0880bb1dab03 | machine-learning-based-intrusion-detection-1 | 2307.01570 | null | https://arxiv.org/abs/2307.01570v1 | https://arxiv.org/pdf/2307.01570v1.pdf | Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction | Internet of things (IoT) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are highly vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a v... | ['Hung Tran', 'Thien Van Luong', 'Tuan-Cuong Vuong', 'Vu-Duc Ngo'] | 2023-07-04 | null | null | null | null | ['intrusion-detection', 'network-intrusion-detection'] | ['miscellaneous', 'miscellaneous'] | [ 2.41134971e-01 -3.12910080e-01 -3.03773701e-01 -1.87605977e-01
1.41127497e-01 -4.76529747e-01 3.80074769e-01 5.72170079e-01
-4.42803115e-01 5.86885810e-01 -5.05600393e-01 -5.50350606e-01
-5.45292974e-01 -1.24320745e+00 4.90798205e-02 -8.04982781e-01
-5.18548936e-02 1.32274315e-01 3.59720737e-01 2.34834254... | [5.22662353515625, 7.141569137573242] |
9d16eb2f-3172-48bb-9606-c543bffb362f | balancing-profit-risk-and-sustainability-for | 2207.02134 | null | https://arxiv.org/abs/2207.02134v1 | https://arxiv.org/pdf/2207.02134v1.pdf | Balancing Profit, Risk, and Sustainability for Portfolio Management | Stock portfolio optimization is the process of continuous reallocation of funds to a selection of stocks. This is a particularly well-suited problem for reinforcement learning, as daily rewards are compounding and objective functions may include more than just profit, e.g., risk and sustainability. We developed a novel... | ['Christian W. Omlin', 'Charl Maree'] | 2022-06-06 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-6.65141523e-01 1.47125810e-01 -3.59967500e-01 1.50265098e-01
-6.82650805e-01 -6.78953111e-01 6.52853727e-01 1.70448154e-01
-6.88116372e-01 1.24035168e+00 3.81251991e-01 -5.01705885e-01
-4.96113241e-01 -1.08299446e+00 -5.54548442e-01 -6.75091922e-01
-3.86170089e-01 6.07245326e-01 8.50402638e-02 -5.25068581... | [4.351058006286621, 3.6806254386901855] |
12d82b46-3a75-4b53-945d-3f56f6dc4840 | easy-and-efficient-transformer-scalable-1 | null | null | https://aclanthology.org/2022.naacl-industry.8 | https://aclanthology.org/2022.naacl-industry.8.pdf | Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model | Recently, large-scale transformer-based models have been proven to be effective over various tasks across many domains. Nevertheless, applying them in industrial production requires tedious and heavy works to reduce inference costs. To fill such a gap, we introduce a scalable inference solution: Easy and Efficient Tran... | ['Zeng Zhao', 'Xiaoxi Mao', 'Changjie Fan', 'Bai Liu', 'Rongsheng Zhang', 'Ziyang Luo', 'Duan Wang', 'Jingzhen Ding', 'Yadong Xi', 'Gongzheng li'] | null | null | null | null | naacl-acl-2022-7 | ['inference-optimization'] | ['audio'] | [-1.23699918e-01 -4.19730507e-02 -8.66938531e-02 -3.49977821e-01
-9.49500203e-01 -4.56409454e-01 2.10737780e-01 -3.97763789e-01
-7.06192181e-02 5.51653624e-01 -4.90518436e-02 -8.35985243e-01
3.23477864e-01 -1.11654985e+00 -9.14674163e-01 -4.66255009e-01
5.68808556e-01 6.16089880e-01 4.67401564e-01 -5.03640361... | [8.73317813873291, 3.604215621948242] |
267abd60-b489-4a08-87cd-7bdbcdc4ac67 | light-field-for-rf | 1901.03953 | null | http://arxiv.org/abs/1901.03953v1 | http://arxiv.org/pdf/1901.03953v1.pdf | Light-Field for RF | Most computer vision systems and computational photography systems are
visible light based which is a small fraction of the electromagnetic (EM)
spectrum. In recent years radio frequency (RF) hardware has become more widely
available, for example, many cars are equipped with a RADAR, and almost every
home has a WiFi de... | ['Ramesh Raskar', 'Manikanta Kotaru', 'Guy Satat', 'Sachin Katti'] | 2019-01-13 | null | null | null | null | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 7.52741218e-01 -1.59262478e-01 2.28583351e-01 -1.87798455e-01
1.99783891e-01 -5.81119120e-01 5.72807074e-01 -7.42471039e-01
-5.85125148e-01 8.10017347e-01 -6.88784719e-02 -3.56506348e-01
-4.48512249e-02 -1.20854676e+00 -3.74913007e-01 -1.14143503e+00
5.86312711e-01 -2.17846483e-01 2.01536380e-02 -2.11906433... | [10.118849754333496, -2.643252372741699] |
82b2a0aa-5795-47ee-bee2-a6f8e7dbc389 | inverse-path-tracing-for-joint-material-and-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Azinovic_Inverse_Path_Tracing_for_Joint_Material_and_Lighting_Estimation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Azinovic_Inverse_Path_Tracing_for_Joint_Material_and_Lighting_Estimation_CVPR_2019_paper.pdf | Inverse Path Tracing for Joint Material and Lighting Estimation | Modern computer vision algorithms have brought significant advancement to 3D geometry reconstruction. However, illumination and material reconstruction remain less studied, with current approaches assuming very simplified models for materials and illumination. We introduce Inverse Path Tracing, a novel approach to join... | [' Matthias Niessner', ' Anton Kaplanyan', ' Tzu-Mao Li', 'Dejan Azinovic'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['lighting-estimation'] | ['computer-vision'] | [ 6.27840757e-01 -6.54314280e-01 8.01988542e-01 -3.89941752e-01
-4.45283502e-01 -4.66484487e-01 6.56253040e-01 1.05846375e-02
-1.88175544e-01 8.11500072e-01 -2.04455405e-01 1.94080211e-02
-2.14209035e-01 -8.84941876e-01 -6.39916003e-01 -8.21315825e-01
4.27099109e-01 6.78011894e-01 -7.37121701e-02 1.12557001... | [9.734335899353027, -3.0745701789855957] |
7694a274-1759-4db4-b359-a307889bb72f | spatial-reasoning-for-few-shot-object | 2211.01080 | null | https://arxiv.org/abs/2211.01080v1 | https://arxiv.org/pdf/2211.01080v1.pdf | Spatial Reasoning for Few-Shot Object Detection | Although modern object detectors rely heavily on a significant amount of training data, humans can easily detect novel objects using a few training examples. The mechanism of the human visual system is to interpret spatial relationships among various objects and this process enables us to exploit contextual information... | ['Seong-Whan Lee', 'Hong-Gyu Jung', 'Geonuk Kim'] | 2022-11-02 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 1.87926769e-01 4.02711285e-03 1.99895844e-01 -5.62252462e-01
-1.10173680e-01 -6.44944429e-01 8.62824857e-01 4.74456489e-01
-7.35101104e-01 3.76214027e-01 -1.28432885e-01 1.14235006e-01
-8.31531435e-02 -8.10114086e-01 -9.17100191e-01 -3.59519690e-01
-1.82854369e-01 -3.24630775e-02 8.57430279e-01 -1.89733610... | [9.884894371032715, 1.627812147140503] |
40522563-7b83-484f-a115-9f5471a85bad | visualization-of-contributions-to-open-source | 2010.08874 | null | https://arxiv.org/abs/2010.08874v1 | https://arxiv.org/pdf/2010.08874v1.pdf | Visualization of Contributions to Open-Source Projects | We want to analyze visually, to what extend team members and external developers contribute to open-source projects. This gives a high-level impression about collaboration in that projects. We achieve this by recording provenance of the development process and use graph drawing on the resulting provenance graph. Our gr... | ['Andreas Schreiber'] | 2020-10-17 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [-3.34362566e-01 2.90862948e-01 5.06769896e-01 -3.21155079e-02
-2.04317629e-01 -8.91860068e-01 4.89685774e-01 4.89907384e-01
1.77920461e-01 4.14496243e-01 4.34712231e-01 -4.02236640e-01
-6.76736534e-02 -5.80802679e-01 -4.04663473e-01 1.39329180e-01
-2.13946223e-01 -3.81751865e-01 4.43247110e-01 -2.41618380... | [7.847101211547852, 7.661530494689941] |
e54002f2-310e-4021-b252-f5d4137887f8 | convolutional-neural-network-achieves-human | 1802.09697 | null | http://arxiv.org/abs/1802.09697v1 | http://arxiv.org/pdf/1802.09697v1.pdf | Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification | Music genre classification is one example of content-based analysis of music
signals. Traditionally, human-engineered features were used to automatize this
task and 61% accuracy has been achieved in the 10-genre classification.
However, it's still below the 70% accuracy that humans could achieve in the
same task. Here,... | ['Mingwen Dong'] | 2018-02-27 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 3.75435442e-01 -4.56041694e-01 1.86999619e-01 -1.80656239e-02
-5.36227286e-01 -6.79624498e-01 1.59344837e-01 5.43947816e-02
-4.32083160e-01 2.42664978e-01 2.00794324e-01 6.52828291e-02
-2.04144016e-01 -6.65637374e-01 -4.11703616e-01 -4.98737872e-01
9.39377174e-02 -9.18241125e-03 1.43832117e-01 -1.35669664... | [15.78613567352295, 5.173326015472412] |
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