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440b9b5b-1b94-4424-8ac1-4bd6812e09bd | instruct-neuraltalker-editing-audio-driven | 2306.10813 | null | https://arxiv.org/abs/2306.10813v1 | https://arxiv.org/pdf/2306.10813v1.pdf | Instruct-NeuralTalker: Editing Audio-Driven Talking Radiance Fields with Instructions | Recent neural talking radiance field methods have shown great success in photorealistic audio-driven talking face synthesis. In this paper, we propose a novel interactive framework that utilizes human instructions to edit such implicit neural representations to achieve real-time personalized talking face generation. Gi... | ['Bo Yan', 'Weimin Tan', 'Reian He', 'Yuqi Sun'] | 2023-06-19 | null | null | null | null | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 5.66261351e-01 1.90709636e-01 1.68490022e-01 -5.52520454e-01
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1.93095252e-01 -6.31151438e-01 -6.04996979e-01 -4.78302568e-01
3.65328938e-01 -4.58854809e-03 -6.23347179e-04 -3.00093919... | [13.11867618560791, -0.44803112745285034] |
df2e54cc-995c-4a79-b7d7-e11290836d0c | ebsr-feature-enhanced-burst-super-resolution | null | null | https://openaccess.thecvf.com/content/CVPR2021W/NTIRE/html/Luo_EBSR_Feature_Enhanced_Burst_Super-Resolution_With_Deformable_Alignment_CVPRW_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021W/NTIRE/papers/Luo_EBSR_Feature_Enhanced_Burst_Super-Resolution_With_Deformable_Alignment_CVPRW_2021_paper.pdf | EBSR: Feature Enhanced Burst Super-Resolution With Deformable Alignment | We propose a novel architecture to handle the problem of multi-frame super-resolution (MFSR). The proposed framework is known as Enhanced Burst Super-Resolution (EBSR), which divides the MFSR problem into three parts: alignment, fusion, and reconstruction. We propose a Feature Enhanced Pyramid Cascading and Deformable ... | ['Shuaicheng Liu', 'Jian Sun', 'Haoqiang Fan', 'Lanpeng Jia', 'Youwei Li', 'Xuan Mo', 'Lei Yu', 'Ziwei Luo'] | 2021-06-15 | null | null | null | proceedings-of-the-ieee-cvf-conference-on | ['multi-frame-super-resolution', 'burst-image-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 4.79322523e-01 -2.38483697e-01 3.05181503e-01 -3.36055845e-01
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8.31767619e-02 -1.12880498e-01 5.35712600e-01 -4.52863783... | [11.001164436340332, -1.8890049457550049] |
88582fc5-afaa-4795-a932-824f049e87c3 | augmented-transformer-achieves-97-and-85-for | 2003.02804 | null | https://arxiv.org/abs/2003.02804v2 | https://arxiv.org/pdf/2003.02804v2.pdf | State-of-the-Art Augmented NLP Transformer models for direct and single-step retrosynthesis | We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using a text-like representation of chemical reactions (SMILES) and Natural Language Processing neural network Transformer architecture. We showed that data augmentation, which is a powerful method used i... | ['Igor V. Tetko', 'Pavel Karpov', 'Guillaume Godin', 'Ruud Van Deursen'] | 2020-03-05 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 7.79197872e-01 8.64383355e-02 -3.06361914e-01 1.76891744e-01
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7.51052946e-02 5.29558063e-01 2.85053849e-01 -3.75081867... | [4.490431308746338, 6.105625152587891] |
798ada7a-f90a-41cd-b330-13662c097474 | adaptive-transfer-learning-for-plant | 2201.05261 | null | https://arxiv.org/abs/2201.05261v1 | https://arxiv.org/pdf/2201.05261v1.pdf | Adaptive Transfer Learning for Plant Phenotyping | Plant phenotyping (Guo et al. 2021; Pieruschka et al. 2019) focuses on studying the diverse traits of plants related to the plants' growth. To be more specific, by accurately measuring the plant's anatomical, ontogenetical, physiological and biochemical properties, it allows identifying the crucial factors of plants' g... | ['Jingrui He', 'Kaiyu Guan', 'Sheng Wang', 'Elizabeth A. Ainsworth', 'Jun Wu'] | 2022-01-14 | null | null | null | null | ['plant-phenotyping', 'gpr', 'gpr'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 3.82825911e-01 -1.25424862e-01 -1.82780817e-01 1.12673670e-01
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1.97301388e-01 2.84454465e-01 5.22607975e-02 -2.11461395... | [9.184743881225586, -1.566736102104187] |
cd34e8ef-3fb4-4fa7-b6cb-456dbdd3b76a | uav-crowd-violent-and-non-violent-crowd | 2208.06702 | null | https://arxiv.org/abs/2208.06702v1 | https://arxiv.org/pdf/2208.06702v1.pdf | UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV | Unmanned Aerial Vehicle (UAV) has gained significant traction in the recent years, particularly the context of surveillance. However, video datasets that capture violent and non-violent human activity from aerial point-of-view is scarce. To address this issue, we propose a novel, baseline simulator which is capable of ... | ['Mayamin Hamid Raha', 'Shahriar Ali Bijoy', 'Tonmoay Deb', 'Mahieyin Rahmun'] | 2022-08-13 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 2.51239210e-01 -8.62348229e-02 5.94068229e-01 -1.64194591e-02
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-3.34599733e-01 3.37108076e-01 7.05783129e-01 -2.68465191... | [7.412619113922119, -1.3567650318145752] |
043a2992-cd29-47a7-9c5c-4486f32125df | person-recognition-using-facial-micro | 2306.13907 | null | https://arxiv.org/abs/2306.13907v1 | https://arxiv.org/pdf/2306.13907v1.pdf | Person Recognition using Facial Micro-Expressions with Deep Learning | This study investigates the efficacy of facial micro-expressions as a soft biometric for enhancing person recognition, aiming to broaden the understanding of the subject and its potential applications. We propose a deep learning approach designed to capture spatial semantics and motion at a fine temporal resolution. Ex... | ['David Mendlovic', 'Mor-Avi Azulay', 'Khen Cohen', 'Yuval Ringel', 'Tuval Kay'] | 2023-06-24 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [-2.19042245e-02 -5.75793564e-01 -4.55805302e-01 -6.41024530e-01
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-1.06714360e-01 -2.75546134e-01 -6.25392437e-01 -2.01100543... | [13.622872352600098, 1.812815546989441] |
c84d01ce-0bf1-4b7a-9111-69782f666875 | a-side-by-side-comparison-of-transformers-for | 2307.03378 | null | https://arxiv.org/abs/2307.03378v1 | https://arxiv.org/pdf/2307.03378v1.pdf | A Side-by-side Comparison of Transformers for English Implicit Discourse Relation Classification | Though discourse parsing can help multiple NLP fields, there has been no wide language model search done on implicit discourse relation classification. This hinders researchers from fully utilizing public-available models in discourse analysis. This work is a straightforward, fine-tuned discourse performance comparison... | ['Jason Hyung-Jong Lee', 'BongSeok Yang', 'Bruce W. Lee'] | 2023-07-07 | null | null | null | null | ['discourse-parsing', 'relation-classification', 'implicit-discourse-relation-classification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.05414963e-01 1.17672431e+00 -8.43960345e-01 -1.70122892e-01
-9.91407216e-01 -6.21166408e-01 1.25925016e+00 4.86421317e-01
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1.14655532e-01 4.53432411e-01 2.05747411e-01 -3.36644620... | [10.864924430847168, 9.336297988891602] |
dd8667ef-9c10-4df0-9e8e-c99164893d72 | bio-inspired-gait-imitation-of-hexapod-robot | 2004.05450 | null | https://arxiv.org/abs/2004.05450v1 | https://arxiv.org/pdf/2004.05450v1.pdf | Bio-inspired Gait Imitation of Hexapod Robot Using Event-Based Vision Sensor and Spiking Neural Network | Learning how to walk is a sophisticated neurological task for most animals. In order to walk, the brain must synthesize multiple cortices, neural circuits, and diverse sensory inputs. Some animals, like humans, imitate surrounding individuals to speed up their learning. When humans watch their peers, visual data is pro... | ['Yan Fang', 'Arijit Raychowdhury', 'Justin Ting', 'Ashwin Sanjay Lele'] | 2020-04-11 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 2.11831465e-01 -5.96670583e-02 2.25020602e-01 1.77844867e-01
5.54582059e-01 -3.17909598e-01 3.68396580e-01 -4.91616547e-01
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1.20429546e-01 6.21945821e-02 6.79476321e-01 -6.30550608... | [8.198049545288086, 2.3616535663604736] |
e9d52eba-db07-4ecb-b368-e1f95c155e19 | detecting-adversarial-examples-in-batches-a | 2206.08738 | null | https://arxiv.org/abs/2206.08738v1 | https://arxiv.org/pdf/2206.08738v1.pdf | Detecting Adversarial Examples in Batches -- a geometrical approach | Many deep learning methods have successfully solved complex tasks in computer vision and speech recognition applications. Nonetheless, the robustness of these models has been found to be vulnerable to perturbed inputs or adversarial examples, which are imperceptible to the human eye, but lead the model to erroneous out... | ['Peter Steinbach', 'Danush Kumar Venkatesh'] | 2022-06-17 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 5.15693724e-01 3.00894082e-01 4.61885601e-01 -3.29975009e-01
-7.12823808e-01 -9.60640728e-01 8.84015620e-01 3.96968096e-01
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-4.78056252e-01 4.46095258e-01 4.06774253e-01 2.86605097... | [5.655708312988281, 7.79729700088501] |
3c726f66-8a5c-4217-8fb7-d4da50ebafe6 | domain-adaptive-cascade-r-cnn-for-mitosis | 2109.00965 | null | https://arxiv.org/abs/2109.00965v2 | https://arxiv.org/pdf/2109.00965v2.pdf | Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge | We present a summary of the domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images. By comprehensive data augmentation and adapting existing popular detection architecture, our proposed method has achieved an F1 score of 0.7500 on the preliminary test set in MItosis DOmain Generaliz... | ['Jingxin Liu', 'Lian Liu', 'Xiao Mu', 'Ying Cheng', 'Xi Long'] | 2021-09-01 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 4.24679369e-01 2.13983461e-01 -5.83421350e-01 -1.38630271e-01
-1.07465839e+00 -1.79423988e-01 4.75322902e-01 4.75066155e-01
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-1.52051687e-01 4.62134212e-01 3.45069170e-01 -3.28818828... | [15.1071195602417, -3.109243631362915] |
8b217f22-2258-4569-8870-2fbb623f8ea1 | time-series-as-images-vision-transformer-for | 2303.12799 | null | https://arxiv.org/abs/2303.12799v1 | https://arxiv.org/pdf/2303.12799v1.pdf | Time Series as Images: Vision Transformer for Irregularly Sampled Time Series | Irregularly sampled time series are becoming increasingly prevalent in various domains, especially in medical applications. Although different highly-customized methods have been proposed to tackle irregularity, how to effectively model their complicated dynamics and high sparsity is still an open problem. This paper s... | ['Xifeng Yan', 'Shiyang Li', 'Zekun Li'] | 2023-03-01 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 4.16863710e-01 -1.26511276e-01 -3.56722116e-01 -1.90129176e-01
-6.61345541e-01 -5.62995970e-01 5.39950550e-01 1.94847479e-01
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-2.28382125e-01 -4.60992128e-01 -6.18829668e-01 -8.07666183e-01
-3.89603317e-01 3.91099334e-01 -1.12260310e-02 -4.93890420... | [7.315160274505615, 3.2367212772369385] |
9f928017-5292-4716-a697-42b37f13bb75 | beyond-tree-structure-models-a-new-occlusion | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Fu_Beyond_Tree_Structure_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Fu_Beyond_Tree_Structure_ICCV_2015_paper.pdf | Beyond Tree Structure Models: A New Occlusion Aware Graphical Model for Human Pose Estimation | Occlusion is a main challenge for human pose estimation, which is largely ignored in popular tree structure models. The tree structure model is simple and convenient for exact inference, but short in modeling the occlusion coherence especially in the case of self-occlusion. We propose an occlusion aware graphical model... | ['Junge Zhang', 'Lianrui Fu', 'Kaiqi Huang'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['2d-human-pose-estimation'] | ['computer-vision'] | [-3.44063997e-01 1.01713493e-01 -3.63087803e-01 -4.39756870e-01
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-1.63751677e-01 1.06631899e+00 1.89379513e-01 -9.29810703... | [7.003200531005859, -0.8866998553276062] |
438ff477-339a-46ba-ba13-19f3b6c8ff57 | global-optimization-networks | 2202.01277 | null | https://arxiv.org/abs/2202.01277v1 | https://arxiv.org/pdf/2202.01277v1.pdf | Global Optimization Networks | We consider the problem of estimating a good maximizer of a black-box function given noisy examples. To solve such problems, we propose to fit a new type of function which we call a global optimization network (GON), defined as any composition of an invertible function and a unimodal function, whose unique global maxim... | ['Maya Gupta', 'Olexander Mangylov', 'Erez Louidor', 'Sen Zhao'] | 2022-02-02 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-7.47086108e-02 6.52763486e-01 -7.69676864e-02 -8.55087459e-01
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-1.71000898e-01 1.11099088e+00 -3.51983070e-01 1.13793798... | [6.967605113983154, 3.9902615547180176] |
116ffa4a-4228-407e-87b4-1abd9c0a8e94 | bibl-amr-parsing-and-generation-with | null | null | https://aclanthology.org/2022.coling-1.485 | https://aclanthology.org/2022.coling-1.485.pdf | BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning | Abstract Meaning Representation (AMR) offers a unified semantic representation for natural language sentences. Thus transformation between AMR and text yields two transition tasks in opposite directions, i.e., Text-to-AMR parsing and AMR-to-Text generation. Existing AMR studies only focus on one-side improvements despi... | ['Hai Zhao', 'Zuchao Li', 'Ziming Cheng'] | null | null | null | null | coling-2022-10 | ['amr-parsing'] | ['natural-language-processing'] | [ 5.43963850e-01 5.49139082e-01 -2.29204431e-01 -5.50079048e-01
-1.23140109e+00 -5.28974712e-01 8.70878994e-01 -6.49397299e-02
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6.31710768e-01 5.38170934e-01 1.32211819e-01 -4.21492010... | [10.966828346252441, 8.949389457702637] |
1b870cb7-271a-49da-b1ee-764c8c914875 | aerial-pass-panoramic-annular-scene | 2105.07209 | null | https://arxiv.org/abs/2105.07209v1 | https://arxiv.org/pdf/2105.07209v1.pdf | Aerial-PASS: Panoramic Annular Scene Segmentation in Drone Videos | Aerial pixel-wise scene perception of the surrounding environment is an important task for UAVs (Unmanned Aerial Vehicles). Previous research works mainly adopt conventional pinhole cameras or fisheye cameras as the imaging device. However, these imaging systems cannot achieve large Field of View (FoV), small size, and... | ['Jian Bai', 'Kaiwei Wang', 'Xiangdong Zhou', 'Kaikai Wu', 'Kailun Yang', 'Jia Wang', 'Lei Sun'] | 2021-05-15 | null | null | null | null | ['scene-parsing', 'scene-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.15620124e-01 -3.89230102e-01 2.26571057e-02 -5.28284550e-01
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2.83338159e-01 -1.78230792e-01 7.24857748e-01 -9.18148905... | [8.722941398620605, -0.8853453993797302] |
ef0f1123-4fac-4f84-a9a2-2a72632c57d8 | an-efficient-and-scalable-deep-learning | 2011.09577 | null | https://arxiv.org/abs/2011.09577v3 | https://arxiv.org/pdf/2011.09577v3.pdf | An Efficient and Scalable Deep Learning Approach for Road Damage Detection | Pavement condition evaluation is essential to time the preventative or rehabilitative actions and control distress propagation. Failing to conduct timely evaluations can lead to severe structural and financial loss of the infrastructure and complete reconstructions. Automated computer-aided surveying measures can provi... | ['Hassan Zargarzadeh', 'Amir R. Kashani', 'M-Mahdi Naddaf-Sh', 'Sadra Naddaf-sh'] | 2020-11-18 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-6.10773563e-02 -1.90620765e-01 2.80320505e-03 -3.20201993e-01
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4c7dd664-38cd-4c99-b559-d249af9054da | application-of-deep-q-network-in-portfolio | 2003.06365 | null | https://arxiv.org/abs/2003.06365v1 | https://arxiv.org/pdf/2003.06365v1.pdf | Application of Deep Q-Network in Portfolio Management | Machine Learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, face recognition and population analysis. This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market.... | ['Jionglong Su', 'Zhengyong Jiang', 'Yuan Gao', 'Yi Hu', 'Ziming Gao'] | 2020-03-13 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-6.72234774e-01 1.42872175e-02 -3.03407818e-01 1.77836586e-02
6.69910386e-02 -3.90875459e-01 1.31459072e-01 -4.10009027e-01
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-3.29163402e-01 4.08963382e-01 7.80931264e-02 -5.77240705... | [4.473752975463867, 3.9802324771881104] |
c513703b-cdf4-43da-a5f4-2d201cea71b0 | ernie-layout-layout-knowledge-enhanced-pre | 2210.06155 | null | https://arxiv.org/abs/2210.06155v2 | https://arxiv.org/pdf/2210.06155v2.pdf | ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding | Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding. However, most existing methods lack the systematic mining and utilization of layout-centered knowledge, leading to sub-optimal performances. In this paper, we propose ERNIE-Layout, a novel document pre-t... | ['Haifeng Wang', 'Hua Wu', 'Hao Tian', 'Yu Sun', 'Shikun Feng', 'Yin Zhang', 'Yongfeng Chen', 'Weichong Yin', 'Teng Hu', 'Zhengjie Huang', 'Zhenyu Zhang', 'Bin Luo', 'Wenjin Wang', 'Yinxu Pan', 'Qiming Peng'] | 2022-10-12 | null | null | null | null | ['document-image-classification', 'semantic-entity-labeling', 'key-information-extraction'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 3.51637810e-01 -2.78754741e-01 -9.41498280e-02 -3.87549788e-01
-8.30068409e-01 -8.06168318e-01 6.41851306e-01 2.73718625e-01
-2.15967387e-01 1.70781583e-01 4.13339823e-01 -6.44649386e-01
-4.73515958e-01 -6.03814363e-01 -7.40965724e-01 -5.34349620e-01
3.33162636e-01 3.61079961e-01 9.31848660e-02 1.29970923... | [11.557393074035645, 2.3175253868103027] |
77d550ab-60fd-44cc-b617-ea9997d0e871 | unsupervised-constrative-person-re | 2010.07608 | null | https://arxiv.org/abs/2010.07608v2 | https://arxiv.org/pdf/2010.07608v2.pdf | Fully Unsupervised Person Re-identification viaSelective Contrastive Learning | Person re-identification (ReID) aims at searching the same identity person among images captured by various cameras. Unsupervised person ReID attracts a lot of attention recently, due to it works without intensive manual annotation and thus shows great potential of adapting to new conditions. Representation learning pl... | ['Xianming Liu', 'Junjun Jiang', 'Deming Zhai', 'Bo Pang'] | 2020-10-15 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 1.58033833e-01 -6.12727821e-01 -1.06832065e-01 -5.53071201e-01
-4.51684088e-01 -3.07882518e-01 7.33865499e-01 2.40092441e-01
-8.14273238e-01 5.51582098e-01 2.22390503e-01 4.84232545e-01
-3.69850844e-01 -5.66320479e-01 -3.01548719e-01 -9.50077236e-01
-1.11139249e-02 3.76756608e-01 -2.30776578e-01 1.74864873... | [14.746077537536621, 1.0358006954193115] |
d8d40ca1-e379-46f3-a2cd-32724aac37a3 | sparsification-and-filtering-for-spatial | 2203.03991 | null | https://arxiv.org/abs/2203.03991v1 | https://arxiv.org/pdf/2203.03991v1.pdf | Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series | We propose an end-to-end architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a matrix filtering module. This module generates filtered (inverse) correlation graphs from multivariate time series before inputting them into a GNN. In contrast with existing spar... | ['Tomaso Aste', 'Yuanrong Wang'] | 2022-03-08 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 2.57829398e-01 3.20398837e-01 1.19779728e-01 -2.38717973e-01
1.06684707e-01 -4.37832475e-01 7.13972151e-01 3.56935829e-01
5.19927964e-02 4.08539116e-01 7.95179084e-02 -8.15074801e-01
-7.12527335e-01 -1.05295312e+00 -8.39378953e-01 -1.50605157e-01
-1.07648921e+00 4.02114868e-01 -2.34333187e-01 -5.49999833... | [6.84735631942749, 2.922158718109131] |
de0f4bf9-f42a-470b-ae12-1a9b6c94dbee | discriminative-feature-learning-for | 1811.09791 | null | http://arxiv.org/abs/1811.09791v1 | http://arxiv.org/pdf/1811.09791v1.pdf | Discriminative Feature Learning for Unsupervised Video Summarization | In this paper, we address the problem of unsupervised video summarization
that automatically extracts key-shots from an input video. Specifically, we
tackle two critical issues based on our empirical observations: (i) Ineffective
feature learning due to flat distributions of output importance scores for each
frame, and... | ['Yunjae Jung', 'Dahun Kim', 'Sanghyun Woo', 'In So Kweon', 'Donghyeon Cho'] | 2018-11-24 | null | null | null | null | ['unsupervised-video-summarization', 'supervised-video-summarization'] | ['computer-vision', 'computer-vision'] | [ 3.55700016e-01 -2.53778875e-01 -4.28011447e-01 -2.23944947e-01
-8.38221967e-01 -9.26374942e-02 2.15350181e-01 -8.04996490e-02
-4.89450037e-01 6.99320436e-01 7.17882812e-01 1.49682939e-01
-1.82661451e-02 -2.14072213e-01 -7.35825062e-01 -6.66435540e-01
-2.41779476e-01 -3.45986277e-01 4.96635854e-01 1.04161829... | [10.37368106842041, 0.4380369484424591] |
ba8fc417-4014-4650-98ba-095d30323bf2 | what-underlies-rapid-learning-and-systematic | 2107.06994 | null | https://arxiv.org/abs/2107.06994v2 | https://arxiv.org/pdf/2107.06994v2.pdf | Systematic human learning and generalization from a brief tutorial with explanatory feedback | Neural networks have long been used to model human intelligence, capturing elements of behavior and cognition, and their neural basis. Recent advancements in deep learning have enabled neural network models to reach and even surpass human levels of intelligence in many respects, yet unlike humans, their ability to lear... | ['Andrew J. Nam', 'James L. McClelland'] | 2021-07-10 | null | null | null | null | ['systematic-generalization', 'high-school-mathematics'] | ['reasoning', 'reasoning'] | [ 1.75001547e-01 2.06716791e-01 9.31403339e-02 -3.15446734e-01
-2.17567503e-01 -5.99955499e-01 6.93986192e-02 3.69813144e-01
-5.10005593e-01 6.15666330e-01 1.86311632e-01 -3.76030862e-01
-7.70087957e-01 -8.86963844e-01 -4.33447927e-01 -4.01601009e-02
9.79967266e-02 7.57824123e-01 -6.35997877e-02 -4.49221730... | [9.525124549865723, 7.265414237976074] |
aed5982b-c4df-4957-bcc9-ee3b5dd2a885 | hyperef-spectral-hypergraph-coarsening-by | 2210.14813 | null | https://arxiv.org/abs/2210.14813v2 | https://arxiv.org/pdf/2210.14813v2.pdf | HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering | This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge effective resistances. Motivated by the latest theoretical framework for low-resistance-diameter decomposition of simple graphs, HyperEF aims at decomposing large h... | ['Zhuo Feng', 'Ali Aghdaei'] | 2022-10-26 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [-1.85344853e-02 6.49567246e-01 -4.48226184e-01 3.48513603e-01
-5.66654563e-01 -4.54387039e-01 -2.14729339e-01 1.01729974e-01
2.33574808e-01 5.63823760e-01 1.75253730e-02 -3.75174880e-01
-5.59768617e-01 -1.18821371e+00 -4.49237972e-01 -8.65396440e-01
-4.74828869e-01 6.79191291e-01 3.89976561e-01 -2.17172548... | [7.02017068862915, 5.213601589202881] |
858fdeb7-c064-460e-99cb-ed6bc603fd38 | arbitrary-shape-text-detection-via | 2208.12419 | null | https://arxiv.org/abs/2208.12419v1 | https://arxiv.org/pdf/2208.12419v1.pdf | Arbitrary Shape Text Detection via Segmentation with Probability Maps | Arbitrary shape text detection is a challenging task due to the significantly varied sizes and aspect ratios, arbitrary orientations or shapes, inaccurate annotations, etc. Due to the scalability of pixel-level prediction, segmentation-based methods can adapt to various shape texts and hence attracted considerable atte... | ['Xu-Cheng Yin', 'Jie-Bo Hou', 'Lei Chen', 'Xiaobin Zhu', 'Shi-Xue Zhang'] | 2022-08-26 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 3.67580175e-01 -2.98425853e-01 1.03205025e-01 -1.88787535e-01
-7.03334033e-01 -4.14959311e-01 4.39191014e-01 3.66167665e-01
-1.86516076e-01 4.36286002e-01 -3.63604248e-01 -1.08346932e-01
1.74557626e-01 -7.82716870e-01 -6.19810581e-01 -8.48508179e-01
6.09140217e-01 6.97449446e-01 9.39245224e-01 8.14447999... | [12.0801420211792, 2.2722771167755127] |
8e9fb8b9-8b6d-47c0-84b9-74a009ed6bd9 | towards-cover-song-detection-with-siamese | 2005.10294 | null | https://arxiv.org/abs/2005.10294v1 | https://arxiv.org/pdf/2005.10294v1.pdf | Towards Cover Song Detection with Siamese Convolutional Neural Networks | A cover song, by definition, is a new performance or recording of a previously recorded, commercially released song. It may be by the original artist themselves or a different artist altogether and can vary from the original in unpredictable ways including key, arrangement, instrumentation, timbre and more. In this wor... | ['Marko Stamenovic'] | 2020-05-20 | null | null | null | null | ['cover-song-identification'] | ['music'] | [ 5.81406713e-01 -3.42697024e-01 6.62841797e-02 -9.47680231e-03
-1.40588152e+00 -1.09480202e+00 1.38940349e-01 -5.45692742e-02
-1.49240747e-01 8.01733375e-01 1.45397350e-01 1.62919879e-01
4.53877784e-02 -3.91340584e-01 -1.10188007e+00 -5.95439613e-01
-5.96979499e-01 4.92223591e-01 -1.25533089e-01 -8.69407356... | [15.734756469726562, 5.325026512145996] |
a0529885-8335-47fc-9f2a-d57019f3bd92 | audio-visual-speech-and-gesture-recognition | null | null | https://www.mdpi.com/1424-8220/23/4/2284 | https://www.mdpi.com/1424-8220/23/4/2284/pdf?version=1676649264 | Audio-Visual Speech and Gesture Recognition by Sensors of Mobile Devices | Audio-visual speech recognition (AVSR) is one of the most promising solutions for reliable speech recognition, particularly when audio is corrupted by noise. Additional visual information can be used for both automatic lip-reading and gesture recognition. Hand gestures are a form of non-verbal communication and can be ... | ['Elena Ryumina', 'Denis Ivanko', 'Dmitry Ryumin'] | 2023-02-17 | null | null | null | sensors-2023-2 | ['sign-language-recognition', 'gesture-recognition', 'audio-visual-speech-recognition'] | ['computer-vision', 'computer-vision', 'speech'] | [ 2.31869057e-01 -2.92693108e-01 -3.09500694e-01 -1.69139266e-01
-1.17344701e+00 -2.50855148e-01 7.53928304e-01 -2.28465438e-01
-5.94123900e-01 2.94527560e-01 2.72172004e-01 -1.84412763e-01
-3.77167240e-02 -2.11845309e-01 -2.90082157e-01 -9.97527719e-01
2.20509455e-01 3.04104954e-01 1.52982771e-01 1.32942870... | [14.313926696777344, 5.044588565826416] |
da83c443-1e6c-43fb-a0b4-a4f061dcdf4c | a-qualitative-investigation-of-optical-flow | 2204.08791 | null | https://arxiv.org/abs/2204.08791v1 | https://arxiv.org/pdf/2204.08791v1.pdf | A qualitative investigation of optical flow algorithms for video denoising | A good optical flow estimation is crucial in many video analysis and restoration algorithms employed in application fields like media industry, industrial inspection and automotive. In this work, we investigate how well optical flow algorithms perform qualitatively when integrated into a state of the art video denoisin... | ['Hannes Fassold'] | 2022-04-19 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [-1.21304236e-01 -4.97231096e-01 2.85717577e-01 3.80736552e-02
-9.36413705e-02 -3.39123487e-01 5.18157244e-01 -1.58748373e-01
-3.26137722e-01 1.27828455e+00 2.83065110e-01 -1.64045542e-01
-2.05100432e-01 -4.72439826e-01 -6.86693192e-01 -8.55737925e-01
-1.41965926e-01 -1.72022596e-01 3.15383941e-01 -3.70344311... | [10.973615646362305, -1.9529390335083008] |
d3c09810-0658-4c98-ba83-fefde82ba8b7 | one-eye-is-all-you-need-lightweight-ensembles | 2211.11936 | null | https://arxiv.org/abs/2211.11936v1 | https://arxiv.org/pdf/2211.11936v1.pdf | One Eye is All You Need: Lightweight Ensembles for Gaze Estimation with Single Encoders | Gaze estimation has grown rapidly in accuracy in recent years. However, these models often fail to take advantage of different computer vision (CV) algorithms and techniques (such as small ResNet and Inception networks and ensemble models) that have been shown to improve results for other CV problems. Additionally, mos... | ['Rohan Kalahasty', 'Lakshmi Sritan Motati', 'Rishi Athavale'] | 2022-11-22 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-1.10733761e-02 2.34478459e-01 2.68121749e-01 -4.12501156e-01
5.18413596e-02 -2.73303658e-01 3.02844107e-01 -3.19589704e-01
-5.31459510e-01 6.14789546e-01 -6.28405437e-02 -2.34430432e-01
7.01241987e-03 -3.35872084e-01 -5.49621284e-01 -4.24712986e-01
3.06430638e-01 1.38735343e-02 2.56007165e-01 -2.07647830... | [14.124768257141113, 0.0789031982421875] |
6622953c-3182-43f4-a439-e88726eb5740 | plug-and-play-pseudo-label-correction-network | 2206.06607 | null | https://arxiv.org/abs/2206.06607v1 | https://arxiv.org/pdf/2206.06607v1.pdf | Plug-and-Play Pseudo Label Correction Network for Unsupervised Person Re-identification | Clustering-based methods, which alternate between the generation of pseudo labels and the optimization of the feature extraction network, play a dominant role in both unsupervised learning (USL) and unsupervised domain adaptive (UDA) person re-identification (Re-ID). To alleviate the adverse effect of noisy pseudo labe... | ['Jinqiao Wang', 'Ming Tang', 'Guibo Zhu', 'Haiyun Guo', 'Kuan Zhu', 'Tianyi Yan'] | 2022-06-14 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 1.14554942e-01 -7.27666309e-03 -1.37869403e-01 -7.49451935e-01
-2.27179229e-01 -3.50311100e-01 6.29371881e-01 1.60387844e-01
-4.74548548e-01 5.90823293e-01 -9.24247205e-02 1.64234504e-01
-4.83722389e-01 -8.04492056e-01 -2.91983306e-01 -8.72380912e-01
2.41968960e-01 7.62661219e-01 1.19763911e-02 6.37307093... | [14.849331855773926, 1.1271573305130005] |
e2a36e68-a498-4b89-90c3-9d494b4c3fbe | joint-blind-room-acoustic-characterization | 2010.11167 | null | https://arxiv.org/abs/2010.11167v1 | https://arxiv.org/pdf/2010.11167v1.pdf | Joint Blind Room Acoustic Characterization From Speech And Music Signals Using Convolutional Recurrent Neural Networks | Acoustic environment characterization opens doors for sound reproduction innovations, smart EQing, speech enhancement, hearing aids, and forensics. Reverberation time, clarity, and direct-to-reverberant ratio are acoustic parameters that have been defined to describe reverberant environments. They are closely related t... | ['Milos Cernak', 'Paul Callens'] | 2020-10-21 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [-1.18713699e-01 -6.78405225e-01 5.56108952e-01 -2.27021381e-01
-1.06173599e+00 -5.63916445e-01 1.34776488e-01 -3.33214760e-01
-2.89227426e-01 2.65008420e-01 7.85678446e-01 -6.20217264e-01
-9.47168618e-02 -3.04324865e-01 -2.61147320e-01 -7.09345341e-01
-9.85051617e-02 -3.65307122e-01 -2.37971455e-01 -1.23980023... | [15.102478981018066, 5.7777099609375] |
4a89ba4e-bd94-4e2f-8cde-d66a8bf6d025 | instruct-fingpt-financial-sentiment-analysis | 2306.12659 | null | https://arxiv.org/abs/2306.12659v1 | https://arxiv.org/pdf/2306.12659v1.pdf | Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models | Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements. Despite the impressive capabilities of large language models (LLMs) in financial natural language processing (NLP), they still struggle with accurately interpreting ... | ['Xiao-Yang Liu', 'Hongyang Yang', 'Boyu Zhang'] | 2023-06-22 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-2.44687125e-01 -1.49400964e-01 -4.43444729e-01 -5.39278924e-01
-5.55935860e-01 -9.77732301e-01 5.26855111e-01 8.23061526e-01
-4.43325669e-01 5.35049856e-01 4.40089196e-01 -1.02432716e+00
5.18374920e-01 -9.36796188e-01 -6.84391856e-01 -6.35015126e-03
6.47214279e-02 5.76243959e-02 1.00484248e-02 -7.75355220... | [11.105313301086426, 7.212611675262451] |
3eab743c-847f-480c-a722-44984dae07ae | functional-causal-bayesian-optimization | 2306.06409 | null | https://arxiv.org/abs/2306.06409v1 | https://arxiv.org/pdf/2306.06409v1.pdf | Functional Causal Bayesian Optimization | We propose functional causal Bayesian optimization (fCBO), a method for finding interventions that optimize a target variable in a known causal graph. fCBO extends the CBO family of methods to enable functional interventions, which set a variable to be a deterministic function of other variables in the graph. fCBO mode... | ['Silvia Chiappa', 'Alexis Bellot', 'Virginia Aglietti', 'Limor Gultchin'] | 2023-06-10 | null | null | null | null | ['gaussian-processes', 'bayesian-optimization'] | ['methodology', 'methodology'] | [ 3.98112297e-01 4.18342859e-01 -4.67325449e-01 -4.72087339e-02
-5.89529037e-01 -4.56333846e-01 7.26825476e-01 2.64040321e-01
-1.59008563e-01 9.80967820e-01 4.48496193e-01 -3.24353606e-01
-9.30032253e-01 -1.03709090e+00 -1.01493454e+00 -9.17153597e-01
-6.75887108e-01 4.28797007e-01 -1.94555670e-01 3.10635418... | [7.727949142456055, 5.295323848724365] |
7094d3a4-7308-4906-a63d-8d987f93583d | aisfg-abundant-information-slot-filling | null | null | https://aclanthology.org/2022.naacl-main.308 | https://aclanthology.org/2022.naacl-main.308.pdf | AISFG: Abundant Information Slot Filling Generator | As an essential component of task-oriented dialogue systems, slot filling requires enormous labeled training data in a certain domain. However, in most cases, there is little or no target domain training data is available in the training stage. Thus, cross-domain slot filling has to cope with the data scarcity problem ... | ['LiWen Wang', 'Zhongbao Zhang', 'Junda Ye', 'Yang Yan'] | null | null | null | null | naacl-2022-7 | ['slot-filling', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.33362496e-01 5.63460112e-01 -5.55605292e-01 -3.84051144e-01
-7.63455451e-01 -1.12286560e-01 6.71719253e-01 1.47263795e-01
-4.76943970e-01 1.29499352e+00 1.51888683e-01 -2.21782655e-01
1.01767533e-01 -9.54087913e-01 1.59338545e-02 -2.50230849e-01
4.15510923e-01 1.16929555e+00 7.30780184e-01 -8.00354004... | [12.610681533813477, 7.427403926849365] |
f81e5d4d-43e7-4016-a175-7c633a6d8b28 | grounded-situation-recognition-with | 2111.10135 | null | https://arxiv.org/abs/2111.10135v1 | https://arxiv.org/pdf/2111.10135v1.pdf | Grounded Situation Recognition with Transformers | Grounded Situation Recognition (GSR) is the task that not only classifies a salient action (verb), but also predicts entities (nouns) associated with semantic roles and their locations in the given image. Inspired by the remarkable success of Transformers in vision tasks, we propose a GSR model based on a Transformer e... | ['Suha Kwak', 'Hyeonjun Lee', 'Youngseok Yoon', 'Junhyeong Cho'] | 2021-11-19 | null | null | null | null | ['grounded-situation-recognition', 'situation-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.39792049e-01 1.53181478e-01 -7.96421841e-02 -4.84908253e-01
-6.63324893e-01 -3.11935753e-01 6.85928106e-01 8.89525786e-02
-4.62019503e-01 3.86350125e-01 7.26215065e-01 -1.28789812e-01
1.71573967e-01 -8.56170297e-01 -8.01975131e-01 -4.17721599e-01
1.49508953e-01 4.08675969e-01 4.29697305e-01 -4.07292873... | [10.375890731811523, 1.4444804191589355] |
378617a0-c1e9-4422-80c2-0bf8c94efd7d | stc-speaker-recognition-systems-for-the | 1904.06093 | null | http://arxiv.org/abs/1904.06093v1 | http://arxiv.org/pdf/1904.06093v1.pdf | STC Speaker Recognition Systems for the VOiCES From a Distance Challenge | This paper presents the Speech Technology Center (STC) speaker recognition
(SR) systems submitted to the VOiCES From a Distance challenge 2019. The
challenge's SR task is focused on the problem of speaker recognition in single
channel distant/far-field audio under noisy conditions. In this work we
investigate different... | ['Galina Lavrentyeva', 'Vladimir Volokhov', 'Timur Pekhovsky', 'Sergey Novoselov', 'Artem Ivanov', 'Andrey Shulipa', 'Alexandr Kozlov', 'Aleksei Gusev'] | 2019-04-12 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 2.01723203e-01 -1.74467206e-01 7.70757079e-01 -5.61416507e-01
-1.10630226e+00 -3.06908935e-01 6.52270079e-01 -2.27367714e-01
-6.55476809e-01 3.33269745e-01 7.40205228e-01 -1.16010882e-01
2.47110482e-02 -1.93254501e-02 -3.57524842e-01 -8.94763708e-01
-2.56333470e-01 -2.17401296e-01 -1.86319813e-01 -5.06758869... | [14.49189281463623, 6.0145111083984375] |
6a807c07-d32f-4e39-9a5c-54de1644f789 | tensorkrowch-smooth-integration-of-tensor | 2306.08595 | null | https://arxiv.org/abs/2306.08595v1 | https://arxiv.org/pdf/2306.08595v1.pdf | TensorKrowch: Smooth integration of tensor networks in machine learning | Tensor networks are factorizations of high-dimensional tensors into networks of smaller tensors. They have applications in physics and mathematics, and recently have been proposed as promising machine learning architectures. To ease the integration of tensor networks in machine learning pipelines, we introduce TensorKr... | ['Alejandro Pozas-Kerstjens', 'David Pérez-García', 'José Ramón Pareja Monturiol'] | 2023-06-14 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-8.09014678e-01 -2.41787925e-01 -2.08961815e-01 -4.46215034e-01
6.91397488e-02 -7.56361067e-01 3.00947279e-01 6.98731616e-02
-1.77525043e-01 2.55602360e-01 3.47260356e-01 -8.27613533e-01
-1.41877219e-01 -7.84408331e-01 -3.92994702e-01 -6.01553738e-01
-5.68477273e-01 3.49742323e-01 2.78978497e-01 -1.11799173... | [6.3113789558410645, 5.052757740020752] |
56e8db59-1210-4a41-ace8-74cfce296b74 | fooling-state-of-the-art-deepfake-detection | 2305.05282 | null | https://arxiv.org/abs/2305.05282v2 | https://arxiv.org/pdf/2305.05282v2.pdf | Fooling State-of-the-Art Deepfake Detection with High-Quality Deepfakes | Due to the rising threat of deepfakes to security and privacy, it is most important to develop robust and reliable detectors. In this paper, we examine the need for high-quality samples in the training datasets of such detectors. Accordingly, we show that deepfake detectors proven to generalize well on multiple researc... | ['Peter Eisert', 'Anna Hilsmann', 'Arian Beckmann'] | 2023-05-09 | null | null | null | null | ['deepfake-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [-1.35246903e-01 1.75537229e-01 7.85304885e-03 -3.59968901e-01
-5.46974242e-01 -7.23363101e-01 4.84363645e-01 -4.09741342e-01
-2.01377362e-01 5.25006235e-01 -5.95205463e-02 -2.16029912e-01
3.28584909e-01 -5.66927969e-01 -9.93269145e-01 -2.89253622e-01
1.54697552e-01 1.12167679e-01 -5.28673790e-02 -3.75068545... | [12.655986785888672, 1.0501515865325928] |
deb1913c-437e-4f30-9400-825a1d50ae87 | pre-scaling-and-codebook-design-for-joint | 2111.10527 | null | https://arxiv.org/abs/2111.10527v1 | https://arxiv.org/pdf/2111.10527v1.pdf | Pre-scaling and Codebook Design for Joint Radar and Communication Based on Index Modulation | This paper develops an efficient index modulation (IM) approach for the joint radar-communication (JRC) system based on a multi-carrier multiple-input multiple-output (MIMO) radar. The communication information is embedded into the transmitted radar pulses by selecting the corresponding indices of the carrier frequenci... | ['Christos Masouros', 'Aryan Kaushik', 'Shengyang Chen'] | 2021-11-20 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 8.13005090e-01 -1.22901775e-01 -1.30886734e-01 -1.99090376e-01
-7.17267215e-01 -4.64333922e-01 9.15462255e-01 -8.17052349e-02
-4.55686986e-01 6.93764150e-01 2.83056777e-02 -6.80326402e-01
-8.95971537e-01 -6.44163311e-01 -1.55762872e-02 -9.54194069e-01
-7.75966406e-01 8.82150978e-02 -2.45649189e-01 -2.06366226... | [6.3611741065979, 1.2474673986434937] |
e064ce3b-700b-4bfa-927b-3228b8f5d215 | discriminative-deep-feature-visualization-for | 2306.00402 | null | https://arxiv.org/abs/2306.00402v1 | https://arxiv.org/pdf/2306.00402v1.pdf | Discriminative Deep Feature Visualization for Explainable Face Recognition | Despite the huge success of deep convolutional neural networks in face recognition (FR) tasks, current methods lack explainability for their predictions because of their "black-box" nature. In recent years, studies have been carried out to give an interpretation of the decision of a deep FR system. However, the affinit... | ['Touradj Ebrahimi', 'Yuhang Lu', 'Zewei Xu'] | 2023-06-01 | null | null | null | null | ['face-recognition', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.98862243e-01 7.69326925e-01 -8.17617849e-02 -8.39706957e-01
4.25653309e-01 1.42620206e-01 6.20751679e-01 -3.94495040e-01
5.62022388e-01 3.74753892e-01 3.22193027e-01 -1.49202347e-01
-2.61242181e-01 -4.61471677e-01 -7.78086364e-01 -3.87300372e-01
2.05651864e-01 2.21821554e-02 -2.25598827e-01 -1.04548059... | [10.315152168273926, 2.095500946044922] |
3483eea1-f1f4-406d-a929-a71c7a61e911 | detect-what-you-can-detecting-and | 1406.2031 | null | http://arxiv.org/abs/1406.2031v1 | http://arxiv.org/pdf/1406.2031v1.pdf | Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts | Detecting objects becomes difficult when we need to deal with large shape
deformation, occlusion and low resolution. We propose a novel approach to i)
handle large deformations and partial occlusions in animals (as examples of
highly deformable objects), ii) describe them in terms of body parts, and iii)
detect them wh... | ['Sanja Fidler', 'Roozbeh Mottaghi', 'Raquel Urtasun', 'Xianjie Chen', 'Alan Yuille', 'Xiaobai Liu'] | 2014-06-08 | detect-what-you-can-detecting-and-1 | http://openaccess.thecvf.com/content_cvpr_2014/html/Chen_Detect_What_You_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Chen_Detect_What_You_2014_CVPR_paper.pdf | cvpr-2014-6 | ['semantic-part-detection'] | ['computer-vision'] | [ 1.75424606e-01 1.59556657e-01 1.25105172e-01 -2.01363727e-01
-3.79621297e-01 -8.99333179e-01 5.04875660e-01 3.77303585e-02
-3.42775673e-01 2.40830243e-01 -9.69028473e-02 4.36927795e-01
2.90260285e-01 -6.43246770e-01 -8.45669568e-01 -6.38647676e-01
-2.58355021e-01 6.85259879e-01 9.74049985e-01 -1.72265530... | [9.102627754211426, 0.07874105125665665] |
b2a6f3ee-0217-4795-b819-0b51fe8b8f48 | scalable-hybrid-learning-techniques-for | 2212.10733 | null | https://arxiv.org/abs/2212.10733v1 | https://arxiv.org/pdf/2212.10733v1.pdf | Scalable Hybrid Learning Techniques for Scientific Data Compression | Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compression algorithms that limit errors to primary data, scientists require compression techniques that ... | ['Sanjay Ranka', 'Anand Rangarajan', 'Scott Klasky', 'Jieyang Chen', 'Qian Gong', 'Jaemoon Lee', 'Jong Choi', 'Tania Banerjee'] | 2022-12-21 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 1.32676482e-01 -1.28029272e-01 4.04147012e-03 -2.85032809e-01
-1.02308381e+00 -5.97535111e-02 7.00307488e-01 1.16280282e+00
-7.28413165e-01 6.33084834e-01 8.41078684e-02 -4.57277507e-01
4.80355462e-03 -1.05848122e+00 -1.10520279e+00 -5.97533941e-01
-9.47818384e-02 9.29878652e-01 6.76989257e-02 9.73167494... | [8.293669700622559, 3.117011308670044] |
68efa4f4-45be-450c-8fee-6591bb6a5414 | blindharmony-blind-harmonization-for-mr | 2305.10732 | null | https://arxiv.org/abs/2305.10732v1 | https://arxiv.org/pdf/2305.10732v1.pdf | BlindHarmony: "Blind" Harmonization for MR Images via Flow model | In MRI, images of the same contrast (e.g., T1) from the same subject can show noticeable differences when acquired using different hardware, sequences, or scan parameters. These differences in images create a domain gap that needs to be bridged by a step called image harmonization, in order to process the images succes... | ['Jongho Lee', 'Dong Un Kang', 'Heejoon Byun', 'Hwihun Jeong'] | 2023-05-18 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 2.27457173e-02 -1.30146578e-01 1.03366874e-01 -2.77076185e-01
-7.84379959e-01 -3.95907044e-01 3.03366661e-01 2.17566952e-01
-5.01922250e-01 5.10968745e-01 1.61245286e-01 1.51681434e-02
-1.26569107e-01 -3.27993333e-01 -6.17983222e-01 -9.10158396e-01
1.70408875e-01 3.33309203e-01 2.50249177e-01 3.58584970... | [13.69223690032959, -2.299781084060669] |
5b42fc6c-6219-4799-96a8-6e812683b7ce | fine-tuning-deep-learning-models-for-stereo | 2205.14051 | null | https://arxiv.org/abs/2205.14051v1 | https://arxiv.org/pdf/2205.14051v1.pdf | Fine-tuning deep learning models for stereo matching using results from semi-global matching | Deep learning (DL) methods are widely investigated for stereo image matching tasks due to their reported high accuracies. However, their transferability/generalization capabilities are limited by the instances seen in the training data. With satellite images covering large-scale areas with variances in locations, conte... | ['Rongjun Qin', 'Hessah Albanwan'] | 2022-05-27 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 3.40691328e-01 -3.25777024e-01 3.03412378e-02 -4.02228385e-01
-7.43913949e-01 -4.30155605e-01 7.02474773e-01 4.51622531e-03
-5.66038787e-01 9.12849367e-01 -1.49563560e-02 -2.51553476e-01
-3.21157038e-01 -1.19237053e+00 -6.87172592e-01 -7.22099960e-01
-1.49437726e-01 5.28820872e-01 3.22970748e-01 -3.35455865... | [8.885817527770996, -2.2731239795684814] |
e5c4771b-0adb-4564-a180-8b758077bd28 | speech-denoising-with-auditory-models | 2011.10706 | null | https://arxiv.org/abs/2011.10706v3 | https://arxiv.org/pdf/2011.10706v3.pdf | Speech Denoising with Auditory Models | Contemporary speech enhancement predominantly relies on audio transforms that are trained to reconstruct a clean speech waveform. The development of high-performing neural network sound recognition systems has raised the possibility of using deep feature representations as 'perceptual' losses with which to train denois... | ['Josh H. McDermott', 'Yang Zhang', 'Kaizhi Qian', 'Jenelle Feather', 'Andrew Francl', 'Mark R. Saddler'] | 2020-11-21 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 3.61980170e-01 5.84117174e-02 7.30074525e-01 -5.06412327e-01
-1.10874069e+00 -4.08047080e-01 5.72739005e-01 -7.09380135e-02
-5.45190036e-01 5.44110477e-01 6.23179615e-01 -4.85684425e-02
-1.10477142e-01 -6.99495435e-01 -7.05019772e-01 -7.19980359e-01
-1.79926053e-01 -1.55783147e-01 9.63188782e-02 -4.54401702... | [15.146655082702637, 5.8433003425598145] |
56032dd0-9a20-4f8e-bb0e-73bff3dd8542 | rethinking-the-design-principles-of-robust | 2105.07926 | null | https://arxiv.org/abs/2105.07926v4 | https://arxiv.org/pdf/2105.07926v4.pdf | Towards Robust Vision Transformer | Recent advances on Vision Transformer (ViT) and its improved variants have shown that self-attention-based networks surpass traditional Convolutional Neural Networks (CNNs) in most vision tasks. However, existing ViTs focus on the standard accuracy and computation cost, lacking the investigation of the intrinsic influe... | ['Hui Xue', 'Yuan He', 'Ranjie Duan', 'Shaokai Ye', 'Xiaodan Li', 'Yuefeng Chen', 'Gege Qi', 'Xiaofeng Mao'] | 2021-05-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Mao_Towards_Robust_Vision_Transformer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mao_Towards_Robust_Vision_Transformer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['robust-design'] | ['miscellaneous'] | [-1.77600518e-01 -3.73398334e-01 1.05592854e-01 -8.42265859e-02
-4.82616425e-01 -5.38788736e-01 6.08300924e-01 -4.89197999e-01
-2.64015228e-01 4.25210625e-01 2.19377279e-01 -2.88459450e-01
-8.95765126e-02 -4.89466816e-01 -9.63599384e-01 -7.32390881e-01
2.10139811e-01 -3.15064877e-01 5.37234783e-01 -5.40133953... | [5.459530353546143, 7.947899341583252] |
84afafb0-7df8-4e66-8971-0da4276b2745 | frustratingly-easy-label-projection-for-cross | 2211.15613 | null | https://arxiv.org/abs/2211.15613v4 | https://arxiv.org/pdf/2211.15613v4.pdf | Frustratingly Easy Label Projection for Cross-lingual Transfer | Translating training data into many languages has emerged as a practical solution for improving cross-lingual transfer. For tasks that involve span-level annotations, such as information extraction or question answering, an additional label projection step is required to map annotated spans onto the translated texts. R... | ['Wei Xu', 'Alan Ritter', 'Chao Jiang', 'Yang Chen'] | 2022-11-28 | null | null | null | null | ['word-alignment', 'event-extraction', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.49084741e-01 4.39743511e-03 -4.11809027e-01 -4.77804482e-01
-1.66696584e+00 -8.75656188e-01 4.29758698e-01 2.79836088e-01
-8.24254990e-01 9.02514875e-01 3.85838240e-01 -5.76815784e-01
3.91669691e-01 -2.92911798e-01 -7.50478148e-01 -1.50630236e-01
5.45226872e-01 5.87353170e-01 2.84220278e-01 -1.04115807... | [11.213830947875977, 10.044257164001465] |
e48a067b-e1dc-4ab2-959d-eae76b64a938 | grammatical-error-correction-a-survey-of-the | 2211.05166 | null | https://arxiv.org/abs/2211.05166v4 | https://arxiv.org/pdf/2211.05166v4.pdf | Grammatical Error Correction: A Survey of the State of the Art | Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject-verb agreement, but also orthographic and semantic errors, such as misspellings and word choice er... | ['Ted Briscoe', 'Hwee Tou Ng', 'Hannan Cao', 'Muhammad Reza Qorib', 'Zheng Yuan', 'Christopher Bryant'] | 2022-11-09 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 4.17093843e-01 2.71601379e-01 1.98106632e-01 -6.65816307e-01
-1.08393359e+00 -4.53192264e-01 4.61690813e-01 6.61228657e-01
-7.51683235e-01 1.22647023e+00 3.35768402e-01 -3.46919358e-01
1.08681638e-02 -3.55661809e-01 -6.19131982e-01 -1.56599939e-01
1.03859834e-01 7.76696384e-01 -1.64050624e-01 -6.49767578... | [11.059906959533691, 10.641997337341309] |
6dc46fa7-2579-446c-90b7-aafaab7186b7 | domain-aware-no-reference-image-quality | 1911.00673 | null | https://arxiv.org/abs/1911.00673v3 | https://arxiv.org/pdf/1911.00673v3.pdf | Domain Fingerprints for No-reference Image Quality Assessment | Human fingerprints are detailed and nearly unique markers of human identity. Such a unique and stable fingerprint is also left on each acquired image. It can reveal how an image was degraded during the image acquisition procedure and thus is closely related to the quality of an image. In this work, we propose a new no-... | ['Jing-Hao Xue', 'Yujiu Yang', 'Weihao Xia', 'Jing Xiao'] | 2019-11-02 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 3.40829074e-01 -5.68616092e-01 -1.41059849e-02 -2.99835950e-01
-6.59503758e-01 -6.73836410e-01 4.62556332e-01 -2.22283915e-01
-1.61984004e-02 3.69213432e-01 9.85936075e-02 1.79630414e-01
-4.28689420e-01 -7.82930195e-01 -5.40410399e-01 -7.78227389e-01
1.82224885e-01 9.90769342e-02 6.49382770e-02 -8.58663488... | [11.790627479553223, -1.893448829650879] |
b6484b25-bba9-4bac-bc17-684bffc16986 | inspecting-the-geographical | 2305.11080 | null | https://arxiv.org/abs/2305.11080v1 | https://arxiv.org/pdf/2305.11080v1.pdf | Inspecting the Geographical Representativeness of Images from Text-to-Image Models | Recent progress in generative models has resulted in models that produce both realistic as well as relevant images for most textual inputs. These models are being used to generate millions of images everyday, and hold the potential to drastically impact areas such as generative art, digital marketing and data augmentat... | ['Danish Pruthi', 'R. Venkatesh Babu', 'Abhipsa Basu'] | 2023-05-18 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 7.61865266e-03 3.04937541e-01 -1.13095399e-02 -2.50784252e-02
-6.93493605e-01 -9.77886140e-01 1.17910838e+00 1.44814536e-01
-4.05355334e-01 6.73733711e-01 8.39555740e-01 -2.15930194e-01
2.60511160e-01 -1.13710105e+00 -8.31672728e-01 -2.53564239e-01
4.20833975e-01 4.22339439e-01 -1.26837477e-01 -1.27354279... | [11.404382705688477, 0.6188852190971375] |
2271fdcc-e83e-4760-9a32-0bedce8c2c5d | latent-nerf-for-shape-guided-generation-of-3d | 2211.07600 | null | https://arxiv.org/abs/2211.07600v1 | https://arxiv.org/pdf/2211.07600v1.pdf | Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures | Text-guided image generation has progressed rapidly in recent years, inspiring major breakthroughs in text-guided shape generation. Recently, it has been shown that using score distillation, one can successfully text-guide a NeRF model to generate a 3D object. We adapt the score distillation to the publicly available, ... | ['Daniel Cohen-Or', 'Raja Giryes', 'Or Patashnik', 'Elad Richardson', 'Gal Metzer'] | 2022-11-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Metzer_Latent-NeRF_for_Shape-Guided_Generation_of_3D_Shapes_and_Textures_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Metzer_Latent-NeRF_for_Shape-Guided_Generation_of_3D_Shapes_and_Textures_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-to-3d'] | ['computer-vision'] | [ 3.48564148e-01 2.98854500e-01 2.02497005e-01 -1.86629638e-01
-7.66983390e-01 -7.62793243e-01 9.02485967e-01 -1.82700172e-01
5.97513504e-02 3.31807852e-01 4.04085755e-01 -2.41515204e-01
4.31872532e-03 -1.26280355e+00 -7.19430447e-01 -6.39345288e-01
1.22563511e-01 6.06579483e-01 5.11533096e-02 -1.87717512... | [9.139368057250977, -3.4630701541900635] |
b04e0d6b-bdd4-465e-ace4-c2e3303d8e1b | revisiting-estimation-bias-in-policy | 2301.08442 | null | https://arxiv.org/abs/2301.08442v2 | https://arxiv.org/pdf/2301.08442v2.pdf | Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning | We revisit the estimation bias in policy gradients for the discounted episodic Markov decision process (MDP) from Deep Reinforcement Learning (DRL) perspective. The objective is formulated theoretically as the expected returns discounted over the time horizon. One of the major policy gradient biases is the state distri... | ['Mingfei Sun', 'Jianping He', 'Wei Yang', 'Qiang Fu', 'Xiaoming Duan', 'Deheng Ye', 'Haoxuan Pan'] | 2023-01-20 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-1.82361752e-01 1.96174875e-01 -6.66512489e-01 -1.07964359e-01
-5.11469781e-01 -5.14998794e-01 5.28408170e-01 6.89333230e-02
-8.26542437e-01 1.10723221e+00 1.31046981e-01 -6.72374189e-01
-2.23820761e-01 -6.20662570e-01 -8.93123209e-01 -8.78797472e-01
-1.37588874e-01 3.18801969e-01 5.69119751e-02 -1.67687356... | [4.193652629852295, 2.414416551589966] |
d659e1d9-a140-4573-a12f-917106138e7e | monocular-2d-camera-based-proximity | 2305.17931 | null | https://arxiv.org/abs/2305.17931v1 | https://arxiv.org/pdf/2305.17931v1.pdf | Monocular 2D Camera-based Proximity Monitoring for Human-Machine Collision Warning on Construction Sites | Accident of struck-by machines is one of the leading causes of casualties on construction sites. Monitoring workers' proximities to avoid human-machine collisions has aroused great concern in construction safety management. Existing methods are either too laborious and costly to apply extensively, or lacking spatial pe... | ['Xiaowei Luo', 'Yuexiong Ding'] | 2023-05-29 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 1.02127045e-01 -7.88668916e-02 2.43572041e-01 -1.09780997e-01
-5.89785695e-01 -2.40791708e-01 3.51029128e-01 3.04379404e-01
-6.37182832e-01 -9.86356754e-03 -1.44582227e-01 -4.77946967e-01
-2.99455166e-01 -8.53819788e-01 -4.15445536e-01 -6.73545301e-01
1.25173226e-01 2.31289685e-01 7.02063322e-01 -3.48379493... | [7.726075649261475, -1.1352204084396362] |
6c4b9606-e161-4580-9837-2d4151485360 | the-best-of-both-worlds-combining-model-based | 2205.00508 | null | https://arxiv.org/abs/2205.00508v1 | https://arxiv.org/pdf/2205.00508v1.pdf | The Best of Both Worlds: Combining Model-based and Nonparametric Approaches for 3D Human Body Estimation | Nonparametric based methods have recently shown promising results in reconstructing human bodies from monocular images while model-based methods can help correct these estimates and improve prediction. However, estimating model parameters from global image features may lead to noticeable misalignment between the estima... | ['Charless Fowlkes', 'Jimei Yang', 'Zhe Wang'] | 2022-05-01 | null | null | null | null | ['3d-absolute-human-pose-estimation'] | ['computer-vision'] | [ 1.01942718e-01 2.68394768e-01 -3.08622658e-01 -1.69890493e-01
-6.11335814e-01 -2.56879181e-01 5.45906186e-01 -3.18159997e-01
1.81976363e-01 7.76979685e-01 3.86083633e-01 6.08686566e-01
1.61707804e-01 -6.69057965e-01 -1.07614732e+00 -2.52048999e-01
3.29633534e-01 1.10594332e+00 4.29737955e-01 1.27270281... | [7.060630798339844, -1.1734353303909302] |
8f458e80-d86f-4507-bfc5-34542390a05f | acenet-anatomical-context-encoding-network | 2002.05773 | null | https://arxiv.org/abs/2002.05773v3 | https://arxiv.org/pdf/2002.05773v3.pdf | ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation | Segmentation of brain structures from magnetic resonance (MR) scans plays an important role in the quantification of brain morphology. Since 3D deep learning models suffer from high computational cost, 2D deep learning methods are favored for their computational efficiency. However, existing 2D deep learning methods ar... | ['Yuemeng Li', 'Yong Fan', 'Hongming Li'] | 2020-02-13 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 6.07286356e-02 1.03223033e-01 2.18853027e-01 -7.18449593e-01
-5.48678398e-01 -4.73958850e-02 1.19784623e-01 2.89267808e-01
-6.79685831e-01 4.02686208e-01 3.67674255e-03 -3.43697399e-01
-1.01830345e-02 -7.80812383e-01 -3.65804523e-01 -5.75325072e-01
-4.19051856e-01 5.39672673e-01 4.81826276e-01 2.74474639... | [14.365531921386719, -2.393667221069336] |
ff3b0f68-a404-46e9-a65d-5ec57835c5a0 | yolov7-trainable-bag-of-freebies-sets-new | 2207.02696 | null | https://arxiv.org/abs/2207.02696v1 | https://arxiv.org/pdf/2207.02696v1.pdf | YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors | YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN... | ['Hong-Yuan Mark Liao', 'Alexey Bochkovskiy', 'Chien-Yao Wang'] | 2022-07-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_YOLOv7_Trainable_Bag-of-Freebies_Sets_New_State-of-the-Art_for_Real-Time_Object_Detectors_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_YOLOv7_Trainable_Bag-of-Freebies_Sets_New_State-of-the-Art_for_Real-Time_Object_Detectors_CVPR_2023_paper.pdf | cvpr-2023-1 | ['real-time-object-detection', 'video-object-tracking'] | ['computer-vision', 'computer-vision'] | [-4.90444750e-01 -3.67801309e-01 8.23871866e-02 3.78290296e-01
-5.40796816e-01 -4.57771480e-01 8.04719329e-02 -1.53167218e-01
-6.75660431e-01 2.81821221e-01 -6.82527483e-01 -1.15289599e-01
7.27831364e-01 -8.57992828e-01 -8.21706116e-01 -4.70327973e-01
1.53151199e-01 8.40959400e-02 1.17818677e+00 -1.15036160... | [8.738911628723145, -0.24069388210773468] |
10aec96f-0d16-4de0-8295-79d096fae93c | effect-of-choice-of-probability-distribution | 1910.12383 | null | https://arxiv.org/abs/1910.12383v1 | https://arxiv.org/pdf/1910.12383v1.pdf | Effect of choice of probability distribution, randomness, and search methods for alignment modeling in sequence-to-sequence text-to-speech synthesis using hard alignment | Sequence-to-sequence text-to-speech (TTS) is dominated by soft-attention-based methods. Recently, hard-attention-based methods have been proposed to prevent fatal alignment errors, but their sampling method of discrete alignment is poorly investigated. This research investigates various combinations of sampling methods... | ['Yusuke Yasuda', 'Junichi Yamagishi', 'Xin Wang'] | 2019-10-28 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 4.00921673e-01 -1.87907636e-01 -2.76183069e-01 -3.86823148e-01
-1.17341137e+00 -2.63101637e-01 3.64428043e-01 -3.68808538e-01
-4.36403632e-01 9.51026917e-01 3.39868546e-01 -6.38484478e-01
1.16901360e-01 -1.90546989e-01 -4.56677496e-01 -8.92397404e-01
4.72584903e-01 1.01285899e+00 2.41079032e-01 -1.97665989... | [14.636889457702637, 6.828321933746338] |
6ee04a20-8b29-43e3-84ed-a265ded4a6b7 | eiseg-an-efficient-interactive-segmentation | 2210.08788 | null | https://arxiv.org/abs/2210.08788v2 | https://arxiv.org/pdf/2210.08788v2.pdf | EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle | In recent years, the rapid development of deep learning has brought great advancements to image and video segmentation methods based on neural networks. However, to unleash the full potential of such models, large numbers of high-quality annotated images are necessary for model training. Currently, many widely used ope... | ['Baohua Lai', 'Zeyu Chen', 'Zewu Wu', 'Guowei Chen', 'Shiyu Tang', 'Juncai Peng', 'Lin Han', 'Yizhou Chen', 'Yi Liu', 'Yuying Hao'] | 2022-10-17 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 3.36922795e-01 -6.73617274e-02 2.99871862e-02 -3.90856177e-01
-8.99879575e-01 -5.85447371e-01 -7.39179850e-02 5.12689129e-02
-3.78218979e-01 4.80625302e-01 -5.90437055e-01 -7.13323236e-01
1.97421983e-01 -9.32702243e-01 -3.99116307e-01 -5.39846301e-01
2.02615023e-01 3.44655603e-01 5.04322529e-01 1.43129021... | [9.561454772949219, 0.016164978966116905] |
4347f32d-40b9-4f6e-b446-61c7d803de54 | processing-energy-modeling-for-neural-network | 2306.16755 | null | https://arxiv.org/abs/2306.16755v1 | https://arxiv.org/pdf/2306.16755v1.pdf | Processing Energy Modeling for Neural Network Based Image Compression | Nowadays, the compression performance of neural-networkbased image compression algorithms outperforms state-of-the-art compression approaches such as JPEG or HEIC-based image compression. Unfortunately, most neural-network based compression methods are executed on GPUs and consume a high amount of energy during executi... | ['André Kaup', 'Felix Rievel', 'Andy Regensky', 'Fabian Brand', 'Christian Herglotz'] | 2023-06-29 | null | null | null | null | ['image-compression'] | ['computer-vision'] | [ 4.41988975e-01 -1.50050253e-01 -4.02010471e-01 -2.41974235e-01
5.58024049e-02 2.12282002e-01 2.87874579e-01 3.15735847e-01
-9.58692729e-01 3.61231059e-01 -1.20825738e-01 -4.87198740e-01
6.14169799e-02 -1.12993741e+00 -9.41119313e-01 -6.32111073e-01
-6.09700717e-02 1.97034836e-01 1.65069312e-01 -7.35020638... | [8.474725723266602, 2.9804797172546387] |
a75e15ad-711d-436c-9d10-99775900ef32 | radio-slam-for-6g-systems-at-thz-frequencies | 2212.12388 | null | https://arxiv.org/abs/2212.12388v1 | https://arxiv.org/pdf/2212.12388v1.pdf | Radio SLAM for 6G Systems at THz Frequencies: Design and Experimental Validation | Next-generation wireless networks will see the convergence of communication and sensing, also exploiting the availability of large bandwidths in the Terahertz (THz) spectrum and electrically large antenna arrays on handheld devices. In particular, it is envisaged that user devices will be able to automatically scan the... | ['Davide Dardari', "Raffaele D'Errico", 'Francesco Guidi', 'Anna Guerra', 'Gianni Pasolini', 'Marina Lotti'] | 2022-12-23 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [ 6.62795722e-01 -1.28002539e-02 3.42790931e-01 -6.30873799e-01
-4.56244409e-01 -5.11961162e-01 4.72784281e-01 -2.00589955e-01
-4.44277763e-01 8.69300902e-01 -3.26123297e-01 -3.67578268e-01
-4.90596801e-01 -1.26281238e+00 -4.07003045e-01 -8.64686251e-01
-4.42720920e-01 1.05475473e+00 1.53593659e-01 -1.44288223... | [6.289161205291748, 1.0856685638427734] |
6c99f3bc-7736-453e-8e18-464000942f2c | pre-trained-language-models-for-keyphrase | 2212.10233 | null | https://arxiv.org/abs/2212.10233v1 | https://arxiv.org/pdf/2212.10233v1.pdf | Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study | Neural models that do not rely on pre-training have excelled in the keyphrase generation task with large annotated datasets. Meanwhile, new approaches have incorporated pre-trained language models (PLMs) for their data efficiency. However, there lacks a systematic study of how the two types of approaches compare and ho... | ['Kai-Wei Chang', 'Wasi Uddin Ahmad', 'Di wu'] | 2022-12-20 | null | null | null | null | ['keyphrase-generation', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.72223085e-01 6.71735592e-03 -5.43629229e-01 1.15824617e-01
-1.02474272e+00 -6.75485134e-01 1.04420626e+00 2.75730699e-01
-8.17374825e-01 9.27577853e-01 5.98208010e-01 -5.55252850e-01
3.50150727e-02 -8.78259897e-01 -8.04627657e-01 -2.75184184e-01
7.93372467e-02 3.99954826e-01 2.48268723e-01 -3.41058701... | [12.205060958862305, 8.945391654968262] |
986cc64c-b9bc-4d08-95ca-0f691b661f88 | an-equivalent-circuit-approach-to-distributed | 2305.14607 | null | https://arxiv.org/abs/2305.14607v1 | https://arxiv.org/pdf/2305.14607v1.pdf | An Equivalent Circuit Approach to Distributed Optimization | Distributed optimization is an essential paradigm to solve large-scale optimization problems in modern applications where big-data and high-dimensionality creates a computational bottleneck. Distributed optimization algorithms that exhibit fast convergence allow us to fully utilize computing resources and effectively s... | ['Larry Pileggi', 'Aayushya Agarwal'] | 2023-05-24 | null | null | null | null | ['distributed-optimization', 'numerical-integration'] | ['methodology', 'miscellaneous'] | [-6.02351189e-01 -4.47057158e-01 -2.73183495e-01 -1.62704661e-01
-8.32179368e-01 -5.84271729e-01 1.56680904e-02 9.94147956e-02
-2.94998169e-01 1.08533382e+00 6.33784905e-02 -3.56485903e-01
-6.62093699e-01 -7.21000373e-01 -6.88263953e-01 -9.68026161e-01
-2.89946079e-01 4.95699137e-01 -5.02261162e-01 -2.32696041... | [6.256715297698975, 4.975368022918701] |
8d3cd312-7175-4f35-8f65-c9775fadaeff | emphcmsalgan-rgb-d-salient-object-detection | 1912.10280 | null | https://arxiv.org/abs/1912.10280v2 | https://arxiv.org/pdf/1912.10280v2.pdf | \emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks | Image salient object detection (SOD) is an active research topic in computer vision and multimedia area. Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem. The main challenge for RGB-D salie... | ['Xiao Wang', 'Zitai Zhou', 'Jin Tang', 'Bin Luo', 'Bo Jiang'] | 2019-12-21 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 5.36273777e-01 5.07871024e-02 3.06109469e-02 2.72972567e-04
-8.55477512e-01 -8.55935588e-02 2.57511109e-01 -7.12157115e-02
-2.77574599e-01 4.50228006e-01 1.79718897e-01 -2.94036535e-03
1.67454511e-01 -6.16782904e-01 -8.17548335e-01 -8.38212907e-01
3.24552119e-01 -4.99892741e-01 8.03643882e-01 -5.63653409... | [9.709718704223633, -0.751375138759613] |
8c712e67-cf9b-4193-88d2-fea3739921cb | delta-training-simple-semi-supervised-text | 1901.07651 | null | https://arxiv.org/abs/1901.07651v3 | https://arxiv.org/pdf/1901.07651v3.pdf | Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings | We propose a novel and simple method for semi-supervised text classification. The method stems from the hypothesis that a classifier with pretrained word embeddings always outperforms the same classifier with randomly initialized word embeddings, as empirically observed in NLP tasks. Our method first builds two sets of... | ['Ceyda Cinarel', 'Hwiyeol Jo'] | 2019-01-22 | delta-training-simple-semi-supervised-text-1 | https://aclanthology.org/D19-1347 | https://aclanthology.org/D19-1347.pdf | ijcnlp-2019-11 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 1.77418321e-01 1.41018599e-01 -4.43121433e-01 -5.52008390e-01
-3.49548161e-01 -5.99948823e-01 8.62832129e-01 6.15622759e-01
-9.07164156e-01 5.09431243e-01 3.39310795e-01 -5.16716421e-01
1.62829265e-01 -7.76136458e-01 -1.16294339e-01 -6.77349567e-01
2.01153174e-01 6.99531972e-01 3.05212826e-01 -1.20511189... | [10.548768997192383, 7.865195274353027] |
a55adbd3-d6f9-479b-873d-a4b7d1817c53 | person-search-in-videos-with-one-portrait | 1807.10510 | null | http://arxiv.org/abs/1807.10510v1 | http://arxiv.org/pdf/1807.10510v1.pdf | Person Search in Videos with One Portrait Through Visual and Temporal Links | In real-world applications, e.g. law enforcement and video retrieval, one
often needs to search a certain person in long videos with just one portrait.
This is much more challenging than the conventional settings for person
re-identification, as the search may need to be carried out in the environments
different from w... | ['Wentao Liu', 'Qingqiu Huang', 'Dahua Lin'] | 2018-07-27 | person-search-in-videos-with-one-portrait-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Qingqiu_Huang_Person_Search_in_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Qingqiu_Huang_Person_Search_in_ECCV_2018_paper.pdf | eccv-2018-9 | ['person-search'] | ['computer-vision'] | [ 1.87976778e-01 -5.73646188e-01 -2.43482143e-02 -1.95162266e-01
-4.90625352e-01 -7.92198122e-01 7.43618548e-01 1.78949401e-01
-5.86643040e-01 6.47914708e-01 1.96640790e-01 2.84178734e-01
-1.96436703e-01 -5.82286119e-01 -4.80541229e-01 -7.12884247e-01
5.12218587e-02 5.95368326e-01 3.24159771e-01 -7.15016052... | [14.768012046813965, 1.0342285633087158] |
569f42b8-dff7-4beb-bb60-4a648584749c | baam-monocular-3d-pose-and-shape | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lee_BAAM_Monocular_3D_Pose_and_Shape_Reconstruction_With_Bi-Contextual_Attention_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lee_BAAM_Monocular_3D_Pose_and_Shape_Reconstruction_With_Bi-Contextual_Attention_CVPR_2023_paper.pdf | BAAM: Monocular 3D Pose and Shape Reconstruction With Bi-Contextual Attention Module and Attention-Guided Modeling | 3D traffic scene comprises various 3D information about car objects, including their pose and shape. However, most recent studies pay relatively less attention to reconstructing detailed shapes. Furthermore, most of them treat each 3D object as an independent one, resulting in losses of relative context inter-objec... | ['Yeong Jun Koh', 'Seong-Gyun Jeong', 'Su-Min Choi', 'HanUl Kim', 'Hyo-Jun Lee'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-car-instance-understanding'] | ['computer-vision'] | [-5.90676628e-02 -6.43501803e-02 1.21285655e-01 -4.33785200e-01
-7.22987354e-01 -3.26798856e-01 7.32741058e-01 -2.93174863e-01
-7.57654458e-02 -8.61650240e-03 7.88426921e-02 -2.28651732e-01
1.38454944e-01 -6.22218549e-01 -9.35194790e-01 -5.15608132e-01
6.65418208e-01 7.51549006e-01 8.23776186e-01 -9.43515673... | [7.770467758178711, -2.558281421661377] |
969aa3e3-ca62-4770-9eeb-7916813020f8 | learning-video-independent-eye-contact | 2210.02033 | null | https://arxiv.org/abs/2210.02033v1 | https://arxiv.org/pdf/2210.02033v1.pdf | Learning Video-independent Eye Contact Segmentation from In-the-Wild Videos | Human eye contact is a form of non-verbal communication and can have a great influence on social behavior. Since the location and size of the eye contact targets vary across different videos, learning a generic video-independent eye contact detector is still a challenging task. In this work, we address the task of one-... | ['Yusuke Sugano', 'Tianyi Wu'] | 2022-10-05 | null | null | null | null | ['contact-detection'] | ['robots'] | [ 1.79335430e-01 -2.84468591e-01 -2.37573698e-01 -4.07773852e-01
-5.58621526e-01 -6.01373196e-01 4.08465236e-01 -4.26454484e-01
-5.79806268e-01 3.54964525e-01 1.07860630e-02 -4.89815101e-02
3.45528305e-01 9.43886414e-02 -7.46264160e-01 -6.91410840e-01
1.91248238e-01 1.01700082e-01 5.02166629e-01 1.83072031... | [14.064658164978027, 0.10031753778457642] |
7eadd19d-b717-482a-977a-f3e884d5b52c | unter-a-unified-knowledge-interface-for | 2305.01624 | null | https://arxiv.org/abs/2305.01624v2 | https://arxiv.org/pdf/2305.01624v2.pdf | UNTER: A Unified Knowledge Interface for Enhancing Pre-trained Language Models | Recent research demonstrates that external knowledge injection can advance pre-trained language models (PLMs) in a variety of downstream NLP tasks. However, existing knowledge injection methods are either applicable to structured knowledge or unstructured knowledge, lacking a unified usage. In this paper, we propose a ... | ['Maosong Sun', 'Zhengyan Zhang', 'Yankai Lin', 'Deming Ye'] | 2023-05-02 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [-1.60570875e-01 3.63715798e-01 -8.27431798e-01 -1.97541654e-01
-6.83954000e-01 -1.04912758e+00 3.66566956e-01 -6.07203022e-02
-5.22545576e-01 1.02486932e+00 4.09659892e-01 -4.84341294e-01
1.16636261e-01 -8.30255747e-01 -9.34125364e-01 -1.49256721e-01
2.47921079e-01 4.64534104e-01 2.13654727e-01 -2.80594565... | [10.407671928405762, 8.2855863571167] |
d5fb4aea-dec5-4590-98cc-439bb760b04f | deepfake-detection-with-inconsistent-head | 2108.12715 | null | https://arxiv.org/abs/2108.12715v1 | https://arxiv.org/pdf/2108.12715v1.pdf | DeepFake Detection with Inconsistent Head Poses: Reproducibility and Analysis | Applications of deep learning to synthetic media generation allow the creation of convincing forgeries, called DeepFakes, with limited technical expertise. DeepFake detection is an increasingly active research area. In this paper, we analyze an existing DeepFake detection technique based on head pose estimation, which ... | ['Robert Bassett', 'Kevin Lutz'] | 2021-08-28 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-1.66650429e-01 4.27931339e-01 -7.03443289e-02 -2.62072146e-01
-5.84556222e-01 -4.42033857e-01 7.52461672e-01 -8.71523440e-01
-1.57074600e-01 5.65541685e-01 3.67957771e-01 -1.18246786e-01
1.48981929e-01 -5.09658813e-01 -7.54968762e-01 -6.41090930e-01
1.22237898e-01 4.20654975e-02 -2.10807279e-01 -3.60443562... | [12.631976127624512, 1.0536731481552124] |
c736b56e-4e5d-4845-9fff-72c37ec9b686 | uwb-at-semeval-2016-task-11-exploring | null | null | https://aclanthology.org/S16-1162 | https://aclanthology.org/S16-1162.pdf | UWB at SemEval-2016 Task 11: Exploring Features for Complex Word Identification | null | ['Michal Konkol'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['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... | [-7.2383294105529785, 3.5662012100219727] |
0ba280e2-f31f-4e5b-961c-9bfd5be1fd87 | self-reinforcement-attention-mechanism-for | 2305.11684 | null | https://arxiv.org/abs/2305.11684v1 | https://arxiv.org/pdf/2305.11684v1.pdf | Self-Reinforcement Attention Mechanism For Tabular Learning | Apart from the high accuracy of machine learning models, what interests many researchers in real-life problems (e.g., fraud detection, credit scoring) is to find hidden patterns in data; particularly when dealing with their challenging imbalanced characteristics. Interpretability is also a key requirement that needs to... | ['Gregoire Jaffre', 'Zaineb Chelly Dagdia', 'Mustapha Lebbah', 'Hanene Azzag', 'Mohamed Djallel Dilmi', 'Kodjo Mawuena Amekoe'] | 2023-05-19 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [ 2.74100512e-01 7.85168186e-02 -1.29765615e-01 -7.79012084e-01
-3.55095267e-01 -2.88826749e-02 2.60247111e-01 6.66514933e-01
-3.68431419e-01 7.06149995e-01 1.96266100e-01 -3.24487627e-01
-1.55314103e-01 -6.98315322e-01 -6.22009397e-01 -5.45328915e-01
2.90042423e-02 4.29385424e-01 -2.41836205e-01 -3.73776555... | [9.299490928649902, 5.244102478027344] |
68692de7-b84f-4453-b9f6-66944987e43b | learning-spatial-attention-for-face-super | 2012.01211 | null | https://arxiv.org/abs/2012.01211v2 | https://arxiv.org/pdf/2012.01211v2.pdf | Learning Spatial Attention for Face Super-Resolution | General image super-resolution techniques have difficulties in recovering detailed face structures when applying to low resolution face images. Recent deep learning based methods tailored for face images have achieved improved performance by jointly trained with additional task such as face parsing and landmark predict... | ['Kwan-Yee K. Wong', 'Zhifeng Li', 'Hao Wang', 'Dihong Gong', 'Chaofeng Chen'] | 2020-12-02 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 1.79733053e-01 7.41562396e-02 9.50302184e-02 -4.17375475e-01
-7.72869468e-01 -1.88432917e-01 2.32886493e-01 -7.25917101e-01
-2.74496786e-02 6.35007143e-01 2.56242841e-01 2.35563502e-01
-7.96037316e-02 -1.03165627e+00 -8.43036294e-01 -5.14822423e-01
-1.00928932e-01 1.42788410e-01 1.44097209e-03 -3.98238182... | [12.800127029418945, -0.07248575985431671] |
dd8e83b5-8f16-4f56-9bfa-87f2c6d5bb9b | a-fuzzy-expert-system-for-earthquake | 1610.04028 | null | http://arxiv.org/abs/1610.04028v2 | http://arxiv.org/pdf/1610.04028v2.pdf | A fuzzy expert system for earthquake prediction, case study: the Zagros range | A methodology for the development of a fuzzy expert system (FES) with
application to earthquake prediction is presented. The idea is to reproduce the
performance of a human expert in earthquake prediction. To do this, at the
first step, rules provided by the human expert are used to generate a fuzzy
rule base. These ru... | ['Farid Atry', 'Mehdi Zare', 'Arash Andalib'] | 2016-10-13 | null | null | null | null | ['earthquake-prediction'] | ['computer-vision'] | [-2.68483534e-02 1.55227274e-01 6.46415532e-01 -2.88086504e-01
2.24456385e-01 -4.33109477e-02 1.69173390e-01 2.73544520e-01
-2.98174083e-01 7.83014834e-01 -2.34382600e-01 -4.95845616e-01
-5.63058197e-01 -1.14388204e+00 -1.82245985e-01 -4.82376963e-01
1.65751114e-01 6.65827453e-01 5.88201344e-01 -9.33960974... | [6.0330810546875, 3.415147066116333] |
30d007a9-be45-4ab6-b628-9321ad0267e5 | an-efficient-cnn-for-spectral-reconstruction | 1804.04647 | null | http://arxiv.org/abs/1804.04647v1 | http://arxiv.org/pdf/1804.04647v1.pdf | An efficient CNN for spectral reconstruction from RGB images | Recently, the example-based single image spectral reconstruction from RGB
images task, aka, spectral super-resolution was approached by means of deep
learning by Galliani et al. The proposed very deep convolutional neural network
(CNN) achieved superior performance on recent large benchmarks. However,
Aeschbacher et al... | ['Radu Timofte', 'Yigit Baran Can'] | 2018-04-12 | null | null | null | null | ['spectral-reconstruction', 'spectral-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 4.91291434e-01 -2.22859815e-01 -8.96161946e-04 -1.33546308e-01
-9.19139564e-01 -2.59581417e-01 5.76382160e-01 -5.52383065e-01
-3.89326125e-01 1.09759367e+00 2.23941773e-01 1.50661409e-01
-2.14171521e-02 -1.03630257e+00 -7.97467470e-01 -7.38215625e-01
1.86160624e-01 8.22687000e-02 1.83202669e-01 -4.07052487... | [10.304314613342285, -1.999470829963684] |
2bef5eed-2d00-4b82-a341-1731a0767e3f | blended-multi-modal-deep-convnet-features-for | 2006.00197 | null | https://arxiv.org/abs/2006.00197v1 | https://arxiv.org/pdf/2006.00197v1.pdf | Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction | Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. The major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR rec... | ['S. N. Shareef', 'M. Bilal', 'J. D. Bodapati', 'O. Jo', 'P. K. R. Maddikunta', 'S. Hakak', 'N. Veeranjaneyulu'] | 2020-05-30 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-1.91323590e-02 -1.46804780e-01 2.11296254e-03 -4.98944312e-01
-6.07520401e-01 -1.83063775e-01 3.55761021e-01 -7.69621432e-02
-5.01578271e-01 9.69262242e-01 4.32535470e-01 -1.70807764e-02
-2.52676368e-01 -7.15885818e-01 -1.61292285e-01 -8.93207729e-01
1.60145223e-01 -2.00982317e-02 4.92930375e-02 1.31932190... | [15.836979866027832, -3.9881322383880615] |
51558238-c960-4eea-a03a-57ffa4c09954 | efficientface-an-efficient-deep-network-with | 2302.11816 | null | https://arxiv.org/abs/2302.11816v1 | https://arxiv.org/pdf/2302.11816v1.pdf | EfficientFace: An Efficient Deep Network with Feature Enhancement for Accurate Face Detection | In recent years, deep convolutional neural networks (CNN) have significantly advanced face detection. In particular, lightweight CNNbased architectures have achieved great success due to their lowcomplexity structure facilitating real-time detection tasks. However, current lightweight CNN-based face detectors trading a... | ['Wankou Yang', 'Jifeng Shen', 'Jianhua Xu', 'Zhijian Wu', 'Jun Li', 'Guangtao Wang'] | 2023-02-23 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [-1.96375132e-01 -1.06267825e-01 5.48730753e-02 -4.55472618e-01
-2.26115689e-01 -1.19089372e-01 4.36402529e-01 -4.24279392e-01
-4.44352776e-01 3.37697715e-01 3.95387933e-02 8.85751843e-02
8.01598430e-02 -7.68250704e-01 -5.55570602e-01 -7.54285097e-01
-2.32210487e-01 -1.27239823e-01 3.23796347e-02 -1.69911057... | [13.333688735961914, 0.7139497399330139] |
325c8cf5-904f-4576-acb7-2fe228d34bf8 | syntactically-informed-unsupervised | null | null | https://aclanthology.org/2021.emnlp-main.203 | https://aclanthology.org/2021.emnlp-main.203.pdf | Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data | Previous works on syntactically controlled paraphrase generation heavily rely on large-scale parallel paraphrase data that is not easily available for many languages and domains. In this paper, we take this research direction to the extreme and investigate whether it is possible to learn syntactically controlled paraph... | ['Yufeng Chen', 'Jinan Xu', 'Changjian Hu', 'Yao Meng', 'Yujie Zhang', 'Deyi Xiong', 'Mingtong Liu', 'Erguang Yang'] | null | null | null | null | emnlp-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.69549930e-01 1.25801802e-01 -1.52505860e-01 -4.45414811e-01
-8.00123334e-01 -7.83176899e-01 6.19058967e-01 -3.38604778e-01
-2.28628919e-01 8.39966834e-01 5.02673090e-01 -5.01111269e-01
3.49340171e-01 -1.08513403e+00 -1.09333527e+00 -4.51246649e-01
8.20477188e-01 3.68606836e-01 -2.61077940e-01 -4.17561084... | [11.67210578918457, 9.313986778259277] |
ab801610-bfb6-4322-9a27-d90544ac6633 | invariances-and-data-augmentation-for | 1711.04845 | null | http://arxiv.org/abs/1711.04845v1 | http://arxiv.org/pdf/1711.04845v1.pdf | Invariances and Data Augmentation for Supervised Music Transcription | This paper explores a variety of models for frame-based music transcription,
with an emphasis on the methods needed to reach state-of-the-art on human
recordings. The translation-invariant network discussed in this paper, which
combines a traditional filterbank with a convolutional neural network, was the
top-performin... | ['Sham M. Kakade', 'Dean Foster', 'Zaid Harchaoui', 'John Thickstun'] | 2017-11-13 | null | null | null | null | ['music-transcription'] | ['music'] | [ 4.43982095e-01 -1.63290814e-01 -2.51703352e-01 -2.44058464e-02
-8.80254328e-01 -7.83101022e-01 3.84878963e-01 -4.62993950e-01
-4.15913016e-01 5.26091337e-01 6.72027111e-01 1.54603779e-01
-3.72588009e-01 -2.87120342e-01 -5.54493785e-01 -4.80430275e-01
-1.11879073e-01 7.15052783e-02 -5.11731863e-01 -2.39743665... | [15.83660888671875, 5.351710319519043] |
0ddf1fad-a7fe-48c6-b89c-6893666a013b | class-guided-image-to-image-diffusion-cell | 2303.08863 | null | https://arxiv.org/abs/2303.08863v2 | https://arxiv.org/pdf/2303.08863v2.pdf | Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels | Image-to-image reconstruction problems with free or inexpensive metadata in the form of class labels appear often in biological and medical image domains. Existing text-guided or style-transfer image-to-image approaches do not translate to datasets where additional information is provided as discrete classes. We introd... | ['Carola-Bibiane Schönlieb', 'Yinhai Wang', 'Elizabeth Mouchet', 'Guy Williams', 'Praveen Anand', 'Jan Oscar Cross-Zamirski'] | 2023-03-15 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 8.58188450e-01 -1.81711745e-02 -2.11044997e-01 -7.22306669e-01
-1.11513388e+00 -7.10850358e-01 8.51620913e-01 1.76267534e-01
-6.00465596e-01 8.99775624e-01 2.86722898e-01 -1.65646330e-01
-2.74792969e-01 -4.57905948e-01 -8.29897285e-01 -9.43952978e-01
3.05708379e-01 7.91764200e-01 2.96299547e-01 4.28420514... | [11.282906532287598, -0.3488941490650177] |
349cc2bf-b579-4924-8134-4ce1e0b2782c | decoupled-multimodal-distilling-for-emotion | 2303.13802 | null | https://arxiv.org/abs/2303.13802v1 | https://arxiv.org/pdf/2303.13802v1.pdf | Decoupled Multimodal Distilling for Emotion Recognition | Human multimodal emotion recognition (MER) aims to perceive human emotions via language, visual and acoustic modalities. Despite the impressive performance of previous MER approaches, the inherent multimodal heterogeneities still haunt and the contribution of different modalities varies significantly. In this work, we ... | ['Zhen Cui', 'Yuanzhi Wang', 'Yong Li'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Decoupled_Multimodal_Distilling_for_Emotion_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Decoupled_Multimodal_Distilling_for_Emotion_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-9.39119309e-02 -1.70392677e-01 -3.25560793e-02 -2.37571850e-01
-4.83652830e-01 -5.70398808e-01 5.53591311e-01 1.05399586e-01
-5.69420815e-01 4.68144923e-01 2.69325286e-01 5.27470894e-02
-1.23155922e-01 -6.17477894e-01 -4.53588426e-01 -9.83379483e-01
9.72055644e-02 2.58740902e-01 -1.87434599e-01 -3.98853779... | [13.085381507873535, 4.99919319152832] |
f2bea63f-b4db-4a88-8c35-352516fb29a9 | conic-colon-nuclei-identification-and | 2111.14485 | null | https://arxiv.org/abs/2111.14485v1 | https://arxiv.org/pdf/2111.14485v1.pdf | CoNIC: Colon Nuclei Identification and Counting Challenge 2022 | Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology (CPath). However, automatic recognition of different nuclei is faced with ... | ['Nasir Rajpoot', 'Fayyaz Minhas', 'Shan E Ahmed Raza', 'David Snead', 'Thomas Leech', 'Giorgos Hadjigeorghiou', 'Quoc Dang Vu', 'Mostafa Jahanifar', 'Simon Graham'] | 2021-11-29 | null | null | null | null | ['explainable-models', 'nuclear-segmentation'] | ['computer-vision', 'medical'] | [ 4.84747767e-01 -4.55097295e-02 4.31038029e-02 -1.55122206e-01
-9.30357575e-01 -9.07474220e-01 5.32338083e-01 8.86349142e-01
-8.03693235e-01 7.04774082e-01 1.97904423e-01 -3.91173840e-01
-4.26134616e-02 -4.97899622e-01 -1.23181619e-01 -1.13054121e+00
5.07658496e-02 9.07329500e-01 3.58015329e-01 -2.00483575... | [15.070718765258789, -3.1342720985412598] |
89803d67-fed6-4862-82db-27bea6792b13 | text-preprocessing-and-its-implications-in-a | null | null | https://aclanthology.org/2021.ranlp-srw.13 | https://aclanthology.org/2021.ranlp-srw.13.pdf | Text Preprocessing and its Implications in a Digital Humanities Project | This paper focuses on data cleaning as part of a preprocessing procedure applied to text data retrieved from the web. Although the importance of this early stage in a project using NLP methods is often highlighted by researchers, the details, general principles and techniques are usually left out due to consideration o... | ['Alistair Plum', 'Maria Kunilovskaya'] | null | null | null | null | ranlp-2021-9 | ['text-annotation'] | ['natural-language-processing'] | [ 2.05627978e-01 8.12471099e-03 1.43720523e-01 -4.75663006e-01
-7.99193084e-01 -9.87555385e-01 6.75437033e-01 9.54559505e-01
-7.71351695e-01 5.33802330e-01 8.22997630e-01 -3.48700613e-01
-2.98826724e-01 -7.04037666e-01 -5.42806506e-01 -5.93339741e-01
3.10299754e-01 3.41556579e-01 -1.18218340e-01 -1.88350558... | [10.017542839050293, 9.840523719787598] |
aa7ab65a-8873-417f-b268-aaebe32d4926 | a-hybrid-deep-learning-framework-for-covid-19 | 2107.03904 | null | https://arxiv.org/abs/2107.03904v2 | https://arxiv.org/pdf/2107.03904v2.pdf | A hybrid deep learning framework for Covid-19 detection via 3D Chest CT Images | In this paper, we present a hybrid deep learning framework named CTNet which combines convolutional neural network and transformer together for the detection of COVID-19 via 3D chest CT images. It consists of a CNN feature extractor module with SE attention to extract sufficient features from CT scans, together with a ... | ['Shuang Liang'] | 2021-07-08 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-1.03637375e-01 -3.72906984e-03 -1.16965033e-01 -2.74342060e-01
-1.10885942e+00 -1.35207534e-01 2.92582452e-01 -8.58241245e-02
-6.04661226e-01 3.70132238e-01 2.26137146e-01 -4.02185410e-01
-1.25710219e-01 -8.59433591e-01 -5.79532921e-01 -6.48522615e-01
-3.08439136e-01 9.31254983e-01 4.03895736e-01 -5.30880876... | [15.263042449951172, -1.9276984930038452] |
4b710391-84ba-4f62-ab11-4a9b79d45466 | punctuation-restoration-in-swedish-through | 2202.06769 | null | https://arxiv.org/abs/2202.06769v1 | https://arxiv.org/pdf/2202.06769v1.pdf | Punctuation restoration in Swedish through fine-tuned KB-BERT | Presented here is a method for automatic punctuation restoration in Swedish using a BERT model. The method is based on KB-BERT, a publicly available, neural network language model pre-trained on a Swedish corpus by National Library of Sweden. This model has then been fine-tuned for this specific task using a corpus of ... | ['John Björkman Nilsson'] | 2022-02-14 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 1.38311654e-01 5.48160851e-01 2.85932600e-01 -4.03091311e-01
-8.81921053e-01 -6.35964334e-01 4.72672433e-01 2.77184993e-01
-8.06539893e-01 1.05074298e+00 2.16556266e-01 -6.47674263e-01
-1.47809401e-01 -5.32135308e-01 -6.25537932e-01 -5.09236693e-01
2.97951221e-01 5.06469548e-01 1.69766501e-01 -4.47764635... | [11.053295135498047, 10.152790069580078] |
9f1388e3-9184-4582-8c5c-3b288f2d687a | diffss-diffusion-model-for-few-shot-semantic | 2307.00773 | null | https://arxiv.org/abs/2307.00773v1 | https://arxiv.org/pdf/2307.00773v1.pdf | DifFSS: Diffusion Model for Few-Shot Semantic Segmentation | Diffusion models have demonstrated excellent performance in image generation. Although various few-shot semantic segmentation (FSS) models with different network structures have been proposed, performance improvement has reached a bottleneck. This paper presents the first work to leverage the diffusion model for FSS ta... | ['Bo Yan', 'Siyuan Chen', 'Weimin Tan'] | 2023-07-03 | null | null | null | null | ['few-shot-image-segmentation', 'image-generation'] | ['computer-vision', 'computer-vision'] | [ 6.27300620e-01 1.40968561e-01 -1.00649081e-01 -4.54026401e-01
-5.08835256e-01 -5.21365583e-01 6.24975741e-01 -3.25634152e-01
-2.28465125e-01 5.08607566e-01 3.62599641e-02 5.93827143e-02
1.01946943e-01 -8.99432182e-01 -6.28624737e-01 -6.46092474e-01
4.21738505e-01 3.44022840e-01 8.67058456e-01 -4.75757629... | [9.688645362854004, 0.6339647173881531] |
6c37feb4-3fdc-4f76-ac88-82da84e48600 | zhijun-wu-chinese-semantic-dependency-parsing | null | null | https://aclanthology.org/S12-1058 | https://aclanthology.org/S12-1058.pdf | Zhijun Wu: Chinese Semantic Dependency Parsing with Third-Order Features | null | ['Xinxin Li', 'Zhijun Wu', 'Xuan Wang'] | 2012-07-01 | null | null | null | semeval-2012-7 | ['transition-based-dependency-parsing', 'semantic-dependency-parsing'] | ['natural-language-processing', '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... | [-7.346733570098877, 3.732409954071045] |
146349d4-f20a-4887-afde-b2076d3b2388 | barriers-for-the-performance-of-graph-neural | 2306.02555 | null | https://arxiv.org/abs/2306.02555v1 | https://arxiv.org/pdf/2306.02555v1.pdf | Barriers for the performance of graph neural networks (GNN) in discrete random structures. A comment on~\cite{schuetz2022combinatorial},\cite{angelini2023modern},\cite{schuetz2023reply} | Recently graph neural network (GNN) based algorithms were proposed to solve a variety of combinatorial optimization problems, including Maximum Cut problem, Maximum Independent Set problem and similar other problems~\cite{schuetz2022combinatorial},\cite{schuetz2022graph}. The publication~\cite{schuetz2022combinatorial}... | ['David Gamarnik'] | 2023-06-05 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 3.74174029e-01 4.18344229e-01 -1.95520520e-01 5.12458235e-02
-4.51137215e-01 -6.85978711e-01 4.87155855e-01 3.22756767e-01
-4.96261597e-01 1.24799752e+00 -3.16375852e-01 -8.27784896e-01
-1.08062589e+00 -1.09103727e+00 -7.20238924e-01 -7.84806311e-01
-6.89227045e-01 6.38641953e-01 2.09793281e-02 -4.94943589... | [6.556680202484131, 5.3163838386535645] |
577d8b18-248d-4a3b-aef8-79d522a20819 | gridtopix-training-embodied-agents-with | 2105.00931 | null | https://arxiv.org/abs/2105.00931v2 | https://arxiv.org/pdf/2105.00931v2.pdf | GridToPix: Training Embodied Agents with Minimal Supervision | While deep reinforcement learning (RL) promises freedom from hand-labeled data, great successes, especially for Embodied AI, require significant work to create supervision via carefully shaped rewards. Indeed, without shaped rewards, i.e., with only terminal rewards, present-day Embodied AI results degrade significantl... | ['Alexander Schwing', 'Luca Weihs', 'Aniruddha Kembhavi', 'Svetlana Lazebnik', 'Iou-Jen Liu', 'Unnat Jain'] | 2021-04-14 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jain_GridToPix_Training_Embodied_Agents_With_Minimal_Supervision_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jain_GridToPix_Training_Embodied_Agents_With_Minimal_Supervision_ICCV_2021_paper.pdf | iccv-2021-1 | ['pointgoal-navigation'] | ['robots'] | [-1.46272779e-01 3.71582329e-01 2.09245086e-02 2.11192518e-01
-7.64388382e-01 -8.04303586e-01 8.22279572e-01 -1.95383668e-01
-7.70612538e-01 1.07818711e+00 7.74257183e-02 -2.86246598e-01
-1.52299613e-01 -7.20699489e-01 -9.19069409e-01 -7.03667223e-01
-6.08063340e-01 6.09512746e-01 1.18883051e-01 -9.06800151... | [4.291147232055664, 0.9834454655647278] |
2eb4b42d-8d40-4090-b50d-549a02ff966b | longitudinal-quantitative-assessment-of-covid | 2103.07240 | null | https://arxiv.org/abs/2103.07240v2 | https://arxiv.org/pdf/2103.07240v2.pdf | Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs | Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation. Image segmentation methods have proven to help quantify the disease burden and even help predict the outcome. The availabi... | ['Thomas Wendler', 'Nassir Navab', 'Rickmer Braren', 'Egon Burian', 'Tobias Czempiel', 'Matthias Keicher', 'Ashkan Khakzar', 'Magdalini Paschali', 'Leili Goli', 'Seong Tae Kim'] | 2021-03-12 | null | null | null | null | ['covid-19-image-segmentation'] | ['computer-vision'] | [ 8.98738205e-02 -6.43937111e-01 -6.08103760e-02 -5.21027185e-02
-3.74888569e-01 -3.09666872e-01 2.02315435e-01 3.72499883e-01
-2.99793750e-01 4.98849779e-01 1.07939102e-01 -4.04171526e-01
-2.19696805e-01 -7.76322126e-01 -9.23163295e-02 -6.74485743e-01
-4.53423023e-01 1.20070672e+00 3.53338331e-01 3.39370340... | [15.446248054504395, -1.8366994857788086] |
8e3a2c77-2930-41ae-9dba-5ed6dfeaa92c | jsi-gan-gan-based-joint-super-resolution-and | 1909.04391 | null | https://arxiv.org/abs/1909.04391v2 | https://arxiv.org/pdf/1909.04391v2.pdf | JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video | Joint learning of super-resolution (SR) and inverse tone-mapping (ITM) has been explored recently, to convert legacy low resolution (LR) standard dynamic range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for the growing need of UHD HDR TV/broadcasting applications. However, previous CNN-based m... | ['Munchurl Kim', 'Soo Ye Kim', 'Jihyong Oh'] | 2019-09-10 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 6.12143159e-01 1.19098425e-01 -7.80732110e-02 -3.57321411e-01
-1.03202617e+00 -8.70826393e-02 4.61776942e-01 -7.53222525e-01
-6.14678711e-02 9.12201703e-01 4.38818127e-01 -1.53312489e-01
1.98252678e-01 -9.89585578e-01 -8.13483596e-01 -7.62307107e-01
2.34384194e-01 -2.46577948e-01 1.90613419e-01 -4.38843429... | [11.057978630065918, -1.9655081033706665] |
4694dac5-94a7-4cb1-ba28-e37f72eff4cc | regular-splitting-graph-network-for-3d-human | 2305.05785 | null | https://arxiv.org/abs/2305.05785v1 | https://arxiv.org/pdf/2305.05785v1.pdf | Regular Splitting Graph Network for 3D Human Pose Estimation | In human pose estimation methods based on graph convolutional architectures, the human skeleton is usually modeled as an undirected graph whose nodes are body joints and edges are connections between neighboring joints. However, most of these methods tend to focus on learning relationships between body joints of the sk... | ['A. Ben Hamza', 'Tanvir Hassan'] | 2023-05-09 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-6.44057691e-02 4.98909622e-01 -1.70499489e-01 -1.24038823e-01
2.24393696e-01 -3.25862348e-01 3.20708424e-01 1.53383791e-01
-4.28243190e-01 2.41987213e-01 5.04722774e-01 4.21779841e-01
-2.95369416e-01 -7.28377819e-01 -6.28711939e-01 -4.28675473e-01
-7.60633230e-01 7.37002075e-01 4.91240650e-01 -5.48559487... | [7.093308925628662, -0.5873224139213562] |
50c754b9-9964-4bae-910e-f731bc68cf79 | contrastive-transformer-based-multiple | 2203.12121 | null | https://arxiv.org/abs/2203.12121v2 | https://arxiv.org/pdf/2203.12121v2.pdf | Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection | Current polyp detection methods from colonoscopy videos use exclusively normal (i.e., healthy) training images, which i) ignore the importance of temporal information in consecutive video frames, and ii) lack knowledge about the polyps. Consequently, they often have high detection errors, especially on challenging poly... | ['Gustavo Carneiro', 'Johan W Verjans', 'Yuanhong Chen', 'Chong Wang', 'Yuyuan Liu', 'Fengbei Liu', 'Guansong Pang', 'Yu Tian'] | 2022-03-23 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 6.09911501e-01 7.12532997e-02 -1.93281978e-01 -5.50949499e-02
-7.36654937e-01 -4.34254557e-01 1.95758775e-01 7.87956536e-01
-3.73353064e-01 1.91621587e-01 -3.88763566e-03 -2.24058568e-01
-1.75402254e-01 -6.29338801e-01 -9.83952165e-01 -7.74370074e-01
-4.51248616e-01 -1.09901861e-03 5.13919175e-01 1.74179614... | [14.013113021850586, -3.193646192550659] |
a05ac5ce-bdf2-4a7e-8aca-e6cbd3e2db0e | bait-barometer-for-information | 2206.07535 | null | https://arxiv.org/abs/2206.07535v2 | https://arxiv.org/pdf/2206.07535v2.pdf | BaIT: Barometer for Information Trustworthiness | This paper presents a new approach to the FNC-1 fake news classification task which involves employing pre-trained encoder models from similar NLP tasks, namely sentence similarity and natural language inference, and two neural network architectures using this approach are proposed. Methods in data augmentation are exp... | ['Callum Rhys Tilbury', 'Jeroen van Mourik', 'Oisín Nolan'] | 2022-06-15 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [ 6.13324940e-01 7.00160325e-01 -6.36490464e-01 -8.32340360e-01
-1.09055269e+00 -1.37372613e-01 7.77187943e-01 6.29202306e-01
-6.61370039e-01 1.14676046e+00 5.94407439e-01 -4.15175021e-01
1.49294376e-01 -8.28288138e-01 -8.66055846e-01 -2.38523677e-01
2.09641472e-01 7.59161472e-01 2.77211983e-02 -8.71906400... | [8.26508617401123, 10.181344985961914] |
9490c708-7266-48c3-ba12-da712143bad8 | ungeneralizable-contextual-logistic-bandit-in | 2212.07632 | null | https://arxiv.org/abs/2212.07632v1 | https://arxiv.org/pdf/2212.07632v1.pdf | Ungeneralizable Contextual Logistic Bandit in Credit Scoring | The application of reinforcement learning in credit scoring has created a unique setting for contextual logistic bandit that does not conform to the usual exploration-exploitation tradeoff but rather favors exploration-free algorithms. Through sufficient randomness in a pool of observable contexts, the reinforcement le... | ['Seksan Kiatsupaibul', 'Kantapong Visantavarakul', 'Pojtanut Manopanjasiri'] | 2022-12-15 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-1.11752778e-01 2.27059752e-01 -8.70205760e-01 -1.33403614e-01
-8.49422932e-01 -7.14105666e-01 4.88076955e-01 1.54305249e-01
-7.14043558e-01 1.11263895e+00 2.89169550e-01 -4.29576159e-01
-4.46134716e-01 -8.97373021e-01 -4.36347902e-01 -8.24205041e-01
-1.17129214e-01 9.38443542e-01 -2.68391687e-02 1.65390387... | [4.402288913726807, 3.0330331325531006] |
7582e4bc-bca2-45bd-9f05-1feef466f783 | musclemap-towards-video-based-activated | 2303.00952 | null | https://arxiv.org/abs/2303.00952v2 | https://arxiv.org/pdf/2303.00952v2.pdf | MuscleMap: Towards Video-based Activated Muscle Group Estimation | In this paper, we tackle the new task of video-based Activated Muscle Group Estimation (AMGE) aiming at identifying active muscle regions during physical activity. To this intent, we provide the MuscleMap136 dataset featuring >15K video clips with 136 different activities and 20 labeled muscle groups. This dataset open... | ['Rainer Stiefelhagen', 'M. Saquib Sarfraz', 'Jiaming Zhang', 'Kailun Yang', 'Alina Roitberg', 'David Schneider', 'Kunyu Peng'] | 2023-03-02 | null | null | null | null | ['video-classification', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 5.08608699e-01 -2.35999569e-01 -7.46030927e-01 1.82710811e-01
-6.99697495e-01 -4.69308317e-01 3.94448280e-01 -3.15048367e-01
-6.17576182e-01 7.59534538e-01 3.23553532e-01 1.31252229e-01
-2.28967458e-01 -4.58337456e-01 -8.41207922e-01 -7.44278133e-01
-4.39110130e-01 2.51409948e-01 3.71351272e-01 -1.44642349... | [8.368563652038574, 0.6097179651260376] |
330cac37-3f9f-4f6d-9a12-357119567ee8 | cross-speaker-style-transfer-with-prosody | 2107.12562 | null | https://arxiv.org/abs/2107.12562v1 | https://arxiv.org/pdf/2107.12562v1.pdf | Cross-speaker Style Transfer with Prosody Bottleneck in Neural Speech Synthesis | Cross-speaker style transfer is crucial to the applications of multi-style and expressive speech synthesis at scale. It does not require the target speakers to be experts in expressing all styles and to collect corresponding recordings for model training. However, the performances of existing style transfer methods are... | ['Lei He', 'Shifeng Pan'] | 2021-07-27 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [-5.74422348e-03 -2.65212327e-01 -7.15330094e-02 -4.18971837e-01
-1.06673837e+00 -7.51068056e-01 4.57076997e-01 -4.17694718e-01
-1.22891851e-01 6.82233453e-01 4.60443288e-01 -5.33206500e-02
4.00920719e-01 -3.27594072e-01 -3.92142534e-01 -8.76159966e-01
3.48940462e-01 2.57495433e-01 -7.94054344e-02 -5.67469418... | [14.939105987548828, 6.554594039916992] |
78cbcd19-c7fd-44ad-bdd4-db2edff8f52c | characterization-and-learning-of-causal-1 | 2301.09028 | null | https://arxiv.org/abs/2301.09028v1 | https://arxiv.org/pdf/2301.09028v1.pdf | Characterization and Learning of Causal Graphs with Small Conditioning Sets | Constraint-based causal discovery algorithms learn part of the causal graph structure by systematically testing conditional independences observed in the data. These algorithms, such as the PC algorithm and its variants, rely on graphical characterizations of the so-called equivalence class of causal graphs proposed by... | ['Murat Kocaoglu'] | 2023-01-22 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 2.48419389e-01 1.42310292e-01 -6.95566237e-01 -2.97883779e-01
-4.32865053e-01 -7.59673238e-01 4.37462986e-01 2.97459632e-01
3.78816389e-02 9.40501392e-01 3.13995272e-01 -8.10525239e-01
-9.16535318e-01 -1.04597795e+00 -8.92656267e-01 -4.72141922e-01
-9.04432118e-01 5.47962129e-01 1.88807547e-01 3.26706529... | [7.8135528564453125, 5.339077949523926] |
2f1a8463-f374-4a1c-9ea4-fa93f348a3ea | statik-structure-and-text-for-inductive | null | null | https://aclanthology.org/2022.findings-naacl.46 | https://aclanthology.org/2022.findings-naacl.46.pdf | StATIK: Structure and Text for Inductive Knowledge Graph Completion | Knowledge graphs (KGs) often represent knowledge bases that are incomplete. Machine learning models can alleviate this by helping automate graph completion. Recently, there has been growing interest in completing knowledge bases that are dynamic, where previously unseen entities may be added to the KG with many missing... | ['Greg Ver Steeg', 'Aram Galstyan', 'Murali Annavaram', 'Mehrnoosh Mirtaheri', 'Keshav Balasubramanian', 'Elan Markowitz'] | null | null | null | null | findings-naacl-2022-7 | ['inductive-knowledge-graph-completion'] | ['knowledge-base'] | [-2.84500457e-02 9.32417870e-01 -6.06726587e-01 -1.15929730e-01
-7.14878500e-01 -7.69166708e-01 5.67910671e-01 6.85867429e-01
-3.47391337e-01 1.02067149e+00 6.84975564e-01 -3.05672348e-01
-1.77752420e-01 -1.15400541e+00 -9.24282551e-01 1.14127301e-01
-2.77295977e-01 8.21124613e-01 8.37687999e-02 -1.98796898... | [9.168331146240234, 8.09712028503418] |
50401ecc-c431-4e64-b0fd-cb38df4577b0 | weakly-supervised-3d-human-pose-learning-via | 2003.07581 | null | https://arxiv.org/abs/2003.07581v1 | https://arxiv.org/pdf/2003.07581v1.pdf | Weakly-Supervised 3D Human Pose Learning via Multi-view Images in the Wild | One major challenge for monocular 3D human pose estimation in-the-wild is the acquisition of training data that contains unconstrained images annotated with accurate 3D poses. In this paper, we address this challenge by proposing a weakly-supervised approach that does not require 3D annotations and learns to estimate 3... | ['Pavlo Molchanov', 'Umar Iqbal', 'Jan Kautz'] | 2020-03-17 | weakly-supervised-3d-human-pose-learning-via-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Iqbal_Weakly-Supervised_3D_Human_Pose_Learning_via_Multi-View_Images_in_the_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Iqbal_Weakly-Supervised_3D_Human_Pose_Learning_via_Multi-View_Images_in_the_CVPR_2020_paper.pdf | cvpr-2020-6 | ['monocular-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.17227012e-01 2.19288692e-01 -1.11503318e-01 -5.39052427e-01
-9.37750041e-01 -6.14623964e-01 2.76798159e-01 -3.80602032e-01
-6.13595307e-01 6.35893047e-01 1.43301412e-01 3.23778689e-01
2.67502755e-01 -1.14732392e-01 -1.02593410e+00 -2.09935814e-01
2.65742630e-01 1.05472243e+00 2.92935580e-01 -9.98916328... | [6.984831809997559, -0.9112699031829834] |
feeec8ce-7e41-46ae-9255-3c0072130bea | model-of-the-weak-reset-process-in-hfox | 2107.06064 | null | https://arxiv.org/abs/2107.06064v2 | https://arxiv.org/pdf/2107.06064v2.pdf | Model of the Weak Reset Process in HfOx Resistive Memory for Deep Learning Frameworks | The implementation of current deep learning training algorithms is power-hungry, owing to data transfer between memory and logic units. Oxide-based RRAMs are outstanding candidates to implement in-memory computing, which is less power-intensive. Their weak RESET regime, is particularly attractive for learning, as it al... | ['Damien Querlioz', 'Jean-Michel Portal', 'Elisa Vianello', 'Etienne Nowak', 'Jacques-Olivier Klein', 'Axel Laborieux', 'Tifenn Hirtzlin', 'Marc Bocquet', 'Atreya Majumdar'] | 2021-07-02 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.62136167e-01 -3.79411936e-01 -1.59302175e-01 -3.63763385e-02
-1.73068643e-02 -1.98587313e-01 3.14215541e-01 3.49957049e-01
-6.61683023e-01 8.46285403e-01 -4.85413074e-01 -3.67280662e-01
-1.06404752e-01 -1.02950966e+00 -8.10154855e-01 -1.15557337e+00
2.10948333e-01 4.10577625e-01 6.26055539e-01 -4.54165608... | [8.232172966003418, 2.5553107261657715] |
116a6529-ae5d-48cd-95a9-50964cae7fd2 | fairvis-visual-analytics-for-discovering | 1904.05419 | null | https://arxiv.org/abs/1904.05419v4 | https://arxiv.org/pdf/1904.05419v4.pdf | FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning | The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people. Despite the benefits algorithmic systems may bring, models can reflect, inject, or exacerbate implicit and explicit societal biases into their outputs, disadvantaging certain demogra... | ['Jamie Morgenstern', 'Minsuk Kahng', 'Ángel Alexander Cabrera', 'Fred Hohman', 'Will Epperson', 'Duen Horng Chau'] | 2019-04-10 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [-9.78620425e-02 4.16165411e-01 -6.12265766e-01 -7.14017749e-01
-4.77371961e-02 -4.26041454e-01 7.85238683e-01 8.03468287e-01
-3.16990823e-01 6.32713079e-01 6.22178614e-01 -6.96173668e-01
-4.07036999e-03 -8.71615112e-01 -1.92747518e-01 -2.51394004e-01
-2.79812783e-01 4.38211799e-01 -3.80287647e-01 -1.82414845... | [8.915249824523926, 5.4866557121276855] |
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