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检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"
8962 条 记 录,以下是341-350 订阅
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Embedded Contours Extraction for High-Speed Scene Dynamics Based on a Neuromorphic Temporal Contrast vision Sensor
Embedded Contours Extraction for High-Speed Scene Dynamics B...
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ieee conference on computer vision and pattern recognition
作者: Belbachir, A. N. Hofstaetter, M. Milosevic, N. Schoen, P. Austrian Res Ctr GmbH ARC Smart Syst Div A-1220 Vienna Austria
The paper presents a compact vision system for efficient contours extraction in high-speed applications. By exploiting the ultra high temporal resolution and the sparse representation of the sensors data in reacting t... 详细信息
来源: 评论
Contrastive Domain Adaptation
Contrastive Domain Adaptation
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Thota, Mamatha Leontidis, Georgios Univ Lincoln Sch Comp Sci Lincoln LN6 7TS England Univ Aberdeen Dept Comp Sci Aberdeen AB24 3UE Scotland
Recently, contrastive self-supervised learning has become a key component for learning visual representations across many computer vision tasks and benchmarks. However, contrastive learning in the context of domain ad... 详细信息
来源: 评论
Kernel ELM and CNN based Facial Age Estimation  29
Kernel ELM and CNN based Facial Age Estimation
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29th ieee conference on computer vision and pattern recognition (CVPR)
作者: Gurpinar, Furkan Kaya, Heysem Dibeklioglu, Hamdi Salah, Albert Ali Bogazici Univ Program Computat Sci & Engn Istanbul Turkey Namik Kemal Univ Dept Comp Engn Tekirdag Turkey Delft Univ Technol Pattern Recognit & Bioinformat Grp Delft Netherlands Bogazici Univ Dept Comp Engn Istanbul Turkey
e propose a two-level system for apparent age estimation from facial images. Our system first classifies samples into overlapping age groups. Within each group, the apparent age is estimated with local regressors, who... 详细信息
来源: 评论
Perceptual Loss for Robust Unsupervised Homography Estimation
Perceptual Loss for Robust Unsupervised Homography Estimatio...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Koguciuk, Daniel Arani, Elahe Zonooz, Bahram NavInfo Europe Adv Res Lab Eindhoven Netherlands
Homography estimation is often an indispensable step in many computer vision tasks. The existing approaches, however, are not robust to illumination and/or larger viewpoint changes. In this paper, we propose bidirecti... 详细信息
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A vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees
A Vision-based System for Traffic Anomaly Detection using De...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Aboah, Armstrong Shoman, Maged Mandal, Vishal Davami, Sayedomidreza Adu-Gyamfi, Yaw Sharma, Anuj Univ Missouri Columbia Dept Civil & Environm Engn Columbia MO 65211 USA Iowa State Univ Dept Civil & Environm Engn Ames IA USA
Any intelligent traffic monitoring system must be able to detect anomalies such as traffic accidents in real time. In this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting ano... 详细信息
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The Role of Shape for Domain Generalization on Sparsely-Textured Images
The Role of Shape for Domain Generalization on Sparsely-Text...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Nazari, Narges Honarvar Kovashka, Adriana Univ Pittsburgh Pittsburgh PA 15260 USA
State-of-the-art object recognition methods do not generalize well to unseen domains. Work in domain generalization has attempted to bridge domains by increasing feature compatibility, but has focused on standard, app... 详细信息
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NTIRE 2022 Challenge on Learning the Super-Resolution Space
NTIRE 2022 Challenge on Learning the Super-Resolution Space
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Lugmayr, Andreas Danelljan, Martin Timofte, Radu Kim, Kang-wook Kim, Younggeun Lee, Jae-young Li, Zechao Pan, Jinshan Shim, Dongseok Song, Ki-Ung Tang, Jinhui Wang, Cong Zhao, Zhihao Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland
This paper reviews the NTIRE 2022 challenge on learning the super-Resolution space. This challenge aims to raise awareness that the super-resolution problem is ill-posed. Since many high-resolution images map to the s... 详细信息
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Deep Image Compression with Latent Optimization and Piece-wise Quantization Approximation
Deep Image Compression with Latent Optimization and Piece-wi...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wu, Yuyang Qi, Zhiyang Zheng, Huiming Tao, Lvfang Gao, Wei Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China
Benefit from its capability of learning high-dimensional compact representation from raw data, the auto-encoders are widely used in various tasks of data compression. In particular, for deep image compression, auto-en... 详细信息
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Experiments on an RGB-D Wearable vision System for Egocentric Activity recognition
Experiments on an RGB-D Wearable Vision System for Egocentri...
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27th ieee conference on computer vision and pattern recognition (CVPR)
作者: Moghimi, Mohammad Azagra, Pablo Montesano, Luis Murillo, Ana C. Belongie, Serge Univ Calif San Diego La Jolla CA 92093 USA Univ Zaragoza DIIS I3A Zaragoza Spain Cornell Tech New York NY USA
This work describes and explores novel steps towards activity recognition from an egocentric point of view. Activity recognition is a broadly studied topic in computer vision, but the unique characteristics of wearabl... 详细信息
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An Effective Temporal Localization Method with Multi-View 3D Action recognition for Untrimmed Naturalistic Driving Videos
An Effective Temporal Localization Method with Multi-View 3D...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Manh Tung Tran Minh Quan Vu Ngoc Duong Hoang Khac-Hoai Nam Bui Viettel Grp Viettel Cyperspace Ctr Hanoi Vietnam
Naturalistic driving studies with computer vision techniques have become an emergent research issue. The objective is to classify the distracted behavior actions by drivers. Specifically, this issue is regarded as tem... 详细信息
来源: 评论