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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"
6678 条 记 录,以下是1511-1520 订阅
排序:
Recurrent attention models for depth-based person identification
Recurrent attention models for depth-based person identifica...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Haque, Albert Alahi, Alexandre Fei-Fei, Li Computer Science Department Stanford University United States
We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark. Our approach leverages unique 4D spatio-temporal s... 详细信息
来源: 评论
Constructing canonical regions for fast and effective view selection
Constructing canonical regions for fast and effective view s...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Wang, Wencheng Gao, Tianhao State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China
In view selection, little work has been done for optimizing the search process; views must be densely distributed and checked individually. Thus, evaluating poor views wastes much time, and a poor view may even be mis... 详细信息
来源: 评论
A hierarchical pose-based approach to complex action understanding using dictionaries of actionlets and motion poselets
A hierarchical pose-based approach to complex action underst...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Lillo, Ivan Niebles, Juan Carlos Soto, Alvaro P. Universidad Catolica de Chile Santiago Chile Stanford University United States Universidad del Norte Colombia
In this paper, we introduce a new hierarchical model for human action recognition using body joint locations. Our model can categorize complex actions in videos, and perform spatio-temporal annotations of the atomic a... 详细信息
来源: 评论
Information bottleneck learning using privileged information for visual recognition
Information bottleneck learning using privileged information...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Motiian, Saeid Piccirilli, Marco Adjeroh, Donald A. Doretto, Gianfranco West Virginia University MorgantownWV26508 United States
We explore the visual recognition problem from a main data view when an auxiliary data view is available during training. This is important because it allows improving the training of visual classifiers when paired ad... 详细信息
来源: 评论
Inferring forces and learning human utilities from videos
Inferring forces and learning human utilities from videos
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Zhu, Yixin Jiang, Chenfanfu Zhao, Yibiao Terzopoulos, Demetri Zhu, Song-Chun UCLA Center for Vision Cognition Learning and Autonomy United States UCLA Computer Graphics and Vision Laboratory United States
We propose a notion of affordance that takes into account physical quantities generated when the human body interacts with real-world objects, and introduce a learning framework that incorporates the concept of human ... 详细信息
来源: 评论
Linear shape deformation models with local support using graph-based structured matrix factorisation
Linear shape deformation models with local support using gra...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Bernard, Florian Gemmar, Peter Hertel, Frank Goncalves, Jorge Thunberg, Johan Centre Hospitalier de Luxembourg Luxembourg Luxembourg Centre for Systems Biomedicine University of Luxembourg Luxembourg Trier University of Applied Sciences Trier Germany
Representing 3D shape deformations by highdimensional linear models has many applications in computer vision and medical imaging. Commonly, using Principal Components Analysis a low-dimensional subspace of the high-di... 详细信息
来源: 评论
Learning structured inference neural networks with label relations
Learning structured inference neural networks with label rel...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Hu, Hexiang Zhou, Guang-Tong Deng, Zhiwei Liao, Zicheng Mori, Greg School of Computing Science Simon Fraser University BurnabyBC Canada College of Computer Science and Technology Zhejiang University Hangzhou Zhejiang China
Images of scenes have various objects as well as abundant attributes, and diverse levels of visual categorization are possible. A natural image could be assigned with finegrained labels that describe major components,... 详细信息
来源: 评论
The next best underwater view
The next best underwater view
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Sheinin, Mark Schechner, Yoav Y. Viterbi Faculty of Electrical Engineering Technion - Israel Inst. of Technology Haifa32000 Israel
To image in high resolution large and occlusion-prone scenes, a camera must move above and around. Degradation of visibility due to geometric occlusions and distances is exacerbated by scattering, when the scene is in... 详细信息
来源: 评论
Regularity-driven building facade matching between aerial and street views
Regularity-driven building facade matching between aerial an...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Wolff, Mark Collins, Robert T. Liu, Yanxi School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA16802 United States
We present an approach for detecting and matching building facades between aerial view and street-view images. We exploit the regularity of urban scene facades as captured by their lattice structures and deduced from ... 详细信息
来源: 评论
What value do explicit high level concepts have in vision to language problems?
What value do explicit high level concepts have in vision to...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Wu, Qi Shen, Chunhua Liu, Lingqiao Dick, Anthony Van Den Hengel, Anton School of Computer Science University of Adelaide Australia
Much recent progress in vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represe... 详细信息
来源: 评论