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检索条件"任意字段=2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011"
21179 条 记 录,以下是91-100 订阅
排序:
Scene Text recognition using Part-based Tree-structured Character Detection
Scene Text Recognition using Part-based Tree-structured Char...
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26th ieee conference on computer vision and pattern recognition (cvpr)
作者: Shi, Cunzhao Wang, Chunheng Xiao, Baihua Zhang, Yang Gao, Song Zhang, Zhong CASIA State Key Lab Management & Control Complex Syst Beijing Peoples R China
Scene text recognition has inspired great interests from the computer vision community in recent years. In this paper, we propose a novel scene text recognition method using part-based tree-structured character detect... 详细信息
来源: 评论
Nonparametric Part Transfer for Fine-grained recognition  27
Nonparametric Part Transfer for Fine-grained Recognition
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27th ieee conference on computer vision and pattern recognition (cvpr)
作者: Goering, Christoph Rodner, Erik Freytag, Alexander Denzler, Joachim Univ Jena Comp Vis Grp D-07745 Jena Germany
In the following paper, we present an approach for fine-grained recognition based on a new part detection method. In particular, we propose a nonparametric label transfer technique which transfers part constellations ... 详细信息
来源: 评论
Global Contrast based Salient Region Detection
Global Contrast based Salient Region Detection
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ieee conference on computer vision and pattern recognition (cvpr)
作者: Cheng, Ming-Ming Zhang, Guo-Xin Mitra, Niloy J. Huang, Xiaolei Hu, Shi-Min Tsinghua Univ TNList Beijing Peoples R China UCL Dept Comp Sci London WC1E 6BT England Lehigh Univ Dept Comp Sci & Engn Bethlehem PA 18015 USA Univ Oxford Oxford OX1 2JD England Tsinghua Univ Dept Comp Sci & Technol TNList Beijing Peoples R China
Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks including image segmentation, ... 详细信息
来源: 评论
What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
What You Saw is Not What You Get: Domain Adaptation Using As...
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ieee conference on computer vision and pattern recognition (cvpr)
作者: Kulis, Brian Saenko, Kate Darrell, Trevor UC Berkeley EECS Berkeley CA USA ICSI Berkeley CA USA
In real-world applications, "what you saw" during training is often not "what you get" during deployment: the distribution and even the type and dimensionality of features can change from one datas... 详细信息
来源: 评论
Fast Cost-Volume Filtering for Visual Correspondence and Beyond
Fast Cost-Volume Filtering for Visual Correspondence and Bey...
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ieee conference on computer vision and pattern recognition (cvpr)
作者: Rhemann, Christoph Hosni, Asmaa Bleyer, Michael Rother, Carsten Gelautz, Margrit Vienna Univ Technol A-1040 Vienna Austria Vienna Univ Technol Inst Software Technol & Interact Sys Interact Media Sys Grp A-1040 Vienna Austria Microsoft Res Cambridge Cambridge England
Many computer vision tasks can be formulated as labeling problems. The desired solution is often a spatially smooth labeling where label transitions are aligned with color edges of the input image. We show that such s... 详细信息
来源: 评论
Attribute-Based Detection of Unfamiliar Classes with Humans in the Loop
Attribute-Based Detection of Unfamiliar Classes with Humans ...
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26th ieee conference on computer vision and pattern recognition (cvpr)
作者: Wah, Catherine Belongie, Serge Univ Calif San Diego Dept Comp Sci & Engn San Diego CA 92103 USA
Recent work in computer vision has addressed zero-shot learning or unseen class detection, which involves categorizing objects without observing any training examples. However, these problems assume that attributes or... 详细信息
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Learning Attributes Equals Multi-Source Domain Generalization  29
Learning Attributes Equals Multi-Source Domain Generalizatio...
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2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Gan, Chuang Yang, Tianbao Gong, Boqing Tsinghua Univ IIIS Beijing Peoples R China Univ Iowa Iowa City IA 52242 USA Univ Cent Florida CRCV Orlando FL 32816 USA
Attributes possess appealing properties and benefit many computer vision problems, such as object recognition, learning with humans in the loop, and image retrieval. Whereas the existing work mainly pursues utilizing ... 详细信息
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Predicate Logic based Image Grammars for Complex pattern recognition
Predicate Logic based Image Grammars for Complex Pattern Rec...
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ieee-computer-Society conference on computer vision and pattern recognition Workshops
作者: Shet, Vinay Singh, Maneesh Bahlmann, Claus Ramesh, Visvanathan Siemens Corp Res Princeton NJ USA
来源: 评论
Efficient stereo with multiple windowing
Efficient stereo with multiple windowing
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1997 ieee computer Society conference on computer vision and pattern recognition (cvpr 97)
作者: Fusiello, A Roberto, V Trucco, E Univ of Udine Udine Italy
We present a new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions. The algorithm is an adaptive, multi-window scheme using left-right consistency to compute disparity an... 详细信息
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Learning a Discriminative Filter Bank within a CNN for Fine-grained recognition  31
Learning a Discriminative Filter Bank within a CNN for Fine-...
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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Wang, Yaming Morariu, Vlad I. Davis, Larry S. Univ Maryland College Pk MD 20742 USA Adobe Res San Jose CA USA
Compared to earlier multistage frameworks using CNN features, recent end-to-end deep approaches for finegrained recognition essentially enhance the mid-level learning capability of CNNs. Previous approaches achieve th... 详细信息
来源: 评论