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检索条件"任意字段=1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1992"
6449 条 记 录,以下是1571-1580 订阅
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Sparse coding for classification via discrimination ensemble
Sparse coding for classification via discrimination ensemble
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Quan, Yuhui Xu, Yong Sun, Yuping Huang, Yan Ji, Hui School of Computer Science and Engineering South China Univ. of Tech. Guangzhou510006 China School of Automation Science and Engineering South China Univ. of Tech. Guangzhou510006 China Department of Mathematics National University of Singapore Singapore117542 Singapore
Discriminative sparse coding has emerged as a promising technique in image analysis and recognition, which couples the process of classifier training and the process of dictionary learning for improving the discrimina... 详细信息
来源: 评论
Latent factor guided convolutional neural networks for age-invariant face recognition
Latent factor guided convolutional neural networks for age-i...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Wen, Yandong Li, Zhifeng Qiao, Yu School of Electronic and Information Engineering South China University of Technology China Shenzhen Key Lab of Comp. Vis. and Pat. Rec. Shenzhen Institutes of Advanced Technology CAS China
While considerable progresses have been made on face recognition, age-invariant face recognition (AIFR) still remains a major challenge in real world applications of face recognition systems. The major difficulty of A... 详细信息
来源: 评论
Occlusion-free face alignment: Deep Regression Networks coupled with de-corrupt autoencoders
Occlusion-free face alignment: Deep Regression Networks coup...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Zhang, Jie Kan, Meina Shan, Shiguang Chen, Xilin Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology China
Face alignment or facial landmark detection plays an important role in many computer vision applications, e.g., face recognition, facial expression recognition, face animation, etc. However, the performance of face al... 详细信息
来源: 评论
Robust scene text recognition with automatic rectification
Robust scene text recognition with automatic rectification
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Shi, Baoguang Wang, Xinggang Lyu, Pengyuan Yao, Cong Bai, Xiang School of Electronic Information and Communications Huazhong University of Science and Technology China
Recognizing text in natural images is a challenging task with many unsolved problems. Different from those in documents, words in natural images often possess irregular shapes, which are caused by perspective distorti... 详细信息
来源: 评论
Learning to match aerial images with deep attentive architectures
Learning to match aerial images with deep attentive architec...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Altwaijry, Hani Trulls, Eduard Hays, James Fua, Pascal Belongie, Serge Department of Computer Science Cornell University United States Cornell Tech United States Switzerland School of Interactive Computing College of Computing Georgia Institute of Technology United States
Image matching is a fundamental problem in computer vision. In the context of feature-based matching, SIFT and its variants have long excelled in a wide array of applications. However, for ultra-wide baselines, as in ... 详细信息
来源: 评论
Recurrently target-attending tracking
Recurrently target-attending tracking
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Cui, Zhen Xiao, Shengtao Feng, Jiashi Yan, Shuicheng Research Center for Learning Science Southeast University Nanjing210096 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore 360 Artificial Intelligence Institute Beijing China
Robust visual tracking is a challenging task in computer vision. Due to the accumulation and propagation of estimation error, model drifting often occurs and degrades the tracking performance. To mitigate this problem... 详细信息
来源: 评论
Fine-grained image classification by exploring bipartite-graph labels
Fine-grained image classification by exploring bipartite-gra...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Zhou, Feng Lin, Yuanqing NEC Labs United States
Given a food image, can a fine-grained object recognition engine tell "which restaurant which dish" the food belongs to? Such ultra-fine grained image recognition is the key for many applications like search... 详细信息
来源: 评论
Temporal epipolar regions
Temporal epipolar regions
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Dar, Mor Moses, Yael Efi Arazi School of Computer Science Interdisciplinary Center Herzliya46150 Israel
Dynamic events are often photographed by a number of people from different viewpoints at different times, resulting in an unconstrained set of images. Finding the corresponding moving features in each of the images al... 详细信息
来源: 评论
Slicing convolutional neural network for crowd video understanding
Slicing convolutional neural network for crowd video underst...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Shao, Jing Loy, Chen Change Kang, Kai Wang, Xiaogang Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Department of Information Engineering Chinese University of Hong Kong Hong Kong
Learning and capturing both appearance and dynamic representations are pivotal for crowd video understanding. Convolutional Neural Networks (CNNs) have shown its remarkable potential in learning appearance representat... 详细信息
来源: 评论
Fashion style in 128 floats: Joint ranking and classification using weak data for feature extraction
Fashion style in 128 floats: Joint ranking and classificatio...
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2016 ieee conference on computer vision and pattern recognition, cvpr 2016
作者: Simo-Serra, Edgar Ishikawa, Hiroshi Department of Computer Science and Engineering Waseda University Tokyo Japan
We propose a novel approach for learning features from weakly-supervised data by joint ranking and classification. In order to exploit data with weak labels, we jointly train a feature extraction network with a rankin... 详细信息
来源: 评论