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检索条件"任意字段=29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"
90 条 记 录,以下是71-80 订阅
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Automatic recognition of Emotions and Membership in Group Videos  29
Automatic Recognition of Emotions and Membership in Group Vi...
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29th ieee conference on computer vision and pattern recognition (cvpr)
作者: Mou, Wenxuan Gunes, Hatice Patras, Ioannis Queen Mary Univ London London England Univ Cambridge Cambridge England
Automatic affect analysis and understanding has become a well established research area in the last two decades. However, little attention has been paid to the analysis of the affect expressed in group settings, eithe... 详细信息
来源: 评论
A Comprehensive Analysis of Deep Learning Based Representation for Face recognition  29
A Comprehensive Analysis of Deep Learning Based Representati...
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29th ieee conference on computer vision and pattern recognition (cvpr)
作者: Ghazi, Mostafa Mehdipour Ekenel, Hazim Kemal Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Istanbul Tech Univ Dept Comp Engn Istanbul Turkey
Deep learning based approaches have been dominating the face recognition field due to the significant performance improvement they have provided on the challenging wild datasets. these approaches have been extensively... 详细信息
来源: 评论
Covert Video Classification by Codebook Growing pattern  29
Covert Video Classification by Codebook Growing Pattern
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29th ieee conference on computer vision and pattern recognition (cvpr)
作者: Du, Liang Lang, Haitao Tian, Ying-Li Tan, Chiu C. Wu, Jie Ling, Haibin Temple Univ Philadelphia PA 19122 USA Beijing Univ Chem Technol Beijing Peoples R China City Univ New York New York NY USA
Recent advances in visual data acquisition and Internet technologies make it convenient and popular to collect and share videos. these activities, however, also raise the issue of privacy invasion. One potential priva... 详细信息
来源: 评论
A Combined EM and Visual Tracking Probabilistic Model for Robust Mosaicking: Application to Fetoscopy  29
A Combined EM and Visual Tracking Probabilistic Model for Ro...
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29th ieee conference on computer vision and pattern recognition (cvpr)
作者: Tella, Marcel Daga, Pankaj Chadebecq, Francois thompson, Stephen Shakir, Dzhoshkun I. Dwyer, George Wimalasundera, Ruwan Deprest, Jan Stoyanov, Danail Vercauteren, Tom Ourselin, Sebastien UCL Translat Imaging Grp CMIC London WC1E 6BT England UCL Surg Robot Vis Grp CMIC London WC1E 6BT England Univ Hosp Leuven Dept Obstet & Gynaecol Leuven Belgium Univ Coll Hosp London England
Twin-to-Twin Transfusion Syndrome (TTTS) is a progressive pregnancy complication in which inter-twin vascular connections in the shared placenta result in a blood flow imbalance between the twins. the most effective t... 详细信息
来源: 评论
What do you do when you know that you don't know?  29
What do you do when you know that you don't know?
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29th ieee conference on computer vision and pattern recognition Workshops (cvprW)
作者: Bendale, Abhijit Boult, Terrance E. Univ Colorado Colorado Springs Colorado Springs CO 80918 USA
Real-world biometrics recognition problems often have two unknowns: the person be recognized, as well as a hidden unknown - missing data. If we choose to ignore data that is occasionally missing, we sacrifice accuracy... 详细信息
来源: 评论
PETS 2016: Dataset and Challenge  29
PETS 2016: Dataset and Challenge
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29th ieee conference on computer vision and pattern recognition Workshops (cvprW)
作者: Patino, Luis Cane, Tom Vallee, Alain Ferryman, James Univ Reading Computat Vis Grp Reading RG6 6AY Berks England BMT Grp Ltd Teddington TW11 8LZ Middx England SAGEM F-92659 Boulogne France
this paper describes the datasets and computer vision challenges that form part of the PETS 2016 workshop. PETS 2016 addresses the application of on-board multi sensor surveillance for protection of mobile critical as... 详细信息
来源: 评论
A Comparison of Human and Automated Face Verification Accuracy on Unconstrained Image Sets  29
A Comparison of Human and Automated Face Verification Accura...
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29th ieee conference on computer vision and pattern recognition Workshops (cvprW)
作者: Blanton, Austin Allen, Kristen C. Miller, Tim Kalka, Nathan D. Jain, Anil K. Noblis Reston VA 20191 USA Michigan State Univ E Lansing MI 48824 USA
Automatic face recognition technologies have seen significant improvements in performance due to a combination of advances in deep learning and availability of larger datasets for training deep networks. Since recogni... 详细信息
来源: 评论
CALIPER: Continuous Authentication Layered with Integrated PKI Encoding recognition  29
CALIPER: Continuous Authentication Layered with Integrated P...
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29th ieee conference on computer vision and pattern recognition Workshops (cvprW)
作者: Rudd, Ethan M. Boult, Terrance E. Univ Colorado Vis & Secur Technol VAST Lab Colorado Springs CO 80907 USA
Architectures relying on continuous authentication require a secure way to challenge the user's identity without trusting that the Continuous Authentication Subsystem (CAS) has not been compromised, i.e., that the... 详细信息
来源: 评论
Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit recognition and Facial Landmark Detection  29
Constrained Joint Cascade Regression Framework for Simultane...
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2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Wu, Yue Ji, Qiang Rensselaer Polytech Inst ECSE Dept 110 8th St Troy NY 12180 USA
Cascade regression framework has been shown to be effective for facial landmark detection. It starts from an initial face shape and gradually predicts the face shape update from the local appearance features to genera... 详细信息
来源: 评论
Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled  29
Deep Hand: How to Train a CNN on 1 Million Hand Images When ...
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2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Koller, Oscar Ney, Hermann Bowden, Richard Rhein Westfal TH Aachen Human Language Technol & Pattern Recog Aachen Germany Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England
this work presents a new approach to learning a frame-based classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm. this allows the CNN to be trained on a vast number of exampl... 详细信息
来源: 评论