Mark一下CVPR 2012应该读的Oral
2012-11-12 17:17
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大部分作者主页上已经挂出了今年cvpr的文章,该看看了,下面的文章是我打算近期看的,希望能坚持。
Discriminative Virtual Views for Cross-View Action Recognition
Detecting activities of daily living in first-person camera views
We are not Contortionist: Coupled Adaptive Learning for Head and Body Orientation Estimation in Surveillance Video
Understanding Collective Crowd Behaviors:Learning Mixture Model of Dynamic Pedestrian-Agents
Max-Margin Early Event Detectors
Weakly Supervised Structured Output Learning for Semantic Segmentation
Contextual Boost for Pedestrian Detection
Accidental pinhole and pinspeck cameras: revealing the scene outside the picture
Finding Animals: Semantic Segmentation using Regions and Parts
A Unified Approach to Salient Object Detection via Low Rank Matrix Recovery
Pedestrian detection at 100 frames per second
Stream-based Joint Exploration-Exploitation Active Learning
Beyond Spatial Pyramids: Receptive Field Learning for Pooled Image Features
A-Optimal Non-negative Projection for Image Representation
另外:
Sparse Representation for Face Recognition based on Discriminative Low-Rank.Dictionary
Learning
相关介绍:
1.http://www.cvchina.info/2012/04/06/cvpr2012-paper/
2.CVPR 2012 关于Low Rank的几篇文章
Discriminative Virtual Views for Cross-View Action Recognition
Detecting activities of daily living in first-person camera views
We are not Contortionist: Coupled Adaptive Learning for Head and Body Orientation Estimation in Surveillance Video
Understanding Collective Crowd Behaviors:Learning Mixture Model of Dynamic Pedestrian-Agents
Max-Margin Early Event Detectors
Weakly Supervised Structured Output Learning for Semantic Segmentation
Contextual Boost for Pedestrian Detection
Accidental pinhole and pinspeck cameras: revealing the scene outside the picture
Finding Animals: Semantic Segmentation using Regions and Parts
A Unified Approach to Salient Object Detection via Low Rank Matrix Recovery
Pedestrian detection at 100 frames per second
Stream-based Joint Exploration-Exploitation Active Learning
Beyond Spatial Pyramids: Receptive Field Learning for Pooled Image Features
A-Optimal Non-negative Projection for Image Representation
另外:
Sparse Representation for Face Recognition based on Discriminative Low-Rank.Dictionary
Learning
相关介绍:
1.http://www.cvchina.info/2012/04/06/cvpr2012-paper/
2.CVPR 2012 关于Low Rank的几篇文章
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