Learning to Parse Pictures of People
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Estimating Human Body Configurations Using Shape Context Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Pictorial Structures for Object Recognition
International Journal of Computer Vision
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Learning structured prediction models: a large margin approach
Learning structured prediction models: a large margin approach
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Building Models of Animals from Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images
International Journal of Computer Vision
Exploring the spatial hierarchy of mixture models for human pose estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Appearance sharing for collective human pose estimation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Efficient human parsing based on sketch representation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Discriminative hierarchical part-based models for human parsing and action recognition
The Journal of Machine Learning Research
Learning visual symbols for parsing human poses in images
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We show quantitative evidence that a full relational model of the body performs better at upper body parsing than the standard tree model, despite the need to adopt approximate inference and learning procedures. Our method uses an approximate search for inference, and an approximate structure learning method to learn. We compare our method to state of the art methods on our dataset (which depicts a wide range of poses), on the standard Buffy dataset, and on the reduced PASCAL dataset published recently. Our results suggest that the Buffy dataset over emphasizes poses where the arms hang down, and that leads to generalization problems.