1. Human Action Recognition by Learning Bases of Action Attributes and Parts
Bangpeng Yao, Xiaoye Jiang, Aditya Khosla, Andy Lai Lin, Leonidas Guibas, and Li Fei-Fei
Stanford University

2. Outline Introduction Action Bases
Learning the Dual-Sparse Action Bases and Reconstruction Coefficients
Experiments

3. Introduction Human action recognition in still images Contributions
A general image classification problem
Human-object interaction
Parts + Attributes
Contributions
Represent each image by using a sparse set of action bases that are meaningful to the content of the image
Effectively learn these bases given far-from-perfect detections of action attributes and parts without meticulous human labeling

4. Action Bases Attributes and parts
Attributes: verb, learned by discriminative classifiers
Parts: object parts and poselets, learned by pre-trained object detectors and poselet detectors
A vector of the normalized confidence scores obtained from these classifiers and detectors is used to represent this image.

5. Action Bases High-order interactions of image attributes and parts
is used to represent each image and SVMs are trained for action classification

6. Dual-sparsity Learning

8. Experiments
PASCAL actions
Stanford 40 actions

9. PASCAL

10. Stanford 40 actions