human action recognition by learning bases of action attributes and parts bangpeng yao, xiaoye...
TRANSCRIPT
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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
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Outline
• Introduction• Action Bases• Learning the Dual-Sparse Action Bases and
Reconstruction Coefficients• Experiments
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Introduction
• Human action recognition in still images• 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
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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.
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Action Bases
• High-order interactions of image attributes and parts
• is used to represent each image and SVMs are trained for action classification
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Dual-sparsity Learning
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Experiments
• PASCAL actions• Stanford 40 actions
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• PASCAL
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• Stanford 40 actions