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Pose-Guided Hierarchical Semantic Decomposition and Composition for Human Parsing.

Beibei Yang, Changqian Yu, Jin-Gang Yu

    IEEE Transactions on Cybernetics
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    This study introduces a novel framework for human parsing, improving accuracy by decomposing and composing body parts hierarchically with pose guidance. The method achieves state-of-the-art results on benchmark datasets.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Human parsing, a fine-grained semantic segmentation task, requires understanding human body parts.
    • Existing methods often treat human parsing as general semantic segmentation, neglecting the hierarchical relationships between human parts.

    Purpose of the Study:

    • To propose a pose-guided hierarchical semantic decomposition and composition framework for improved human parsing.
    • To address the limitations of existing methods in capturing the inherent relationships among hierarchical human parts.

    Main Methods:

    • Developed a Semantic Maintained Decomposition and Composition (SMDC) module for progressive disassembly and assembly of human parts.
    • Introduced a Pose Distillation (PC) module to integrate pose information, converting discrete pose predictions into continuous regions for parsing.
    • Ensured atomic semantic labels are maintained throughout decomposition and composition to prevent error propagation.

    Main Results:

    • Achieved 55.21% mean Intersection of Union (mIoU) on the Look-Into-Person (LIP) dataset.
    • Obtained 69.88% mIoU on the PASCAL-Person-Part dataset.
    • Demonstrated superior performance compared to state-of-the-art methods in human parsing.

    Conclusions:

    • The proposed pose-guided hierarchical framework significantly enhances human parsing accuracy.
    • The integration of pose information via knowledge distillation effectively bridges the gap between discrete pose estimation and continuous region parsing.
    • The method offers a robust solution for fine-grained semantic segmentation of human bodies.