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    This study introduces Graph Theory-based Class Activation Mapping (GT-CAM) for explainable skeleton-based behavior recognition using Graph Convolutional Networks (GCN). GT-CAM enhances interpretability by considering node interactions, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Graph Convolutional Networks (GCNs) excel in skeleton-based behavior recognition but lack transparency.
    • Existing Class Activation Map (CAM) methods for GCNs often ignore crucial node interactions.
    • Explainability is vital for advancing deep learning model development and trust.

    Purpose of the Study:

    • To develop a novel explainable AI method for GCNs in behavior recognition.
    • To address the limitations of existing CAM algorithms by incorporating node interactions.
    • To provide a more comprehensive understanding of GCN decision-making processes.

    Main Methods:

    • Proposed Graph Theory-based Class Activation Mapping (GT-CAM) integrating Shapley values and gradient weights.
    • Developed a method for calculating Shapley values of node coalitions to reduce computational cost.
    • Introduced a rationality evaluation method using bipartite and cooperative game theory.
    • Implemented an efficient Monte Carlo-based calculation for the coalition rationality coefficient.

    Main Results:

    • GT-CAM generates activation maps highlighting critical nodes and revealing cooperative dynamics between nodes or subgraphs.
    • The proposed coalition-based Shapley value calculation significantly reduces computational burden.
    • The rationality evaluation method ensures robust and meaningful coalition partitioning.
    • Experimental results show GT-CAM surpasses existing interpretation methods in visualization and quantitative analysis.

    Conclusions:

    • GT-CAM offers a significant advancement in the explainability of GCNs for skeleton-based behavior recognition.
    • The game theory approach effectively captures node interactions, leading to more insightful explanations.
    • GT-CAM provides a computationally efficient and quantitatively superior alternative for GCN interpretability.