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Updated: Oct 23, 2025

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Extremely Lightweight Skeleton-Based Action Recognition With ShiftGCN+.

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    Summary
    This summary is machine-generated.

    ShiftGCN introduces efficient graph convolutional networks (GCNs) for skeleton-based action recognition, significantly reducing computational costs and improving speed. A further optimized model, ShiftGCN++, offers comparable performance with even greater efficiency for low-power devices.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Graph Convolutional Networks (GCNs) excel in skeleton-based action recognition.
    • Current GCN methods suffer from high computational costs (over 15 GFLOPs) and inflexible receptive fields.
    • Existing adaptive modules offer limited efficiency gains due to regular GCN structures.

    Purpose of the Study:

    • To propose a novel Shift Graph Convolutional Network (ShiftGCN) to address the efficiency and flexibility limitations of existing GCNs.
    • To develop an extremely computation-efficient model, ShiftGCN++, for low-power devices.
    • To enhance the receptive field flexibility of both spatial and temporal graphs in action recognition.

    Main Methods:

    • Introduced novel shift graph operations combined with lightweight point-wise convolutions.
    • Developed ShiftGCN with flexible receptive fields for spatial and temporal graphs.
    • Incorporated four techniques to create the highly efficient ShiftGCN++ model.

    Main Results:

    • ShiftGCN achieved over 10x fewer FLOPs and 4x practical speedup compared to state-of-the-art methods.
    • ShiftGCN++ demonstrated comparable performance with 6x fewer FLOPs and 2x practical speedup.
    • Both models significantly outperform existing methods on three skeleton-based action recognition datasets.

    Conclusions:

    • ShiftGCN offers a highly efficient and effective solution for skeleton-based action recognition.
    • ShiftGCN++ provides a computationally efficient alternative suitable for resource-constrained environments.
    • The proposed shift graph operations enhance both performance and efficiency in GCN-based action recognition.