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    This study introduces a novel method for zero-shot action recognition, using a visually connected graph and grouped attention graph convolutional networks (GAGCNs) to transfer knowledge from seen to unseen action categories.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Action recognition models struggle with novel classes due to the limited data.
    • Zero-shot learning aims to recognize unseen actions by leveraging knowledge from seen classes.

    Purpose of the Study:

    • To develop a method for zero-shot action recognition that bridges the knowledge gap between seen and unseen action categories.
    • To effectively transfer knowledge from visual features to semantic space for improved recognition of novel actions.

    Main Methods:

    • Extracting visual features for all actions.
    • Constructing a visually connected graph to link seen actions with visually similar unseen categories.
    • Employing grouped attention graph convolutional networks (GAGCNs) for knowledge transfer.

    Main Results:

    • The proposed GAGCN method effectively transfers knowledge from seen to unseen action categories.
    • Experimental evaluations on HMDB51, UCF101, and NTU RGB + D datasets demonstrate superior performance compared to state-of-the-art methods.

    Conclusions:

    • The GAGCN approach offers a promising solution for zero-shot action recognition.
    • Visual association and graph-based knowledge transfer are effective strategies for extending action recognition to novel classes.