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Multi-Granularity Anchor-Contrastive Representation Learning for Semi-Supervised Skeleton-Based Action Recognition.

Xiangbo Shu, Binqian Xu, Liyan Zhang

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    Summary
    This summary is machine-generated.

    This study introduces Multi-granularity Anchor-Contrastive representation Learning (MAC-Learning) for skeleton-based action recognition. MAC-Learning enhances feature representation by contrasting data across multiple granularities, improving performance in semi-supervised settings.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Semi-supervised skeleton-based action recognition faces challenges in extracting discriminative features from limited labeled and abundant unlabeled data.
    • Contrastive learning is a common approach but struggles with global features, ambiguous sample pairs, and single-granularity comparisons.

    Purpose of the Study:

    • To address limitations in current contrastive learning methods for skeleton-based action recognition.
    • To propose a novel Multi-granularity Anchor-Contrastive representation Learning (MAC-Learning) framework.

    Main Methods:

    • Developed MAC-Learning to learn multi-granularity representations using inter- and intra-granularity contrastive pretext tasks.
    • Introduced a Multi-granularity Anchor-Contrastive Loss (MAC-Loss) using reliable soft-positive/negative pairs based on an anchor graph.
    • Utilized learnable and structural-link skeletons across local, context, and global views.

    Main Results:

    • MAC-Learning effectively captures local motion information missed by global-granularity methods.
    • The proposed MAC-Loss mitigates issues arising from ambiguous and hard-defined positive/negative pairs.
    • Experiments demonstrated superior performance of MAC-Learning on NTU RGB+D and Northwestern-UCLA datasets.

    Conclusions:

    • MAC-Learning offers a more robust and effective approach to semi-supervised skeleton-based action recognition.
    • The multi-granularity contrastive strategy and anchor-based loss significantly enhance representation learning.
    • This method advances the state-of-the-art in action recognition using skeletal data.