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Related Experiment Video

Updated: Sep 22, 2025

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X-Invariant Contrastive Augmentation and Representation Learning for Semi-Supervised Skeleton-Based Action

Binqian Xu, Xiangbo Shu, Yan Song

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    |May 26, 2022
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    Summary
    This summary is machine-generated.

    This study introduces X-CAR, a novel framework for semi-supervised skeleton-based action recognition. X-CAR enhances feature learning by adaptively combining augmentations and representations in a single stage, improving accuracy with limited labeled data.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Semi-supervised skeleton-based action recognition faces challenges due to limited labeled data.
    • Existing methods often use a two-stage approach with random augmentation, leading to limited representation learning capabilities.
    • Inconsistency between random augmentations and learned representations hinders performance.

    Purpose of the Study:

    • To propose a novel X-invariant Contrastive Augmentation and Representation learning (X-CAR) framework.
    • To achieve rotation-shear-scale (X) invariant features through a one-stage learning process.
    • To improve the accuracy of skeleton-based action recognition in semi-supervised settings.

    Main Methods:

    • Developed a one-stage framework (X-CAR) integrating augmentation and representation learning.
    • Introduced an Adaptive-combination Augmentation (AA) mechanism with learnable factors for adaptive skeleton transformations (rotation, shear, scale).
    • Implemented a Pull-Push Contrastive Loss (PPCL) to refine sample relationships and relax predefined positive/negative sample allocation.

    Main Results:

    • The X-CAR framework demonstrated superior performance in semi-supervised action recognition.
    • Achieved better accuracy compared to existing competitive methods on benchmark datasets (NTU RGB+D, North-Western UCLA).
    • The one-stage approach and adaptive augmentation improved the consistency between learned augmentations and representations.

    Conclusions:

    • X-CAR effectively addresses the limitations of two-stage methods in semi-supervised skeleton-based action recognition.
    • The proposed adaptive augmentation and contrastive loss enhance the learning of invariant features.
    • X-CAR offers a promising direction for improving action recognition accuracy with limited labeled data.