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Self-Supervised Action Representation Learning Based on Asymmetric Skeleton Data Augmentation.

Hualing Zhou1,2, Xi Li1,2, Dahong Xu1,2

  • 1College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.

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Summary
This summary is machine-generated.

This study introduces an asymmetric data augmentation strategy to improve skeleton-based action recognition. The novel approach enhances feature learning for better performance in downstream tasks.

Keywords:
action representationcontrastive learningdata augmentationself-supervised

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Skeleton-based action recognition is crucial for human behavior analysis.
  • Existing contrastive learning methods often use simple augmentations, limiting feature learning depth.

Purpose of the Study:

  • To enhance skeleton-based action representations using an improved contrastive learning strategy.
  • To address the limitations of simple augmentation in current methods.

Main Methods:

  • Proposed an asymmetric data augmentation strategy tailored for 3D skeleton data.
  • Applied view-specific augmentations within a dual-branch pipeline.
  • Introduced random probability activation to prevent extreme augmentation of joint views.

Main Results:

  • The asymmetric augmentation strategy significantly improved the quality of learned action representations.
  • Experiments on NTU RGB + D datasets demonstrated the effectiveness of the proposed method.

Conclusions:

  • The asymmetric data augmentation strategy is effective for skeleton-based action recognition.
  • This method offers a promising direction for advancing contrastive learning in this domain.