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Related Experiment Video

Updated: Jun 7, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Learning multi-granularity skeleton representations via hierarchical graph contrastive learning.

Yaxiong Liu1, Xianfeng Zhai2,3

  • 1Qinghai Normal University, Qinghai, 810000, China.

Scientific Reports
|June 5, 2026
PubMed
Summary
This summary is machine-generated.

Hierarchical Graph Contrastive Learning (HGCL) enhances skeleton-based action recognition by addressing label reliance and data augmentation issues. This method improves accuracy and reduces feature distances, outperforming existing models in various scenarios.

Related Experiment Videos

Last Updated: Jun 7, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Skeleton-based action recognition is robust to environmental variations but struggles with data requirements and augmentation.
  • Existing methods often fail to effectively utilize joint confidence and can disrupt action topology during augmentation.

Purpose of the Study:

  • To introduce Hierarchical Graph Contrastive Learning (HGCL) for improved skeleton-based action recognition.
  • To address challenges of label reliance, topological destruction during augmentation, and underutilized joint confidence in a unified framework.

Main Methods:

  • HGCL employs a novel augmentation strategy with topology constraints and adaptive perturbation.
  • A hierarchical contrastive objective balances global and part-level motion semantics.
  • Joint detection confidence is integrated into graph convolution and representation aggregation.

Main Results:

  • HGCL achieved 91.2% (UCF101) and 66.8% (HMDB51) Top-1 accuracy, surpassing InfoGCN.
  • Significant improvements were observed in few-shot learning (21.6 pp on UCF101) and under noisy conditions.
  • Feature analysis showed reduced intra-class (54.5%) and increased inter-class (90.3%) distances.

Conclusions:

  • HGCL offers a robust and effective approach to skeleton-based action recognition.
  • The unified framework successfully tackles key limitations of previous methods.
  • HGCL demonstrates strong performance in few-shot learning and noisy environments, validating its self-supervised representation learning capabilities.