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Related Experiment Video

Updated: Jun 3, 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

Spatial-Temporal Self-Compensating Graph Convolutional Network for Skeleton-Based Action Recognition Under Data

Xing Li, Qiang Geng, Qian Huang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 1, 2026
    PubMed
    Summary
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    This study introduces a Spatial Temporal Self-compensating Graph Convolutional Network (STSc-GCN) to improve skeleton-based human action recognition. The novel network enhances robustness against real-world data constraints like occlusion and missing frames.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Skeleton-based human action recognition is crucial for computer vision.
    • Existing methods struggle with real-world data issues like occlusion and missing frames.
    • These limitations reduce the practical applicability of current techniques.

    Purpose of the Study:

    • To develop a robust method for skeleton-based human action recognition.
    • To address performance degradation caused by data constraints.
    • To improve the reliability of action recognition in real-world scenarios.

    Main Methods:

    • Propose a Spatial Temporal Self-compensating Graph Convolutional Network (STSc-GCN).
    • Utilize a data self-compensation mechanism leveraging human movement patterns.

    Related Experiment Videos

    Last Updated: Jun 3, 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

  • Implement Collaborative Motion Spatial Compensation (CMSC) and Meta-action Sharpening Temporal Compensation (MSTC) modules.

    • Main Results:

    • STSc-GCN achieves state-of-the-art performance on four constrained datasets.
    • Demonstrates superior results on three standard datasets.
    • Confirms effectiveness in both constrained and general scenarios.

    Conclusions:

    • STSc-GCN effectively mitigates performance degradation from data constraints.
    • The proposed method enhances robustness and adaptability in human action recognition.
    • The approach shows significant promise for real-world computer vision applications.