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

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Published on: April 21, 2023

Semantics-Assisted Training Graph Convolution Network for Skeleton-Based Action Recognition.

Huangshui Hu¹, Yu Cao¹, Yue Fang¹

¹College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.

Sensors (Basel, Switzerland)

|April 28, 2025

Summary

This summary is machine-generated.

This study introduces a novel semantics-assisted training graph convolution network (SAT-GCN) for skeleton-based action recognition. The SAT-GCN enhances accuracy by leveraging semantic information and incorporating angle features, outperforming existing models.

Keywords:

action recognition feature fusion semantic relationships semantics-assisted training

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Corticospinal Excitability Modulation During Action Observation

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Published on: December 31, 2013

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Skeleton-based action recognition models often overlook semantic relationships within actions.
Existing methods primarily focus on joint features, neglecting valuable semantic information.

Purpose of the Study:

To propose a novel Semantics-Assisted Training Graph Convolution Network (SAT-GCN) for improved skeleton-based action recognition.
To enhance recognition accuracy and reduce model parameters by integrating semantic information.

Main Methods:

A multi-feature skeleton encoder extracts joint, bone, and angle features.
Contrastive loss between skeleton and text features guides network training.
Feature fusion integrates multi-modal features before graph convolution.

Main Results:

The proposed SAT-GCN significantly improves action recognition accuracy.
The model achieves superior performance with a reduced number of parameters.
Experiments on NTU RGB+D 60, NTU RGB+D 120, and NW-UCLA datasets validate the effectiveness.

Conclusions:

The SAT-GCN effectively utilizes semantic information for skeleton-based action recognition.
Incorporating angle features aids in distinguishing similar actions.
The developed model offers a more efficient and accurate approach to action recognition.