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LI-AGCN: A Lightweight Initialization-Enhanced Adaptive Graph Convolutional Network for Effective Skeleton-Based

Qingsheng Xie¹, Hongmin Deng¹

¹College of Electronics and Information Engineering, Sichuan University, No. 24, Section 1, Yihuan Road, Wuhou District, Chengdu 610065, China.

Sensors (Basel, Switzerland)

|December 11, 2025

Summary

This summary is machine-generated.

A new lightweight initialization-enhanced adaptive graph convolutional network (LI-AGCN) improves skeleton-based action recognition by optimizing spatial feature extraction. This model achieves high accuracy with low computational complexity.

Keywords:

coordinate-based branch graph convolutional network initialization-enhanced model lightweight skeleton-based action recognition

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Graph Convolutional Networks (GCNs) are key in skeleton-based action recognition.
Prior GCN models often neglect heuristic initialization's impact on spatial feature extraction, limiting performance.

Purpose of the Study:

Introduce a lightweight initialization-enhanced adaptive graph convolutional network (LI-AGCN).
Improve spatiotemporal feature extraction and computational efficiency in action recognition.

Main Methods:

LI-AGCN uses three coordinate-based input branches (CIB) for dynamic graph structure adjustment.
Incorporates a lightweight, multi-scale temporal module and a spatio-temporal-channel attention module.

Main Results:

Achieved excellent performance on NTU RGB+D, NTU RGB+D 120, and UAV-Human datasets.
Obtained 90.03% accuracy on NTU RGB+D cross-subject benchmark with only 0.18 million parameters.

Conclusions:

The proposed LI-AGCN effectively captures spatiotemporal features with low complexity.
Initialization enhancement is crucial for optimal GCN performance in action recognition.