LI-AGCN:一种轻量级初始化增强的自适应图形卷积网络,用于有效的基于骨架的动作识别
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
概括
一个新的轻量级初始化增强的自适应图形卷积网络 (LI-AGCN) 通过优化空间特征提取来改进基于骨架的动作识别. 这种模型在低计算复杂度的情况下实现了高精度.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 图形卷积网络 (GCN) 是基于骨架的动作识别的关键.
- 之前的GCN模型往往忽视了启发启发对空间特征提取的影响,从而限制了性能.
研究的目的:
- 引入一个轻量级的初始化增强的自适应图形卷积网络 (LI-AGCN).
- 改善时空特征提取和动作识别中的计算效率.
主要方法:
- 对于动态图形结构调整,LI-AGCN使用了三个基于坐标的输入分支 (CIB).
- 包含一个轻量级的,多尺度的时间模块和一个时空通道注意模块.
主要成果:
- 在NTU RGB+D,NTU RGB+D 120和UAV-Human数据集上取得了出色的性能.
- 在只有0.18万个参数的NTU RGB+D跨学科基准上获得了90.03%的准确性.
结论:
- 拟议的LI-AGCN有效地捕捉了低复杂度的时空特征.
- 启动增强对于在动作识别中实现最佳 GCN 性能至关重要.
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