集群联合时空图注意力网络,用于基于骨架的动作识别.
Tao Yu1, Sandro Pinto1, Tiago Gomes1
1Centro Algoritmi, University do Minho, 4800-058 Guimarães, Portugal.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
集群联合时空图注意网络 (CF-STGAT) 在客户端异质性下改善基于骨架的动作识别. 这种新的框架通过使用注意力机制动态分组客户来提高模型的趋同性和准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 基于骨架的动作识别的联合学习 (FL) 由于客户端异质性而具有挑战性,导致模型漂移和与标准方法 (如FedAvg.
- 当前FL方法在客户数据分布和模型参数出现显著变化时,难以保持稳定的性能.
研究的目的:
- 引入一个新的集群FL框架,集群联合时空图注意网络 (CF-STGAT),旨在解决基于骨架的动作识别中的客户端异质性.
- 通过动态分组客户端并执行注意力加权的集群间融合来提高FL模型的稳定性和准确性.
主要方法:
- 从局部空间时间图注意力网络 (STGAT) 模型中利用注意力衍生的时空统计数据来动态分组客户.
- 实现了一个服务器端过程,涉及K-means集群的基于多头参数的关注描述符的提取,规范化和PCA投影.
- 利用注意力相似度权重计算,通过轻量级的融合步骤来规范集群模型的全球参考,保持本地培训不变.
主要成果:
- 在NTU RGB+D 60/120数据集上,CF-STGAT的表现始终优于强大的FL基线,与FedAvg相比,实现了显著的绝对顶级-1准确性收益.
- 在不同评估设置 (X-Sub/X-Setup) 中表现出更好的性能,在NTU 60上显著增长 +0.84/+4.09,在NTU 120.0上增长 +7.98/+4.18.
- 观察到更顺的每个客户培训轨迹和更低的终端测试损失,表明模型稳定性和趋同性得到了增强.
结论:
- 注意引导的集群和集群间的融合是有效的补充策略,用于减轻FL的集团内部变异和跨集群分歧.
- 在强烈的客户异质性下,CF-STGAT框架为基于骨架的行动识别提供了强大的解决方案,而不会改变本地培训程序.
- 拟议的方法通过利用动态客户群组和模型对齐的注意力机制,有效地提高联合学习性能.
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