通过梯度协调通过非IID数据对人类姿势估计的联合学习
Peng Ni1, Dan Xiang2, Dawei Jiang1
1School of Applied Technology, Changchun University of Technology, Changchun 130012, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
联合梯度协调 (FedGH) 通过解决非IID数据中的梯度冲突,在分散的环境中改进人类姿势估计. 这种强大的优化增强了分布式计算机视觉任务的模型准确性和融合.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 人体姿势估计中的分散数据往往呈现出非独立且相同分布 (非IID) 的特征.
- 传统的联合学习聚合策略与梯度冲突作斗争,阻碍了非IID环境中的模型融合和准确性.
研究的目的:
- 提出一个新的联合学习聚合策略,联邦梯度协调 (FedGH),以解决人类姿势估计的非IID设置中的梯度冲突.
- 提高分布式人类姿势估计模型的准确性和稳定性.
主要方法:
- 通过测量梯度差异,FedGH协调客户端更新方向.
- 它将梯度投影校正与参数重建机制集成在一起,以协调更新.
- 该策略在自建机器人数据集和MPII人类姿势数据集上进行了测试.
主要成果:
- 在MPII数据集上,FedGH实现了47.14%和66.31%的正确关键点 (PCK) 的平均百分比,超过了联邦自适应权重 (FedAW).
- 在自己构建的数据集上,FedGH在肩部检测方面达到86.4%的PCK,超过其他方法的20-30%.
- 使用FedGH的关键点热图回归模型在自建数据集的10轮内实现了超过98%的准确性.
结论:
- 在非IID环境中,FedGH有效地减轻了梯度冲突,为分布式人类姿势估计提供了更强大的优化解决方案.
- 拟议的方法显示了对联合的人体姿势估计任务的准确性和趋同的显著改进.
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