通过隐性热图学习来提高基于整体的人类姿势估计
Congju Du1, Zengqiang Yan1, Zixiang Xiong2
1School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
概括
本研究引入了一种新的隐性热图学习框架,用于人类姿势的估计,通过解决关键点的模糊性和实现竞争性表现来改进基于整体的方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 人体姿势估计方法包括热图,回归和基于整体的方法.
- 基于整体的方法提供了诸如端到端学习之类的优势,但表现较差.
- 现有的方法在隐性热图的形状和变异方面存在模糊性.
研究的目的:
- 重新审视和增强人类姿势估计的基于整体的方法.
- 提出一种新的隐性热图学习框架,以减轻模两可的问题.
- 为了实现竞争性表现,同时保持基于整体的方法的好处.
主要方法:
- 引入了简单的隐式热图规范化 (SIHN),以取代软max规范化,以有效地定位关键点.
- 拟议的微分空间到分布式转换 (DSDT) 将隐性热图映射到转换系数,解决形状和方差模糊性.
- 在隐式热图生成过程中实施了基于Wasserstein距离的约束 (WDC) 以进行稳定的监督.
主要成果:
- 拟议的框架有效地减轻了隐式热图中的形状和变异模糊性.
- 在MSCOCO和MPII数据集上实现了与传统基于热图的方法相比具有竞争力的性能.
- 保持了基于整体的方法的固有优势,例如端到端的学习.
结论:
- 新的隐性热图学习框架显著增强了基于整体的人类姿势估计.
- 拟议的SIHN,DSDT和WDC方法有效地解决关键点的模两可,并提高性能.
- 这项研究为推进基于整体的人类姿势估计技术提供了一个有希望的方向.
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