基于自主监督学习的面部动作单元表示,带有合并的先验约束
概括
本研究介绍了SsupAU,这是一个自我监督的模型,用于从未标记的视频中学习面部动作单元 (AU) 表示. 它克服了注释的局限性,使人类表达更好地理解.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 面部动作单位 (AU) 对于理解人类表情至关重要.
- 监督学习的AU识别需要大量的手册注释,限制了现实世界的表现.
- 由于注释成本,精确的AU本地化和表征具有挑战性.
研究的目的:
- 提出一个端到端的自我监督模型 (SsupAU) 来从未标记的面部视频中学习AU表示.
- 克服传统的监督AU识别方法中手动注释的局限性.
- 在现实的场景中实现强大的AU识别.
主要方法:
- 使用自动编码器将输入面分解为六个组件,包括光几何元素和2D流场AU.
- 逐渐构建了正规中性,中性姿势和表情面孔,以便在没有监督的情况下解开组件.
- 使用身份一致性和平均面部假设来构建正规中性面部和解AUs.
主要成果:
- 与基准数据集上的最先进方法相比,在AU表示学习中取得了更好的表现.
- 在将输入面分解为重建的有意义因素方面表现出卓越的能力.
- 从未标记的面部视频中成功学习了AU表示,验证了自我监督的方法.
结论:
- 拟议的SsupAU模型提供了一种有效的自我监督的方法来学习面部动作单元表示.
- 这种方法显著减少了对手工注释的依赖,为更实用的表达式理解系统铺平了道路.
- 该方法对推进面部表情分析和人机交互研究有前途.
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