分离双重对比学习用于跨域面部行动单元检测
概括
这项研究引入了一种新的脱双重对比适应 (D2CA) 方法,用于强大的跨域面部动作单元 (AU) 检测. D2CA有效地将面部表情特征与域特定变异分开,大大提高了检测准确度.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 生物识别信息 生物识别信息
背景情况:
- 目前基于视觉的面部动作单元 (AU) 检测方法与域变异作斗争.
- 跨领域的AU检测仍然是一个探索不足的研究领域,限制了现实世界的应用性.
研究的目的:
- 开发一种强大的跨域面部AU检测方法.
- 为了学习一个纯化的AU表示,在不同的领域内从语义上对齐.
- 为了实现对跨域面部图像合成的直观控制.
主要方法:
- 提议解双重对比适应 (D2CA) 以将隐藏的表示分解为与AU相关的和与AU无关的组件.
- 通过评估带有修改的AU或域属性的合成面部来使用特征脱.
- 使用双重对比学习 (图像和特征级) 来加强脱,特别是在有限的数据多样性的情况下.
主要成果:
- D2CA成功地将AU和域因素脱,使得视觉上令人愉快的跨域合成面部图像.
- 该方法比最先进的跨领域AU检测方法取得了显著的性能改进.
- 在各种跨领域场景中,观察到F1平均得分提高了6% - 14%.
结论:
- D2CA为学习专用分离与AU相关的因素和与领域相关的因素提供了有效的框架.
- 这种方法提高了跨域AU检测精度和面部图像合成能力.
- 这项工作解决了当前面部表情识别系统的关键局限性.
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