Abstract
Recently how to introduce large amounts of unlabeled facial images in the wild into supervised Facial Action Unit (AU) detection frameworks has become a challenging problem. In this paper, we propose a new AU detection framework where multi-task learning is introduced to jointly learn AU domain separation and reconstruction and facial landmark detection by sharing the parameters of homostructural facial extraction modules. In addition, we propose a new feature alignment scheme based on contrastive learning by simple projectors and an improved contrastive loss, which adds four additional intermediate supervisors to promote the feature reconstruction process. Experimental results on two benchmarks demonstrate our superiority against the state-of-the-art methods for AU detection in the wild.
Abstract (translated)
近年来，如何将大量未标注的野面部图像引入到监督面部动作单元（AU）检测框架中已成为一个具有挑战性的问题。在本文中，我们提出了一种新的人脸动作单元（AU）检测框架，引入了多任务学习来共同学习AU领域分离和重建以及通过共享同构面部提取模块的 facial特征检测。此外，我们提出了一个基于对比学习的新特征对齐方案，通过简单投影器实现，并改进了对比损失，从而增加了四个中间监督器，以促进特征重构过程。在两个基准测试上进行的实验结果表明，我们在野生环境中的人脸动作单元（AU）检测方法相对于最先进的方法具有优越性。
URL
https://arxiv.org/abs/2310.05207
