  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.
