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 detection frameworks has become a challenging problem. In this paper%2C 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%2C we propose a new feature alignment scheme based on contrastive learning by simple projectors and an improved contrastive loss%2C 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.?quality=80&w=800)
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