Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations.
We thus define a new domain adaptation setting called Few-shot One-class Domain Adaptation (FODA), where adaptation only relies on a limited number of target bonafide samples.
In this paper, we emphatically summarize that learning an adaptive label distribution on ordinal regression tasks should follow three principles.
However, many other generalizable cues are unexplored for face anti-spoofing, which limits their performance under cross-dataset testing.
Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network with the proposed regional and temporal based auxiliary task learning (RTATL) framework.
In order to incorporate the intra-level AU relation and inter-level AU regional relevance simultaneously, a multi-level AU relation graph is constructed and graph convolution is performed to further enhance AU regional features of each level.
In this paper, we propose a self-domain adaptation framework to leverage the unlabeled test domain data at inference.