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Self-Supervised Representation Learning From Videos for ...
Self-Supervised Representation Learning from Videos for facial Action Unit Detection Yong Li1,2 , Jiabei Zeng1 , Shiguang Shan1,2,3,4 , Xilin Chen1,2. 1. Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China 2. University of Chinese Academy of Sciences, Beijing 100049, China 3. CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 200031, China 4. Peng Cheng Laboratory, Shenzhen, 518055, China { , sgshan, Abstract In this paper, we aim to learn discriminative representa- tion for facial action unit (AU) detection from large amount re-generate of Videos without manual annotations. Inspired by the AU-related AU movements fact that facial actions are the movements of facial mus- feature . AU-changed cles, we depict the movements as the transformation be- tween two face images in different frames and use it as the self-supervisory signal to learn the representations.}
fact that facial actions are the movements of facial mus-cles, we depict the movements as the transformation be-tween two face images in different frames and use it as the self-supervisory signal to learn the representations. How-ever, under the uncontrolled condition, the transformation is caused by both facial actions and head motions. To re-
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