Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection

• URL: http://arxiv.org/abs/2503.08977v1
• Date: Wed, 12 Mar 2025 00:42:17 GMT
• Title: Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection
• Authors: Yong Li, Menglin Liu, Zhen Cui, Yi Ding, Yuan Zong, Wenming Zheng, Shiguang Shan, Cuntai Guan,
• Abstract summary: We propose a decoupled doubly contrastive adaptation (D$2$CA) approach to learn a purified AU representation.<n>D$2$CA is trained to disentangle AU and domain factors by assessing the quality of synthesized faces.<n>It consistently outperforms state-of-the-art cross-domain AU detection approaches.
• Score: 66.80386429324196
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite the impressive performance of current vision-based facial action unit (AU) detection approaches, they are heavily susceptible to the variations across different domains and the cross-domain AU detection methods are under-explored. In response to this challenge, we propose a decoupled doubly contrastive adaptation (D$^2$CA) approach to learn a purified AU representation that is semantically aligned for the source and target domains. Specifically, we decompose latent representations into AU-relevant and AU-irrelevant components, with the objective of exclusively facilitating adaptation within the AU-relevant subspace. To achieve the feature decoupling, D$^2$CA is trained to disentangle AU and domain factors by assessing the quality of synthesized faces in cross-domain scenarios when either AU or domain attributes are modified. To further strengthen feature decoupling, particularly in scenarios with limited AU data diversity, D$^2$CA employs a doubly contrastive learning mechanism comprising image and feature-level contrastive learning to ensure the quality of synthesized faces and mitigate feature ambiguities. This new framework leads to an automatically learned, dedicated separation of AU-relevant and domain-relevant factors, and it enables intuitive, scale-specific control of the cross-domain facial image synthesis. Extensive experiments demonstrate the efficacy of D$^2$CA in successfully decoupling AU and domain factors, yielding visually pleasing cross-domain synthesized facial images. Meanwhile, D$^2$CA consistently outperforms state-of-the-art cross-domain AU detection approaches, achieving an average F1 score improvement of 6\%-14\% across various cross-domain scenarios.

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