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• W2970293673 abstract "Facial action unit (AU) detection in the wild is a challenging problem, due to the unconstrained variability in facial appearances and the lack of accurate AU annotations. Most existing methods either depend on impractical labor-intensive labeling by experts, or inaccurate pseudo labels. In this paper, we propose an end-to-end semi-supervised unconstrained AU detection framework, which transfers accurate AU labels from a constrained source domain to an unconstrained target domain by exploiting accurate labels of AU-related facial landmarks. Specifically, we map a source image with AU label and a target image without AU label into a latent feature domain by combining source landmark-related feature with target landmark-free feature. Due to the combination of source AU-related information and target AU-free information, the latent feature domain with the transferred source AU label can be learned by maximizing the target-domain AU detection performance. Moreover, to disentangle landmark-related and landmark-free features, we introduce a novel landmark adversarial loss which can solve the multi-player minimax game in adversarial learning. Our framework can also be naturally extended for use with target-domain pseudo AU labels. Extensive experiments show that our method soundly outperforms the baselines, upper-bounds and state-of-the-art approaches on the challenging BP4D, GFT and EmotioNet benchmarks. The code for our method is available at this https URL" @default.
• W2970293673 created "2019-09-05" @default.
• W2970293673 creator A5017901486 @default.
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• W2970293673 creator A5084218062 @default.
• W2970293673 date "2019-03-25" @default.
• W2970293673 modified "2023-09-27" @default.
• W2970293673 title "Semi-Supervised Unconstrained Action Unit Detection via Latent Feature Domain" @default.
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