Feb. 12, 2024, 5:42 a.m. | Ziqiao Shang Bin Liu Fei Teng Tianrui Li

cs.LG updates on arXiv.org arxiv.org

The predominant approach to facial action unit (AU) detection revolves around a supervised multi-label binary classification problem. Existing methodologies often encode pixel-level information of AUs, thereby imposing substantial demands on model complexity and expressiveness. Moreover, this practice elevates the susceptibility to overfitting due to the presence of noisy AU labels. In the present study, we introduce a contrastive learning framework enhanced by both supervised and self-supervised signals. The objective is to acquire discriminative features, deviating from the conventional pixel-level learning …

binary classification complexity cs.ai cs.cv cs.lg detection encode feature information labels overfitting pixel practice

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