Learning Contrastive Feature Representations for Facial Action Unit
  Detection
Ziqiao Shang Bin Liu Fei Teng Tianrui Li
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 paradigm within the domain of AU detection. To address the challenge
posed by noisy AU labels, we augment the supervised signal through the
introduction of a self-supervised signal. This augmentation is achieved through
positive sample sampling, encompassing three distinct types of positive sample
pairs. Furthermore, to mitigate the imbalanced distribution of each AU type, we
employ an importance re-weighting strategy tailored for minority AUs. The
resulting loss, denoted as AUNCE, is proposed to encapsulate this strategy. Our
experimental assessments, conducted on two widely-utilized benchmark datasets
(BP4D and DISFA), underscore the superior performance of our approach compared
to state-of-the-art methods in the realm of AU detection.