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Facial action unit detection has emerged as an important task within facialexpression analysis, aimed at detecting specific pre-defined, objective facialexpressions, such as lip tightening and cheek raising. This paper presents oursubmission to the Affective Behavior Analysis in-the-wild (ABAW) 2023Competition for AU detection. We propose a multi-modal method for facial actionunit detection with visual, acoustic, and lexical features extracted from thelarge pre-trained models. To provide high-quality details for visual featureextraction, we apply super-resolution and face alignment to the training dataand show potential performance gain. Our approach achieves the F1 score of52.3% on the official validation set of the 5th ABAW Challenge.

Multi-modal Facial Action Unit Detection with Large Pre-trained Models for the 5th Competition on Affective Behavior Analysis in-the-wild