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

Understand

Facial action unit detection has emerged as an important task within facial expression analysis, aimed at detecting specific pre-defined, objective facial expressions, such as lip tightening and cheek raising.

• This paper presents our submission to the Affective Behavior Analysis in-the-wild (ABAW) 2023 Competition for AU detection.
• We propose a multi-modal method for facial action unit detection with visual, acoustic, and lexical features extracted from the large pre-trained models.
• To provide high-quality details for visual feature extraction, we apply super-resolution and face alignment to the training data and show potential performance gain.