Vision Transformer for Action Units Detection
This work addresses fine-grained facial analysis for affective behavior analysis, but it is incremental as it adapts existing methods to a specific competition task.
The paper tackled the problem of Facial Action Units detection in videos by proposing a Vision Transformer-based approach, achieving a 14% improvement over the baseline model in the ABAW 2023 challenge and results comparable to top teams from ABAW 2022.
Facial Action Units detection (FAUs) represents a fine-grained classification problem that involves identifying different units on the human face, as defined by the Facial Action Coding System. In this paper, we present a simple yet efficient Vision Transformer-based approach for addressing the task of Action Units (AU) detection in the context of Affective Behavior Analysis in-the-wild (ABAW) competition. We employ the Video Vision Transformer(ViViT) Network to capture the temporal facial change in the video. Besides, to reduce massive size of the Vision Transformers model, we replace the ViViT feature extraction layers with the CNN backbone (Regnet). Our model outperform the baseline model of ABAW 2023 challenge, with a notable 14% difference in result. Furthermore, the achieved results are comparable to those of the top three teams in the previous ABAW 2022 challenge.