  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.
