  Affective behavior analysis plays an important role in human-computer
interaction, customer marketing, health monitoring. ABAW Challenge and
Aff-Wild2 dataset raise the new challenge for classifying basic emotions and
regression valence-arousal value under in-the-wild environments. In this paper,
we present an affective expression analysis model that deals with the above
challenges. Our approach includes STAT and Temporal Module for fine-tuning
again face feature model. We experimented on Aff-Wild2 dataset, a large-scale
dataset for ABAW Challenge with the annotations for both the categorical and
valence-arousal emotion. We achieved the expression score 0.543 and
valence-arousal score 0.534 on the validation set.
