  The in-the-wild affective behavior analysis has been an important study. In
this paper, we submit our solutions for the 5th Workshop and Competition on
Affective Behavior Analysis in-the-wild (ABAW), which includes V-A Estimation,
Facial Expression Classification and AU Detection Sub-challenges. We propose a
Transformer Encoder with Multi-Head Attention framework to learn the
distribution of both the spatial and temporal features. Besides, there are
virious effective data augmentation strategies employed to alleviate the
problems of sample imbalance during model training. The results fully
demonstrate the effectiveness of our proposed model based on the Aff-Wild2
dataset.
