  Facial valence/arousal, expression and action unit are related tasks in
facial affective analysis. However, the tasks only have limited performance in
the wild due to the various collected conditions. The 4th competition on
affective behavior analysis in the wild (ABAW) provided images with
valence/arousal, expression and action unit labels. In this paper, we introduce
multi-task learning framework to enhance the performance of three related tasks
in the wild. Feature sharing and label fusion are used to utilize their
relations. We conduct experiments on the provided training and validating data.
