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

Multi-Task Learning for Emotion Descriptors Estimation at the fourth ABAW Challenge