  Facial behavior analysis is a broad topic with various categories such as
facial emotion recognition, age, and gender recognition. Many studies focus on
individual tasks while the multi-task learning approach is still an open
research issue and requires more research. In this paper, we present our
solution and experiment result for the Multi-Task Learning challenge of the
Affective Behavior Analysis in-the-wild competition. The challenge is a
combination of three tasks: action unit detection, facial expression
recognition, and valance-arousal estimation. To address this challenge, we
introduce a cross-attentive module to improve multi-task learning performance.
Additionally, a facial graph is applied to capture the association among action
units. As a result, we achieve the evaluation measure of 128.8 on the
validation data provided by the organizers, which outperforms the baseline
result of 30.
