  Affective Analysis is not a single task, and the valence-arousal value,
expression class, and action unit can be predicted at the same time. Previous
researches did not pay enough attention to the entanglement and hierarchical
relation of these three facial attributes. We propose a novel model named
feature pyramid networks for multi-task affect analysis. The hierarchical
features are extracted to predict three labels and we apply a teacher-student
training strategy to learn from pretrained single-task models. Extensive
experiment results demonstrate the proposed model outperforms other models.
This is a submission to The 2nd Workshop and Competition on Affective Behavior
Analysis in the wild (ABAW). The code and model are available for research
purposes at https://github.com/ryanhe312/ABAW2-FPNMAA.
