Feature Pyramid Network for Multi-task Affective Analysis

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 failed to take them as a whole task or ignore the entanglement and hierarchical relation of this 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 teacher-student training strategy to learn from pretrained single-task models. Extensive experiment results demonstrate the proposed model outperform 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.