Abstract
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. The code and model are available for research purposes at $\href{this https URL}{\text{this link}}$.
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URL
https://arxiv.org/abs/2107.03670
