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 URL
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 URL
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,
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failed to take them as a whole task or ignore
did not pay enough attention to
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this
these
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outperform
outperforms
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in-the-wild
in the wild