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Due to the collection of big data and the development of deep learning,research to predict human emotions in the wild is being actively conducted. Wedesigned a multi-task model using ABAW dataset to predict valence-arousal,expression, and action unit through audio data and face images at in realworld. We trained model from the incomplete label by applying the knowledgedistillation technique. The teacher model was trained as a supervised learningmethod, and the student model was trained by using the output of the teachermodel as a soft label. As a result we achieved 2.40 in Multi Task Learning taskvalidation dataset.

Multitask Emotion Recognition Model with Knowledge Distillation and Task Discriminator