Multitask Emotion Recognition Model with Knowledge Distillation and Task Discriminator
This work addresses emotion recognition for real-world applications, but it is incremental as it builds on existing datasets and techniques.
The paper tackled the problem of predicting human emotions in the wild by designing a multi-task model using audio and face images, achieving a score of 2.40 on the validation dataset.
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. We designed a multi-task model using ABAW dataset to predict valence-arousal, expression, and action unit through audio data and face images at in real world. We trained model from the incomplete label by applying the knowledge distillation technique. The teacher model was trained as a supervised learning method, and the student model was trained by using the output of the teacher model as a soft label. As a result we achieved 2.40 in Multi Task Learning task validation dataset.