A transformer-based approach to video frame-level prediction in Affective Behaviour Analysis In-the-wild
This work addresses emotion recognition in uncontrolled environments, but it is incremental as it applies an existing transformer architecture to a specific competition dataset.
The authors tackled emotion classification in the wild by proposing a transformer-based model for the 5th Affective Behavior Analysis In-the-wild Competition, achieving a score of 0.4775 on the validation set of Aff-Wild2.
In recent years, transformer architecture has been a dominating paradigm in many applications, including affective computing. In this report, we propose our transformer-based model to handle Emotion Classification Task in the 5th Affective Behavior Analysis In-the-wild Competition. By leveraging the attentive model and the synthetic dataset, we attain a score of 0.4775 on the validation set of Aff-Wild2, the dataset provided by the organizer.