Exploring Expression-related Self-supervised Learning for Affective Behaviour Analysis

Understand

This paper explores an expression-related self-supervised learning (SSL) method (ContraWarping) to perform expression classification in the 5th Affective Behavior Analysis in-the-wild (ABAW) competition.

• Affective datasets are expensive to annotate, and SSL methods could learn from large-scale unlabeled data, which is more suitable for this task.
• By evaluating on the Aff-Wild2 dataset, we demonstrate that ContraWarping outperforms most existing supervised methods and shows great application potential in the affective analysis area.
• Codes will be released on: https://github.com/youqingxiaozhua/ABAW5.