ABAW Challenge

The Affective Behavior Analysis in-the-wild (ABAW) challenge focuses on advancing the automatic recognition of human emotions and behaviors from unconstrained real-world videos. Current research emphasizes multi-task learning approaches, often employing deep learning architectures like Convolutional Neural Networks (CNNs) and Transformers, to simultaneously predict continuous affect dimensions (valence and arousal), discrete expressions, and facial action units. Success in this challenge has significant implications for developing more human-centered technologies, improving human-computer interaction, and furthering our understanding of human emotion.