The article presents results from the sixth Affective Behavior Analysis in-the-wild (ABAW) competition focusing on facial expression recognition, valence-arousal estimation, and emotion intensity prediction. The study introduces lightweight models based on various architectures trained to recognize emotions from static photos. These models significantly improve quality metrics compared to existing techniques. The research emphasizes the importance of accurate emotion analysis for human-centered technologies and highlights challenges faced in unconstrained environments. The authors aim to construct single models that are fair, explainable, trustworthy, and privacy-conscious while achieving high performance in real-world scenarios.
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by Andrey V. Sa... at arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.11590.pdfDeeper Inquiries