From Affect to Complex Behavior: Advancing Multimodal Human-Centered AI at the 10th ABAW Workshop & Competition

2026-05-24

AI Summary

This work addresses the modeling and understanding of human affect and complex behaviors in real-world, unconstrained settings, tackling core challenges such as continuous and discrete emotion recognition alongside high-level behavioral analysis. Through a dual-track approach combining competitions and publications, it introduces novel tasks—including emotion mimicry intensity estimation, detection of hesitation and ambivalence, and fine-grained violence recognition—thereby extending the frontiers of traditional affective computing. Leveraging a large-scale in-the-wild multimodal dataset, the study establishes a standardized benchmark by integrating pose and motion estimation, robust modeling techniques, and fairness-aware evaluation protocols. This effort provides an authoritative platform for advancing research in affective and behavioral understanding, fostering collaborative innovation and practical deployment of multimodal human-centered AI systems.

Abstract

The 10th Affective & Behavior Analysis in-the-Wild (ABAW) Workshop and Competition, held at CVPR 2026, continues to advance research on modelling, analysis, understanding of human affect and behavior in real-world, unconstrained environments. The workshop maintains its dual structure, comprising both a competition and a paper track. The ABAW Competition introduces a diverse set of challenges targeting key aspects of affective and behavioral understanding, including continuous affect (valence-arousal) estimation, discrete affect (expression and action unit) recognition, as well as more complex behavior analysis tasks, such as emotional mimicry intensity estimation, ambivalence/hesitancy recognition and fine-grained violence detection. These challenges are built upon large-scale in-the-wild datasets, providing comprehensive benchmarks for state-of-the-art approaches. In parallel, the paper track presents a wide range of contributions spanning pose, motion & behavior estimation, affect modelling & multimodal learning, benchmarks, datasets & evaluation protocols, fairness, robustness & deployment. Overall, the 10th ABAW Workshop and Competition continues to serve as a key platform for benchmarking, collaboration and innovation, shaping the development of next-generation multimodal, human-centered AI systems.

Problem

• affective computing
• behavior analysis
• in-the-wild
• multimodal AI
• human-centered AI

Innovation

• multimodal learning
• in-the-wild behavior analysis
• affective computing
• complex human behavior
• benchmark dataset