Engagement Estimation in Human-Robot and Human-Human Interactions across Domain, Age and Culture: MultiMediate'26 Challenge

원문 초록

Understanding engagement in human interaction is essential for modelling assistive and context-aware AI systems. Previously, the MultiMediate challenge has focused on estimating momentary continuous engagement values in adult conversation across cultures, achieving high levels of generalisability across domains. However, engagement can be measured in different metrics and manifest differently between children and adults. MultiMediate'26 therefore expands the evaluated domain set to include the PInSoRo dataset of Child-Child and Child-Robot play sessions in English. These sessions were annotated categorically with task and social engagement, diversifying engagement types, age ranges and interaction types. In this paper, we describe the MultiMediate'26 challenge and its goals, introduce the NoXi, NoXi-J, MPIIGI, and PInSoRo datasets, showcase extracted multi-modal features, present our baseline model for engagement estimation across all domains, and discuss challenge submissions and approaches. In an effort to provide a transparent and welcoming challenge environment, baseline code, leaderboard, datasets, and extracted features are publicly available at https://github.com/hcmlab/MultiMediate_26.