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A graph-based Mixture-of-Experts (MoE) framework that explicitly separates self and social influences to enable interpretable engagement modeling, which outperforms baselines by up to 64.1% while offering interpretable insights into engagement dynamics across language and gender groups.
Automatic engagement prediction is a significant aspect of Human-Computer Interaction (HCI) and affective computing, enabling systems to capture user interest and deliver timely interventions. In dyadic conversations, a person’s (target’s) engagement can be influenced by both their own behavioral signals and those of t...
This work introduces XPlainVerse, a large-scale benchmark designed for joint deepfake detection and human-centered explanation, and proposes novel metrics, EntityScore and EvidenceScore, that measure reasoning fidelity by checking whether explanations correctly identify manipulated entities and visual evidence.
The Explainable Deepfake Detection Challenge at ACM Multimedia 2026 is designed to benchmark this joint capability of classification metrics with semantic similarity, simplicity, and intent-aware grounding metrics that assess whether explanations identify the relevant manipulated entities and supporting visual evidence...
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