发表机构
DIBRIS-Università degli Studi di Genova; MaLGa - Machine Learning Genoa center; Istituto Italiano di Tecnologia（热那亚大学DIBRIS学院; 马耳他热那亚机器学习中心; 意大利技术研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出受互视觉注意心理学启发的可解释社交参与模型，结合头部姿态估计与几何推理，通过定量实验和定性可视化验证，可支持相关人员理解群体互动动态。
AI 中文摘要
从非语言视觉数据中理解社交互动对行为分析和活动监测具有重要意义。我们提出一种受互视觉注意心理学理论启发的可解释社交参与计算模型。该框架并非端到端学习互动模式，而是显式建模二元视觉注意，并将这些线索聚合为个体及群体参与度的可解释度量。此模块化框架结合了最先进的头部姿态估计与轻量级几何推理，生成的解释对非技术用户仍可理解。我们通过定量实验在多种数据上评估该方法，并通过定性可视化展示其实用性，旨在支持教师、护理人员及社会工作者理解群体互动动态。
英文摘要
Understanding social interactions from non-verbal visual data is important for behavior analysis and activity monitoring. We propose an interpretable computational model of social engagement inspired by psychological theories of mutual visual attention. Rather than learning interaction patterns end-to-end, our framework explicitly models dyadic visual attention and aggregates these cues into interpretable measures of individual and group engagement. The resulting modular framework combines state-of-the-art head orientation estimation with lightweight geometric reasoning, producing explanations that remain accessible to non-technical users. We evaluate the proposed approach on a variety of data through quantitative experiments and demonstrate its practical usefulness with qualitative visualizations designed to support teachers, caregivers, and social workers in understanding group interaction dynamics.
CommentsECCV 2026 Workshop - 3rd Human-inspired Computer Vision
