基于互视注意力建模社交参与度，解释性强且适合教育者使用
Human-Inspired Social Engagement Analysis via Interpretable Mutual Visual Attention
- 显式建模两人间的视线交互，结合头部朝向与几何推理
- 在多数据集上实现可解释的个体与群体参与度评估
- 可视化结果直观易懂，适用于教师、护理人员等非技术用户
从非语言视觉数据中理解社会互动对行为分析和活动监测至关重要。我们提出一种受心理学互视注意力理论启发的可解释计算模型，用于社交参与度分析。不同于端到端学习互动模式，该框架显式建模双人视觉注意力，并将这些线索聚合为可解释的个体与群体参与度指标。所提出的模块化框架融合了最先进的头部朝向估计与轻量级几何推理，生成的解释对非技术人员仍保持可读性。我们在多种数据集上通过定量实验评估该方法，并通过定性可视化展示其实际价值，助力教师、照护者及社工理解群体互动动态。
原文摘要 · Abstract (English)
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.
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