自动分析家长陪读中干预策略，无需训练即可精准识别。
InterventionLens: A Multi-Agent Framework for Detecting ASD Intervention Strategies in Parent-Child Shared Reading
- 多智能体协作分析视频中的多模态互动内容
- 在ASD-HI数据集上F1达79.44%，比基线高19.72%
- 适合自闭症家庭干预研究与临床辅助工具开发
基于家庭的干预方式如家长-儿童共读，为自闭症谱系障碍（ASD）儿童提供了低成本支持路径。然而，自然情境下分析照护者干预策略通常依赖专家标注，存在成本高、耗时长且难扩展的问题。为此，我们提出InterventionLens，一个端到端的多智能体系统，可从共读视频中自动检测并时间分割照护者干预策略。该系统无需任务特定模型训练或微调，通过协同多智能体架构整合多模态交互内容，实现细粒度策略分析。在ASD-HI数据集上的实验表明，InterventionLens整体F1得分为79.44%，较基线提升19.72%。结果表明，InterventionLens是分析家庭环境中自闭症儿童共读干预策略的有力工具。更多资源将发布于项目主页。
原文摘要 · Abstract (English)
Home-based interventions like parent-child shared reading provide a cost-effective approach for supporting children with autism spectrum disorder (ASD). However, analyzing caregiver intervention strategies in naturalistic home interactions typically relies on expert annotation, which is costly, time-intensive, and difficult to scale. To address this challenge, we propose InterventionLens, an end-to-end multi-agent system for automatically detecting and temporally segmenting caregiver intervention strategies from shared reading videos. Without task-specific model training or fine-tuning, InterventionLens uses a collaborative multi-agent architecture to integrate multimodal interaction content and perform fine-grained strategy analysis. Experiments on the ASD-HI dataset show that InterventionLens achieves an overall F1 score of 79.44\%, outperforming the baseline by 19.72\%. These results suggest that InterventionLens is a promising system for analyzing caregiver intervention strategies in home-based ASD shared reading settings. Additional resources will be released on the project page.
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