发表机构
Guangdong University of Technology; Queen Mary University of London; The Hong Kong Polytechnic University（广东工业大学; 伦敦玛丽女王大学; 香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出BehavDep框架，通过稀疏因子分解结合语义桥关联行为概念，在弱监督下学习视频级抑郁倾向得分并聚合信息实现用户级多模态抑郁评估，该方法性能最优且可解释性强。
AI 中文摘要
多模态抑郁评估为分析与抑郁相关的行为模式提供了一种有前景的方法。然而，现有方法通常依赖密集且不透明的多模态表示，难以解释其预测背后的行为模式。本研究引入BehavDep，这是一种基于稀疏因子的框架，它将多模态行为表示分解为稀疏潜在因子，并通过语义桥将这些因子与具有行为意义的概念关联起来。为解决用户级标注与异构视频级行为之间的不匹配问题，BehavDep进一步在弱监督下学习视频级抑郁倾向得分，并聚合多个观测结果的信息以进行用户级评估。大量实验表明，BehavDep在整体评估性能上达到最优，同时揭示了互补的模态贡献、不同观测结果间的异构行为模式以及对概念级编辑的预测响应。这些结果表明，BehavDep为分析用于抑郁评估的多模态行为表示提供了一种结构化且可解释的方法。
英文摘要
Multimodal depression assessment offers a promising approach to analyzing behavioral patterns associated with depression. However, existing methods often rely on dense and opaque multimodal representations, making it difficult to interpret the behavioral patterns underlying their predictions. In this work, we introduce BehavDep, a sparse factor-based framework that decomposes multimodal behavioral representations into sparse latent factors and associates them with behaviorally meaningful concepts through a semantic bridge. To address the mismatch between user-level annotations and heterogeneous video-level behaviors, BehavDep further learns video-level depression tendency scores under weak supervision and aggregates information across multiple observations for user-level assessment. Extensive experiments demonstrate that BehavDep achieves the best overall assessment performance while revealing complementary modality contributions, heterogeneous behavioral patterns across observations, and prediction responses to concept-level editing. These results show that BehavDep provides a structured and interpretable approach to analyzing multimodal behavioral representations for depression assessment.
