arXiv cs.CL· Guimin Hu, Zihao Song, Jiachen Luo, Jiayuan Xie, Ruichu Cai·· 9 天前AI 评分22
BehavDep：从稀疏表示到行为洞察的多模态抑郁评估框架
From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment
AI 导读
BehavDep 是一个基于稀疏因子的多模态抑郁评估框架，将多模态行为表示分解为稀疏潜在因子，并通过语义桥与具有行为意义的概
正文 · 原文
Abstract: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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.11787 [cs.CL] |
| (or arXiv:2610.11787v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11787 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Guimin Hu [view email]
[v1]
Thu, 8 Oct 2026 12:03:06 UTC (1,705 KB)
来源：arXiv cs.CL · arxiv.org