From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment
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Computer Science > Computation and Language
arXiv:2610.11787 (cs)
[Submitted on 8 Oct 2026]
Title:From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment
View a PDF of the paper titled From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment, by Guimin Hu and 4 other authors
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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)
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View a PDF of the paper titled From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment, by Guimin Hu and 4 other authors
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