Mitigating Symptom Heterogeneity in Multimodal Depression Estimation via Level Separation and Deviation Regression

Multimodal Depression Estimation (MDE) aims to infer individual depression scores by analyzing various signals, such as visual, auditory, and language signals etc. Compared to Multimodal Depression Detection (MDD) methods that only provide discrete labels, MDE can provide a more refined score evaluation. However, symptom heterogeneity leads to differences in external behaviors among patients with similar depressive states, which limits the performance of direct regression MDE methods. To address this issue, we propose a Combined Depression Level and Deviation (CDLD) method for MDE, which separates samples at different depression levels and further analyzes subtle deviations within same level to improve estimation performance. Specifically, the Multilevel Depression Separation module constructs depression levels with inherent commonalities based on psychological theories and models the ordinality of these levels, thereby separating samples with different levels of depression. Building on this, the Level-specific Deviation Regression module contrasts sample features relative to level-specific anchors, regressing the subtle depression deviation. Finally, the depression level and deviation are integrated to infer the depression score more accurately. Experiments on the DAIC-WOZ, CMDC, SEARCH, and AVEC 2014 datasets demonstrate that the proposed coarse-to-fine method effectively mitigates the impact of symptom heterogeneity on depression estimation performance, showing significant advantages in MDE tasks. The code is publicly available at https://github.com/LIU70KG/CDLD.