Modeling Multimodal Depression Diagnosis From the Perspective of Local Depressive Representation
Bibliographic record
Abstract
Depression recognition is critical for early detection and treatment. Existing works focus on modeling coarse-grained multimodal representation to estimate the depression level. However, these approaches often overlook the inherent locality of depressive representation, resulting in weak and sparse depressive frames being overlooked. In addition, they neglect the inter modal correlations and intra-modal patterns of mood change, limiting the learning of multimodal complementary information. Therefore, we present a Locality-Aware Multimodal Depression (LAMD) recognition model. Specifically, LAMD contains three innovations: 1) Considering the sparsity of depressive features, we propose an Adaptive Temporal Attention (ATA) module to adaptively highlight keyframes with depressive features and suppress irrelevant frames. Additionally, we introduce Segment Information Sharing (SIS) strategy to overcome the limitation of inter-segment independence, enabling global awareness of depressive features within the whole segment. 2) We revisit the audio-video multimodal interaction from the perspectives of inter-modal correlation and intra-modal smoothness, introducing frame-level multimodal attention consistency constraints and smooth constraints. Furthermore, we propose a local cross attention to enhance the inter-modal interactions in adjacent time. 3) Extensive experiments on several datasets demonstrate that LAMD achieves superior performance, with up to 7.21 RMSE and 76.77% F1-score on the AVEC2014 and NJAD dataset, outperforming the prior art by a notable 0.22% and 1.88% margin, respectively. Moreover, visual analysis reveals that LAMD can adaptively perceive depressive keyframes and focus on fine-grained facial regions known for capturing subtle depressive expressions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".