MDN: A Deep Maximization-Differentiation Network for Spatio-Temporal Depression Detection
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Bibliographic record
Abstract
Deep learning (DL) models have been successfully applied in video-based affective computing, allowing, for instance, to recognize emotions and mood, or to estimate the intensity of pain or stress of individuals based on their facial expressions. Despite the recent advances with state-of-the-art DL models for spatio-temporal recognition of facial expressions associated with depressive behaviour, some key challenges remain in the cost-effective application of 3D-CNNs: (1) 3D convolutions usually employ structures with fixed temporal depth that decreases the potential to extract discriminative representations due to the usually small difference of spatio-temporal variations along different depression levels; and (2) the computational complexity of these models with consequent susceptibility to overfitting. To address these challenges, we propose a novel DL architecture called the Maximization and Differentiation Network (MDN) in order to effectively represent facial expression variations that are relevant for depression assessment. The MDN, operating without 3D convolutions, explores multiscale temporal information using a maximization block that captures smooth facial variations and a difference block that encodes sudden facial variations. Extensive experiments using our proposed MDN with models with 100 and 152 layers result in improved performance while reducing the number of parameters by more than <inline-formula><tex-math notation="LaTeX">$3\times$</tex-math></inline-formula> when compared with 3D ResNet models. Our model also outperforms other 3D models and achieves state-of-the-art results for depression detection. Code available at: <uri>https://github.com/wheidima/MDN</uri> .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it