EviDep: Uncertainty-Aware Multimodal Depression Estimation via Disentangled Evidential Learning
Quick summary
arXiv:2604.16579v3 Announce Type: replace-cross Abstract: Audio--visual recordings provide complementary cues for estimating depression severity, but their informativeness varies across time and modalities. Point predictions alone do not express the uncertainty associated with these estimates. We present EviDep, a multimodal evidential regression framework that integrates multi-scale temporal modeling and shared--private representation learning for uncertainty-aware depression estimation. Frequency-aware Feature Extraction decomposes behavioral feature sequences into multiple frequency bands a
Key takeaways
- arXiv:2604.16579v3 Announce Type: replace-cross Abstract: Audio--visual recordings provide complementary cues for estimating depression severity, but their informativeness varies across time and modalities.
- Point predictions alone do not express the uncertainty associated with these estimates.
- We present EviDep, a multimodal evidential regression framework that integrates multi-scale temporal modeling and shared--private representation learning for uncertainty-aware depression estimation.
Why it matters
“EviDep: Uncertainty-Aware Multimodal Depression Estimation via Disentangled Evidential Learning” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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