SPIN Unprocessed July 8, 2026 ai_technology research
Uncovering Latent Depression Severity for Binary Depression Detection via Advantage-weighting Ranking
View original on arxiv.orgOverview
arXiv:2607.05901v1 Announce Type: new Abstract: Automatic depression detection using audio-visual data faces significant challenges, particularly in disentangling overlapping feature distributions and establishing robust decision boundaries. To address this, we propose a fine-grained multimodal framework featuring a temporal encoder and a mutual transformer to facilitate deep cross-modal fusion. Our core contribution is the Binary Advantage-weighting Ranking Loss, which optimizes the latent spac
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Artificial Intelligence
View all →- SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation
- PCBWorld: A Benchmark Environment for Engine-Grounded PCB Design Automation
- StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems
- Onnes: A Physics-Grounded Multi-Agent LLM Simulator for Cryogenic Fault Diagnosis in Quantum Computing Infrastructure
- TurnOPD: Making On-Policy Distillation Turn-Aware for Efficient Long-Horizon Agent Training
- From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO