SAGE-OR: Semi-supervised Adaptive Scene Graph Generation for Operating Rooms
Current surgical scene graph generation methods depend on dense multi-modal supervision and specialized hardware (synchronized RGB-D sensors, calibration rigs), making dataset construction expensive and restricting all existing benchmarks to simulated environments. We propose SAGE-OR, a feature-centric framework that replaces the traditional detect-then-reason paradigm with a decoupled representation-reasoning paradigm in which localization is derived from frozen foundation models, encoded implicitly in pre-computed features, and used without any localization supervision, while a lightweight graph transformer performs relational reasoning over cached features. We employ a semi-supervised ...
| Authors | Brandon Leblanc, Charalambos Poullis |
|---|---|
| Primary category | cs.CV |
| Published | 15 August 2026 |
| Last updated at source | 9 October 2026 |
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