SegRAG: Retrieval Augmented Spatial Prompting for Open Vocabulary Semantic Segmentation
Frozen segmentation foundation models often fail when the target class appears in a form that is weakly represented during pretraining. To address this problem, we introduce SegRAG, a retrieval-augmented inference-time spatial prompting pipeline for open-vocabulary semantic segmentation that uses frozen models without updating their weights. SegRAG builds a compact class-indexed memory from annotated references. When multiple references are available, Intra-Class Cohesion Distillation (ICCD) filters DINOv3 patch descriptors by cross-image foreground agreement. With one reference, foreground descriptors are retained directly without ICCD. Topographic Similarity Grounding (TSG) then turns h...
| Authors | Abderrahmene Boudiaf, Irfan Hussain, Sajid Javed |
|---|---|
| Primary category | cs.CV |
| Published | 17 May 2026 |
| Last updated at source | 30 September 2026 |
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