REBASE: Reference-Background Subspace Elimination for Training-Free In-Context Segmentation
Training-free in-context segmentation enables new object categories to be introduced at inference time from a single annotated reference image, eliminating the retraining and memory overhead of class-incremental learning. Recent approaches achieve this by combining vision foundation models for semantic correspondence with promptable segmentation networks like SAM. However, their performance is fundamentally limited by the quality of the cross-image similarity map; shared contextual backgrounds between the reference and query systematically elevate similarity in non-target regions, degrading prompt localization. We present REBASE, a training-free framework that explicitly suppresses these ...
| Authors | Mantha Sai Gopal, Jaison Saji Chacko, Harsh Nandwana et al. |
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| Primary category | cs.CV |
| Published | 10 July 2026 |
| Last updated at source | 3 October 2026 |
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