# One-Shot Segmentation

The work presents a training-free, one-shot framework for scientific image segmentation that uses a single annotated reference image to combine frozen DINOv3 with SAM: a background direction estimated from the reference background drives background-adaptive orthogonal projection of DINOv3 patch features to suppress artifact-related directions, cosine similarity then localizes candidate regions, and generated prompts feed SAM. Evaluated on red-blood-cell microscopy, structured-illumination pool boiling, and chest radiography, it improves mean IoU over the strongest baseline by 5.15 points on microscopy and 33.36 points on pool boiling, while matching SAM2 on chest radiographs.

The work presents a training-free, one-shot framework for scientific image segmentation that uses a single annotated reference image to combine frozen DINOv3 with SAM: a background direction estimated from the reference background drives background-adaptive orthogonal projection of DINOv3 patch features to suppress artifact-related directions, cosine similarity then localizes candidate regions, and generated prompts feed SAM. Evaluated on red-blood-cell microscopy, structured-illumination pool boiling, and chest radiography, it improves mean IoU over the strongest baseline by 5.15 points on microscopy and 33.36 points on pool boiling, while matching SAM2 on chest radiographs.

The work proposes RPG-SAM, a SAM2-based training-free one-shot polyp segmentation framework that uses Reliability-Weighted Prototype Mining (RWPM) to weight support foreground prototypes by a contrast factor and a reverse purity factor while using background prototypes as negative anchors for noise suppression, Geometric Adaptive Selection (GAS) to pick binarization thresholds dynamically from morphological solidity and scale consensus, and a Prior-guided Iterative Refinement (PIR) loop to polish boundaries, reaching 78.65% mIoU and 85.65% mDice on Kvasir, surpassing ProtoSAM by 5.56% and 4.11% respectively, with comparisons also reported on three-center PolypGen, CVC-ClinicDB, and CVC-ColonDB.

The work proposes RPG-SAM, a SAM2-based training-free one-shot polyp segmentation framework that uses Reliability-Weighted Prototype Mining (RWPM) to weight support foreground prototypes by a contrast factor and a reverse purity factor while using background prototypes as negative anchors for noise suppression, Geometric Adaptive Selection (GAS) to pick binarization thresholds dynamically from morphological solidity and scale consensus, and a Prior-guided Iterative Refinement (PIR) loop to polish boundaries, reaching 78.65% mIoU and 85.65% mDice on Kvasir, surpassing ProtoSAM by 5.56% and 4.11% respectively, with comparisons also reported on three-center PolypGen, CVC-ClinicDB, and CVC-ColonDB.