Purdue team's training-free one-shot segmentation framework lifts mean IoU by 33.36 points over the strongest baseline on pool-boiling images
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.