Token-Level Decisions for Training-Free Few-Shot Segmentation of Overhead Imagery

Few-shot segmentation asks a model to delineate a class in a query image from a handful of annotated examples. The need is most acute in remote sensing, where labels are scarce and the imagery departs from the photographs vision backbones are pretrained on. A growing family of methods answers it without training by reading the prediction off a frozen self-supervised encoder, and its design effort has concentrated on the class model that turns support features into a score. We argue that accuracy is governed largely by what surrounds the class model, first by how finely the decision is committed in space and then by how well the feature geometry is refined on the support. FROST is a training-free segmenter built on this position. It projects a positional component out of the frozen DINOv3 tokens, whitens a within-class scatter estimated from the support under heavy shrinkage, scores every token by a density ratio whose threshold the Bayes rule fixes at zero, and commits a label per token rather than per cluster, with nothing tuned per dataset. A component study attributes 13.5 mIoU to the granularity of the decision, more than every other component together, and 5.1 to the feature refinement. An oracle labelling of every decision unit shows the first to be a bound rather than a scoring failure, since coarsening the unit costs almost exactly the accuracy it forecloses, and the effect is not ours alone, since moving the decision of a competing method to the token raises it by 2.3 mIoU. Across seventeen remote-sensing benchmarks FROST surpasses both training-free and learning-based methods, leading by 5.5 mIoU from a single annotated example and by a wider margin at every larger support set, while remaining among the smallest models compared.