# Exemplar: Classical Priors Complement Frozen Features for Few-Shot Microscopy Segmentation at Native Resolution

arXiv: https://arxiv.org/abs/2609.03080 · HTML: https://mlsanity.com/paper/2609.03080/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 2 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation](https://mlsanity.com/paper/1907.12347/) (2019)
- [Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching](https://mlsanity.com/paper/2305.13310/) (2023)
- [UniverSeg: Universal Medical Image Segmentation](https://mlsanity.com/paper/2304.06131/) (2023)
- [Fast End-to-End Trainable Guided Filter](https://mlsanity.com/paper/1803.05619/) (2018)
- [Tyche: Stochastic In-Context Learning for Medical Image Segmentation](https://mlsanity.com/paper/2401.13650/) (2024)
- [Show and Segment: Universal Medical Image Segmentation via In-Context Learning](https://mlsanity.com/paper/2503.19359/) (2025)
- [INSID3: Training-Free In-Context Segmentation with DINOv3](https://mlsanity.com/paper/2603.28480/) (2026)
- [FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures](https://mlsanity.com/paper/2404.00130/) (2024)
- [Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation](https://mlsanity.com/paper/2508.21529/) (2025)
- [Evaluating Vision Foundation Models for Pixel and Object Classification in Microscopy](https://mlsanity.com/paper/2603.19802/) (2026)
- [HyperBank: A Differentiable Bank of Classical Priors for Few-Shot Spheroid Microscopy Segmentation](https://mlsanity.com/paper/2607.10684/) (2026)

## Abstract (arXiv)

Segmenting a new biomedical dataset usually means a domain-specific model trained on substantial annotation, or a foundation model steered at inference time. We present Exemplar, a few-shot segmenter that fuses a frozen DINOv3 backbone with a fixed bank of classical native-resolution filter responses in one lightweight head, fitted from the support masks alone. In the few-mask, native-resolution regime, classical priors and frozen self-supervised features are complementary: fused in one head, a single fixed configuration spans eleven biomedical imaging datasets. Under the same head, the classical bank alone reaches 0.693 on the eleven-dataset panel, scored by foreground intersection-over-union or centreline Dice, and the frozen features alone 0.672; the bank leads on seven of the eleven and the features on the rest, and fused they reach 0.782. Against five forward-pass few-shot methods, Exemplar leads in 54 of 55 method-dataset comparisons, 52 of them significant after Holm correction. From a single annotated mask it reaches 0.703 on the same panel, against 0.682 for a from-scratch nnU-Net trained on that same mask. At eight masks nnU-Net overtakes it on the panel mean, chiefly on centreline agreement, but takes 16-77x longer to fit.