INSID3: Training-Free In-Context Segmentation with DINOv3
In-context segmentation (ICS) aims to segment arbitrary concepts, e.g., objects, parts, or personalized instances, given one annotated visual examples. Existing work relies on (i) fine-tuning vision foundation models (VFMs), which improves in-domain results but harms generalization, or (ii) combines multiple frozen VFMs, which preserves generalization but yields architectural complexity and fixed segmentation granularities. We revisit ICS from a minimalist perspective and ask: Can a single self-supervised backbone support both semantic matching and segmentation, without any supervision or auxiliary models? We show that scaled-up dense self-supervised features from DINOv3 exhibit strong spatial structure and semantic correspondence. We introduce INSID3, a training-free approach that segments concepts at varying granularities only from frozen DINOv3 features, given an in-context example. INSID3 achieves state-of-the-art results across one-shot semantic, part, and personalized segmentation, outperforming previous work by +7.5 % mIoU, while using 3x fewer parameters and without any mask or category-level supervision. Code is available at https://github.com/visinf/INSID3 .
69cbc34c5bd847a9dd477498
Claudia Cuttano
claimed_verified
2026-04-01T08:35:56.302Z
699ecd1ed5093f64efe0030d
/avatars/bb8a1fd454f368d916a22ffb8b7ccb22.svg
Claudia Cuttano
claudiacuttano
user
claudiacuttano
69cbc34c5bd847a9dd477499
Gabriele Trivigno
69cbc34c5bd847a9dd47749a
Christoph Reich
69cbc34c5bd847a9dd47749b
Daniel Cremers
69cbc34c5bd847a9dd47749c
Carlo Masone
69cbc34c5bd847a9dd47749d
Stefan Roth
INSID3: Training-Free In-Context Segmentation with DINOv3
In-context segmentation (ICS) aims to segment arbitrary concepts, e.g., objects, parts, or personalized instances, given one annotated visual examples. Existing work relies on (i) fine-tuning vision foundation models (VFMs), which improves in-domain results but harms generalization, or (ii) combines multiple frozen VFMs, which preserves generalization but yields architectural complexity and fixed segmentation granularities. We revisit ICS from a minimalist perspective and ask: Can a single self-supervised backbone support both semantic matching and segmentation, without any supervision or auxiliary models? We show that scaled-up dense self-supervised features from DINOv3 exhibit strong spatial structure and semantic correspondence. We introduce INSID3, a training-free approach that segments concepts at varying granularities only from frozen DINOv3 features, given an in-context example. INSID3 achieves state-of-the-art results across one-shot semantic, part, and personalized segmentation, outperforming previous work by +7.5 % mIoU, while using 3x fewer parameters and without any mask or category-level supervision. Code is available at https://github.com/visinf/INSID3 .
INSID3 demonstrates that frozen DINOv3 features can support versatile segmentation tasks without supervision or auxiliary models, achieving superior performance with reduced parameters.
in-context segmentation vision foundation models dense self-supervised features DINOv3 training-free approach semantic matching segmentation granularity mask supervision category-level supervision