DAUPNet: Domain-Aware Uncertainty Modeling for Reliable Prototype Discrimination in Cross-Domain Few-Shot Semantic Segmentation
Quick Answer
DAUPNet introduces a novel framework for cross-domain few-shot semantic segmentation by employing uncertainty-aware prototype discrimination, achieving 72.6% and 76.7% average mIoU in 1-shot and 5-shot settings, respectively.
Quick Take
This approach enhances prototype matching reliability under significant domain shifts, particularly benefiting medical domain applications.
Key Points
- DAUPNet harmonizes hierarchical support-query features for stable evidence.
- Prototypes are represented probabilistically to capture boundary and appearance ambiguity.
- Uncertainty estimation regulates contrastive optimization for improved segmentation.
- Achieved significant performance gains in two medical domains.
- Code for DAUPNet is publicly available for further research.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Cross-domain few-shot semantic segmentation (CD-FSS) has predominantly been formulated as learning domain-invariant representations or improving support-query correspondence. Nevertheless, large domain shifts still make prototype matching unreliable: inconsistent hierarchical responses corrupt the support representation, deterministic prototypes cannot express boundary and appearance ambiguity, and treating prototypes with different reliability equally during optimization weakens foreground-background separation. We therefore propose DAUPNet, a unified framework that reformulates cross-domain prototype matching as uncertainty-aware prototype discrimination. DAUPNet first harmonizes hierarchical support-query features to provide stable evidence, then represents foreground and background prototypes probabilistically, and finally uses their estimated uncertainty to regulate contrastive optimization. On four standard target domains, DAUPNet achieves 72.6% and 76.7% average mIoU in the 1-shot and 5-shot settings, respectively, including substantial gains on the two medical domains. These results demonstrate that modeling prototype uncertainty and incorporating it into optimization provides a robust and interpretable approach to CD-FSS under severe domain shift. The code is available at this https URL
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.16308 [cs.CV] |
| (or arXiv:2607.16308v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.16308 arXiv-issued DOI via DataCite |
Submission history
From: Lei Yuan [view email]
[v1]
Tue, 14 Jul 2026 14:48:54 UTC (15,480 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
ProMoE-FL introduces a Prototype-conditioned Mixture-of-Experts framework for multimodal federated learning, effectively addressing missing modalities. It outperforms existing methods on four chest X-ray datasets, demonstrating superior feature synthesis capabilities in both homogeneous and heterogeneous settings.