SAM3 for Cross-Domain Few-Shot Segmentation via Dual-Branch Candidate Calibration

Cross-Domain Few-Shot Segmentation (CD-FSS) aims to segment novel categories in unseen domains from a few annotated support examples. Existing methods often rely on source-domain episodic training and remain sensitive to domain shifts. Motivated by advances in vision foundation models, we explore a fully frozen Segment Anything Model 3 (SAM3) for training-free CD-FSS. We observe that class-name text improves visual prompting in some domains but impairs it in others. To address this inconsistency, we propose a dual-branch candidate calibration framework. Support and query images are arranged on a shared canvas, where visual-only and visual-text branches generate complementary candidate masks. We assess candidate reliability using SAM3 confidence, support-query prototype consistency, and cross-branch agreement, then select and merge reliable masks into the final prediction. The framework requires no source-domain training, parameter updates, or additional learnable modules. Extensive experiments on diverse CD-FSS benchmarks demonstrate its effectiveness and superior average performance over the compared training-required and training-free methods.