Training-free Cross-domain Few-shot Segmentation via Robust Semantic Representation and Matching

Cross-domain Few-shot Segmentation (CD-FSS) aims to tra-nsfer knowledge learned from source domain to distinct target domains,segmenting unseen target classes with only a few annotated samples.Although existing methods have made significant progress, they stillrely on training or fine-tuning processes, which incur high computationalcosts and risk overfitting. We observe that when powerful and general-purpose vision foundation models are incorporated into these methods,their performance shows only marginal improvement or even degradesdue to overfitting. To address this, we eliminate trainable parameters andpropose a training-free framework to avoid both training overhead andoverfitting. Built upon the self-supervised vision encoder DINOv3, ourframework addresses cross-domain challenges through three core mod-ules. First, the Semantic-aware Feature Re-fusion (SAFR) module iden-tifies and re-fuses features that emphasize semantic patterns, generatingrepresentations with enhanced semantic discriminability. Additionally,the Adaptive Support Enhancement (ASE) module narrows semanticgaps between support and query through robust query information ag-gregation. Finally, the Hybrid Prototype Matching (HPM) module inte-grates matching results from diverse prototypes to adapt to varying se-mantic complexity across domains. Extensive experiments on four targetdomain datasets demonstrate that our method achieves state-of-the-artperformance in CD-FSS without any training. Our code is available athttps://github.com/Sparkling-Water/RSRM.