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Cross-domain few-shot segmentation (CD-FSS) aims to achieve semanticsegmentation in previously unseen domains with a limited number of annotatedsamples. Although existing CD-FSS models focus on cross-domain featuretransformation, relying exclusively on inter-domain knowledge transfer may leadto the loss of critical intra-domain information. To this end, we propose anovel residual transformation network (RestNet) that facilitates knowledgetransfer while retaining the intra-domain support-query feature information.Specifically, we propose a Semantic Enhanced Anchor Transform (SEAT) modulethat maps features to a stable domain-agnostic space using advanced semantics.Additionally, an Intra-domain Residual Enhancement (IRE) module is designed tomaintain the intra-domain representation of the original discriminant space inthe new space. We also propose a mask prediction strategy based on prototypefusion to help the model gradually learn how to segment. Our RestNet cantransfer cross-domain knowledge from both inter-domain and intra-domain withoutrequiring additional fine-tuning. Extensive experiments on ISIC, Chest X-ray,and FSS-1000 show that our RestNet achieves state-of-the-art performance. Ourcode will be available soon.

RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network