Cross-domain few-shot segmentation (CD-FSS) aims to achieve semantic
segmentation in previously unseen domains with a limited number of annotated
samples. Although existing CD-FSS models focus on cross-domain feature
transformation, relying exclusively on inter-domain knowledge transfer may lead
to the loss of critical intra-domain information. To this end, we propose a
novel residual transformation network (RestNet) that facilitates knowledge
transfer while retaining the intra-domain support-query feature information.
Specifically, we propose a Semantic Enhanced Anchor Transform (SEAT) module
that maps features to a stable domain-agnostic space using advanced semantics.
Additionally, an Intra-domain Residual Enhancement (IRE) module is designed to
maintain the intra-domain representation of the original discriminant space in
the new space. We also propose a mask prediction strategy based on prototype
fusion to help the model gradually learn how to segment. Our RestNet can
transfer cross-domain knowledge from both inter-domain and intra-domain without
requiring additional fine-tuning. Extensive experiments on ISIC, Chest X-ray,
and FSS-1000 show that our RestNet achieves state-of-the-art performance. Our
code will be available soon.

提出了一种新型的残差转换网络（RestNet），在保留域内支持 - 查询特征信息的同时，通过语义增强锚点转换模块（SEAT）将特征映射到稳定的域无关空间，以及通过域内残差增强模块（IRE）在新空间中维持原始判别空间的域内表示。提出了基于原型融合的掩模预测策略，帮助模型逐渐学习如何进行分割。实验证明，RestNet 在 ISIC、胸部 X 光和 FSS-1000 数据集上取得了最先进的性能。