Cross-domain few-shot segmentation (CD-FSS) aims to segment objects of novel classes in new domains, which is often challenging due to the diverse characteristics of target domains and the limited availability of support data. Most CD-FSS methods redesign and retrain in-domain FSS models using abundant base data from the source domain, which are effective but costly to train. To address these issues, we propose adapting informative model structures of the well-trained FSS model for target domains by learning domain characteristics from few-shot labeled support samples during inference, thereby eliminating the need for source domain retraining. Specifically, we first adaptively identify domain-specific model structures by measuring parameter importance using a novel structure Fisher score in a data-dependent manner. Then, we progressively train the selected informative model structures with hierarchically constructed training samples, progressing from fewer to more support shots. The resulting Informative Structure Adaptation (ISA) method effectively addresses domain shifts and equips existing well-trained in-domain FSS models with flexible adaptation capabilities for new domains, eliminating the need to redesign or retrain CD-FSS models on base data. Extensive experiments validate the effectiveness of our method, demonstrating superior performance across multiple CD-FSS benchmarks. Codes are at https://github.com/fanq15/ISA.

翻译：跨域少样本分割（CD-FSS）旨在对新领域中的新类别目标进行分割，由于目标领域特征的多样性及支持数据的有限性，该任务通常具有挑战性。现有CD-FSS方法大多基于源域丰富的基类数据重新设计并重训域内FSS模型，虽有效但训练成本高昂。为解决此问题，我们提出在推理阶段通过从少样本标注支持样本中学习领域特征，直接适配已训练完备的FSS模型的信息化结构至目标域，从而避免源域重训练。具体而言，我们首先通过数据依赖的新型结构费希尔分数度量参数重要性，自适应识别领域特定模型结构；随后，利用分层构建的训练样本（从较少到较多支持样本）对选定的信息化模型结构进行渐进式训练。所提出的信息化结构适配（ISA）方法有效应对领域偏移，使现有训练完备的域内FSS模型获得面向新领域的灵活适配能力，无需基于基类数据重新设计或重训CD-FSS模型。大量实验验证了本方法的有效性，在多个CD-FSS基准测试中均展现出优越性能。代码发布于 https://github.com/fanq15/ISA。