Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting
novel categories from a distinct domain using only limited exemplars. In this
paper, we undertake a comprehensive study of CD-FSS and uncover two crucial
insights: (i) the necessity of a fine-tuning stage to effectively transfer the
learned meta-knowledge across domains, and (ii) the overfitting risk during the
na\"ive fine-tuning due to the scarcity of novel category examples. With these
insights, we propose a novel cross-domain fine-tuning strategy that addresses
the challenging CD-FSS tasks. We first design Bi-directional Few-shot
Prediction (BFP), which establishes support-query correspondence in a
bi-directional manner, crafting augmented supervision to reduce the overfitting
risk. Then we further extend BFP into Iterative Few-shot Adaptor (IFA), which
is a recursive framework to capture the support-query correspondence
iteratively, targeting maximal exploitation of supervisory signals from the
sparse novel category samples. Extensive empirical evaluations show that our
method significantly outperforms the state-of-the-arts (+7.8\%), which verifies
that IFA tackles the cross-domain challenges and mitigates the overfitting
simultaneously. Code will be made available.

跨领域少样本分割 (CD-FSS) 需要仅使用有限样本从不同领域中对新颖类别进行分割，论文发现通过 fine-tuning 阶段有效地在领域之间转移学习得到的元知识的必要性以及由于新颖类别样本稀缺导致朴素的 fine-tuning 存在过拟合风险，并提出了一种解决这一挑战的新型跨领域 fine-tuning 策略，通过设计双向少样本预测 (BFP) 和迭代少样本适配器 (IFA) 来降低过拟合风险并最大化利用稀疏的新颖类别样本的监督信号，实验证明该方法显著优于当前技术水平 (+7.8%)，验证了 IFA 能同时应对跨领域挑战和减轻过拟合问题。代码将提供。