Abstract
Cross-Domain Few-Shot Semantic Segmentation (CD-FSS) seeks to segment unknown classes in unseen domains using only a few annotated examples. This setting is inherently challenging: source and target domains exhibit substantial distribution shifts, label spaces are disjoint, and support images are scarce--making standard episodic methods unreliable and computationally demanding at test time. To address these constraints, we propose DistillFSS, a framework that embeds support-set knowledge directly into a model's parameters through a teacher--student distillation process. By internalizing few-shot reasoning into a dedicated layer within the student network, DistillFSS eliminates the need for support images at test time, enabling fast, lightweight inference, while allowing efficient extension to novel classes in unseen domains through rapid teacher-driven specialization. Combined with fine-tuning, the approach scales efficiently to large support sets and significantly reduces computational overhead. To evaluate the framework under realistic conditions, we introduce a new CD-FSS benchmark spanning medical imaging, industrial inspection, and remote sensing, with disjoint label spaces and variable support sizes. Experiments show that DistillFSS matches or surpasses state-of-the-art baselines, particularly in multi-class and multi-shot scenarios, while offering substantial efficiency gains. The code is available at this https URL.
Abstract (translated)
跨域少量样本语义分割（CD-FSS）旨在仅使用少数标注示例，在未见领域中对未知类别进行分割。这种设置具有固有的挑战性：源和目标领域的分布差异显著，标签空间不相交，并且支持图像稀缺——这些因素使得标准的分段方法在测试时不可靠且计算成本高昂。为了解决这些问题，我们提出了DistillFSS框架，该框架通过教师-学生蒸馏过程将支持集知识直接嵌入到模型参数中。通过将少量样本推理内化为学生网络中的专用层，DistillFSS消除了测试时对支持图像的需求，实现了快速轻量级的推断，并且能够通过快速的教师驱动专业化有效地扩展至未见领域中的新类别。结合微调技术，该方法可以有效处理大量支持集并显著减少计算开销。 为了在现实条件下评估该框架，我们引入了一个新的CD-FSS基准测试，涵盖了医疗成像、工业检测和遥感领域，并且具有不相交的标签空间和可变的支持集大小。实验表明，DistillFSS在多类别和多样本场景中与最先进的基线方法相当或超越它们，并提供了显著的效率提升。 该框架的代码可在以下链接获取：[请在此处插入实际的URL]。
URL
https://arxiv.org/abs/2512.05613
