广西师范大学学报（自然科学版）2026，Vol.44Issue(4)：1-27,27.DOI:10.16088/j.issn.1001-6600.2025081302
跨域少样本图像语义分割方法综述
A review of cross-domain few-shot image semantic segmentation methods
摘要
Abstract
Cross-domain few-shot image semantic segmentation is widely applied in fields such as medical image analysis and remote sensing image processing.This paper focuses on the field of cross-domain few-shot image semantic segmentation,presenting the first systematic review in this direction.Firstly,the development from image semantic segmentation and few-shot image semantic segmentation to cross-domain few-shot image semantic segmentation is outlined,identifying the core challenges as domain shift,scarcity of annotated data,and insufficient model generalization capability.Subsequently,nine commonly used datasets and five key evaluation metrics are summarized.Existing cross-domain few-shot image semantic segmentation methods are categorized into ten subclasses from three dimensions:feature alignment strategies,model architecture design,and data utilization approaches;and their key strategies are analyzed.Finally,the limitations of current methods and potential future research directions are discussed,aiming to provide researchers with a comprehensive overview of the current state and emerging trends in this field.关键词
深度学习/图像语义分割/跨域/少样本/特征对齐Key words
deep learning/image semantic segmentation/cross-domain/few-shot/feature alignment分类
信息技术与安全科学引用本文复制引用
唐程华,易见兵,吴欣,熊文武,王敬永..跨域少样本图像语义分割方法综述[J].广西师范大学学报（自然科学版）,2026,44(4):1-27,27.基金项目
国家自然科学基金(62366017) （62366017）
江西省自然科学基金(20181BAB202004) （20181BAB202004）
江西省研究生创新专项资金(YC2024-S570,YC2024-S572) （YC2024-S570,YC2024-S572）
赣州市重点研发计划(GZ2024YLJ273) （GZ2024YLJ273）
