广西师范大学学报（自然科学版） ›› 2026, Vol. 44 ›› Issue (4): 1-27.doi: 10.16088/j.issn.1001-6600.2025081302

• 综述 •    下一篇

跨域少样本图像语义分割方法综述

唐程华1,2, 易见兵1,2*, 吴欣1,2, 熊文武1,2, 王敬永3   

  1. 1.江西理工大学 信息工程学院, 江西 赣州 341000;
    2.多维智能感知与控制江西省重点实验室(江西理工大学), 江西 赣州 341000;
    3.龙南鼎泰电子科技有限公司, 江西 赣州 341000
  • 收稿日期:2025-08-13 修回日期:2025-12-08 出版日期:2026-07-05 发布日期:2026-07-01
  • 通讯作者: 易见兵(1980—), 男, 江西宜春人, 江西理工大学副教授, 博士。E-mail: yijianbing8@jxust.edu.cn
  • 基金资助:
    国家自然科学基金(62366017);江西省自然科学基金(20181BAB202004);江西省研究生创新专项资金(YC2024-S570,YC2024-S572);赣州市重点研发计划(GZ2024YLJ273)

A review of cross-domain few-shot image semantic segmentation methods

Tang Chenghua1,2, Yi Jianbing1,2*, Wu Xin1,2, Xiong Wenwu1,2, Wang Jingyong3   

  1. 1. College of Information Engineering, Jiangxi University of Science and Technology, Ganzhou Jiangxi 341000, China;
    2. Jiangxi Province Key Laboratory of Multidimensional Intelligent Perception and Control (Jiangxi University of Science and Technology), Ganzhou Jiangxi 341000, China;
    3. Longnan Dingtai Electronic Technology Co., Ltd, Ganzhou Jiangxi 341000, China
  • Received:2025-08-13 Revised:2025-12-08 Online:2026-07-05 Published:2026-07-01

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摘要： 跨域少样本图像语义分割在医学影像分析、遥感图像处理等领域应用广泛。本文聚焦跨域少样本图像语义分割领域,此前未见该方向的系统性综述。本文首先梳理图像语义分割、少样本图像语义分割到跨域少样本图像语义分割的发展历程,明确跨域少样本图像语义分割的核心挑战为域偏移、标注数据稀缺及模型泛化能力不足;接着,整理9类常用数据集及5项核心评价指标;然后,从特征对齐策略、模型架构设计、数据利用方式3个维度,将现有跨域少样本图像语义分割方法划分为10个子类并剖析其关键策略;最后,对现有方法的局限性和未来的发展趋势进行探讨,旨在为广大研究者提供相关领域的前沿动态和研究现状分析,并为未来研究提供参考。

关键词: 深度学习, 图像语义分割, 跨域, 少样本, 特征对齐

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.

Key words: deep learning, image semantic segmentation, cross-domain, few-shot, feature alignment

中图分类号:  TP391.41

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