Journal of Guangxi Normal University(Natural Science Edition) ›› 2026, Vol. 44 ›› Issue (4): 1-27.doi: 10.16088/j.issn.1001-6600.2025081302

• Review •     Next Articles

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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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

CLC Number:  TP391.41
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