跨域少样本图像语义分割方法综述OA

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.