Cascaded State Space and Contrastive Learning for Cross-Domain Few-Shot Segmentation

Current cross-domain few-shot semantic segmentation (CD-FSS) faces multiple challenges, including inconsistent feature mapping among domains and insufficient utilization of low-level information and background information from the source domain. To address these issues, this article proposes a novel cascade feature enhancement and contrastive learning framework to improve the generalization capability of CD-FSS. Within this framework, we first introduce a cascade feature enhancement module to construct distinctive feature representations, enhancing the model’s transferability across domains. By effectively integrating multilevel feature information from support images, this module strengthens the representation capability of query images. Second, we employ contrastive learning to form positive and negative sample pairs for the foreground and background, capturing rich correlations between them. Finally, the iterative prototype enhancement module we propose gradually refines the correspondence between the support image and the query image through iteration, making full use of the embedded supervisory information in the limited support samples. Experimental results demonstrate that the proposed method outperforms existing approaches on multiple benchmark datasets, achieving up to a 9.7% improvement over state-of-the-art methods.