CGPRNet: Cross-Scale Graph Prototype Refinement for Robust Few-Shot Semantic Segmentation

Few-shot semantic segmentation (FSS) learns to segment novel classes with only a few annotated support images. Despite the promising results of recent methods, there remain three critical limitations: (1) current FSS methods do not explicitly model the cross-scale semantic dependencies between support and query; (2) they generate prototypes by simple embedding statistics while disregarding the progressive refinement ability of graph; (3) they fuse the multi-scale information in a principled manner. To address these challenges, We propose CGPR, a framework that jointly integrates query-conditioned graph prototype refinement, adaptive multi-scale matching, and dual-branch feature encoding to improve prototype quality, feature representation, and robustness to cross-scale variation. Concretely, we design a Cross-Attention Graph Prototype Generator to iteratively refine the prototypes by aggregating the cross-scale support-query relationships, and a Multi-Scale Prototype Matching module to compute the similarity at different receptive fields in an adaptive way. Additionally, we introduce a dual-branch feature encoder with frozen generic features and trainable task-specific features to prevent overfitting while maintaining discriminative power. We conduct extensive experiments on PASCAL-5i, COCO-20i, and FSS-1000 datasets to show the superiority of CGPRNet over the previous state-of-the-art methods. Under both 1-shot and 5-shot settings, our CGPRNet obtains better mIoU, FB-IoU and Dice scores on these three benchmarks, demonstrating the effectiveness of cross-scale and query-conditioned prototype refinement learning for robust few-shot segmentation.