MSCA: A few-shot segmentation framework driven by multi-scale cross-attention and information extraction

Abstract

Few-Shot Semantic Segmentation (FSS) aims to achieve precise pixel-level segmentation of target objects in query images using only a small number of annotated support images. The main challenge lies in effectively capturing and transferring critical information from support samples while establishing fine-grained semantic associations between query and support images to improve segmentation accuracy. However, existing methods struggle with spatial alignment issues caused by intra-class variations and inter-class visual similarities, and they fail to fully integrate high-level and low-level decoder features. To address these limitations, we propose a novel framework based on cross-scale interactive attention mechanisms. This framework employs a hybrid mask-guided multi-scale feature fusion strategy, constructing a cross-scale attention network that spans from local details to global context. It dynamically enhances target region representation and alleviates spatial misalignment issues. Furthermore, we design a hierarchical multi-axis decoding architecture that progressively integrates multi-resolution feature pathways, enabling the model to focus on semantic associations within foreground regions. Experimental results show that our Multi-Scale Cross-Attention (MSCA) model performs exceptionally well on the PASCAL-5i and COCO-20i benchmark datasets, achieving highly competitive results. Notably, the model contains only 1.86 million learnable parameters, demonstrating its efficiency and practical applicability.