Query-support semantic correlation mining for few-shot segmentation

Given one or several annotation samples, few-shot segmentation has the capability to complete the segmentation of unseen target objects, thereby reducing the need for extensive data annotation. However, due to the scarcity and diversity of data, it becomes challenging to employ a few support samples effectively for guiding the segmentation of one query sample. Most existing approaches are committed to excavating support samples' information while ignoring the crucial relationship between query and support samples. To address this limitation and capture richer mutual information, we propose a query-support semantic correlation mining module that serves as an elaborate guide for subsequent dense matching. Specifically, two kinds of query guide maps are generated that leverage global and local similarities to learn the comprehensive correlation between multi-scale support and query features. By incorporating this module, the network can better execute dense matching and mitigate the possibility of misclassification effectively. In addition, a dual-attention module is introduced to obtain a discriminating initial prototype representation to avert prototype bias. Furthermore, our method generalizes well in the k-shot setting. Extensive experiments on PASCAL -5i and COCO-20i datasets demonstrate that the proposed method is potent and successful, which yields competitive segmentation results with state-of-the-art methods.