Abstract: Existing few-shot semantic segmentation methods commonly employ simple fusion strategies to propagate cross-branch information, making it challenging to capture intricate feature interactions. To address this issue, a mutually guided conditional coupling network is proposed to efficiently fuse support-query branch information. The network incorporates a coupling information generation module, which focuses on regions in the support feature space highly similar to the query features, enabling each pixel in the query features to adaptively integrate pixel-level information from the support features. Additionally, to mitigate the adverse impact of querying image background on fusion effectiveness, a conditional coupling module is designed. In this module, the generated conditions are utilized to suppress information absorption in the query image background regions, effectively avoiding interference from background information on the results. Extensive experiments and analyses on PASCAL-5ⁱ and COCO-20ⁱ datasets demonstrate significant improvements compared to baseline methods.