Beyond Simple Extraction: Unleashing the Potential of Encoder Interaction in Few-Shot Segmentation

Few-shot segmentation (FSS) aims to segment objects of new categories instructed by only a few annotated samples. Previous research primarily focused on designing interactions between the query features and the support features during the decoding phase, neglecting the exploration of support-query interactions during the encoding phase. This could introduce mismatches from pre-training tasks, thereby affecting the accurate representation of the target categories. In this paper, we re-evaluate the importance of interactions during the encoding phase and propose a novel FSS framework, BSENet, which incorporates interactive operations at all stages. Specifically, the Prompt-based Feature Module (PFM) and the Masked Cross Interaction Module (MCI) are jointly designed for encoding feature interaction. PFM enhances semantic interactions between query and support through the transfer of semantic tokens, and MCI enhances pixel-level visual interactions by employing pixel-level correlation maps. Furthermore, considering the risk of latent novel categories in the support images, a Foreground-Centric Cropping (FCC) strategy is developed for pre-processing support set. Extensive experiments on the PASCAL-5ⁱ and COCO-20ⁱ benchmarks show that BSENet achieves competitive FSS accuracy, surpassing previous methods that only considered support-query interactions during the decoding phase. The code and results can be found from the link of https://github.com/X-Hang/BSENet TMM26. © 2026 IEEE.