Xu, D; Yu, S; Zhou, J; Guo, F; Li, L; Chen, J; (2025) Multi-scale prototype convolutional network for few-shot semantic segmentation. PLoS ONE , 20 (4) , Article e0319905. 10.1371/journal.pone.0319905.

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Abstract

Few-shot semantic segmentation aims to accurately segment objects from a limited amount of annotated data, a task complicated by intra-class variations and prototype representation challenges. To address these issues, we propose the Multi-Scale Prototype Convolutional Network (MPCN). Our approach introduces a Prior Mask Generation (PMG) module, which employs dynamic kernels of varying sizes to capture multi-scale object features. This enhances the interaction between support and query features, thereby improving segmentation accuracy. Additionally, we present a Multi-Scale Prototype Extraction (MPE) module to overcome the limitations of MAP (Mean Average Precision). By augmenting support set features, assessing spatial importance, and utilizing multi-scale downsampling, we obtain a more accurate prototype set. Extensive experiments conducted on the PASCAL-5i and COCO-20i datasets demonstrate that our method achieves superior performance in both 1-shot and 5-shot settings.

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