Two-Stage Category-Guided Frequency Modulation for Few-Shot Semantic Segmentation  

作　　者：Yiming Tang Yanqiu Chen 

机构地区：[1]School of Computer Science,Fudan University,Shanghai,200438,China

出　　处：《Computers, Materials & Continua》2025年第5期1707-1726,共20页计算机、材料和连续体(英文)

摘　　要：Semantic segmentation of novel object categories with limited labeled data remains a challenging problem in computer vision.Few-shot segmentation methods aim to address this problem by recognizing objects from specific target classes with a few provided examples.Previous approaches for few-shot semantic segmentation typically represent target classes using class prototypes.These prototypes are matched with the features of the query set to get segmentation results.However,class prototypes are usually obtained by applying global average pooling on masked support images.Global pooling discards much structural information,which may reduce the accuracy of model predictions.To address this issue,we propose a Category-Guided Frequency Modulation(CGFM)method.CGFM is designed to learn category-specific information in the frequency space and leverage it to provide a twostage guidance for the segmentation process.First,to self-adaptively activate class-relevant frequency bands while suppressing irrelevant ones,we leverage the Dual-Perception Gaussian Band Pre-activation(DPGBP)module to generate Gaussian filters using class embedding vectors.Second,to further enhance category-relevant frequency components in activated bands,we design a Support-Guided Category Response Enhancement(SGCRE)module to effectively introduce support frequency components into the modulation of query frequency features.Experiments on the PASCAL-5^(i) and COCO-20^(i) datasets demonstrate the promising performance of our model.

关 键 词：Few-shot semantic segmentation frequency feature category representation 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]