CAMSNet:Few-Shot Semantic Segmentation via Class Activation Map and Self-Cross Attention Block  

作　　者：Jingjing Yan Xuyang Zhuang Xuezhuan Zhao Xiaoyan Shao Jiaqi Han 

机构地区：[1]School of Computer Science,Zhengzhou University of Aeronautics,Zhengzhou,450046,China [2]National Key Laboratory of Air-Based Information Perception and Fusion,China Airborne Missile Academy,Luoyang,471000,China

出　　处：《Computers, Materials & Continua》2025年第3期5363-5386,共24页计算机、材料和连续体(英文)

基　　金：supported by funding from the following sources:National Natural Science Foundation of China(U1904119);Research Programs of Henan Science and Technology Department(232102210033,232102210054);Chongqing Natural Science Foundation(CSTB2023NSCQ-MSX0070);Henan Province Key Research and Development Project(231111212000);Aviation Science Foundation(20230001055002);supported by Henan Center for Outstanding Overseas Scientists(GZS2022011).

摘　　要：The key to the success of few-shot semantic segmentation(FSS)depends on the efficient use of limited annotated support set to accurately segment novel classes in the query set.Due to the few samples in the support set,FSS faces challenges such as intra-class differences,background(BG)mismatches between query and support sets,and ambiguous segmentation between the foreground(FG)and BG in the query set.To address these issues,The paper propose a multi-module network called CAMSNet,which includes four modules:the General Information Module(GIM),the Class Activation Map Aggregation(CAMA)module,the Self-Cross Attention(SCA)Block,and the Feature Fusion Module(FFM).In CAMSNet,The GIM employs an improved triplet loss,which concatenates word embedding vectors and support prototypes as anchors,and uses local support features of FG and BG as positive and negative samples to help solve the problem of intra-class differences.Then for the first time,the Class Activation Map(CAM)from the Weakly Supervised Semantic Segmentation(WSSS)is applied to FSS within the CAMA module.This method replaces the traditional use of cosine similarity to locate query information.Subsequently,the SCA Block processes the support and query features aggregated by the CAMA module,significantly enhancing the understanding of input information,leading to more accurate predictions and effectively addressing BG mismatch and ambiguous FG-BG segmentation.Finally,The FFM combines general class information with the enhanced query information to achieve accurate segmentation of the query image.Extensive Experiments on PASCAL and COCO demonstrate that-5i-20ithe CAMSNet yields superior performance and set a state-of-the-art.

关 键 词：Few-shot semantic segmentation semantic segmentation meta learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]