Shallow Feature-driven Dual-edges Localization Network for Weakly Supervised Localization  

作　　者：Wenjun Hui Guanghua Gu Bo Wang 

机构地区：[1]School of Information Science and Engineering,Yanshan University,Qinhuangdao,066000,China [2]Hebei Key Laboratory of Information Transmission and Signal Processing,Qinhuangdao,066000,China

出　　处：《Machine Intelligence Research》2023年第6期923-936,共14页机器智能研究（英文版）

基　　金：This work was partly supported by National Natural Science Foundation of China(No.62072394);Natural Science Foundation of Hebei Province,China(No.F2021203019);Hebei Key Laboratory Project,China(No.202250701010046).

摘　　要：Weakly supervised object localization mines the pixel-level location information based on image-level annotations.The traditional weakly supervised object localization approaches exploit the last convolutional feature map to locate the discriminative regions with abundant semantics.Although it shows the localization ability of classification network,the process lacks the use of shallow edge and texture features,which cannot meet the requirement of object integrity in the localization task.Thus,we propose a novel shallow feature-driven dual-edges localization(DEL)network,in which dual kinds of shallow edges are utilized to mine entire target object regions.Specifically,we design an edge feature mining(EFM)module to extract the shallow edge details through the similarity measurement between the original class activation map and shallow features.We exploit the EFM module to extract two kinds of edges,named the edge of the shallow feature map and the edge of shallow gradients,for enhancing the edge details of the target object in the last convolutional feature map.The total process is proposed during the inference stage,which does not bring extra training costs.Extensive experiments on both the ILSVRC and CUB-200-2011 datasets show that the DEL method obtains consistency and substantial performance improvements compared with the existing methods.

关 键 词：Weakly supervised object localization edge feature mining edge of shallow feature map edge of shallow gradients similarity measurement 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]