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SPA2Net: Structure-Preserved Attention Activated Network for Weakly Supervised Object Localization

文献类型：期刊论文

作者Chen, Dong1; Pan, Xingjia2; Tang, Fan3; Dong, Weiming4; Xu, Changsheng4
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2023
卷号32页码:5779-5793
ISSN号1057-7149
关键词High-order self-correlation class activation map structure preservation weakly supervised object localization
DOI10.1109/TIP.2023.3323793
通讯作者Tang, Fan(tangfan@ict.ac.cn)
英文摘要By exploring the localizable representations in deep CNN, weakly supervised object localization (WSOL) methods could determine the position of the object in each image just trained by the classification task. However, the partial activation problem caused by the discriminant function makes the network unable to locate objects accurately. To alleviate this problem, we propose Structure-Preserved Attention Activated Network (SPA(2)Net), a simple and effective one-stage WSOL framework to explore the ability of structure preservation of deep features. Different from traditional WSOL approaches, we decouple the object localization task from the classification branch to reduce their mutual influence by involving a localization branch which is online refined by a self-supervised structural-preserved localization mask. Specifically, we employ the high-order self-correlation as structural prior to enhance the perception of spatial interaction within convolutional features. By succinctly combining the structural prior with spatial attention, activations by SPA(2)Net will spread from part to the whole object during training. To avoid the structure-missing issue caused by the classification network, we furthermore utilize the restricted activation loss (RAL) to distinguish the difference between foreground and background in the channel dimension. In conjunction with the self-supervised localization branch, SPA(2)Net can directly predict the class-irrelevant localization map while prompting the network to pay more attention to the target region for accurate localization. Extensive experiments on two publicly available benchmarks, including CUB-200-2011 and ILSVRC, show that our SPA(2)Net achieves substantial and consistent performance gains compared with baseline approaches. The code and models are available at https://github.com/MsterDC/SPA2Net.
WOS关键词ACTION RECOGNITION
资助项目National Natural Science Foundation of China[62102162] ; National Natural Science Foundation of China[U20B2070] ; National Natural Science Foundation of China[61832016] ; National Natural Science Foundation of China[61832002] ; Beijing Natural Science Foundation[L221013]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001104979200002
资助机构National Natural Science Foundation of China ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/55089]  
专题多模态人工智能系统全国重点实验室
通讯作者Tang, Fan
作者单位1.Jilin Univ, Sch Artificial Intelligence, Changchun 130012, Peoples R China
2.Momenta, Beijing 100018, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Dong,Pan, Xingjia,Tang, Fan,et al. SPA2Net: Structure-Preserved Attention Activated Network for Weakly Supervised Object Localization[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:5779-5793.
APA Chen, Dong,Pan, Xingjia,Tang, Fan,Dong, Weiming,&Xu, Changsheng.(2023).SPA2Net: Structure-Preserved Attention Activated Network for Weakly Supervised Object Localization.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,5779-5793.
MLA Chen, Dong,et al."SPA2Net: Structure-Preserved Attention Activated Network for Weakly Supervised Object Localization".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):5779-5793.

入库方式： OAI收割

来源：自动化研究所

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