Visual Place Recognition via a Multitask Learning Method With Attentive Feature Aggregation

doi:10.1109/TCDS.2022.3206500

	Visual Place Recognition via a Multitask Learning Method With Attentive Feature Aggregation
	Guan, Peiyu1,2 ; Cao, Zhiqiang1,2 ; Yu, Junzhi3 ; Tan, Min1,2 ; Wang, Shuo1,2
发表期刊	IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
ISSN	2379-8920
	2023-09-01
卷号	15 期号:3 页码:1263-1278
通讯作者	Cao, Zhiqiang(zhiqiang.cao@ia.ac.cn)
摘要	Visual place recognition has gained popularity in recent years. Mainstream convolutional neural network-based methods formulate it as a ranking task and optimize it in the paradigm of deep metric learning, however, the ranking-motivated losses concern only the ranking relationship for each query image and the compactness of intraplace feature distribution is seldom considered. It is still challenging due to varying viewpoints, illuminations, and even dynamic objects. In this article, a novel multitask learning framework is proposed, which combines the existing triplet ranking task and our designed binary classification task to jointly optimize the network for better generalization capability. Specifically, a binary classification network with the corresponding binary cross-entropy loss is designed in the classification task. In this way, the intraplace feature compactness and interplace feature separability are reinforced. At the testing stage, this classification network is discarded without increasing the computation cost. Furthermore, an attention module is presented to promote the network to concentrate on the salient regions by assigning different importance to each spatial position. Our method achieves the top-10 recalls of 97.27%, 94.6%, and 96.93% on Pitts250k-test, Tokyo 24/7, and TokyoTM-val data sets, respectively. Extensive experiments prove that the proposed network can learn discriminative global features with better robustness to viewpoints and environmental variations.
关键词	Attentive feature aggregation multitask learning visual place recognition
DOI	10.1109/TCDS.2022.3206500
关键词[WOS]	IMAGE ; NETVLAD ; NETWORK
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62073322] ; National Natural Science Foundation of China[61633020]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Robotics ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Robotics ; Neurosciences
WOS记录号	WOS:001089186500023
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54392
专题	多模态人工智能系统全国重点实验室
通讯作者	Cao, Zhiqiang
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Peking Univ, Coll Engn, Dept Adv Mfg & Robot, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Guan, Peiyu,Cao, Zhiqiang,Yu, Junzhi,et al. Visual Place Recognition via a Multitask Learning Method With Attentive Feature Aggregation[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2023,15(3):1263-1278.
APA	Guan, Peiyu,Cao, Zhiqiang,Yu, Junzhi,Tan, Min,&Wang, Shuo.(2023).Visual Place Recognition via a Multitask Learning Method With Attentive Feature Aggregation.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,15(3),1263-1278.
MLA	Guan, Peiyu,et al."Visual Place Recognition via a Multitask Learning Method With Attentive Feature Aggregation".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 15.3(2023):1263-1278.