Visual Place Recognition via a Multitask Learning Method With Attentive Feature Aggregation
文献类型：期刊论文
| 作者 | Guan, Peiyu2,3 ; Cao, Zhiqiang2,3; Yu, Junzhi1; Tan, Min2,3; Wang, Shuo2,3
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| 刊名 | IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
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| 出版日期 | 2023-09-01 |
| 卷号 | 15期号:3页码:1263-1278 |
| 关键词 | Attentive feature aggregation multitask learning visual place recognition |
| ISSN号 | 2379-8920 |
| DOI | 10.1109/TCDS.2022.3206500 |
| 通讯作者 | Cao, Zhiqiang(zhiqiang.cao@ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | IMAGE ; NETVLAD ; NETWORK |
| 资助项目 | National Natural Science Foundation of China[62073322] ; National Natural Science Foundation of China[61633020] |
| WOS研究方向 | Computer Science ; Robotics ; Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:001089186500023 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54391]  |
| 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.Peking Univ, Coll Engn, Dept Adv Mfg & Robot, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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