CASIA OpenIR
Two-Level Attention Network With Multi-Grain Ranking Loss for Vehicle Re-Identification
Guo, Haiyun1,2; Zhu, Kuan1,2; Tang, Ming1,2; Wang, Jinqiao1,2
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2019-09-01
Volume28Issue:9Pages:4328-4338
Corresponding AuthorZhu, Kuan(kuan.zhu@nlpr.ia.ac.cn)
AbstractVehicle re-identification (re-ID) aims to identify the same vehicle across multiple non-overlapping cameras, which is rather a challenging task. On the one hand, subtle changes in viewpoint and illumination condition can make the same vehicle look much different. On the other hand, different vehicles, even different vehicle models, may look quite similar. In this paper, we propose a novel Two-level Attention network supervised by a Multi-grain Ranking loss (TAMR) to learn an efficient feature embedding for the vehicle re-ID task. The two-level attention network consisting of hard part-level attention and soft pixel-level attention can adaptively extract discriminative features from the visual appearance of vehicles. The former one is designed to localize the salient vehicle parts, such as windscreen and car head. The latter one gives an additional attention refinement at pixel level to focus on the distinctive characteristics within each part. In addition, we present a multi-grain ranking loss to further enhance the discriminative ability of learned features. We creatively take the multi-grain relationship between vehicles into consideration. Thus, not only the discrimination between different vehicles but also the distinction between different vehicle models is constrained. Finally, the proposed network can learn a feature space, where both intra-class compactness and interclass discrimination are well guaranteed. Extensive experiments demonstrate the effectiveness of our approach and we achieve state-of-the-art results on two challenging datasets, including VehicleID and Vehicle-1M.
KeywordTwo-level attention network multi-grain ranking loss vehicle re-identification feature embedding
DOI10.1109/TIP.2019.2910408
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61806200]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000473641100011
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26856
Collection中国科学院自动化研究所
Corresponding AuthorZhu, Kuan
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Guo, Haiyun,Zhu, Kuan,Tang, Ming,et al. Two-Level Attention Network With Multi-Grain Ranking Loss for Vehicle Re-Identification[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(9):4328-4338.
APA Guo, Haiyun,Zhu, Kuan,Tang, Ming,&Wang, Jinqiao.(2019).Two-Level Attention Network With Multi-Grain Ranking Loss for Vehicle Re-Identification.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(9),4328-4338.
MLA Guo, Haiyun,et al."Two-Level Attention Network With Multi-Grain Ranking Loss for Vehicle Re-Identification".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.9(2019):4328-4338.
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