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Two-Level Attention Network With Multi-Grain Ranking Loss for Vehicle Re-Identification

文献类型：期刊论文

作者Guo, Haiyun1,2; Zhu, Kuan1,2; Tang, Ming1,2; Wang, Jinqiao1,2
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2019-09-01
卷号28期号:9页码:4328-4338
关键词Two-level attention network multi-grain ranking loss vehicle re-identification feature embedding
ISSN号1057-7149
DOI10.1109/TIP.2019.2910408
通讯作者Zhu, Kuan(kuan.zhu@nlpr.ia.ac.cn)
英文摘要Vehicle 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.
资助项目National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61806200]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000473641100011
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/173211/26855]  
专题自动化研究所_模式识别国家重点实验室_图像与视频分析团队
通讯作者Zhu, Kuan
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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