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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 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
2019-09 | |
卷号 | 28期号:9页码:4328-4338 |
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. |
关键词 | Two-level attention network multi-grain ranking loss vehicle re-identification feature embedding |
DOI | 10.1109/TIP.2019.2910408 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61806200] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61806200] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000473641100011 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/26856 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
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 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
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. |
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