Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| Multi-Granularity Mutual Learning Network for Object Re-Identification | |
Tu, Mingfei1,2; Zhu, Kuan1,2; Guo, Haiyun2,3 ; Miao, Qinghai1; Zhao, Chaoyang3; Zhu, Guibo2; Qiao, Honglin4; Huang, Gaopan4; Tang, Ming; Wang, Jinqiao1,2,3
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| Source Publication | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| ISSN | 1524-9050 |
| 2022-03-29 | |
| Pages | 12 |
| Corresponding Author | Guo, Haiyun(haiyun.guo@nlpr.ia.ac.cn) ; Miao, Qinghai(miaoqh@ucas.ac.cn) |
| Abstract | Object re-identification (re-ID), which is key and fundamental technology for intelligent transportation systems, is a challenging task including person re-ID and vehicle re-ID. It aims to retrieve a given target object from the gallery images captured by different cameras. In this task, it is necessary to extract fine-grained and discriminative features to deal with complex inter-class and intra-class variations caused by the changes of camera viewpoints and object poses. Existing methods focus on learning discriminative local features to improve the re-ID performance. Some state-of-the-art methods use key point detection model to locate local features, which also increases the additional computational cost as side effect. Another type of method focuses on how to learn features of different granularity from rigid stripes of different scales. However, there is little attention paid to how to effectively coalesce multi-granularity features without additional calculation cost. To tackle this issue, this paper proposes the Multi-granularity Mutual Learning Network (MMNet) and makes two contributions. 1) We introduce the multi-granularity jigsaw puzzle module into object re-ID to impel the network to learn local discriminative features from multiple visual granularities by breaking spatial correlation in original images. 2) We propose a parameter-free multi-scale feature reconstruction module to facilitate mutual learning of features at multiple grain levels, thereby both global features and local features have strong representation capabilities. Extensive experiments demonstrate the effectiveness of our proposed modules and the superiority of our method over various state-of-the-art methods on both person and vehicle re-ID benchmarks. |
| Keyword | Feature extraction Visualization Task analysis Intelligent transportation systems Image reconstruction Semantics Licenses Object re-identification mutual learning feature reconstruction solving jigsaw puzzle |
| DOI | 10.1109/TITS.2021.3137954 |
| WOS Keyword | VEHICLE REIDENTIFICATION |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | Key-Area Research and Development Program of Guangdong Province[2019B010153001] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61976210] ; National Natural Science Foundation of China[62002356] ; National Natural Science Foundation of China[62002357] ; National Natural Science Foundation of China[62076235] ; National Natural Science Foundation of China[62006230] ; Open Research Projects of Zhejiang Laboratory[2021KH0AB07] ; Alibaba Group through Alibaba Innovative Research Program |
| Funding Organization | Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China ; Open Research Projects of Zhejiang Laboratory ; Alibaba Group through Alibaba Innovative Research Program |
| WOS Research Area | Engineering ; Transportation |
| WOS Subject | Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
| WOS ID | WOS:000777339700001 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Sub direction classification | 图像视频处理与分析 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/48199 |
| Collection | 模式识别国家重点实验室_图像与视频分析 |
| Corresponding Author | Guo, Haiyun; Miao, Qinghai |
| Affiliation | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100864, Peoples R China 3.Object Eye Inc, Beijing 100078, Peoples R China 4.Alibaba Cloud, Beijing 100102, Peoples R China |
| First Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Tu, Mingfei,Zhu, Kuan,Guo, Haiyun,et al. Multi-Granularity Mutual Learning Network for Object Re-Identification[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022:12. |
| APA | Tu, Mingfei.,Zhu, Kuan.,Guo, Haiyun.,Miao, Qinghai.,Zhao, Chaoyang.,...&Wang, Jinqiao.(2022).Multi-Granularity Mutual Learning Network for Object Re-Identification.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,12. |
| MLA | Tu, Mingfei,et al."Multi-Granularity Mutual Learning Network for Object Re-Identification".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022):12. |
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