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  2. 自动化研究所
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  4. 紫东太初大模型研究中心
  5. 图像与视频分析团队
Multi-Granularity Mutual Learning Network for Object Re-Identification

文献类型：期刊论文

作者Tu, Mingfei3,4; Zhu, Kuan3,4; Guo, Haiyun2,4; Miao, Qinghai3; Zhao, Chaoyang2; Zhu, Guibo4; Qiao, Honglin1; Huang, Gaopan1; Tang, Ming; Wang, Jinqiao2,3,4
刊名IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
出版日期2022-03-29
页码12
关键词Feature extraction Visualization Task analysis Intelligent transportation systems Image reconstruction Semantics Licenses Object re-identification mutual learning feature reconstruction solving jigsaw puzzle
ISSN号1524-9050
DOI10.1109/TITS.2021.3137954
通讯作者Guo, Haiyun(haiyun.guo@nlpr.ia.ac.cn) ; Miao, Qinghai(miaoqh@ucas.ac.cn)
英文摘要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.
WOS关键词VEHICLE REIDENTIFICATION
资助项目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
WOS研究方向Engineering ; Transportation
语种英语
WOS记录号WOS:000777339700001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/48198]  
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
紫东太初大模型研究中心
通讯作者Guo, Haiyun; Miao, Qinghai
作者单位1.Alibaba Cloud, Beijing 100102, Peoples R China
2.Object Eye Inc, Beijing 100078, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100864, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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