Progressive Context-Aware Graph Feature Learning for Target Re-Identification
文献类型：期刊论文
| 作者 | Cao, Min2 ; Ding, Cong2; Chen, Chen3; Dou, Hao3; Hu, Xiyuan4; Yan, Junchi1,5
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| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
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| 出版日期 | 2023 |
| 卷号 | 25页码:1230-1242 |
| ISSN号 | 1520-9210 |
| 关键词 | Target re-identification graph convolutional network feature learning contextual information graph feature learning |
| DOI | 10.1109/TMM.2022.3140647 |
| 通讯作者 | Chen, Chen(chen.chen@ia.ac.cn) |
| 英文摘要 | This paper aims at robust and discriminative feature learning for target re-identification (Re-ID). In addition to paying attention to the individual appearance information as in most Re-ID methods, we further utilize the abundant contextual information as additional clues to guide the feature learning. Graph as a format of structured data is used to represent the target sample with its context. It describes the first-order appearance information of the samples and the second-order topological relationship information among samples, based on which we compute the feature representation by learning a graph feature embedding. We provide a detailed analysis of graph convolutional network mechanism applied in target Re-ID and propose a novel progressive context-aware graph feature learning method, in which the message passing is dominated by a pre-defined adjacency relationship followed by a learned relationship in a self-adaptive way. The proposed method fully exploits and utilizes contextual information at a low cost for Re-ID. Extensive experiments on five Re-ID benchmarks demonstrate the state-of-the-art performance of the proposed method. |
| WOS关键词 | PERSON REIDENTIFICATION ; NEURAL-NETWORK |
| 资助项目 | National Science Foundation of China (NSFC)[61906194] ; National Science Foundation of China (NSFC)[62002252] ; Collaborative Innovation Center of Novel Software Technology and Industrialization ; Liaoning Collaboration Innovation Center for CSLE |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000970791100016 |
| 资助机构 | National Science Foundation of China (NSFC) ; Collaborative Innovation Center of Novel Software Technology and Industrialization ; Liaoning Collaboration Innovation Center for CSLE |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53218]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Chen, Chen |
| 作者单位 | 1.Shanghai Jiao Tong Univ, AI Inst, MoE Key Lab Artificial Intelligence, Shanghai 200240, Peoples R China 2.Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210093, Jiangsu, Peoples R China 5.Shanghai Jiao Tong Univ, AI Inst, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Min,Ding, Cong,Chen, Chen,et al. Progressive Context-Aware Graph Feature Learning for Target Re-Identification[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2023,25:1230-1242. |
| APA | Cao, Min,Ding, Cong,Chen, Chen,Dou, Hao,Hu, Xiyuan,&Yan, Junchi.(2023).Progressive Context-Aware Graph Feature Learning for Target Re-Identification.IEEE TRANSACTIONS ON MULTIMEDIA,25,1230-1242. |
| MLA | Cao, Min,et al."Progressive Context-Aware Graph Feature Learning for Target Re-Identification".IEEE TRANSACTIONS ON MULTIMEDIA 25(2023):1230-1242. |
入库方式： OAI收割
来源：自动化研究所
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