Knowledge Commons of Institute of Automation,CAS
Tensor Multi-Task Learning for Person Re-Identification | |
Zhang, Zhizhong1,2; Xie, Yuan3; Zhang, Wensheng1,2; Tang, Yongqiang4; Tian, Qi5 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
2020 | |
卷号 | 29期号:1页码:2463-2477 |
摘要 | This article presents a tensor multi-task model for person re-identification (Re-ID). Due to discrepancy among cameras, our approach regards Re-ID from multiple cameras as different but related classification tasks, each task corresponding to a specific camera. In each task, we distinguish the person identity as a one-vs-all linear classification problem, where one classifier is associated with a specific person. By constructing all classifiers into a task-specific projection matrix, the proposed method could utilize all the matrices to form a tensor structure, and jointly train all the tasks in a uniform tensor space. In this space, by assuming the features of the same person under different cameras are generated from a latent subspace, and different identities under the same perspective share similar patterns, the high-order correlations, not only across different tasks but also within a certain task, can be captured by utilizing a new type of low-rank tensor constraint. Therefore, the learned classifiers transform the original feature vector into the latent space, where feature distributions across cameras can be well-aligned. Moreover, this model can be incorporated into multiple visual features to boost the performance, and easily extended to the unsupervised setting. Extensive experiments and comparisons with recent Re-ID methods manifest the competitive performance of our method. |
关键词 | Cameras Task analysis Measurement Visualization Training Computational modeling Person re-identification multi-task learning tensor optimization |
DOI | 10.1109/TIP.2019.2949929 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Shanghai Key Laboratory of Trustworthy Computing ; National Natural Science Foundation of China[61432008] ; National Key R&D Program of China[2017YFC0803700] ; Beijing Municipal Natural Science Foundation[4182067] ; National Natural Science Foundation of China[61772524] ; Fundamental Research Funds for the Central Universities ; National Natural Science Foundation of China[61472423] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61472423] ; Fundamental Research Funds for the Central Universities ; National Natural Science Foundation of China[61772524] ; Beijing Municipal Natural Science Foundation[4182067] ; National Key R&D Program of China[2017YFC0803700] ; National Natural Science Foundation of China[61432008] ; Shanghai Key Laboratory of Trustworthy Computing |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000507869900020 |
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/173211/29512 |
专题 | 多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队 精密感知与控制研究中心 |
通讯作者 | Zhang, Wensheng; Tian, Qi |
作者单位 | 1.Chinese Acad Sci Ences, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 3.East China Normal Univ, Sch Comp Sci & Technol, Shanghai 200241, Peoples R China 4.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China 5.Huawei Noahs Ark Lab, Comp Vis, Shenzhen 51800, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Zhizhong,Xie, Yuan,Zhang, Wensheng,et al. Tensor Multi-Task Learning for Person Re-Identification[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29(1):2463-2477. |
APA | Zhang, Zhizhong,Xie, Yuan,Zhang, Wensheng,Tang, Yongqiang,&Tian, Qi.(2020).Tensor Multi-Task Learning for Person Re-Identification.IEEE TRANSACTIONS ON IMAGE PROCESSING,29(1),2463-2477. |
MLA | Zhang, Zhizhong,et al."Tensor Multi-Task Learning for Person Re-Identification".IEEE TRANSACTIONS ON IMAGE PROCESSING 29.1(2020):2463-2477. |
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