Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
An end-to-end exemplar association for unsupervised person Re-identification | |
Wu, Jinlin1,2,3; Yang, Yang1,2,3; Lei, Zhen1,2,3; Wang, Jinqiao1,2,3; Li, Stan Z.4; Tiwari, Prayag5; Pandey, Hari Mohan6 | |
发表期刊 | NEURAL NETWORKS |
ISSN | 0893-6080 |
2020-09-01 | |
卷号 | 129页码:43-54 |
摘要 | Tracklet association methods learn the cross camera retrieval ability though associating underlying cross camera positive samples, which have proven to be successful in unsupervised person re-identification task. However, most of them use poor-efficiency association strategies which costs long training hours but gains the low performance. To solve this, we propose an effective end-to-end exemplar associations (EEA) framework in this work. EEA mainly adapts three strategies to improve efficiency: (1) end-to-end exemplar-based training, (2) exemplar association and (3) dynamic selection threshold. The first one is to accelerate the training process, while the others aim to improve the tracklet association precision. Compared with existing tracklet associating methods, EEA obviously reduces the training cost and achieves the higher performance. Extensive experiments and ablation studies on seven RE-ID datasets demonstrate the superiority of the proposed EEA over most state-of-the-art unsupervised and domain adaptation RE-ID methods. (C) 2020 Elsevier Ltd. All rights reserved. |
关键词 | End-to-end exemplar-based training Exemplar association Dynamic selection threshold |
DOI | 10.1016/j.neunet.2020.05.015 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Plan[2019YFC2003901] ; Chinese National Natural Science Foundation[61806203] ; Chinese National Natural Science Foundation[61876178] ; Chinese National Natural Science Foundation[61872367] ; Chinese National Natural Science Foundation[61976229] |
项目资助者 | National Key Research and Development Plan ; Chinese National Natural Science Foundation |
WOS研究方向 | Computer Science ; Neurosciences & Neurology |
WOS类目 | Computer Science, Artificial Intelligence ; Neurosciences |
WOS记录号 | WOS:000555927200005 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
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/173211/40345 |
专题 | 模式识别国家重点实验室_图像与视频分析 |
通讯作者 | Lei, Zhen |
作者单位 | 1.Chinese Acad Sci, Inst Automat, CBSR, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Westlake Univ, Sch Engn, Hangzhou, Peoples R China 5.Univ Padua, Dept Informat Engn, Padua, Italy 6.Edge Hill Univ, Dept Comp Sci, Ormskirk, England |
第一作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室 |
通讯作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Wu, Jinlin,Yang, Yang,Lei, Zhen,et al. An end-to-end exemplar association for unsupervised person Re-identification[J]. NEURAL NETWORKS,2020,129:43-54. |
APA | Wu, Jinlin.,Yang, Yang.,Lei, Zhen.,Wang, Jinqiao.,Li, Stan Z..,...&Pandey, Hari Mohan.(2020).An end-to-end exemplar association for unsupervised person Re-identification.NEURAL NETWORKS,129,43-54. |
MLA | Wu, Jinlin,et al."An end-to-end exemplar association for unsupervised person Re-identification".NEURAL NETWORKS 129(2020):43-54. |
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