Uncertainty-optimized deep learning model for small-scale person re-identification | |
Zhao, Cairong1; Chen, Kang1; Zang, Di1; Zhang, Zhaoxiang2; Zuo, Wangmeng3; Mia, Duoqian1 | |
发表期刊 | SCIENCE CHINA-INFORMATION SCIENCES |
ISSN | 1674-733X |
2019-12-01 | |
卷号 | 62期号:12页码:13 |
通讯作者 | Zhao, Cairong(zhaocairong@tongji.edu.cn) |
摘要 | In recent years, deep learning has developed rapidly and is widely used in various fields, such as computer vision, speech recognition, and natural language processing. For end-to-end person re-identification, most deep learning methods rely on large-scale datasets. Relatively few methods work with small-scale datasets. Insufficient training samples will affect neural network accuracy significantly. This problem limits the practical application of person re-identification. For small-scale person re-identification, the uncertainty of person representation and the overfitting problem associated with deep learning remain to be solved. Quantifying the uncertainty is difficult owing to complex network structures and the large number of hyperparameters. In this study, we consider the uncertainty of pedestrian representation for small-scale person re-identification. To reduce the impact of uncertain person representations, we transform parameters into distributions and conduct multiple sampling by using multilevel dropout in a testing process. We design an improved Monte Carlo strategy that considers both the average distance and shortest distance for matching and ranking. When compared with state-of-the-art methods, the proposed method significantly improve accuracy on two small-scale person re-identification datasets and is robust on four large-scale datasets. |
关键词 | person re-identification uncertainty analysis deep learning |
DOI | 10.1007/s11432-019-2675-3 |
关键词[WOS] | GAP |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61673299] ; National Natural Science Foundation of China[61203247] ; National Natural Science Foundation of China[61573259] ; National Natural Science Foundation of China[61573255] ; National Natural Science Foundation of China[61876218] ; Fundamental Research Funds for the Central Universities ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; National Natural Science Foundation of China[61673299] ; National Natural Science Foundation of China[61203247] ; National Natural Science Foundation of China[61573259] ; National Natural Science Foundation of China[61573255] ; National Natural Science Foundation of China[61876218] ; Fundamental Research Funds for the Central Universities ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) |
项目资助者 | National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000498592200001 |
出版者 | SCIENCE PRESS |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/29356 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Zhao, Cairong |
作者单位 | 1.Tongji Univ, Dept Comp Sci & Technol, Shanghai 201804, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China |
推荐引用方式 GB/T 7714 | Zhao, Cairong,Chen, Kang,Zang, Di,et al. Uncertainty-optimized deep learning model for small-scale person re-identification[J]. SCIENCE CHINA-INFORMATION SCIENCES,2019,62(12):13. |
APA | Zhao, Cairong,Chen, Kang,Zang, Di,Zhang, Zhaoxiang,Zuo, Wangmeng,&Mia, Duoqian.(2019).Uncertainty-optimized deep learning model for small-scale person re-identification.SCIENCE CHINA-INFORMATION SCIENCES,62(12),13. |
MLA | Zhao, Cairong,et al."Uncertainty-optimized deep learning model for small-scale person re-identification".SCIENCE CHINA-INFORMATION SCIENCES 62.12(2019):13. |
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