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Uncertainty-optimized deep learning model for small-scale person re-identification

文献类型：期刊论文

作者Zhao, Cairong3; Chen, Kang3; Zang, Di3; Zhang, Zhaoxiang2; Zuo, Wangmeng1; Mia, Duoqian3
刊名SCIENCE CHINA-INFORMATION SCIENCES
出版日期2019-12-01
卷号62期号:12页码:13
ISSN号1674-733X
关键词person re-identification uncertainty analysis deep learning
DOI10.1007/s11432-019-2675-3
通讯作者Zhao, Cairong(zhaocairong@tongji.edu.cn)
英文摘要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.
WOS关键词GAP
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)
WOS研究方向Computer Science ; Engineering
语种英语
出版者SCIENCE PRESS
WOS记录号WOS:000498592200001
资助机构National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)
源URL[http://ir.ia.ac.cn/handle/173211/29355]  
专题自动化研究所_智能感知与计算研究中心
通讯作者Zhao, Cairong
作者单位1.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
3.Tongji Univ, Dept Comp Sci & Technol, Shanghai 201804, 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.

入库方式： OAI收割

来源：自动化研究所

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