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Multi-Objective Matrix Normalization for Fine-Grained Visual Recognition

文献类型：期刊论文

作者Min, Shaobo1; Yao, Hantao2; Xie, Hongtao1; Zha, Zheng-Jun1; Zhang, Yongdong1
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2020
卷号29页码:4996-5009
关键词Visualization Graphics processing units Feature extraction Convergence Optimization Covariance matrices Training Fine-grained visual recognition bilinear pooling matrix normalization multi-objective optimization
ISSN号1057-7149
DOI10.1109/TIP.2020.2977457
通讯作者Xie, Hongtao(htxie@ustc.edu.cn) ; Zhang, Yongdong(zhyd73@ustc.edu.cn)
英文摘要Bilinear pooling achieves great success in fine-grained visual recognition (FGVC). Recent methods have shown that the matrix power normalization can stabilize the second-order information in bilinear features, but some problems, e.g., redundant information and over-fitting, remain to be resolved. In this paper, we propose an efficient Multi-Objective Matrix Normalization (MOMN) method that can simultaneously normalize a bilinear representation in terms of square-root, low-rank, and sparsity. These three regularizers can not only stabilize the second-order information, but also compact the bilinear features and promote model generalization. In MOMN, a core challenge is how to jointly optimize three non-smooth regularizers of different convex properties. To this end, MOMN first formulates them into an augmented Lagrange formula with approximated regularizer constraints. Then, auxiliary variables are introduced to relax different constraints, which allow each regularizer to be solved alternately. Finally, several updating strategies based on gradient descent are designed to obtain consistent convergence and efficient implementation. Consequently, MOMN is implemented with only matrix multiplication, which is well-compatible with GPU acceleration, and the normalized bilinear features are stabilized and discriminative. Experiments on five public benchmarks for FGVC demonstrate that the proposed MOMN is superior to existing normalization-based methods in terms of both accuracy and efficiency. The code is available: https://github.com/mboboGO/MOMN.
资助项目National Key Research and Development Program of China[2017YFC0820600] ; National Nature Science Foundation of China[61525206] ; National Nature Science Foundation of China[U1936210] ; National Postdoctoral Programme for Innovative Talents[BX20180358] ; Youth Innovation Promotion Association Chinese Academy of Sciences[2017209] ; Fundamental Research Funds for the Central Universities[WK2100100030]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000522226700005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Nature Science Foundation of China ; National Postdoctoral Programme for Innovative Talents ; Youth Innovation Promotion Association Chinese Academy of Sciences ; Fundamental Research Funds for the Central Universities
源URL[http://ir.ia.ac.cn/handle/173211/38713]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Xie, Hongtao; Zhang, Yongdong
作者单位1.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Peoples R China
2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100864, Peoples R China
推荐引用方式
GB/T 7714		 Min, Shaobo,Yao, Hantao,Xie, Hongtao,et al. Multi-Objective Matrix Normalization for Fine-Grained Visual Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:4996-5009.
APA Min, Shaobo,Yao, Hantao,Xie, Hongtao,Zha, Zheng-Jun,&Zhang, Yongdong.(2020).Multi-Objective Matrix Normalization for Fine-Grained Visual Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,4996-5009.
MLA Min, Shaobo,et al."Multi-Objective Matrix Normalization for Fine-Grained Visual Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):4996-5009.

入库方式： OAI收割

来源：自动化研究所

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