Knowledge Commons of Institute of Automation,CAS
Multi-Objective Matrix Normalization for Fine-Grained Visual Recognition | |
Min, Shaobo1; Yao, Hantao2![]() | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
![]() |
ISSN | 1057-7149 |
2020 | |
卷号 | 29页码:4996-5009 |
通讯作者 | Xie, Hongtao(htxie@ustc.edu.cn) ; Zhang, Yongdong(zhyd73@ustc.edu.cn) |
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. |
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 |
DOI | 10.1109/TIP.2020.2977457 |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | 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 |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000522226700005 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/38714 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
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. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论