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Fine-grained visual classification with multi-scale features based on self-supervised attention filtering mechanism
Chen, Haiyuan1; Cheng, Lianglun1; Huang, Guoheng1; Zhang, Ganghan1; Lan, Jiaying1; Yu, Zhiwen2; Pun, Chi Man3; Ling, Wing Kuen4
2022-03
Source PublicationApplied Intelligence
ISSN0924-669X
Volume52Issue:13Pages:15673-15689
Abstract

Although the existing Fine-Grained Visual Classification (FGVC) researches has made some progress, there are still some deficiencies need to be refined. Specifically, 1. The feature maps are used directly by most methods after they are extracted from the original images, which lacks further processing of feature maps and may lead irrelevant features to negatively affect network performance; 2. In many methods, the utilize of feature maps is relatively simple, and the relationship between feature maps that helpful for accurate classification is ignored. 3. Due to the high similarity between subcategories as well as the randomness and instability of training, the network prediction results may sometimes not accurate enough. To this end, we propose an efficient Self-supervised Attention Filtering and Multi-scale Features Network (SA-MFN) to improve the accuracy of FGVC, which consists of three modules. The first one is the Self-supervised Attention Map Filter, which is proposed to extract the initial attention maps of subcategories and filter out the most distinguishable and representative local attention maps. The second module is the Multi-scale Attention Map Generator, which extracts a global spatial feature map from the filtered attention maps and then concatenates it with the filtered attention maps. The third module is the Reiterative Prediction, in which the first prediction result of the network is re-utilized by this module to improve the accuracy and stability. Experimental results show that our SA-MFN outperforms the state-of-the-art methods on multiple fine-grained classification datasets, especially on the dataset of Stanford Cars, the proposed network achieves the accuracy of 94.7%.

KeywordAttention Mechanism Feature Filtering Fine-grained Visual Classification Self-supervised Learning
DOI10.1007/s10489-022-03232-w
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000770218700005
Scopus ID2-s2.0-85126376338
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorHuang, Guoheng
Affiliation1.School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China
2.School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
3.Department of Computer and Information Science, University of Macau, 999078, Macao
4.School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China
Recommended Citation
GB/T 7714
Chen, Haiyuan,Cheng, Lianglun,Huang, Guoheng,et al. Fine-grained visual classification with multi-scale features based on self-supervised attention filtering mechanism[J]. Applied Intelligence,2022,52(13):15673-15689.
APA Chen, Haiyuan,Cheng, Lianglun,Huang, Guoheng,Zhang, Ganghan,Lan, Jiaying,Yu, Zhiwen,Pun, Chi Man,&Ling, Wing Kuen.(2022).Fine-grained visual classification with multi-scale features based on self-supervised attention filtering mechanism.Applied Intelligence,52(13),15673-15689.
MLA Chen, Haiyuan,et al."Fine-grained visual classification with multi-scale features based on self-supervised attention filtering mechanism".Applied Intelligence 52.13(2022):15673-15689.
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