Bidirectional Attention-Recognition Model for Fine-Grained Object Classification

doi:10.1109/TMM.2019.2954747

	Bidirectional Attention-Recognition Model for Fine-Grained Object Classification
	Liu, Chuanbin 1; Xie, Hongtao 1; Zha, Zhengjun 1; Yu, Lingyun 1; Chen, Zhineng2 ; Zhang, Yongdong 1
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
ISSN	1520-9210
	2020-07-01
卷号	22 期号:7 页码:1785-1795
通讯作者	Xie, Hongtao(htxie@ustc.edu.cn) ; Zhang, Yongdong(zhyd73@ustc.edu.cn)
摘要	Fine-grained object classification (FGOC) is a challenging research topic in multimedia computing with machine learning, which faces two pivotal conundrums: focusing attention on the discriminate part regions, and then processing recognition with the part-based features. Existing approaches generally adopt a unidirectional two-step structure, that first locate the discriminate parts and then recognize the part-based features. However, they neglect the truth that part localization and feature recognition can be reinforced in a bidirectional process. In this paper, we propose a novel bidirectional attention-recognition model (BARM) to actualize the bidirectional reinforcement for FGOC. The proposed BARM consists of one attention agent for discriminate part regions proposing and one recognition agent for feature extraction and recognition. Meanwhile, a feedback flow is creatively established to optimize the attention agent directly by recognition agent. Therefore, in BARM the attention agent and the recognition agent can reinforce each other in a bidirectional way and the overall framework can be trained end-to-end without neither object nor parts annotations. Moreover, a novel Multiple Random Erasing data augmentation is proposed, and it exhibits impressive pertinency and superiority for FGOC. Conducted on several extensive FGOC benchmarks, BARM outperforms the present state-of-the-art methods in classification accuracy. Furthermore, BARM exhibits a clear interpretability and keeps consistent with the human perception in visualization experiments.
关键词	Fine-grained object classification interpretable machine learning visual attention pattern recognition data augmentation
DOI	10.1109/TMM.2019.2954747
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[61771468] ; Youth Innovation Promotion Association Chinese Academy of Sciences[2017209]
项目资助者	National Key Research and Development Program of China ; National Nature Science Foundation of China ; Youth Innovation Promotion Association Chinese Academy of Sciences
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000545990500011
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：34[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/40042
专题	数字内容技术与服务研究中心_远程智能医疗
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, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Chuanbin,Xie, Hongtao,Zha, Zhengjun,et al. Bidirectional Attention-Recognition Model for Fine-Grained Object Classification[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2020,22(7):1785-1795.
APA	Liu, Chuanbin,Xie, Hongtao,Zha, Zhengjun,Yu, Lingyun,Chen, Zhineng,&Zhang, Yongdong.(2020).Bidirectional Attention-Recognition Model for Fine-Grained Object Classification.IEEE TRANSACTIONS ON MULTIMEDIA,22(7),1785-1795.
MLA	Liu, Chuanbin,et al."Bidirectional Attention-Recognition Model for Fine-Grained Object Classification".IEEE TRANSACTIONS ON MULTIMEDIA 22.7(2020):1785-1795.