Attribute-Guided Feature Learning for Few-Shot Image Recognition

doi:10.1109/TMM.2020.2993952

	Attribute-Guided Feature Learning for Few-Shot Image Recognition
	Zhu, Yaohui 1,2; Min, Weiqing 1,2; Jiang, Shuqiang 1,2
	2021
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
ISSN	1520-9210
卷号	23 页码:1200-1209
摘要	Few-shot image recognition has become an essential problem in the field of machine learning and image recognition, and has attracted more and more research attention. Typically, most few-shot image recognition methods are trained across tasks. However, these methods are apt to learn an embedding network for discriminative representations of training categories, and thus could not distinguish well for novel categories. To establish connections between training and novel categories, we use attribute-related representations for few-shot image recognition and propose an attribute-guided two-layer learning framework, which is capable of learning general feature representations. Specifically, few-shot image recognition trained over tasks and attribute learning trained over images share the same network in a multi-task learning framework. In this way, few-shot image recognition learns feature representations guided by attributes, and is thus less sensitive to novel categories compared with feature representations only using category supervision. Meanwhile, the multi-layer features associated with attributes are aligned with category learning on multiple levels respectively. Therefore we establish a two-layer learning mechanism guided by attributes to capture more discriminative representations, which are complementary compared with a single-layer learning mechanism. Experimental results on CUB-200, AWA and MiniImageNet datasets demonstrate our method effectively improves the performance.
关键词	Image recognition Training Task analysis Semantics Standards Measurement Visualization Attribute learning few-shot learning image recognition
DOI	10.1109/TMM.2020.2993952
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61532018] ; National Natural Science Foundation of China[61972378] ; National Natural Science Foundation of China[U1936203] ; National Natural Science Foundation of China[U19B2040] ; Beijing Natural Science Foundation[L182054] ; National Program for Special Support of Eminent Professionals ; National Program for Support of Top-Notch Young Professionals
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:000645068200004
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：45[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17795
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Jiang, Shuqiang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, CAS, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Yaohui,Min, Weiqing,Jiang, Shuqiang. Attribute-Guided Feature Learning for Few-Shot Image Recognition[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:1200-1209.
APA	Zhu, Yaohui,Min, Weiqing,&Jiang, Shuqiang.(2021).Attribute-Guided Feature Learning for Few-Shot Image Recognition.IEEE TRANSACTIONS ON MULTIMEDIA,23,1200-1209.
MLA	Zhu, Yaohui,et al."Attribute-Guided Feature Learning for Few-Shot Image Recognition".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):1200-1209.