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Attribute-Guided Feature Learning for Few-Shot Image Recognition

文献类型：期刊论文

作者Zhu, Yaohui1,2; Min, Weiqing1,2; Jiang, Shuqiang1,2
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2021
卷号23页码:1200-1209
关键词Image recognition Training Task analysis Semantics Standards Measurement Visualization Attribute learning few-shot learning image recognition
ISSN号1520-9210
DOI10.1109/TMM.2020.2993952
英文摘要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.
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
语种英语
WOS记录号WOS:000645068200004
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/17794]  
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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, CAS, Beijing 100190, 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.

入库方式： OAI收割

来源：计算技术研究所

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