CASIA OpenIR
Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector
Du, Chen1,2; Wang, Chunheng1; Wang, Yanna1; Shi, Cunzhao1; Xiao, Baihua1
Source PublicationPATTERN RECOGNITION LETTERS
ISSN0167-8655
2020
Volume129Pages:108-114
Corresponding AuthorWang, Yanna(chunheng.wang@ia.ac.cn)
AbstractDifferent layers of deep convolutional neural networks(CNNs) can encode different-level information. High-layer features always contain more semantic information, and low-layer features contain more detail information. However, low-layer features suffer from the background clutter and semantic ambiguity. During visual recognition, the feature combination of the low-layer and high-level features plays an important role in context modulation. If directly combining the high-layer and low-layer features, the background clutter and semantic ambiguity may be caused due to the introduction of detailed information. In this paper, we propose a general network architecture to concatenate CNN features of different layers in a simple and effective way, called Selective Feature Connection Mechanism (SFCM). Low-level features are selectively linked to high-level features with a feature selector which is generated by high-level features. The proposed connection mechanism can effectively overcome the above-mentioned drawbacks. We demonstrate the effectiveness, superiority, and universal applicability of this method on multiple challenging computer vision tasks, including image classification, scene text detection, and image-to-image translation. (C) 2019 Elsevier B.V. All rights reserved.
KeywordFeature combination Network architecture Selective feature connection mechanism Convolutional neural network
DOI10.1016/j.patrec.2019.11.015
Indexed BySCI
Language英语
Funding ProjectKey Programs of the Chinese Academy of Sciences[ZDBS-SSWJSC003] ; Key Programs of the Chinese Academy of Sciences[ZDBS-SSW-JSC004] ; Key Programs of the Chinese Academy of Sciences[ZDBS-SSWJSC005] ; National Natural Science Foundation of China (NSFC)[61601462] ; National Natural Science Foundation of China (NSFC)[61531019] ; National Natural Science Foundation of China (NSFC)[71621002] ; Key Programs of the Chinese Academy of Sciences[ZDBS-SSWJSC003] ; Key Programs of the Chinese Academy of Sciences[ZDBS-SSW-JSC004] ; Key Programs of the Chinese Academy of Sciences[ZDBS-SSWJSC005] ; National Natural Science Foundation of China (NSFC)[61601462] ; National Natural Science Foundation of China (NSFC)[61531019] ; National Natural Science Foundation of China (NSFC)[71621002]
Funding OrganizationKey Programs of the Chinese Academy of Sciences ; National Natural Science Foundation of China (NSFC)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000504641500016
PublisherELSEVIER
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/29462
Collection中国科学院自动化研究所
Corresponding AuthorWang, Yanna
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Du, Chen,Wang, Chunheng,Wang, Yanna,et al. Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector[J]. PATTERN RECOGNITION LETTERS,2020,129:108-114.
APA Du, Chen,Wang, Chunheng,Wang, Yanna,Shi, Cunzhao,&Xiao, Baihua.(2020).Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector.PATTERN RECOGNITION LETTERS,129,108-114.
MLA Du, Chen,et al."Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector".PATTERN RECOGNITION LETTERS 129(2020):108-114.
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