Knowledge Commons of Institute of Automation，CAS
| Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector | |
Du, Chen1,2; Wang, Chunheng1 ; Wang, Yanna1; Shi, Cunzhao1; Xiao, Baihua1
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| Source Publication | PATTERN RECOGNITION LETTERS
 |
| ISSN | 0167-8655 |
| 2020 | |
| Volume | 129Pages:108-114 |
| Corresponding Author | Wang, Yanna(chunheng.wang@ia.ac.cn) |
| Abstract | Different 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. |
| Keyword | Feature combination Network architecture Selective feature connection mechanism Convolutional neural network |
| DOI | 10.1016/j.patrec.2019.11.015 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] ; 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 Organization | Key Programs of the Chinese Academy of Sciences ; National Natural Science Foundation of China (NSFC) |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence |
| WOS ID | WOS:000504641500016 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/29462 |
| Collection | 中国科学院自动化研究所 |
| Corresponding Author | Wang, Yanna |
| Affiliation | 1.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 Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute 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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