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Squeeze-and-Excitation Networks
文献类型：期刊论文
| Author | Hu, Jie2,3,7; Shen, Li5; Albanie, Samuel5; Sun, Gang3,6 ; Wu, Enhua1,2,4,7
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| Source | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| Issued Date | 2020-08-01 |
| Volume | 42Issue:8Pages:2011-2023 |
| Keyword | Squeeze-and-excitation image representations attention convolutional neural networks |
| ISSN | 0162-8828 |
| DOI | 10.1109/TPAMI.2019.2913372 |
| Corresponding Author | Shen, Li(lishen@robots.ox.ac.uk) |
| English Abstract | The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at each layer. A broad range of prior research has investigated the spatial component of this relationship, seeking to strengthen the representational power of a CNN by enhancing the quality of spatial encodings throughout its feature hierarchy. In this work, we focus instead on the channel relationship and propose a novel architectural unit, which we term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We show that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. We further demonstrate that SE blocks bring significant improvements in performance for existing state-of-the-art CNNs at slight additional computational cost. Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2.251 percent, surpassing the winning entry of 2016 by a relative improvement of similar to 25 percent. Models and code are available at https://github.com/hujie-frank/SENet. |
| Funding Project | NSFC[61632003] ; NSFC[61620106003] ; NSFC[61672502] ; NSFC[61571439] ; National Key R&D Program of China[2017YFB1002701] ; Macao FDCT Grant[068/2015/A2] ; EPSRC AIMS CDT[EP/L015897/1] |
| WOS Keyword | VISUAL-ATTENTION ; MODEL |
| WOS Research Area | Computer Science ; Engineering |
| Language | 英语 |
| WOS ID | WOS:000545415400015 |
| Publisher | IEEE COMPUTER SOC |
| Funding Organization | NSFC ; National Key R&D Program of China ; Macao FDCT Grant ; EPSRC AIMS CDT |
| 源URL | [http://ir.ia.ac.cn/handle/173211/40089]  |
| Collection | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| Corresponding Author | Shen, Li |
| Affiliation | 1.Univ Macau, AI Ctr, Taipa, Macao, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Momenta, Dongsheng Plaza A,8 Zhongguancun East Rd, Beijing 100083, Peoples R China 4.Univ Macau, Fac Sci & Technol, Taipa, Macao, Peoples R China 5.Univ Oxford, Visual Geometry Grp, Oxford OX1 2JD, England 6.Chinese Acad Sci, Inst Automat, LIAMA NLPR, Beijing 100190, Peoples R China 7.Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Hu, Jie,Shen, Li,Albanie, Samuel,et al. Squeeze-and-Excitation Networks[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2020,42(8):2011-2023. |
| APA | Hu, Jie,Shen, Li,Albanie, Samuel,Sun, Gang,&Wu, Enhua.(2020).Squeeze-and-Excitation Networks.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,42(8),2011-2023. |
| MLA | Hu, Jie,et al."Squeeze-and-Excitation Networks".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 42.8(2020):2011-2023. |
入库方式： OAI收割
来源：自动化研究所
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