Knowledge Commons of Institute of Automation，CAS
| Anisotropic Convolution for Image Classification | |
Li, Wenjuan1 ; Li, Bing1,2; Yuan, Chunfeng1; Li, Yangxi3; Wu, Haohao4; Hu, Weiming1,5,6; Wang, Fangshi4
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| 发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| ISSN | 1057-7149 |
| 2020 | |
| 卷号 | 29页码:5584-5595 |
| 通讯作者 | Li, Bing(bli@nlpr.ia.ac.cn) |
| 摘要 | Convolutional neural networks are built upon simple but useful convolution modules. The traditional convolution has a limitation on feature extraction and object localization due to its fixed scale and geometric structure. Besides, the loss of spatial information also restricts the networks' performance and depth. To overcome these limitations, this paper proposes a novel anisotropic convolution by adding a scale factor and a shape factor into the traditional convolution. The anisotropic convolution augments the receptive fields flexibly and dynamically depending on the valid sizes of objects. In addition, the anisotropic convolution is a generalized convolution. The traditional convolution, dilated convolution and deformable convolution can be viewed as its special cases. Furthermore, in order to improve the training efficiency and avoid falling into a local optimum, this paper introduces a simplified implementation of the anisotropic convolution. The anisotropic convolution can be applied to arbitrary convolutional networks and the enhanced networks are called ACNs (anisotropic convolutional networks). Experimental results show that ACNs achieve better performance than many state-of-the-art methods and the baseline networks in tasks of image classification and object localization, especially in classification task of tiny images. |
| 关键词 | Convolution Shape Kernel Feature extraction Task analysis Training Neural networks Anisotropic convolution image classification object localization |
| DOI | 10.1109/TIP.2020.2985875 |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | Beijing Natural Science Foundation[JQ18018] ; Beijing Natural Science Foundation[L172051] ; Beijing Natural Science Foundation[L182058] ; National Key RD Plan[2017YFB1002801] ; Natural Science Foundation of China[U1936204] ; Natural Science Foundation of China[U1803119] ; Natural Science Foundation of China[U1736106] ; Natural Science Foundation of China[61876100] ; Natural Science Foundation of China[61751212] ; Natural Science Foundation of China[61721004] ; Natural Science Foundation of China[61906192] ; Natural Science Foundation of China[61972397] ; Natural Science Foundation of China[61772225] ; NSFC-General Technology Collaborative Fund for Basic Research[U1636218] ; Key Research Program of Frontier Sciences, CAS[YZDJ-SSW-JSC040] ; Science and Technology Service Network Initiative, CAS[KFJ-STS-SCYD-317] ; CAS External Cooperation Key Project ; Youth Innovation Promotion Association, CAS |
| 项目资助者 | Beijing Natural Science Foundation ; National Key RD Plan ; Natural Science Foundation of China ; NSFC-General Technology Collaborative Fund for Basic Research ; Key Research Program of Frontier Sciences, CAS ; Science and Technology Service Network Initiative, CAS ; CAS External Cooperation Key Project ; Youth Innovation Promotion Association, CAS |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000529943000003 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 七大方向——子方向分类 | 图像视频处理与分析 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/39390 |
| 专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
| 通讯作者 | Li, Bing |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Peoples Daily Online, State Key Lab Commun Content Cognit, Beijing 100733, Peoples R China 3.Coordinat Ctr China CNCERT CC, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China 4.Beijing Jiaotong Univ, Sch Software Engn, Beijing 100093, Peoples R China 5.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100039, Peoples R China |
| 第一作者单位 | 模式识别国家重点实验室 |
| 通讯作者单位 | 模式识别国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Li, Wenjuan,Li, Bing,Yuan, Chunfeng,et al. Anisotropic Convolution for Image Classification[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:5584-5595. |
| APA | Li, Wenjuan.,Li, Bing.,Yuan, Chunfeng.,Li, Yangxi.,Wu, Haohao.,...&Wang, Fangshi.(2020).Anisotropic Convolution for Image Classification.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,5584-5595. |
| MLA | Li, Wenjuan,et al."Anisotropic Convolution for Image Classification".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):5584-5595. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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