Multi-Domain Image-to-Image Translation via a Unified Circular Framework
文献类型：期刊论文
| 作者 | Wang, Yuxi1,2 ; Zhang, Zhaoxiang1,2; Hao, Wangli1,2; Song, Chunfeng1,2
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 出版日期 | 2021 |
| 卷号 | 30页码:670-684 |
| 关键词 | Task analysis Semantics Visualization Generative adversarial networks Generators Feature extraction Meteorology Image-to-image transfer multiple domain pairs sharing knowledge module GANs |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2020.3037528 |
| 通讯作者 | Zhang, Zhaoxiang(zhaoxiang.zhang@ia.ac.cn) |
| 英文摘要 | The image-to-image translation aims to learn the corresponding information between the source and target domains. Several state-of-the-art works have made significant progress based on generative adversarial networks (GANs). However, most existing one-to-one translation methods ignore the correlations among different domain pairs. We argue that there is common information among different domain pairs and it is vital to multiple domain pairs translation. In this paper, we propose a unified circular framework for multiple domain pairs translation, leveraging a shared knowledge module across numerous domains. One selected translation pair can benefit from the complementary information from other pairs, and the sharing knowledge is conducive to mutual learning between domains. Moreover, absolute consistency loss is proposed and applied in the corresponding feature maps to ensure intra-domain consistency. Furthermore, our model can be trained in an end-to-end manner. Extensive experiments demonstrate the effectiveness of our approach on several complex translation scenarios, such as Thermal IR switching, weather changing, and semantic transfer tasks. |
| WOS关键词 | ADVERSARIAL NETWORKS |
| 资助项目 | Major Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of China[61836014] ; National Natural Science Foundation of China[61761146004] ; National Natural Science Foundation of China[61773375] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000597161500005 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Major Project for New Generation of AI ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/42692]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Zhang, Zhaoxiang |
| 作者单位 | 1.Chinese Acad Sci, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yuxi,Zhang, Zhaoxiang,Hao, Wangli,et al. Multi-Domain Image-to-Image Translation via a Unified Circular Framework[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:670-684. |
| APA | Wang, Yuxi,Zhang, Zhaoxiang,Hao, Wangli,&Song, Chunfeng.(2021).Multi-Domain Image-to-Image Translation via a Unified Circular Framework.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,670-684. |
| MLA | Wang, Yuxi,et al."Multi-Domain Image-to-Image Translation via a Unified Circular Framework".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):670-684. |
入库方式： OAI收割
来源：自动化研究所
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