Multi-Domain Image-to-Image Translation via a Unified Circular Framework | |
Wang, Yuxi1,2; Zhang, Zhaoxiang1,2; Hao, Wangli1,2; Song, Chunfeng1,2 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
2020 | |
期号 | 30页码:670-684 |
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. |
关键词 | Task analysis Semantics Visualization Generative adversarial networks Generators Feature extraction Meteorology Image-to-image transfer sharing knowledge module multiple domain pairs GANs |
DOI | 10.1109/TIP.2020.3037528 |
关键词[WOS] | ADVERSARIAL NETWORKS |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | Major Project for New Generation of AI ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000597161500005 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 多模态智能 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42693 |
专题 | 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 |
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 | 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,2020(30):670-684. |
APA | Wang, Yuxi,Zhang, Zhaoxiang,Hao, Wangli,&Song, Chunfeng.(2020).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(2020):670-684. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
TIP-MDI2I-Published.(3399KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论