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Automatic semantic style transfer using deep convolutional neural networks and soft masks

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成果类型:
期刊论文
作者:
Zhao, Hui-Huang*;Rosin, Paul L.;Lai, Yu-Kun;Wang, Yao-Nan
通讯作者:
Zhao, Hui-Huang
作者机构:
[Zhao, Hui-Huang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
[Lai, Yu-Kun; Rosin, Paul L.] Cardiff Univ, Sch Comp Sci & Informat, Cardiff, Wales.
[Wang, Yao-Nan] Hunan Univ, Coll Elect & Informat Engn, Changsha, Peoples R China.
通讯机构:
[Zhao, Hui-Huang] H
Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
语种:
英文
关键词:
Deep neural networks;Style transfer;Soft mask;Semantic segmentation
期刊:
VISUAL COMPUTER
ISSN:
0178-2789
年:
2020
卷:
36
期:
7
页码:
1307-1324
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China [61503128]; Science and Technology Plan Project of Hunan Province [2016TP1020]; Scientific Research Fund of Hunan Provincial Education DepartmentHunan Provincial Education Department [16C0226, 18A333]; Hengyang guided science and technology projects and Application-oriented Special Disciplines [Hengkefa [2018]60-31]; Double FirstClass University Project of Hunan Province [Xiangjiaotong [2018]469]; Hunan Province Special Funds of Central Government for Guiding Local Science and Technology Development [2018CT5001]; Subject Group Construction Project of Hengyang Normal University [18XKQ02, 61733004]
机构署名:
本校为第一且通讯机构
院系归属:
计算机科学与技术学院
摘要:
This paper presents an automatic image synthesis method to transfer the style of an example image to a content image. When standard neural style transfer approaches are used, the textures and colours in different semantic regions of the style image are often applied inappropriately to the content image, ignoring its semantic layout and ruining the transfer result. In order to reduce or avoid such effects, we propose a novel method based on automatically segmenting the objects and extracting their soft semantic masks from the style and content images, in order to preserve the structure of the c...

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