Panoramic image style transfer technology based on multi-attention fusion

Author:

Xiang Xin1ORCID,Ye Wujian1ORCID,Liu Yijun1ORCID

Affiliation:

1. School of Information Engineering, Guangdong University of Technology, China

Funder

This research was funded in part by Key Area R&D Program of Guangdong Province with grants No.2018B030338001,No.2018B010115002 and No.2018B010107003 in part by the Basic and Applied Basic Research Project of Guangzhou Basic Research Program with grant 202201010595,in part by the Guangdong Education Department and in part by the Guangdong University of Technology with grant 220413548.

Publisher

ACM

Reference21 articles.

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2. Neural Style Transfer: A Review

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4. Gatys L A , Ecker A S , Bethge M. Image style transfer using convolutional neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition , 2016 : 2414-2423. Gatys L A, Ecker A S, Bethge M. Image style transfer using convolutional neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2016: 2414-2423.

5. Johnson J , Alahi A , Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution[C]//European conference on computer vision . Springer , Cham , 2016 : 694-711. Johnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution[C]//European conference on computer vision. Springer, Cham, 2016: 694-711.

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