Multi-Style Shape Matching GAN for Text Images
Author:
Affiliation:
1. Department of Informatics, The University of Electro-Communications
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Link
https://www.jstage.jst.go.jp/article/transinf/E107.D/4/E107.D_2023IHP0010/_pdf
Reference29 articles.
1. [1] S. Yang, Z. Wang, Z. Wang, N. Xu, J. Liu, and Z. Guo, “Controllable artistic text style transfer via shape-matching GAN,” Proc. IEEE Computer Vision and Pattern Recognition, pp.4442-4451, 2019.
2. [2] T. Park, M.-Y. Liu, T.-C. Wang, and J.-Y. Zhu, “Semantic image synthesis with spatially-adaptive normalization,” Proc. IEEE Computer Vision and Pattern Recognition, pp.2337-2346, 2019.
3. [3] P. Zhu, R. Abdal, Y. Qin, and P. Wonka, “SEAN: Image synthesis with semantic region-adaptive normalization,” Proc. IEEE Computer Vision and Pattern Recognition, pp.5104-5113, 2020.
4. [4] H. Yuan and K. Yanai, “Multi-style transfer generative adversarial network for text images,” Proc. IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR), pp.63-69, IEEE, 2021. https://ieeexplore.ieee.org/abstract/document/9565534.
5. [5] P. Isola, J.-Y. Zhu, T. Zhou, and A.A. Efros, “Image-to-image translation with conditional adversarial networks,” Proc. IEEE Computer Vision and Pattern Recognition, pp.1125-1134, 2017.
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