Abstract
In this paper, the method of two-stage generation and two-stage discrimination (2G2D) is proposed to generate high-resolution and more realistic images. It is a simple but effective way to synthesize images based on text descriptions. Our method generates the refined foreground image in the first stage, and then combines the text description to generate the final high-resolution image in second stage. We demonstrate the performance of the proposed method on the Caltech-UCSD Birds (CUB) dataset. Through the experimental results, our model can improve the resolution and the authenticity of content of the synthetic image better than the existing state-of-the-art methods.
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Acknowledgement
This research was supported by 2018GZ0517, 2019YFS0146, 2019YFS0155 which supported by Sichuan Provincial Science and Technology Department, 2018KF003 Supported by State Key Laboratory of ASIC & System, Science and Technology Planning Project of Guangdong Province 2017B010110007.
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Zhang, Z. et al. (2019). Text to Image Synthesis Using Two-Stage Generation and Two-Stage Discrimination. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11776. Springer, Cham. https://doi.org/10.1007/978-3-030-29563-9_12
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DOI: https://doi.org/10.1007/978-3-030-29563-9_12
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