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In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control parts of the image generation according to natural language descriptions.
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio · 2015
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Neural photo editing with introspective adversarial networks
A. Brock, T. Lim, J. M. Ritchie, and N. Weston · 2016
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Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
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Generative adversarial text to image synthesis
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee · 2016
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Learning what and where to draw
S. E. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele, and H. Lee · 2016
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Text-adaptive generative adversarial networks: manipulating images with natural language
S. Nam, Y. Kim, and S. J. Kim · 2018
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StackGAN++
H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. N. Metaxas · 2018
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Progressive attention guided recurrent network for salient object detection
X. Zhang, T. Wang, J. Qi, H. Lu, and G. Wang · 2018
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