SegAttnGAN: Text to Image Generation with Segmentation Attention

25 May 2020 Yuchuan Gou Qiancheng Wu Minghao Li Bo Gong Mei Han

In this paper, we propose a novel generative network (SegAttnGAN) that utilizes additional segmentation information for the text-to-image synthesis task. As the segmentation data introduced to the model provides useful guidance on the generator training, the proposed model can generate images with better realism quality and higher quantitative measures compared with the previous state-of-art methods... (read more)

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