  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. We achieved Inception Score of 4.84 on the CUB dataset
and 3.52 on the Oxford-102 dataset. Besides, we tested the self-attention
SegAttnGAN which uses generated segmentation data instead of masks from
datasets for attention and achieved similar high-quality results, suggesting
that our model can be adapted for the text-to-image synthesis task.
