论文标题
Segattngan：文本到图像生成，分段关注
SegAttnGAN: Text to Image Generation with Segmentation Attention
论文作者
论文摘要
在本文中，我们提出了一个新颖的生成网络（Segattngan），该网络利用文本对图像综合任务的其他分割信息。由于对模型引入的分割数据提供了有关发电机培训的有用指导，因此与先前的最新方法相比，提出的模型可以生成具有更好现实主义质量和更高定量措施的图像。我们在CUB数据集上获得了4.84的成立分数，而Oxford-102数据集的成立得分为3.52。此外，我们测试了使用生成的分割数据而不是来自数据集的掩模的自我发项，以引起注意并获得了相似的高质量结果，这表明我们的模型可以适用于文本对图像合成任务。
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.
