Fine-grained Semantic Constraint in Image Synthesis

In this paper, we propose a multi-stage and high-resolution model for image synthesis that uses fine-grained attributes and masks as input. With a fine-grained attribute, the proposed model can detailedly constrain the features of the generated image through rich and fine-grained semantic information in the attribute. With mask as prior, the model in this paper is constrained so that the generated images conform to visual senses, which will reduce the unexpected diversity of samples generated from the generative adversarial network. This paper also proposes a scheme to improve the discriminator of the generative adversarial network by simultaneously discriminating the total image and sub-regions of the image. In addition, we propose a method for optimizing the labeled attribute in datasets, which reduces the manual labeling noise. Extensive quantitative results show that our image synthesis model generates more realistic images.

https://arxiv.org/abs/2101.04558

在本文中,我们提出一个多阶段的高分辨率图像生成模型,这个模型可以利用精细粒度的属性标签和掩膜作为输入。通过精细粒度的属性标签提供的语义信息,我们提出的模型可以在细节上限制生成图像的特征。通过掩膜输入,模型可以生成符合视觉直觉的图像并且减少生成非期望的异常图像。本文还提出了一种新的架构通过同时输入整体图像以及部分图像用来提升判别器的行呢个。另外,我们的方法通过优化数据集中的属性标签以减少人工标注带来的早哦生。实验证明我们的模型可以生成更加真实的图像。

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