SuperStyleNet：使用基于超像素的风格编码器进行深度图像合成
SuperStyleNet: Deep Image Synthesis with Superpixel Based Style Encoder
摘要：
用于图像合成的现有方法利用基于卷积和池化层的堆栈的样式编码器来从输入图像生成样式代码。然而，编码矢量不一定包含相应图像的局部信息，因为小尺度对象往往通过这种缩小过程被“冲走”。在本文中，我们提出了基于超像素的风格编码器的深度图像合成，称为SuperStyleNet。首先，我们直接提取的风格代码从原始图像的超像素的基础上考虑局部对象。其次，我们恢复空间关系的矢量化的风格代码的基础上图形分析。因此，所提出的网络实现了高质量的图像合成，通过映射到语义标签的风格代码。实验结果表明，该方法优于国家的最先进的视觉质量和定量测量。此外，我们还通过调整样式代码来实现精细的空间样式编辑。
英文摘要：
Existing methods for image synthesis utilized a style encoder based on stacks of convolutions and pooling layers to generate style codes from input images. However, the encoded vectors do not necessarily contain local information of the corresponding images since small-scale objects are tended to "wash away" through such downscaling procedures. In this paper, we propose deep image synthesis with superpixel based style encoder, named as SuperStyleNet. First, we directly extract the style codes from the original image based on superpixels to consider local objects. Second, we recover spatial relationships in vectorized style codes based on graphical analysis. Thus, the proposed network achieves high-quality image synthesis by mapping the style codes into semantic labels. Experimental results show that the proposed method outperforms state-of-the-art ones in terms of visual quality and quantitative measurements. Furthermore, we achieve elaborate spatial style editing by adjusting style codes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
