论文标题
DeepObjstyle：基于对象的深度照片样式转移
DeepObjStyle: Deep Object-based Photo Style Transfer
论文作者
论文摘要
样式转移的主要挑战之一是输出图像和输入（样式和内容）图像之间的适当图像特征监督。有效的策略是在样式的对象和内容图像之间定义对象图。但是，当样式和内容图像中有不同类型和数字的语义对象时，这种映射就无法确定。它还导致样式传输输出的内容不匹配，这可以降低结果的视觉质量。我们提出了一种基于对象的样式转移方法，称为DeepObjStyle，以供培训数据独立的框架中的样式监督。 DeepObjStyle保留了对象的语义，并在挑战性的情况下实现了更好的样式转移，当样式和内容图像具有不匹配的图像特征时。我们还执行包含单词云的图像的样式转移，以证明DeepObjStyle可以实现适当的图像特征监督。我们使用定量比较和用户研究来验证结果。
One of the major challenges of style transfer is the appropriate image features supervision between the output image and the input (style and content) images. An efficient strategy would be to define an object map between the objects of the style and the content images. However, such a mapping is not well established when there are semantic objects of different types and numbers in the style and the content images. It also leads to content mismatch in the style transfer output, which could reduce the visual quality of the results. We propose an object-based style transfer approach, called DeepObjStyle, for the style supervision in the training data-independent framework. DeepObjStyle preserves the semantics of the objects and achieves better style transfer in the challenging scenario when the style and the content images have a mismatch of image features. We also perform style transfer of images containing a word cloud to demonstrate that DeepObjStyle enables an appropriate image features supervision. We validate the results using quantitative comparisons and user studies.
