通过混合部分卷积改进遮蔽风格迁移
Improving Masked Style Transfer using Blended Partial Convolution
AI总结：
本文提出基于部分卷积的风格迁移网络，精准应用于目标区域，并通过内部混合技术弥补区域选择的不完美，提升视觉和量化效果。
摘要：
艺术风格迁移长期以来依赖于卷积和变压器神经网络的发展。大多数算法将艺术风格应用于整个图像，但个别用户可能只需要将风格应用于图像中的特定区域。标准做法是在风格化后简单地对图像进行遮蔽。本文表明这种做法倾向于不恰当地捕捉目标区域的风格特征。我们提出了一种基于部分卷积的风格迁移网络，能够准确地将风格特征仅应用于目标区域。此外，我们还提出了网络内部的混合技术，以弥补区域选择的不完美。我们通过SA-1B数据集中的示例展示了这种改进在视觉和量化上的提升。代码可在https://github.com/davidmhart/StyleTransferMasked公开获取。
英文摘要：
Artistic style transfer has long been possible with the advancements of convolution- and transformer-based neural networks. Most algorithms apply the artistic style transfer to the whole image, but individual users may only need to apply a style transfer to a specific region in the image. The standard practice is to simply mask the image after the stylization. This work shows that this approach tends to improperly capture the style features in the region of interest. We propose a partial-convolution-based style transfer network that accurately applies the style features exclusively to the region of interest. Additionally, we present network-internal blending techniques that account for imperfections in the region selection. We show that this visually and quantitatively improves stylization using examples from the SA-1B dataset. Code is publicly available at https://github.com/davidmhart/StyleTransferMasked.
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。
