利用混合部分卷积提升掩码风格迁移效果
Improving Masked Style Transfer using Blended Partial Convolution
August 7, 2025
作者: Seyed Hadi Seyed, Ayberk Cansever, David Hart
cs.AI
摘要
随着卷积神经网络和基于Transformer的神经网络的发展,艺术风格迁移技术早已成为可能。大多数算法将艺术风格迁移应用于整幅图像,但个别用户可能仅需对图像中的特定区域进行风格迁移。标准做法是在风格化后简单地使用遮罩处理图像。本研究表明,这种方法往往无法准确捕捉感兴趣区域的风格特征。我们提出了一种基于部分卷积的风格迁移网络,能够精确地将风格特征仅应用于感兴趣区域。此外,我们还引入了网络内部融合技术,以应对区域选择中的不完美之处。通过SA-1B数据集中的示例,我们证明了这种方法在视觉和量化指标上均提升了风格化效果。代码已公开于https://github.com/davidmhart/StyleTransferMasked。
English
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.