CAP-VSTNet：内容亲和性保持的通用风格迁移
CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer
AI总结：
针对内容亲和性损失导致伪影的问题，提出CAP-VSTNet框架，结合新型可逆残差网络与无偏线性变换模块，并利用Matting Laplacian损失，在通用风格迁移中实现了优于SOTA的定性与定量效果。
摘要：
包含特征亲和性和像素亲和性的内容亲和性损失是导致照片级真实感和视频风格迁移中出现伪影的主要问题。本文提出了一种名为CAP-VSTNet的新框架，由新型可逆残差网络和无偏线性变换模块组成，用于通用风格迁移。该可逆残差网络不仅能保持内容亲和性，且不像传统可逆网络引入冗余信息，从而促进更好的风格化。借助能解决线性变换导致的像素亲和性损失问题的Matting Laplacian训练损失，所提框架在通用风格迁移上适用且有效。大量实验表明，CAP-VSTNet能产生优于SOTA方法的定性和定量结果。
英文摘要：
Content affinity loss including feature and pixel affinity is a main problem which leads to artifacts in photorealistic and video style transfer. This paper proposes a new framework named CAP-VSTNet, which consists of a new reversible residual network and an unbiased linear transform module, for versatile style transfer. This reversible residual network can not only preserve content affinity but not introduce redundant information as traditional reversible networks, and hence facilitate better stylization. Empowered by Matting Laplacian training loss which can address the pixel affinity loss problem led by the linear transform, the proposed framework is applicable and effective on versatile style transfer. Extensive experiments show that CAP-VSTNet can produce better qualitative and quantitative results in comparison with the state-of-the-art methods.
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。
