用Transformer和对比学习重新思考任意风格迁移
Rethink Arbitrary Style Transfer with Transformer and Contrastive Learning
AI总结：
本文提出SCIN和ICL方法优化特征对齐与风格关系理解，并引入PE替代VGG捕捉风格特征，显著提升了任意风格迁移的图像质量并有效减少伪影。
摘要：
任意风格迁移在研究中受到广泛关注，并拥有大量实际应用。现有方法要么采用交叉注意力将深层风格属性融入内容属性，要么使用自适应归一化调整内容特征，均无法生成高质量的风格化图像。本文引入一种创新技术来提升风格化图像的质量。首先，我们提出风格一致性实例归一化（SCIN），该方法用于优化内容与风格特征之间的对齐。此外，我们开发了基于实例的对比学习（ICL）方法，旨在理解各种风格之间的关系，从而提升生成风格化图像的质量。鉴于VGG网络更擅长提取分类特征，而需要更好地适应捕捉风格特征，我们还引入了感知编码器（PE）来捕捉风格特征。大量实验表明，与现有最先进方法相比，我们提出的方法能生成高质量的风格化图像，并有效防止伪影。
英文摘要：
Arbitrary style transfer holds widespread attention in research and boasts numerous practical applications. The existing methods, which either employ cross-attention to incorporate deep style attributes into content attributes or use adaptive normalization to adjust content features, fail to generate high-quality stylized images. In this paper, we introduce an innovative technique to improve the quality of stylized images. Firstly, we propose Style Consistency Instance Normalization (SCIN), a method to refine the alignment between content and style features. In addition, we have developed an Instance-based Contrastive Learning (ICL) approach designed to understand the relationships among various styles, thereby enhancing the quality of the resulting stylized images. Recognizing that VGG networks are more adept at extracting classification features and need to be better suited for capturing style features, we have also introduced the Perception Encoder (PE) to capture style features. Extensive experiments demonstrate that our proposed method generates high-quality stylized images and effectively prevents artifacts compared with the existing 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。
