AEANet：用于任意风格迁移的亲和力增强注意力网络
AEANet: Affinity Enhanced Attentional Networks for Arbitrary Style Transfer
AI总结：
针对现有风格迁移方法易破坏内容图像纹理的问题，提出AEANet，通过内容与风格亲和力增强注意力模块、混合注意力模块及局部不相似性损失，有效保留内容结构与艺术特征，在任意风格迁移中优于现有最先进方法。
摘要：
任意艺术风格迁移是一个结合理性学术研究与感性艺术创作的研究领域。它旨在根据目标艺术风格从内容图像生成新图像，在保持内容纹理结构信息的同时融入风格图像的艺术特征。然而，现有的风格迁移方法在风格转换过程中往往会显著破坏内容图像的纹理线条。为解决这些问题，我们提出了亲和力增强注意力网络，包括内容亲和力增强注意力（CAEA）模块、风格亲和力增强注意力（SAEA）模块和混合注意力（HA）模块。CAEA和SAEA模块首先利用注意力增强内容和风格表示，随后通过细节增强（DE）模块强化细节特征。混合注意力模块根据内容特征分布调整风格特征分布。我们还引入了基于亲和注意力的局部不相似性损失，能更好地保持与内容图像和风格图像的亲和力。实验表明，我们的工作在任意风格迁移中取得了优于其他最先进方法的结果。
英文摘要：
Arbitrary artistic style transfer is a research area that combines rational academic study with emotive artistic creation. It aims to create a new image from a content image according to a target artistic style, maintaining the content's textural structural information while incorporating the artistic characteristics of the style image. However, existing style transfer methods often significantly damage the texture lines of the content image during the style transformation. To address these issues, we propose affinity-enhanced attentional network, which include the content affinity-enhanced attention (CAEA) module, the style affinity-enhanced attention (SAEA) module, and the hybrid attention (HA) module. The CAEA and SAEA modules first use attention to enhance content and style representations, followed by a detail enhanced (DE) module to reinforce detail features. The hybrid attention module adjusts the style feature distribution based on the content feature distribution. We also introduce the local dissimilarity loss based on affinity attention, which better preserves the affinity with content and style images. Experiments demonstrate that our work achieves better results in arbitrary style transfer than other 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。
