提升任意风格迁移中内容纹理的保留能力
AEANet: Affinity Enhanced Attentional Networks for Arbitrary Style Transfer
- 设计注意力增强模块,分别优化内容与风格特征
- 新损失函数有效保持内容与风格图像的相似性
- 适合需要高保真风格迁移的应用场景
任意艺术风格迁移是融合理性研究与情感创作的领域,旨在根据目标艺术风格生成新图像,同时保持内容图像的纹理结构信息并融入风格图像的艺术特征。然而,现有方法在风格转换过程中常严重破坏内容图像的纹理线条。为此,本文提出亲和力增强注意力网络(AEANet),包含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与SAEA模块首先通过注意力机制增强内容与风格表示,再经细节增强(DE)模块强化细节特征;混合注意力模块则根据内容特征分布调整风格特征分布。此外,引入基于亲和力注意力的局部不相似性损失,更有效地保持内容与风格图像间的亲和性。实验表明,本方法在任意风格迁移任务上优于现有最先进方法。
原文摘要 · Abstract (English)
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.
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