Exemplar-based image translation refers to the task of generating images with the desired style, while conditioning on certain input image. Most of the current methods learn the correspondence between two input domains and lack the mining of information within the domains. In this paper, we propose a more general learning approach by considering two domain features as a whole and learning both inter-domain correspondence and intra-domain potential information interactions. Specifically, we propose a Cross-domain Feature Fusion Transformer (CFFT) to learn inter- and intra-domain feature fusion. Based on CFFT, the proposed CFFT-GAN works well on exemplar-based image translation. Moreover, CFFT-GAN is able to decouple and fuse features from multiple domains by cascading CFFT modules. We conduct rich quantitative and qualitative experiments on several image translation tasks, and the results demonstrate the superiority of our approach compared to state-of-the-art methods. Ablation studies show the importance of our proposed CFFT. Application experimental results reflect the potential of our method.

本文提出了一种更通用的学习方法，通过将两个领域特征作为一个整体来学习inter-domain correspondence和intra-domain potential information interactions，其中，我们提出了一种跨域特征融合转换器(Cross-domain Feature Fusion Transformer, CFFT)来学习inter- and intra-domain feature fusion，基于该方法的CFFT-GAN在示例图像翻译方面表现良好，能够通过级联CFFT模块解耦和多域特征融合。我们在几个图像翻译任务上进行了丰富的定量和定性实验，并证明了与最先进方法相比，我们的方法具有卓越的优势。消融研究表明了我们所提出的CFFT的重要性。应用实验结果反映了我们的方法的潜力。

CFFT-GAN:基于Transformer的跨域特征融合范例图像翻译