We present a novel framework for exemplar based image translation. Recent
advanced methods for this task mainly focus on establishing cross-domain
semantic correspondence, which sequentially dominates image generation in the
manner of local style control. Unfortunately, cross-domain semantic matching is
challenging; and matching errors ultimately degrade the quality of generated
images. To overcome this challenge, we improve the accuracy of matching on the
one hand, and diminish the role of matching in image generation on the other
hand. To achieve the former, we propose a masked and adaptive transformer (MAT)
for learning accurate cross-domain correspondence, and executing context-aware
feature augmentation. To achieve the latter, we use source features of the
input and global style codes of the exemplar, as supplementary information, for
decoding an image. Besides, we devise a novel contrastive style learning
method, for acquire quality-discriminative style representations, which in turn
benefit high-quality image generation. Experimental results show that our
method, dubbed MATEBIT, performs considerably better than state-of-the-art
methods, in diverse image translation tasks. The codes are available at
https://github.com/AiArt-HDU/MATEBIT.

提出了一个用于样例图像翻译的新框架，称为 MATEBIT，其基于交叉域语义对应和全局风格编码，使用掩模和自适应变换器提高了匹配的准确性，并制定了新的对比风格学习方法来获得高质量图像生成。