Unpaired Image-to-image Translation is a new rising and challenging vision
problem that aims to learn a mapping between unaligned image pairs in diverse
domains. Recent advances in this field like MUNIT and DRIT mainly focus on
disentangling content and style/attribute from a given image first, then
directly adopting the global style to guide the model to synthesize new domain
images. However, this kind of approaches severely incurs contradiction if the
target domain images are content-rich with multiple discrepant objects. In this
paper, we present a simple yet effective instance-aware image-to-image
translation approach (INIT), which employs the fine-grained local (instance)
and global styles to the target image spatially. The proposed INIT exhibits
three import advantages: (1) the instance-level objective loss can help learn a
more accurate reconstruction and incorporate diverse attributes of objects; (2)
the styles used for target domain of local/global areas are from corresponding
spatial regions in source domain, which intuitively is a more reasonable
mapping; (3) the joint training process can benefit both fine and coarse
granularity and incorporates instance information to improve the quality of
global translation. We also collect a large-scale benchmark for the new
instance-level translation task. We observe that our synthetic images can even
benefit real-world vision tasks like generic object detection.

本文提出了一种基于实例感知的图像翻译方法，通过使用细粒度的本地和全局风格来处理目标图像，从而实现更准确的线性重建并结合不同对象的多样属性，同时为目标域的局部和全局领域分别使用与源域相应空间区域匹配的风格，从而在精细度和粗糙度上同时受益，改进全局转换的质量，最后基于此方法构建了新的基准测试集并通过实验验证可以帮助实现通用对象检测。