Language-driven image editing can significantly save the laborious image
editing work and be friendly to the photography novice. However, most similar
work can only deal with a specific image domain or can only do global
retouching. To solve this new task, we first present a new language-driven
image editing dataset that supports both local and global editing with editing
operation and mask annotations. Besides, we also propose a baseline method that
fully utilizes the annotation to solve this problem. Our new method treats each
editing operation as a sub-module and can automatically predict operation
parameters. Not only performing well on challenging user data, but such an
approach is also highly interpretable. We believe our work, including both the
benchmark and the baseline, will advance the image editing area towards a more
general and free-form level.

本文介绍了一种利用语言驱动的图像编辑技术节省大量编辑工作量并对摄影新手友好，作者提出了一种支持本地和全局编辑的新技术方法，并构建了一个数据集用于支持本文所提出方法的研发，并且该方法在用户数据测试中表现良好，能够自动预测操作参数，具有高度的可解释性。