This paper presents a new synthesis-based approach for batch image
processing. Unlike existing tools that can only apply global edits to the
entire image, our method can apply fine-grained edits to individual objects
within the image. For example, our method can selectively blur or crop specific
objects that have a certain property. To facilitate such fine-grained image
editing tasks, we propose a neuro-symbolic domain-specific language (DSL) that
combines pre-trained neural networks for image classification with other
language constructs that enable symbolic reasoning. Our method can
automatically learn programs in this DSL from user demonstrations by utilizing
a novel synthesis algorithm. We have implemented the proposed technique in a
tool called ImageEye and evaluated it on 50 image editing tasks. Our evaluation
shows that ImageEye is able to automate 96% of these tasks.

本文介绍了一种新的基于综合的批量图像处理方法，通过利用预训练神经网络和其他语言构造使得对象级别的编辑成为可能，并提出了一种 neuro-symbolic DSL 语言，该方法能够自动学习这种语言，并在该方法的工具 ImageEye 上进行实现并评估，结果显示 ImageEye 能够自动完成 96% 的图像编辑任务。