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.

翻译：本文提出了一种基于程序合成的新方法，用于批量图像处理。与现有只能对整幅图像进行全局编辑的工具不同，我们的方法能够对图像中的单个对象进行精细编辑。例如，我们的方法可以针对具有特定属性的对象进行选择性模糊或裁剪。为了实现这种细粒度图像编辑任务，我们提出了一种结合了用于图像分类的预训练神经网络与其他能进行符号推理的语言构造的神经符号领域特定语言。我们的方法能够通过一种新颖的合成算法，从用户演示中自动学习该领域特定语言中的程序。我们已在名为ImageEye的工具中实现了所提出的技术，并在50个图像编辑任务上对其进行了评估。评估结果显示，ImageEye能够自动化完成其中96%的任务。