Abstract
An early stage of developing user-facing applications is creating a wireframe to layout the interface. Once a wireframe has been created it is given to a developer to implement in code. Developing boiler plate user interface code is time consuming work but still requires an experienced developer. In this dissertation we present two approaches which automates this process, one using classical computer vision techniques, and another using a novel application of deep semantic segmentation networks. We release a dataset of websites which can be used to train and evaluate these approaches. Further, we have designed a novel evaluation framework which allows empirical evaluation by creating synthetic sketches. Our evaluation illustrates that our deep learning approach outperforms our classical computer vision approach and we conclude that deep learning is the most promising direction for future research.
Abstract (translated)
开发面向用户的应用程序的早期阶段是创建一个线框来布局界面。一旦创建了一个线框，它就被提供给开发人员在代码中实现。开发锅炉板用户界面代码是一项耗时的工作，但仍然需要有经验的开发人员。在本文中，我们提出了两种自动化这一过程的方法，一种是使用经典的计算机视觉技术，另一种是使用深度语义分割网络的新应用。我们发布了一个网站数据集，可以用来培训和评估这些方法。此外，我们还设计了一个新的评估框架，允许通过创建合成草图进行经验评估。我们的评估表明，我们的深度学习方法优于传统的计算机视觉方法，我们认为，深度学习是未来研究最有前景的方向。
URL
https://arxiv.org/abs/1905.13750
