Visual elements in an information presentation are often spatially and semantically grouped hierarchically for effective message delivery. Studying the hierarchical grouping information can help researchers and designers better explore layout structures and understand design demographics. However, recovering hierarchical grouping is challenging due to a large number of possibilities for compositing visual elements into a single-page design. This paper introduces an automatic approach that takes the layout of visual elements as input and returns the hierarchical grouping as output. To understand information presentations, we first contribute a dataset of 23,072 information presentations with diverse layouts to the community. Next, we propose our technique with a Transformer-based model to predict relatedness between visual elements and a bottom-up algorithm to produce the hierarchical grouping. Finally, we evaluate our technique through a technical experiment and a user study with 30 designers. The results show that the proposed technique is promising.

翻译：信息展示中的视觉元素通常为了有效传递信息而在空间和语义上进行层级分组。研究层级分组信息有助于研究者和设计者更好地探索布局结构并理解设计特征。然而，由于将视觉元素组合成单页设计存在大量可能性，恢复层级分组颇具挑战性。本文提出一种自动化方法，以视觉元素布局为输入，输出层级分组结果。为理解信息展示，我们首先向学术界贡献了一个包含23,072个多样化布局信息展示的数据集。接着，我们提出基于Transformer的模型预测视觉元素间的相关性，并采用自底向上算法生成层级分组。最后，通过技术实验和30名设计师参与的用户研究进行评估，结果表明所提方法具有良好前景。