CANVASEMB: Learning Layout Representation with Large-scale Pre-training for Graphic Design

Yuxi Xie, Danqing Huang, Jinpeng Wang, Chin-Yew Lin

Keywords: Layout Representation, Pre-training

Abstract:

Layout representation, which models visual elements in a canvas and their inter-relations, plays a crucial role in graphic design intelligence.
With a large variety of layout designs and the unique characteristic of layouts that visual elements are defined as a list of categorical (e.g. shape type) and numerical (e.g. position and size) properties, it is challenging to learn a general and compact representation with limited data. Inspired by the recent success of self-supervised pre-training techniques in various natural language processing tasks, in this paper, we propose CanvasEmb (Canvas Embedding), which pre-trains deep representation from unlabeled graphic designs by jointly conditioning on all the context elements in the same canvas, with a multi-dimensional feature encoder and a multi-task learning objective. The pre-trained CanvasEmb model can be fine-tuned with just one additional output layer and with a small size of training data to create models for a wide range of downstream tasks. We verify our approach with presentation slides data. We construct a large-scale dataset with more than one million slides, and propose two novel layout understanding tasks with human labeling sets, namely element role labeling and image captioning. Evaluation results on these two tasks show that our model with fine-tuning achieves state-of-the-art performances. Furthermore, we conduct a deep analysis aiming to understand the modeling mechanism of CanvasEmb, and demonstrate its great potential use on more applications such as layout auto completion and layout retrieval.

One-sentence Summary: we propose CanvasEmb for layout representation learning, which pre-trains deep representation from large-scale unlabeled graphic designs and facilitates downstream tasks for design intelligence.

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