RL-CSDia: Representation Learning of Computer Science Diagrams

TL;DR

This paper introduces a new dataset and a multi-branch neural network model for understanding complex computer science diagrams, addressing challenges like data scarcity and semantic confusion.

Contribution

It constructs the first comprehensive dataset of computer science diagrams with detailed annotations and proposes a novel Diagram Parsing Net for improved diagram understanding.

Findings

DPN outperforms baseline models on diagram classification

The dataset contains over 1,200 annotated diagrams

Topology, visual features, and text improve understanding accuracy

Abstract

Recent studies on computer vision mainly focus on natural images that express real-world scenes. They achieve outstanding performance on diverse tasks such as visual question answering. Diagram is a special form of visual expression that frequently appears in the education field and is of great significance for learners to understand multimodal knowledge. Current research on diagrams preliminarily focuses on natural disciplines such as Biology and Geography, whose expressions are still similar to natural images. Another type of diagrams such as from Computer Science is composed of graphics containing complex topologies and relations, and research on this type of diagrams is still blank. The main challenges of graphic diagrams understanding are the rarity of data and the confusion of semantics, which are mainly reflected in the diversity of expressions. In this paper, we construct a novel dataset of graphic diagrams named Computer Science Diagrams (CSDia). It contains more than 1,200 diagrams and exhaustive annotations of objects and relations. Considering the visual noises caused by the various expressions in diagrams, we introduce the topology of diagrams to parse topological structure. After that, we propose Diagram Parsing Net (DPN) to represent the diagram from three branches: topology, visual feature, and text, and apply the model to the diagram classification task to evaluate the ability of diagrams understanding. The results show the effectiveness of the proposed DPN on diagrams understanding.