C2F-CHART: A Curriculum Learning Approach to Chart Classification

TL;DR

This paper introduces C2F-CHART, a novel curriculum learning method that improves chart classification accuracy by leveraging coarse-to-fine training strategies based on inter-class similarities.

Contribution

The paper proposes a new coarse-to-fine curriculum learning approach for chart classification, enhancing performance over existing methods.

Findings

Outperforms state-of-the-art on ICPR 2022 dataset

Utilizes inter-class similarities for curriculum design

Demonstrates improved classification accuracy

Abstract

In scientific research, charts are usually the primary method for visually representing data. However, the accessibility of charts remains a significant concern. In an effort to improve chart understanding pipelines, we focus on optimizing the chart classification component. We leverage curriculum learning, which is inspired by the human learning process. In this paper, we introduce a novel training approach for chart classification that utilizes coarse-to-fine curriculum learning. Our approach, which we name C2F-CHART (for coarse-to-fine) exploits inter-class similarities to create learning tasks of varying difficulty levels. We benchmark our method on the ICPR 2022 CHART-Infographics UB UNITEC PMC dataset, outperforming the state-of-the-art results.