用分阶段学习提升图表分类准确率，让模型像人一样逐步进阶。
C2F-CHART: A Curriculum Learning Approach to Chart Classification
- 按难度从粗到细设计学习任务，利用类别相似性构建阶梯式训练
- 在ICPR 2022图表数据集上超越现有最佳水平
- 适合需要提升图表理解能力的研究者和开发者
在科学研究中，图表是呈现数据的主要视觉方式，但其可访问性仍是一个重大挑战。为优化图表理解流程，本文聚焦于改进图表分类环节。我们采用受人类学习过程启发的课程学习方法，提出一种新的图表分类训练策略——C2F-CHART（粗到细）。该方法利用类别间的相似性，构建不同难度的学习任务，实现由易到难的渐进式训练。我们在ICPR 2022 CHART-Infographics UB UNITEC PMC数据集上进行了评估，结果表明该方法优于当前最优水平。
原文摘要 · Abstract (English)
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.
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