构建涵盖30种图表类型的通用数据集，助力多模态模型评估
ChartComplete: A Taxonomy-based Inclusive Chart Dataset
- 基于可视化领域分类体系，覆盖30种图表类型
- 仅包含分类后的图表图像，无标注学习信号
- 适合评估多模态大模型在多样化图表理解能力
随着深度学习与计算机视觉的发展，图表理解领域正迅速演进。特别是多模态大语言模型（MLLMs）在图表理解方面展现出高效与高精度。为准确评估MLLMs性能，研究社区已开发多个基准数据集。然而，这些数据集均局限于少量图表类型。为此，我们提出ChartComplete数据集，其基于可视化领域的图表分类体系，涵盖30种不同图表类型。该数据集由分类后的图表图像构成，不包含学习信号。我们以原始形态向社区发布ChartComplete，以便后续研究使用与拓展。
原文摘要 · Abstract (English)
With advancements in deep learning (DL) and computer vision techniques, the field of chart understanding is evolving rapidly. In particular, multimodal large language models (MLLMs) are proving to be efficient and accurate in understanding charts. To accurately measure the performance of MLLMs, the research community has developed multiple datasets to serve as benchmarks. By examining these datasets, we found that they are all limited to a small set of chart types. To bridge this gap, we propose the ChartComplete dataset. The dataset is based on a chart taxonomy borrowed from the visualization community, and it covers thirty different chart types. The dataset is a collection of classified chart images and does not include a learning signal. We present the ChartComplete dataset as is to the community to build upon it.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。