数据与计算发展前沿2025，Vol.7Issue(1)：19-37,19.DOI:10.11871/jfdc.issn.2096-742X.2025.01.002
图表问答研究综述
Review of Research on Chart Question Answering
摘要
Abstract
[Objective]The purpose of this paper is to comprehensively review the research progress of Chart Question Answering(CQA)technology,analyze existing models and methods,and ex-plore future development directions.[Methods]Firstly,CQA models are divided into two cate-gories:deep learning-based and multi-modal large models.Deep learning-based methods are further subdivided into end-to-end models and two-stage models in this paper.Subsequently,the three core processes taken by the deep learning-based CQA are deeply analyzed,and a de-tailed classification along with an in-depth analysis of the existing processing methods for each process is provided.CQA models based on multi-modal large models are also explored in this paper,with their advantages,limitations,and future development directions being analyzed.[Results]The current research status of CQA technology is comprehensively summarized,and an in-depth analysis of existing models and methods is conducted.It is found that deep learning-based CQA mod-els perform well in handling standard chart types and simple tasks,but fall short when facing complex,non-stan-dardized charts or tasks requiring deep reasoning.In contrast,CQA models based on multi-modal large models show great potential,but the improvement in model performance often comes with an increase in model size and computational complexity.Future research should focus on developing more lightweight question answering mod-els and enhancing model interpretability.关键词
图表问答/视觉问答/深度学习/多模态大语言模型Key words
chart question answering/visual question answering/deep learning/multi-modal large language models引用本文复制引用
马秋平,张琪,赵晓凡..图表问答研究综述[J].数据与计算发展前沿,2025,7(1):19-37,19.基金项目
中央高校基本科研业务费项目(2024JKF18) （2024JKF18）
