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arXiv · 2404.01548

mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning

Jingxuan Wei·Nan Xu·Guiyong Chang·Yin Luo·BiHui Yu·Ruifeng Guo
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Abstract

In the fields of computer vision and natural language processing, multimodal chart question-answering, especially involving color, structure, and textless charts, poses significant challenges. Traditional methods, which typically involve either direct multimodal processing or a table-to-text conversion followed by language model analysis, have limitations in effectively handling these complex scenarios. This paper introduces a novel multimodal chart question-answering model, specifically designed to address these intricate tasks. Our model integrates visual and linguistic processing, overcoming the constraints of existing methods. We adopt a dual-phase training approach: the initial phase focuses on aligning image and text representations, while the subsequent phase concentrates on optimizing the model's interpretative and analytical abilities in chart-related queries. This approach has demonstrated superior performance on multiple public datasets, particularly in handling color, structure, and textless chart questions, indicating its effectiveness in complex multimodal tasks.

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Jingxuan Wei, Nan Xu, Guiyong Chang, Yin Luo, BiHui Yu, Ruifeng Guo. 2024-04-02. mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning. https://doi.org/10.1016/j.patcog.2025.112348

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