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.
@article{arxiv.2404.01548,
title = {mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning},
author = {Jingxuan Wei and Nan Xu and Guiyong Chang and Yin Luo and BiHui Yu and Ruifeng Guo},
journal= {arXiv preprint arXiv:2404.01548},
year = {2024}
}