mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning
Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Wei, Jingxuan, Xu, Nan, Chang, Guiyong, Luo, Yin, Yu, BiHui, Guo, Ruifeng
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4393927305