EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding
<|reference_start|>EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding: Chart understanding enables automated data analysis for humans, which requires models to achieve highly accurate visual comprehension. While existing Visual Language Models (VLMs) have shown progress in chart understanding, the lack of high-quality training data and comprehensive evaluation benchmarks hinders VLM chart comprehension. In this paper, we introduce EvoChart, a novel self-training method for generating synthetic chart data to enhance VLMs' capabilities in real-world chart comprehension. We also propose EvoChart-QA, a noval benchmark for measuring models' chart comprehension abilities in real-world scenarios. Specifically, EvoChart is a unique self-training data synthesis approach that simultaneously produces high-quality training corpus and a high-performance chart understanding model. EvoChart-QA consists of 650 distinct real-world charts collected from 140 different websites and 1,250 expert-curated questions that focus on chart understanding. Experimental results on various open-source and proprietary VLMs tested on EvoChart-QA demonstrate that even the best proprietary model, GPT-4o, achieves only 49.8% accuracy. Moreover, the EvoChart method significantly boosts the performance of open-source VLMs on real-world chart understanding tasks, achieving 54.2% accuracy on EvoChart-QA.<|reference_end|>
@article{huang2024evochart:,
    title={EvoChart: A Benchmark and a Self-Training Approach Towards Real-World
  Chart Understanding},
    author={Muye Huang, Lai Han, Xinyu Zhang, Wenjun Wu, Jie Ma, Lingling Zhang,
  Jun Liu},
    journal={arXiv preprint arXiv:2409.01577},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01577},
    primaryClass={cs.CV}
}