Cited By
View all- Yang WHe JLi Q(2024)ChartLine: Automatic Detection and Tracing of Curves in Scientific Line Charts Using Spatial-Sequence Feature Pyramid NetworkSensors10.3390/s2421701524:21(7015)Online publication date: 31-Oct-2024
OneChart: Purify the Chart Structural Extraction via One Auxiliary Token
Authors: 
Jinyue Chen, Lingyu Kong, Haoran Wei, Chenglong Liu, Zheng Ge, Liang Zhao, Jianjian Sun, Chunrui Han, Xiangyu ZhangAuthors Info & Claims
Pages 147 - 155
Abstract
Chart parsing poses a significant challenge due to the diversity of styles, values, texts, and so forth. Even advanced large vision-language models (LVLMs) with billions of parameters struggle to handle such tasks satisfactorily. To address this, we propose OneChart: a reliable agent specifically devised for the structural extraction of chart information. Similar to popular LVLMs, OneChart incorporates an autoregressive main body. Uniquely, to enhance the reliability of the numerical parts of the output, we introduce an auxiliary token placed at the beginning of the total tokens along with an additional decoder. The numerically optimized (auxiliary) token allows subsequent tokens for chart parsing to capture enhanced numerical features through causal attention. Furthermore, with the aid of the auxiliary token, we have devised a self-evaluation mechanism that enables the model to gauge the reliability of its chart parsing results by providing confidence scores for the generated content. Compared to current state-of-the-art (SOTA) chart parsing models, e.g., DePlot, ChartVLM, ChartAst, OneChart significantly outperforms in Average Precision (AP) for chart structural extraction across multiple public benchmarks, despite enjoying only 0.2 billion parameters. Moreover, as a chart parsing agent, it also brings 10%+ accuracy gains for the popular LVLM (LLaVA-1.6) in the downstream ChartQA benchmark. Our code, model and datasets ara available at https://onechartt.github.io.
References
[1]
Mubashara Akhtar, Oana Cocarascu, and Elena Simperl. 2023. Reading and reasoning over chart images for evidence-based automated fact-checking. arXiv preprint arXiv:2301.11843 (2023).
[2]
Jeonghun Baek, Geewook Kim, Junyeop Lee, Sungrae Park, Dongyoon Han, Sangdoo Yun, Seong Joon Oh, and Hwalsuk Lee. 2019. What is wrong with scene text recognition model comparisons' dataset and model analysis. In Proceedings of the IEEE/CVF international conference on computer vision. 4715--4723.
[3]
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966 (2023).
[4]
Lukas Blecher, Guillem Cucurull, Thomas Scialom, and Robert Stojnic. 2023. Nougat: Neural optical understanding for academic documents. arXiv preprint arXiv:2308.13418 (2023).
[5]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, Vol. 33 (2020), 1877--1901.
[6]
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023. Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality. https://lmsys.org/blog/2023-03--30-vicuna/.
[7]
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416 (2022).
[8]
Runpei Dong, Chunrui Han, Yuang Peng, Zekun Qi, Zheng Ge, Jinrong Yang, Liang Zhao, Jianjian Sun, Hongyu Zhou, Haoran Wei, et al. 2023. Dreamllm: Synergistic multimodal comprehension and creation. arXiv preprint arXiv:2309.11499 (2023).
[9]
Ali Mazraeh Farahani, Peyman Adibi, Alireza Darvishy, Mohammad Saeed Ehsani, and Hans-Peter Hutter. 2023. Automatic chart understanding: a review. IEEE Access (2023).
[10]
Yucheng Han, Chi Zhang, Xin Chen, Xu Yang, Zhibin Wang, Gang Yu, Bin Fu, and Hanwang Zhang. 2023. Chartllama: A multimodal llm for chart understanding and generation. arXiv preprint arXiv:2311.16483 (2023).
[11]
Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Martin Eisenschlos. 2020. TaPas: Weakly supervised table parsing via pre-training. arXiv preprint arXiv:2004.02349 (2020).
[12]
Shankar Kantharaj, Xuan Long Do, Rixie Tiffany Ko Leong, Jia Qing Tan, Enamul Hoque, and Shafiq Joty. 2022. Opencqa: Open-ended question answering with charts. arXiv preprint arXiv:2210.06628 (2022).
[13]
Shankar Kantharaj, Rixie Tiffany Ko Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, and Shafiq Joty. 2022. Chart-to-text: A large-scale benchmark for chart summarization. arXiv preprint arXiv:2203.06486 (2022).
[14]
Hei Law and Jia Deng. 2018. Cornernet: Detecting objects as paired keypoints. In Proceedings of the European conference on computer vision (ECCV). 734--750.
[15]
Kenton Lee, Mandar Joshi, Iulia Raluca Turc, Hexiang Hu, Fangyu Liu, Julian Martin Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, and Kristina Toutanova. 2023. Pix2struct: Screenshot parsing as pretraining for visual language understanding. In International Conference on Machine Learning. PMLR, 18893--18912.
[16]
Vladimir I Levenshtein et al. 1966. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, Vol. 10. Soviet Union, 707--710.
[17]
Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, and Yasemin Altun. 2023. DePlot: One-shot visual language reasoning by plot-to-table translation. In Findings of the 61st Annual Meeting of the Association for Computational Linguistics. https://arxiv.org/abs/2212.10505
[18]
Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, and Julian Martin Eisenschlos. 2022. Matcha: Enhancing visual language pretraining with math reasoning and chart derendering. arXiv preprint arXiv:2212.09662 (2022).
[19]
Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, and Dong Yu. 2023 d. Mmc: Advancing multimodal chart understanding with large-scale instruction tuning. arXiv preprint arXiv:2311.10774 (2023).
[20]
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. 2023. Improved baselines with visual instruction tuning. arXiv preprint arXiv:2310.03744 (2023).
[21]
Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. 2024. LLaVA-NeXT: Improved reasoning, OCR, and world knowledge. https://llava-vl.github.io/blog/2024-01--30-llava-next/
[22]
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023. Visual Instruction Tuning.
[23]
Xiaoyi Liu, Diego Klabjan, and Patrick NBless. 2019. Data extraction from charts via single deep neural network. arXiv preprint arXiv:1906.11906 (2019).
[24]
Junyu Luo, Zekun Li, Jinpeng Wang, and Chin-Yew Lin. 2021. Chartocr: Data extraction from charts images via a deep hybrid framework. In Proceedings of the IEEE/CVF winter conference on applications of computer vision. 1917--1925.
[25]
Ahmed Masry, Parsa Kavehzadeh, Xuan Long Do, Enamul Hoque, and Shafiq Joty. 2023. Unichart: A universal vision-language pretrained model for chart comprehension and reasoning. arXiv preprint arXiv:2305.14761 (2023).
[26]
Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. 2022. ChartQA: A benchmark for question answering about charts with visual and logical reasoning. arXiv preprint arXiv:2203.10244 (2022).
[27]
Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, and Ping Luo. 2024. Chartassisstant: A universal chart multimodal language model via chart-to-table pre-training and multitask instruction tuning. arXiv preprint arXiv:2401.02384 (2024).
[28]
Nitesh Methani, Pritha Ganguly, Mitesh M. Khapra, and Pratyush Kumar. 2020. PlotQA: Reasoning over Scientific Plots. In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). 1516--1525. https://doi.org/10.1109/WACV45572.2020.9093523
[29]
Prerna Mishra and Urmila Shrawankar. 2022. Human vs. machine eye for chart interpretation. In 2022 IEEE Region 10 Symposium (TENSYMP). IEEE, 1--6.
[30]
Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kry'sci'nski, Hailey Schoelkopf, Riley Kong, Xiangru Tang, et al. 2022. Fetaqa: Free-form table question answering. Transactions of the Association for Computational Linguistics, Vol. 10 (2022), 35--49.
[31]
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748--8763.
[32]
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, Vol. 21, 140 (2020), 1--67.
[33]
Chinmayee Rane, Seshasayee Mahadevan Subramanya, Devi Sandeep Endluri, Jian Wu, and C Lee Giles. 2021. Chartreader: Automatic parsing of bar-plots. In 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 318--325.
[34]
Haoran Wei, Lingyu Kong, Jinyue Chen, Liang Zhao, Zheng Ge, Jinrong Yang, Jianjian Sun, Chunrui Han, and Xiangyu Zhang. 2023. Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models. arXiv preprint arXiv:2312.06109 (2023).
[35]
Haoran Wei, Lingyu Kong, Jinyue Chen, Liang Zhao, Zheng Ge, En Yu, Jianjian Sun, Chunrui Han, and Xiangyu Zhang. 2024. Small Language Model Meets with Reinforced Vision Vocabulary. arXiv preprint arXiv:2401.12503 (2024).
[36]
Renqiu Xia, Bo Zhang, Haoyang Peng, Ning Liao, Peng Ye, Botian Shi, Junchi Yan, and Yu Qiao. 2023. Structchart: Perception, structuring, reasoning for visual chart understanding. arXiv preprint arXiv:2309.11268 (2023).
[37]
Renqiu Xia, Bo Zhang, Hancheng Ye, Xiangchao Yan, Qi Liu, Hongbin Zhou, Zijun Chen, Min Dou, Botian Shi, Junchi Yan, and Yu Qiao. 2024. ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning. arxiv: 2402.12185 [cs.CV]
[38]
Liang Xu. [n.,d.]. NLPCC2019: Large-Scale Chinese Datasets for NLP. http://github.com/brightmart/nlp_chinese_corpus.
[39]
Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Yuhao Dan, Chenlin Zhao, Guohai Xu, Chenliang Li, Junfeng Tian, et al. 2023. mplug-docowl: Modularized multimodal large language model for document understanding. arXiv preprint arXiv:2307.02499 (2023).
[40]
En Yu, Liang Zhao, Yana Wei, Jinrong Yang, Dongming Wu, Lingyu Kong, Haoran Wei, Tiancai Wang, Zheng Ge, Xiangyu Zhang, et al. 2023. Merlin: Empowering Multimodal LLMs with Foresight Minds. arXiv preprint arXiv:2312.00589 (2023).
[41]
Ao Zhang, Wei Ji, and Tat-Seng Chua. 2023. NExT-Chat: An LMM for Chat, Detection and Segmentation. arxiv: 2311.04498 [cs.CV]
[42]
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068 (2022).
[43]
Liang Zhao, En Yu, Zheng Ge, Jinrong Yang, Haoran Wei, Hongyu Zhou, Jianjian Sun, Yuang Peng, Runpei Dong, Chunrui Han, et al. 2023. Chatspot: Bootstrapping multimodal llms via precise referring instruction tuning. arXiv preprint arXiv:2307.09474 (2023).
[44]
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023. MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models. arXiv preprint arXiv:2304.10592 (2023).
Index Terms
- OneChart: Purify the Chart Structural Extraction via One Auxiliary Token
Recommendations
Using one EWMA chart to jointly monitor the process mean and variance
Two control charts are usually used to monitor the process mean and variance separately. The mean is monitored using the $${\bar{{X}}}$$chart while the variance using either the standard deviation, S chart, or the range, R chart. Recently, numerous ...
FlexAttention for Efficient High-Resolution Vision-Language Models
Computer Vision – ECCV 2024AbstractCurrent high-resolution vision-language models encode images as high-resolution image tokens and exhaustively take all these tokens to compute attention, which significantly increases the computational cost. To address this problem, we propose ...
An adaptive multivariate EWMA chart
Highlights- A new adaptive multivariate EWMA chart is proposed for the process mean.
- The ...
AbstractThe adaptive multivariate EWMA (AMEWMA) and adaptive multivariate CUSUM (AMCUSUM) charts are recently proposed as they provide an overall good detection over a range of mean shift sizes than their non-adaptive conventional ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
- General Chairs:
- Jianfei Cai,
- Mohan Kankanhalli,
- Balakrishnan Prabhakaran,
- Susanne Boll,
- Program Chairs:
- Ramanathan Subramanian,
- Liang Zheng,
- Vivek K. Singh,
- Pablo Cesar,
- Lexing Xie,
- Dong Xu
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 October 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
MM '24
Sponsor:
MM '24: The 32nd ACM International Conference on Multimedia
October 28 - November 1, 2024
Melbourne VIC, Australia
Acceptance Rates
MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 456Total Downloads
- Downloads (Last 12 months)456
- Downloads (Last 6 weeks)248
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
Cited By
View all- Yang WHe JLi Q(2024)ChartLine: Automatic Detection and Tracing of Curves in Scientific Line Charts Using Spatial-Sequence Feature Pyramid NetworkSensors10.3390/s2421701524:21(7015)Online publication date: 31-Oct-2024
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in