Summary of From Pixels to Insights: a Survey on Automatic Chart Understanding in the Era Of Large Foundation Models, by Kung-hsiang Huang et al.
From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models
by Kung-Hsiang Huang, Hou Pong Chan, Yi R. Fung, Haoyi Qiu, Mingyang Zhou, Shafiq Joty, Shih-Fu Chang, Heng Ji
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent survey paper reviews the advancements in chart understanding, a crucial aspect of data analysis, leveraging large foundation models. The study provides an overview of recent developments, challenges, and future directions in this area. Foundation models, such as language models, have revolutionized natural language processing tasks and are being applied to chart understanding tasks. The paper explores various tasks, including classification-based and generation-based approaches, along with tool augmentation techniques that enhance performance. It also discusses the state-of-the-art performance of each task and potential areas for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Chart understanding is a vital part of data analysis, helping us make informed decisions. Recently, large foundation models have made significant progress in this area. A new survey paper looks at what’s been happening, what challenges there are, and where we might go from here. It shows how these big models can be used to understand charts better. |
Keywords
» Artificial intelligence » Classification » Natural language processing
