Summary of Transformers Utilization in Chart Understanding: a Review Of Recent Advances & Future Trends, by Mirna Al-shetairy et al.
Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends
by Mirna Al-Shetairy, Hanan Hindy, Dina Khattab, Mostafa M. Aref
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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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 medium-difficulty summary of this paper is that it reviews State-of-The-Art (SoTA) frameworks for Chart Understanding (CU), which involves processing chart images, text, tables, and user queries. The review focuses on transformer-based End-to-End (E2E) solutions that have improved CU performance significantly. It analyzes relevant benchmarking datasets and evaluation techniques, identifies key challenges, and outlines promising future directions for advancing CU solutions. The paper also explores recent advancements in frameworks addressing various CU tasks, including single-task and multi-task approaches. Pre-trained and prompt-engineering-based techniques are explored within multi-task frameworks. The review discusses leading architectures, datasets, and pre-training tasks, as well as ongoing challenges in OCR dependency, low-resolution images, and visual reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper reviews the latest advancements in Chart Understanding (CU), which is important because it can help us better understand complex data. CU involves looking at charts, graphs, and other visuals, along with text and tables, to make sense of them. The review looks at how special computers called transformers have improved CU a lot. It also talks about the challenges that remain, like making sure these computer models don’t get too confused by low-quality images or needing help understanding what’s in the charts. |
Keywords
» Artificial intelligence » Multi task » Prompt » Transformer
