Summary of See Then Tell: Enhancing Key Information Extraction with Vision Grounding, by Shuhang Liu et al.
See then Tell: Enhancing Key Information Extraction with Vision Grounding
by Shuhang Liu, Zhenrong Zhang, Pengfei Hu, Jiefeng Ma, Jun Du, Qing Wang, Jianshu Zhang, Chenyu Liu
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel end-to-end model called STNet is introduced to deliver precise answers with relevant vision grounding for visually rich documents integrating text, complex layouts, and imagery. This approach bypasses traditional Optical Character Recognition (OCR) methods, which can introduce latency, computational overhead, and errors. STNet uses a unique |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, a new way to understand documents is introduced. It’s called STNet, and it helps computers see important parts of images when answering questions about the document. This approach is better than old methods that use Optical Character Recognition (OCR) because it doesn’t have as many mistakes or take as long. The model looks at specific parts of the image using a special token called |
Keywords
» Artificial intelligence » Decoder » Grounding » Question answering » Token
