Summary of Towards Human-like Machine Comprehension: Few-shot Relational Learning in Visually-rich Documents, by Hao Wang et al.
Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents
by Hao Wang, Tang Li, Chenhui Chu, Nengjun Zhu, Rui Wang, Pinpin Zhu
First submitted to arxiv on: 23 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 This research focuses on improving document AI approaches by considering non-textual cues, such as color and font styles, in Visually-Rich Documents (VRDs). The authors propose a variational approach that incorporates relational 2D-spatial priors and prototypical rectification techniques for few-shot relational learning. This method aims to generate relation representations that are more aware of the spatial context and unseen relations, similar to human perception. The proposed approach outperforms existing methods on two new benchmarks built upon existing supervised datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping computers understand documents better by using visual clues like colors and font styles. Right now, computer programs don’t do a great job of understanding these kinds of documents because they don’t consider the extra information that humans use to figure out what’s important. The authors came up with a new way for computers to learn from small amounts of examples by paying attention to where things are on the page and how they’re related. |
Keywords
* Artificial intelligence * Attention * Few shot * Supervised
