Summary of An Efficient System For Automatic Map Storytelling — a Case Study on Historical Maps, by Ziyi Liu et al.
An Efficient System for Automatic Map Storytelling – A Case Study on Historical Maps
by Ziyi Liu, Claudio Affolter, Sidi Wu, Yizi Chen, Lorenz Hurni
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel and lightweight map-captioning counterpart aims to generate captions relevant to historical maps, addressing the limitations of existing methods. The system fine-tunes a state-of-the-art vision-language model (CLIP) to generate captions related to historical maps, enriched with GPT-3.5 to tell a brief story about the map’s context. A decision tree architecture is proposed to only generate relevant captions for specific map types. The system demonstrates invariance to text alterations in maps and can be easily adapted and extended to other map types or scaled to larger systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper aims to improve our ability to understand historical maps by creating a machine that can describe what we see on the map. Current methods are not very good at this because they were trained on lots of pictures, but not many maps. The new method uses a special kind of AI called CLIP and adds some extra information from another AI called GPT-3.5 to make it better. It also has a special way of deciding what to say about the map that makes sure it’s only saying things that are relevant. This system can handle changes in the text on the map and can be used for different types of maps. |
Keywords
» Artificial intelligence » Decision tree » Gpt » Language model
