Summary of Figuring Out Figures: Using Textual References to Caption Scientific Figures, by Stanley Cao and Kevin Liu
Figuring out Figures: Using Textual References to Caption Scientific Figures
by Stanley Cao, Kevin Liu
First submitted to arxiv on: 25 Jun 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 The paper proposes an innovative approach for generating figure captions in scientific papers using a variant of a CLIP+GPT-2 encoder-decoder model with cross-attention. By leveraging the SciCap datasets and incorporating textual metadata from the original paper, such as titles, abstracts, and references, the authors demonstrate improved performance compared to previous work. The study highlights the importance of considering both image and text features in generating accurate captions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way for computers to write descriptions of pictures in scientific papers. It uses a special kind of language model that looks at both the picture and some extra information from the paper, like the title and abstract. This helps the computer generate better descriptions than previous attempts. The study shows that by using this approach, it’s possible to create more accurate captions. |
Keywords
» Artificial intelligence » Cross attention » Encoder decoder » Gpt » Language model
