Summary of From Pixels to Prose: a Large Dataset Of Dense Image Captions, by Vasu Singla et al.
From Pixels to Prose: A Large Dataset of Dense Image Captions
by Vasu Singla, Kaiyu Yue, Sukriti Paul, Reza Shirkavand, Mayuka Jayawardhana, Alireza Ganjdanesh, Heng Huang, Abhinav Bhatele, Gowthami Somepalli, Tom Goldstein
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 The paper introduces PixelProse, a large dataset of synthetically generated image captions designed to bridge the gap in existing web-scraped datasets. The dataset consists of over 16 million captions generated using cutting-edge vision-language models, ensuring detailed and accurate descriptions. To ensure data integrity, the authors rigorously analyze the dataset for problematic content such as CSAM, PII, and toxicity. Additionally, they provide valuable metadata like watermark presence and aesthetic scores to aid in further filtering. The paper hopes that PixelProse will become a valuable resource for future vision-language research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PixelProse is a new way to get image captions that are really detailed and accurate. Right now, many datasets are made up of images found on the internet, but these images often don’t have good descriptions. The authors created PixelProse by using computers to generate over 16 million captions for images. They also checked the dataset to make sure it’s safe and doesn’t include bad things like child abuse material or mean language. The authors think that PixelProse will be really helpful for people doing research on vision-language models. |
