From Pixels to Prose: A Large Dataset of Dense Image Captions
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arXiv
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| Format: | Preprint |
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2024
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| author | Singla, Vasu Yue, Kaiyu Paul, Sukriti Shirkavand, Reza Jayawardhana, Mayuka Ganjdanesh, Alireza Huang, Heng Bhatele, Abhinav Somepalli, Gowthami Goldstein, Tom |
| author_facet | Singla, Vasu Yue, Kaiyu Paul, Sukriti Shirkavand, Reza Jayawardhana, Mayuka Ganjdanesh, Alireza Huang, Heng Bhatele, Abhinav Somepalli, Gowthami Goldstein, Tom |
| contents | Training large vision-language models requires extensive, high-quality image-text pairs. Existing web-scraped datasets, however, are noisy and lack detailed image descriptions. To bridge this gap, we introduce PixelProse, a comprehensive dataset of over 16M (million) synthetically generated captions, leveraging cutting-edge vision-language models for detailed and accurate descriptions. To ensure data integrity, we rigorously analyze our dataset for problematic content, including child sexual abuse material (CSAM), personally identifiable information (PII), and toxicity. We also provide valuable metadata such as watermark presence and aesthetic scores, aiding in further dataset filtering. We hope PixelProse will be a valuable resource for future vision-language research. PixelProse is available at https://huggingface.co/datasets/tomg-group-umd/pixelprose |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10328 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Pixels to Prose: A Large Dataset of Dense Image Captions Singla, Vasu Yue, Kaiyu Paul, Sukriti Shirkavand, Reza Jayawardhana, Mayuka Ganjdanesh, Alireza Huang, Heng Bhatele, Abhinav Somepalli, Gowthami Goldstein, Tom Computer Vision and Pattern Recognition Computation and Language Machine Learning Training large vision-language models requires extensive, high-quality image-text pairs. Existing web-scraped datasets, however, are noisy and lack detailed image descriptions. To bridge this gap, we introduce PixelProse, a comprehensive dataset of over 16M (million) synthetically generated captions, leveraging cutting-edge vision-language models for detailed and accurate descriptions. To ensure data integrity, we rigorously analyze our dataset for problematic content, including child sexual abuse material (CSAM), personally identifiable information (PII), and toxicity. We also provide valuable metadata such as watermark presence and aesthetic scores, aiding in further dataset filtering. We hope PixelProse will be a valuable resource for future vision-language research. PixelProse is available at https://huggingface.co/datasets/tomg-group-umd/pixelprose |
| title | From Pixels to Prose: A Large Dataset of Dense Image Captions |
| topic | Computer Vision and Pattern Recognition Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2406.10328 |