Summary of Omnicorpus: a Unified Multimodal Corpus Of 10 Billion-level Images Interleaved with Text, by Qingyun Li et al.

OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text

by Qingyun Li, Zhe Chen, Weiyun Wang, Wenhai Wang, Shenglong Ye, Zhenjiang Jin, Guanzhou Chen, Yinan He, Zhangwei Gao, Erfei Cui, Jiashuo Yu, Hao Tian, Jiasheng Zhou, Chao Xu, Bin Wang, Xingjian Wei, Wei Li, Wenjian Zhang, Bo Zhang, Pinlong Cai, Licheng Wen, Xiangchao Yan, Zhenxiang Li, Pei Chu, Yi Wang, Min Dou, Changyao Tian, Xizhou Zhu, Lewei Lu, Yushi Chen, Junjun He, Zhongying Tu, Tong Lu, Yali Wang, Limin Wang, Dahua Lin, Yu Qiao, Botian Shi, Conghui He, Jifeng Dai

First submitted to arxiv on: 12 Jun 2024

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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 paper introduces OmniCorpus, a massive image-text interleaved dataset that combines 10 billion images and text tokens. The dataset is designed to facilitate multimodal learning and fine-tuning of large language models. Compared to existing datasets like MMC4 and OBELICS, OmniCorpus has a larger scale (15 times), more diverse sources, and greater flexibility. The authors validate the quality and effectiveness of the dataset through comprehensive analysis and experiments. This paper provides a solid foundation for future multimodal model research.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper makes a big difference in how we learn from images and text together. Imagine reading books with pictures, but instead of flipping pages, you can jump back and forth between words and pictures. That’s what this dataset does – it lets computers learn to understand both at the same time! The dataset is huge (10 billion images!), comes from many different places, and works well for training special kinds of computer models. This will help make computers even better at understanding us.