Summary of Explore the Limits Of Omni-modal Pretraining at Scale, by Yiyuan Zhang et al.
Explore the Limits of Omni-modal Pretraining at Scale
by Yiyuan Zhang, Handong Li, Jing Liu, Xiangyu Yue
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Multimodal Context (MiCo) pretraining paradigm enables the development of omni-modal intelligence, capable of understanding any modality and learning universal representations. By scaling up the numbers of modalities and amount of data, MiCo shows significant emergent abilities in multimodal learning. The pretrained models establish 37 new records for state-of-the-art performance across single-modality perception benchmarks (10 modalities), cross-modality understanding tasks (25), and multimodal large language model benchmarks (18). This research contributes to the development of omni-modal intelligence. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re working on a way to make computers understand different types of information, like images, sound, or text. This new approach, called MiCo, helps machines learn from lots of different sources and remember important things. The test results are really good! MiCo did better than the best models before it in many tasks, like recognizing what’s in a picture or answering questions about something you’ve seen. |
Keywords
* Artificial intelligence * Large language model * Pretraining