Summary of Ovis: Structural Embedding Alignment For Multimodal Large Language Model, by Shiyin Lu et al.
Ovis: Structural Embedding Alignment for Multimodal Large Language Model
by Shiyin Lu, Yang Li, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Han-Jia Ye
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed Ovis architecture is a novel multimodal large language model that aims to seamlessly integrate visual and textual information. The current approach typically combines pre-trained models through a connector, but this misalignment between different embedding strategies hinders the fusion of these modalities. To address this issue, Ovis integrates an additional learnable visual embedding table into the visual encoder’s process. This structured approach mirrors the method used for generating textual embeddings. Empirical evaluations on various multimodal benchmarks show that Ovis outperforms open-source models and even surpasses proprietary models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Ovis is a new kind of computer model that helps machines understand pictures and words better. Right now, these kinds of models are tricky to use because they mix up the way they look at pictures and words. The researchers came up with a new idea called Ovis that makes it easier for machines to understand both pictures and words. They used a special trick to make this happen, which is like taking a picture of something and then looking at it in different ways to get more meaning out of it. When they tested Ovis, it did better than other models on lots of tasks, showing that it’s a good way to make machines understand pictures and words. |
Keywords
» Artificial intelligence » Embedding » Encoder » Large language model
