Summary of Mini-gemini: Mining the Potential Of Multi-modality Vision Language Models, by Yanwei Li et al.
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
by Yanwei Li, Yuechen Zhang, Chengyao Wang, Zhisheng Zhong, Yixin Chen, Ruihang Chu, Shaoteng Liu, Jiaya Jia
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 researchers introduce Mini-Gemini, a framework designed to enhance the performance of multi-modality Vision Language Models (VLMs). Despite advancements in VLMs for basic visual dialog and reasoning, a gap remains between these models and more advanced ones like GPT-4 and Gemini. To narrow this gap, the authors focus on three aspects: high-resolution visual tokens, high-quality data, and VLM-guided generation. They propose using an additional visual encoder to refine high-resolution visual tokens without increasing the token count. A new dataset is constructed to promote precise image comprehension and reasoning-based generation, expanding the capabilities of current VLMs. Mini-Gemini supports a range of large language models (LLMs) from 2B to 34B and achieves leading performance in several zero-shot benchmarks, even surpassing private models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mini-Gemini is a new way to make computers understand pictures better. It’s like a special tool that helps computers see and understand what’s in a picture. Right now, computers can do some simple things with pictures, but they’re not as good as humans. The Mini-Gemini team wants to change this by making computers better at understanding pictures and doing tasks with them. They did three important things to make this happen: they made the computer see more details in a picture, created a new way to get high-quality data, and used the computer to help generate new images. This makes computers better at understanding and working with pictures. |
Keywords
» Artificial intelligence » Encoder » Gemini » Gpt » Token » Zero shot
