Summary of Enhancing Perception Capabilities Of Multimodal Llms with Training-free Fusion, by Zhuokun Chen et al.
Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion
by Zhuokun Chen, Jinwu Hu, Zeshuai Deng, Yufeng Wang, Bohan Zhuang, Mingkui Tan
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel integration framework, VisionFuse, is proposed to enhance the visual perception of Multimodal LLMs (MLLMs) without requiring additional training. This framework efficiently utilizes multiple off-the-shelf MLLMs to align vision encoders with language models, leveraging insights into the focus regions and feature distributions of different MLLM families. By concatenating tokens generated by selected vision encoders and merging the parameters of language models, VisionFuse reduces deployment overhead while achieving substantial improvements in multimodal tasks. Comprehensive evaluations across multiple benchmarks demonstrate an average performance increase of over 4% when integrating MiniGemini-8B and SLIME-8B. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VisionFuse is a new way to help machines understand pictures better. It takes advantage of many different language models that already know how to process images, and combines their knowledge to make one powerful language model. This makes it easier to use these models for tasks like image recognition or question answering. The idea behind VisionFuse comes from the fact that different language models focus on different parts of an image when they look at it. By combining this information, VisionFuse can make a single language model that is much better at understanding images. |
Keywords
» Artificial intelligence » Language model » Question answering
