Summary of Rethinking Visual Prompting For Multimodal Large Language Models with External Knowledge, by Yuanze Lin et al.
Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge
by Yuanze Lin, Yunsheng Li, Dongdong Chen, Weijian Xu, Ronald Clark, Philip Torr, Lu Yuan
First submitted to arxiv on: 5 Jul 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 paper proposes a new approach to improve the performance of multimodal large language models (MLLMs) in understanding fine-grained or spatially dense visual information. Current MLLMs are good at understanding images but struggle with tasks that require detailed knowledge of localized elements. The authors draw inspiration from the Retrieval-Augmented Generation concept and propose a visual prompt approach to integrate external knowledge gleaned from specialized vision models into MLLMs. This involves embedding fine-grained knowledge information directly into a spatial embedding map as a visual prompt, which can be easily incorporated into various MLLMs like LLaVA and Mipha. The proposed method improves the performance of MLLMs across nine benchmarks, enhancing their fine-grained context-aware capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making machines better at understanding images. Right now, they’re good at general things, but struggle with details. To fix this, the researchers came up with a new way to teach them by using special computer vision models that can understand tiny parts of an image. They then combine these models with language models to make the machines smarter and more accurate. |
Keywords
» Artificial intelligence » Embedding » Prompt » Retrieval augmented generation
