Summary of Exploring the Transferability Of Visual Prompting For Multimodal Large Language Models, by Yichi Zhang et al.
Exploring the Transferability of Visual Prompting for Multimodal Large Language Models
by Yichi Zhang, Yinpeng Dong, Siyuan Zhang, Tianzan Min, Hang Su, Jun Zhu
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 Transferable Visual Prompting (TVP) method aims to improve the performance of diverse Multimodal Large Language Models (MLLMs) on downstream tasks by optimizing shared parameters for a specific task. TVP generates visual prompts that can be applied to different models, enhancing their performance after training on only one model. To address cross-model feature corruption, two strategies are introduced: Feature Consistency Alignment and Task Semantics Enrichment. The effectiveness of TVP is validated through extensive experiments with 6 modern MLLMs on various tasks, including object recognition, counting, multimodal reasoning, and hallucination correction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal Large Language Models (MLLMs) are super powerful! But they’re not perfect. They can’t do everything by themselves, so we need to help them get better at specific tasks. We created a new way to make MLLMs better called Transferable Visual Prompting (TVP). TVP makes special pictures that help different MLLMs do their jobs better after training on just one model. It’s like giving them a boost! To make sure these prompts work well with many models, we came up with two clever ideas: making sure the prompts don’t mess up the models’ features and adding more meaning to the prompts so they’re really helpful. |
Keywords
» Artificial intelligence » Alignment » Hallucination » Prompting » Semantics
