Summary of Visual Prompting in Multimodal Large Language Models: a Survey, by Junda Wu et al.
Visual Prompting in Multimodal Large Language Models: A Survey
by Junda Wu, Zhehao Zhang, Yu Xia, Xintong Li, Zhaoyang Xia, Aaron Chang, Tong Yu, Sungchul Kim, Ryan A. Rossi, Ruiyi Zhang, Subrata Mitra, Dimitris N. Metaxas, Lina Yao, Jingbo Shang, Julian McAuley
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This research survey presents the first comprehensive overview of visual prompting methods for multimodal large language models (MLLMs). It focuses on visual prompt generation, compositional reasoning, and learning, highlighting existing categorizations and generative methods for automatic prompt annotations. The study also examines ways to align visual encoders with backbone LLMs, improving MLLM’s visual grounding, object referring, and compositional abilities. Additionally, it summarizes model training and in-context learning methods to enhance perception and understanding of visual prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual prompting is a new way for large language models (LLMs) to understand pictures. This paper looks at many different ways to do this, like making prompts from scratch or using images to teach the model what objects are. The study shows how these methods can help MLLMs become better at understanding visual instructions and improving their abilities to recognize objects and follow instructions. |
Keywords
» Artificial intelligence » Grounding » Prompt » Prompting
