Summary of Parameter-efficient Fine-tuning Medical Multimodal Large Language Models For Medical Visual Grounding, by Jinlong He et al.
Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding
by Jinlong He, Pengfei Li, Gang Liu, Shenjun Zhong
First submitted to arxiv on: 31 Oct 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 multimodal approach to language models enables them to excel in tasks that involve multiple types of input, such as text and images. However, the high cost of training these models and the need for large amounts of medical data pose significant challenges for developing models specifically designed for medical applications. Additionally, the free-text nature of answers makes it difficult for these models to produce output in a prescribed format, which is necessary for tasks like visual grounding. To address this challenge, we propose Parameter-efficient Fine-tuning (PFMVG) for medical multimodal large language models (MLLMs). Our model achieves competitive results on a public benchmark dataset for medical visual grounding and outperforms GPT-4v. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical language models can be used to understand and analyze images. However, these models are expensive to train and require lots of medical data. They also struggle with tasks that require specific answers. For example, if you give a model a description of a body part and an image, it should tell you where the part is in the picture. We want to make language models better at this task by proposing a new way to fine-tune them for medical use. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Grounding » Parameter efficient
