Summary of Surgical-lvlm: Learning to Adapt Large Vision-language Model For Grounded Visual Question Answering in Robotic Surgery, by Guankun Wang et al.
Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery
by Guankun Wang, Long Bai, Wan Jun Nah, Jie Wang, Zhaoxi Zhang, Zhen Chen, Jinlin Wu, Mobarakol Islam, Hongbin Liu, Hongliang Ren
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO); Image and Video Processing (eess.IV)
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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 Surgical-LVLM model is a personalized large vision-language model designed for complex surgical scenarios. It leverages pre-trained models and specialized Visual Perception LoRA (VP-LoRA) blocks to excel in understanding visual-language tasks within surgical contexts. The model includes the Token-Interaction (TIT) module, which strengthens the interaction between the grounding module and language responses. Surgical-LVLM sets new performance standards on benchmarks such as EndoVis-17-VQLA, EndoVis-18-VQLA, and the newly introduced EndoVis Conversations dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Surgical Visual Question Answering (Surgical-VQA) is a technology that helps robots and doctors work together. It’s like having a super smart assistant in the operating room! Currently, these models can only answer simple questions, but they struggle with harder scenarios because they don’t understand how things are connected or how to combine different types of information. The new Surgical-LVLM model is designed to fix this problem by using a special type of AI that’s really good at understanding complex visual and language tasks. This breakthrough could lead to more personalized surgical mentorship, making surgeries safer and more effective. |
Keywords
» Artificial intelligence » Grounding » Language model » Lora » Question answering » Token
