Summary of Llava-vsd: Large Language-and-vision Assistant For Visual Spatial Description, by Yizhang Jin et al.
LLaVA-VSD: Large Language-and-Vision Assistant for Visual Spatial Description
by Yizhang Jin, Jian Li, Jiangning Zhang, Jianlong Hu, Zhenye Gan, Xin Tan, Yong Liu, Yabiao Wang, Chengjie Wang, Lizhuang Ma
First submitted to arxiv on: 9 Aug 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 The proposed Large Language-and-Vision Assistant for Visual Spatial Description (LLaVA-VSD) aims to generate texts that describe the spatial relationships between objects within images. Traditional methods often neglect world knowledge and lack general language capabilities, whereas LLaVA-VSD is designed for classification, description, and open-ended description of visual spatial relationships. The model constructs a VSD instruction-following dataset using given figure-caption pairs and employs LoRA to fine-tune the assistant, which has 13 billion parameters and supports high-resolution images. Finally, a large language model (Qwen-2) refines the generated sentences for diversity and accuracy. LLaVA-VSD demonstrates excellent multimodal conversational capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual Spatial Description (VSD) is like teaching computers to describe what’s happening in pictures. Right now, computers can only say things like “dog is next to cat.” But VSD wants to do more – it wants computers to understand the whole story behind a picture and generate text that explains the relationships between objects. The team created a special computer model called LLaVA-VSD to help with this task. It uses lots of data, including pictures and words, to learn how to describe spatial relationships in images. The result is a super smart computer that can understand what’s happening in pictures and generate text about it. |
Keywords
» Artificial intelligence » Classification » Large language model » Lora
