Summary of Llava-sg: Leveraging Scene Graphs As Visual Semantic Expression in Vision-language Models, by Jingyi Wang et al.
LLaVA-SG: Leveraging Scene Graphs as Visual Semantic Expression in Vision-Language Models
by Jingyi Wang, Jianzhong Ju, Jian Luan, Zhidong Deng
First submitted to arxiv on: 29 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 A novel approach to enhance large vision-language models’ (VLMs’) visual understanding capabilities is proposed by introducing a Scene Graph Expression (SGE) module. This module extracts and structurally expresses complex semantic information within images, improving VLMs’ foundational perception and understanding abilities. The SGE module addresses the limitation of traditional ViT-based vision encoders that fragmentally perceive images. Experimental results demonstrate significant performance enhancements in vision-language tasks, indicating the effectiveness of the SGE module in preserving intricate semantic details and facilitating better visual understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large vision-language models have a problem with understanding images because they break them into small pieces. This makes it hard for them to learn what’s going on in an image. To fix this, researchers propose a new way to look at images that extracts important information about the scene. This helps the model understand images better and do tasks like recognizing objects or scenes. The results show that this new approach really works well. |
Keywords
* Artificial intelligence * Vit
