Summary of Covis: a Collaborative Framework For Fine-grained Graphic Visual Understanding, by Xiaoyu Deng et al.
CoVis: A Collaborative Framework for Fine-grained Graphic Visual Understanding
by Xiaoyu Deng, Zhengjian Kang, Xintao Li, Yongzhe Zhang, Tianmin Guo
First submitted to arxiv on: 27 Nov 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 CoVis framework aims to improve the quality and efficiency of visual information transmission by developing a collaborative approach for fine-grained visual understanding. The framework combines a cascaded dual-layer segmentation network with a large-language-model (LLM) based content generator to extract knowledge from an image and generate visual analytics. This enables observers to comprehend imagery from a more holistic perspective, outperforming current methods in feature extraction and generating more comprehensive visual descriptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoVis is a new way to understand pictures better. Right now, we rely on our own knowledge and experience when looking at images, which can be limited. CoVis helps by using artificial intelligence to extract as much information as possible from an image, then creating detailed summaries of what’s in the picture. This makes it easier for people to understand what they’re seeing. |
Keywords
» Artificial intelligence » Feature extraction » Large language model
