Summary of Vired: Prediction Of Visual Relations in Engineering Drawings, by Chao Gu et al.
ViRED: Prediction of Visual Relations in Engineering Drawings
by Chao Gu, Ke Lin, Yiyang Luo, Jiahui Hou, Xiang-Yang Li
First submitted to arxiv on: 2 Sep 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 This research proposes a novel vision-based relation detection model called ViRED to identify associations between tables and circuits in electrical engineering drawings. The model consists of three parts: a vision encoder, an object encoder, and a relation decoder. Implemented using PyTorch, ViRED achieves an accuracy of 96% in relation prediction on the engineering drawing dataset, outperforming existing methods. Additionally, the model can inference quickly even when dealing with complex drawings containing numerous objects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists created a new way to understand diagrams used by engineers. These diagrams are important because they help us build things like electrical circuits. The problem is that most computer programs that try to understand these diagrams only look at the words, not the pictures. This doesn’t work well when there are many images in the diagram. To fix this, the researchers created a new model called ViRED that looks at both the text and the images in the diagram. They tested their model on some diagrams and found that it was very good at understanding the relationships between different parts of the diagram. |
Keywords
» Artificial intelligence » Decoder » Encoder » Inference
