Summary of Greedyvig: Dynamic Axial Graph Construction For Efficient Vision Gnns, by Mustafa Munir et al.
GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNs
by Mustafa Munir, William Avery, Md Mostafijur Rahman, Radu Marculescu
First submitted to arxiv on: 10 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 method for designing Vision Graph Neural Networks (ViGs) called Dynamic Axial Graph Construction (DAGC), which addresses the inefficient k-nearest neighbor operation used in traditional ViG construction. The authors also introduce GreedyViG, a hybrid CNN-GNN architecture that leverages DAGC and achieves state-of-the-art performance on various computer vision tasks, including image classification, object detection, instance segmentation, and semantic segmentation. The proposed models outperform existing architectures, such as Vision GNN and Vision HyperGraph Neural Network (ViHGNN), in terms of accuracy, GPU memory consumption, and parameter efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates new ways to design computer vision models that are faster and more accurate. It introduces a new method for building graph neural networks called Dynamic Axial Graph Construction. This helps solve a problem with the way graphs are built in traditional computer vision models. The researchers also created a new architecture that combines two types of models, CNNs and GNNs, to get even better results on tasks like image recognition, object detection, and more. Their models beat existing ones in terms of how well they do, how much memory they use, and how many parameters they have. |
Keywords
» Artificial intelligence » Cnn » Gnn » Image classification » Instance segmentation » Nearest neighbor » Neural network » Object detection » Semantic segmentation
