Summary of Wignet: Windowed Vision Graph Neural Network, by Gabriele Spadaro and Marco Grangetto and Attilio Fiandrotti and Enzo Tartaglione and Jhony H. Giraldo
WiGNet: Windowed Vision Graph Neural Network
by Gabriele Spadaro, Marco Grangetto, Attilio Fiandrotti, Enzo Tartaglione, Jhony H. Giraldo
First submitted to arxiv on: 1 Oct 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 paper introduces a novel approach to Graph Neural Networks (GNNs) called WiGNet, designed for efficient image processing. Unlike previous architectures, WiGNet partitions the image into windows and constructs a graph within each window, reducing computational complexity. The model achieves competitive results on the ImageNet-1k benchmark dataset and demonstrates adaptability using the CelebA-HQ dataset as a downstream task with higher-resolution images. WiGNet’s efficient processing capabilities make it a promising solution for deploying vision GNNs in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WiGNet is a new way to use computer programs (called Graph Neural Networks) to analyze images. These programs are good at recognizing patterns, but they can be slow and use a lot of memory when working with big images. WiGNet solves this problem by breaking the image into smaller pieces (called windows) and processing each one separately. This makes it faster and uses less memory. The program works well on big datasets like ImageNet-1k and can even work with very high-resolution images. |
