Summary of Scaling Up Your Kernels: Large Kernel Design in Convnets Towards Universal Representations, by Yiyuan Zhang et al.
Scaling Up Your Kernels: Large Kernel Design in ConvNets towards Universal Representations
by Yiyuan Zhang, Xiaohan Ding, Xiangyu Yue
First submitted to arxiv on: 10 Oct 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 paper proposes a novel approach to designing modern Convolutional Neural Networks (ConvNets) by leveraging large convolutional kernels. The authors demonstrate that using a few large kernels instead of stacking multiple smaller ones can lead to superior performance and efficiency. They introduce UniRepLKNet, an architecture specifically designed for large-kernel ConvNets, which emphasizes their unique ability to capture extensive spatial information without deep layer stacking. The proposed approach achieves state-of-the-art results on various tasks, including ImageNet (88.0%), ADE20K (55.6%), and COCO box AP (56.4%). Additionally, the authors show that large-kernel ConvNets possess larger effective receptive fields and a higher shape bias compared to smaller-kernel CNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding better ways to design computer vision models called Convolutional Neural Networks (ConvNets). The researchers found that instead of using many small pieces, it’s better to use a few big pieces to process images. They developed a new model called UniRepLKNet that works well on many tasks like recognizing objects in pictures or videos. This new approach is faster and more accurate than previous methods. |
