Summary of Remote Sensing Image Segmentation Using Vision Mamba and Multi-scale Multi-frequency Feature Fusion, by Yice Cao et al.
Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion
by Yice Cao, Chenchen Liu, Zhenhua Wu, Wenxin Yao, Liu Xiong, Jie Chen, Zhixiang Huang
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 In this paper, researchers address the challenge of processing high-resolution satellite images efficiently and accurately. Current CNN-based segmentation algorithms achieve good results but are limited by their computational complexity, making them impractical for wide application. To overcome this, the authors introduce a state space model (SSM) and propose a hybrid semantic segmentation network called CVMH-UNet. This approach uses a cross-scanning visual state space block to capture global information from multiple directions and incorporates convolutional neural networks to acquire local features. Additionally, a multi-frequency multi-scale feature fusion block is designed to enhance information utilization and provide refined feature fusion. Experimental results on renowned datasets show that CVMH-UNet outperforms current leading-edge segmentation algorithms while maintaining low computational complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to process high-resolution satellite images quickly and accurately. Right now, some computer programs can do this job well but they take too long to run. The authors of this paper come up with a new way to solve this problem using something called a state space model (SSM) and a special kind of network called CVMH-UNet. This new approach uses two main parts: one that looks at the big picture and another that zooms in on details. It also has a special feature that helps make sure all the information is used correctly. The results show that this new method works better than others and is faster too! |
Keywords
» Artificial intelligence » Cnn » Semantic segmentation » Unet
