Summary of Bafnet: Bilateral Attention Fusion Network For Lightweight Semantic Segmentation Of Urban Remote Sensing Images, by Wentao Wang and Xili Wang
BAFNet: Bilateral Attention Fusion Network for Lightweight Semantic Segmentation of Urban Remote Sensing Images
by Wentao Wang, Xili Wang
First submitted to arxiv on: 16 Sep 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 This paper proposes a novel lightweight bilateral semantic segmentation network called BAFNet for efficiently segmenting high-resolution urban remote sensing images. The model consists of two paths: the dependency path, which utilizes large kernel attention to capture long-range dependencies, and the remote-local path, comprising multi-scale local attention and efficient remote attention. A feature aggregation module is designed to utilize features from both paths. BAFNet outperforms advanced lightweight models in accuracy on public datasets Vaihingen and Potsdam, while also demonstrating comparable performance to non-lightweight state-of-the-art methods despite significant reductions in computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to analyze satellite images, called BAFNet. It’s designed to work with limited computer power and small image samples. The method uses two paths: one for looking at big picture details and another for focusing on local features. By combining these two approaches, the model can accurately identify different parts of an image even when it’s not very powerful. This new approach was tested on real-world satellite images and showed similar results to more complex models that require much more computer power. |
Keywords
» Artificial intelligence » Attention » Semantic segmentation
