Summary of Lkaseg:remote-sensing Image Semantic Segmentation with Large Kernel Attention and Full-scale Skip Connections, by Xuezhi Xiang et al.
LKASeg:Remote-Sensing Image Semantic Segmentation with Large Kernel Attention and Full-Scale Skip Connections
by Xuezhi Xiang, Yibo Ning, Lei Zhang, Denis Ombati, Himaloy Himu, Xiantong Zhen
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 The paper proposes a novel network for remote sensing image semantic segmentation called LKASeg, which combines Large Kernel Attention (LKA) and Full-Scale Skip Connections (FSC). The LKA-based decoder extracts global features while avoiding the computational overhead of self-attention and providing channel adaptability. FSC is used to achieve full-scale feature learning and fusion between the encoder and decoder. Experiments on the ISPRS Vaihingen dataset show that the model achieves mF1 and mIoU scores of 90.33% and 82.77%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new method for analyzing satellite images, called LKASeg. It helps computers understand what’s in these images by combining two techniques: Large Kernel Attention (LKA) and Full-Scale Skip Connections (FSC). This lets the computer learn more features from the image without getting too complicated. The researchers tested their approach on a well-known dataset and got good results. |
Keywords
» Artificial intelligence » Attention » Decoder » Encoder » Self attention » Semantic segmentation
