Summary of Lsknet: a Foundation Lightweight Backbone For Remote Sensing, by Yuxuan Li et al.
LSKNet: A Foundation Lightweight Backbone for Remote Sensing
by Yuxuan Li, Xiang Li, Yimian Dai, Qibin Hou, Li Liu, Yongxiang Liu, Ming-Ming Cheng, Jian Yang
First submitted to arxiv on: 18 Mar 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 lightweight Large Selective Kernel Network (LSKNet) backbone that dynamically adjusts its large spatial receptive field to better model the ranging context of various objects in remote sensing scenarios. The LSKNet architecture is designed to leverage prior knowledge embedded within remote sensing images, which can be valuable for recognizing objects and reducing errors caused by mistakenly recognizing objects without referencing a sufficiently long-range context. The proposed approach sets new state-of-the-art scores on standard remote sensing classification, object detection, and semantic segmentation benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special pictures taken from far away (called remote sensing images) to recognize things like buildings or cars. Right now, computers are not very good at doing this because they don’t understand the context of what they’re looking at. This means that if a computer sees a picture of a building and thinks it’s a car, it will say it’s a car even though it’s wrong. To fix this problem, scientists have created a new way to look at these pictures using something called a Large Selective Kernel Network (LSKNet). This approach is special because it can adjust its “vision” to see things in different ways, depending on what it’s looking at. This makes the computer much better at recognizing things correctly. |
Keywords
* Artificial intelligence * Classification * Object detection * Semantic segmentation
