Summary of Unsupervised Few-shot Continual Learning For Remote Sensing Image Scene Classification, by Muhammad Anwar Ma’sum et al.
Unsupervised Few-Shot Continual Learning for Remote Sensing Image Scene Classification
by Muhammad Anwar Ma’sum, Mahardhika Pratama, Ramasamy Savitha, Lin Liu, Habibullah, Ryszard Kowalczyk
First submitted to arxiv on: 4 Jun 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 The paper proposes an unsupervised flat-wide learning approach (UNISA) for remote sensing image scene classifications that doesn’t require labelled samples. This is a crucial advancement in the field, as traditional continual learning models rely on massive labelled datasets which aren’t feasible for remote sensing applications. UNISA combines prototype scattering and positive sampling to learn representations without catastrophic forgetting. The authors’ numerical study using remote sensing and hyperspectral datasets confirms the effectiveness of their solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at recognizing scenes in images taken from space or airplanes. Right now, these computers need a lot of labelled data to keep getting better, which isn’t practical because it’s hard to get labels for all those images. The authors came up with a new way to teach the computer without needing labels. They call it UNISA and it works by learning patterns in the images and then using that knowledge to make predictions even when new types of images come along. This is important because remote sensing is used in many areas like environmental monitoring, urban planning, and disaster response. |
Keywords
» Artificial intelligence » Continual learning » Unsupervised
