Summary of When Are Foundation Models Effective? Understanding the Suitability For Pixel-level Classification Using Multispectral Imagery, by Yiqun Xie et al.
When are Foundation Models Effective? Understanding the Suitability for Pixel-Level Classification Using Multispectral Imagery
by Yiqun Xie, Zhihao Wang, Weiye Chen, Zhili Li, Xiaowei Jia, Yanhua Li, Ruichen Wang, Kangyang Chai, Ruohan Li, Sergii Skakun
First submitted to arxiv on: 17 Apr 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 Foundation models have achieved impressive performance in various language and vision tasks. This raises expectations about their potential in other domains like satellite remote sensing. Foundation models like Prithvi, Segment-Anything-Model, and ViT have been built to test their capabilities in broader applications. However, it is essential to understand whether foundation models are always suitable for different remote sensing tasks. This paper aims to enhance the understanding of foundation models’ suitability for pixel-level classification using multispectral imagery at moderate resolution by comparing them with traditional machine learning (ML) and regular-size deep learning models. The results show that traditional ML models often perform similarly or better than foundation models, especially when texture is not crucial for classification. Deep learning models showed more promising results for tasks relying on texture, but the difference between foundation models and deep learning models was not significant. Ultimately, the suitability of foundation models depends on the alignment between self-supervised learning tasks and real downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models are very large deep learning models that have been successful in language and vision tasks. They can be used for satellite remote sensing, but it’s important to know when they’re suitable or not. In this paper, we compare foundation models with smaller models and traditional machine learning (ML) models. We found that ML models often do just as well or better than foundation models, especially when texture isn’t needed for classification. However, deep learning models are good for tasks that rely on texture. |
Keywords
» Artificial intelligence » Alignment » Classification » Deep learning » Machine learning » Self supervised » Vit
