Summary of Augmenting Prototype Network with Transmix For Few-shot Hyperspectral Image Classification, by Chun Liu et al.
Augmenting Prototype Network with TransMix for Few-shot Hyperspectral Image Classification
by Chun Liu, Longwei Yang, Dongmei Dong, Zheng Li, Wei Yang, Zhigang Han, Jiayao Wang
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Few-shot hyperspectral image classification aims to identify pixel classes based on a limited number of labeled pixels. To achieve this, fixed-size patches are often used as features for classification. However, boundary patches are challenging due to mixed spectral information from multiple classes. The authors propose an augmented prototype network (APNT) with TransMix, which uses transformers to learn pixel-to-pixel relations and adaptively attends to different pixels. Unlike traditional methods, APNT mixes and labels synthetic patches to enlarge the number of training samples and enhance their diversity. Experimental results show that APNT outperforms existing methods in few-shot hyperspectral image classification, demonstrating state-of-the-art performance and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to identify what’s in a picture by only seeing a few areas labeled. That’s the challenge of few-shot hyperspectral image classification. Current methods use small pieces of the picture as features, but these boundary pieces are tricky because they have information from multiple classes mixed together. The researchers propose a new approach that uses special computer algorithms to learn how different parts of the picture relate to each other and focus on important areas. They also create fake patches to help train the model, making it better at recognizing patterns in the pictures. This new method performs really well and is more robust than existing methods. |
Keywords
» Artificial intelligence » Classification » Few shot » Image classification
