Summary of Memory-guided Network with Uncertainty-based Feature Augmentation For Few-shot Semantic Segmentation, by Xinyue Chen et al.
Memory-guided Network with Uncertainty-based Feature Augmentation for Few-shot Semantic Segmentation
by Xinyue Chen, Miaojing Shi
First submitted to arxiv on: 1 Jun 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 The paper proposes a novel approach to few-shot semantic segmentation (FSS) that alleviates the dependence on large-scale training data. The method, called class-shared memory (CSM), uses learnable memory vectors to encode elemental object patterns from base classes and improve distribution alignment between base and novel classes. Additionally, an uncertainty-based feature augmentation (UFA) module is introduced to cope with intra-class variance across images, enhancing the model’s robustness. Experimental results on PASCAL-5i and COCO-20i datasets demonstrate the superior performance of this approach over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to make computers better at recognizing objects in pictures, even when they don’t have much training data. This is called few-shot semantic segmentation (FSS). The new method uses something called class-shared memory (CSM) and uncertainty-based feature augmentation (UFA) to help the computer learn about different types of objects and recognize them more accurately. By using these new techniques, the computer can do a better job of recognizing objects in pictures, even when it hasn’t seen many examples before. |
Keywords
» Artificial intelligence » Alignment » Few shot » Semantic segmentation
