Summary of Compositional Few-shot Class-incremental Learning, by Yixiong Zou et al.
Compositional Few-Shot Class-Incremental Learning
by Yixiong Zou, Shanghang Zhang, Haichen Zhou, Yuhua Li, Ruixuan Li
First submitted to arxiv on: 27 May 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 proposed few-shot class-incremental learning (FSCIL) method aims to improve the ability of AI models to learn from novel classes with only a few samples, mimicking human capabilities. The approach is inspired by cognitive science and involves identifying visual primitives from learned knowledge and composing new concepts using these transferred primitives. A compositional model based on set similarities is proposed, featuring primitive composition and primitive reuse modules. The primitive composition module utilizes Centered Kernel Alignment (CKA) similarity to approximate the similarity between primitive sets, while the primitive reuse module enhances reusability by classifying inputs based on primitives replaced with closest primitives from other classes. Experimental results on three datasets demonstrate improved performance and interpretability compared to current state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for machines to learn about new things with just a few examples, like humans do. It’s called few-shot class-incremental learning (FSCIL). The idea is to break down what we already know into smaller parts or “primitives” and then use these primitives to figure out new things. The method uses a special way of comparing sets of data, called Centered Kernel Alignment (CKA), to help it learn. It also has a special module that helps it reuse the knowledge it’s gained from other classes to learn about new ones. Tests on three different datasets show that this method works better than other methods and is easier to understand. |
Keywords
» Artificial intelligence » Alignment » Few shot
