Summary of Less Is More: a Closer Look at Semantic-based Few-shot Learning, by Chunpeng Zhou et al.
Less is More: A Closer Look at Semantic-based Few-Shot Learning
by Chunpeng Zhou, Haishuai Wang, Xilu Yuan, Zhi Yu, Jiajun Bu
First submitted to arxiv on: 10 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 This paper proposes a simple yet effective framework for few-shot learning tasks that leverages the textual information and pre-trained language model to facilitate learning. The framework explicitly exploits the zero-shot capability of the pre-trained language model with learnable prompts, combining visual features with textual features for inference without complex fusion modules. Additionally, self-ensemble and distillation techniques are applied to enhance performance. Experimental results on four widely used few-shot datasets demonstrate impressive classification accuracy, particularly in the 1-shot learning task, surpassing state-of-the-art methods by an average of 3.0%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way for computers to learn new things really quickly using only a few examples. Right now, it’s hard to teach computers new things without lots of training data. But what if we could use text and language models to help them learn? The researchers propose a simple solution that uses the power of language to improve learning. They tested their idea on four different datasets and found that it worked really well, especially when they only had one example to work with. This is important because it could make it easier for computers to learn new things in the future. |
Keywords
» Artificial intelligence » 1 shot » Classification » Distillation » Few shot » Inference » Language model » Zero shot
