Summary of Prompt As Free Lunch: Enhancing Diversity in Source-free Cross-domain Few-shot Learning Through Semantic-guided Prompting, by Linhai Zhuo et al.
Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting
by Linhai Zhuo, Zheng Wang, Yuqian Fu, Tianwen Qian
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 The proposed SeGD-VPT framework tackles the challenge of source-free cross-domain few-shot learning (CD-FSL) by leveraging textual modality to enhance training sample diversity. The method consists of two phases: increasing feature diversity using diversity prompts and semantically meaningful learning, followed by deep prompt tuning for transfer capability enhancement. This framework is capable of achieving comparable performance to state-of-the-art source-utilized models while outperforming existing methods under the source-free CD-FSL setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new approach to cross-domain few-shot learning that uses textual modality to increase training sample diversity. It’s like a special kind of training for machine learning models that helps them learn from different types of data. The method has two parts: first, it adds variety to the training samples by giving each one a different prompt. Then, it uses these prompts to help the model learn more about the classes it’s trying to recognize. This approach is tested on several datasets and performs well compared to other methods. |
Keywords
» Artificial intelligence » Few shot » Machine learning » Prompt
