Summary of Exploring Cross-domain Few-shot Classification Via Frequency-aware Prompting, by Tiange Zhang et al.
Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting
by Tiange Zhang, Qing Cai, Feng Gao, Lin Qi, Junyu Dong
First submitted to arxiv on: 24 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 A novel approach to cross-domain few-shot learning is proposed, addressing the limitation of existing methods that neglect the reliance on high-frequency cues by deep networks. The Frequency-Aware Prompting method with mutual attention is introduced, which simulates human visual perception by selecting different frequency cues for new recognition tasks. A frequency-aware prompting mechanism and a mutual attention module are designed to learn generalizable inductive bias under cross-domain few-shot learning settings. Experimental results demonstrate the effectiveness of the proposed method, achieving robust performance improvements over existing methods. The proposed approach is a plug-and-play module that can be directly applied to most off-the-shelf CD-FLS methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help machines learn from very few examples across different types of data is developed. This method makes sure computers don’t just rely on easy-to-spot patterns, but also look at other important details. The approach works by giving the computer a way to select which patterns to focus on for each new task it’s trying to learn. This helps the computer learn more accurately and robustly from the limited examples it has. The results show that this method can improve the performance of existing machine learning approaches. |
Keywords
» Artificial intelligence » Attention » Few shot » Machine learning » Prompting
