Summary of Mind the Gap Between Prototypes and Images in Cross-domain Finetuning, by Hongduan Tian et al.
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning
by Hongduan Tian, Feng Liu, Zhanke Zhou, Tongliang Liu, Chengqi Zhang, Bo Han
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 paper proposes a novel method called contrastive prototype-image adaptation (CoPA) to improve cross-domain few-shot classification (CFC). CoPA adapts different transformations for prototypes and images, similar to CLIP, by treating prototypes as text prompts. This approach is shown to achieve state-of-the-art performance on the Meta-Dataset benchmark more efficiently than previous methods. The authors also demonstrate that CoPA learns better representation clusters and enlarges the gap between prototype and image representations, leading to minimal validation loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoPA is a new way to make computers better at learning from small amounts of data. Right now, most AI systems are good at doing one thing well, but struggle when they have to do something completely different. CoPA helps fix this by creating two different paths for the computer to follow: one for images and another for words (like in a sentence). This allows the system to learn from small amounts of data more effectively. The results show that CoPA is better than other methods at solving these types of problems. |
Keywords
* Artificial intelligence * Classification * Few shot
