Summary of Transitive Vision-language Prompt Learning For Domain Generalization, by Liyuan Wang et al.
Transitive Vision-Language Prompt Learning for Domain Generalization
by Liyuan Wang, Yan Jin, Zhen Chen, Jinlin Wu, Mengke Li, Yang Lu, Hanzi Wang
First submitted to arxiv on: 29 Apr 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 recent learning method based on vision-language pre-training has shown great potential in domain generalization tasks. However, existing methods still struggle to balance domain invariance and class separability, which are crucial for current DG problems. This paper proposes a novel prompt learning strategy that leverages deep vision prompts to address domain invariance while utilizing language prompts to ensure class separability. The approach also incorporates adaptive weighting mechanisms to balance these two aspects. Extensive experiments demonstrate the effectiveness of this method, achieving state-of-the-art performance on three datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help deep models learn from different types of data without getting confused. Right now, most models struggle to do this because they’re not good at balancing two important things: staying consistent across different types of data and making sure the differences between classes are clear. The authors came up with a new idea that uses both visual and language prompts to help deep models get better at domain generalization. |
Keywords
» Artificial intelligence » Domain generalization » Prompt
