Summary of Promptta: Prompt-driven Text Adapter For Source-free Domain Generalization, by Haoran Zhang et al.
PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization
by Haoran Zhang, Shuanghao Bai, Wanqi Zhou, Jingwen Fu, Badong Chen
First submitted to arxiv on: 21 Sep 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 This paper proposes the Prompt-Driven Text Adapter (PromptTA) method for source-free domain generalization, which leverages style features and resampling to capture comprehensive domain knowledge. The approach involves developing a transferable linear classifier based on diverse style features extracted from text and learned prompts or deriving domain-unified text representations from domain banks. To further leverage this rich domain information, the authors introduce a text adapter that learns from these style features for efficient domain information storage. The method achieves state-of-the-art performance in extensive experiments conducted on four benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers adapt to new situations without seeing examples of those situations before. It proposes a way to do this using language and visual models, which are trained on large amounts of text data. The approach captures the essence of different domains, such as images of cats or dogs, by analyzing their characteristics. This allows the model to make accurate predictions even when it hasn’t seen similar examples before. |
Keywords
» Artificial intelligence » Domain generalization » Prompt
