Craft: Cross-modal Aligned Features Improve Robustness of Prompt Tuning
| Autor: | Sun, Jingchen, Sharma, Rohan, Lokhande, Vishnu Suresh, Chen, Changyou |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Prompt Tuning has emerged as a prominent research paradigm for adapting vision-language models to various downstream tasks. However, recent research indicates that prompt tuning methods often lead to overfitting due to limited training samples. In this paper, we propose a Cross-modal Aligned Feature Tuning (Craft) method to address this issue. Cross-modal alignment is conducted by first selecting anchors from the alternative domain and deriving relative representations of the embeddings for the selected anchors. Optimizing for a feature alignment loss over anchor-aligned text and image modalities creates a more unified text-image common space. Overfitting in prompt tuning also deteriorates model performance on out-of-distribution samples. To further improve the prompt model's robustness, we propose minimizing Maximum Mean Discrepancy (MMD) over the anchor-aligned feature spaces to mitigate domain shift. The experiment on four different prompt tuning structures consistently shows the improvement of our method, with increases of up to $6.1\%$ in the Base-to-Novel generalization task, $5.8\%$ in the group robustness task, and $2.7\%$ in the out-of-distribution tasks. The code will be available at https://github.com/Jingchensun/Craft Comment: 15pages |
| Databáze: | arXiv |
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