Re-parameterized Low-rank Prompt: Generalize a Vision-Language Model within 0.5K Parameters

• URL: http://arxiv.org/abs/2312.10813v2
• Date: Thu, 11 Jan 2024 12:51:12 GMT
• Title: Re-parameterized Low-rank Prompt: Generalize a Vision-Language Model within 0.5K Parameters
• Authors: Tianxiang Hao, Mengyao Lyu, Hui Chen, Sicheng Zhao, Jungong Han, Guiguang Ding
• Abstract summary: We develop a new type of prompt, Re- parameterized Low-rank Prompt (RLP), for both efficient and effective adaptation. On a series of tasks over 11 datasets, RLP significantly increases the average downstream accuracy of classic prompt tuning by up to 5.25% using merely 0.5K parameters.
• Score: 75.28536311904489
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of large pre-trained vision-language models, how to effectively transfer the knowledge of such foundational models to downstream tasks becomes a hot topic, especially in a data-deficient scenario. Recently, prompt tuning has become a popular solution. When adapting the vision-language models, researchers freeze the parameters in the backbone and only design and tune the prompts. On the one hand, the delicate design of prompt tuning exhibits strong performance. On the other hand, complicated structures and update rules largely increase the computation and storage cost. Motivated by the observation that the evolution pattern of the generalization capability in visual-language models aligns harmoniously with the trend of rank variations in the prompt matrix during adaptation, we design a new type of prompt, Re-parameterized Low-rank Prompt (RLP), for both efficient and effective adaptation. Our method could largely reduce the number of tunable parameters and storage space, which is quite beneficial in resource-limited scenarios. Extensive experiments further demonstrate the superiority of RLP. In particular, RLP shows comparable or even stronger performance than the latest state-of-the-art methods with an extremely small number of parameters. On a series of tasks over 11 datasets, RLP significantly increases the average downstream accuracy of classic prompt tuning by up to 5.25% using merely 0.5K parameters.

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