Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models

• URL: http://arxiv.org/abs/2303.06571v2
• Date: Thu, 17 Aug 2023 08:58:00 GMT
• Title: Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models
• Authors: Juncheng Li, Minghe Gao, Longhui Wei, Siliang Tang, Wenqiao Zhang, Mengze Li, Wei Ji, Qi Tian, Tat-Seng Chua, Yueting Zhuang
• Abstract summary: We introduce a novel Gradient-RegulAted Meta-prompt learning framework. It helps pre-training models adapt to downstream tasks in a parameter -- and data -- efficient way. GRAM can be easily incorporated into various prompt tuning methods in a model-agnostic way.
• Score: 137.74524357614285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-training models to adapt to downstream tasks in a parameter -- and data -- efficient way, by learning the ``soft prompts'' to condition frozen pre-training models. Though effective, it is particularly problematic in the few-shot scenario, where prompt tuning performance is sensitive to the initialization and requires a time-consuming process to find a good initialization, thus restricting the fast adaptation ability of the pre-training models. In addition, prompt tuning could undermine the generalizability of the pre-training models, because the learnable prompt tokens are easy to overfit to the limited training samples. To address these issues, we introduce a novel Gradient-RegulAted Meta-prompt learning (GRAM) framework that jointly meta-learns an efficient soft prompt initialization for better adaptation and a lightweight gradient regulating function for strong cross-domain generalizability in a meta-learning paradigm using only the unlabeled image-text pre-training data. Rather than designing a specific prompt tuning method, our GRAM can be easily incorporated into various prompt tuning methods in a model-agnostic way, and comprehensive experiments show that GRAM brings about consistent improvement for them in several settings (i.e., few-shot learning, cross-domain generalization, cross-dataset generalization, etc.) over 11 datasets. Further, experiments show that GRAM enables the orthogonal methods of textual and visual prompt tuning to work in a mutually-enhanced way, offering better generalizability beyond the uni-modal prompt tuning methods.

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