Prompt Learning via Meta-Regularization
Jinyoung Park Juyeon Ko Hyunwoo J. Kim
Pre-trained vision-language models have shown impressive success on various
computer vision tasks with their zero-shot generalizability. Recently, prompt
learning approaches have been explored to efficiently and effectively adapt the
vision-language models to a variety of downstream tasks. However, most existing
prompt learning methods suffer from task overfitting since the general
knowledge of the pre-trained vision language models is forgotten while the
prompts are finetuned on a small data set from a specific target task. To
address this issue, we propose a Prompt Meta-Regularization (ProMetaR) to
improve the generalizability of prompt learning for vision-language models.
Specifically, ProMetaR meta-learns both the regularizer and the soft prompts to
harness the task-specific knowledge from the downstream tasks and task-agnostic
general knowledge from the vision-language models. Further, ProMetaR augments
the task to generate multiple virtual tasks to alleviate the meta-overfitting.
In addition, we provide the analysis to comprehend how ProMetaR improves the
generalizability of prompt tuning in the perspective of the gradient alignment.
Our extensive experiments demonstrate that our ProMetaR improves the
generalizability of conventional prompt learning methods under
base-to-base/base-to-new and domain generalization settings. The code of
ProMetaR is available at https://github.com/mlvlab/ProMetaR.