  Vision Transformer (ViT) models have recently emerged as powerful and
versatile models for various visual tasks. Recently, a work called PMF has
achieved promising results in few-shot image classification by utilizing
pre-trained vision transformer models. However, PMF employs full fine-tuning
for learning the downstream tasks, leading to significant overfitting and
storage issues, especially in the remote sensing domain. In order to tackle
these issues, we turn to the recently proposed parameter-efficient tuning
methods, such as VPT, which updates only the newly added prompt parameters
while keeping the pre-trained backbone frozen. Inspired by VPT, we propose the
Meta Visual Prompt Tuning (MVP) method. Specifically, we integrate the VPT
method into the meta-learning framework and tailor it to the remote sensing
domain, resulting in an efficient framework for Few-Shot Remote Sensing Scene
Classification (FS-RSSC). Furthermore, we introduce a novel data augmentation
strategy based on patch embedding recombination to enhance the representation
and diversity of scenes for classification purposes. Experiment results on the
FS-RSSC benchmark demonstrate the superior performance of the proposed MVP over
existing methods in various settings, such as various-way-various-shot,
various-way-one-shot, and cross-domain adaptation.
