Can I read MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification on EtoBox?

MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification by Zhu, Junjie; Li, Yiying; Qiu, Chunping; Yang, Ke; Guan, Naiyang; Yi, Xiaodong is a scholarly article available to read on EtoBox.

What is MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification about?

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 p

Author

Zhu, Junjie; Li, Yiying; Qiu, Chunping; Yang, Ke; Guan, Naiyang; Yi, Xiaodong

Published

2023

Language

EN