Small but Mighty: Dynamic Wavelet Expert-Guided Fine-Tuning of Large-Scale Models for Optical Remote Sensing Object Segmentation
Yanguang Sun, Chao Wang, Jian Yang, and Lei Luo

TL;DR
This paper introduces WEFT, a novel fine-tuning paradigm that efficiently adapts large-scale models for optical remote sensing image segmentation using wavelet expert guidance, reducing training costs while improving performance.
Contribution
The paper proposes a dynamic wavelet expert-guided fine-tuning method that leverages fewer trainable parameters to adapt large models for remote sensing segmentation tasks.
Findings
Outperforms 21 SOTA methods on three datasets
Achieves top results in camouflage, natural, and medical scenarios
Reduces training costs compared to full-parameter fine-tuning
Abstract
Accurately localizing and segmenting relevant objects from optical remote sensing images (ORSIs) is critical for advancing remote sensing applications. Existing methods are typically built upon moderate-scale pre-trained models and employ diverse optimization strategies to achieve promising performance under full-parameter fine-tuning. In fact, deeper and larger-scale foundation models can provide stronger support for performance improvement. However, due to their massive number of parameters, directly adopting full-parameter fine-tuning leads to pronounced training difficulties, such as excessive GPU memory consumption and high computational costs, which result in extremely limited exploration of large-scale models in existing works. In this paper, we propose a novel dynamic wavelet expert-guided fine-tuning paradigm with fewer trainable parameters, dubbed WEFT, which efficiently…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsRemote-Sensing Image Classification · Advanced Neural Network Applications · Domain Adaptation and Few-Shot Learning
