Parameter Efficient Fine-tuning of Vision Transformers for Remote Sensing Scene Understanding
With the rapid development of deep learning and computer vision technologies,fine-tuning pre-trained models for remote sensing tasks often requires substantial computational resources.To reduce memory requirements and training costs,a method called"Multi-Fusion Adapter(MuFA)"for fine-tuning remote sensing models is proposed in this paper.MuFA introduces a fusion module that combines bottleneck modules with different down sample rates and connects them in parallel with the original vision Transformer model.During training,the parameters of the original vision Transformer model are frozen,and only the MuFA module and classification head are fine-tuned.Experimental results demonstrate that MuFA achieves superior performance on the UCM and NWPU-RESISC45 remote sensing scene classification datasets,surpassing other parameter efficient fine-tuning methods.Therefore,MuFA not only maintains model performance but also reduces resource overhead,making it highly promising for various remote sensing applications.
万方数据
维普
