Adapting Vision Transformer for Efficient Change Detection
This incremental improvement reduces computational barriers for researchers in remote sensing change detection.
The paper tackles the high resource cost of fine-tuning vision transformers for change detection by proposing an efficient tuning approach that freezes the pretrained encoder and adds minimal parameters, achieving competitive or better results on six benchmarks with training times as low as half an hour and 9 GB memory usage.
Most change detection models based on vision transformers currently follow a "pretraining then fine-tuning" strategy. This involves initializing the model weights using large scale classification datasets, which can be either natural images or remote sensing images. However, fully tuning such a model requires significant time and resources. In this paper, we propose an efficient tuning approach that involves freezing the parameters of the pretrained image encoder and introducing additional training parameters. Through this approach, we have achieved competitive or even better results while maintaining extremely low resource consumption across six change detection benchmarks. For example, training time on LEVIR-CD, a change detection benchmark, is only half an hour with 9 GB memory usage, which could be very convenient for most researchers. Additionally, the decoupled tuning framework can be extended to any pretrained model for semantic change detection and multi temporal change detection as well. We hope that our proposed approach will serve as a part of foundational model to inspire more unified training approaches on change detection in the future.