  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.
