  Recently, numerous methods have achieved impressive performance in remote
sensing object detection, relying on convolution or transformer architectures.
Such detectors typically have a feature backbone to extract useful features
from raw input images. For the remote sensing domain, a common practice among
current detectors is to initialize the backbone with pre-training on ImageNet
consisting of natural scenes. Fine-tuning the backbone is then typically
required to generate features suitable for remote-sensing images. However, this
could hinder the extraction of basic visual features in long-term training,
thus restricting performance improvement. To mitigate this issue, we propose a
novel method named DBF (Dynamic Backbone Freezing) for feature backbone
fine-tuning on remote sensing object detection. Our method aims to handle the
dilemma of whether the backbone should extract low-level generic features or
possess specific knowledge of the remote sensing domain, by introducing a
module called 'Freezing Scheduler' to dynamically manage the update of backbone
features during training. Extensive experiments on DOTA and DIOR-R show that
our approach enables more accurate model learning while substantially reducing
computational costs. Our method can be seamlessly adopted without additional
effort due to its straightforward design.
