Rethinking Feature Backbone Fine-tuning for Remote Sensing Object Detection
<|reference_start|>Rethinking Feature Backbone Fine-tuning for Remote Sensing Object Detection: 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.<|reference_end|>
@article{kim2024rethinking,
    title={Rethinking Feature Backbone Fine-tuning for Remote Sensing Object
  Detection},
    author={Yechan Kim and JongHyun Park and SooYeon Kim and Moongu Jeon},
    journal={arXiv preprint arXiv:2407.15143},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15143},
    primaryClass={cs.CV cs.AI}
}