Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection
<|reference_start|>Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection: Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains using only data from a single source domain during training. This is a practical yet challenging task as it requires the model to address domain shift without incorporating target domain data into training. In this paper, we propose a novel phrase grounding-based style transfer (PGST) approach for the task. Specifically, we first define textual prompts to describe potential objects for each unseen target domain. Then, we leverage the grounded language-image pre-training (GLIP) model to learn the style of these target domains and achieve style transfer from the source to the target domain. The style-transferred source visual features are semantically rich and could be close to imaginary counterparts in the target domain. Finally, we employ these style-transferred visual features to fine-tune GLIP. By introducing imaginary counterparts, the detector could be effectively generalized to unseen target domains using only a single source domain for training. Extensive experimental results on five diverse weather driving benchmarks demonstrate our proposed approach achieves state-of-the-art performance, even surpassing some domain adaptive methods that incorporate target domain images into the training process.The source codes and pre-trained models will be made available.<|reference_end|>
@article{li2024phrase,
    title={Phrase Grounding-based Style Transfer for Single-Domain Generalized
  Object Detection},
    author={Hao Li, Wei Wang, Cong Wang, Zhigang Luo, Xinwang Liu, Kenli Li and
  Xiaochun Cao},
    journal={arXiv preprint arXiv:2402.01304},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01304},
    primaryClass={cs.CV}
}