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.

单域广义目标检测提高模型泛化能力，只使用来自单一源域的数据进行训练。本文提出了一种基于短语定位的风格转移方法（PGST）来实现此任务。通过定义文本提示并利用已有模型学习目标域的风格，并将源域的视觉特征转移到目标域。最终，我们使用这些风格转移的视觉特征对模型进行微调，使其在只使用单一源域进行训练的情况下能够有效地适用于未见过的目标域。实验证明，我们的方法在五个不同的天气驾驶基准数据集上取得了最先进的性能，甚至超过了一些将目标域图像纳入训练过程的领域自适应方法。源代码和预训练模型将会提供。