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.

翻译：单域广义目标检测旨在仅使用训练中的单个源域数据，增强模型对多个未见目标域的泛化能力。这是一个实用但具有挑战性的任务，因为它要求模型在未将目标域数据纳入训练的情况下处理域偏移。本文提出一种新颖的基于短语定位的风格迁移（PGST）方法来解决该任务。具体而言，我们首先为每个未见目标域定义文本提示，以描述潜在物体。然后，利用接地语言-图像预训练（GLIP）模型学习这些目标域的风格，实现从源域到目标域的风格迁移。经过风格迁移的源域视觉特征语义丰富，且能接近目标域中的想象对应物。最后，我们使用这些风格迁移后的视觉特征对GLIP进行微调。通过引入想象对应物，检测器仅需使用单一源域训练即可有效泛化到未见目标域。在五个不同天气驾驶基准上的大量实验结果表明，所提方法实现了最先进性能，甚至超越了某些将目标域图像纳入训练过程的域自适应方法。源代码和预训练模型将公开发布。