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。

基于短语引导的风格转移的单域通用目标检测

Phrase Grounding-based Style Transfer for Single-Domain Generalized  Object Detection

This paper provides a novel framework for single-domain generalized object
detection (i.e., Single-DGOD), where we are interested in learning and
maintaining the semantic structures of self-augmented compound cross-domain
samples to enhance the model's generalization ability. Different from DGOD
trained on multiple source domains, Single-DGOD is far more challenging to
generalize well to multiple target domains with only one single source domain.
Existing methods mostly adopt a similar treatment from DGOD to learn
domain-invariant features by decoupling or compressing the semantic space.
However, there may have two potential limitations: 1) pseudo attribute-label
correlation, due to extremely scarce single-domain data; and 2) the semantic
structural information is usually ignored, i.e., we found the affinities of
instance-level semantic relations in samples are crucial to model
generalization. In this paper, we introduce Semantic Reasoning with Compound
Domains (SRCD) for Single-DGOD. Specifically, our SRCD contains two main
components, namely, the texture-based self-augmentation (TBSA) module, and the
local-global semantic reasoning (LGSR) module. TBSA aims to eliminate the
effects of irrelevant attributes associated with labels, such as light, shadow,
color, etc., at the image level by a light-yet-efficient self-augmentation.
Moreover, LGSR is used to further model the semantic relationships on instance
features to uncover and maintain the intrinsic semantic structures. Extensive
experiments on multiple benchmarks demonstrate the effectiveness of the
proposed SRCD.

本研究提出了一种单领域广义目标检测（Single-DGOD）的新框架，其中我们利用自我增强的复合跨域样本的语义结构来提高模型的泛化能力，提出了具有两个主要组成部分的语义推理与复合域（SRCD）来进行 Single-DGOD。