Single-domain generalization (S-DG) aims to generalize a model to unseen environments with a single-source domain. However, most S-DG approaches have been conducted in the field of classification. When these approaches are applied to object detection, the semantic features of some objects can be damaged, which can lead to imprecise object localization and misclassification. To address these problems, we propose an object-aware domain generalization (OA-DG) method for single-domain generalization in object detection. Our method consists of data augmentation and training strategy, which are called OA-Mix and OA-Loss, respectively. OA-Mix generates multi-domain data with multi-level transformation and object-aware mixing strategy. OA-Loss enables models to learn domain-invariant representations for objects and backgrounds from the original and OA-Mixed images. Our proposed method outperforms state-of-the-art works on standard benchmarks. Our code is available at https://github.com/WoojuLee24/OA-DG.

翻译：单源领域泛化旨在利用单一源领域将模型泛化至未见过的环境。然而，现有单源领域泛化方法主要集中于分类任务。当将这些方法应用于目标检测时，部分目标语义特征可能遭到破坏，从而导致目标定位不精确与分类错误。针对上述问题，我们提出了一种面向目标检测单源领域泛化的目标感知领域泛化方法。该方法包含数据增强与训练策略两个核心组件，分别命名为OA-Mix和OA-Loss。其中，OA-Mix通过多层级变换与目标感知混合策略生成多领域数据；OA-Loss则使模型能够从原始图像与OA-Mix增强图像中学习目标和背景的域不变表征。实验结果表明，所提方法在标准基准测试中优于现有最优方法。相关代码已在https://github.com/WoojuLee24/OA-DG 公开。