Domain generalisation aims to promote the learning of domain-invariant features while suppressing domain-specific features, so that a model can generalise better to previously unseen target domains. An approach to domain generalisation for object detection is proposed, the first such approach applicable to any object detection architecture. Based on a rigorous mathematical analysis, we extend approaches based on feature alignment with a novel component for performing class conditional alignment at the instance level, in addition to aligning the marginal feature distributions across domains at the image level. This allows us to fully address both components of domain shift, i.e. covariate and concept shift, and learn a domain agnostic feature representation. We perform extensive evaluation with both one-stage (FCOS, YOLO) and two-stage (FRCNN) detectors, on a newly proposed benchmark comprising several different datasets for autonomous driving applications (Cityscapes, BDD10K, ACDC, IDD) as well as the GWHD dataset for precision agriculture, and show consistent improvements to the generalisation and localisation performance over baselines and state-of-the-art.

翻译：领域泛化旨在促进模型学习领域不变特征，同时抑制领域特定特征，从而使模型能够更好地泛化到先前未见过的目标领域。本文提出了一种适用于目标检测的领域泛化方法，这是首个可应用于任意目标检测架构的此类方法。基于严格的数学分析，我们在图像层级对齐领域间边缘特征分布的基础上，扩展了基于特征对齐的方法，引入了一种在实例层级执行类别条件对齐的新颖组件。这使得我们能够完整处理领域偏移的两个组成部分——即协变量偏移与概念偏移，并学习领域无关的特征表示。我们使用单阶段检测器（FCOS、YOLO）与两阶段检测器（FRCNN）在新提出的基准测试上进行了广泛评估，该基准包含多个自动驾驶应用数据集（Cityscapes、BDD10K、ACDC、IDD）以及精准农业领域的GWHD数据集。实验结果表明，相较于基线方法与当前最优技术，我们的方法在泛化性能与定位精度方面均取得了持续改进。