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：域泛化旨在促进域不变特征的学习，同时抑制域特定特征，从而使模型能够更好地泛化至先前未见的目标域。本文提出了一种面向目标检测的域泛化方法，这是首个适用于任意目标检测架构的此类方法。基于严谨的数学分析，我们将基于特征对齐的方法进行扩展，引入了一个新颖的组件，该组件在实例层级执行类条件对齐，同时还在图像层级对齐跨域的边际特征分布。这使得我们能够全面应对域漂移的两个组成部分，即协变量漂移和概念漂移，并学习到一种域无关的特征表示。我们使用单阶段（FCOS、YOLO）和双阶段（FRCNN）检测器进行了广泛评估，评估基准包含多个面向自动驾驶应用的数据集（Cityscapes、BDD10K、ACDC、IDD）以及面向精准农业的GWHD数据集，结果表明，与基线方法和现有最优方法相比，我们在泛化性能和定位性能上均取得了一致的提升。