  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.
