Domain generalization and synthetic data in object detection: the enabler, the probe, and the gap
Object detection models often experience performance degradation when deployed under distribution shifts, caused by for example changes in weather type, operational environment, or object appearance. Domain Generalization (DG) aims to develop models that remain robust under such shifts and generalize well to unseen domains. DG research specifically focused on object detection models is scarce, although these models face additional challenges around localization and multi-scale representations. Synthetic data is a promising tool to support in DG, by enabling large-scale generation of diverse new samples. In this paper, we present an object detection-centric review of DG and examine the rol...
| Authors | Elfi I. S. Hofmeijer, Ella P. Fokkinga, Friso G. Heslinga et al. |
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| Primary category | cs.CV |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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