Mixture of Prototype Experts with Preference-Guided Routing for Single Domain Generalized Object Detection

Single Domain Generalized Object Detection (SDGOD) trains a detector on a single labeled source domain for generalization to unseen domains. Existing approaches learn invariant features or broaden the source distribution, but limited source diversity prevents them from modeling complex combinations of unseen visual conditions. We propose Mixture of Prototype Experts (MoPE), a framework that factorizes visual conditions into atomic prototypes across photometric, textural, and spatial dimensions, each paired with a corresponding expert. To learn input-adaptive expert composition, we formulate routing as a preference learning problem over multiple coexisting visual attributes, enabling the router to assign higher selection probabilities to scene-relevant prototype experts than to nonrelevant ones. The selected expert outputs are then sparsely aggregated with a shared expert for multi-scale feature fusion. This preference learning is grounded in a Multi-Style Augmentation (MSA) strategy that broadens illumination, weather, and appearance variations without target-domain information while preserving object content and geometry, ensuring the router observes sufficient diversity for learning generalizable routing preferences. Extensive experiments demonstrate consistent generalization gains across unseen weather conditions, artistic styles, and different backbone architectures while maintaining a favorable accuracy-efficiency trade-off. Code and data will be publicly available.