Vision-Language Models Empowered Nighttime Object Detection With Consistency Sampler and Hallucination Feature...

Current object detectors often suffer performance degradation when applied to cross-domain scenarios, particularly under challenging visual conditions such as nighttime scenes. This is primarily due to the I3 problems: Inadequate sampling of instance-level features, Indistinguishable feature representation across domains and Inaccurate generation for identical category participation. To address these challenges, we propose a domain-adaptive detection framework that enables robust generalization across different visual domains without introducing any additional inference overhead. The framework comprises three key components. Specifically, the centerness-category consistency sampler alleviates inadequate sampling by selecting representative instance-level features, while the paired centerness consistency loss enforces alignment between classification and localization. Second, VLM-based orthogonality enhancement leverages frozen vision-language encoders with an orthogonal projection loss to improve cross-domain feature distinguishability. Third, hallucination feature generator synthesizes robust instance-level features for missing categories, ensuring balanced category participation across domains. Extensive experiments on multiple datasets covering various domain adaptation and generalization settings demonstrate that our method consistently outperforms state-of-the-art detectors, achieving up to 5.5 mAP improvement, with particularly strong gains in nighttime adaptation.