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Despite the success of deep learning-based object detection methods in recentyears, it is still challenging to make the object detector reliable in adverseweather conditions such as rain and snow. For the robust performance of objectdetectors, unsupervised domain adaptation has been utilized to adapt thedetection network trained on clear weather images to adverse weather images.While previous methods do not explicitly address weather corruption duringadaptation, the domain gap between clear and adverse weather can be decomposedinto two factors with distinct characteristics: a style gap and a weather gap.In this paper, we present an unsupervised domain adaptation framework forobject detection that can more effectively adapt to real-world environmentswith adverse weather conditions by addressing these two gaps separately. Ourmethod resolves the style gap by concentrating on style-related information ofhigh-level features using an attention module. Using self-supervisedcontrastive learning, our framework then reduces the weather gap and acquiresinstance features that are robust to weather corruption. Extensive experimentsdemonstrate that our method outperforms other methods for object detection inadverse weather conditions.

DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions