DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather
  Conditions
Minsik Jeon Junwon Seo Jihong Min
Despite the success of deep learning-based object detection methods in recent
years, it is still challenging to make the object detector reliable in adverse
weather conditions such as rain and snow. For the robust performance of object
detectors, unsupervised domain adaptation has been utilized to adapt the
detection network trained on clear weather images to adverse weather images.
While previous methods do not explicitly address weather corruption during
adaptation, the domain gap between clear and adverse weather can be decomposed
into two factors with distinct characteristics: a style gap and a weather gap.
In this paper, we present an unsupervised domain adaptation framework for
object detection that can more effectively adapt to real-world environments
with adverse weather conditions by addressing these two gaps separately. Our
method resolves the style gap by concentrating on style-related information of
high-level features using an attention module. Using self-supervised
contrastive learning, our framework then reduces the weather gap and acquires
instance features that are robust to weather corruption. Extensive experiments
demonstrate that our method outperforms other methods for object detection in
adverse weather conditions.