Semantic Segmentation in Multiple Adverse Weather Conditions with Domain
  Knowledge Retention
Xin Yang Wending Yan Yuan Yuan Michael Bi Mi Robby T. Tan
Semantic segmentation's performance is often compromised when applied to
unlabeled adverse weather conditions. Unsupervised domain adaptation is a
potential approach to enhancing the model's adaptability and robustness to
adverse weather. However, existing methods encounter difficulties when
sequentially adapting the model to multiple unlabeled adverse weather
conditions. They struggle to acquire new knowledge while also retaining
previously learned knowledge.To address these problems, we propose a semantic
segmentation method for multiple adverse weather conditions that incorporates
adaptive knowledge acquisition, pseudolabel blending, and weather composition
replay. Our adaptive knowledge acquisition enables the model to avoid learning
from extreme images that could potentially cause the model to forget. In our
approach of blending pseudo-labels, we not only utilize the current model but
also integrate the previously learned model into the ongoing learning process.
This collaboration between the current teacher and the previous model enhances
the robustness of the pseudo-labels for the current target. Our weather
composition replay mechanism allows the model to continuously refine its
previously learned weather information while simultaneously learning from the
new target domain. Our method consistently outperforms the stateof-the-art
methods, and obtains the best performance with averaged mIoU (%) of 65.7 and
the lowest forgetting (%) of 3.6 against 60.1 and 11.3, on the ACDC datasets
for a four-target continual multi-target domain adaptation.