A semantic segmentation method designed to sequentially adapt the model to various unlabeled adverse weather conditions.

Abstract

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 (Hoyer et al. 2023), on the ACDC datsets for a four-target continual multi-target domain adaptation.

Framework

Our architecture for adapting a model to n adverse weather conditions in n steps in a sequential manner. The architecture consists of several key components: (1) Adaptive knowledge acquisition, where the model is guided to avoid learning the areas that could lead to a forgetting problem. (2) Pseudo-label blending, where the previous teacher is involved for enhancing the pseudo-label. (3) Weather composition replay, where the weather vectors from previous steps are composed into the current target image for revising on previously learned knowledge.

Results

The model's performance after trained on multiple adverse weather conditions sequentially. (ACDC dataset)

Citation