| Summary: | Semantic segmentation based on optical images can provide comprehensive scene information for intelligent vehicle systems, thus aiding in scene perception and decision making. However, under adverse weather conditions (such as fog), the performance of methods can be compromised due to incomplete observations. Considering the success of domain adaptation in recent years, we believe it is reasonable to transfer knowledge from clear and existing annotated datasets to images with fog. Technically, we follow the main workflow of the previous SDAT-Former method, which incorporates fog and style-factor knowledge into the teacher segmentor to generate better pseudo-labels for guiding the student segmentor, but we identify and address some issues, achieving significant improvements. Firstly, we introduce a consistency loss for learning from multiple source data to better converge the performance of each component. Secondly, we apply positional encoding to the features of fog-invariant adversarial learning, strengthening the model’s ability to handle the details of foggy entities. Furthermore, to address the complexity and noise in the original version, we integrate a simple but effective masked learning technique into a unified, end-to-end training process. Finally, we regularize the knowledge transfer in the original method through re-weighting. We tested our SDAT-Former++ on mainstream benchmarks for semantic segmentation in foggy scenes, demonstrating improvements of 3.3%, 4.8%, and 1.1% (as measured by the mIoU) on the ACDC, Foggy Zurich, and Foggy Driving datasets, respectively, compared to the original version.
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