Model Adaptation with Synthetic and Real Data
for Semantic Dense Foggy Scene Understanding
Abstract. This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made
in semantic scene understanding, it is mainly related to clear-weather
scenes. Extending recognition methods to adverse weather conditions
such as fog is crucial for outdoor applications. In this paper, we propose
a novel method, named Curriculum Model Adaptation (CMAda), which
gradually adapts a semantic segmentation model from light synthetic fog
to dense real fog in multiple steps, using both synthetic and real foggy
data. In addition, we present three other main stand-alone contributions:
1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich
dataset comprising 3808 real foggy images, with pixel-level semantic annotations for 16 images with dense fog. Our experiments show that 1)
our fog simulation slightly outperforms a state-of-the-art competing simulation with respect to the task of semantic foggy scene understanding
(SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly by leveraging unlabeled real foggy data. The
datasets and code will be made publicly available