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Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding by Dengxin Dai; Christos Sakaridis; Simon Hecker; Luc Van Gool is a Computer Science article available to read on EtoBox.

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This work addresses the problem of semantic scene understanding under fog. Although marked progress has been made in semantic scene understanding, it is mainly concentrated on clear-weather scenes. Extending semantic segmentation 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 labeled synthetic foggy data and unlabeled real foggy data. The method is based on the fact that the results of semantic segmentation in moderately adverse conditions (light fog) can be bootstrapped to solve the same problem in highly adverse conditions (dense fog). CMAda is extensible to other adverse conditions and provides a new paradigm for learning with synthetic data and unlabeled real data. In addition, we present four 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) a novel fog densification method for real foggy scenes without known depth;

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Author

Dengxin Dai; Christos Sakaridis; Simon Hecker; Luc Van Gool

Publisher

Springer Science and Business Media LLC

Published

2019

Language

EN

Field

Computer Science (Physical Sciences)