Multi-Model Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation

no code implementations • 9 Dec 2020 • Naif Alshammari, Samet Akcay, Toby P. Breckon

Using this architectural formulation with dense skip connections, our model achieves comparable performance to contemporary approaches at a fraction of the overall model complexity.

Domain Adaptation Scene Segmentation +1

Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation

no code implementations • 9 Dec 2020 • Naif Alshammari, Samet Akcay, Toby P. Breckon

For optimal performance in semantic segmentation, our model generates depth to be used as complementary source information with RGB in the segmentation network.

Domain Adaptation Monocular Depth Estimation +4

Multi-Task Learning for Automotive Foggy Scene Understanding via Domain Adaptation to an Illumination-Invariant Representation

no code implementations • 17 Sep 2019 • Naif Alshammari, Samet Akçay, Toby P. Breckon

Joint scene understanding and segmentation for automotive applications is a challenging problem in two key aspects:- (1) classifying every pixel in the entire scene and (2) performing this task under unstable weather and illumination changes (e. g. foggy weather), which results in poor outdoor scene visibility.

Domain Adaptation Multi-Task Learning +1