Semantic Foggy Scene Understanding with Synthetic Data

Christos Sakaridis; Dengxin Dai; Luc Van Gool

doi:10.3929/ethz-b-000254367

ScienceGate Book Chapters

JOURNAL ARTICLE

Semantic Foggy Scene Understanding with Synthetic Data

Christos Sakaridis Dengxin Dai Luc Van Gool

Year: 2018 Journal: International Journal of Computer Vision Vol: 126 (9)Pages: 973-992 Publisher: Springer Science+Business Media

DOI: 10.3929/ethz-b-000254367

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Artificial intelligence Leverage (statistics) Ground truth Synthetic data Segmentation Convolutional neural network Transfer of learning Computer vision Supervised learning Object detection Pipeline (software) Pattern recognition (psychology) Machine learning Artificial neural network

Metrics

1176

Cited By

50.53

FWCI (Field Weighted Citation Impact)

Refs

1.00

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Image Enhancement Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Video Surveillance and Tracking Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Flood Risk Assessment and Management

Physical Sciences → Environmental Science → Global and Planetary Change

Semantic Foggy Scene Understanding with Synthetic Data

Metrics

Citation History

Topics

Related Documents

Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding

Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding

Semantic Understanding of Foggy Scenes with Purely Synthetic Data

Enhanced 3D Scene Reconstruction with Semantic Understanding Using Synthetic Data and Deep Learning

Improving RGB-Thermal Semantic Scene Understanding With Synthetic Data Augmentation for Autonomous Driving