Adverse weather and illumination conditions (e.g. fog, rain, snow, low light, nighttime, glare and shadows) create visibility problems for the sensors that power automated systems. Many outdoor applications such as autonomous cars and surveillance systems are required to operate smoothly in the frequent scenarios of bad weather. While rapid progress is being made in this direction, the performance of current vision algorithms is still mainly benchmarked under clear weather conditions (good weather, favorable lighting). Even the top-performing algorithms undergo a severe performance degradation under adverse conditions. The aim of this workshop is to promote research into the design of robust vision algorithms for adverse weather and lighting conditions.
Invited Speakers for V4AS@CVPR’23
Daniel Cremers
TU Munich
Judy Hoffman
Georgia Tech
Felix Heide
Princeton University
Adam Kortylewski
MPI for Informatics
Werner Ritter
Mercedes-Benz AG
Patrick Pérez
valeo.ai
Robby T. Tan
NUS
Eren Erdal Aksoy
Halmstad University
Tim Barfoot
University of Toronto
Organizers
Dengxin Dai
Huawei Zurich
Christos Sakaridis
ETH Zurich
Lukas Hoyer
ETH Zurich
Haoran Wang
MPI for Informatics
Wim Abbeloos
Toyota Motor Europe
Daniel Olmeda Reino
Toyota Motor Europe
Jiri Matas
CTU in Prague
Bernt Schiele
MPI for Informatics
Luc Van Gool
ETH Zurich & KU Leuven