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’21
Wolfram Burgard
University of Freiburg
Daniel Cremers
TU Munich
Felix Heide
Princeton University
Dan Hendrycks
UC Berkeley
Patrick Pérez
valeo.ai
Boxin Shi
Beijing University
Robby T. Tan
NUS
