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’22

Vishal M. Patel
Johns Hopkins Uni.

Mario Fritz
CISPA

Werner Ritter
Mercedes-Benz AG

Patrick Pérez
valeo.ai

Peter Kontschieder
Meta

Kate Saenko
Boston University

Sen Wang
Heriot-Watt University

Raoul de Charette
INRIA
Organizers

Dengxin Dai
MPI for Informatics

Christos Sakaridis
ETH Zurich

Martin Hahner
ETH Zurich

Robby T. Tan
NUS

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