  Modern models that perform system-critical tasks such as segmentation and
localization exhibit good performance and robustness under ideal conditions
(i.e. daytime, overcast) but performance degrades quickly and often
catastrophically when input conditions change. In this work, we present a
domain adaptation system that uses light-weight input adapters to pre-processes
input images, irrespective of their appearance, in a way that makes them
compatible with off-the-shelf computer vision tasks that are trained only on
inputs with ideal conditions. No fine-tuning is performed on the off-the-shelf
models, and the system is capable of incrementally training new input adapters
in a self-supervised fashion, using the computer vision tasks as supervisors,
when the input domain differs significantly from previously seen domains. We
report large improvements in semantic segmentation and topological localization
performance on two popular datasets, RobotCar and BDD.
