  We propose a new problem formulation and a corresponding evaluation framework
to advance research on unsupervised domain adaptation for semantic image
segmentation. The overall goal is fostering the development of adaptive
learning systems that will continuously learn, without supervision, in
ever-changing environments. Typical protocols that study adaptation algorithms
for segmentation models are limited to few domains, adaptation happens offline,
and human intervention is generally required, at least to annotate data for
hyper-parameter tuning. We argue that such constraints are incompatible with
algorithms that can continuously adapt to different real-world situations. To
address this, we propose a protocol where models need to learn online, from
sequences of temporally correlated images, requiring continuous, frame-by-frame
adaptation. We accompany this new protocol with a variety of baselines to
tackle the proposed formulation, as well as an extensive analysis of their
behaviors, which can serve as a starting point for future research.
