  In this work, we address the task of unsupervised domain adaptation (UDA) for
semantic segmentation in presence of multiple target domains: The objective is
to train a single model that can handle all these domains at test time. Such a
multi-target adaptation is crucial for a variety of scenarios that real-world
autonomous systems must handle. It is a challenging setup since one faces not
only the domain gap between the labeled source set and the unlabeled target
set, but also the distribution shifts existing within the latter among the
different target domains. To this end, we introduce two adversarial frameworks:
(i) multi-discriminator, which explicitly aligns each target domain to its
counterparts, and (ii) multi-target knowledge transfer, which learns a
target-agnostic model thanks to a multi-teacher/single-student distillation
mechanism.The evaluation is done on four newly-proposed multi-target benchmarks
for UDA in semantic segmentation. In all tested scenarios, our approaches
consistently outperform baselines, setting competitive standards for the novel
task.
