  In this work, we define and address a novel domain adaptation (DA) problem in
semantic scene segmentation, where the target domain not only exhibits a data
distribution shift w.r.t. the source domain, but also includes novel classes
that do not exist in the latter. Different to "open-set" and "universal domain
adaptation", which both regard all objects from new classes as "unknown", we
aim at explicit test-time prediction for these new classes. To reach this goal,
we propose a framework that leverages domain adaptation and zero-shot learning
techniques to enable "boundless" adaptation in the target domain. It relies on
a novel architecture, along with a dedicated learning scheme, to bridge the
source-target domain gap while learning how to map new classes' labels to
relevant visual representations. The performance is further improved using
self-training on target-domain pseudo-labels. For validation, we consider
different domain adaptation set-ups, namely synthetic-2-real, country-2-country
and dataset-2-dataset. Our framework outperforms the baselines by significant
margins, setting competitive standards on all benchmarks for the new task. Code
and models are available at https://github.com/valeoai/buda.
