  Unsupervised domain adaptation (UDA) is an important topic in the computer
vision community. The key difficulty lies in defining a common property between
the source and target domains so that the source-domain features can align with
the target-domain semantics. In this paper, we present a simple and effective
mechanism that regularizes cross-domain representation learning with a
domain-agnostic prior (DAP) that constrains the features extracted from source
and target domains to align with a domain-agnostic space. In practice, this is
easily implemented as an extra loss term that requires a little extra costs. In
the standard evaluation protocol of transferring synthesized data to real data,
we validate the effectiveness of different types of DAP, especially that
borrowed from a text embedding model that shows favorable performance beyond
the state-of-the-art UDA approaches in terms of segmentation accuracy. Our
research reveals that UDA benefits much from better proxies, possibly from
other data modalities.
