  Adapting semantic segmentation models to new domains is an important but
challenging problem. Recently enlightening progress has been made, but the
performance of existing methods are unsatisfactory on real datasets where the
new target domain comprises of heterogeneous sub-domains (e.g., diverse weather
characteristics). We point out that carefully reasoning about the multiple
modalities in the target domain can improve the robustness of adaptation
models. To this end, we propose a condition-guided adaptation framework that is
empowered by a special attentive progressive adversarial training (APAT)
mechanism and a novel self-training policy. The APAT strategy progressively
performs condition-specific alignment and attentive global feature matching.
The new self-training scheme exploits the adversarial ambivalences of easy and
hard adaptation regions and the correlations among target sub-domains
effectively. We evaluate our method (DCAA) on various adaptation scenarios
where the target images vary in weather conditions. The comparisons against
baselines and the state-of-the-art approaches demonstrate the superiority of
DCAA over the competitors.
