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.

该研究提出了一种基于 APAT 和自我培训的条件引导适应框架，能够有效地推理出目标域中的多模态特征，从而提高语义分割模型在天气等异构子域中的鲁棒性。各种实验结果表明，该方法（DCAA）在目标域的天气变化等复杂情景下，相比基线和现有方法有着显著的优越之处。