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。

利用多种特性和对抗性矛盾实现域自适应分割

Exploiting Diverse Characteristics and Adversarial Ambivalence for  Domain Adaptive Segmentation

The last several years have seen intensive interest in exploring
neural-network-based models for machine comprehension (MC) and question
answering (QA). In this paper, we approach the problems by closely modelling
questions in a neural network framework. We first introduce syntactic
information to help encode questions. We then view and model different types of
questions and the information shared among them as an adaptation task and
proposed adaptation models for them. On the Stanford Question Answering Dataset
(SQuAD), we show that these approaches can help attain better results over a
competitive baseline.

本文通过在神经网络框架中密切模拟问题，引入句法信息来帮助编码问题，并将不同类型的问题和共享信息建模为适应性任务，并提出适应性模型，从而在 Stanford 问题回答数据集（SQuAD）上证明这些方法可帮助获得比竞争基准更好的结果。