Due to the poor illumination and the difficulty in annotating, nighttime conditions pose a significant challenge for autonomous vehicle perception systems. Unsupervised domain adaptation (UDA) has been widely applied to semantic segmentation on such images to adapt models from normal conditions to target nighttime-condition domains. Self-training (ST) is a paradigm in UDA, where a momentum teacher is utilized for pseudo-label prediction, but a confirmation bias issue exists. Because the one-directional knowledge transfer from a single teacher is insufficient to adapt to a large domain shift. To mitigate this issue, we propose to alleviate domain gap by incrementally considering style influence and illumination change. Therefore, we introduce a one-stage Dual-Teacher Bi-directional Self-training (DTBS) framework for smooth knowledge transfer and feedback. Based on two teacher models, we present a novel pipeline to respectively decouple style and illumination shift. In addition, we propose a new Re-weight exponential moving average (EMA) to merge the knowledge of style and illumination factors, and provide feedback to the student model. In this way, our method can be embedded in other UDA methods to enhance their performance. For example, the Cityscapes to ACDC night task yielded 53.8 mIoU (\%), which corresponds to an improvement of +5\% over the previous state-of-the-art. The code is available at \url{https://github.com/hf618/DTBS}.

翻译：由于光照不足和标注困难，夜间条件对自动驾驶车辆感知系统构成了重大挑战。无监督域自适应（UDA）已被广泛应用于此类图像的语义分割，以将模型从正常条件适应到目标夜间条件域。自训练（ST）是UDA中的一种范式，采用动量教师进行伪标签预测，但存在确认偏差问题。因为单教师的单向知识传递不足以应对较大的域偏移。为缓解该问题，我们提出通过逐步考虑风格影响和光照变化来缩小域差距。因此，我们引入了一个单阶段双教师双向自训练（DTBS）框架，用于平滑的知识传递与反馈。基于两个教师模型，我们提出了一种新颖的流程，分别解耦风格和光照偏移。此外，我们提出了一种新的重加权指数移动平均（EMA）方法，融合风格和光照因素的知识，并向学生模型提供反馈。通过这种方式，我们的方法可嵌入其他UDA方法中以提高其性能。例如，Cityscapes到ACDC夜间任务取得了53.8的平均交并比（%），较先前最优方法提升了+5%。代码见\url{https://github.com/hf618/DTBS}。