Existing object detectors encounter challenges in handling domain shifts
between training and real-world data, particularly under poor visibility
conditions like fog and night. Cutting-edge cross-domain object detection
methods use teacher-student frameworks and compel teacher and student models to
produce consistent predictions under weak and strong augmentations,
respectively. In this paper, we reveal that manually crafted augmentations are
insufficient for optimal teaching and present a simple yet effective framework
named Adversarial Defense Teacher (ADT), leveraging adversarial defense to
enhance teaching quality. Specifically, we employ adversarial attacks,
encouraging the model to generalize on subtly perturbed inputs that effectively
deceive the model. To address small objects under poor visibility conditions,
we propose a Zoom-in Zoom-out strategy, which zooms-in images for better
pseudo-labels and zooms-out images and pseudo-labels to learn refined features.
Our results demonstrate that ADT achieves superior performance, reaching 54.5%
mAP on Foggy Cityscapes, surpassing the previous state-of-the-art by 2.6% mAP.

现有的目标检测器在处理训练和真实世界数据之间的领域转变方面面临挑战，尤其是在雾天和夜晚等低能见度条件下。我们提出了一种名为 Adversarial Defense Teacher（ADT）的简单而有效的框架，通过利用对抗性防御来提高教学质量，并通过 Zoom-in Zoom-out 策略解决低能见度条件下的小目标问题。我们的结果表明，ADT 在 Foggy Cityscapes 上达到了 54.5％的 mAP，超过了先前的最先进技术 2.6％的 mAP。