Adversarial Defense Teacher for Cross-Domain Object Detection under Poor
  Visibility Conditions
Kaiwen Wang Yinzhe Shen Martin Lauer
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.