We tackle the problem of domain adaptation in object detection, where there is a significant domain shift between a source (a domain with supervision) and a target domain (a domain of interest without supervision). As a widely adopted domain adaptation method, the self-training teacher-student framework (a student model learns from pseudo labels generated from a teacher model) has yielded remarkable accuracy gain on the target domain. However, it still suffers from the large amount of low-quality pseudo labels (e.g., false positives) generated from the teacher due to its bias toward the source domain. To address this issue, we propose a self-training framework called Adaptive Unbiased Teacher (AUT) leveraging adversarial learning and weak-strong data augmentation during mutual learning to address domain shift. Specifically, we employ feature-level adversarial training in the student model, ensuring features extracted from the source and target domains share similar statistics. This enables the student model to capture domain-invariant features. Furthermore, we apply weak-strong augmentation and mutual learning between the teacher model on the target domain and the student model on both domains. This enables the teacher model to gradually benefit from the student model without suffering domain shift. We show that AUT demonstrates superiority over all existing approaches and even Oracle (fully supervised) models by a large margin. For example, we achieve 50.9% (49.3%) mAP on Foggy Cityscape (Clipart1K), which is 9.2% (5.2%) and 8.2% (11.0%) higher than previous state-of-the-art and Oracle, respectively

我们提出了自适应教师（AT）框架来解决使用教师-学生框架进行目标检测的半监督学习中由于域差异而产生的问题，AT利用领域对抗学习和弱-强数据增强来缓解这一问题。

跨域适应目标检测教师