  We address the task of domain adaptation in object detection, where there is
a domain gap between a domain with annotations (source) and a domain of
interest without annotations (target). As an effective semi-supervised learning
method, the teacher-student framework (a student model is supervised by the
pseudo labels from a teacher model) has also yielded a large accuracy gain in
cross-domain object detection. However, it suffers from the domain shift and
generates many low-quality pseudo labels (\textit{e.g.,} false positives),
which leads to sub-optimal performance. To mitigate this problem, we propose a
teacher-student framework named Adaptive Teacher (AT) which leverages domain
adversarial learning and weak-strong data augmentation to address the domain
gap. Specifically, we employ feature-level adversarial training in the student
model, allowing features derived from the source and target domains to share
similar distributions. This process ensures the student model produces
domain-invariant features. Furthermore, we apply weak-strong augmentation and
mutual learning between the teacher model (taking data from the target domain)
and the student model (taking data from both domains). This enables the teacher
model to learn the knowledge from the student model without being biased to the
source domain. We show that AT demonstrates superiority over 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.
