  Addressing the challenge of domain shift between datasets is vital in
maintaining model performance. In the context of cross-domain object detection,
the teacher-student framework, a widely-used semi-supervised model, has shown
significant accuracy improvements. However, existing methods often overlook
class differences, treating all classes equally, resulting in suboptimal
results. Furthermore, the integration of instance-level alignment with a
one-stage detector, essential due to the absence of a Region Proposal Network
(RPN), remains unexplored in this framework. In response to these shortcomings,
we introduce a novel teacher-student model named Versatile Teacher (VT). VT
differs from previous works by considering class-specific detection difficulty
and employing a two-step pseudo-label selection mechanism, referred to as
Class-aware Pseudo-label Adaptive Selection (CAPS), to generate more reliable
pseudo labels. These labels are leveraged as saliency matrices to guide the
discriminator for targeted instance-level alignment. Our method demonstrates
promising results on three benchmark datasets, and extends the alignment
methods for widely-used one-stage detectors, presenting significant potential
for practical applications. Code is available at
https://github.com/RicardooYoung/VersatileTeacher.
