  This work tackles the unsupervised cross-domain object detection problem
which aims to generalize a pre-trained object detector to a new target domain
without labels. We propose an uncertainty-aware model adaptation method, which
is based on two motivations: 1) the estimation and exploitation of model
uncertainty in a new domain is critical for reliable domain adaptation; and 2)
the joint alignment of distributions for inputs (feature alignment) and outputs
(self-training) is needed. To this end, we compose a Bayesian CNN-based
framework for uncertainty estimation in object detection, and propose an
algorithm for generation of uncertainty-aware pseudo-labels. We also devise a
scheme for joint feature alignment and self-training of the object detection
model with uncertainty-aware pseudo-labels. Experiments on multiple
cross-domain object detection benchmarks show that our proposed method achieves
state-of-the-art performance.
