  False positive is one of the most serious problems brought by agnostic domain
shift in domain adaptive pedestrian detection. However, it is impossible to
label each box in countless target domains. Therefore, it yields our attention
to suppress false positive in each target domain in an unsupervised way. In
this paper, we model an object detection task into a ranking task among
positive and negative boxes innovatively, and thus transform a false positive
suppression problem into a box re-ranking problem elegantly, which makes it
feasible to solve without manual annotation. An attached problem during box
re-ranking appears that no labeled validation data is available for
cherrypicking. Considering we aim to keep the detection of true positive
unchanged, we propose box number alignment, a self-supervised evaluation
metric, to prevent the optimized model from capacity degeneration. Extensive
experiments conducted on cross-domain pedestrian detection datasets have
demonstrated the effectiveness of our proposed framework. Furthermore, the
extension to two general unsupervised domain adaptive object detection
benchmarks also supports our superiority to other state-of-the-arts.
