  Domain adaptive object detection (DAOD) is a promising way to alleviate
performance drop of detectors in new scenes. Albeit great effort made in single
source domain adaptation, a more generalized task with multiple source domains
remains not being well explored, due to knowledge degradation during their
combination. To address this issue, we propose a novel approach, namely
target-relevant knowledge preservation (TRKP), to unsupervised multi-source
DAOD. Specifically, TRKP adopts the teacher-student framework, where the
multi-head teacher network is built to extract knowledge from labeled source
domains and guide the student network to learn detectors in unlabeled target
domain. The teacher network is further equipped with an adversarial
multi-source disentanglement (AMSD) module to preserve source domain-specific
knowledge and simultaneously perform cross-domain alignment. Besides, a
holistic target-relevant mining (HTRM) scheme is developed to re-weight the
source images according to the source-target relevance. By this means, the
teacher network is enforced to capture target-relevant knowledge, thus
benefiting decreasing domain shift when mentoring object detection in the
target domain. Extensive experiments are conducted on various widely used
benchmarks with new state-of-the-art scores reported, highlighting the
effectiveness.
