DUA-DA: Distillation-based Unbiased Alignment for Domain Adaptive Object
  Detection
Yongchao Feng Shiwei Li Yingjie Gao Ziyue Huang Yanan Zhang Qingjie Liu Yunhong Wang
Though feature-alignment based Domain Adaptive Object Detection (DAOD) have
achieved remarkable progress, they ignore the source bias issue, i.e. the
aligned features are more favorable towards the source domain, leading to a
sub-optimal adaptation. Furthermore, the presence of domain shift between the
source and target domains exacerbates the problem of inconsistent
classification and localization in general detection pipelines. To overcome
these challenges, we propose a novel Distillation-based Unbiased Alignment
(DUA) framework for DAOD, which can distill the source features towards a more
balanced position via a pre-trained teacher model during the training process,
alleviating the problem of source bias effectively. In addition, we design a
Target-Relevant Object Localization Network (TROLN), which can mine
target-related knowledge to produce two classification-free metrics (IoU and
centerness). Accordingly, we implement a Domain-aware Consistency Enhancing
(DCE) strategy that utilizes these two metrics to further refine classification
confidences, achieving a harmonization between classification and localization
in cross-domain scenarios. Extensive experiments have been conducted to
manifest the effectiveness of this method, which consistently improves the
strong baseline by large margins, outperforming existing alignment-based works.