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.

提出了基于蒸馏的无偏对齐 (Distillation-based Unbiased Alignment, DUA) 框架及目标相关对象定位网络 (Target-Relevant Object Localization Network, TROLN)，解决了领域自适应目标检测中源偏差和领域转移问题，通过蒸馏方式调整源特征位置，利用目标相关知识生成无需分类的两个度量指标 (IoU 和 centerness)，并采用领域感知的一致性增强策略 (DCE) 进一步改善分类一致性和定位准确性，在跨领域情景中显著提升性能，超过已有基于对齐的方法。