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.

翻译：尽管基于特征对齐的域自适应目标检测（DAOD）取得了显著进展，但仍忽略了源域偏差问题，即对齐后的特征更倾向于源域，导致自适应效果次优。此外，源域与目标域之间存在域偏移，进一步加剧了通用检测流程中分类与定位不一致的问题。为克服这些挑战，我们提出了一种新颖的基于蒸馏的无偏对齐（DUA）框架，该框架通过训练过程中预训练的教师模型将源域特征蒸馏至更均衡的位置，从而有效缓解源域偏差问题。同时，我们设计了目标相关目标定位网络（TROLN），该网络可挖掘目标相关知识以生成两种无需分类的度量指标（IoU与中心度）。基于此，我们实现了域感知一致性增强（DCE）策略，利用这两种度量对分类置信度进行进一步修正，从而实现跨域场景下分类与定位的协调。大量实验证明了该方法的有效性，其在强基线上显著提升了性能，超越了现有基于对齐的工作。