Though feature-alignment based Domain Adaptive Object Detection (DAOD) methods have achieved remarkable progress, they ignore the source bias issue, i.e., the detector tends to acquire more source-specific knowledge, impeding its generalization capabilities in the target domain. Furthermore, these methods face a more formidable challenge in achieving consistent classification and localization in the target domain compared to the source domain. To overcome these challenges, we propose a novel Distillation-based Source Debiasing (DSD) framework for DAOD, which can distill domain-agnostic knowledge from a pre-trained teacher model, improving the detector's performance on both domains. In addition, we design a Target-Relevant Object Localization Network (TROLN), which can mine target-related localization information from source and target-style mixed data. Accordingly, we present a Domain-aware Consistency Enhancing (DCE) strategy, in which these information are formulated into a new localization representation to further refine classification scores in the testing stage, achieving a harmonization between classification and localization. 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方法已取得显著进展，但它们忽略了源域偏差问题，即检测器倾向于获取更多源域特定知识，从而阻碍其在目标域中的泛化能力。此外，与源域相比，这些方法在目标域中实现一致的分类与定位面临更大挑战。为克服这些问题，我们提出了一种新颖的基于蒸馏的源域去偏框架DSD-DA，该框架能从预训练的教师模型中蒸馏域无关知识，提升检测器在两个域上的性能。同时，我们设计了目标相关对象定位网络TROLN，该网络能从源域与目标域风格混合数据中挖掘目标相关定位信息。在此基础上，我们提出了域感知一致性增强策略DCE，将这些信息整合为新的定位表示，在测试阶段进一步优化分类得分，实现分类与定位的协调。大量实验验证了该方法的有效性，能在强基线基础上实现大幅持续提升，超越现有基于对齐的方法。