  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.
