Domain adaptive object detection (DAOD) aims to generalize an object detector trained on labeled source-domain data to a target domain without annotations, the core principle of which is \emph{source-target feature alignment}. Typically, existing approaches employ adversarial learning to align the distributions of the source and target domains as a whole, barely considering the varying significance of distinct regions, say instances under different circumstances and foreground \emph{vs} background areas, during feature alignment. To overcome the shortcoming, we investigates a differential feature alignment strategy. Specifically, a prediction-discrepancy feedback instance alignment module (dubbed PDFA) is designed to adaptively assign higher weights to instances of higher teacher-student detection discrepancy, effectively handling heavier domain-specific information. Additionally, an uncertainty-based foreground-oriented image alignment module (UFOA) is proposed to explicitly guide the model to focus more on regions of interest. Extensive experiments on widely-used DAOD datasets together with ablation studies are conducted to demonstrate the efficacy of our proposed method and reveal its superiority over other SOTA alternatives. Our code is available at https://github.com/EstrellaXyu/Differential-Alignment-for-DAOD.

翻译：领域自适应目标检测（DAOD）旨在将基于标注源领域数据训练的目标检测器推广至无标注的目标领域，其核心原理是**源-目标特征对齐**。现有方法通常采用对抗学习来整体对齐源领域与目标领域的分布，而很少考虑在特征对齐过程中不同区域（例如不同情境下的实例以及前景与背景区域）的重要性差异。为克服这一不足，本文研究了一种差分特征对齐策略。具体而言，我们设计了一个预测差异反馈实例对齐模块（称为PDFA），该模块能够自适应地为教师-学生检测差异较大的实例分配更高权重，从而有效处理更强的领域特定信息。此外，本文提出了一个基于不确定性的前景导向图像对齐模块（UFOA），以显式引导模型更多地关注感兴趣区域。我们在广泛使用的DAOD数据集上进行了大量实验及消融研究，结果证明了所提方法的有效性，并揭示了其相对于其他先进替代方案的优越性。我们的代码公开于 https://github.com/EstrellaXyu/Differential-Alignment-for-DAOD。