Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. Specifically, UDA methods try to align the source and target representations to improve the generalization on the target domain. Further, UDA methods work under the assumption that the source data is accessible during the adaptation process. However, in real-world scenarios, the labelled source data is often restricted due to privacy regulations, data transmission constraints, or proprietary data concerns. The Source-Free Domain Adaptation (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data. In this paper, we explore the SFDA setting for the task of adaptive object detection. To this end, we propose a novel training strategy for adapting a source-trained object detector to the target domain without source data. More precisely, we design a novel contrastive loss to enhance the target representations by exploiting the objects relations for a given target domain input. These object instance relations are modelled using an Instance Relation Graph (IRG) network, which are then used to guide the contrastive representation learning. In addition, we utilize a student-teacher based knowledge distillation strategy to avoid overfitting to the noisy pseudo-labels generated by the source-trained model. Extensive experiments on multiple object detection benchmark datasets show that the proposed approach is able to efficiently adapt source-trained object detectors to the target domain, outperforming previous state-of-the-art domain adaptive detection methods. Code and models are provided in \href{https://viudomain.github.io/irg-sfda-web/}{https://viudomain.github.io/irg-sfda-web/}.

翻译：无监督域适应是解决领域偏移问题的有效方法。具体而言，无监督域适应方法通过对齐源域和目标域的特征表示来提升在目标域上的泛化能力。此外，这些方法假设在适应过程中可以访问源域数据。然而，在实际场景中，由于隐私法规、数据传输限制或数据所有权问题，带标签的源域数据往往受到限制。无源域适应设置旨在通过无需访问源域数据即可使源域训练模型适应目标域的方式来缓解上述问题。本文探索了面向自适应目标检测任务的无源域适应设置。为此，我们提出了一种新颖的训练策略，能够在无源域数据的情况下使源域训练的目标检测器适应目标域。更精确地说，我们设计了一种新颖的对比损失函数，通过利用给定目标域输入中对象之间的关系来增强目标域特征表示。这些对象实例关系通过实例关系图网络进行建模，并用于引导对比表示学习。此外，我们采用了基于师生模型的知识蒸馏策略，以避免对源域训练模型生成的噪声伪标签过拟合。在多个目标检测基准数据集上的大量实验表明，所提方法能够高效地将源域训练的目标检测器适应到目标域，并优于之前最先进的域适应检测方法。代码和模型已开源至\href{https://viudomain.github.io/irg-sfda-web/}{https://viudomain.github.io/irg-sfda-web/}。