  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/}.
