  Self-supervised pre-training, based on the pretext task of instance
discrimination, has fueled the recent advance in label-efficient object
detection. However, existing studies focus on pre-training only a feature
extractor network to learn transferable representations for downstream
detection tasks. This leads to the necessity of training multiple
detection-specific modules from scratch in the fine-tuning phase. We argue that
the region proposal network (RPN), a common detection-specific module, can
additionally be pre-trained towards reducing the localization error of
multi-stage detectors. In this work, we propose a simple pretext task that
provides an effective pre-training for the RPN, towards efficiently improving
downstream object detection performance. We evaluate the efficacy of our
approach on benchmark object detection tasks and additional downstream tasks,
including instance segmentation and few-shot detection. In comparison with
multi-stage detectors without RPN pre-training, our approach is able to
consistently improve downstream task performance, with largest gains found in
label-scarce settings.
