  Recent self-supervised pretraining methods for object detection largely focus
on pretraining the backbone of the object detector, neglecting key parts of
detection architecture. Instead, we introduce DETReg, a new self-supervised
method that pretrains the entire object detection network, including the object
localization and embedding components. During pretraining, DETReg predicts
object localizations to match the localizations from an unsupervised region
proposal generator and simultaneously aligns the corresponding feature
embeddings with embeddings from a self-supervised image encoder. We implement
DETReg using the DETR family of detectors and show that it improves over
competitive baselines when finetuned on COCO, PASCAL VOC, and Airbus Ship
benchmarks. In low-data regimes DETReg achieves improved performance, e.g.,
when training with only 1% of the labels and in the few-shot learning settings.
