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.
@article{arxiv.2106.04550,
title = {DETReg: Unsupervised Pretraining with Region Priors for Object Detection},
author = {Amir Bar and Xin Wang and Vadim Kantorov and Colorado J Reed and Roei Herzig and Gal Chechik and Anna Rohrbach and Trevor Darrell and Amir Globerson},
journal= {arXiv preprint arXiv:2106.04550},
year = {2023}
}