  Unsupervised representation learning achieves promising performances in
pre-training representations for object detectors. However, previous approaches
are mainly designed for image-level classification, leading to suboptimal
detection performance. To bridge the performance gap, this work proposes a
simple yet effective representation learning method for object detection, named
patch re-identification (Re-ID), which can be treated as a contrastive pretext
task to learn location-discriminative representation unsupervisedly, possessing
appealing advantages compared to its counterparts. Firstly, unlike
fully-supervised person Re-ID that matches a human identity in different camera
views, patch Re-ID treats an important patch as a pseudo identity and
contrastively learns its correspondence in two different image views, where the
pseudo identity has different translations and transformations, enabling to
learn discriminative features for object detection. Secondly, patch Re-ID is
performed in Deeply Unsupervised manner to learn multi-level representations,
appealing to object detection. Thirdly, extensive experiments show that our
method significantly outperforms its counterparts on COCO in all settings, such
as different training iterations and data percentages. For example, Mask R-CNN
initialized with our representation surpasses MoCo v2 and even its
fully-supervised counterparts in all setups of training iterations (e.g. 2.1
and 1.1 mAP improvement compared to MoCo v2 in 12k and 90k iterations
respectively). Code will be released at https://github.com/dingjiansw101/DUPR.
