  Unsupervised pre-training aims at learning transferable features that are
beneficial for downstream tasks. However, most state-of-the-art unsupervised
methods concentrate on learning global representations for image-level
classification tasks instead of discriminative local region representations,
which limits their transferability to region-level downstream tasks, such as
object detection. To improve the transferability of pre-trained features to
object detection, we present Deeply Unsupervised Patch Re-ID (DUPR), a simple
yet effective method for unsupervised visual representation learning. The patch
Re-ID task treats individual patch as a pseudo-identity and contrastively
learns its correspondence in two views, enabling us to obtain discriminative
local features for object detection. Then the proposed patch Re-ID is performed
in a deeply unsupervised manner, appealing to object detection, which usually
requires multilevel feature maps. Extensive experiments demonstrate that DUPR
outperforms state-of-the-art unsupervised pre-trainings and even the ImageNet
supervised pre-training on various downstream tasks related to object
detection.
