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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 multi-level 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.

Deeply Unsupervised Patch Re-Identification for Pre-Training Object Detectors