  Self-supervised learning has been widely used to obtain transferrable
representations from unlabeled images. Especially, recent contrastive learning
methods have shown impressive performances on downstream image classification
tasks. While these contrastive methods mainly focus on generating invariant
global representations at the image-level under semantic-preserving
transformations, they are prone to overlook spatial consistency of local
representations and therefore have a limitation in pretraining for localization
tasks such as object detection and instance segmentation. Moreover,
aggressively cropped views used in existing contrastive methods can minimize
representation distances between the semantically different regions of a single
image.
  In this paper, we propose a spatially consistent representation learning
algorithm (SCRL) for multi-object and location-specific tasks. In particular,
we devise a novel self-supervised objective that tries to produce coherent
spatial representations of a randomly cropped local region according to
geometric translations and zooming operations. On various downstream
localization tasks with benchmark datasets, the proposed SCRL shows significant
performance improvements over the image-level supervised pretraining as well as
the state-of-the-art self-supervised learning methods.
  Code is available at https://github.com/kakaobrain/scrl
