  We present a self-supervised learning (SSL) method suitable for semi-global
tasks such as object detection and semantic segmentation. We enforce local
consistency between self-learned features, representing corresponding image
locations of transformed versions of the same image, by minimizing a
pixel-level local contrastive (LC) loss during training. LC-loss can be added
to existing self-supervised learning methods with minimal overhead. We evaluate
our SSL approach on two downstream tasks -- object detection and semantic
segmentation, using COCO, PASCAL VOC, and CityScapes datasets. Our method
outperforms the existing state-of-the-art SSL approaches by 1.9% on COCO object
detection, 1.4% on PASCAL VOC detection, and 0.6% on CityScapes segmentation.
