  While self-supervised learning has enabled effective representation learning
in the absence of labels, for vision, video remains a relatively untapped
source of supervision. To address this, we propose Pixel-level Correspondence
(PiCo), a method for dense contrastive learning from video. By tracking points
with optical flow, we obtain a correspondence map which can be used to match
local features at different points in time. We validate PiCo on standard
benchmarks, outperforming self-supervised baselines on multiple dense
prediction tasks, without compromising performance on image classification.
