Pixel-level Correspondence for Self-Supervised Learning from Video

Understand

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.