# Pixel-level Correspondence for Self-Supervised Learning from Video

Source: [Pixel-level Correspondence for Self-Supervised Learning from Video](<https://www.sota2.com/research/paper/68fa6cf31e209131cea36c4a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:21:36.840Z

Authors: Yash Sharma, Yi Zhu, Chris Russell, Thomas Brox

Published: 2022-07-08T12:50:13.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2207.03866>)

[PDF](<https://arxiv.org/pdf/2207.03866.pdf>)