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arXiv · 2207.03866

Pixel-level Correspondence for Self-Supervised Learning from Video

Yash Sharma·Yi Zhu·Chris Russell·Thomas Brox
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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.

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Yash Sharma, Yi Zhu, Chris Russell, Thomas Brox. 2022-07-08. Pixel-level Correspondence for Self-Supervised Learning from Video. https://arxiv.org/abs/2207.03866

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