Pixel-level Correspondence for Self-Supervised Learning from Video

• URL: http://arxiv.org/abs/2207.03866v1
• Date: Fri, 8 Jul 2022 12:50:13 GMT
• Title: Pixel-level Correspondence for Self-Supervised Learning from Video
• Authors: Yash Sharma, Yi Zhu, Chris Russell, Thomas Brox
• Abstract summary: Pixel-level Correspondence (PiCo) is a method for dense contrastive learning from video. We validate PiCo on standard benchmarks, outperforming self-supervised baselines on multiple dense prediction tasks.
• Score: 56.24439897867531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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