Pixel-level Correspondence for Self-Supervised Learning from VideoDownload PDF

26 May 2022, 20:09 (modified: 23 Jul 2022, 02:24)ICML 2022 Pre-training WorkshopReaders: Everyone
Keywords: self-supervised, unsupervised, representation, video, optical flow, dense prediction, contrastive learning
TL;DR: Improve dense prediction, while maintaining global classification performance, by enabling dense contrastive learning on video via off-the-shelf optical flow.
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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