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Self-supervised learning using video by using optical flow to track points with PiCo

Self-supervised learning using video by using optical flow to track points with PiCo


Pixel-level Correspondence for Self-Supervised Learning from Video



While self-supervised learning has enabled effective representation learning in the absence of labels, for vision, video remains a relatively untapped source of supervision.


... propose Pixel-level Correspondence (PiCo), a method for dense contrastive learning from video.


By tracking points with optical flow, ... obtain a correspondence map which can be used to match local features at different points in time.


... PiCo on standard benchmarks, outperforming self-supervised baselines on multiple dense prediction tasks, without compromising performance on image classification.



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