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Recent interest in self-supervised dense tracking has yielded rapid progress,but performance still remains far from supervised methods. We propose a densetracking model trained on videos without any annotations that surpassesprevious self-supervised methods on existing benchmarks by a significant margin(+15%), and achieves performance comparable to supervised methods. In thispaper, we first reassess the traditional choices used for self-supervisedtraining and reconstruction loss by conducting thorough experiments thatfinally elucidate the optimal choices. Second, we further improve on existingmethods by augmenting our architecture with a crucial memory component. Third,we benchmark on large-scale semi-supervised video object segmentation(aka.dense tracking), and propose a new metric: generalizability. Our first twocontributions yield a self-supervised network that for the first time iscompetitive with supervised methods on standard evaluation metrics of densetracking. When measuring generalizability, we show self-supervised approachesare actually superior to the majority of supervised methods. We believe thisnew generalizability metric can better capture the real-world use-cases fordense tracking, and will spur new interest in this research direction.

MAST: A Memory-Augmented Self-supervised Tracker