MAST: A Memory-Augmented Self-supervised Tracker
This work addresses the need for annotation-free dense tracking in computer vision, offering a competitive self-supervised approach that could reduce reliance on labeled data.
The paper tackles the problem of self-supervised dense tracking, which lags behind supervised methods, by proposing a memory-augmented model that surpasses previous self-supervised methods by 15% and achieves performance comparable to supervised methods on standard benchmarks.
Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods. We propose a dense tracking model trained on videos without any annotations that surpasses previous self-supervised methods on existing benchmarks by a significant margin (+15%), and achieves performance comparable to supervised methods. In this paper, we first reassess the traditional choices used for self-supervised training and reconstruction loss by conducting thorough experiments that finally elucidate the optimal choices. Second, we further improve on existing methods 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 two contributions yield a self-supervised network that for the first time is competitive with supervised methods on standard evaluation metrics of dense tracking. When measuring generalizability, we show self-supervised approaches are actually superior to the majority of supervised methods. We believe this new generalizability metric can better capture the real-world use-cases for dense tracking, and will spur new interest in this research direction.