Self-Supervised Any-Point Tracking by Contrastive Random Walks
<|reference_start|>Self-Supervised Any-Point Tracking by Contrastive Random Walks: We present a simple, self-supervised approach to the Tracking Any Point (TAP) problem. We train a global matching transformer to find cycle consistent tracks through video via contrastive random walks, using the transformer's attention-based global matching to define the transition matrices for a random walk on a space-time graph. The ability to perform "all pairs" comparisons between points allows the model to obtain high spatial precision and to obtain a strong contrastive learning signal, while avoiding many of the complexities of recent approaches (such as coarse-to-fine matching). To do this, we propose a number of design decisions that allow global matching architectures to be trained through self-supervision using cycle consistency. For example, we identify that transformer-based methods are sensitive to shortcut solutions, and propose a data augmentation scheme to address them. Our method achieves strong performance on the TapVid benchmarks, outperforming previous self-supervised tracking methods, such as DIFT, and is competitive with several supervised methods.<|reference_end|>
@article{shrivastava2024self-supervised,
    title={Self-Supervised Any-Point Tracking by Contrastive Random Walks},
    author={Ayush Shrivastava, Andrew Owens},
    journal={arXiv preprint arXiv:2409.16288},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16288},
    primaryClass={cs.CV}
}