We present DINO-Tracker -- a new framework for long-term dense tracking in
video. The pillar of our approach is combining test-time training on a single
video, with the powerful localized semantic features learned by a pre-trained
DINO-ViT model. Specifically, our framework simultaneously adopts DINO's
features to fit to the motion observations of the test video, while training a
tracker that directly leverages the refined features. The entire framework is
trained end-to-end using a combination of self-supervised losses, and
regularization that allows us to retain and benefit from DINO's semantic prior.
Extensive evaluation demonstrates that our method achieves state-of-the-art
results on known benchmarks. DINO-tracker significantly outperforms
self-supervised methods and is competitive with state-of-the-art supervised
trackers, while outperforming them in challenging cases of tracking under
long-term occlusions.

DINO-Tracker 是一种新的视频长期密集跟踪框架，通过在单个视频上进行测试时间训练，并结合预训练的 DINO-ViT 模型中学到的强大的局部语义特征，从而适应测试视频的动态观察，同时训练一种直接利用改进特征的跟踪器，通过自监督损失和正则化的组合实现端到端训练，并在已知基准测试中取得了最先进的结果；DINO-Tracker 在长期遮挡跟踪的挑战性案例中显著优于自监督方法，而且在竞争中超越了最先进的监督跟踪器。