  We introduce Diff-Tracker, a novel approach for the challenging unsupervised
visual tracking task leveraging the pre-trained text-to-image diffusion model.
Our main idea is to leverage the rich knowledge encapsulated within the
pre-trained diffusion model, such as the understanding of image semantics and
structural information, to address unsupervised visual tracking. To this end,
we design an initial prompt learner to enable the diffusion model to recognize
the tracking target by learning a prompt representing the target. Furthermore,
to facilitate dynamic adaptation of the prompt to the target's movements, we
propose an online prompt updater. Extensive experiments on five benchmark
datasets demonstrate the effectiveness of our proposed method, which also
achieves state-of-the-art performance.
