  Unsupervised learning has been popular in various computer vision tasks,
including visual object tracking. However, prior unsupervised tracking
approaches rely heavily on spatial supervision from template-search pairs and
are still unable to track objects with strong variation over a long time span.
As unlimited self-supervision signals can be obtained by tracking a video along
a cycle in time, we investigate evolving a Siamese tracker by tracking videos
forward-backward. We present a novel unsupervised tracking framework, in which
we can learn temporal correspondence both on the classification branch and
regression branch. Specifically, to propagate reliable template feature in the
forward propagation process so that the tracker can be trained in the cycle, we
first propose a consistency propagation transformation. We then identify an
ill-posed penalty problem in conventional cycle training in backward
propagation process. Thus, a differentiable region mask is proposed to select
features as well as to implicitly penalize tracking errors on intermediate
frames. Moreover, since noisy labels may degrade training, we propose a
mask-guided loss reweighting strategy to assign dynamic weights based on the
quality of pseudo labels. In extensive experiments, our tracker outperforms
preceding unsupervised methods by a substantial margin, performing on par with
supervised methods on large-scale datasets such as TrackingNet and LaSOT. Code
is available at https://github.com/FlorinShum/ULAST.
