  Tracking requires building a discriminative model for the target in the
inference stage. An effective way to achieve this is online learning, which can
comfortably outperform models that are only trained offline. Recent research
shows that visual tracking benefits significantly from the unification of
visual tracking and segmentation due to its pixel-level discrimination.
However, it imposes a great challenge to perform online learning for such a
unified model. A segmentation model cannot easily learn from prior information
given in the visual tracking scenario. In this paper, we propose TrackMLP: a
novel meta-learning method optimized to learn from only partial information to
resolve the imposed challenge. Our model is capable of extensively exploiting
limited prior information hence possesses much stronger target-background
discriminability than other online learning methods. Empirically, we show that
our model achieves state-of-the-art performance and tangible improvement over
competing models. Our model achieves improved average overlaps of66.0%,67.1%,
and68.5% in VOT2019, VOT2018, and VOT2016 datasets, which are 6.4%,7.3%,
and6.4% higher than our baseline. Code will be made publicly available.
