  Offline Siamese networks have achieved very promising tracking performance,
especially in accuracy and efficiency. However, they often fail to track an
object in complex scenes due to the incapacity in online update. Traditional
updaters are difficult to process the irregular variations and sampling noises
of objects, so it is quite risky to adopt them to update Siamese networks. In
this paper, we first present a two-stage one-shot learner, which can predict
the local parameters of primary classifier with object samples from diverse
stages. Then, an updatable Siamese network is proposed based on the learner
(SiamTOL), which is able to complement online update by itself. Concretely, we
introduce an extra inputting branch to sequentially capture the latest object
features, and design a residual module to update the initial exemplar using
these features. Besides, an effective multi-aspect training loss is designed
for our network to avoid overfit. Extensive experimental results on several
popular benchmarks including OTB100, VOT2018, VOT2019, LaSOT, UAV123 and GOT10k
manifest that the proposed tracker achieves the leading performance and
outperforms other state-of-the-art methods
