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Updatable Siamese Tracker with Two-stage One-shot Learning
[article]
2021
arXiv
pre-print
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
arXiv:2104.15049v1
fatcat:wzhotgpqcfczlo6dqzlg6xbcye