  Visual Object Tracking (VOT) can be seen as an extended task of Few-Shot
Learning (FSL). While the concept of FSL is not new in tracking and has been
previously applied by prior works, most of them are tailored to fit specific
types of FSL algorithms and may sacrifice running speed. In this work, we
propose a generalized two-stage framework that is capable of employing a large
variety of FSL algorithms while presenting faster adaptation speed. The first
stage uses a Siamese Regional Proposal Network to efficiently propose the
potential candidates and the second stage reformulates the task of classifying
these candidates to a few-shot classification problem. Following such a
coarse-to-fine pipeline, the first stage proposes informative sparse samples
for the second stage, where a large variety of FSL algorithms can be conducted
more conveniently and efficiently. As substantiation of the second stage, we
systematically investigate several forms of optimization-based few-shot
learners from previous works with different objective functions, optimization
methods, or solution space. Beyond that, our framework also entails a direct
application of the majority of other FSL algorithms to visual tracking,
enabling mutual communication between researchers on these two topics.
Extensive experiments on the major benchmarks, VOT2018, OTB2015, NFS, UAV123,
TrackingNet, and GOT-10k are conducted, demonstrating a desirable performance
gain and a real-time speed.
