  A strong visual object tracker nowadays relies on its well-crafted modules,
which typically consist of manually-designed network architectures to deliver
high-quality tracking results. Not surprisingly, the manual design process
becomes a particularly challenging barrier, as it demands sufficient prior
experience, enormous effort, intuition, and perhaps some good luck. Meanwhile,
neural architecture search has gaining grounds in practical applications as a
promising method in tackling the issue of automated search of feasible network
structures. In this work, we propose a novel cell-level differentiable
architecture search mechanism with early stopping to automate the network
design of the tracking module, aiming to adapt backbone features to the
objective of Siamese tracking networks during offline training. Besides, the
proposed early stopping strategy avoids over-fitting and performance collapse
problems leading to generalization improvement. The proposed approach is
simple, efficient, and with no need to stack a series of modules to construct a
network. Our approach is easy to be incorporated into existing trackers, which
is empirically validated using different differentiable architecture
search-based methods and tracking objectives. Extensive experimental
evaluations demonstrate the superior performance of our approach over five
commonly-used benchmarks.
