  Multi-modal feature fusion as a core investigative component of RGBT tracking
emerges numerous fusion studies in recent years. However, existing RGBT
tracking methods widely adopt fixed fusion structures to integrate multi-modal
feature, which are hard to handle various challenges in dynamic scenarios. To
address this problem, this work presents a novel \emph{A}ttention-based
\emph{F}usion rou\emph{ter} called AFter, which optimizes the fusion structure
to adapt to the dynamic challenging scenarios, for robust RGBT tracking. In
particular, we design a fusion structure space based on the hierarchical
attention network, each attention-based fusion unit corresponding to a fusion
operation and a combination of these attention units corresponding to a fusion
structure. Through optimizing the combination of attention-based fusion units,
we can dynamically select the fusion structure to adapt to various challenging
scenarios. Unlike complex search of different structures in neural architecture
search algorithms, we develop a dynamic routing algorithm, which equips each
attention-based fusion unit with a router, to predict the combination weights
for efficient optimization of the fusion structure. Extensive experiments on
five mainstream RGBT tracking datasets demonstrate the superior performance of
the proposed AFter against state-of-the-art RGBT trackers. We release the code
in https://github.com/Alexadlu/AFter.
