  We introduce a novel backbone architecture to improve target-perception
ability of feature representation for tracking. Specifically, having observed
that de facto frameworks perform feature matching simply using the outputs from
backbone for target localization, there is no direct feedback from the matching
module to the backbone network, especially the shallow layers. More concretely,
only the matching module can directly access the target information (in the
reference frame), while the representation learning of candidate frame is blind
to the reference target. As a consequence, the accumulation effect of
target-irrelevant interference in the shallow stages may degrade the feature
quality of deeper layers. In this paper, we approach the problem from a
different angle by conducting multiple branch-wise interactions inside the
Siamese-like backbone networks (InBN). At the core of InBN is a general
interaction modeler (GIM) that injects the prior knowledge of reference image
to different stages of the backbone network, leading to better
target-perception and robust distractor-resistance of candidate feature
representation with negligible computation cost. The proposed GIM module and
InBN mechanism are general and applicable to different backbone types including
CNN and Transformer for improvements, as evidenced by our extensive experiments
on multiple benchmarks. In particular, the CNN version (based on SiamCAR)
improves the baseline with 3.2/6.9 absolute gains of SUC on LaSOT/TNL2K,
respectively. The Transformer version obtains SUC scores of 65.7/52.0 on
LaSOT/TNL2K, which are on par with recent state of the arts. Code and models
will be released.
