  Recently, Siamese-based trackers have achieved promising performance in
visual tracking. Most recent Siamese-based trackers typically employ a
depth-wise cross-correlation (DW-XCorr) to obtain multi-channel correlation
information from the two feature maps (target and search region). However,
DW-XCorr has several limitations within Siamese-based tracking: it can easily
be fooled by distractors, has fewer activated channels, and provides weak
discrimination of object boundaries. Further, DW-XCorr is a handcrafted
parameter-free module and cannot fully benefit from offline learning on
large-scale data. We propose a learnable module, called the asymmetric
convolution (ACM), which learns to better capture the semantic correlation
information in offline training on large-scale data. Different from DW-XCorr
and its predecessor(XCorr), which regard a single feature map as the
convolution kernel, our ACM decomposes the convolution operation on a
concatenated feature map into two mathematically equivalent operations, thereby
avoiding the need for the feature maps to be of the same size (width and
height)during concatenation. Our ACM can incorporate useful prior information,
such as bounding-box size, with standard visual features. Furthermore, ACM can
easily be integrated into existing Siamese trackers based on DW-XCorror XCorr.
To demonstrate its generalization ability, we integrate ACM into three
representative trackers: SiamFC, SiamRPN++, and SiamBAN. Our experiments reveal
the benefits of the proposed ACM, which outperforms existing methods on six
tracking benchmarks. On the LaSOT test set, our ACM-based tracker obtains a
significant improvement of 5.8% in terms of success (AUC), over the baseline.
