  Siamese tracking has achieved groundbreaking performance in recent years,
where the essence is the efficient matching operator cross-correlation and its
variants. Besides the remarkable success, it is important to note that the
heuristic matching network design relies heavily on expert experience.
Moreover, we experimentally find that one sole matching operator is difficult
to guarantee stable tracking in all challenging environments. Thus, in this
work, we introduce six novel matching operators from the perspective of feature
fusion instead of explicit similarity learning, namely Concatenation,
Pointwise-Addition, Pairwise-Relation, FiLM, Simple-Transformer and
Transductive-Guidance, to explore more feasibility on matching operator
selection. The analyses reveal these operators' selective adaptability on
different environment degradation types, which inspires us to combine them to
explore complementary features. To this end, we propose binary channel
manipulation (BCM) to search for the optimal combination of these operators.
BCM determines to retrain or discard one operator by learning its contribution
to other tracking steps. By inserting the learned matching networks to a strong
baseline tracker Ocean, our model achieves favorable gains by $67.2 \rightarrow
71.4$, $52.6 \rightarrow 58.3$, $70.3 \rightarrow 76.0$ success on OTB100,
LaSOT, and TrackingNet, respectively. Notably, Our tracker, dubbed AutoMatch,
uses less than half of training data/time than the baseline tracker, and runs
at 50 FPS using PyTorch. Code and model will be released at
https://github.com/JudasDie/SOTS.
