  Robustness and discrimination power are two fundamental requirements in
visual object tracking. In most tracking paradigms, we find that the features
extracted by the popular Siamese-like networks cannot fully discriminatively
model the tracked targets and distractor objects, hindering them from
simultaneously meeting these two requirements. While most methods focus on
designing robust correlation operations, we propose a novel target-dependent
feature network inspired by the self-/cross-attention scheme. In contrast to
the Siamese-like feature extraction, our network deeply embeds cross-image
feature correlation in multiple layers of the feature network. By extensively
matching the features of the two images through multiple layers, it is able to
suppress non-target features, resulting in instance-varying feature extraction.
The output features of the search image can be directly used for predicting
target locations without extra correlation step. Moreover, our model can be
flexibly pre-trained on abundant unpaired images, leading to notably faster
convergence than the existing methods. Extensive experiments show our method
achieves the state-of-the-art results while running at real-time. Our feature
networks also can be applied to existing tracking pipelines seamlessly to raise
the tracking performance. Code will be available.
