  Correlation has a critical role in the tracking field, especially in recent
popular Siamese-based trackers. The correlation operation is a simple fusion
method that considers the similarity between the template and the search
region. However, the correlation operation is a local linear matching process,
losing semantic information and easily falling into a local optimum, which may
be the bottleneck in designing high-accuracy tracking algorithms. In this work,
to determine whether a better feature fusion method exists than correlation, a
novel attention-based feature fusion network, inspired by the transformer, is
presented. This network effectively combines the template and search region
features using attention. Specifically, the proposed method includes an
ego-context augment module based on self-attention and a cross-feature augment
module based on cross-attention. First, we present a transformer tracking
(named TransT) method based on the Siamese-like feature extraction backbone,
the designed attention-based fusion mechanism, and the classification and
regression head. Based on the TransT baseline, we further design a segmentation
branch to generate an accurate mask. Finally, we propose a stronger version of
TransT by extending TransT with a multi-template scheme and an IoU prediction
head, named TransT-M. Experiments show that our TransT and TransT-M methods
achieve promising results on seven popular datasets. Code and models are
available at https://github.com/chenxin-dlut/TransT-M.
