We present a Siamese-like Dual-branch network based on solely Transformers
for tracking. Given a template and a search image, we divide them into
non-overlapping patches and extract a feature vector for each patch based on
its matching results with others within an attention window. For each token, we
estimate whether it contains the target object and the corresponding size. The
advantage of the approach is that the features are learned from matching, and
ultimately, for matching. So the features are aligned with the object tracking
task. The method achieves better or comparable results as the best-performing
methods which first use CNN to extract features and then use Transformer to
fuse them. It outperforms the state-of-the-art methods on the GOT-10k and
VOT2020 benchmarks. In addition, the method achieves real-time inference speed
(about $40$ fps) on one GPU. The code and models will be released.

提出了一种仅基于 Transformer 的连体双支路网络，用于目标跟踪。该方法比第一步使用 CNN 提取特征，然后使用 Transformer 融合它们的最佳方法产生更好或可比的结果，在 GOT-10k 和 VOT2020 基准上优于现有技术。此外，该方法实现了实时推理速度（约 40fps）并且代码和模型将被公布。