Optimization based tracking methods have been widely successful by
integrating a target model prediction module, providing effective global
reasoning by minimizing an objective function. While this inductive bias
integrates valuable domain knowledge, it limits the expressivity of the
tracking network. In this work, we therefore propose a tracker architecture
employing a Transformer-based model prediction module. Transformers capture
global relations with little inductive bias, allowing it to learn the
prediction of more powerful target models. We further extend the model
predictor to estimate a second set of weights that are applied for accurate
bounding box regression. The resulting tracker relies on training and on test
frame information in order to predict all weights transductively. We train the
proposed tracker end-to-end and validate its performance by conducting
comprehensive experiments on multiple tracking datasets. Our tracker sets a new
state of the art on three benchmarks, achieving an AUC of 68.5% on the
challenging LaSOT dataset.

本文提出了一种优化基于追踪算法，采用 Transformer 模型实现的目标模型预测模块，并进一步扩展模型预测器以估计第二组权重以实现精确的边界框回归，该跟踪器在多个跟踪数据集上进行了广泛的实验，并在三个基准测试中取得了 68.5% 的 AUC 值。