Compared with previous two-stream trackers, the recent one-stream tracking
pipeline, which allows earlier interaction between the template and search
region, has achieved a remarkable performance gain. However, existing
one-stream trackers always let the template interact with all parts inside the
search region throughout all the encoder layers. This could potentially lead to
target-background confusion when the extracted feature representations are not
sufficiently discriminative. To alleviate this issue, we propose a generalized
relation modeling method based on adaptive token division. The proposed method
is a generalized formulation of attention-based relation modeling for
Transformer tracking, which inherits the merits of both previous two-stream and
one-stream pipelines whilst enabling more flexible relation modeling by
selecting appropriate search tokens to interact with template tokens. An
attention masking strategy and the Gumbel-Softmax technique are introduced to
facilitate the parallel computation and end-to-end learning of the token
division module. Extensive experiments show that our method is superior to the
two-stream and one-stream pipelines and achieves state-of-the-art performance
on six challenging benchmarks with a real-time running speed.

本文提出了一种基于适应性标记划分的关系建模方法，通过与模板标记的交互来改善现有的单流跟踪器中可能出现的目标 - 背景混淆问题，并引入注意掩蔽策略和 Gumbel-Softmax 技术，以促进标记划分模块的并行计算和端到端的学习，在六个具有挑战性的基准测试中实现了最先进的性能。