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.

翻译：与以往的双流跟踪器相比，近期通过允许模板与搜索区域更早交互的单流跟踪流水线取得了显著的性能提升。然而，现有单流跟踪器始终让模板与搜索区域内所有部分在所有编码器层中持续交互。当提取的特征表示区分性不足时，这可能导致目标与背景混淆。为解决此问题，我们提出一种基于自适应令牌划分的广义关系建模方法。该方法将基于注意力的关系建模推广至Transformer跟踪领域，融合了以往双流与单流流水线的优点，同时通过选择适当搜索令牌与模板令牌交互，实现更灵活的关系建模。本文引入注意力掩蔽策略与Gumbel-Softmax技术，以支持令牌划分模块的并行计算与端到端学习。大量实验表明，本方法优于双流与单流流水线，在六个具有挑战性的基准测试中以实时运行速度达到最先进性能。