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：与先前双流跟踪器相比，近期提出的单流跟踪框架通过允许模板与搜索区域提前交互，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的所有部分在所有编码器层中进行交互。当提取的特征表示缺乏足够判别力时，这可能导致目标与背景混淆。为解决该问题，我们提出一种基于自适应令牌划分的广义关系建模方法。该方法是对Transformer跟踪中基于注意力机制的关系建模的广义表述，既继承了先前双流与单流框架的优势，又通过选择适当的搜索令牌与模板令牌交互实现更灵活的关系建模。我们引入注意力掩码策略与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框架，在六个具有挑战性的基准数据集上以实时运行速度达到了最先进的性能。