Siamese trackers contain two core stages, i.e., learning the features of both target and search inputs at first and then calculating response maps via the cross-correlation operation, which can also be used for regression and classification to construct typical one-shot detection tracking framework. Although they have drawn continuous interest from the visual tracking community due to the proper trade-off between accuracy and speed, both stages are easily sensitive to the distracters in search branch, thereby inducing unreliable response positions. To fill this gap, we advance Siamese trackers with two novel non-local blocks named Nocal-Siam, which leverages the long-range dependency property of the non-local attention in a supervised fashion from two aspects. First, a target-aware non-local block (T-Nocal) is proposed for learning the target-guided feature weights, which serve to refine visual features of both target and search branches, and thus effectively suppress noisy distracters. This block reinforces the interplay between both target and search branches in the first stage. Second, we further develop a location-aware non-local block (L-Nocal) to associate multiple response maps, which prevents them inducing diverse candidate target positions in the future coming frame. Experiments on five popular benchmarks show that Nocal-Siam performs favorably against well-behaved counterparts both in quantity and quality.

中文翻译：

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Nocal-Siam：使用高级非本地块改进视觉功能和响应，以进行实时暹罗跟踪

暹罗跟踪器包含两个核心阶段，即首先学习目标输入和搜索输入的特征，然后通过互相关运算来计算响应图，也可以将其用于回归和分类以构建典型的单次检测跟踪框架。尽管由于准确性和速度之间的适当折衷，它们一直引起视觉跟踪社区的关注，但是这两个阶段都对搜索分支中的干扰因素很敏感，从而导致了不可靠的响应位置。为了填补这一空白，我们用两个名为Nocal-Siam的新型非本地块推进了暹罗跟踪器，它们从两个方面以监督的方式利用了非本地注意力的远程依赖性。第一，提出了一种目标感知的非局部块（T-Nocal），用于学习目标导向的特征权重，该权重可用于细化目标和搜索分支的视觉特征，从而有效地抑制嘈杂的干扰因素。在第一阶段，此块加强了目标分支和搜索分支之间的相互作用。其次，我们进一步开发了一个位置感知的非局部块（L-Nocal）来关联多个响应图，这可以防止它们在未来的帧中引发不同的候选目标位置。在五个流行基准上进行的实验表明，Nocal-Siam在数量和质量上均优于行为良好的同类产品。在第一阶段，此块加强了目标分支和搜索分支之间的相互作用。其次，我们进一步开发了一个位置感知的非局部块（L-Nocal）来关联多个响应图，这可以防止它们在未来的帧中引发不同的候选目标位置。在五个流行基准上进行的实验表明，Nocal-Siam在数量和质量上均优于行为良好的同类产品。在第一阶段，此块加强了目标分支和搜索分支之间的相互作用。其次，我们进一步开发了一个位置感知的非局部块（L-Nocal）来关联多个响应图，这可以防止它们在未来的帧中引发不同的候选目标位置。在五个流行基准上进行的实验表明，Nocal-Siam在数量和质量上均优于行为良好的同类产品。