In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from high memory costs, which restrict their applicability to mobile devices with tight memory budgets. To address this issue, we propose a distilled Siamese tracking framework to learn small, fast and accurate trackers (students), which capture critical knowledge from large Siamese trackers (teachers) by a teacher-students knowledge distillation model. This model is intuitively inspired by the one teacher versus multiple students learning method typically employed in schools. In particular, our model contains a single teacher-student distillation module and a student-student knowledge sharing mechanism. The former is designed using a tracking-specific distillation strategy to transfer knowledge from a teacher to students. The latter is utilized for mutual learning between students to enable in-depth knowledge understanding. Extensive empirical evaluations on several popular Siamese trackers demonstrate the generality and effectiveness of our framework. Moreover, the results on five tracking benchmarks show that the proposed distilled trackers achieve compression rates of up to 18× and frame-rates of 265 FPS, while obtaining comparable tracking accuracy compared to base models.

中文翻译：

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用于视觉跟踪的蒸馏连体网络。

近年来，基于 Siamese 网络的跟踪器显着提高了实时跟踪的最新水平。尽管取得了成功，但 Siamese 跟踪器往往会遭受高内存成本的困扰，这限制了它们在内存预算紧张的移动设备上的适用性。为了解决这个问题，我们提出了一个蒸馏 Siamese 跟踪框架来学习小型、快速和准确的跟踪器（学生），它通过师生知识蒸馏模型从大型 Siamese 跟踪器（教师）捕获关键知识。该模型的直观灵感来自于学校通常采用的单教师对多学生学习方法。特别是，我们的模型包含单个教师-学生蒸馏模块和学生-学生知识共享机制。前者是使用特定于跟踪的蒸馏策略设计的，以将知识从教师转移到学生。后者用于学生之间的相互学习，以实现深入的知识理解。对几种流行的 Siamese 跟踪器的广泛实证评估证明了我们框架的通用性和有效性。此外，五个跟踪基准的结果表明，所提出的蒸馏跟踪器可实现高达 18 倍的压缩率和 265 FPS 的帧速率，同时获得与基础模型相当的跟踪精度。对几种流行的 Siamese 跟踪器的广泛实证评估证明了我们框架的通用性和有效性。此外，五个跟踪基准的结果表明，所提出的蒸馏跟踪器可实现高达 18 倍的压缩率和 265 FPS 的帧速率，同时获得与基础模型相当的跟踪精度。对几种流行的 Siamese 跟踪器的广泛实证评估证明了我们框架的通用性和有效性。此外，五个跟踪基准的结果表明，所提出的蒸馏跟踪器可实现高达 18 倍的压缩率和 265 FPS 的帧速率，同时获得与基础模型相当的跟踪精度。