夜间无人机跟踪的互学习知识蒸馏
Mutual-Learning Knowledge Distillation for Nighttime UAV Tracking
AI总结：
针对夜间无人机跟踪中低光增强器计算负担重且与跟踪方法耦合不足的问题，提出互学习知识蒸馏框架MLKD，通过教师知识迁移和多学生互学习训练紧凑跟踪器MLKD-Track，在真实测试中验证了有效性。
摘要：
夜间无人机（UAV）跟踪得益于不可或缺的即插即用低光增强器。然而，低光增强器的引入增加了无人机的额外计算负担，显著阻碍了实时无人机应用的发展。同时，这些最先进（SOTA）的增强器与先进的白天无人机跟踪方法缺乏紧密耦合。为解决上述问题，本工作提出了一种新颖的用于夜间无人机跟踪的互学习知识蒸馏框架，即MLKD。该框架旨在通过从教师的知识迁移和多个学生之间的知识共享，学习一个紧凑且快速的夜间跟踪器。具体而言，采用基于SOTA增强器和卓越跟踪骨干的先进教师，指导仅基于紧密耦合感知跟踪骨干的学生直接提取夜间目标特征。为解决单一学生的偏置学习，构建了具有不同蒸馏方法的多样轻量级学生，以关注教师知识的不同方面。此外，设计了一个创新的互学习空间，在训练阶段逐帧选举出优秀的学生候选者来协助其余学生。最后，通过测试数据集选出最终的最佳学生，即MLKD-Track。大量实验证明了MLKD和MLKD-Track的有效性和优越性。MLKD-Track的实用性在具有不同挑战场景的真实世界测试中得到了验证。代码可在https://github.com/lyfeng001/MLKD获取。
英文摘要：
Nighttime unmanned aerial vehicle (UAV) tracking has been facilitated with indispensable plug-and-play low-light enhancers. However, the introduction of low-light enhancers increases the extra computational burden for the UAV, significantly hindering the development of real-time UAV applications. Meanwhile, these state-of-the-art (SOTA) enhancers lack tight coupling with the advanced daytime UAV tracking approach. To solve the above issues, this work proposes a novel mutual-learning knowledge distillation framework for nighttime UAV tracking, i.e., MLKD. This framework is constructed to learn a compact and fast nighttime tracker via knowledge transferring from the teacher and knowledge sharing among various students. Specifically, an advanced teacher based on a SOTA enhancer and a superior tracking backbone is adopted for guiding the student based only on the tight coupling-aware tracking backbone to directly extract nighttime object features. To address the biased learning of a single student, diverse lightweight students with different distillation methods are constructed to focus on various aspects of the teacher's knowledge. Moreover, an innovative mutual-learning room is designed to elect the superior student candidate to assist the remaining students frame-by-frame in the training phase. Furthermore, the final best student, i.e., MLKD-Track, is selected through the testing dataset. Extensive experiments demonstrate the effectiveness and superiority of MLKD and MLKD-Track. The practicality of the MLKD-Track is verified in real-world tests with different challenging situations. The code is available at https://github.com/lyfeng001/MLKD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
