Mutual-Learning Knowledge Distillation for Nighttime UAV Tracking
CoRR(2023)
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/vision4robotics/MLKD.
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