It remains very challenging to build a pedestrian detection system for real
world applications, which demand for both accuracy and speed. This work
presents a novel hierarchical knowledge distillation framework to learn a
lightweight pedestrian detector, which significantly reduces the computational
cost and still holds the high accuracy at the same time. Following the
`teacher--student' diagram that a stronger, deeper neural network can teach a
lightweight network to learn better representations, we explore multiple
knowledge distillation architectures and reframe this approach as a unified,
hierarchical distillation framework. In particular, the proposed distillation
is performed at multiple hierarchies, multiple stages in a modern detector,
which empowers the student detector to learn both low-level details and
high-level abstractions simultaneously. Experiment result shows that a student
model trained by our framework, with 6 times compression in number of
parameters, still achieves competitive performance as the teacher model on the
widely used pedestrian detection benchmark.

本研究提出了一种新的层次化知识蒸馏框架，以学习轻量级行人检测器，该方法可以显著降低计算成本并同时保持高精度。实验结果表明，使用我们的框架训练的学生模型，在参数数量压缩了 6 倍的情况下，仍然可以在广泛使用的行人检测基准上实现与老师模型相当的性能。