Current state-of-the-art object detectors are at the expense of high
computational costs and are hard to deploy to low-end devices. Knowledge
distillation, which aims at training a smaller student network by transferring
knowledge from a larger teacher model, is one of the promising solutions for
model miniaturization. In this paper, we investigate each module of a typical
detector in depth, and propose a general distillation framework that adaptively
transfers knowledge from teacher to student according to the task specific
priors. The intuition is that simply distilling all information from teacher to
student is not advisable, instead we should only borrow priors from the teacher
model where the student cannot perform well. Towards this goal, we propose a
region proposal sharing mechanism to interflow region responses between the
teacher and student models. Based on this, we adaptively transfer knowledge at
three levels, \emph{i.e.}, feature backbone, classification head, and bounding
box regression head, according to which model performs more reasonably.
Furthermore, considering that it would introduce optimization dilemma when
minimizing distillation loss and detection loss simultaneously, we propose a
distillation decay strategy to help improve model generalization via gradually
reducing the distillation penalty. Experiments on widely used detection
benchmarks demonstrate the effectiveness of our method. In particular, using
Faster R-CNN with FPN as an instantiation, we achieve an accuracy of $39.0\%$
with Resnet-50 on COCO dataset, which surpasses the baseline $36.3\%$ by
$2.7\%$ points, and even better than the teacher model with $38.5\%$ mAP.

该论文提出了一种基于知识蒸馏的对象检测模型微调方法，采用区域提议共享机制和自适应知识转移方法，根据模型的不同表现区分性能好坏，同时使用知识衰减策略帮助提高模型的泛化性能。实验结果表明，该方法在 COOC 数据集上取得了比基线模型更好的检测结果。