Contemporary segmentation methods are usually based on deep fully
convolutional networks (FCNs). However, the layer-by-layer convolutions with a
growing receptive field is not good at capturing long-range contexts such as
lane markers in the scene. In this paper, we address this issue by designing a
distillation method that exploits label structure when training segmentation
network. The intuition is that the ground-truth lane annotations themselves
exhibit internal structure. We broadcast the structure hints throughout a
teacher network, i.e., we train a teacher network that consumes a lane label
map as input and attempts to replicate it as output. Then, the attention maps
of the teacher network are adopted as supervisors of the student segmentation
network. The teacher network, with label structure information embedded, knows
distinctly where the convolution layers should pay visual attention into. The
proposed method is named as Label-guided Attention Distillation (LGAD). It
turns out that the student network learns significantly better with LGAD than
when learning alone. As the teacher network is deprecated after training, our
method do not increase the inference time. Note that LGAD can be easily
incorporated in any lane segmentation network.

本文提出一种基于标签引导的注意力蒸馏方法（LGAD），用于训练分割网络以更好地捕捉诸如车道标记之类的长程上下文信息。通过将标签结构信息嵌入教师网络中，并利用其注意力图作为学生网络的监督信号，能够显著提高学生网络的训练效果。