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。

基于标签引导的车道线分割注意力蒸馏

Label-guided Attention Distillation for Lane Segmentation

Neural sequence labelling approaches have achieved state of the art results
in morphological tagging. We evaluate the efficacy of four standard sequence
labelling models on Sanskrit, a morphologically rich, fusional Indian language.
As its label space can theoretically contain more than 40,000 labels, systems
that explicitly model the internal structure of a label are more suited for the
task, because of their ability to generalise to labels not seen during
training. We find that although some neural models perform better than others,
one of the common causes for error for all of these models is mispredictions
due to syncretism.

本研究评估了四种标准的序列标注模型在梵语单词形态标注方面的效果，发现尽管一些神经模型表现更好，但所有模型的一个常见错误原因是由于不同单词形态混淆造成的错误。