Accurate semantic segmentation models typically require significant
computational resources, inhibiting their use in practical applications. Recent
works rely on well-crafted lightweight models to achieve fast inference.
However, these models cannot flexibly adapt to varying accuracy and efficiency
requirements. In this paper, we propose a simple but effective slimmable
semantic segmentation (SlimSeg) method, which can be executed at different
capacities during inference depending on the desired accuracy-efficiency
tradeoff. More specifically, we employ parametrized channel slimming by
stepwise downward knowledge distillation during training. Motivated by the
observation that the differences between segmentation results of each submodel
are mainly near the semantic borders, we introduce an additional boundary
guided semantic segmentation loss to further improve the performance of each
submodel. We show that our proposed SlimSeg with various mainstream networks
can produce flexible models that provide dynamic adjustment of computational
cost and better performance than independent models. Extensive experiments on
semantic segmentation benchmarks, Cityscapes and CamVid, demonstrate the
generalization ability of our framework.

本文提出了一种简单而有效的可调节语义分割方法（SlimSeg），该方法可以根据所期望的准确度和效率平衡在不同容量下进行推理，并通过阶梯式向下蒸馏的参数化通道精简来进行训练，并引入了边界引导的语义分割损失来改进每个子模型的性能，在 Cityscapes 和 CamVid 等语义分割基准测试中展示了我们提出的 SlimSeg 在各种主流网络上的灵活性能和比独立模型更好的性能。