  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.
