Performance of a pre-trained semantic segmentation model is likely to substantially decrease on data from a new domain. We show a pre-trained model can be adapted to unlabelled target domain data by calculating soft-label prototypes under the domain shift and making predictions according to the prototype closest to the vector with predicted class probabilities. The proposed adaptation procedure is fast, comes almost for free in terms of computational resources and leads to considerable performance improvements. We demonstrate the benefits of such label calibration on the highly-practical synthetic-to-real semantic segmentation problem.

翻译：预训练的语义分割模型在新领域数据上的性能可能会显著下降。我们证明，通过计算领域迁移下的软标签原型，并根据与预测类别概率向量最接近的原型进行预测，可以将预训练模型适应于无标签的目标领域数据。所提出的自适应过程速度快、在计算资源方面几乎零成本，并能带来显著的性能提升。我们在高度实用的合成到真实语义分割问题上展示了这种标签校准的优越性。