Label Calibration for Semantic Segmentation Under Domain Shift
Ondrej Bohdal Da Li Timothy Hospedales
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.