Multi-Source Soft Pseudo-Label Learning with Domain Similarity-based Weighting for Semantic Segmentation
Shigemichi Matsuzaki,Hiroaki Masuzawa,Jun Miura,Shigemichi Matsuzaki,Hiroaki Masuzawa,Jun Miura
This paper describes a method of domain adap-tive training for semantic segmentation using multiple source datasets that are not necessarily relevant to the target dataset. We propose a soft pseudo-label generation method by integrating predicted object probabilities from multiple source models. The prediction of each source model is weighted based on the estimated domain similarity between the so...
