MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation

arXiv:2311.18331v2 Announce Type: replace
Abstract: Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains, but due to the absence of style diversity during training, enhancing performance on unseen target domains using only single source domain data remains a challenging task. Generation of simulated data is a feasible alternative to retrieving large style-diverse real-world datasets as it is a cumbersome and budget-intensive process. However, the large domain-specfic inconsistencies between simulated and real-world data pose a …

abstract arxiv cs.ai cs.cv data diversity domain domains exemplary feature networks neural networks performance resolution segmentation semantic sim style tasks training type understanding