To improve the segmentation performance and solve the problem of poor generalization of the model in different data domains,we propose a method based on depth information for semantic segmenta-tion in the context of unsupervised domain adaptation.It includes a Depth-aware Adaptation Frame-work(DAF)and a Intra-domain Adaptation(IDA)strategy.Firstly,DAF is proposed to adapt domains by capitalizing on the inherent correlations of semantic and depth information.Then a novel light-weight depth estimation network is designed provide additional depth information,and we fuse semantic and depth information by cross-task interaction,then align the distribution in depth-aware space between source and target domains.Finally,IDA strategy is proposed to bridge the distribution gap inside the target domain.To this end,a depth-aware ranking strategy is presented to separate target domain into sub-source domain and sub-target domain,and then we perform the alignment between sub-source domain and sub-target domain.Experiments on SYNTHIA-2-Cityscapes and SYNTHIA-2-Mapillary cross-domain tasks show that our method achieves significant improvement(46.7%mIoU and 73.3%mIoU,respec-tively)compared with the similar methods.
维普
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