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Unsupervised domain adaptive (UDA) semantic segmentation aims to assign a predetermined semantic label to every single pixel of the unannotated target data by exploiting a model that is trained on the labeled source data. Numerous current methods only display concern for grouping similar features together but ignore dispersing those features across various classes, so that some feature representations can not be well-separated. Therefore, we propose to employ contrastive learning (CL) method to increase the similarity of pixel features, propelling similar features closer and dispelling different ones far away. Furthermore, due to the domain shift, the UDA model frequently has poor generalization on the target domain. Accordingly, we design an optimal transport (OT) module to enhance UDA by comparing and aligning sample distributions to minimize transport loss between them. By taking advantage of this, the domain shift can be efficaciously mitigated by bringing the target probability distribution closer to that of the source. Specially, due to its simplicity, our OT module can be integrated into various UDA methods. In light of the aforementioned viewpoints, we put forth an ingenious approach, named OTCLDA, which successfully combines OT and CL while enhancing the performance of the UDA model. Multitudinous experiments demonstrate the importance of our method involving OT and CL. It significantly gains mIoU of 75. 1% on benchmark GTA Cityscapes, and 66. 9% on SYNTHIA Cityscapes respectively, displaying a competitive performance compared with previous works. The source code of OTCLDA is publicly available at https: //github. com/YYDSDD/OTCLDA.
Fan et al. (Wed,) studied this question.