Unsupervised domain adaptation has been used to reduce the domain shift, which would improve the performance of semantic segmentation on unlabeled real-world data. How-ever, existing methods do not cater to the specific characteristics of traffic scene elements, leading to suboptimal alignment outcomes. In this paper, we propose a novel domain adaptation method for semantic segmentation of road scenes via unsu-pervised alignment of traffic elements. Firstly, a self-training framework is developed, which distinguishes the differential features between dynamic and static traffic elements, providing more accurate alignment training. Then, we designed a dynamic and static traffic elements alignment module to achieve cross-domain feature matching between the source and the target domain images. The cosine similarity maximization is applied to the alignment of dynamic traffic elements, while the prototype learning is utilized for the static traffic elements. Furthermore, the element alignment loss functions for dynamic and static traffic elements are designed to optimize the alignment modules. The experimental results demonstrate that the proposed method is superior to the existing methods on G TAS→Cityscapes task, which is applicable to semantic segmentation of road scenes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Unsupervised Alignment of Traffic Elements for Domain Adaptive Semantic Segmentation


    Contributors:
    Gao, Yuan (author) / Li, Yaochen (author) / Zhang, Tengwen (author) / Qiu, Chao (author) / Liu, Yuehu (author)


    Publication date :

    2024-09-24


    Size :

    1872518 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Threshold-Adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation

    Yan, Weihao / Qian, Yeqiang / Wang, Chunxiang et al. | IEEE | 2023



    Domain-Incremental Semantic Segmentation for Traffic Scenes

    Liu, Yazhou / Chen, Haoqi / Lasang, Pongsak et al. | IEEE | 2025


    Unsupervised Domain Adaptation via Shared Content Representation for Semantic Segmentation

    Hubschneider, Christian / Birkenbach, Marius / Zollner, J. Marius | IEEE | 2021


    Continual Unsupervised Domain Adaptation for Semantic Segmentation by Online Frequency Domain Style Transfer

    Termohlen, Jan-Aike / Klingner, Marvin / Brettin, Leon J. et al. | IEEE | 2021