This paper addresses the challenges in multi-target domain adaptive (MTDA) for semantic segmentation, aiming to learn a single model capable of adapting to multi-target domains. Existing methods solely focus on visual appearance (style) discrepancies, overlooking contextual variations across multi-target domains, resulting in limited performance. We propose a novel approach termed Masking-augmented Collaborative Domain Congregation (MacDC) to handle both style gap and contextual gap among multi-target domains. MacDC achieves this goal by generating image-level and region-level intermediate domains among multi-target domains. To further strengthen contextual alignment, MacDC applies multi-context masking that enforces the model’s understanding of diverse contexts. Notably, MacDC directly learns a single model for multi-target domain adaptation, significantly reducing training times and model parameters. Despite its simplicity, MacDC demonstrates superior performance compared to state-of-the-art MTDA segmentation methods on the syn-to-real and real-to-real benchmarks.


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    Titel :

    MacDC: Masking-augmented Collaborative Domain Congregation for Multi-target Domain Adaptation in Semantic Segmentation


    Beteiligte:
    Pan, Fei (Autor:in) / He, Dong (Autor:in) / Yin, Xu (Autor:in) / Zhang, Chenshuang (Autor:in) / Kim, Munchurl (Autor:in)


    Erscheinungsdatum :

    02.06.2024


    Format / Umfang :

    4727857 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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