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Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13694))

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Abstract

Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Adaptation (ProCA), a simple and efficient contrastive learning method for unsupervised domain adaptive semantic segmentation. Previous domain adaptation methods merely consider the alignment of the intra-class representational distributions across various domains, while the inter-class structural relationship is insufficiently explored, resulting in the aligned representations on the target domain might not be as easily discriminated as done on the source domain anymore. Instead, ProCA incorporates inter-class information into class-wise prototypes, and adopts the class-centered distribution alignment for adaptation. By considering the same class prototypes as positives and other class prototypes as negatives to achieve class-centered distribution alignment, ProCA achieves state-of-the-art performance on classical domain adaptation tasks, i.e., GTA5 \(\rightarrow \) Cityscapes and SYNTHIA \(\rightarrow \) Cityscapes. Code is available at ProCA.

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Author information

Authors and Affiliations

  1. Tencent Youtu Lab, Shanghai, China

    Zhengkai Jiang, Yuxi Li, Yabiao Wang, Ying Tai & Chengjie Wang

  2. The Chinese University of Hong Kong, Ma Liu Shui, Hong Kong

    Ceyuan Yang & Peng Gao

Authors
  1. Zhengkai Jiang
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  2. Yuxi Li
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  3. Ceyuan Yang
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  4. Peng Gao
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  5. Yabiao Wang
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  6. Ying Tai
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  7. Chengjie Wang
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Corresponding authors

Correspondence to Yabiao Wang or Chengjie Wang .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Jiang, Z. et al. (2022). Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13694. Springer, Cham. https://doi.org/10.1007/978-3-031-19830-4_3

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  • DOI: https://doi.org/10.1007/978-3-031-19830-4_3

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