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Domain-Agnostic Priors for Semantic Segmentation Under Unsupervised Domain Adaptation and Domain Generalization

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Abstract

In computer vision, an important challenge to deep neural networks comes from adjusting the varying properties of different image domains. To study this problem, researchers have been investigating a practical setting in which the deep neural networks are trained on a labeled source domain and then transferred to an unlabeled or even unseen target domain. The major difficulty lies in the potential domain gap, which essentially arises from the overfitting in the source domain. Hence, it is important to introduce generalized priors to alleviate the issue. From this perspective, this paper presents a novel framework that forces visual features to align with domain-agnostic priors (DAP). Specifically, we study two kinds of priors, (i) language-guided embedding and (ii) class-level relationship, and we believe that more such priors can be constructed. Our framework, referred to as DAP, is evaluated on both unsupervised domain adaptation (UDA) and domain generalization (DG) where the target domain is unlabeled and even unseen, respectively. We use the standard benchmark that performs transfer semantic segmentation on synthesized datasets (i.e., GTAv and SYNTHIA) and a real dataset (i.e., Cityscapes). Experiments validate the effectiveness of DAP with competitive accuracy in all tasks. In particular, language-guided priors work sufficiently well for UDA, while class-level priors serve as useful complements for DG. The proposed frameworks shed light that domain transfer benefits from better proxies, possibly from other modalities.

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Data Availability

Datasets used in this work are publicly available and can be downloaded according to the related papers which are introduced in Sect. 4.1.

Code Availability

The code is available at this URL: https://github.com/xinyuehuo/DAP.

Notes

  1. https://platform.openai.com/tokenizer.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Contract 62021001, and in part by the Fundamental Research Funds for the Central Universities under contract WK3490000007. It was also supported by the GPU cluster built by MCC Lab of USTC.

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  1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China

    Xinyue Huo, Wengang Zhou & Houqiang Li

  2. Huawei Cloud, Shenzhen, 518055, China

    Xinyue Huo, Lingxi Xie, Hengtong Hu & Qi Tian

  3. Hefei University of Technology, Hefei, 230009, China

    Hengtong Hu

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Huo, X., Xie, L., Hu, H. et al. Domain-Agnostic Priors for Semantic Segmentation Under Unsupervised Domain Adaptation and Domain Generalization. Int J Comput Vis (2024). https://doi.org/10.1007/s11263-024-02041-7

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