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Weakly Supervised Method for Domain Adaptation in Instance Segmentation

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Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14495))

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Abstract

The domain adaptation of an instance segmentation model has gained much attention. However, manual annotation is tedious and self-training contains too much pseudolabel noise. Inspired by weakly supervised methods, we propose a method to handle these challenges by limited verification signals and label propagation. Semantic trees are constructed to explore the relation between samples by using a clustering method; Then, reliable pseudolabels are verified and propagated to unreliable labels, which improves instance segmentation model by employing the updated samples. Experiments on public datasets demonstrate that the proposed approach is competitive with state-of-the-art approaches.

J. Sun and Y. Tian— Equal contribution.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (61976188, 61972351, 62111530300). The authors declare no conflicts of interest.

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, 310018, China

    Jie Sun, Yan Tian, Hao Wang, Zhaoyi Jiang & Xun Wang

  2. Shining 3D Tech Co., Ltd., Hangzhou, 311258, China

    Jialei Wang

  3. School of Mathematical and Computational Sciences, Massey University, Auckland, 0632, New Zealand

    Zhaocheng Xu

Authors
  1. Jie Sun
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  2. Yan Tian
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  3. Jialei Wang
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  4. Zhaocheng Xu
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  5. Hao Wang
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  6. Zhaoyi Jiang
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  7. Xun Wang
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Corresponding author

Correspondence to Yan Tian .

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Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneve, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

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Sun, J. et al. (2024). Weakly Supervised Method for Domain Adaptation in Instance Segmentation. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14495. Springer, Cham. https://doi.org/10.1007/978-3-031-50069-5_18

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  • DOI: https://doi.org/10.1007/978-3-031-50069-5_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50068-8

  • Online ISBN: 978-3-031-50069-5

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