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Training-Free Region Prediction with Stable Diffusion

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MultiMedia Modeling (MMM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14557))

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Abstract

Semantic segmentation models require a large number of images with pixel-level annotations for training, which is a costly problem. In this study, we propose a method called StableSeg that infers region masks of any classes without needs of additional training by using an image synthesis foundation model, Stable Diffusion, pre-trained with five billion image-text pair data. We also propose StableSeg++, which uses the pseudo-masks generated by StableSeg to estimate the optimal weights of the attention maps, and can infer better region masks. We show the effectiveness of the proposed methods by the experiments on five datasets.

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Acknowledgements

This work was supported by JSPS KAKENHI Grant Numbers, 21H05812, 22H00540, 22H00548, and 22K19808.

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Authors and Affiliations

  1. The University of Electro-Communications, Tokyo, Japan

    Yuma Honbu & Keiji Yanai

Authors
  1. Yuma Honbu
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  2. Keiji Yanai
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Corresponding author

Correspondence to Keiji Yanai .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  2. Delft University of Technology, Delft, The Netherlands

    Alan Hanjalic

  3. Delft University of Technology, Delft, The Netherlands

    Cynthia Liem

  4. University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring

  5. Reykjavik University, Reykjavik, Iceland

    Björn Þór Jónsson

  6. Microsoft Research Lab – Asia, Beijing, China

    Bei Liu

  7. The University of Tokyo, Tokyo, Japan

    Yoko Yamakata

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Honbu, Y., Yanai, K. (2024). Training-Free Region Prediction with Stable Diffusion. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14557. Springer, Cham. https://doi.org/10.1007/978-3-031-53302-0_2

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  • DOI: https://doi.org/10.1007/978-3-031-53302-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53301-3

  • Online ISBN: 978-3-031-53302-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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