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MMM-GCN: Multi-Level Multi-Modal Graph Convolution Network for Video-Based Person Identification

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MultiMedia Modeling (MMM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13833))

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Abstract

Video-based multi-modal person identification has attracted rising research interest recently to address the inadequacies of single-modal identification in unconstrained scenes. Most existing methods model video-level and multi-modal-level information of target video respectively, which suffer from separation of different levels and insufficient information contained in a specific video. In this paper, we introduce extra neighbor-level information for the first time to enhance the informativeness of target video. Then a Multi-Level(neighbor-level, multi-modal-level, and video-level) and Multi-Modal GCN model is proposed, to capture correlation among different levels and achieve adaptive fusion in a unified model. Experiments on iQIYI-VID-2019 dataset show that MMM-GCN significantly outperforms current state-of-the-art methods, proving its superiority and effectiveness. Besides, we point out feature fusion is heavily polluted by noisy nodes that result in a suboptimal result. Further improvement could be explored on this basis to approach the performance upper bound of our paradigm.

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Notes

  1. 1.

    http://challenge.ai.iqiyi.com/detail?raceId=5c767dc41a6fa0ccf53922e7.

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Author information

Authors and Affiliations

  1. Dahua Technology Co., Ltd., Hangzhou, China

    Ziyan Liao, Dening Di, Jingsong Hao, Jiang Zhang, Shulei Zhu & Jun Yin

Authors
  1. Ziyan Liao
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  2. Dening Di
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  3. Jingsong Hao
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  4. Jiang Zhang
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  5. Shulei Zhu
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  6. Jun Yin
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Corresponding author

Correspondence to Jingsong Hao .

Editor information

Editors and Affiliations

  1. University of Bergen, Bergen, Norway

    Duc-Tien Dang-Nguyen

  2. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  3. Radboud University Nijmegen, Nijmegen, The Netherlands

    Martha Larson

  4. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  5. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  6. National Institute of Information and Communications Technology, Tokyo, Japan

    Minh-Son Dao

  7. Department of Information Science and Media Studies, University of Bergen, Bergen, Norway

    Christoph Trattner

  8. La Trobe University, Melbourne, VIC, Australia

    Phoebe Chen

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Cite this paper

Liao, Z., Di, D., Hao, J., Zhang, J., Zhu, S., Yin, J. (2023). MMM-GCN: Multi-Level Multi-Modal Graph Convolution Network for Video-Based Person Identification. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13833. Springer, Cham. https://doi.org/10.1007/978-3-031-27077-2_1

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  • DOI: https://doi.org/10.1007/978-3-031-27077-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27076-5

  • Online ISBN: 978-3-031-27077-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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