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VISIONE at Video Browser Showdown 2023

  • Conference paper
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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

In this paper, we present the fourth release of VISIONE, a tool for fast and effective video search on a large-scale dataset. It includes several search functionalities like text search, object and color-based search, semantic and visual similarity search, and temporal search. VISIONE uses ad-hoc textual encoding for indexing and searching video content, and it exploits a full-text search engine as search backend. In this new version of the system, we introduced some changes both to the current search techniques and to the user interface.

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Notes

  1. 1.

    http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1.

  2. 2.

    https://github.com/facebookresearch/faiss.

  3. 3.

    https://lucene.apache.org/.

  4. 4.

    Please note that some of these changes were already integrated in VISIONE some weeks before the last VBS competition.

  5. 5.

    https://doi.org/10.5281/zenodo.7194300.

  6. 6.

    https://wordnet.princeton.edu/.

References

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Acknowledgements

This work was partially funded by AI4Media - A European Excellence Centre for Media, Society and Democracy (EC, H2020 n. 951911) and INAROS, CNR4C program (Tuscany POR FSE CUP B53D21008060008).

Author information

Authors and Affiliations

  1. ISTI-CNR, Via G. Moruzzi 1, 56124, Pisa, Italy

    Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Fabrizio Falchi, Claudio Gennaro, Nicola Messina, Lucia Vadicamo & Claudio Vairo

Authors
  1. Giuseppe Amato
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  2. Paolo Bolettieri
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  3. Fabio Carrara
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  4. Fabrizio Falchi
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  5. Claudio Gennaro
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  6. Nicola Messina
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  7. Lucia Vadicamo
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  8. Claudio Vairo
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Corresponding author

Correspondence to Lucia Vadicamo .

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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Amato, G. et al. (2023). VISIONE at Video Browser Showdown 2023. 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_48

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  • DOI: https://doi.org/10.1007/978-3-031-27077-2_48

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  • Online ISBN: 978-3-031-27077-2

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