Skip to main content
Springer Nature Link
Log in

Waseda_Meisei_SoftBank at Video Browser Showdown 2024

  • Conference paper
  • First Online:
MultiMedia Modeling (MMM 2024)

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

Included in the following conference series:

  • 952 Accesses

Abstract

This paper presents our first interactive video browser system “System 4 Vision”. Because our system is based on the system that achieved the highest video retrieval accuracy in the AVS task of the TRECVID benchmark 2022, high retrieval accuracy can be expected in the Video Browser Showdown competition. Our system is characterized by the availability of rich text input, including complicated multiple conditions as queries, because our system uses the visual-semantic embedding method represented by Contrastive Language-Image Pre-training (CLIP).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/mlfoundations/open_clip.

References

  1. Frome, A., et al.: DeViSE: a deep visual-semantic embedding model. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), vol. 26 (2013)

    Google Scholar 

  2. Schoeffmann, K., Lokoč, J., Bailer, W.: 10 years of video browser showdown. In: MMAsia 2020: ACM Multimedia Asia (2022)

    Google Scholar 

  3. Faghri, F., Fleet, D.J., Kiros, R., Fidler, S.: VSE++: improved visual-semantic embeddings. arXiv:1707.05612 (2017)

  4. Lee, K.-H., Chen, X., Hua, G., Hu, H., He, X.: Stacked cross attention for image-text matching. In: Proceedings of European Conference on Computer Vision (ECCV) (2018)

    Google Scholar 

  5. Liu, C., Mao, Z., Zhang, T., Xie, H., Wang, B., Zhang, Y.: Graph structured network for image-text matching. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  6. Radford, A., et al.: Learning transferable visual models from natural language supervision. arXiv:2103.00020 (2021)

  7. Mu, N., Kirillov, A., Wagner, D., Xie, S.: SLIP: self-supervision meets language-image pre-training. arXiv:2112.12750 (2021)

  8. Schuhmann, C., et al.: LAION-5B: an open large-scale dataset for training next generation image-text models. In: 36th Conference on Neural Information Processing Systems (NeurIPS) (2022)

    Google Scholar 

  9. Ueki, K., Suzuki, Y., Takushima, H., Okamoto, H., Tanoue, H., Hori, T.: Waseda_Meisei_SoftBank at TRECVID 2022 ad-hoc video search. In: Notebook paper of the TRECVID 2022 Workshop (2022)

    Google Scholar 

Download references

Acknowledgments

This work was partially supported by the Telecommunications Advancement Foundation.

Author information

Authors and Affiliations

  1. Meisei University, 2-1-1 Hodokubo, Hino, Tokyo, 191-8506, Japan

    Kazuya Ueki

  2. Softbank Corp, Kaigan 1-7-1, Minato-ku, Tokyo, 105-7529, Japan

    Takayuki Hori, Yuma Suzuki, Hiroki Takushima, Hayato Tanoue, Haruki Sato, Takumi Takada & Aiswariya Manoj Kumar

  3. Waseda University, 3-4-1, Okubo, Shinjuku-ku, Tokyo, 169-8555, Japan

    Takayuki Hori

Authors
  1. Takayuki Hori
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kazuya Ueki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuma Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hiroki Takushima
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hayato Tanoue
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Haruki Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Takumi Takada
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Aiswariya Manoj Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Takayuki Hori .

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

Rights and permissions

Reprints and permissions

Copyright information

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

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hori, T. et al. (2024). Waseda_Meisei_SoftBank at Video Browser Showdown 2024. 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_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-53302-0_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53301-3

  • Online ISBN: 978-3-031-53302-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics