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QIVISE: A Quantum-Inspired Interactive Video Search Engine in VBS2023

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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 a quantum-inspired interactive video search engine (QIVISE), which will be tested in VBS2023. QIVISE aims at assisting the user in dealing with Known-Item Search and Ad-hoc Video Search tasks with high efficiency and accuracy. QIVISE is based on a text-image encoder to achieve multi-modal embedding and introduces multiple interaction possibilities, including a novel quantum-inspired interaction on paradigm, label search, and multi-modal search to refine the retrieval results via user’s interaction and feedback.

W. Song, J. He and X. Li—These authors contribute equally to this work.

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References

  1. Chen, Z., Zhu, B.: Some formal analysis of Rocchio’s similarity-based relevance feedback algorithm. Inf. Retrieval 5, 61–86 (2002). https://doi.org/10.1023/A:1012730924277

    Article  MATH  Google Scholar 

  2. Gan, J., Tao, Y.: DBSCAN revisited: mis-claim, un-fixability, and approximation. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (2015). https://doi.org/10.1145/2723372.2737792

  3. Hezel, N., Schall, K., Jung, K., Barthel, K.U.: Efficient search and browsing of large-scale video collections with vibro. In: Þór Jónsson, B., et al. (eds.) MMM 2022. LNCS, vol. 13142, pp. 487–492. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98355-0_43

    Chapter  Google Scholar 

  4. Kenton, J.D., Toutanova, L.K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT (2019)

    Google Scholar 

  5. Li, J., Li, D., Xiong, C., Hoi, S.: Blip: bootstrapping language-image pre-training for unified vision-language understanding and generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2022). https://doi.org/10.48550/arXiv.2201.12086

  6. Lokoč, J., et al.: Is the reign of interactive search eternal? Findings from the video browser showdown 2020. ACM Trans. Multimedia Comput. Commun. Appl. 17(3), 1–26 (2021)

    Article  Google Scholar 

  7. Rossetto, L., Amiri Parian, M., Gasser, R., Giangreco, I., Heller, S., Schuldt, H.: Deep learning-based concept detection in vitrivr. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, W.-H., Vrochidis, S. (eds.) MMM 2019. LNCS, vol. 11296, pp. 616–621. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05716-9_55

    Chapter  Google Scholar 

  8. Uprety, S., Gkoumas, D., Song, D.: A survey of quantum theory inspired approaches to information retrieval. ACM Comput. Surv. (2021). https://doi.org/10.1145/3402179

    Article  Google Scholar 

  9. Wang, P., Hou, Y., Li, Z., Zhang, Y.: QIRM: a quantum interactive retrieval model for session search. Neurocomputing (2021). https://doi.org/10.1016/j.neucom.2021.04.013

    Article  Google Scholar 

  10. Wu, H., et al.: UniKeyphrase: a unified extraction and generation framework for keyphrase prediction. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP (2021). https://doi.org/10.48550/arXiv.2106.04847

  11. https://github.com/milvus-io/milvus

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (No. U1903214, 61876135) and by Ministry of Education Industry-University Cooperation and Collaborative Education Projects (No. 202102246004). The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.

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Authors and Affiliations

  1. National Engineering Research Center for Multimedia Software (NERCMS), Wuhan, China

    Weixi Song, Jiangshan He, Xinghan Li, Shiwei Feng & Chao Liang

  2. Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan, China

    Weixi Song, Jiangshan He, Xinghan Li, Shiwei Feng & Chao Liang

  3. School of Computer Science, Wuhan University, Wuhan, China

    Weixi Song, Jiangshan He, Xinghan Li, Shiwei Feng & Chao Liang

Authors
  1. Weixi Song
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  2. Jiangshan He
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  3. Xinghan Li
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  4. Shiwei Feng
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  5. Chao Liang
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Corresponding author

Correspondence to Chao Liang .

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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Song, W., He, J., Li, X., Feng, S., Liang, C. (2023). QIVISE: A Quantum-Inspired Interactive Video Search Engine in VBS2023. 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_52

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  • DOI: https://doi.org/10.1007/978-3-031-27077-2_52

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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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