Skip to main content

VERGE in VBS 2021

  • Conference paper
  • First Online:
MultiMedia Modeling (MMM 2021)

Abstract

This paper presents VERGE, an interactive video search engine that supports efficient browsing and searching into a collection of images or videos. The framework involves a variety of retrieval approaches as well as reranking and fusion capabilities. A Web application enables users to create queries and view the results in a fast and friendly manner.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dong, J., Li, X., Xu, C., Ji, S., He, Y., et al.: Dual encoding for zero-example video retrieval. In: Proceedings of IEEE Conference on CVPR 2019, pp. 9346–9355 (2019)

    Google Scholar 

  2. Faghri, F., Fleet, D.J., et al.: VSE++: improving visual-semantic embeddings with hard negatives. In: Proceedings of the British Machine Vision Conference (BMVC) (2018)

    Google Scholar 

  3. Galanopoulos, D., Mezaris, V.: Attention mechanisms, signal encodings and fusion strategies for improved ad-hoc video search with dual encoding networks. In: Proceedings of the ACM International Conference on Multimedia Retrieval, (ICMR 2020). ACM (2020)

    Google Scholar 

  4. Gkountakos, K., Dimou, A., Papadopoulos, G.T., Daras, P.: Incorporating textual similarity in video captioning schemes. In: 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1–6. IEEE (2019)

    Google Scholar 

  5. Ye, G., Li, Y., Xu, H., et al.: EventNet: a large scale structured concept library for complex event detection in video. In: Proceedings of the ACM MM (2015)

    Google Scholar 

  6. Hara, K., et al.: Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and imagenet? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  7. Jegou, H., et al.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 117–128 (2010)

    Article  Google Scholar 

  8. Kay, W., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017)

  9. Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)

  10. Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., et al.: OpenImages: a public dataset for large-scale multi-label and multi-class image classification (2017). https://storage.googleapis.com/openimages/web/index.html

  11. Li, Y., Song, Y., Cao, L., Tetreault, J., et al.: TGIF: a new dataset and benchmark on animated GIF description. In: Proceedings of IEEE CVPR 2016 (2016)

    Google Scholar 

  12. Markatopoulou, F., Moumtzidou, A., Galanopoulos, D., et al.: ITI-CERTH participation in TRECVID 2017. In: Proceedings of the TRECVID 2017 Workshop, USA (2017)

    Google Scholar 

  13. Pittaras, N., Markatopoulou, F., Mezaris, V., Patras, I.: Comparison of fine-tuning and extension strategies for deep convolutional neural networks. In: Amsaleg, L., Guðmundsson, G.Þ., Gurrin, C., Jónsson, B.Þ., Satoh, S. (eds.) MMM 2017. LNCS, vol. 10132, pp. 102–114. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51811-4_9

    Chapter  Google Scholar 

  14. Schoeffmann, K.: Video browser showdown 2012–2019: a review. In: 2019 International Conference on Content-Based Multimedia Indexing (CBMI), pp. 1–4. IEEE (2019)

    Google Scholar 

  15. Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 (2019)

  16. Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  17. Tan, W.R., Chan, C.S., Aguirre, H.E., Tanaka, K.: Ceci n’est pas une pipe: a deep convolutional network for fine-art paintings classification. In: 2016 IEEE ICIP, pp. 3703–3707. IEEE (2016)

    Google Scholar 

  18. Venugopalan, S., Rohrbach, M., Donahue, J., et al.: Sequence to sequence-video to text. In: Proceedings of the IEEE ICCV, pp. 4534–4542 (2015)

    Google Scholar 

  19. Xu, J., Mei, T., Yao, T., Rui, Y.: MSR-VTT: a large video description dataset for bridging video and language. In: The IEEE Conference on CVPR, June 2016

    Google Scholar 

  20. Zhou, B., Lapedriza, A., et al.: Places: a 10 million image database for scene recognition. IEEE Trans. PAMI 40(6), 1452–1464 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This work has been supported by the EU’s Horizon 2020 research and innovation programme under grant agreements H2020-825079 Mind-Spaces, H2020-779962 V4Design, H2020-780656 ReTV, and H2020-832921 MIRROR.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stelios Andreadis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Andreadis, S. et al. (2021). VERGE in VBS 2021. In: Lokoč, J., et al. MultiMedia Modeling. MMM 2021. Lecture Notes in Computer Science(), vol 12573. Springer, Cham. https://doi.org/10.1007/978-3-030-67835-7_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-67835-7_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67834-0

  • Online ISBN: 978-3-030-67835-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics