VISIONE 5.0: Enhanced User Interface and AI Models for VBS2024
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-53302-0_29
Reference30 articles.
1. Amato, G., et al.: The VISIONE video search system: exploiting off-the-shelf text search engines for large-scale video retrieval. J. Imag. 7(5), 76 (2021)
2. Amato, G., et al.: Visione: a large-scale video retrieval system with advanced search functionalities. In: Proceedings of the 2023 ACM International Conference on Multimedia Retrieval,D pp. 649–653 (2023)
3. Amato, G., et al.: VISIONE at video browser showdown 2023. In: Dang-Nguyen, D.-T., et al. (eds.) MultiMedia Modeling: 29th International Conference, MMM 2023, Bergen, Norway, January 9–12, 2023, Proceedings, Part I, pp. 615–621. Springer International Publishing, Cham (2023). https://doi.org/10.1007/978-3-031-27077-2_48
4. Amato, G., et al.: VISIONE Feature Repository for VBS: Multi-Modal Features and Detected Objects from LapGyn100 Dataset, October 2023. https://doi.org/10.5281/zenodo.10013328
5. Amato, G., et al.: VISIONE Feature Repository for VBS: Multi-Modal Features and Detected Objects from MVK Dataset (2023). https://doi.org/10.5281/zenodo.8355037
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