Abstract
In this paper, we introduce a multi-user hierarchical video search tool called Videofall. Our objective, in the Video Browser Showdown (VBS) 2022, is to explore if Videofall interactive video retrieval under time constraints is a useful approach to take, given the overhead of requiring multiple users. It is our conjecture that combining different skills of normal users can support a master user to retrieve target videos efficiently. The system is designed on top of the CLIP pre-trained model and the video keyframes are embedded into a vector space in which queries would also be encoded to facilitate retrieval.
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Acknowledgments
This research was conducted with the financial support of Science Foundation Ireland under Grant Agreement No. 18/CRT/6223, and 13/RC/2106_P2 at the ADAPT SFI Research Centre at DCU. ADAPT, the SFI Research Centre for AI-Driven Digital Content Technology, is funded by Science Foundation Ireland through the SFI Research Centres Programme.
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Nguyen, TN., Puangthamawathanakun, B., Healy, G., Nguyen, B.T., Gurrin, C., Caputo, A. (2022). Videofall - A Hierarchical Search Engine for VBS2022. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_48
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DOI: https://doi.org/10.1007/978-3-030-98355-0_48
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