Open Challenges of Interactive Video Search and Evaluation

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Útgefandi

Association for Computing Machinery, Inc

Útdráttur

During the last 10 years of Video Browser Showdown (VBS), there were many different approaches tested for known-item search and ad-hoc search tasks. Undoubtedly, teams incorporating state-of-the-art models from the machine learning domain had an advantage over teams focusing just on interactive interfaces. On the other hand, VBS results indicate that effective means of interaction with a search system is still necessary to accomplish challenging search tasks. In this tutorial, we summarize successful deep models tested at the Video Browser Showdown as well as interfaces designed on top of corresponding distance/similarity spaces. Our broad experience with competition organization and evaluation will be presented as well, focusing on promising findings and also challenging problems from the most recent iterations of the Video Browser Showdown.

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Publisher Copyright: © 2022 Owner/Author.

Efnisorð

interactive retrieval, retrieval evaluation, video retrieval, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Human-Computer Interaction, Software

Citation

Loko, J, Schoeffmann, K, Bailer, W, Rossetto, L & Jónsson, B Þ 2022, Open Challenges of Interactive Video Search and Evaluation. in MM 2022 - Proceedings of the 30th ACM International Conference on Multimedia. MM 2022 - Proceedings of the 30th ACM International Conference on Multimedia, Association for Computing Machinery, Inc, pp. 7383-7385, 30th ACM International Conference on Multimedia, MM 2022, Lisboa, Portugal, 10/10/22. https://doi.org/10.1145/3503161.3546973
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