% Werner Bailer
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@InProceedings{Schoeffmann2021,
  author = {Klaus Schoeffmann and Jakub Lokoc and Werner Bailer},
  booktitle = {Proceedings of the 2nd ACM International Conference on Multimedia in Asia},
  title = {{10 years of video browser showdown}},
  year = {2021},
  month = {mar},
  pages = {1--3},
  publisher = {ACM},
  abstract = {The Video Browser Showdown (VBS) has influenced the Multimedia community already for 10 years now. More than 30 unique teams from over 21 countries participated in the VBS since 2012 already. In 2021, we are celebrating the 10th anniversary of VBS, where 17 international teams compete against each other in an unprecedented contest of fast and accurate multimedia retrieval. In this tutorial we discuss the motivation and details of the VBS contest, including its history, rules, evaluation metrics, and achievements for multimedia retrieval. We talk about the properties of specific VBS retrieval systems and their unique characteristics, as well as existing open-source tools that can be used as a starting point for participating for the first time. Participants of this tutorial get a detailed understanding of the VBS and its search systems, and see the latest developments of interactive video retrieval.},
  doi = {10.1145/3444685.3450215},
  url = {https://dl.acm.org/doi/10.1145/3444685.3450215}
}
@Article{Rossetto2021,
  author = {Luca Rossetto and Ralph Gasser and Jakub Lokoc and Werner Bailer and Klaus Schoeffmann and Bernd Muenzer and Tomas Soucek and Phuong Anh Nguyen and Paolo Bolettieri and Andreas Leibetseder and Stefanos Vrochidis},
  journal = {IEEE Transactions on Multimedia},
  title = {{Interactive Video Retrieval in the Age of Deep Learning - Detailed Evaluation of VBS 2019}},
  year = {2021},
  issn = {1941-0077},
  month = mar,
  pages = {243--256},
  volume = {23},
  abstract = {Despite the fact that automatic content analysis has made remarkable progress over the last decade - mainly due to significant advances in machine learning - interactive video retrieval is still a very challenging problem, with an increasing relevance in practical applications. The Video Browser Showdown (VBS) is an annual evaluation competition that pushes the limits of interactive video retrieval with state-of-the-art tools, tasks, data, and evaluation metrics. In this paper, we analyse the results and outcome of the 8th iteration of the VBS in detail. We first give an overview of the novel and considerably larger V3C1 dataset and the tasks that were performed during VBS 2019. We then go on to describe the search systems of the six international teams in terms of features and performance. And finally, we perform an in-depth analysis of the per-team success ratio and relate this to the search strategies that were applied, the most popular features, and problems that were experienced. A large part of this analysis was conducted based on logs that were collected during the competition itself. This analysis gives further insights into the typical search behavior and differences between expert and novice users. Our evaluation shows that textual search and content browsing are the most important aspects in terms of logged user interactions. Furthermore, we observe a trend towards deep learning based features, especially in the form of labels generated by artificial neural networks. But nevertheless, for some tasks, very specific content-based search features are still being used. We expect these findings to contribute to future improvements of interactive video search systems.},
  doi = {10.1109/tmm.2020.2980944},
  keywords = {Interactive Video Retrieval, Video Browsing, Video Content Analysis, Content-based Retrieval, Evaluations},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  url = {https://ieeexplore.ieee.org/document/9037125}
}
@Article{Lokoc2021,
  author = {Jakub Lokoc and Patrik Vesely and Frantisek Mejzlik and Gregor Kovalcik and Tomas Soucek and Luca Rossetto and Klaus Schoeffmann and Werner Bailer and Cathal Gurrin and Loris Sauter and Jaeyub Song and Stefanos Vrochidis and Jiaxin Wu and Björn Thor Jonsson},
  journal = {ACM Transactions on Multimedia Computing, Communications, and Applications},
  title = {{Is the Reign of Interactive Search Eternal? Findings from the Video Browser Showdown 2020}},
  year = {2021},
  issn = {1551-6865},
  month = {jul},
  number = {3},
  pages = {1--26},
  volume = {17},
  abstract = {Comprehensive and fair performance evaluation of information retrieval systems represents an essential task for the current information age. Whereas Cranfield-based evaluations with benchmark datasets support development of retrieval models, significant evaluation efforts are required also for user-oriented systems that try to boost performance with an interactive search approach. This article presents findings from the 9th Video Browser Showdown, a competition that focuses on a legitimate comparison of interactive search systems designed for challenging known-item search tasks over a large video collection. During previous installments of the competition, the interactive nature of participating systems was a key feature to satisfy known-item search needs, and this article continues to support this hypothesis. Despite the fact that top-performing systems integrate the most recent deep learning models into their retrieval process, interactive searching remains a necessary component of successful strategies for known-item search tasks. Alongside the description of competition settings, evaluated tasks, participating teams, and overall results, this article presents a detailed analysis of query logs collected by the top three performing systems, SOMHunter, VIRET, and vitrivr. The analysis provides a quantitative insight to the observed performance of the systems and constitutes a new baseline methodology for future events. The results reveal that the top two systems mostly relied on temporal queries before a correct frame was identified. An interaction log analysis complements the result log findings and points to the importance of result set and video browsing approaches. Finally, various outlooks are discussed in order to improve the Video Browser Showdown challenge in the future.},
  doi = {10.1145/3445031},
  keywords = {Interactive video retrieval, deep learning, interactive search evaluation},
  publisher = {Association for Computing Machinery (ACM)},
  url = {https://dl.acm.org/doi/10.1145/3445031}
}
@Article{Lokoc2018,
  title = {On influential trends in interactive video retrieval: Video Browser Showdown 2015-2017},
  author = {Jakub Lokoč and Werner Bailer and Klaus Schöffmann and Bernd Münzer and George M. Awad},
  journal = {IEEE Transactions on Multimedia},
  year = {2018},
  month = {April},
  abstract = {The last decade has seen innovations that make video recording, manipulation, storage and sharing easier than ever before, thus impacting many areas of life. New video retrieval scenarios emerged as well, which challenge the state-of-the-art video retrieval approaches. Despite recent advances in content analysis, video retrieval can still benefit from involving the human user in the loop. We present our experience with a class of interactive video retrieval scenarios and our methodology to stimulate the evolution of new interactive video retrieval approaches. More specifically, the Video Browser Showdown evaluation campaign is thoroughly analyzed, focusing on the years 2015-2017. Evaluation scenarios, objectives and metrics are presented, complemented by the results of the annual evaluations. The results reveal promising interactive video retrieval techniques adopted by the most successful tools and confirm assumptions about the different complexity of various types of interactive retrieval scenarios. A comparison of the interactive retrieval tools with automatic approaches (including fully automatic and manual query formulation) participating in the TRECVID 2016 Ad-hoc Video Search (AVS) task is discussed. Finally, based on the results of data analysis, a substantial revision of the evaluation methodology for the following years of the Video Browser Showdown is provided.},
  doi = {10.1109/TMM.2018.2830110},
  url = {https://ieeexplore.ieee.org/document/8352047/?tp=&arnumber=8352047&filter%3DAND(p_IS_Number:4456689)}
}