Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019

Rossetto, Luca; Gasser, Ralph; Lokoc, Jakub; Bailer, Werner; Schoeffmann, Klaus; Muenzer, Bernd; Soucek, Tomas; Nguyen, Phuong Anh; Bolettieri, Paolo; Leibetseder, Andreas; Vrochidis, Stefanos

doi:10.1109/tmm.2020.2980944

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Rossetto, L., Gasser, R., Lokoc, J., Bailer, W., Schoeffmann, K., Muenzer, B., Soucek, T., Nguyen, P. A., Bolettieri, P., Leibetseder, A., & Vrochidis, S. (2021). Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019. IEEE Transactions on Multimedia, 23, 243–256. https://doi.org/10.1109/tmm.2020.2980944

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 o