Abstract
During the last three years, the most successful systems at the Video Browser Showdown employed effective retrieval models where raw video data are automatically preprocessed in advance to extract semantic or low-level features of selected frames or shots. This enables users to express their search intents in the form of keywords, sketch, query example, or their combination. In this paper, we present new extensions to our interactive video retrieval system VIRET that won Video Browser Showdown in 2018 and achieved the second place at Video Browser Showdown 2019 and Lifelog Search Challenge 2019. The new features of the system focus both on updates of retrieval models and interface modifications to help users with query specification by means of informative visualizations.
Cite
CITATION STYLE
Lokoč, J., Kovalčík, G., & Souček, T. (2020). VIRET at Video Browser Showdown 2020. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11962 LNCS, pp. 784–789). Springer. https://doi.org/10.1007/978-3-030-37734-2_70
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