Abstract
The results of the last Video Browser Showdown in Bangkok 2018 show that multimodal search with interactive query reformulation represents a competitive search strategy for all the evaluated task categories. Therefore, we plan to target the effectiveness of involved retrieval models by making use of the most recent deep network architectures in the new version of our interactive video retrieval VIRET tool. Specifically, we apply the NasNet deep convolutional neural network architecture for automatic annotation and similarity search in the set of selected frames from the provided video collection. In addition, we implement temporal sequence queries and subimage similarity search to provide higher query formulation flexibility for users.
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CITATION STYLE
Lokoč, J., Kovalčík, G., Souček, T., Moravec, J., Bodnár, J., & Čech, P. (2019). Viret tool meets nasnet. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11296 LNCS, pp. 597–601). Springer Verlag. https://doi.org/10.1007/978-3-030-05716-9_52
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