Deep Learning-based Concept Detection in vitrivr

Rossetto, Luca and Amiri Parian, Mahnaz and Gasser, Ralph and Giangreco, Ivan and Heller, Silvan and Schuldt, Heiko. (2019) Deep Learning-based Concept Detection in vitrivr. In: Proceedings of the 25th International Conference on MultiMedia Modeling (MMM'19). Cham, pp. 616-621.

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Official URL: https://edoc.unibas.ch/68619/

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This paper presents the most recent additions to the vitrivr retrieval stack, which will be put to the test in the context of the 2019 Video Browser Showdown (VBS). The vitrivr stack has been extended by approaches for detecting, localizing, or describing concepts and actions in video scenes using various convolutional neural networks. Leveraging those additions, we have added support for searching the video collection based on semantic sketches. Furthermore, vitrivr offers new types of labels for text-based retrieval. In the same vein, we have also improved upon vitrivr's pre-existing capabilities for extracting text from video through scene text recognition. Moreover, the user interface has received a major overhaul so as to make it more accessible to novice users, especially for query formulation and result exploration.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Databases and Information Systems (Schuldt)
UniBasel Contributors:Schuldt, Heiko and Rossetto, Luca and Amiri Parian, Mahnaz and Gasser, Ralph and Giangreco, Ivan and Heller, Silvan
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Series Name: Lecture Notes in Computer Science book series
Issue Number:11296
Note:Publication type according to Uni Basel Research Database: Conference paper
Identification Number:
Last Modified:04 Mar 2019 10:59
Deposited On:04 Mar 2019 10:59

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