ŠTEFÁNIK, Michal and Vít NOVOTNÝ. Video699: Interconnecting Lecture Recordings with Study Materials. 2019.
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Basic information
Original name Video699: Interconnecting Lecture Recordings with Study Materials
Authors ŠTEFÁNIK, Michal (703 Slovakia, guarantor, belonging to the institution) and Vít NOVOTNÝ (203 Czech Republic, belonging to the institution).
Edition 2019.
Other information
Original language English
Type of outcome Presentations at conferences
Field of Study 10200 1.2 Computer and information sciences
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
WWW Scientific poster Presentation slides
RIV identification code RIV/00216224:14330/19:00109519
Organization unit Faculty of Informatics
Keywords (in Czech) umělá inteligence; strojové učení; zpracování digitálního obrazu; získávání znalostí; Siamské neuronové sítě; konvoluční neuronové sítě; hluboké neuronové sítě; segmentace obrazu; segmentace instancí; massive open online courseware
Keywords in English artificial intelligence; machine learning; digital image processing; information retrieval; Siamese neural networks; convolutional neural networks; deep neural networks; image segmentation; instance segmentation; massive open online courseware
Tags machine learning
Tags International impact
Changed by Changed by: RNDr. Vít Novotný, Ph.D., učo 409729. Changed: 1/11/2021 09:38.
Abstract

Our work is a scientific poster that was presented at the ML Prague 2019 conference during February 22–24, 2019.

Recording lectures is a common practice in the academia nowadays and lays foundation to massive open online courseware. Although lecture slides are often recorded along with the lecturer, machine-readable information about the lecture slides is rarely preserved. This prevents full-text search in the recordings and makes the lectures inaccessible to the blind and partially sighted members of the audience.

In our work, we present several neural architectures that work in lockstep to segment lecture recordings and to map the individual segments to shown lecture slides. We also present a new dataset, which has been produced at the Masaryk University in Brno, Czechia, and which is used to train and evaluate our system. We evaluate the performance of the individual neural architectures.

Links
MUNI/A/1145/2018, interní kód MUName: Aplikovaný výzkum na FI: softwarové architektury kritických infrastruktur, bezpečnost počítačových systémů, techniky pro zpracování a vizualizaci velkých dat a rozšířená realita.
Investor: Masaryk University, Critical Infrastructure Software Architectures, Computer Systems Security, Data Processing and Visualization Techniques, and Augmented Reality, Category A
MUNI/33/11/2017, interní kód MUName: Automatické provazování videozáznamu přednášek se studijními materiály (Acronym: fimu-video-699)
Investor: Masaryk University
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