Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder

A new method for the detection of important scenes in baseball videos via a time-lag-aware multimodal variational autoencoder (Tl-MVAE) is presented in this paper. Tl-MVAE estimates latent features calculated from tweet, video, and audio features extracted from tweets and videos. Then, important sce...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 21; no. 6; p. 2045
Main Authors:	Hirasawa, Kaito, Maeda, Keisuke, Ogawa, Takahiro, Haseyama, Miki
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 14.03.2021 MDPI
Subjects:	Baseball detection of important scenes Influence Methods multimodal variational autoencoder Neural networks Sensors Social networks sports video time-lags Twitter Twitter time-lags multimodal variational autoencoder detection of important scenes sports video
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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