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

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
Main Authors:	Kaito Hirasawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
Format:	Article
Language:	English
Published:	MDPI AG 2021-03-01
Series:	Sensors
Subjects:	multimodal variational autoencoder detection of important scenes Twitter sports video time-lags
Online Access:	https://www.mdpi.com/1424-8220/21/6/2045

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