A Crossmodal Approach to Multimodal Fusion in Video Hyperlinking

With the recent resurgence of neural networks and the proliferation of massive amounts of unlabeled multimodal data, recommendation systems and multimodal retrieval systems based on continuous representation spaces and deep learning methods are becoming of great interest. Multimodal representations...

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Veröffentlicht in:	IEEE multimedia Jg. 25; H. 2; S. 11 - 23
Hauptverfasser:	Vukotic, Vedran, Raymond, Christian, Gravier, Guillaume
Format:	Magazine Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Schlagworte:	Artificial neural networks Coders Computer architecture Computer Science Computer Vision and Pattern Recognition crossmodal deep learning Hypertext systems Information Retrieval Machine learning Multimedia multimodal autoencoders multimodal fusion multimodal retrieval Neural and Evolutionary Computing Neural networks Recommender systems Representations Retrieval Streaming media Task analysis Training unsupervised representation learning video hyperlinking video retrieval Visualization deep learning bidirectional learning multimodal fusion tied weights shared weights multimodal autoencoders unsupervised representation learning video retrieval neural networks multimodal retrieval video hyperlinking crossmodal
ISSN:	1070-986X, 1941-0166
Online-Zugang:	Volltext
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