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
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Zusammenfassung: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 are typically obtained with autoencoders that reconstruct multimodal data. In this article, we describe an alternative method to perform high-level multimodal fusion that leverages crossmodal translation by means of symmetrical encoders cast into a bidirectional deep neural network (BiDNN). Using the lessons learned from multimodal retrieval, we present a BiDNN-based system that performs video hyperlinking and recommends interesting video segments to a viewer. Results established using TRECVIDs 2016 video hyperlinking benchmarking initiative show that our method obtained the best score, thus defining the state of the art.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1070-986X
1941-0166
DOI:10.1109/MMUL.2018.023121161