A Crossmodal Approach to Multimodal Fusion in Video Hyperlinking

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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.