Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion

• URL: http://arxiv.org/abs/2006.08159v1
• Date: Mon, 15 Jun 2020 06:42:04 GMT
• Title: Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion
• Authors: Yang Wang
• Abstract summary: Multi-modal or multi-view data has surged as a major stream for big data, where each modal/view encodes individual property of data objects. Most of the existing state-of-the-art focused on how to fuse the energy or information from multi-modal spaces to deliver a superior performance. Deep neural networks have exhibited as a powerful architecture to well capture the nonlinear distribution of high-dimensional multimedia data.
• Score: 6.225190099424806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of web technology, multi-modal or multi-view data has surged as a major stream for big data, where each modal/view encodes individual property of data objects. Often, different modalities are complementary to each other. Such fact motivated a lot of research attention on fusing the multi-modal feature spaces to comprehensively characterize the data objects. Most of the existing state-of-the-art focused on how to fuse the energy or information from multi-modal spaces to deliver a superior performance over their counterparts with single modal. Recently, deep neural networks have exhibited as a powerful architecture to well capture the nonlinear distribution of high-dimensional multimedia data, so naturally does for multi-modal data. Substantial empirical studies are carried out to demonstrate its advantages that are benefited from deep multi-modal methods, which can essentially deepen the fusion from multi-modal deep feature spaces. In this paper, we provide a substantial overview of the existing state-of-the-arts on the filed of multi-modal data analytics from shallow to deep spaces. Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition and fusion over multi-modal spaces. Finally, we share our viewpoints regarding some future directions on this field.

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