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. This 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-arts 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 been 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 article, we provide a substantial overview of the existing state-of-the-arts in the field 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 in this field.

中文翻译：

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深度多模态数据分析调查：协作、竞争和融合

随着网络技术的发展，多模态或多视图数据已成为大数据的主流，其中每个模态/视图编码数据对象的单个属性。通常，不同的模式是相辅相成的。这一事实激发了对融合多模态特征空间以全面表征数据对象的大量研究关注。大多数现有的最先进技术都集中在如何融合来自多模态空间的能量或信息，以提供比单模态空间更优越的性能。最近，深度神经网络被展示为一种强大的架构，可以很好地捕捉高维多媒体数据的非线性分布，对于多模态数据自然也是如此。进行了大量的实证研究以证明其受益于深度多模态方法的优势，这可以从本质上加深多模态深度特征空间的融合。在本文中，我们对从浅到深的多模态数据分析领域现有的最新技术进行了实质性概述。在整个调查中，我们进一步指出该领域的关键组成部分是协作、对抗性竞争和多模态空间的融合。最后，我们就该领域的一些未来方向分享我们的观点。我们对从浅到深的多模态数据分析领域现有的最新技术进行了实质性概述。在整个调查中，我们进一步指出该领域的关键组成部分是协作、对抗性竞争和多模态空间的融合。最后，我们就该领域的一些未来方向分享我们的观点。我们对从浅到深的多模态数据分析领域现有的最新技术进行了实质性概述。在整个调查中，我们进一步指出该领域的关键组成部分是协作、对抗性竞争和多模态空间的融合。最后，我们就该领域的一些未来方向分享我们的观点。