论文标题
深度多模式数据分析的调查：协作，竞争和融合
Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion
论文作者
论文摘要
随着Web技术的开发，多模式或多视图数据已成为大数据的主要流，其中每个模态/视图编码数据对象的单个属性。通常，不同的方式是彼此互补的。这样的事实激发了许多研究关注，以融合多模式特征空间，以全面地表征数据对象。大多数现有的最新最先进的重点是如何融合多模式空间中的能量或信息，以提供与单式模式相比的卓越性能。最近，深层神经网络已作为一种强大的体系结构展示，可以很好地捕获高维多媒体数据的非线性分布，因此自然地适用于多模式数据。进行了大量的经验研究，以证明其优势从深层多模式方法中受益，这些方法可以从本质上加深多模式的深度特征空间的融合。在本文中，我们对从浅层到深空间的多模式数据分析提交的现有最新技术进行了实质概述。在整个调查中，我们进一步指出，该领域的关键组成部分用于多模式空间的协作，对抗性竞争和融合。最后，我们对该领域的一些未来方向分享了我们的观点。
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.
