论文标题
用于智能数据分析的多模式和跨模式AI
Multimodal and Crossmodal AI for Smart Data Analysis
论文作者
论文摘要
最近，多模式和跨模式AI技术吸引了社区的注意。前者的目标是收集脱节和异质数据，以补偿补充信息以增强健壮的预测。后者的目标是利用一种模式来通过发现它们之间的共同关注共享来预测另一种方式。尽管两种方法都共享相同的目标：从收集的原始数据中生成智能数据，但前者需要更多的方式，而后者则旨在减少各种方式。本文首先讨论了多模式和跨模式AI在智能数据分析中的作用。然后，我们介绍了多模式和跨模式AI框架（MMCRAI），以平衡上述方法，并易于扩展到不同的域。该框架集成到XDATAPF（交叉数据平台https://www.xdata.nict.jp/）中。我们还介绍并讨论了基于此框架和XDATAPF的各种应用程序。
Recently, the multimodal and crossmodal AI techniques have attracted the attention of communities. The former aims to collect disjointed and heterogeneous data to compensate for complementary information to enhance robust prediction. The latter targets to utilize one modality to predict another modality by discovering the common attention sharing between them. Although both approaches share the same target: generate smart data from collected raw data, the former demands more modalities while the latter aims to decrease the variety of modalities. This paper first discusses the role of multimodal and crossmodal AI in smart data analysis in general. Then, we introduce the multimodal and crossmodal AI framework (MMCRAI) to balance the abovementioned approaches and make it easy to scale into different domains. This framework is integrated into xDataPF (the cross-data platform https://www.xdata.nict.jp/). We also introduce and discuss various applications built on this framework and xDataPF.
