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Recently, the multimodal and crossmodal AI techniques have attracted theattention of communities. The former aims to collect disjointed andheterogeneous data to compensate for complementary information to enhancerobust prediction. The latter targets to utilize one modality to predictanother modality by discovering the common attention sharing between them.Although both approaches share the same target: generate smart data fromcollected raw data, the former demands more modalities while the latter aims todecrease the variety of modalities. This paper first discusses the role ofmultimodal and crossmodal AI in smart data analysis in general. Then, weintroduce the multimodal and crossmodal AI framework (MMCRAI) to balance theabovementioned approaches and make it easy to scale into different domains.This framework is integrated into xDataPF (the cross-data platformhttps://www.xdata.nict.jp/). We also introduce and discuss various applicationsbuilt on this framework and xDataPF.

Multimodal and Crossmodal AI for Smart Data Analysis