Multimodal Machine Learning: A Survey and Taxonomy

0162-8828 (c) 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI , IEEET ransactions on Pattern Analysis and Machine IntelligenceTRANSACTIONS OF PATTERN ANALYSIS AND Machine INTELLIGENCE1 Multimodal Machine Learning: A Survey and TaxonomyTadas Baltru saitis, Chaitanya Ahuja, and Louis-Philippe MorencyAbstract Our experience of the world is Multimodal - we see objects, hear sounds, feel texture, smell odors, and taste to the way in which something happens or is experienced and a research problem is characterized asmultimodalwhenit includes multiple such modalities. In order for Artificial Intelligence to make progress in understanding the world around us, it needsto be able to interpret such Multimodal signals Machine learningaims to build models that can process and relateinformation from multiple modalities.

Multimodal machine learning aims to build models that can process and relate information from multiple modalities. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself

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