35
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Multimodal Machine Learning: A Survey and Taxonomy.

      Read this article at

      ScienceOpenPublisherPubMed
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together. 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 and presents them in a common taxonomy. We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning. This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research.

          Related collections

          Author and article information

          Journal
          IEEE Trans Pattern Anal Mach Intell
          IEEE transactions on pattern analysis and machine intelligence
          Institute of Electrical and Electronics Engineers (IEEE)
          1939-3539
          0098-5589
          February 2019
          : 41
          : 2
          Article
          10.1109/TPAMI.2018.2798607
          29994351
          ff24966f-a6ef-4e12-bbda-8b243382f70e
          History

          Comments

          Comment on this article