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Multimodal Classification: Current Landscape, Taxonomy and Future Directions

arXiv 2109.09020 ↗First posted 18 Sep 2021cs.LG cs.AIcited by 0

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Abstract (arXiv)

Multimodal classification research has been gaining popularity in many domains that collect more data from multiple sources including satellite imagery, biometrics, and medicine. However, the lack of consistent terminology and architectural descriptions makes it difficult to compare different existing solutions. We address these challenges by proposing a new taxonomy for describing such systems based on trends found in recent publications on multimodal classification. Many of the most difficult aspects of unimodal classification have not yet been fully addressed for multimodal datasets including big data, class imbalance, and instance level difficulty. We also provide a discussion of these challenges and future directions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 194 references that are in the map. First posted on arXiv 18 Sep 2021.

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31 of 194 references.

References · 31

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19 more
  1. 2018Multimodal Machine Learning for Automated ICD Codingcited by 40
  2. 2017A Survey on Multi-View Clusteringcited by 32
  3. 2019EmbraceNet: A robust deep learning architecture for multimodal classificationcited by 30
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  19. 2020Automatic Chronic Degenerative Diseases Identification Using Enteric Nervous System Imagescited by 2

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