Multimodal Classification: Current Landscape, Taxonomy and Future Directions
This work addresses a foundational problem for researchers in multimodal classification by organizing the field, but it is incremental as it synthesizes existing trends without introducing new methods.
The paper tackles the lack of consistent terminology and architectural descriptions in multimodal classification by proposing a new taxonomy based on recent publications, and it discusses unresolved challenges like big data and class imbalance.
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.