Multimodal Classification: Current Landscape, Taxonomy and Future Directions

Autor:	Sleeman IV, William C., Kapoor, Rishabh, Ghosh, Preetam
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence I.5.2
Druh dokumentu:	Working Paper
Popis:	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. Comment: 24 pages, 3 tables, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.09020 Zobrazit plný text záznamu View this record from Arxiv