IEEE Transactions on Pattern Analysis and Machine Intelligence· 2018Q1· Review
Multimodal Machine Learning: A Survey and Taxonomy
- 4,343citations
- Q1SCImago
- 2018year
Short summary
This survey introduces a novel taxonomy for multimodal machine learning, moving beyond simple fusion categories to identify core challenges like representation, translation, alignment, fusion, and co-learning.
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Key points
- Multimodal machine learning integrates data from various senses (vision, audio, touch, etc.) for AI understanding.
- A new taxonomy identifies five core challenges: representation, translation, alignment, fusion, and co-learning.
- This framework moves beyond basic 'early' and 'late' fusion categorization.
- The taxonomy aims to guide future research in the field.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science