Multimodal machine learning: A survey and taxonomy
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 …
odors, and taste flavors. Modality refers to the way in which something happens or is …
Foundations & trends in multimodal machine learning: Principles, challenges, and open questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
A review of affective computing: From unimodal analysis to multimodal fusion
Affective computing is an emerging interdisciplinary research field bringing together
researchers and practitioners from various fields, ranging from artificial intelligence, natural …
researchers and practitioners from various fields, ranging from artificial intelligence, natural …
A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics
Background and objectives Over the past two decades, medical imaging has been
extensively apply to diagnose diseases. Medical experts continue to have difficulties for …
extensively apply to diagnose diseases. Medical experts continue to have difficulties for …
Deep multimodal learning: A survey on recent advances and trends
The success of deep learning has been a catalyst to solving increasingly complex machine-
learning problems, which often involve multiple data modalities. We review recent advances …
learning problems, which often involve multiple data modalities. We review recent advances …
Trusted multi-view classification with dynamic evidential fusion
Existing multi-view classification algorithms focus on promoting accuracy by exploiting
different views, typically integrating them into common representations for follow-up tasks …
different views, typically integrating them into common representations for follow-up tasks …
Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
Deep multimodal fusion by channel exchanging
Deep multimodal fusion by using multiple sources of data for classification or regression has
exhibited a clear advantage over the unimodal counterpart on various applications. Yet …
exhibited a clear advantage over the unimodal counterpart on various applications. Yet …
Bi-bimodal modality fusion for correlation-controlled multimodal sentiment analysis
Multimodal sentiment analysis aims to extract and integrate semantic information collected
from multiple modalities to recognize the expressed emotions and sentiment in multimodal …
from multiple modalities to recognize the expressed emotions and sentiment in multimodal …
[HTML][HTML] Conversational memory network for emotion recognition in dyadic dialogue videos
Emotion recognition in conversations is crucial for the development of empathetic machines.
Present methods mostly ignore the role of inter-speaker dependency relations while …
Present methods mostly ignore the role of inter-speaker dependency relations while …