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 …
Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions
Sentiment analysis (SA) has gained much traction In the field of artificial intelligence (AI) and
natural language processing (NLP). There is growing demand to automate analysis of user …
natural language processing (NLP). There is growing demand to automate analysis of user …
Multimodal sentiment analysis based on fusion methods: A survey
Sentiment analysis is an emerging technology that aims to explore people's attitudes toward
an entity. It can be applied in a variety of different fields and scenarios, such as product …
an entity. It can be applied in a variety of different fields and scenarios, such as product …
Harnessing multimodal data integration to advance precision oncology
Advances in quantitative biomarker development have accelerated new forms of data-driven
insights for patients with cancer. However, most approaches are limited to a single mode of …
insights for patients with cancer. However, most approaches are limited to a single mode of …
Decoupled multimodal distilling for emotion recognition
Human multimodal emotion recognition (MER) aims to perceive human emotions via
language, visual and acoustic modalities. Despite the impressive performance of previous …
language, visual and acoustic modalities. Despite the impressive performance of previous …
Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis
Abstract Representation Learning is a significant and challenging task in multimodal
learning. Effective modality representations should contain two parts of characteristics: the …
learning. Effective modality representations should contain two parts of characteristics: the …
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 …
Misa: Modality-invariant and-specific representations for multimodal sentiment analysis
Multimodal Sentiment Analysis is an active area of research that leverages multimodal
signals for affective understanding of user-generated videos. The predominant approach …
signals for affective understanding of user-generated videos. The predominant approach …
State of the art: a review of sentiment analysis based on sequential transfer learning
Recently, sequential transfer learning emerged as a modern technique for applying the
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …
[HTML][HTML] Multimodal transformer for unaligned multimodal language sequences
Human language is often multimodal, which comprehends a mixture of natural language,
facial gestures, and acoustic behaviors. However, two major challenges in modeling such …
facial gestures, and acoustic behaviors. However, two major challenges in modeling such …