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 …
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 learning with transformers: A survey
Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …
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 …
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 …
Improving multimodal fusion with hierarchical mutual information maximization for multimodal sentiment analysis
In multimodal sentiment analysis (MSA), the performance of a model highly depends on the
quality of synthesized embeddings. These embeddings are generated from the upstream …
quality of synthesized embeddings. These embeddings are generated from the upstream …
Cdtrans: Cross-domain transformer for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled
source domain to a different unlabeled target domain. Most existing UDA methods focus on …
source domain to a different unlabeled target domain. Most existing UDA methods focus on …
Disentangled representation learning for multimodal emotion recognition
Multimodal emotion recognition aims to identify human emotions from text, audio, and visual
modalities. Previous methods either explore correlations between different modalities or …
modalities. Previous methods either explore correlations between different modalities or …
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 …
Are multimodal transformers robust to missing modality?
Multimodal data collected from the real world are often imperfect due to missing modalities.
Therefore multimodal models that are robust against modal-incomplete data are highly …
Therefore multimodal models that are robust against modal-incomplete data are highly …