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 …

Combining complementary information from multiple modalities is intuitively appealing for
improving the performance of learning-based approaches. However, it is challenging to fully …

Deep Learning has implemented a wide range of applications and has become increasingly
popular in recent years. The goal of multimodal deep learning is to create models that can …

In recent years, deep learning algorithms have rapidly revolutionized artificial intelligence,
particularly machine learning, enabling researchers and practitioners to extend previously …

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 …

Learning multimodal representations involves integrating information from multiple
heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world …

Deep learning methods haverevolutionized speech recognition, image recognition, and
natural language processing since 2010. Each of these tasks involves a single modality in …

The world provides us with data of multiple modalities. Intuitively, models fusing data from
different modalities outperform their uni-modal counterparts, since more information is …

The research progress in multimodal learning has grown rapidly over the last decade in
several areas, especially in computer vision. The growing potential of multimodal data …

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 …