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

Multimodal learning aims to build models that can process and relate information from
multiple modalities. Despite years of development in this field, it still remains challenging to …

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 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 …

Despite the remarkable success of deep multi-modal learning in practice, it has not been
well-explained in theory. Recently, it has been observed that the best uni-modal network …

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 …

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

Multimodal learning which integrates data from diverse sensory modes plays a pivotal role
in artificial intelligence. However existing multimodal learning methods often struggle with …

Classification using multimodal data arises in many machine learning applications. It is
crucial not only to model cross-modal relationship effectively but also to ensure robustness …

Deep learning has been successfully applied to multimodal representation learning
problems, with a common strategy to learning joint representations that are shared across …