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 …

Deep vision multimodal learning aims at combining deep visual representation learning with
other modalities, such as text, sound, and data collected from other sensors. With the fast …

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 …

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

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 …

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 …

Deep multimodal learning has achieved great progress in recent years. However, current
fusion approaches are static in nature, ie, they process and fuse multimodal inputs with …

Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …

With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …

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