Sight and hearing are two senses that play a vital role in human communication and scene
understanding. To mimic human perception ability, audio-visual learning, aimed at …

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 …

The inherent challenge of multimodal fusion is to precisely capture the cross-modal
correlation and flexibly conduct cross-modal interaction. To fully release the value of each …

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 …

We abstract the features (ie learned representations) of multi-modal data into 1) uni-modal
features, which can be learned from uni-modal training, and 2) paired features, which can …

Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to
compensate for their inherent limitations. However, existing MML methods often optimize a …

It has been a hot research topic to enable machines to understand human emotions in
multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion …

Recent advances in learning multi-modal representation have witnessed the success in
biomedical domains. While established techniques enable handling multi-modal …

One primary topic of multimodal learning is to jointly incorporate heterogeneous information
from different modalities. However most models often suffer from unsatisfactory multimodal …

Learning effective representations from unlabeled data is a challenging task for point cloud
understanding. As the human visual system can map concepts learned from 2D images to …