Title: Multimodal Representation Learning

Abstract:

With rapid development of social networks and search engines, a surge of interests has been witnessed in jointly analyzing of multimodal data such as text, image, audio and video. To cope with this situation, information acquisition and processing have to be transformed from the form of single media to multimedia. Therefore, challenges stemming from the “media gap”, which means that representations of different media types are inconsistent, are appealing increasing attention. Recently, deep neural networks(DNN), a major breakthrough in machine learning, have been employed to learn better multimodal representations. This talk introduces a taxonomy of multimodal machine learning from representation, translation, fusion, alignment, and co-learning. Our recent studies in multimodal representation are presented with new multimodal algorithms and exciting multimodal applications.

More Information is at

https://infoscale.eai-conferences.org/2021/keynotes/