Research Area:  Machine Learning

Abstract:

Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications and Big Data, Transformer-based multimodal learning has become a hot topic in AI research. This paper presents a comprehensive survey of Transformer techniques oriented at multimodal data. The main contents of this survey include: (1) a background of multimodal learning, Transformer ecosystem, and the multimodal Big Data era, (2) a systematic review of Vanilla Transformer, Vision Transformer, and multimodal Transformers, from a geometrically topological perspective, (3) a review of multimodal Transformer applications, via two important paradigms, i.e., for multimodal pretraining and for specific multimodal tasks, (4) a summary of the common challenges and designs shared by the multimodal Transformer models and applications, and (5) a discussion of open problems and potential research directions for the community.

Keywords:  
Neural Network
Multimodal Applications
Multimodal Learning
Transformer Ecosystem
Multimodal Pretraining
Potential Research Directions

Author(s) Name:  Peng Xu, Xiatian Zhu, David A. Clifton

Publisher name:  IEEE

DOI:  https://doi.org/10.1109/TPAMI.2023.3275156

Volume Information:  Volume: 45

Paper Link:   https://ieeexplore.ieee.org/abstract/document/10123038