  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 theoretical 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.
