Multimodal Learning with Transformers: A Survey

Peng Xu; Xiatian Zhu; David A. Clifton

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arXiv · 2206.06488

Multimodal Learning with Transformers: A Survey

Peng Xu·Xiatian Zhu·David A. Clifton

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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 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.

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Peng Xu, Xiatian Zhu, David A. Clifton. 2022-06-13. Multimodal Learning with Transformers: A Survey. https://arxiv.org/abs/2206.06488

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