# Multimodal Learning with Transformers: A Survey

**Authors:** Peng Xu, Xiatian Zhu, David A. Clifton  
**arXiv:** [2206.06488](https://arxiv.org/abs/2206.06488)  
**Date:** 2022-06-13  
**Summary page:** https://www.summarizepaper.com/en/paper/2206.06488v1/

## Key points

- The paper is a comprehensive survey of Transformer techniques for multimodal data
- Transformers have achieved great success in various machine learning tasks
- Multimodal learning with Transformers has become a hot topic in AI research
- The survey provides background information on multimodal learning, the Transformer ecosystem, and multimodal big data
- Three types of Transformers are reviewed: Vanilla Transformer, Vision Transformer, and Multimodal Transformers
- Applications of multimodal Transformers include multimodal pretraining and specific tasks like image captioning or video understanding
- Common challenges and design considerations for multimodal Transformer models are discussed, including data representation fusion, cross-modal alignment scalability, and interpretability
- Open problems and potential research directions for improving the performance and applicability of multimodal Transformers are identified

## Summary

The paper titled "Multimodal Learning with Transformers: A Survey" by Peng Xu, Xiatian Zhu, and David A. Clifton presents a comprehensive survey of Transformer techniques oriented at multimodal data. Transformers have emerged as a promising neural network learner and have achieved great success in various machine learning tasks. With the recent prevalence of multimodal applications and the availability of big data, Transformer-based multimodal learning has become a hot topic in AI research. The survey begins by providing background information on multimodal learning, the Transformer ecosystem, and the era of multimodal big data. It then delves into a theoretical review of three types of Transformers: Vanilla Transformer, Vision Transformer, and Multimodal Transformers. This review is conducted from a geometrically topological perspective, offering insights into their underlying principles. Next, the paper explores various applications of multimodal Transformers through two important paradigms: multimodal pretraining and specific multimodal tasks. The authors discuss how these models can be used for pretraining on large-scale datasets containing multiple modalities as well as their effectiveness in addressing specific tasks such as image captioning or video understanding. Furthermore, the survey highlights common challenges and design considerations shared by multimodal Transformer models and applications. These include issues related to data representation fusion, cross-modal alignment scalability to large datasets and interpretability. Finally, the paper concludes with a discussion on open problems and potential research directions for the community. It identifies areas where further investigation is needed to improve the performance and applicability of multimodal Transformers in real-world scenarios. Overall, this survey provides an extensive overview of Transformer techniques applied to multimodal data. It not only covers theoretical aspects but also explores practical applications and discusses challenges faced by researchers in this field; thus contributing to advancing our understanding of how Transformers can effectively handle complex multimodal information and paving the way for future developments in this area of AI research.