Multimodal Learning With Transformers: A Survey

Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. This paper presents a comprehensive survey of Transformer techniques oriented at multimodal data.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 Thanks to the recent prevalence of multimodal applications and Big Data, Transformer-based multimodal learning has become a hot topic in AI research.
• 2 This paper presents a comprehensive survey of Transformer techniques oriented at multimodal data.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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