Transformer Models for Multimodal Sequence Learning

Transformer models, initially developed for natural language processing, have shown remarkable success in modeling sequences across diverse domains like computer vision and audio. Their ability to capture long-range dependencies makes them ideal for multimodal sequence learning, where data from multiple modalities such as text, images, and audio are processed jointly. This paper reviews the architectures, strategies, and applications of transformer models in multimodal sequence learning, high- lighting recent innovations from 2022 to 2025 and addressing key challenges.