Multimodal Transformers

Abstract

Multimodal learning has emerged as a powerful paradigm for processing and integrating heterogeneous data such as text, images, audio, and sensor streams. With the advancement of deep learning, transformer-based architectures have shown exceptional capability in modeling long-range dependencies and learning contextual representations. This research focuses on the design and implementation of a Multimodal Transformer model capable of jointly analyzing multiple modalities to enhance prediction accuracy and contextual understanding. The study evaluates different fusion strategies—early fusion, late fusion, and cross-modal attention—to identify the most effective configuration. Experimental results demonstrate that the multimodal transformer significantly outperforms unimodal baselines, achieving improved accuracy, robustness, and generalization. The findings highlight the potential of multimodal transformers in real-world applications such as healthcare analytics, autonomous systems, and human–computer interaction. Future improvements may include large-scale pretraining, edge-aware transformer modules, and adaptive modality selection.