Multimodal AI integrates different data modalities, such as text, image, audio, or video, into a common model to solve complex tasks that require multiple sources of information.
These models learn to establish connections between the modalities by creating shared representations or links between the different data types.
Typical approaches use neural networks with specialized encoders for each modality and combine them through shared latent spaces or cross-attention mechanisms.
Examples include multimodal transformer models that process image and text information together to generate image captions or implement visual question-answering systems.
Multimodal AI enhances performance in tasks such as multimodal search, multimodal content generation, and multimodal understanding.