Multimodal Models

Multimodal models are a type of Artificial Intelligence Models that can process and understand data from multiple modalities, such as text, images, and audio, to make predictions or generate outputs.


  • Multimodal models are valuable for tasks that involve multiple types of data, such as video understanding, Sentiment Analysis, and content generation.
  • Fusion: The features from different modalities are combined or fused to create a comprehensive representation of the input data. The multimodal model uses the fused representation to make predictions or generate outputs.
  • Effective fusion of features from different modalities is a key challenge in developing multimodal models.
  • These models have applications in areas such as healthcare, autonomous vehicles, and Natural Language Processing (NLP).
  • Multimodal NLP models integrate Features that are extracted from different modalities into an Natural Language Processing model.