Multimodal Learning
Training AI models to understand and generate content across multiple data types (text, images, audio, video).
Overview
Multimodal learning involves training models that can process, understand, and generate content across multiple modalities — text, images, audio, video, and structured data. Rather than separate models for each modality, multimodal models learn shared representations that capture cross-modal relationships.
Key Details
Modern multimodal models like GPT-4V, Gemini, and Claude 3 can reason across images and text simultaneously. Architectures include early fusion (combining modalities at the input), late fusion (combining at the output), and cross-attention mechanisms. Multimodal learning enables applications like visual question answering, image-guided text generation, video understanding, and embodied AI systems that perceive and interact with the physical world.