Multimodal AI

Multimodal AI models can understand and generate content across multiple modalities—text, images, audio, and video.

What is Multimodal AI?

Unlike traditional models that focus on a single type of data, multimodal models can:

• Understand images and answer questions about them (Visual Question Answering)
• Generate images from text descriptions (Text-to-Image)
• Transcribe and understand audio (Speech-to-Text)
• Create videos from prompts (Text-to-Video)

Popular Multimodal Models

Model	Modalities	Key Feature
GPT-4V	Text + Image	Vision understanding
LLaVA	Text + Image	Open-source alternative
Gemini	Text + Image + Audio	Native multimodal
CLIP	Text + Image	Zero-shot classification
Whisper	Audio → Text	Robust transcription

Architecture Approaches

Late Fusion

• Process each modality separately, combine at the end
• Simpler but less integrated understanding

Early Fusion

• Combine modalities at input level
• Better cross-modal understanding but more complex

Cross-Attention

• Each modality attends to others during processing
• Used in models like Flamingo and LLaVA

Related Concepts

• Transformer Architecture
• Context Length
• Embeddings