Multimodal AI Models

What is Multimodal AI? Multimodal AI processes and generates content across multiple modalities: text, images, audio, video, and beyond. These models can understand images, answer questions about them, generate images from text, and more.

Vision-Language Models (VLMs)

Leading Commercial VLMs

Open Source VLMs

VLM Architecture

[Image] → [Vision Encoder] → [Projection] ↘
                                           → [LLM] → [Response]
[Text Prompt] ─────────────────────────── ↗

Components 1. Vision Encoder: ViT, CLIP, or EVA models (~300M-1B params) 2. Projection Layer: Maps image embeddings to text embedding space 3. LLM Backbone: Processes projected image + text tokens together

Use Cases

Document Understanding

• OCR and text extraction
• Form and table parsing
• Receipt and invoice processing
• Handwriting recognition

Visual Question Answering

• "What is happening in this image?"
• "Count the number of people"
• "What brand is shown?"

Chart and Diagram Analysis

• Data extraction from graphs
• Technical diagram interpretation
• Scientific figure understanding

Image Generation Models

Best Practices

• Use high-resolution images when possible
• Be specific in visual questions
• Combine multiple frames for video understanding
• Verify OCR results for critical applications