Multimodal Embeddings

Overview

Multimodal embeddings map different data modalities (text, images, audio, video) into a unified vector space where similar concepts are close regardless of modality.

Key Concept

Same semantic meaning → Similar embeddings, even across modalities:

• Text: "A red car"
• Image: Photo of red car
• Audio: "Red car" spoken → All map to similar vectors

Capabilities

Cross-Modal Search

• Text query → Find images
• Image query → Find text descriptions
• Audio query → Find relevant videos

Understanding

• Image captioning
• Visual question answering
• Video understanding
• Audio-visual learning

Models

Text + Image

• CLIP (OpenAI): Text-image matching
• ALIGN (Google): Large-scale alignment
• UForm: Efficient multimodal

Full Multimodal

• Gemini Embedding 2: Text, image, video, audio, documents
• ImageBind (Meta): 6 modalities
• LLaVA: Language + vision

Use Cases

• Visual Search: Find products from images
• Content Moderation: Cross-modal policy enforcement
• Creative Tools: Generate images from text
• Accessibility: Image descriptions for blind users
• Media Archives: Search across text, images, video

Advantages

• Unified Search: One interface, all modalities
• Richer Understanding: Combine modalities
• Flexibility: Handle diverse inputs
• Powerful Applications: Enable new use cases

Challenges

• Model Complexity: Larger, more complex
• Training Data: Requires aligned multimodal data
• Computational Cost: More expensive inference

Pricing

Model APIs charge per input, vary by modality. Self-hosting has compute costs.