Multimodal Embedding
Embeddings that map different data types (text, images, audio) into the same vector space, enabling cross-modal search and comparison.
Why It Matters
Multimodal embeddings enable searching images with text, finding similar audio and video, and building truly multimodal AI applications.
Example
Embedding both product images and text descriptions in the same space so searching 'red leather handbag' finds matching products whether described in text or shown in photos.
Think of it like...
Like a universal Rosetta Stone that translates any type of content into a shared language — text, images, and audio can all be compared and searched together.
Related Terms
Embedding
A numerical representation of data (text, images, etc.) as a vector of numbers in a high-dimensional space. Similar items are placed closer together in this space, enabling machines to understand semantic relationships.
CLIP
Contrastive Language-Image Pre-training — an OpenAI model trained to understand the relationship between images and text. CLIP can match images to text descriptions without being trained on specific image categories.
Multimodal AI
AI systems that can process and generate multiple types of data — text, images, audio, video — within a single model. Multimodal models understand the relationships between different data types.
Semantic Search
Search that understands the meaning and intent behind a query rather than just matching keywords. It uses embeddings to find results that are conceptually related even if they use different words.
Vision-Language Model
An AI model that can process both visual and textual inputs, understanding images and generating text about them. VLMs combine computer vision with language understanding.