Fine-tuning Multimodal Embedding Models
General-purpose multimodal embedding models like CLIP position multiple data types in a unified vector space — semantically related concepts cluster together, dissimilar items remain distant.
The core problem
CLIP and similar models may perform poorly in domain-specific use cases. Fine-tuning adapts the embedding space to align with domain-specific semantic relationships.
Multimodal embeddings
Models like CLIP can embed images and text into the same vector space, enabling cross-modal retrieval (e.g., search images with text queries or vice versa).
Approach
Fine-tune on domain data (demonstrated with YouTube video content) to align the embedding space with domain-specific content and retrieval patterns.
Part 4 in a series on multimodal AI, following coverage of multimodal RAG with CLIP.