How does multimodal retrieval (image + text) work?
hardAnswer
- Use a shared-embedding model like CLIP (Radford 2021), SigLIP (2023), Jina CLIP, or Cohere Embed v3-Image.
- These map images and text into a common vector space via contrastive training.
- At retrieval time, embed a text query, do ANN search over image embeddings (or vice versa).
- Applications: image search from text, semantic album search, cross-modal RAG (retrieve images relevant to a question).
- Combine with a multimodal LLM (GPT-4o, Claude, Gemini) for downstream generation over retrieved images.
Check yourself — multiple choice
- Separate spaces
- CLIP / SigLIP / Jina CLIP: shared embedding space via contrastive → cross-modal ANN + multimodal LLM for downstream generation
- Only images
- Impossible
Multimodal retrieval: CLIP-family shared embeddings → text ↔ image ANN + multimodal LLM.
#embeddings#retrieval#multimodal
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