GELATO architecture enables efficient multimodal embeddings

Researchers have introduced GELATO (Geometry-preserving Embeddings via Locked Aligned Towers), a new multimodal embedding model architecture. GELATO extends existing text embedding models by incorporating frozen encoders for images and audio, with only the connecting components trained. This approach results in efficient training and maintains the original text embedding quality while achieving competitive performance against larger multimodal models. AI

IMPACT Enables more efficient training of multimodal models, potentially lowering the barrier to entry for complex AI applications.

RANK_REASON The cluster contains an arXiv paper detailing a new model architecture and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• GELATO
• Gotit.pub
• Han Xiao
• Hugging Face
• jina-embeddings-v5-omni
• Jina Embeddings v5 Text models
• ScienceCast

• paper
• model release

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