Fusion Embedding Unifies Text, Image, Video, and Audio Modalities

Researchers have introduced Fusion Embedding, a novel approach to create a unified embedding space capable of representing text, images, video, and audio. This system leverages a frozen vision-language backbone, with new connector modules trained to integrate audio data without altering the base model. The resulting fusion-embedding-1 and fusion-embedding-2 models demonstrate strong performance in cross-modal retrieval, including emergent audio-image retrieval capabilities without explicit audio-visual training data. The team has released the code, weights, and evaluation tools, highlighting the efficiency of training on a single GPU in a matter of hours. AI

IMPACT Enables unified cross-modal search and retrieval, potentially simplifying multimodal AI applications.

RANK_REASON Research paper detailing a new model architecture and its capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Abdul Basit Tonmoy
• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Fusion Embedding
• fusion-embedding-1
• fusion-embedding-2
• Gotit.pub
• Hugging Face
• ScienceCast

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →