MoEMB scales multimodal embeddings with efficient Mixture-of-Experts models

Researchers have introduced MoEMB, a novel approach to scaling universal multimodal embeddings using an efficient mixture-of-experts (MoE) architecture. This method allows for increased encoder capacity while maintaining a single-vector, non-autoregressive encoding, which is crucial for embedding models. MoEMB achieves state-of-the-art performance on benchmarks like MMEB-V2 and MRMR, outperforming models with significantly more active parameters and less compute. The study also explores adaptive computation strategies to further enhance the efficiency of MoE-based embedding models for large-scale applications such as retrieval and recommendation systems. AI

IMPACT This research could lead to more efficient and scalable multimodal embedding models, improving performance in large-scale retrieval and recommendation systems.

RANK_REASON The cluster describes a new research paper detailing a novel method for scaling multimodal embeddings using mixture-of-experts models.

Read on arXiv cs.AI →

• arXiv
• Hugging Face
• mixture of experts
• MMEB-V2
• Think-Then-Embed
• universal multimodal embedding
• alphaXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• IArxiv
• Multimodal Embedding
• ScienceCast
• WeMM-Embedding

• model release
• paper
• infra

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