Researchers outline framework for unifying embedding models across modalities
A new paper on arXiv proposes a framework to unify embedding models across different data types like text, images, and audio. The authors argue that current research remains fragmented by modality, despite shared mathematical foundations. They introduce a three-step approach—coverage, comparability, and compositionality—to bridge gaps between specialized embedding models.
Key points
- Proposes a three-step framework—coverage, comparability, and compositionality—for unifying embedding models across modalities
- Argues current research is fragmented by modality despite shared mathematical foundations
- Advocates joint embedding as the ultimate goal for compositionality in omnimodal systems
The paper suggests three paths toward unification: transformation (to broaden coverage), alignment (to enable comparability), and joint embedding (to achieve compositionality). It also highlights modality-specific challenges that remain underexplored. The authors claim this framework provides the first cross-cutting treatment of omnimodal embedding research, aiming to guide future work in the field.
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