Survey: omnimodal embedding research is converging toward unified principles
codefuse-ai · hf · 2026-11-03
A survey and roadmap from Ant Group, Shanghai Jiao Tong University, ANU and others argues embedding research, though sharing mathematical foundations, has long been fragmented by modality with little cross-community exchange. Driven by contrastive learning, Transformers, and granularity shifts, cross-modal embedding research is converging toward shared principles.
The paper organizes the field by three progressively harder capability dimensions—coverage, comparability, and compositionality—mapping to three unification paths: transformation broadens coverage, alignment enables comparability, and joint embedding targets compositionality (the endpoint the authors advocate).
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