From Fragmentation to Fusion: A Survey and Roadmap for Omnimodal Embeddings

Embedding models underpin modern AI systems across various modalities. Research on embedding models is converging toward common principles driven by contrastive learning and Transformer architectures. A framework of coverage, comparability, and compositionality is proposed to reason across this convergence. Joint embedding is advocated as the terminus for unification.