Survey outlines unification of omnimodal embeddings
Original title: From Fragmentation to Fusion: A Survey and Roadmap for Omnimodal Embeddings
Position paper traces convergence of embedding research across modalities toward joint embeddings for compositionality.
Embedding models share mathematical foundations yet research remains fragmented by modality. Convergence occurs through contrastive learning, Transformers and granularity shifts. A framework of coverage, comparability and compositionality guides unification via transformation, alignment and joint embedding. This provides the first unified cross-cutting treatment of omnimodal embeddings.
- Fragmentation obscures deeper convergence in embedding research
- Three capabilities: coverage, comparability, compositionality
- Transformation broadens coverage
- Alignment enables comparability
- Joint embedding targets compositionality
From the original article
1 min read at huggingface.co
Embedding models underpin virtually every modern AI system, from search engines to recommendation systems. Despite shared mathematical foundations, research on embedding models remains fragmented by modality: the communities develop largely in isolation, seldom exchanging insights. We argue that this fragmentation obscures a deeper convergence.
By Ziyin Zhang, Yuyu Guo, Ying Zhang · huggingface.co
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