Omni-Interactive Universal Embedder
Multimodal representation learning has been shifting from traditional two-tower architectures to large language model (LLM)-based embedders due to their strong instruction-following capabilities. Despite this progress, existing approaches primarily focus on language and image modalities, which also remain the dominant modalities for user-conditioned interactions in current embedders. In this paper, we propose the first Omni-Interactive Universal Embedder (OmniUE), which not only learns a unified embedding space across text, video, and audio by leveraging intermediate-layer representations from dedicated learnable tokens, but also supports omni-interactive querying, enabling users to provi...
| Authors | Wei-Yao Wang, Kazuya Tateishi, Shuyang Cui et al. |
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| Primary category | cs.AI |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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