2026
MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction
ICLR 2026oral
Universal multimodal embedding models have achieved great success in capturing semantic relevance between queries and candidates. However, current methods either condense queries and candidates into a single vector, potentially limiting the expressiveness for fine-grained information, or produce too…