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MoEMB: Scaling Universal Multimodal Embeddings with Efficient Mixture-of-Experts Models
Universal multimodal embedding (UME) increasingly demands encoder's capacity for handling a broad range of tasks and modalities with increased complexity. Prior scaling methods either increase the representation size, retrieval effort, or scales the encoder into a heavy multimodal LLM. Recent works,
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