AI RESEARCH
UniNote: A Unified Embedding Model for Multimodal Representation and Ranking
arXiv CS.CV
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ArXi:2605.29287v1 Announce Type: cross Item-to-Item (I2I) retrieval is a fundamental part of modern content platforms, ing critical industrial workflows from recommendation engines to content auditing. While multimodal embedding methods have advanced general retrieval, they often falter in I2I scenarios due to the challenges of balancing global content representation with fine-grained local retrieval, the systemic inefficiency of decoupled embedding-and-ranking pipelines, and the inherent trade-offs between model precision and serving latency.