UMER framework unifies embedding and ranking for multimodal retrieval

Researchers have introduced UMER, a novel framework designed to unify embedding and ranking for universal multimodal retrieval tasks. This approach employs Pair-Aware Discriminative Reasoning, which contrasts query-candidate pairs to identify relevant matching and discrepancy evidence, unlike previous methods that reasoned in isolation. UMER jointly learns contrastive embeddings for efficient global matching and discriminative ranking for pairwise relevance judgment within a single multimodal large language model (MLLM). The framework has demonstrated state-of-the-art performance on the MMEB-V2 benchmark. AI

IMPACT Enhances multimodal retrieval by improving semantic reasoning and efficiency for complex tasks.

RANK_REASON The cluster contains a research paper detailing a new method for multimodal retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Chain-of-Thought
• MMEB-V2
• multimodal large language model
• Pair-Aware Discriminative Reasoning

• paper
• model release

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