New pruning method slashes visual document retrieval model size

Researchers have developed Structural Anchor Pruning (SAP), a novel training-free method to compress visual document retrieval models. SAP addresses the significant storage overhead of multi-vector indexes in these models by identifying and pruning redundant visual tokens without requiring query-dependent training. The framework utilizes a Score Retention diagnostic and a visual in-degree centrality scorer to effectively reduce index size while maintaining high retrieval accuracy. AI

IMPACT Introduces a technique to reduce storage costs for visual document retrieval systems, potentially enabling wider deployment.

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

Read on arXiv cs.CL →

• Structural Anchor Pruning
• Visual Document Retrieval
• Zhuchenyang Liu

• paper
• infra

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