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MINER module enhances multimodal document retrieval by fusing internal transformer signals

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced MINER, a novel plug-in module designed to enhance the efficiency of visual document retrieval. MINER probes and fuses internal representations from transformer layers into a single compact embedding, addressing the trade-off between quality and efficiency in existing retrieval methods. This approach aims to improve retrieval accuracy without increasing storage or latency, outperforming current dense single-vector retrievers on several benchmarks. AI

IMPACT MINER could lead to more efficient and accurate visual document search systems, reducing costs for platforms that handle large volumes of visual data.

RANK_REASON This is a research paper detailing a new method for improving retrieval efficiency in visual documents.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

MINER module enhances multimodal document retrieval by fusing internal transformer signals

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Weien Li, Rui Song, Zeyu Li, Haochen Liu, Gonghao Zhang, Difan Jiao, Zhenwei Tang, Bowei He, Haolun Wu, Xue Liu, Ye Yuan ·

    MINER: Mining Multimodal Internal Representation for Efficient Retrieval

    arXiv:2605.06460v1 Announce Type: new Abstract: Visual document retrieval has become essential for accessing information in visually rich documents. Existing approaches fall into two camps. Late-interaction retrievers achieve strong quality through fine-grained token-level matchi…

  2. arXiv cs.LG TIER_1 English(EN) · Ye Yuan ·

    MINER: Mining Multimodal Internal Representation for Efficient Retrieval

    Visual document retrieval has become essential for accessing information in visually rich documents. Existing approaches fall into two camps. Late-interaction retrievers achieve strong quality through fine-grained token-level matching but store hundreds of vectors per page, incur…

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