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New methods boost visual document retrieval efficiency

By PulseAugur Editorial · [4 sources] ·

Two new research papers propose methods to improve the efficiency of late-interaction visual document retrieval systems. The first paper introduces Generative Late-Interaction Embeddings (GLIE), which uses a small set of learned vectors to regenerate full embedding sets on demand, significantly reducing storage requirements while maintaining high accuracy. The second paper focuses on query-aware token budgeting, suggesting that dynamically allocating retrieval resources based on the query can recover a higher percentage of the full-token score compared to static pooling methods. Both approaches aim to make large-scale deployment of visual document retrieval more cost-effective. AI

IMPACT These methods could significantly reduce the computational and storage costs associated with large-scale visual document retrieval systems.

RANK_REASON Two arXiv papers proposing novel methods for visual document retrieval.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New methods boost visual document retrieval efficiency

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Two arXiv papers proposing novel methods for visual document retrieval.
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COVERAGE [4]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Naeemullah Khan ·

    Generative Late-Interaction Embeddings For Visual Document Retrieval

    Late-interaction retrieval is the state-of-the-art for visual document search, but it pays for its accuracy in storage. Existing compression methods retain a subset or local average of the N~1,000 vectors per page. Under aggressive storage budgets, however, these methods degrade …

  2. arXiv cs.AI TIER_1 English(EN) · PS Rishi, Rajeev Ranjan Dwivedi, Vinod K Kurmi ·

    Query-Aware Token Budgeting for Efficient Late-Interaction Visual Document Retrieval

    arXiv:2609.07262v1 Announce Type: cross Abstract: Late-interaction visual document retrievers preserve fine-grained page evidence by storing many token embeddings per page, but the resulting storage and query-time interaction costs make large-scale deployment expensive. Pooling d…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Generative Late-Interaction Embeddings For Visual Document Retrieval

    Generative Late-Interaction Embeddings compress visual document retrieval vectors by learning a small basis set that regenerates full embeddings on demand, improving accuracy under tight storage limits without retraining the encoder.

  4. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Vinod K Kurmi ·

    Query-Aware Token Budgeting for Efficient Late-Interaction Visual Document Retrieval

    Late-interaction visual document retrievers preserve fine-grained page evidence by storing many token embeddings per page, but the resulting storage and query-time interaction costs make large-scale deployment expensive. Pooling document tokens before indexing offers a natural re…

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