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FlexVDR framework optimizes visual document retrieval with adaptive budget allocation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed FlexVDR, a new framework designed to optimize visual document retrieval by adaptively allocating computational budgets. This system constructs a family of representation routes, allowing it to select the most efficient configuration for each query based on predicted retrieval utility and user-defined resource preferences. Experiments on public datasets demonstrate that FlexVDR can maintain high retrieval quality while significantly reducing computation by choosing more compact routes for individual queries, offering flexibility without retraining. AI

IMPACT This framework could lead to more efficient AI systems for processing and retrieving information from visual documents.

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

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

FlexVDR framework optimizes visual document retrieval with adaptive budget allocation

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The cluster contains a research paper detailing a new framework for visual document retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xuming Hu ·

    FlexVDR: Learning to Allocate Matryoshka Representation Budgets for Visual Document Retrieval

    Visual document retrieval relies on rich multi-vector representations to capture fine-grained evidence in document pages, but encoding and matching these representations can be computationally expensive. Existing compression and elastic representation methods expose smaller confi…

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