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New QART module adapts visual token budgets to text queries

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a Query-Adaptive Recurrent Tokenizer (QART) that makes visual representations query-steerable and budget-adaptive. This module allows vision and vision-language models to allocate token budgets based on the information required for a specific text query, rather than using a fixed budget. QART recurrently constructs a compact representation, using reconstruction quality over query-relevant visual support to control token allocation. Experiments on Visual7W and VQAv2 datasets showed QART achieving high accuracy with significantly reduced token counts, outperforming existing methods like SteerViT on certain benchmarks. AI

IMPACT This approach could lead to more efficient and context-aware visual processing in AI models, reducing computational load and improving performance on vision-language tasks.

RANK_REASON The cluster describes a new method presented in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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

New QART module adapts visual token budgets to text queries

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The cluster describes a new method presented in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Bavesh Balaji, Pranav Gopinath, Pouya Sadeghi, Sirisha Rambhatla ·

    Query-adaptive Recurrent Tokenization

    arXiv:2610.22957v1 Announce Type: new Abstract: Visual information worth extracting from an image and the token budget it warrants depend on the text query. Yet modern vision and vision-language models largely encode images independently of the query and allocate a fixed visual t…

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