Magnifying What Matters: Attention-Guided Adaptive Rendering for Visual Text Comprehension
Quick Answer
The study introduces AGAR (Attention-Guided Adaptive Rendering), a model-agnostic method that enhances Visual Text Comprehension (VTC) by leveraging VLMs' attention mechanisms.
Quick Take
The study introduces AGAR (Attention-Guided Adaptive Rendering), a model-agnostic method that enhances Visual Text Comprehension (VTC) by leveraging VLMs' attention mechanisms. AGAR improves performance across nine VTC benchmarks, showing significant gains in answer accuracy without requiring additional training. This approach effectively addresses the limitations of existing VTC pipelines by dynamically adjusting rendered text based on localized attention.
Key Points
- AGAR identifies top-K important visual patches using VLM's middle-to-late layer attention.
- Extensive experiments show AGAR improves off-the-shelf VLMs as a plug-and-play enhancement.
- The method yields further gains when combined with VLM post-training.
- AGAR remains robust under visual and text input degradation.
- The study reveals a localization-without-utilization regime in existing VTC pipelines.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 12898v1 Announce Type: new Abstract: Visual Text Comprehension (VTC) renders text into images for a vision-language model (VLM) to read, sidestepping LLM context-window limits and powering applications from long-page OCR to multi-page memory QA. Yet existing VTC pipelines treat rendering and layout as a fixed, content-agnostic preprocessing step and offer little mechanistic understanding of how VLMs internally process visualized text.
Through a focused empirical study on VTC QA tasks, we reveal that VLMs exhibit a localization-without-utilization regime: evidence-localizing attention emerges sharply in the middle-to-late layers and is largely decoupled from answer correctness, yet simply enlarging the localized spans on the rendered page recovers a large fraction of the failures.
Building on these observations, we propose AGAR (Attention-Guided Adaptive Rendering), a training-free, model-agnostic method that leverages a VLM's own middle-to-late layer attention to identify the top-K important visual patches, maps them back to word spans, and re-renders the page with those spans enlarged before re-inferring the answer.
Extensive experiments across nine VTC benchmarks (short-form, long-context, and multi-page memory QA) and four VLM backbones show that AGAR (i)consistently improves off-the-shelf VLMs as a plug-and-play enhancement, (ii)composes with VLM post-training to yield further gains, and (iii)remains robust under both visual- and text-side input degradation.
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