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ARXIV:2604.04838 · VISION-LANGUAGE MODELS · SUBMITTED 07 APR · 20:11 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04838VISION-LANGUAGE MODELSSUBMITTED 07 APR · 20:11 UTCFRESHNESS UNKNOWNHaoxuan Han · Weijie Wang · Zeyu Zhang · Yefei He · Bohan Zhuang · arXiv
A framework that strategically degrades image quality to improve VQA accuracy by forcing models to focus on essential information.
Opportunity summary
Pain A framework that strategically degrades image quality to improve VQA accuracy by forcing models to focus on essential information.
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A framework that strategically degrades image quality to improve VQA accuracy by forcing models to focus on essential information. In this paper,we propose Degradation-Driven Prompting (DDP), a novel framework that improves VQA performance by…
Recent advancements in Vision-Language Models (VLMs) have significantly pushed the boundaries of Visual Question Answering (VQA).However,high-resolution details can sometimes become noise that leads to hallucinations or reasoning errors. In this paper,we propose Degradation-Driven Prompting…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this paper,we propose Degradation-Driven Prompting (DDP), a novel framework that improves VQA performance by strategically reducing image fidelity to force models to focus…
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A framework that strategically degrades image quality to improve VQA accuracy by forcing models to focus on essential information.
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10.48550/arXiv.2604.04838A framework that strategically degrades image quality to improve VQA accuracy by forcing models to focus on essential information.
Abstract
Recent advancements in Vision-Language Models (VLMs) have significantly pushed the boundaries of Visual Question Answering (VQA).However,high-resolution details can sometimes become noise that leads to hallucinations or reasoning errors. In this paper,we propose Degradation-Driven Prompting (DDP), a novel framework that improves VQA performance by strategically reducing image fidelity to force models to focus on essential structural information. We evaluate DDP across two distinct tasks. Physical attributes targets images prone to human misjudgment, where DDP employs a combination of 80p downsampling, structural visual aids (white background masks and orthometric lines), and In-Context Learning (ICL) to calibrate the model's focus. Perceptual phenomena addresses various machine-susceptible visual anomalies and illusions, including Visual Anomaly (VA), Color (CI), Motion(MI),Gestalt (GI), Geometric (GSI), and Visual Illusions (VI).For this task, DDP integrates a task-classification stage with specialized tools such as blur masks and contrast enhancement alongside downsampling. Our experimental results demonstrate that less is more: by intentionally degrading visual inputs and providing targeted structural prompts, DDP enables VLMs to bypass distracting textures and achieve superior reasoning accuracy on challenging visual benchmarks.
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PROBLEM
A framework that strategically degrades image quality to improve VQA accuracy by forcing models to focus on essential information. In this paper,we propose Degradation-Driven Prompting (DDP), a novel framework that improves VQA performance by strategically reducing image fidelit...
METHOD
Recent advancements in Vision-Language Models (VLMs) have significantly pushed the boundaries of Visual Question Answering (VQA).However,high-resolution details can sometimes become noise that leads to hallucinations or reasoning errors. In this paper,we propose Degradation-Driv...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this paper,we propose Degradation-Driven Prompting (DDP), a novel framework that improves VQA performance by strategically reducing image fidelity to force models to focus on essential structural infor...
WHY NOW
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework that strategically degrades image quality to improve VQA accuracy by forcing models to focus on essential information. In this paper,we propose Degradation-Driven Prompting (DDP), a novel framework that improves VQA performance by strategically reducing image fidelity to force models to focus on essential structural information.
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partial
Recent advancements in Vision-Language Models (VLMs) have significantly pushed the boundaries of Visual Question Answering (VQA).However,high-resolution details can sometimes become noise that leads to hallucinations or reasoning errors. In this paper,we propose Degradation-Driven Prompting (DDP), a novel framework that improves VQA performance by strategically reducing image fidelity to force models to focus on essential structural information.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this paper,we propose Degradation-Driven Prompting (DDP), a novel framework that improves VQA performance by strategically reducing image fidelity to force models to focus on essential structural information. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework that strategically degrades image quality to improve VQA accuracy by forcing models to focus on essential information.
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