Less Detail, Better Answers: Degradation-Driven Prompting for VQA
Haoxuan Han, Weijie Wang, Zeyu Zhang, Yefei He, Bohan Zhuang
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Topics
3D Vision
Paper type
Benchmark
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arXiv categories
Why It Matters
DDP shows that deliberately blurring images can make AI answer visual questions more accurately by forcing it to focus on core structures instead of distracting details. This flips conventional wisdom—less data can mean better performance, and it’s easy to plug into existing VQA systems.
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.