AI RESEARCH
Less Detail, Better Answers: Degradation-Driven Prompting for VQA
arXiv CS.CV
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ArXi:2604.04838v1 Announce Type: new 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.