DR-VQA: Decompose-then-Reconstruct for Visual Question Answering in BLV Assistance

Visual impairment affects over 200 million individuals globally, creating significant challenges in daily visual tasks. While vision-language models offer transformative assistive potential, existing systems based on Multimodal Large Language Models (MLLMs) face a serious cross-contamination problem when processing real-world images captured by blind and low-vision (BLV) users: when jointly processing imperfect images and specific questions, current models are often misled by question assumptions rather than adhering to visual facts, generating hallucinations about objects not present in the image. We introduce DR-VQA (Decompose-then-Reconstruct Visual Question Answering), a novel framework that balances user intent with visual facts. Our approach prevents cross-contamination through structured reasoning. Our approach deliberately separates image processing from question analysis, ensuring model-generated descriptions are strictly based on image facts without being influenced by questions. Subsequently, through a structured decomposition mechanism, the system generates targeted sub-questions relevant to user intent, gradually aligning visual descriptions with user needs while minimizing question bias. During final synthesis, a memory-reset LLM reconstructs the reasoning chain with detailed information to generate responses that either provide evidence-supported conclusions or transparently acknowledge information limitations. Experimental evaluations demonstrate our framework's effectiveness in reducing hallucination risks while improving answer accuracy. By systematically balancing user intent with factual visual evidence, this work advances BLV-assistive technologies from probabilistic outputs to reliable visual assistance services.