大视觉-语言模型是否准备好指导盲人和低视力者？
Are Large Vision-Language Models Ready to Guide Blind and Low-Vision Individuals?
AI总结：
本文提出统一框架，通过用户研究和定制数据集，开发出更高效的评估器，提升大视觉-语言模型在盲人和低视力用户导航中的应用性能。
摘要：
大视觉-语言模型（LVLMs）展示了协助盲人或低视力（BLV）个体的有前途的方向。然而，测量其在真实场景中的真正效用具有挑战性，因为评估其描述是否BLV信息性需要与评估标准场景描述根本不同的方法。尽管
英文摘要：
Large Vision-Language Models (LVLMs) demonstrate a promising direction for assisting individuals with blindness or low-vision (BLV). Yet, measuring their true utility in real-world scenarios is challenging because evaluating whether their descriptions are BLV-informative requires a fundamentally different approach from assessing standard scene descriptions. While the "VLM-as-a-metric" or "LVLM-as-a-judge" paradigm has emerged, existing evaluators still fall short of capturing the unique requirements of BLV-centric evaluation, lacking at least one of the following key properties: (1) High correlation with human judgments, (2) Long instruction understanding, (3) Score generation efficiency, and (4) Multi-dimensional assessment. To this end, we propose a unified framework to bridge the gap between automated evaluation and actual BLV needs. First, we conduct an in-depth user study with BLV participants to understand and quantify their navigational preferences, curating VL-GUIDEDATA, a large-scale BLV user-simulated preference dataset containing image-request-response-score pairs. We then leverage the dataset to develop an accessibility-aware evaluator, VL-GUIDE-S, which outperforms existing (L)VLM judges in both human alignment and inference efficiency. Notably, its effectiveness extends beyond a single domain, demonstrating strong performance across multiple fine-grained, BLV-critical dimensions. We hope our work lays as a foundation for automatic AI judges that advance safe, barrier-free navigation for BLV users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
