Are Large Vision-Language Models Ready to Guide Blind and Low-Vision Individuals?
Summary: arXiv:2510.00766v2 Announce Type: replace-cross
Large Vision-Language Models (LVLMs) have emerged as a promising technology for supporting individuals with blindness or low-vision (BLV). However, assessing their effectiveness in practical environments poses unique challenges. Unlike standard scene descriptions, the utility of LVLMs for BLV individuals requires a different evaluative approach to ensure that their outputs are genuinely informative and helpful.
Challenges in Evaluating LVLMs for BLV Needs
Current evaluation paradigms, such as the “VLM-as-a-metric” and “LVLM-as-a-judge,” have been developed. Nevertheless, these frameworks often fail to meet the specific requirements essential for BLV-centric evaluations. The inadequacies are primarily observed in the following areas:
- High correlation with human judgments: Existing evaluators often do not align closely with how BLV users interpret information.
- Long instruction understanding: Models frequently struggle to comprehend and follow detailed instructions necessary for effective assistance.
- Score generation efficiency: Current systems may take too long to provide feedback, reducing their practical applicability.
- Multi-dimensional assessment: Evaluators often lack the ability to assess multiple important aspects of the information provided.
Proposed Solutions and Framework
To address these challenges, researchers propose a unified framework that connects automated evaluation with the actual needs of BLV individuals. The first step in this process involved conducting an in-depth user study with BLV participants to gain insights into their navigational preferences. This study led to the creation of VL-GUIDEDATA, a comprehensive dataset consisting of image-request-response-score pairs tailored to BLV users.
Development of VL-GUIDE-S
Leveraging the VL-GUIDEDATA dataset, the researchers developed an innovative accessibility-aware evaluator known as VL-GUIDE-S. This new evaluator has shown remarkable performance, surpassing existing LVLM judges in both alignment with human feedback and inference efficiency. Key features of VL-GUIDE-S include:
- Enhanced accuracy in understanding and meeting the needs of BLV users.
- Improved efficiency in generating responses and evaluations.
- Strong performance across various dimensions critical to BLV users’ experiences.
Conclusion
The research underscores the importance of tailoring AI technologies to meet the specific needs of underserved populations, such as those with blindness or low vision. By establishing a robust framework and developing advanced evaluators like VL-GUIDE-S, the hope is to pave the way for more effective, automated solutions that facilitate safe and barrier-free navigation for BLV individuals. This foundational work is expected to inspire further advancements in the realm of AI and accessibility.