自动修复网页无障碍缺陷，保持原设计不变
WebAccessVL: Violation-Aware VLM for Web Accessibility
- 用视觉和代码联合训练模型，精准定位并修正网页问题
- 每网站仅0.211个违规，较原始数据降低96%
- 适合需要自动化无障碍优化的开发与测试团队
我们提出一种视觉语言模型（VLM），可自动编辑网页HTML以修复符合WCAG2标准的无障碍缺陷，同时保留原始设计。将该任务建模为有监督的图像条件程序合成问题，模型在给定代码及其视觉渲染的前提下学习修正HTML。我们构建了WebAccessVL数据集，包含人工修正的无障碍缺陷。进一步提出一种基于违规检测描述的条件化VLM，支持测试阶段的迭代式检查-优化循环。在开放API与开放权重模型上进行广泛评估，结果表明，本方法实现每网站0.211个违规，相比原始数据5.34个减少96%，优于GPT-5达87%。感知研究验证，修复后网页在视觉外观和内容完整性上更接近原版。
原文摘要 · Abstract (English)
We present a vision-language model (VLM) that automatically edits website HTML to address violations of the Web Content Accessibility Guidelines 2 (WCAG2) while preserving the original design. We formulate this as a supervised image-conditioned program synthesis task, where the model learns to correct HTML given both the code and its visual rendering. We create WebAccessVL, a website dataset with manually corrected accessibility violations. We then propose a violation-conditioned VLM that further takes the detected violations' descriptions from a checker as input. This conditioning enables an iterative checker-in-the-loop refinement strategy at test time. We conduct extensive evaluation on both open API and open-weight models. Empirically, our method achieves 0.211 violations per website, a 96.0\% reduction from the 5.34 violations in raw data and 87\% better than GPT-5. A perceptual study also confirms that our edited websites better maintain the original visual appearance and content.
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