Researchers from AC at the KI Institute will present two papers tackling real-world challenges using AI: web accessibility and industrial anomaly detection. The first, titled "Making the Web More Inclusive: Enter AccessGuru", will be presented at the ASSETS conference, a premier venue on Computers and Accessibility. The second, titled "MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning", will be presented at ICCV , a leading conference in computer vision. Below is a summary of both papers:
Making the Web More Inclusive: Enter AccessGuru
Despite the availability of accessibility guidelines like WCAG, most websites still present barriers for users with disabilities. This paper introduces AccessGuru, a system that leverages Large Language Models (LLMs) to automatically detect and correct accessibility violations in HTML code.
AccessGuru is guided by a novel taxonomy of syntactic, semantic, and layout violations, and combines rule-based tools with LLM reasoning over code and visuals.
It reduces violation scores by up to 84%, outperforming existing tools, and achieves 73% similarity to human-generated semantic corrections. A benchmark dataset of 3,500 real-world violations is also released to support future research.
This work demonstrates how LLMs can meaningfully automate accessibility efforts and foster a more inclusive Web.
Fathallah, N., Hernández, D., & Staab, S. (2025). AccessGuru: Leveraging LLMs to detect and correct web accessibility violations in HTML code. ASSETS 2025. http://arxiv.org/abs/2507.19549 .
MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning
In manufacturing, quality control remains a critical yet complex task, especially when multiple defect types are involved. MultiADS introduces a system capable of detecting and segmenting a wide range of anomalies (e.g., scratches, bends, holes), even in zero-shot settings.
By combining visual analysis with descriptive textual input, and using a curated Knowledge Base for Anomalies, MultiADS generalizes to unseen defect types without requiring prior visual examples, and consistently outperforms state-of-the-art models across several benchmarks, offering a robust and scalable solution for industrial inspection tasks.
Sadikaj, Y., Zhou, H., Halilaj, L., Schmid, S., Staab, S., & Plant, C. (2025). MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning. ICCV 2025. https://arxiv.org/abs/2504.06740 .