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most citedApproximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings

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cs.CV2026

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation

Nadeem Nazer, Hongkuan Zhou, Lavdim Halilaj +2

Recent vision-language models (VLMs) like CLIP have shown impressive anomaly detection performance under significant distribution shift by utilizing high-level semantic information…

cs.CV2026

GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models

Hongkuan Zhou, Tristan Rehm, Nadeem Nazer +3

Industrial inspection requires more than binary anomaly detection: a practical system should determine whether an anomaly exists, localize the defective region, identify the defect…

cs.CV2025

Seeing and Knowing in the Wild: Open-domain Visual Entity Recognition with Large-scale Knowledge Graphs via Contrastive Learning

Hongkuan Zhou, Lavdim Halilaj, Sebastian Monka +5

Open-domain visual entity recognition aims to identify and link entities depicted in images to a vast and evolving set of real-world concepts, such as those found in Wikidata. Unli…

cs.CV2025

MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning

Ylli Sadikaj, Hongkuan Zhou, Lavdim Halilaj +3

Precise optical inspection in industrial applications is crucial for minimizing scrap rates and reducing the associated costs. Besides merely detecting if a product is anomalous or…

cs.CV2025

Robust Visual Representation Learning with Multi-modal Prior Knowledge for Image Classification Under Distribution Shift

Hongkuan Zhou, Lavdim Halilaj, Sebastian Monka +4

Despite the remarkable success of deep neural networks (DNNs) in computer vision, they fail to remain high-performing when facing distribution shifts between training and testing d…

cs.CV2024

Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving

Mehdi Azarafza, Mojtaba Nayyeri, Charles Steinmetz +2

Large Language Models (LLMs) have garnered significant attention for their ability to understand text and images, generate human-like text, and perform complex reasoning tasks. How…