activity
20192026
most citedSegmentation-Based Deep-Learning Approach for Surface-Defect Detection

869 citations · 1.3k across the 14 of their papers we have counts for

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15 papers · 1 filter

cs.CV2026

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors

Matic Fučka, Vitjan Zavrtanik, Danijel Skočaj

Zero-shot anomaly detection aims to detect and localise abnormal regions in the image without access to any in-domain training images. While recent approaches leverage vision-langu…

cs.CV2025

SALAD -- Semantics-Aware Logical Anomaly Detection

Matic Fučka, Vitjan Zavrtanik, Danijel Skočaj

Recent surface anomaly detection methods excel at identifying structural anomalies, such as dents and scratches, but struggle with logical anomalies, such as irregular or missing o…

cs.CV20258 cited

No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes

Blaž Rolih, Matic Fučka, Danijel Skočaj

Surface defect detection is a critical task across numerous industries, aimed at efficiently identifying and localising imperfections or irregularities on manufactured components.…

cs.CV2025

A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection

Muhammad Aqeel, Danijel Skocaj, Marco Cristani +1

Industrial and medical anomaly detection faces critical challenges from data scarcity and prohibitive annotation costs, particularly in evolving manufacturing and healthcare settin…

cs.CV202413 cited

Dense Center-Direction Regression for Object Counting and Localization with Point Supervision

Domen Tabernik, Jon Muhovič, Danijel Skočaj

Object counting and localization problems are commonly addressed with point supervised learning, which allows the use of less labor-intensive point annotations. However, learning b…

cs.CV202413 cited

Center Direction Network for Grasping Point Localization on Cloths

Domen Tabernik, Jon Muhovič, Matej Urbas +1

Object grasping is a fundamental challenge in robotics and computer vision, critical for advancing robotic manipulation capabilities. Deformable objects, like fabrics and cloths, p…