38 citations · 59 across the 9 of their papers we have counts for
9 papers
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…
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…
A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation
Jer Pelhan, Alan Lukežič, Vitjan Zavrtanik +1
Low-shot object counters estimate the number of objects in an image using few or no annotated exemplars. Objects are localized by matching them to prototypes, which are constructed…
DAVE -- A Detect-and-Verify Paradigm for Low-Shot Counting
Jer Pelhan, Alan Lukežič, Vitjan Zavrtanik +1
Low-shot counters estimate the number of objects corresponding to a selected category, based on only few or no exemplars annotated in the image. The current state-of-the-art estima…
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation
Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
RGB-based surface anomaly detection methods have advanced significantly. However, certain surface anomalies remain practically invisible in RGB alone, necessitating the incorporati…
TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection
Matic Fučka, Vitjan Zavrtanik, Danijel Skočaj
Surface anomaly detection is a vital component in manufacturing inspection. Current discriminative methods follow a two-stage architecture composed of a reconstructive network foll…
A Low-Shot Object Counting Network With Iterative Prototype Adaptation
Nikola Djukic, Alan Lukezic, Vitjan Zavrtanik +1
We consider low-shot counting of arbitrary semantic categories in the image using only few annotated exemplars (few-shot) or no exemplars (no-shot). The standard few-shot pipeline…
DSR -- A dual subspace re-projection network for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images. Such approaches are pr…
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative m…