no code implementations • 16 Nov 2023 • Matic Fučka, Vitjan Zavrtanik, Danijel Skočaj
We propose a novel transparency-based diffusion process, where the transparency of anomalous regions is progressively increased, restoring their normal appearance accurately and maintaining the appearance of anomaly-free regions without loss of detail.
Ranked #8 on Anomaly Detection on VisA
1 code implementation • 2 Nov 2023 • Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
(ii) We tackle the lack of diverse industrial depth datasets by introducing a simulation process for learning informative depth features in the depth encoder.
Depth Anomaly Detection and Segmentation RGB+3D Anomaly Detection and Segmentation
2 code implementations • 2 Aug 2022 • 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.
Ranked #1 on Supervised Defect Detection on KolektorSDD2
Supervised Defect Detection Unsupervised Anomaly Detection +1
no code implementations • 8 Nov 2021 • Álvaro García Faura, Dejan Štepec, Tomaž Martinčič, Danijel Skočaj
A key component towards an improved and fast cancer diagnosis is the development of computer-assisted tools.
3 code implementations • 17 Aug 2021 • Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance.
Ranked #15 on Anomaly Detection on VisA
2 code implementations • 13 Apr 2021 • Jakob Božič, Domen Tabernik, Danijel Skočaj
We also show that mixed supervision with only a handful of fully annotated samples added to weakly labelled training images can result in performance comparable to the fully supervised model's performance but at a significantly lower annotation cost.
Ranked #1 on Defect Detection on KolektorSDD2
2 code implementations • 17 Oct 2020 • Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
Visual anomaly detection addresses the problem of classification or localization of regions in an image that deviate from their normal appearance.
Ranked #6 on Anomaly Detection on AeBAD-V
1 code implementation • 15 Jul 2020 • Jakob Božič, Domen Tabernik, Danijel Skočaj
We demonstrate the performance of the end-to-end training scheme and the proposed extensions on three defect detection datasets - DAGM, KolektorSDD and Severstal Steel defect dataset - where we show state-of-the-art results.
Ranked #1 on Defect Detection on DAGM2007 (Average Precision metric)
1 code implementation • 1 Apr 2019 • Domen Tabernik, Danijel Skočaj
Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory.
5 code implementations • 20 Mar 2019 • Domen Tabernik, Samo Šela, Jure Skvarč, Danijel Skočaj
This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection.
Ranked #3 on Defect Detection on KolektorSDD