1 code implementation • 25 Apr 2024 • Jer Pelhan, Alan Lukežič, Vitjan Zavrtanik, Matej Kristan
DAVE outperforms the top density-based counters by ~20% in the total count MAE, it outperforms the most recent detection-based counter by ~20% in detection quality and sets a new state-of-the-art in zero-shot as well as text-prompt-based counting.
Ranked #1 on Object Counting on FSC147
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
1 code implementation • ICCV 2023 • Nikola Djukic, Alan Lukezic, Vitjan Zavrtanik, Matej Kristan
The standard few-shot pipeline follows extraction of appearance queries from exemplars and matching them with image features to infer the object counts.
Ranked #3 on Object Counting on FSC147
1 code implementation • 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
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 • ICCV 2021 • Vitjan Zavrtanik, Matej Kristan, Danijel Skocaj
Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance.
Ranked #4 on Anomaly Detection on AeBAD-V
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