Ardelean, AT;
Weyrich, T;
(2024)
Blind Localization and Clustering of Anomalies in Textures.
In:
Proceedings IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2024.
(pp. pp. 3900-3909).
Institute of Electrical and Electronics Engineers (IEEE)
Preview |
Text
2404.12246v1.pdf - Accepted Version Download (6MB) | Preview |
Abstract
Anomaly detection and localization in images is a growing field in computer vision. In this area, a seemingly understudied problem is anomaly clustering, i.e., identifying and grouping different types of anomalies in a fully unsupervised manner. In this work, we propose a novel method for clustering anomalies in largely stationary images (textures) in a blind setting. That is, the input consists of normal and anomalous images without distinction and without labels. What contributes to the difficulty of the task is that anomalous regions are often small and may present only subtle changes in appearance, which can be easily overshadowed by the genuine variance in the texture. Moreover, each anomaly type may have a complex appearance distribution. We introduce a novel scheme for solving this task using a combination of blind anomaly localization and contrastive learning. By identifying the anomalous regions with high fidelity, we can restrict our focus to those regions of interest; then, contrastive learning is employed to increase the separability of different anomaly types and reduce the intra-class variation. Our experiments show that the proposed solution yields significantly better results compared to prior work, setting a new state of the art. Project page: reality.tf.fau.de/pub/ardelean2024blind.html.
| Type: | Proceedings paper |
|---|---|
| Title: | Blind Localization and Clustering of Anomalies in Textures |
| Event: | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2024 |
| Location: | Seattle, WA, USA |
| Dates: | 17th-18th June 2024 |
| ISBN-13: | 979-8-3503-6547-4 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CVPRW63382.2024.00394 |
| Publisher version: | https://doi.org/10.1109/cvprw63382.2024.00394 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10205198 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
