Blind Localization and Clustering of Anomalies in Textures

Ardelean AT, Weyrich T (2024)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2024

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 3900-3909

Conference Proceedings Title: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Event location: Seattle, WA US

ISBN: 9798350365474

DOI: 10.1109/CVPRW63382.2024.00394

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.

Authors with CRIS profile

Andrei-Timotei Ardelean Lehrstuhl für Informatik 15 (Digital Reality) Tim Weyrich Lehrstuhl für Informatik 15 (Digital Reality)

How to cite

APA:

Ardelean, A.-T., & Weyrich, T. (2024). Blind Localization and Clustering of Anomalies in Textures. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 3900-3909). Seattle, WA, US: Institute of Electrical and Electronics Engineers Inc..

MLA:

Ardelean, Andrei-Timotei, and Tim Weyrich. "Blind Localization and Clustering of Anomalies in Textures." Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024, Seattle, WA Institute of Electrical and Electronics Engineers Inc., 2024. 3900-3909.

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