Ardelean, AT;
Weyrich, T;
(2024)
High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis.
In:
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2024.
(pp. pp. 1123-1133).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
We propose a novel method for Zero-Shot Anomaly Localization on textures. The task refers to identifying abnormal regions in an otherwise homogeneous image. To obtain a high-fidelity localization, we leverage a bijective mapping derived from the 1-dimensional Wasserstein Distance. As opposed to using holistic distances between distributions, the proposed approach allows pinpointing the non-conformity of a pixel in a local context with increased precision. By aggregating the contribution of the pixel to the errors of all nearby patches, we obtain a reliable anomaly score estimate. We validate our solution on several datasets and obtain more than a 40% reduction in error over the previous state of the art on the MVTec AD dataset in a zero-shot setting. Also see reality.tf.fau.de/pub/ardelean2024highfidelity.html.
| Type: | Proceedings paper |
|---|---|
| Title: | High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis |
| Event: | IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2024 |
| Location: | Waikoloa, HI, USA |
| Dates: | 3rd-8th January 2024 |
| ISBN-13: | 979-8-3503-1892-0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/WACV57701.2024.00117 |
| Publisher version: | https://doi.org/10.1109/wacv57701.2024.00117 |
| 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/10205199 |
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