Software & Data Downloads — LTOAD
Long-Tailed Online Anomaly Detection dataset for identifing defect regions of a given image.
Anomaly detection (AD) identifies the defect regions of a given image. Recent works have studied AD, focusing on learning AD without abnormal images, with long-tailed distributed training data, and using a unified model for all classes. In addition, online AD learning has also been explored. We expand in both directions to a realistic setting by considering the novel task of long-tailed online AD (LTOAD).
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Related Publications
- , "Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts", IEEE International Conference on Computer Vision (ICCV), October 2025.
BibTeX TR2025-124 PDF Data- @inproceedings{Yang2025oct,
- author = {Yang, Chiao-An and Peng, Kuan-Chuan and Yeh, Raymond},
- title = {{Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts}},
- booktitle = {IEEE International Conference on Computer Vision (ICCV)},
- year = 2025,
- month = oct,
- url = {https://www.merl.com/publications/TR2025-124}
- }
- , "Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts", IEEE International Conference on Computer Vision (ICCV), October 2025.
Software & Data Downloads
Access data at https://doi.org/10.5281/zenodo.16283852.