Abstract
In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of “learning shortcuts”, wherein the model fails to learn the patterns of normal samples as it should, opting instead for shortcuts such as identity mapping or artificial noise elimination. Consequently, the model becomes unable to reconstruct genuine anomalies as normal instances, resulting in a failure of anomaly detection. To counter this issue, we present a novel unified feature reconstruction-based anomaly detection framework termed RLR (Reconstruct features from a Learnable Reference representation). Unlike previous methods, RLR utilizes learnable reference representations to compel the model to learn normal feature patterns explicitly, thereby prevents the model from succumbing to the “learning shortcuts” issue. Additionally, RLR incorporates locality constraints into the learnable reference to facilitate more effective normal pattern capture and utilizes a masked learnable key attention mechanism to enhance robustness. Evaluation of RLR on the 15-category MVTec-AD dataset and the 12-category VisA dataset shows superior performance compared to state-of-the-art methods under the unified setting. Code is available at RLR.
L. He, Z. Jiang, J. Peng and W. Zhu—Equal contribution.
This work was supported by Natural Science Foundation of China under contract 62171139.
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References
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: MVTec AD–a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9592–9600 (2019)
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Uninformed students: student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4183–4192 (2020)
Defard, T., Setkov, A., Loesch, A., Audigier, R.: PaDiM: a patch distribution modeling framework for anomaly detection and localization. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12664, pp. 475–489. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68799-1_35
Deng, H., Li, X.: Anomaly detection via reverse distillation from one-class embedding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9737–9746 (2022)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Fernando, T., Gammulle, H., Denman, S., Sridharan, S., Fookes, C.: Deep learning for medical anomaly detection-a survey. ACM Comput. Surv. (CSUR) 54(7), 1–37 (2021)
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)
Gudovskiy, D., Ishizaka, S., Kozuka, K.: CFLOW-AD: real-time unsupervised anomaly detection with localization via conditional normalizing flows. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 98–107 (2022)
He, B., et al.: Deep transformers without shortcuts: modifying self-attention for faithful signal propagation. In: The Eleventh International Conference on Learning Representations (2023)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840–6851 (2020)
Lee, Y., Kang, P.: AnoViT: unsupervised anomaly detection and localization with vision transformer-based encoder-decoder. IEEE Access 10, 46717–46724 (2022)
Lei, J., Hu, X., Wang, Y., Liu, D.: PyramidFlow: high-resolution defect contrastive localization using pyramid normalizing flow. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14143–14152 (2023)
Liu, Z., Zhou, Y., Xu, Y., Wang, Z.: SimpleNet: a simple network for image anomaly detection and localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20402–20411 (2023)
Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: VT-ADL: a vision transformer network for image anomaly detection and localization. In: 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), pp. 01–06. IEEE (2021)
Pirnay, J., Chai, K.: Inpainting transformer for anomaly detection. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds.) ICIAP 2022. LNCS, vol. 13232, pp. 394–406. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06430-2_33
Rippel, O., Mertens, P., Merhof, D.: Modeling the distribution of normal data in pre-trained deep features for anomaly detection. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 6726–6733. IEEE (2021)
Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318–14328 (2022)
Rudolph, M., Wandt, B., Rosenhahn, B.: Same same but differnet: semi-supervised defect detection with normalizing flows. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1907–1916 (2021)
Rudolph, M., Wehrbein, T., Rosenhahn, B., Wandt, B.: Fully convolutional cross-scale-flows for image-based defect detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1088–1097 (2022)
Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R.: Multiresolution knowledge distillation for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14902–14912 (2021)
Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Tien, T.D., et al.: Revisiting reverse distillation for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 24511–24520 (2023)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wang, C., et et al.: Real-IAD: a real-world multi-view dataset for benchmarking versatile industrial anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22883–22892 (2024)
Yan, X., Zhang, H., Xu, X., Hu, X., Heng, P.A.: Learning semantic context from normal samples for unsupervised anomaly detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3110–3118 (2021)
Yao, X., Li, R., Qian, Z., Luo, Y., Zhang, C.: Focus the discrepancy: intra-and inter-correlation learning for image anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6803–6813 (2023)
Yi, J., Yoon, S.: Patch SVDD: patch-level SVDD for anomaly detection and segmentation. In: Proceedings of the Asian Conference on Computer Vision (2020)
You, Z., et al.: A unified model for multi-class anomaly detection. In: Advances in Neural Information Processing Systems, vol. 35, pp. 4571–4584 (2022)
You, Z., Yang, K., Luo, W., Cui, L., Zheng, Y., Le, X.: ADTR: anomaly detection transformer with feature reconstruction. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) ICONIP 2022. LNCS, vol. 13625, pp. 298–310. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-30111-7_26
Zavrtanik, V., Kristan, M., Skočaj, D.: Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8330–8339 (2021)
Zhang, H., Wang, Z., Wu, Z., Jiang, Y.G.: DiffusionAD: denoising diffusion for anomaly detection. arXiv preprint arXiv:2303.08730 (2023)
Zhang, J., et al.: ADer: a comprehensive benchmark for multi-class visual anomaly detection. arXiv preprint arXiv:2406.03262 (2024)
Zhang, J., et al.: Learning feature inversion for multi-class anomaly detection under general-purpose coco-ad benchmark. arXiv preprint arXiv:2404.10760 (2024)
Zhao, Y.: Just noticeable learning for unsupervised anomaly localization and detection. In: 2022 IEEE International Conference on Multimedia and Expo (ICME), pp. 01–06. IEEE (2022)
Zhao, Y.: OmniAL: a unified CNN framework for unsupervised anomaly localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3924–3933 (2023)
Zou, Y., Jeong, J., Pemula, L., Zhang, D., Dabeer, O.: SPot-the-difference self-supervised pre-training for anomaly detection and segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13690, pp. 392–408. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20056-4_23
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He, L. et al. (2025). Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15125. Springer, Cham. https://doi.org/10.1007/978-3-031-72855-6_13
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