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A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

Anomaly synthesis strategies can effectively enhance unsupervised anomaly detection. However, existing strategies have limitations in the coverage and controllability of anomaly synthesis, particularly for weak defects that are very similar to normal regions. In this paper, we propose Global and Local Anomaly co-Synthesis Strategy (GLASS), a novel unified framework designed to synthesize a broader coverage of anomalies under the manifold and hypersphere distribution constraints of Global Anomaly Synthesis (GAS) at the feature level and Local Anomaly Synthesis (LAS) at the image level. Our method synthesizes near-in-distribution anomalies in a controllable way using Gaussian noise guided by gradient ascent and truncated projection. GLASS achieves state-of-the-art results on the MVTec AD (detection AUROC of 99.9%), VisA, and MPDD datasets and excels in weak defect detection. The effectiveness and efficiency have been further validated in industrial applications for woven fabric defect detection. The code and dataset are available at: https://github.com/cqylunlun/GLASS.

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References

  1. Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622–637. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_39

    Chapter  Google Scholar 

  2. Bae, J., Lee, J.H., Kim, S.: PNI: industrial anomaly detection using position and neighborhood information. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6373–6383 (2023)

    Google Scholar 

  3. Batzner, K., Heckler, L., König, R.: EfficientAD: accurate visual anomaly detection at millisecond-level latencies. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 128–138 (2024)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. Cao, Y., Xu, X., Liu, Z., Shen, W.: Collaborative discrepancy optimization for reliable image anomaly localization. IEEE Trans. Industr. Inf. 19(11), 10674–10683 (2023)

    Article  Google Scholar 

  6. Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3606–3613 (2014)

    Google Scholar 

  7. Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition workshops, pp. 702–703 (2020)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using real NVP. In: International Conference on Learning Representations (2017)

    Google Scholar 

  10. Goyal, S., Raghunathan, A., Jain, M., Simhadri, H.V., Jain, P.: DROCC: deep robust one-class classification. In: International Conference on Machine Learning, vol. 119, pp. 3711–3721. PMLR (2020)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. Hou, J., Zhang, Y., Zhong, Q., Xie, D., Pu, S., Zhou, H.: Divide-and-assemble: learning block-wise memory for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8791–8800 (2021)

    Google Scholar 

  13. Hyun, J., Kim, S., Jeon, G., Kim, S.H., Bae, K., Kang, B.J.: Reconpatch: contrastive patch representation learning for industrial anomaly detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2052–2061 (2024)

    Google Scholar 

  14. Jezek, S., Jonak, M., Burget, R., Dvorak, P., Skotak, M.: Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions. In: International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), pp. 66–71. IEEE (2021)

    Google Scholar 

  15. Lee, S., Lee, S., Song, B.C.: CFA: coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. IEEE Access 10, 78446–78454 (2022)

    Article  Google Scholar 

  16. 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)

    Google Scholar 

  17. Li, C.L., Sohn, K., Yoon, J., Pfister, T.: Cutpaste: Self-supervised learning for anomaly detection and localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9664–9674 (2021)

    Google Scholar 

  18. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  19. Liu, J., et al.: Deep industrial image anomaly detection: a survey. Mach. Intell. Res. 21(1), 104–135 (2024). https://doi.org/10.1007/s11633-023-1459-z

    Article  Google Scholar 

  20. 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)

    Google Scholar 

  21. Pless, R., Souvenir, R.: A survey of manifold learning for images. IPSJ Trans. Comput. Vis. Appl. 1, 83–94 (2009)

    Article  Google Scholar 

  22. Reiss, T., Cohen, N., Bergman, L., Hoshen, Y.: Panda: adapting pretrained features for anomaly detection and segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2806–2814 (2021)

    Google Scholar 

  23. 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)

    Google Scholar 

  24. Ruff, L., et al.: Deep one-class classification. In: International Conference on Machine Learning, pp. 4393–4402. PMLR (2018)

    Google Scholar 

  25. 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)

    Google Scholar 

  26. Schlüter, H.M., Tan, J., Hou, B., Kainz, B.: Natural synthetic anomalies for self-supervised anomaly detection and localization. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV. LNCS, vol. 13691, pp. 474–489. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19821-2_27

    Chapter  Google Scholar 

  27. Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)

    Article  Google Scholar 

  28. Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 761–769 (2016)

    Google Scholar 

  29. Tax, D.M., Duin, R.P.: Support vector data description. Mach. Learn. 54, 45–66 (2004)

    Article  Google Scholar 

  30. 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)

    Google Scholar 

  31. Xiao, F., Sun, R., Fan, J.: Restricted generative projection for one-class classification and anomaly detection. arXiv preprint arXiv:2307.04097 (2023)

  32. Yang, M., Wu, P., Feng, H.: MemSeg: a semi-supervised method for image surface defect detection using differences and commonalities. Eng. Appl. Artif. Intell. 119, 105835 (2023)

    Article  Google Scholar 

  33. Yao, X., Li, R., Zhang, J., Sun, J., Zhang, C.: Explicit boundary guided semi-push-pull contrastive learning for supervised anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 24490–24499 (2023)

    Google Scholar 

  34. You, Z., et al.: A unified model for multi-class anomaly detection. In: Advance in Neural Information Processing System, vol. 35, pp. 4571–4584 (2022)

    Google Scholar 

  35. Yu, J., et al.: Fastflow: unsupervised anomaly detection and localization via 2D normalizing flows. arXiv preprint arXiv:2111.07677 (2021)

  36. 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)

    Google Scholar 

  37. Zavrtanik, V., Kristan, M., Skočaj, D.: Reconstruction by inpainting for visual anomaly detection. Pattern Recogn. 112, 107706 (2021)

    Article  Google Scholar 

  38. Zavrtanik, V., Kristan, M., Skočaj, D.: DSR-a dual subspace re-projection network for surface anomaly detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13691, pp. 539–554. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19821-2_31

    Chapter  Google Scholar 

  39. Zhang, X., Li, S., Li, X., Huang, P., Shan, J., Chen, T.: DeSTSeg: segmentation guided denoising student-teacher for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3914–3923 (2023)

    Google Scholar 

  40. Zhou, Y.: Rethinking reconstruction autoencoder-based out-of-distribution detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7379–7387 (2022)

    Google Scholar 

  41. 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

    Chapter  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant 62303458, Grant 62303461 and Grant U21A20482. This work is also supported by the Beijing Municipal Natural Science Foundation of China under Grant L243018. In addition, we would like to express our gratitude to WEIQIAO Textile for collecting the original images used in the WFDD dataset.

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Qiyu Chen, Huiyuan Luo, Chengkan Lv & Zhengtao Zhang

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Qiyu Chen & Zhengtao Zhang

  3. CASI Vision Technology CO., LTD., Luoyang, China

    Zhengtao Zhang

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  1. Qiyu Chen
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  2. Huiyuan Luo
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  3. Chengkan Lv
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  4. Zhengtao Zhang
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Corresponding author

Correspondence to Chengkan Lv .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Chen, Q., Luo, H., Lv, C., Zhang, Z. (2025). A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization. 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_3

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