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Bringing Attention to Image Anomaly Detection

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Image Analysis and Processing. ICIAP 2022 Workshops (ICIAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13373))

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Abstract

Detecting anomalies in images is a task with several relevant real-world applications, e.g. industrial inspection. Building on the existing RIAD (Reconstruction by Inpainting for visual Anomaly Detection) framework, we introduce an attention-based component to improve the model performance. Furthermore we propose a different approach to image masking which leverages the selection of multiple random patches at a single scale in the original images. Through the provided experimental results we show how the novelties introduced by this work consistently improve the performance of the baseline approach over the various classes of the heterogeneous MVTec benchmark dataset across all the metrics considered.

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References

  1. Abati, D., Porrello, A., Calderara, S., Cucchiara, R.: Latent space autoregression for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 481–490 (2019)

    Google Scholar 

  2. 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 

  3. Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., Steger, C.: The MVTEC anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection. Int. J. Comput. Vis. 129(4), 1038–1059 (2021)

    Article  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 Conference on Computer Vision and Pattern Recognition, pp. 9592–9600 (2019)

    Google Scholar 

  5. 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)

    Google Scholar 

  6. Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving unsupervised defect segmentation by applying structural similarity to autoencoders. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019)

    Google Scholar 

  7. Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249–259 (2018)

    Google Scholar 

  8. Böttger, T., Ulrich, M.: Real-time texture error detection on textured surfaces with compressed sensing. Pattern Recogni. Image Anal. 26, 88–94 (2016)

    Google Scholar 

  9. Steger, C., Ulrich, M., Wiedemann, C.: Machine Vision Algorithms and Applications. Wiley (2018)

    Google Scholar 

  10. Cohen, N., Hoshen, Y.: Sub-image anomaly detection with deep pyramid correspondences. arXiv preprint arXiv:2005.02357 (2020)

  11. Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53–65 (2018)

    Article  Google Scholar 

  12. Deecke, L., Vandermeulen, R., Ruff, L., Mandt, S., Kloft, M.: Image anomaly detection with generative adversarial networks. In: Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11051, pp. 3–17. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10925-7_1

    Chapter  Google Scholar 

  13. Faust, K., et al.: Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction. BMC Bioinform. 19(1), 1–15 (2018)

    Article  Google Scholar 

  14. Kiran, B.R., Thomas, D.M., Parakkal, R.: An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos. J. Imaging 4(2), 36 (2018)

    Google Scholar 

  15. Klushyn, A., Chen, N., Kurle, R., Cseke, B., van der Smagt, P.: Learning hierarchical priors in vaes. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 2866–2875. Curran Associates, Inc. (2019)

    Google Scholar 

  16. Lee, C.K., Cheon, Y.J., Hwang, W.Y.: Studies on the GAN-based anomaly detection methods for the time series data. IEEE Access 9, 73201–73215 (2021)

    Article  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. Liu, B., Wang, D., Lin, K., Tan, P.N., Zhou, J.: Unsupervised anomaly detection by robust collaborative autoencoders (2021)

    Google Scholar 

  19. Ma, X., et al.: Understanding adversarial attacks on deep learning based medical image analysis systems. Pattern Recogn. 110, 107332 (2021)

    Google Scholar 

  20. Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: A simple neural attentive meta-learner. arXiv preprint arXiv:1707.03141 (2017)

  21. Mishra, P., Piciarelli, C., Foresti, G.L.: A neural network for image anomaly detection with deep pyramidal representations and dynamic routing. Int. J. Neural Syst. 30(10), 2050060–2050060 (2020)

    Article  Google Scholar 

  22. Mishra, P., Piciarelli, C., Foresti, G.L.: Image anomaly detection by aggregating deep pyramidal representations. In: Del Bimbo, A., Cucchiara, R., Sclaroff, S., Farinella, G.M., Mei, T., Bertini, M., Escalante, H.J., Vezzani, R. (eds.) ICPR 2021. LNCS, vol. 12664, pp. 705–718. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68799-1_51

    Chapter  Google Scholar 

  23. Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: VT-ADL: a vision transformer network for image anomaly detection and localization. In: 30th IEEE/IES International Symposium on Industrial Electronics (ISIE), June 2021

    Google Scholar 

  24. Napoletano, P., Piccoli, F., Schettini, R.: Anomaly detection in nanofibrous materials by CNN-based self-similarity. Sensors 18(1), 209 (2018)

    Article  Google Scholar 

  25. Oktay, O., et al.: Attention u-net: Learning where to look for the pancreas. ArXiv abs/1804.03999 (2018)

    Google Scholar 

  26. Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(2), 1–38 (2021)

    Google Scholar 

  27. Piciarelli, C., Avola, D., Pannone, D., Foresti, G.L.: A vision-based system for internal pipeline inspection. IEEE Trans. Ind. Inform. 15(6), 3289–3299 (2018). early access

    Google Scholar 

  28. Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-based anomalous event detection. IEEE Trans. Circ. Syst. Video Technol. 18(11), 1544–1554 (2008)

    Article  Google Scholar 

  29. Piciarelli, C., Mishra, P., Foresti, G.L.: Supervised anomaly detection with highly imbalanced datasets using capsule networks. Int. J. Pattern Recogn. Artif. Intell. 35(8), 2152010 (2021)

    Google Scholar 

  30. 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 

  31. Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019)

    Article  Google Scholar 

  32. Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing: methods and applications. J. Manufact. Syst. 48, 144–156 (2018)

    Article  Google Scholar 

  33. Yu, P., Yan, X.: Stock price prediction based on deep neural networks. NeuralComput. Appl.32(6), 1609–1628 (2020)

    Google Scholar 

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

    Google Scholar 

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Acknowledgement

This work was partially supported by the ONRG project N62909-20-1-2075 Target Re-Association for Autonomous Agents (TRAAA).

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Authors and Affiliations

  1. University of Udine, Italy, via delle Scienze 206, 33100, Udine, Italy

    Axel de Nardin, Pankaj Mishra, Claudio Piciarelli & Gian Luca Foresti

Authors
  1. Axel de Nardin
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  2. Pankaj Mishra
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  3. Claudio Piciarelli
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  4. Gian Luca Foresti
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Corresponding author

Correspondence to Axel de Nardin .

Editor information

Editors and Affiliations

  1. National Research Council, Lecce, Italy

    Pier Luigi Mazzeo

  2. Università Politecnica delle Marche, Ancona, Italy

    Emanuele Frontoni

  3. Boston University, Boston, MA, USA

    Stan Sclaroff

  4. National Research Council, Lecce, Italy

    Cosimo Distante

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de Nardin, A., Mishra, P., Piciarelli, C., Foresti, G.L. (2022). Bringing Attention to Image Anomaly Detection. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham. https://doi.org/10.1007/978-3-031-13321-3_11

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  • DOI: https://doi.org/10.1007/978-3-031-13321-3_11

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