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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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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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  • Online ISBN: 978-3-031-13321-3

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