loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Emilie Mathian 1 ; 2 ; Huidong Liu 3 ; Lynnette Fernandez-Cuesta 1 ; Dimitris Samaras 4 ; Matthieu Foll 1 and Liming Chen 2

Affiliations: 1 International Agency for Research on Cancer (IARC-WHO), Lyon, France ; 2 Ecole Centrale de Lyon, Ecully, France ; 3 Amazon, WA, U.S.A. ; 4 Stony Brook University, New York, U.S.A.

Keyword(s): Anomaly Detection, HaloNet, Transformer, Auto-Encoder.

Abstract: Unsupervised anomaly detection and localization is a crucial task in many applications, e.g., defect detection in industry, cancer localization in medicine, and requires both local and global information as enabled by the self-attention in Transformer. However, brute force adaptation of Transformer, e.g., ViT, suffers from two issues: 1) the high computation complexity, making it hard to deal with high-resolution images; and 2) patch-based tokens, which are inappropriate for pixel-level dense prediction tasks, e.g., anomaly localization,and ignores intra-patch interactions. We present HaloAE, the first auto-encoder based on a local 2D version of Transformer with HaloNet allowing intra-patch correlation computation with a receptive field covering 25% of the input image. HaloAE combines convolution and local 2D block-wise self-attention layers and performs anomaly detection and segmentation through a single model. Moreover, because the loss function is generally a weighted sum of sever al losses, we also introduce a novel dynamic weighting scheme to better optimize the learning of the model. The competitive results on the MVTec dataset suggest that vision models incorporating Transformer could benefit from a local computation of the self-attention operation, and its very low computational cost and pave the way for applications on very large images a (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 100.26.179.41

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Mathian, E.; Liu, H.; Fernandez-Cuesta, L.; Samaras, D.; Foll, M. and Chen, L. (2023). HaloAE: A Local Transformer Auto-Encoder for Anomaly Detection and Localization Based on HaloNet. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 325-337. DOI: 10.5220/0011865900003417

@conference{visapp23,
author={Emilie Mathian. and Huidong Liu. and Lynnette Fernandez{-}Cuesta. and Dimitris Samaras. and Matthieu Foll. and Liming Chen.},
title={HaloAE: A Local Transformer Auto-Encoder for Anomaly Detection and Localization Based on HaloNet},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={325-337},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011865900003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - HaloAE: A Local Transformer Auto-Encoder for Anomaly Detection and Localization Based on HaloNet
SN - 978-989-758-634-7
IS - 2184-4321
AU - Mathian, E.
AU - Liu, H.
AU - Fernandez-Cuesta, L.
AU - Samaras, D.
AU - Foll, M.
AU - Chen, L.
PY - 2023
SP - 325
EP - 337
DO - 10.5220/0011865900003417
PB - SciTePress