Inpainting transformer for anomaly detection
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InTra, VT-ADL, and AnoVit directly utilize vanilla transformer and do not figure out why transformer brings improvement.
“However, these methods directly utilize vanilla transformer, and do not figure out why transformer brings improvement.”
From A Unified Model for Multi-Class Anomaly Detection (2022) · cites this paper as [32]
The Introduction (p. 2) explains why InTra uses self-attention: global context helps reconstruct patches without merely copying their local neighborhood. It also describes long residual connections and nonlinear reduction of keys and queries, contrary to the characterization of InTra as simply using a vanilla Transformer. This paper does not establish whether the characterization applies to VT-ADL or AnoVit. (LLM, conf=0.86)
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Cites
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Surface Defect Detection Methods for Industrial Products: A Review
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“[111] propose Inpainting Transformer (InTra), which integrates information from larger regions of the input image.”
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FAPM: Fast Adaptive Patch Memory for Real-Time Industrial Anomaly Detection
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A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection
Tailanian, M., Muse, P., & Pardo, A. (2021) · 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla52953.2021.00035 · Cited by 10 · see citing works
- Authors
- Pirnay, J., & Chai, K.
- Affiliations
- Fujitsu (Germany) (DE)
- Corresponding author
- Pirnay, Jonathan
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2022
- Pages
- 394-406
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3159648608
- Cited by
- 227
- References
- 19
- Citations by year
- 2026: 22 · 2025: 51 · 2024: 73 · 2023: 61 · 2022: 16 · 2021: 4
- FWCI
- 27.78
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- anomaly detection, image inpainting, transformer models, self-attention, convolutional autoencoder, MVTec AD, anomaly segmentation, out-of-distribution detection
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