20.7.3.9.1 Anomalies, Anomaly Detection

Chapter Contents (Back)
Anomaly Detection. Application, Inspection. Inspection, Defects. Defect Detection. General Anomaly Detection.

Yu, Q.[Qianzi], Zhu, K.[Kai], Cao, Y.[Yang], Xia, F.[Feijie], Kang, Y.[Yu],
TF²: Few-Shot Text-Free Training-Free Defect Image Generation for Industrial Anomaly Inspection,
CirSysVideo(34), No. 11, November 2024, pp. 11825-11837.
IEEE DOI 2412
Inspection, Diffusion models, Feature extraction, Image synthesis, Electronic mail, Task analysis, Production, Anomaly generation, anomaly inspection BibRef

Wang, S.Y.[Shu-Yuan], Li, Q.[Qi], Luo, H.Y.[Hui-Yuan], Lv, C.K.[Cheng-Kan], Zhang, Z.T.[Zheng-Tao],
Produce Once, Utilize Twice for Anomaly Detection,
CirSysVideo(34), No. 11, November 2024, pp. 11751-11767.
IEEE DOI 2412
Image reconstruction, Decoding, Anomaly detection, Feature extraction, Accuracy, Semantics, Training data, representations reusing BibRef

Yang, M.H.[Ming-Hui], Liu, J.[Jing], Yang, Z.W.[Zhi-Wei], Wu, Z.Y.[Zhao-Yang],
SLSG: Industrial image anomaly detection with improved feature embeddings and one-class classification,
PR(156), 2024, pp. 110862.
Elsevier DOI 2408
Anomaly detection, One-class classification, Self-supervised learning, Graph convolutional network BibRef

Liu, B.[Binhui], Guo, T.[Tianchu], Luo, B.[Bin], Cui, Z.[Zhen], Yang, J.[Jian],
Cross-Attention Regression Flow for Defect Detection,
IP(33), 2024, pp. 5183-5193.
IEEE DOI 2410
Feature extraction, Anomaly detection, Transforms, Defect detection, Visualization, Fitting, Testing, Defect detection, autoregression BibRef

Chen, Z.H.[Zi-Heng], Lyu, C.Z.[Chen-Zhi], Zhang, L.[Lei], Li, S.K.[Shao-Kang], Xia, B.[Bin],
RDMS: Reverse distillation with multiple students of different scales for anomaly detection,
IET-IPR(18), No. 13, 2024, pp. 3815-3826.
DOI Link Code:
WWW Link. 2411
crack detection, pattern recognition, unsupervised learning BibRef

Wan, Y.H.[Yong-Hao], Feng, A.[Aimin],
DualAD: Dual adversarial network for image anomaly detection?,
IET-CV(18), No. 8, 2024, pp. 1138-1148.
DOI Link 2501
feature extraction, image recognition, image reconstruction, vision defects BibRef

Chen, Z.[Ziyi], Bai, C.Y.[Chen-Yao], Zhu, Y.L.[Yun-Long], Lu, X.W.[Xi-Wen],
TUT: Template-Augmented U-Net Transformer for Unsupervised Anomaly Detection,
SPLetters(31), 2024, pp. 780-784.
IEEE DOI 2404
Image reconstruction, Decoding, Convolution, Vectors, Anomaly detection, Head, Self-supervised learning, unsupervised learning BibRef


Zhou, Z.Y.[Zhe-Yuan], Wang, L.[Le], Fang, N.[Naiyu], Wang, Z.L.[Zi-Li], Qiu, L.[Lemiao], Zhang, S.[Shuyou],
R3D-AD: Reconstruction via Diffusion for 3d Anomaly Detection,
ECCV24(XXXVI: 91-107).
Springer DOI 2412
BibRef

Yao, H.[Hang], Liu, M.[Ming], Yin, Z.[Zhicun], Yan, Z.[Zifei], Hong, X.P.[Xiao-Peng], Zuo, W.M.[Wang-Meng],
Glad: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection,
ECCV24(LXXI: 1-17).
Springer DOI 2412
BibRef

Gui, G.[Guan], Gao, B.B.[Bin-Bin], Liu, J.[Jun], Wang, C.J.[Cheng-Jie], Wu, Y.S.[Yun-Sheng],
Few-shot Anomaly-driven Generation for Anomaly Classification and Segmentation,
ECCV24(LXXXIII: 210-226).
Springer DOI 2412
BibRef

Meng, S.Y.[Shi-Yuan], Meng, W.C.[Wen-Chao], Zhou, Q.H.[Qi-Hang], Li, S.Z.[Shi-Zhong], Hou, W.[Weiye], He, S.[Shibo],
Moead: A Parameter-efficient Model for Multi-class Anomaly Detection,
ECCV24(LXXXV: 345-361).
Springer DOI 2412
BibRef

Shi, J.[Jian], Zhang, P.Y.[Peng-Yi], Zhang, N.[Ni], Ghazzai, H.[Hakim], Wonka, P.[Peter],
Dissolving is Amplifying: Towards Fine-grained Anomaly Detection,
ECCV24(LIX: 377-394).
Springer DOI 2412
BibRef

Lee, J.C.[Joo Chan], Kim, T.[Taejune], Park, E.[Eunbyung], Woo, S.S.[Simon S.], Ko, J.H.[Jong Hwan],
Continuous Memory Representation for Anomaly Detection,
ECCV24(LI: 438-454).
Springer DOI 2412
BibRef

Sträter, L.P.J.[Luc P. J.], Salehi, M.[Mohammadreza], Gavves, E.[Efstratios], Snoek, C.G.M.[Cees G. M.], Asano, Y.M.[Yuki M.],
Generalad: Anomaly Detection Across Domains by Attending to Distorted Features,
ECCV24(XXXVII: 448-465).
Springer DOI 2412
BibRef

Chen, Q.Y.[Qi-Yu], Luo, H.Y.[Hui-Yuan], Lv, C.[Chengkan], Zhang, Z.T.[Zheng-Tao],
A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization,
ECCV24(LXVII: 37-54).
Springer DOI 2412
BibRef

He, L.[Liren], Jiang, Z.K.[Zheng-Kai], Peng, J.L.[Jin-Long], Zhu, W.B.[Wen-Bing], Liu, L.[Liang], Du, Q.[Qiangang], Hu, X.B.[Xia-Bin], Chi, M.[Mingmin], Wang, Y.[Yabiao], Wang, C.J.[Cheng-Jie],
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection,
ECCV24(LXVII: 216-232).
Springer DOI 2412
BibRef

Gao, B.B.[Bin-Bin],
Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt,
ECCV24(LXVII: 454-470).
Springer DOI 2412
BibRef

Isaac-Medina, B.K.S.[Brian K. S.], Gaus, Y.F.A.[Yona Falinie A.], Bhowmik, N.[Neelanjan], Breckon, T.P.[Toby P.],
Towards Open-world Object-based Anomaly Detection via Self-supervised Outlier Synthesis,
ECCV24(LXXI: 196-214).
Springer DOI 2412
BibRef

Qu, Z.[Zhen], Tao, X.[Xian], Prasad, M.[Mukesh], Shen, F.[Fei], Zhang, Z.T.[Zheng-Tao], Gong, X.[Xinyi], Ding, G.G.[Gui-Guang],
VCP-CLIP: A Visual Context Prompting Model for Zero-shot Anomaly Segmentation,
ECCV24(LXIX: 301-317).
Springer DOI 2412
BibRef

Tu, Y.P.[Yuan-Peng], Zhang, B.S.[Bo-Shen], Liu, L.[Liang], Li, Y.X.[Yu-Xi], Zhang, J.N.[Jiang-Ning], Wang, Y.B.[Ya-Biao], Wang, C.J.[Cheng-Jie], Zhao, C.R.[Cai-Rong],
Self-Supervised Feature Adaptation for 3D Industrial Anomaly Detection,
ECCV24(II: 75-91).
Springer DOI 2412
BibRef

Zhu, B.K.[Bing-Ke], Li, H.[Hao], Chen, C.L.[Chang-Lin], Hua, L.J.[Liu-Jie], Wang, J.Q.[Jin-Qiao],
Estate: Expert-Guided State Text Enhancement for Zero-Shot Industrial Anomaly Detection,
ICIP24(1281-1287)
IEEE DOI 2411
Training, Measurement, Image segmentation, Benchmark testing, Task analysis, Anomaly detection, Standards, anomaly detection, text-guided BibRef

Hu, W.R.[Wen-Rui], Xie, Y.[Yuan], Yu, W.[Wei],
TDAD: Trident Distillations for Anomaly Detection,
ICIP24(346-352)
IEEE DOI 2411
Training, Reliability, Task analysis, Anomaly detection, Surface treatment, Unsupervised anomaly detection, self-supervised learning BibRef

Li, G.J.[Guan-Ji], Gao, H.X.[Hong-Xia],
Apnet: Generating Precise Anomaly Prior Information for Mixed-Supervised Defect Detection,
ICIP24(3889-3895)
IEEE DOI 2411
Location awareness, Inductance, Image segmentation, Vector quantization, Pipelines, Object detection BibRef

Li, X.F.[Xiao-Fan], Zhang, Z.Z.[Zhi-Zhong], Tan, X.[Xin], Chen, C.W.[Cheng-Wei], Qu, Y.[Yanyun], Xie, Y.[Yuan], Ma, L.Z.[Li-Zhuang],
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection,
CVPR24(16848-16858)
IEEE DOI Code:
WWW Link. 2410
Training, Learning systems, Codes, Semantics, Prompt engineering, Anomaly Detection, Prompt Learning, Few-Shot Learning BibRef

Zhu, J.[Jiawen], Pang, G.S.[Guan-Song],
Toward Generalist Anomaly Detection via In-Context Residual Learning with Few-Shot Sample Prompts,
CVPR24(17826-17836)
IEEE DOI Code:
WWW Link. 2410
Training, Codes, Computational modeling, Semantics, Benchmark testing, Data models, Anomaly Detection, Few-shot Anomaly Detection BibRef

Murphy, J.[James], Buckley, M.[Maria], Buckley, L.[Léonie], Taylor, A.[Adam], O'Brien, J.[Jake], Namee, B.M.[Brian Mac],
Deploying Machine Learning Anomaly Detection Models to Flight Ready AI Boards,
AI4Space24(6828-6836)
IEEE DOI 2410
Performance evaluation, Space missions, Image edge detection, Computational modeling, Machine learning, Transformers, Hardware, space BibRef

Gupta, S.[Shaurya], Gautam, N.[Neil], Malyala, A.[Anurag],
ATAC-NET: Zoomed View Works Better for Anomaly Detection,
ICIP24(249-255)
IEEE DOI 2411
Training, Deep learning, Visualization, Quality control, Manufacturing, Reliability, anomaly detection, self-explainability, deviation loss BibRef

Lee, M.Y.[Ming-Yu], Choi, J.W.[Jong-Won],
Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation,
CVPR24(26509-26518)
IEEE DOI 2410
Image segmentation, Image synthesis, Generators, Vectors, Stability analysis, Libraries, Data models, Industrial Anomaly Segmentation BibRef

Ugwu, C.I.[Cynthia I.], Casarin, S.[Sofia], Lanz, O.[Oswald],
Fractals as Pre-training Datasets for Anomaly Detection and Localization,
FaDE-TCV24(163-172)
IEEE DOI 2410
Training, Visualization, Data privacy, Solid modeling, Systematics, Feature extraction, Solids, anomaly detection, fractals images, feature-embedding BibRef

Tebbe, J.[Justin], Tayyub, J.[Jawad],
Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection,
VAND24(3940-3949)
IEEE DOI 2410
Location awareness, Technological innovation, Noise reduction, Noise, Diffusion models, anomaly detection, diffusion models, domain adaptation BibRef

Artola, A.[Aitor], Kolodziej, Y.[Yannis], Morel, J.M.[Jean-Michel], Ehret, T.[Thibaud],
Model-guided contrastive fine-tuning for industrial anomaly detection,
VAND24(3981-3991)
IEEE DOI 2410
Location awareness, Visualization, Computational modeling, Neural networks, Contrastive learning, Feature extraction, contrastive learning BibRef

Zhao, Y.[Ying],
LogicAL: Towards logical anomaly synthesis for unsupervised anomaly localization,
VAND24(4022-4031)
IEEE DOI 2410
Location awareness, Manifolds, Image edge detection, Semantics, Production, Anomaly synthesis, Anomaly localization, Anomaly detection BibRef

Baitieva, A.[Aimira], Hurych, D.[David], Besnier, V.[Victor], Bernard, O.[Olivier],
Supervised Anomaly Detection for Complex Industrial Images,
CVPR24(17754-17762)
IEEE DOI Code:
WWW Link. 2410
Image segmentation, Visualization, Computational modeling, Production, Benchmark testing, Product design, Quality assessment BibRef

Zhu, J.[Jiawen], Ding, C.[Choubo], Tian, Y.[Yu], Pang, G.S.[Guan-Song],
Anomaly Heterogeneity Learning for Open-Set Supervised Anomaly Detection,
CVPR24(17616-17626)
IEEE DOI Code:
WWW Link. 2410
Training, Codes, Computational modeling, Collaboration, Rendering (computer graphics), Supervised Anomaly Detection BibRef

Li, H.M.[Hui-Min], Chen, Z.T.[Zhen-Tao], Xu, Y.H.[Yun-Hao], Hu, J.L.[Jun-Lin],
Hyperbolic Anomaly Detection,
CVPR24(17511-17520)
IEEE DOI 2410
Deep learning, Computational modeling, Pipelines, Benchmark testing, Feature extraction, feature embedding BibRef

Zhang, X.[Ximiao], Xu, M.[Min], Zhou, X.Z.[Xiu-Zhuang],
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection,
CVPR24(16699-16708)
IEEE DOI Code:
WWW Link. 2410
Location awareness, Redundancy, Reconstruction algorithms, Feature extraction, Data models, feature selection BibRef

Ho, C.H.[Chih-Hui], Peng, K.C.[Kuan-Chuan], Vasconcelos, N.M.[Nuno M.],
Long-Tailed Anomaly Detection with Learnable Class Names,
CVPR24(12435-12446)
IEEE DOI Code:
WWW Link. 2410
Performance evaluation, Semantics, Training data, Transformers, Image reconstruction, Anomaly Detection, Visual Language Foundational Model BibRef

Wang, C.J.[Cheng-Jie], Zhu, W.B.[Wen-Bing], Gao, B.B.[Bin-Bin], Gan, Z.[Zhenye], Zhang, J.N.[Jiang-Ning], Gu, Z.H.[Zhi-Hao], Qian, S.G.[Shu-Guang], Chen, M.[Mingang], Ma, L.Z.[Li-Zhuang],
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection,
CVPR24(22883-22892)
IEEE DOI 2410
Training, Measurement, Noise, Metals, Production, Benchmark testing, Inspection BibRef

Vieira e Silva, A.L.[André Luiz], Simġes, F.[Francisco], Kowerko, D.[Danny], Schlosser, T.[Tobias], Battisti, F.[Felipe], Teichrieb, V.[Veronica],
Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study,
WACV24(8231-8240)
IEEE DOI 2404
Visualization, Artificial neural networks, Inspection, Anomaly detection, Applications, Remote Sensing, Algorithms. BibRef

Hyun, J.[Jeeho], Kim, S.[Sangyun], Jeon, G.[Giyoung], Kim, S.H.[Seung Hwan], Bae, K.[Kyunghoon], Kang, B.J.[Byung Jun],
ReConPatch: Contrastive Patch Representation Learning for Industrial Anomaly Detection,
WACV24(2041-2050)
IEEE DOI 2404
Representation learning, Training, Measurement, Dimensionality reduction, Visualization, Modulation, Image recognition and understanding BibRef

Bao, T.P.[Tian-Peng], Chen, J.D.[Jia-Dong], Li, W.[Wei], Wang, X.[Xiang], Fei, J.J.[Jing-Jing], Wu, L.W.[Li-Wei], Zhao, R.[Rui], Zheng, Y.[Ye],
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection,
LIMIT23(993-1002)
IEEE DOI 2401
BibRef

Wang, Y.[Yue], Peng, J.L.[Jin-Long], Zhang, J.N.[Jiang-Ning], Yi, R.[Ran], Wang, Y.[Yabiao], Wang, C.J.[Cheng-Jie],
Multimodal Industrial Anomaly Detection via Hybrid Fusion,
CVPR23(8032-8041)
IEEE DOI 2309
BibRef

Jang, J.K.[Jun-Kyu], Hwang, E.[Eugene], Park, S.H.[Sung-Hyuk],
N-pad : Neighboring Pixel-based Industrial Anomaly Detection,
VISION23(4365-4374)
IEEE DOI 2309
BibRef

Long, J.[Jun], Yang, Y.X.[Yu-Xi], Hua, L.[Liujie], Ou, Y.Q.[Yi-Qi],
Self-supervised Augmented Patches Segmentation for Anomaly Detection,
ACCV22(II:93-107).
Springer DOI 2307

WWW Link. BibRef

Ofir, N.[Nati], Yacobi, R.[Ran], Granoviter, O.[Omer], Levant, B.[Boris], Shtalrid, O.[Ore],
Automatic Defect Segmentation by Unsupervised Anomaly Learning,
ICIP22(306-310)
IEEE DOI 2211
Training, Image segmentation, Head, Shape, Manuals, Implants, Semiconductor device manufacture, Defect Segmentation, Contrastive Learning BibRef

Tian, H.[Huang], Li, X.[Xiang], Yang, L.F.[Ling-Feng], Li, J.[Jun], Yang, J.[Jian], Du, W.D.[Wei-Dong],
PPT: Anomaly Detection Dataset of Printed Products with Templates,
ICIP22(506-510)
IEEE DOI 2211
Printing, Industries, Visualization, Inspection, Benchmark testing, Kernel, Anomaly detection, dataset, printed product, template BibRef

Tan, D.S.[Daniel Stanley], Chen, Y.C.[Yi-Chun], Chen, T.P.C.[Trista Pei-Chun], Chen, W.C.[Wei-Chao],
TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions,
WACV21(276-285)
IEEE DOI 2106
Training, Data collection, Noise robustness, Anomaly detection, Image reconstruction BibRef

Barata, A.P.[António Pereira], Takes, F.W.[Frank W.], van den Herik, H.J.[H. Jaap], Veenman, C.J.[Cor J.],
The eXPose Approach to Crosslier Detection,
ICPR21(2312-2319)
IEEE DOI 2105
Used for inspections of loads of waste for disposal. Supervised learning, Europe, Transportation, Companies, Tools, Task analysis, crosslier, anomaly, detection, visualisation BibRef

Racki, D., Tomazevic, D., Skocaj, D.,
A Compact Convolutional Neural Network for Textured Surface Anomaly Detection,
WACV18(1331-1339)
IEEE DOI 1806
cellular neural nets, feature extraction, feedforward neural nets, image classification, Visualization BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Inspection -- Glass, Panes, Panels, Bottles .


Last update:Jan 20, 2025 at 11:36:25