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
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 .