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Code, Anomaly Detection.
WWW Link. Reed-Xiaoli detector
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Elsevier DOI
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Anomaly detection, Isolation forest, Anomaly score, Outliers
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A semantically driven self-supervised algorithm for detecting
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2112
Anomaly detection, Self-supervised learning,
Representation learning, Remote sensing, Multivariate statistics
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Zhang, K.T.[Kai-Tai],
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PEDENet: Image anomaly localization via patch embedding and density
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PRL(153), 2022, pp. 144-150.
Elsevier DOI
2201
Image anomaly detection, Image anomaly localization, Density estimation
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Sato, K.[Kazuki],
Nakata, S.[Satoshi],
Matsubara, T.[Takashi],
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Few-Shot Anomaly Detection Using Deep Generative Models for Grouped
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IEICE(E105-D), No. 2, February 2022, pp. 436-440.
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Wang, Z.P.[Zhi-Peng],
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Unsupervised anomaly detection via dual transformation-aware
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IET-IPR(16), No. 6, 2022, pp. 1657-1668.
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images that are globally or locally different from the training set.
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Two-stage coarse-to-fine image anomaly segmentation and detection
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IVC(139), 2023, pp. 104817.
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Anomaly detection and segmentation,
Convolutional neural network, Pseudo anomaly insertion, Superpixel segmentation
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Tang, T.W.[Ta-Wei],
Hsu, H.[Hakiem],
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Industrial anomaly detection with multiscale autoencoder and deep
feature extractor-based neural network,
IET-IPR(17), No. 6, 2023, pp. 1752-1761.
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image classification, image recognition,
inspection, unsupervised learning
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Chen, Z.[Zhi],
Duan, J.[Jiang],
Kang, L.[Li],
Qiu, G.P.[Guo-Ping],
Supervised Anomaly Detection via Conditional Generative Adversarial
Network and Ensemble Active Learning,
PAMI(45), No. 6, June 2023, pp. 7781-7798.
IEEE DOI
2305
Detectors, Anomaly detection, Generative adversarial networks,
Ensemble learning, Training, Generators, Task analysis,
outlier detection
BibRef
Arias, L.A.S.[Luis Antonio Souto],
Oosterlee, C.W.[Cornelis W.],
Cirillo, P.[Pasquale],
AIDA: Analytic isolation and distance-based anomaly detection
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PR(141), 2023, pp. 109607.
Elsevier DOI
2306
Outlier detection, Anomaly explanation, Isolation, Distance, Ensemble methods
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Wei, S.X.[Shen-Xing],
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Kurniawan, M.R.[Muhammad Rifki],
Ma, Z.H.[Zhi-Heng],
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Topology-preserving transfer learning for weakly-supervised anomaly
detection and segmentation,
PRL(170), 2023, pp. 77-84.
Elsevier DOI
2306
Anomaly detection, Transfer learning,
Weakly-supervised learning, Topology preservation
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Makov”, M.[Michaela],
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PR(151), 2024, pp. 110381.
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Set data, Anomaly detection, Generative models,
One-class classification, Set transformers
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Li, H.X.[Han-Xi],
Hu, J.F.[Jian-Fei],
Li, B.[Bo],
Chen, H.[Hao],
Zheng, Y.B.[Yong-Bin],
Shen, C.H.[Chun-Hua],
Target Before Shooting: Accurate Anomaly Detection and Localization
Under One Millisecond via Cascade Patch Retrieval,
IP(33), 2024, pp. 5606-5621.
IEEE DOI Code:
WWW Link.
2410
Accuracy, Anomaly detection, Measurement, Standards, Prototypes,
Image retrieval, Image reconstruction, Anomaly detection, metric learning
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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,
Task analysis, Production, Anomaly generation,
anomaly inspection
BibRef
Yang, M.H.[Ming-Hui],
Liu, J.[Jing],
Yang, Z.W.[Zhi-Wei],
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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
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Liu, B.[Binhui],
Guo, T.[Tianchu],
Luo, B.[Bin],
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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.
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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
Chen, Q.Y.[Qi-Yu],
Luo, H.Y.[Hui-Yuan],
Gao, H.[Han],
Lv, C.[Chengkan],
Zhang, Z.T.[Zheng-Tao],
Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly
Detection,
CirSysVideo(35), No. 2, February 2025, pp. 1193-1208.
IEEE DOI Code:
WWW Link.
2502
Image reconstruction, Feature extraction, Anomaly detection,
Training, Optimization, Learning (artificial intelligence),
progressive boundary guidance
BibRef
Liu, J.[Jinfan],
Yan, Y.C.[Yi-Chao],
Li, J.J.[Jun-Jie],
Zhao, W.M.[Wei-Ming],
Chu, P.Z.[Peng-Zhi],
Sheng, X.[Xingdong],
Liu, Y.H.[Yun-Hui],
Yang, X.K.[Xiao-Kang],
IPAD: Industrial Process Anomaly Detection Dataset,
CirSysVideo(35), No. 1, January 2025, pp. 380-393.
IEEE DOI Code:
WWW Link.
2502
Anomaly detection, Synthetic data, Pedestrians,
Image reconstruction, Production facilities, Data models,
reconstruction model
BibRef
Wang, C.J.[Cheng-Jie],
Jiang, X.[Xi],
Gao, B.B.[Bin-Bin],
Gan, Z.[Zhenye],
Liu, Y.[Yong],
Zheng, F.[Feng],
Ma, L.Z.[Li-Zhuang],
SoftPatch+: Fully unsupervised anomaly classification and
segmentation,
PR(161), 2025, pp. 111295.
Elsevier DOI Code:
WWW Link.
2502
Anomaly detection, Unsupervised learning, Learn with noise
BibRef
Zhang, J.J.[Jia-Jun],
Yang, Z.W.[Zhou-Wang],
Song, Y.Z.[Yan-Zhi],
DC-AD: A Divide-and-Conquer Method for Few-Shot Anomaly Detection,
PR(162), 2025, pp. 111360.
Elsevier DOI
2503
Few-shot learning, Anomaly detection, Region matching, Benchmarks
BibRef
Pei, M.J.[Ming-Jing],
Zhou, X.[Xiancun],
Huang, Y.[Yourui],
Zhang, F.H.[Feng-Hui],
Pei, M.L.[Ming-Li],
Yang, Y.D.[Ya-Dong],
Zheng, S.J.[Shi-Jian],
Xin, M.[Mai],
Enhancing industrial anomaly detection with Mamba-inspired feature
fusion,
JVCIR(107), 2025, pp. 104368.
Elsevier DOI
2503
Industrial image anomaly detection, Unsupervised learning,
Mamba, Feature fusion
BibRef
Zhang, J.N.[Jiang-Ning],
Chen, X.[Xuhai],
Wang, Y.B.[Ya-Biao],
Wang, C.J.[Cheng-Jie],
Liu, Y.[Yong],
Li, X.T.[Xiang-Tai],
Yang, M.H.[Ming-Hsuan],
Tao, D.C.[Da-Cheng],
Exploring plain ViT features for multi-class unsupervised visual
anomaly detection,
CVIU(253), 2025, pp. 104308.
Elsevier DOI Code:
WWW Link.
2503
Multi-class anomaly detection, Vision transformer,
Unsupervised learning, Feature reconstruction
BibRef
He, L.[Liren],
Jiang, Z.K.[Zheng-Kai],
Peng, J.L.[Jin-Long],
Zhu, W.B.[Wen-Bing],
Liu, L.[Liang],
Du, Q.G.[Qian-Gang],
Hu, X.B.[Xia-Bin],
Chi, M.M.[Ming-Min],
Wang, Y.B.[Ya-Biao],
Wang, C.J.[Cheng-Jie],
Learning Unified Reference Representation for Unsupervised Multi-class
Anomaly Detection,
ECCV24(LXVII: 216-232).
Springer DOI
2412
BibRef
Chen, D.[Dong],
Pan, K.[Kaihang],
Dai, G.Y.[Guang-Yu],
Wang, G.M.[Guo-Ming],
Zhuang, Y.T.[Yue-Ting],
Tang, S.L.[Si-Liang],
Xu, M.L.[Ming-Liang],
Improving Vision Anomaly Detection With the Guidance of Language
Modality,
MultMed(27), 2025, pp. 1410-1419.
IEEE DOI
2503
Detectors, Feature extraction, Anomaly detection, Entropy, Training,
Correlation, Contrastive learning, Semantics, Testing, Symbols,
anomaly detection
BibRef
Wu, G.C.[Gao-Chang],
Zhang, Y.P.[Ya-Peng],
Deng, L.[Lan],
Zhang, J.X.[Jing-Xin],
Chai, T.Y.[Tian-You],
Cross-Modal Learning for Anomaly Detection in Complex Industrial
Process: Methodology and Benchmark,
CirSysVideo(35), No. 3, March 2025, pp. 2632-2645.
IEEE DOI Code:
WWW Link.
2503
Anomaly detection, Smelting, Magnesium, Visualization,
Feature extraction, Transformers, Tokenization, Correlation,
fused magnesium furnace
BibRef
Chen, Z.X.[Zi-Xuan],
Xie, X.H.[Xiao-Hua],
Yang, L.X.[Ling-Xiao],
Lai, J.H.[Jian-Huang],
Hard-Normal Example-Aware Template Mutual Matching for Industrial
Anomaly Detection,
IJCV(133), No. 5, May 2025, pp. 2927-2949.
Springer DOI
2504
BibRef
He, S.[Sihan],
Zhang, T.[Tao],
Song, W.[Wei],
Yu, H.B.[Hong-Bin],
Feature Bank-Guided Reconstruction for Anomaly Detection,
SPLetters(32), 2025, pp. 1480-1484.
IEEE DOI
2504
Image reconstruction, Discrete cosine transforms,
Feature extraction, Anomaly detection, Training, semisupervised learning
BibRef
Yu, Q.[Qien],
Dai, S.X.[Sheng-Xin],
Dong, R.[Ran],
Ikuno, S.[Soichiro],
Attention-based vector quantized variational autoencoder for anomaly
detection by using orthogonal subspace constraints,
PR(164), 2025, pp. 111500.
Elsevier DOI
2504
Industrial image, Anomaly detection, Subspace projection,
Attention mechanism, Vector quantized variational autoencoder
BibRef
Wang, C.[Chen],
Erfani, S.[Sarah],
Alpcan, T.[Tansu],
Leckie, C.[Christopher],
OIL-AD: An anomaly detection framework for decision-making sequences,
PR(166), 2025, pp. 111656.
Elsevier DOI Code:
WWW Link.
2505
Anomaly detection, Offline imitation learning,
Sequential decision-making, Reinforcement learning
BibRef
Li, J.H.[Jia-Hao],
Chen, Y.Q.[Yi-Qiang],
Xing, Y.[Yunbing],
Gu, Y.[Yang],
Lan, X.Y.[Xiang-Yuan],
GSM: Global Semantic Memory,
PR(169), 2026, pp. 111950.
Elsevier DOI
2509
Anomaly detection, Unsupervised learning, Memory
BibRef
Lee, Y.J.[Yu-Jin],
Lim, H.[Harin],
Jang, S.[Seoyoon],
Yoon, H.[Hyunsoo],
UniFormaly: Towards task-agnostic unified framework for visual
anomaly detection,
PR(169), 2026, pp. 111820.
Elsevier DOI Code:
WWW Link.
2509
Anomaly detection, Unified framework,
Off-the-shelf representation, Patch-level memory bank
BibRef
Liu, X.[Xu],
Wu, C.L.[Chun-Lei],
Zhang, H.[Huan],
Wang, L.[Leiquan],
A memory-tree driven network for multi-view fusion anomaly detection,
PR(170), 2026, pp. 112106.
Elsevier DOI
2509
Memory tree, Fusion scheme, Anomaly detection
BibRef
Wang, C.J.[Cheng-Jie],
Zhu, H.[Haokun],
Peng, J.L.[Jin-Long],
Wang, Y.[Yue],
Yi, R.[Ran],
Wu, Y.S.[Yun-Sheng],
Ma, L.Z.[Li-Zhuang],
Zhang, J.N.[Jiang-Ning],
M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via
Multimodal Denoising,
PAMI(47), No. 11, November 2025, pp. 9981-9993.
IEEE DOI
2510
Anomaly detection, Feature extraction, Noise measurement,
Training, Noise, Image reconstruction,
unsupervised learning
BibRef
Wang, Y.[Yue],
Peng, J.L.[Jin-Long],
Zhang, J.N.[Jiang-Ning],
Yi, R.[Ran],
Wang, Y.B.[Ya-Biao],
Wang, C.J.[Cheng-Jie],
Multimodal Industrial Anomaly Detection via Hybrid Fusion,
CVPR23(8032-8041)
IEEE DOI
2309
BibRef
Wang, G.[Gang],
Zou, Y.S.[Yi-Sheng],
He, S.L.[Song-Lin],
Wang, Y.K.[Ya-Kun],
Dai, R.H.[Rui-Hong],
Anomaly Detection and Localization via Reverse Distillation With
Latent Anomaly Suppression,
CirSysVideo(35), No. 10, October 2025, pp. 9592-9607.
IEEE DOI
2510
Decoding, Anomaly detection, Image reconstruction, Training,
Feature extraction, Location awareness, Semantics, Optimization,
knowledge distillation
BibRef
Zhou, Y.X.[Yi-Xuan],
Xu, X.[Xing],
Sun, Z.[Zhe],
Song, J.K.[Jing-Kuan],
Cichocki, A.[Andrzej],
Shen, H.T.[Heng Tao],
VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection
via Hierarchical Vector Quantization,
MultMed(27), 2025, pp. 6884-6895.
IEEE DOI
2510
Anomaly detection, Vector quantization, Data models, Prototypes,
Gaussian distribution, Computational modeling, Adaptation models,
vector quantization
BibRef
Wang, S.[Sihan],
Yuan, Z.[Zhong],
Cheng, S.T.[Shi-Tong],
Chen, H.M.[Hong-Mei],
Peng, D.Z.[De-Zhong],
Granular-ball computing-based Random Walk for anomaly detection,
PR(165), 2025, pp. 111588.
Elsevier DOI Code:
WWW Link.
2505
Anomaly detection, Outlier detection, Granular-ball computing,
Multi-granularity, Random walk
BibRef
Galka, L.[Lukasz],
Optimized Deep Isolation Forest,
PRL(197), 2025, pp. 88-94.
Elsevier DOI
2510
Anomaly detection, Isolation Forest, Deep Isolation Forest,
Neural networks, Outlier detection
BibRef
Nourmohammadi, S.[Sepehr],
Sarp-Yenicesu, A.[Arda],
Rahimzadeh-Arashloo, S.[Shervin],
Oguz, O.S.[Ozgur S.],
Locally adaptive one-class classifier fusion with dynamic Lp-Norm
constraints for robust anomaly detection,
PR(171), 2026, pp. 112204.
Elsevier DOI
2511
One-class classification, Anomaly detection,
Locally adaptive learning, Classifier fusion
BibRef
Zhong, Y.H.[Yuan-Hong],
Yan, G.[Ge],
Hu, Y.[Yongting],
Zhu, D.[Dong],
Zhu, R.[Ruyue],
A Two-Stage Framework With Memory for Anomaly Detection via Video
Decomposition and Bidirectional Consistency,
CirSysVideo(35), No. 11, November 2025, pp. 11377-11389.
IEEE DOI Code:
WWW Link.
2511
Prototypes, Anomaly detection, Training, Feature extraction,
Image reconstruction, Decoding, Streaming media, Data models,
video decomposition
BibRef
Coscia, P.[Pasquale],
Genovese, A.[Angelo],
Piuri, V.[Vincenzo],
Scotti, F.[Fabio],
OneN: Guided attention for natively-explainable anomaly detection,
IVC(163), 2025, pp. 105741.
Elsevier DOI
2511
Anomaly detection, Attention mechanism, Knowledge distillation,
Generative model, Vision transformer
BibRef
Wang, P.X.[Peng-Xiang],
Qin, Y.H.[Yu-Hua],
Zong, X.[Xulin],
Wang, C.[Chaoyue],
Zhang, H.[Hao],
MMFNet: A multi-scale memory fusion network based on simulated
abnormal samples for anomaly detection,
PR(172), 2026, pp. 112581.
Elsevier DOI
2512
Anomaly detection, Anomaly synthesis, Unsupervised learning,
Mechanism-driven data enhancement
BibRef
Rahmaniar, W.[Wahyu],
Suzuki, K.[Kenji],
Multi-AD: cross-domain unsupervised anomaly detection for medical and
industrial applications,
PR(172), 2026, pp. 112486.
Elsevier DOI
2512
Anomaly detection, CNN, Deep learning, Knowledge distillation,
Medical imaging, Industrial imaging
BibRef
Wang, C.[Chuang],
Ning, X.[Xin],
Qian, P.J.[Peng-Jiang],
Hu, W.J.[Wen-Jun],
Yao, J.[Jian],
Ng, E.Y.K.[Eddie-Yin-Kwee],
Lai, K.W.[Khin-Wee],
Wang, S.T.[Shi-Tong],
Distribution entropy regularized multimodal subspace support vector
data description for anomaly detection,
PR(172), 2026, pp. 112478.
Elsevier DOI
2512
One-class classification (OCC), Multimodal data,
Support vector data description (SVDD), Subspace learning
BibRef
Leveni, F.[Filippo],
Magri, L.[Luca],
Alippi, C.[Cesare],
Boracchi, G.[Giacomo],
Preference isolation forest for structure-based anomaly detection,
PR(172), 2026, pp. 112405.
Elsevier DOI
2512
Structure-based anomaly detection, Isolation-based anomaly detection
BibRef
Nakkina, T.G.[Tapan Ganatma],
Zhong, Y.H.[Yu-Hao],
Sumethasorn, P.[Pete],
Tian, H.[Haopeng],
Bukkapatnam, S.[Satish],
When Textures Deceive: Weakly Supervised Industrial Anomaly Detection
with Adapted-Loss CycleGAN,
VAND25(4068-4077)
IEEE DOI
2512
Benchmark testing, Manufacturing, Complexity theory, Labeling,
Anomaly detection, Standards, anomaly localization, cyclegan,
texture complexity
BibRef
Mokhtar, S.[Sassan],
Mousakhan, A.[Arian],
Galesso, S.[Silvio],
Tayyub, J.[Jawad],
Brox, T.[Thomas],
Detect, Classify, Act: Categorizing Industrial Anomalies with
Multi-Modal Large Language Models,
VAND25(4058-4067)
IEEE DOI
2512
Training, Visualization, Accuracy, Annotations,
Large language models, Pipelines, Inspection, Benchmark testing, MLLM
BibRef
Pemula, L.[Latha],
Zhang, D.Q.[Dong-Qing],
Dabeer, O.[Onkar],
Robust AD: A Real World Benchmark Dataset for Robustness in
Industrial Anomaly Detection,
VAND25(4047-4057)
IEEE DOI Code:
WWW Link.
2512
Measurement, Training, Computational modeling, Benchmark testing,
Robustness, Complexity theory, Mirrors, Anomaly detection,
domain generalization
BibRef
Baitieva, A.[Aimira],
Bouaouni, Y.[Yacine],
Briot, A.[Alexandre],
Ameln, D.[Dick],
Khalfaoui, S.[Souhaiel],
Akcay, S.[Samet],
Beyond Academic Benchmarks: Critical Analysis and Best Practices for
Visual Industrial Anomaly Detection,
VAND25(4015-4025)
IEEE DOI Code:
WWW Link.
2512
Measurement, Degradation, Visualization, Production, Benchmark testing,
Predictive models, Inspection, Data models, visual inspection
BibRef
Kruse, M.[Mathis],
Rosenhahn, B.[Bodo],
Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial
Anomaly Detection,
VAND25(3933-3944)
IEEE DOI
2512
Training, Bridges, Estimation, Computer architecture, Production,
Boosting, Anomaly detection, anomaly detection, defect detection,
density estimation
BibRef
Park, Y.[YeongHyeon],
Kang, S.[Sungho],
Kim, M.J.[Myung Jin],
Kim, H.S.[Hyeong Seok],
Yi, J.H.[June-Ho],
Feature Attenuation of Defective Representation Can Resolve
Incomplete Masking on Anomaly Detection,
VAND25(3922-3932)
IEEE DOI
2512
Computational modeling, Scalability, Artificial neural networks,
Benchmark testing, Attenuation, Feature extraction,
feature representation
BibRef
Zhao, Y.[Ying],
AnomalyHybrid: A Domain-Agnostic Generative Framework for General
Anomaly Detection,
SyntaGen25(3118-3127)
IEEE DOI
2512
Training, Image segmentation, Image color analysis,
Image edge detection, Depth measurement, Diversity reception,
GAN
BibRef
Lagos, J.[Juan],
Ali, H.[Haider],
Faroque, A.[Adnan],
Rahtu, E.[Esa],
Heterogeneous Datasets for Unsupervised Image Anomaly Detection,
WACV25(7266-7276)
IEEE DOI Code:
WWW Link.
2505
Location awareness, Measurement, Visualization, Accuracy, Roads,
Benchmark testing, Robustness, Manufacturing, Anomaly detection, DCNN
BibRef
Li, X.F.[Xiao-Fan],
Tan, X.[Xin],
Chen, Z.[Zhuo],
Zhang, Z.Z.[Zhi-Zhong],
Zhang, R.X.[Rui-Xin],
Guo, R.[Rizen],
Jiang, G.[Guanna],
Chen, Y.L.[Yu-Long],
Qu, Y.Y.[Yan-Yun],
Ma, L.Z.[Li-Zhuang],
Xie, Y.[Yuan],
One-for-More: Continual Diffusion Model for Anomaly Detection,
CVPR25(4766-4775)
IEEE DOI Code:
WWW Link.
2508
Training, Memory management, Markov processes, Diffusion models,
Stability analysis, Iterative methods, Anomaly detection,
continual learning
BibRef
Huang, Z.M.[Zi-Ming],
Li, X.[Xurui],
Liu, H.T.[Hao-Tian],
Xue, F.[Feng],
Wang, Y.Z.[Yu-Zhe],
Zhou, Y.[Yu],
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial
Scenarios,
CVPR25(4755-4765)
IEEE DOI Code:
WWW Link.
2508
Representation learning, Codes, Clustering methods, Merging,
Semantics, Crops, Anomaly detection, Faces
BibRef
Ma, W.X.[Wen-Xin],
Zhang, X.[Xu],
Yao, Q.S.[Qing-Song],
Tang, F.[Fenghe],
Wu, C.X.[Chen-Xu],
Li, Y.[Yingtai],
Yan, R.[Rui],
Jiang, Z.H.[Zi-Hang],
Zhou, S.K.[S. Kevin],
AA-CLIP: Enhancing Zero-Shot Anomaly Detection via Anomaly-Aware CLIP,
CVPR25(4744-4754)
IEEE DOI Code:
WWW Link.
2508
Location awareness, Visualization, Biomedical equipment, Codes,
Semantics, Medical services, Lesions, Anomaly detection, clip,
vlm
BibRef
Yang, K.[Kaichen],
Cao, J.J.[Jun-Jie],
Bai, Z.[Zeyu],
Su, Z.X.[Zhi-Xun],
Tagliasacchi, A.[Andrea],
PIAD: Pose and Illumination agnostic Anomaly Detection,
CVPR25(4734-4743)
IEEE DOI Code:
WWW Link.
2508
Accuracy, Autonomous systems, Lighting, Training data,
Anomaly detection, anomaly detection, camera pose estimation, 3dgs, illumination
BibRef
Ye, J.A.[Jian-An],
Zhao, W.G.[Wei-Guang],
Yang, X.[Xi],
Cheng, G.L.[Guang-Liang],
Huang, K.[Kaizhu],
PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly
Detection,
CVPR25(1353-1362)
IEEE DOI Code:
WWW Link.
2508
Point cloud compression, Training, Measurement, Data visualization,
Feature extraction, Vectors, Data models, Anomaly detection,
3d point cloud
BibRef
Mao, K.[Kai],
Wei, P.[Ping],
Lian, Y.Y.[Yi-Yang],
Wang, Y.Y.[Yang-Yang],
Zheng, N.N.[Nan-Ning],
Beyond Single-Modal Boundary: Cross-Modal Anomaly Detection through
Visual Prototype and Harmonization,
CVPR25(9964-9973)
IEEE DOI Code:
WWW Link.
2508
Visualization, Adaptation models, Codes, Semantics, Prototypes,
Data models, Anomaly detection
BibRef
Luo, W.[Wei],
Cao, Y.[Yunkang],
Yao, H.M.[Hai-Ming],
Zhang, X.T.[Xiao-Tian],
Lou, J.A.[Jian-An],
Cheng, Y.Q.[Yu-Qi],
Shen, W.M.[Wei-Ming],
Yu, W.Y.[Wen-Yong],
Exploring Intrinsic Normal Prototypes within a Single Image for
Universal Anomaly Detection,
CVPR25(9974-9983)
IEEE DOI Code:
WWW Link.
2508
Training, Limiting, Prototypes, Coherence,
III-V semiconductor materials, Indium phosphide, feature reconstruction
BibRef
Li, W.Q.[Wen-Qiao],
Zheng, B.[Bozhong],
Xu, X.H.[Xiao-Hao],
Gan, J.Y.[Jin-Ye],
Lu, F.[Fading],
Li, X.[Xiang],
Ni, N.[Na],
Tian, Z.[Zheng],
Huang, X.N.[Xiao-Nan],
Gao, S.H.[Sheng-Hua],
Wu, Y.[Yingna],
Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry,
and Internal Properties,
CVPR25(9984-9993)
IEEE DOI Code:
WWW Link.
2508
Geometry, Deformation, Face recognition, Infrared imaging,
Inspection, Sensors, Laser fusion, Anomaly detection, multisensor
BibRef
Wei, S.[Shun],
Jiang, J.L.[Jie-Lin],
Xu, X.L.[Xiao-Long],
UniNet: A Contrastive Learning-guided Unified Framework with Feature
Selection for Anomaly Detection,
CVPR25(9994-10003)
IEEE DOI
2508
Bridges, Technological innovation, Visualization, Correlation,
Feature extraction, Anomaly detection, anomaly detection, contrastive learning
BibRef
Nafez, M.[Mojtaba],
Koochakian, A.[Amirhossein],
Maleki, A.[Arad],
Habibi, J.[Jafar],
Rohban, M.H.[Mohammad Hossein],
PatchGuard: Adversarially Robust Anomaly Detection and Localization
through Vision Transformers and Pseudo Anomalies,
CVPR25(20383-20394)
IEEE DOI
2508
Location awareness, Training, Training data, Performance gain,
Transformers, Robustness, Anomaly detection, Biomedical imaging
BibRef
Bhunia, A.[Ankan],
Li, C.J.[Chang-Jian],
Bilen, H.[Hakan],
Odd-One-Out: Anomaly Detection by Comparing with Neighbors,
CVPR25(20395-20404)
IEEE DOI
2508
Solid modeling, Correlation, Computational modeling,
Benchmark testing, Object recognition, Anomaly detection, multi-view
BibRef
Guo, J.[Jia],
Lu, S.[Shuai],
Zhang, W.[Weihang],
Chen, F.[Fang],
Li, H.Q.[Hui-Qi],
Liao, H.[Hongen],
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised
Anomaly Detection,
CVPR25(20405-20415)
IEEE DOI Code:
WWW Link.
2508
Philosophical considerations, Computational modeling, Noise, Force,
Transformers, Feature extraction, Noise measurement,
unsupervised learning
BibRef
Wang, F.[Fuyun],
Zhang, T.[Tong],
Wang, Y.Z.[Yuan-Zhi],
Qiu, Y.[Yide],
Liu, X.[Xin],
Guo, X.[Xu],
Cui, Z.[Zhen],
Distribution Prototype Diffusion Learning for Open-set Supervised
Anomaly Detection,
CVPR25(20416-20426)
IEEE DOI
2508
Representation learning, Bridges, Prototypes,
Gaussian distribution, Robustness, Anomaly detection, Dispersion
BibRef
Sun, H.[Han],
Cao, Y.[Yunkang],
Dong, H.[Hao],
Fink, O.[Olga],
Unseen Visual Anomaly Generation,
CVPR25(25508-25517)
IEEE DOI Code:
WWW Link.
2508
Training, Visualization, Limiting, Image synthesis,
Foundation models, Training data, Anomaly detection, Optimization,
diffusion model
BibRef
Akshay, S.[Shilhora],
Narasimhan, N.L.[Niveditha Lakshmi],
George, J.[Jacob],
Balasubramanian, V.N.[Vineeth N.],
A Unified Latent Schrödinger Bridge Diffusion Model for Unsupervised
Anomaly Detection and Localization,
CVPR25(25528-25538)
IEEE DOI Code:
WWW Link.
2508
Location awareness, Bridges, Training, Adaptation models, Transforms,
Robustness, Anomaly detection, Image reconstruction,
diffusion
BibRef
Qu, Z.[Zhen],
Tao, X.[Xian],
Gong, X.[Xinyi],
Qu, S.[ShiChen],
Chen, Q.Y.[Qi-Yu],
Zhang, Z.T.[Zheng-Tao],
Wang, X.G.[Xin-Gang],
Ding, G.[Guiguang],
Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection,
CVPR25(30398-30408)
IEEE DOI Code:
WWW Link.
2508
Training, Uncertainty, Semantics, Probabilistic logic, Vectors,
Probability distribution, oBayes methods, Anomaly detection, CLIP
BibRef
Zhang, J.J.[Jin-Jin],
Wang, G.D.[Guo-Dong],
Jin, Y.Z.[Yi-Zhou],
Huang, D.[Di],
Towards Training-free Anomaly Detection with Vision and Language
Foundation Models,
CVPR25(15204-15213)
IEEE DOI Code:
WWW Link.
2508
Training, Foundation models, Semantic segmentation, Detectors,
Inspection, Robustness, Proposals, Anomaly detection,
vision and language foundation models
BibRef
Zhu, W.B.[Wen-Bing],
Wang, L.[Lidong],
Zhou, Z.Q.[Zi-Qing],
Wang, C.J.[Cheng-Jie],
Pan, Y.R.[Yu-Rui],
Zhang, R.[Ruoyi],
Chen, Z.[Zhuhao],
Cheng, L.J.[Lin-Jie],
Gao, B.B.[Bin-Bin],
Zhang, J.N.[Jiang-Ning],
Gan, Z.[Zhenye],
Wang, Y.X.[Yu-Xie],
Chen, Y.L.[Yu-Long],
Qian, S.G.[Shu-Guang],
Chi, M.[Mingmin],
Peng, B.[Bo],
Ma, L.Z.[Li-Zhuang],
Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial
Anomaly Detection,
CVPR25(15214-15223)
IEEE DOI Code:
WWW Link.
2508
Point cloud compression, Image resolution, Face recognition,
Machine vision, Feature extraction, Robustness, Complexity theory, Anomaly detection
BibRef
Wu, S.[Sheng],
Wang, Y.[Yimi],
Liu, X.D.[Xu-Dong],
Yang, Y.G.[Yu-Guang],
Wang, R.[Runqi],
Guo, G.D.[Guo-Dong],
Doermann, D.[David],
Zhang, B.C.[Bao-Chang],
DFM: Differentiable Feature Matching for Anomaly Detection,
CVPR25(15224-15233)
IEEE DOI
2508
Training, Computational modeling, Transforms, Feature extraction,
Anomaly detection, Optimization, Pattern matching
BibRef
Gu, Z.P.[Zhao-Peng],
Zhu, B.[Bingke],
Zhu, G.[Guibo],
Chen, Y.Y.[Ying-Ying],
Tang, M.[Ming],
Wang, J.Q.[Jin-Qiao],
UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly
Detection,
CVPR25(15194-15203)
IEEE DOI Code:
WWW Link.
2508
Training, Visualization, Semantics, Training data, Standardization,
Data models, Anomaly detection, Context modeling, multimodal
BibRef
Beizaee, F.[Farzad],
Lodygensky, G.A.[Gregory A.],
Desrosiers, C.[Christian],
Dolz, J.[Jose],
Correcting Deviations from Normality: A Reformulated Diffusion Model
for Multi-Class Unsupervised Anomaly Detection,
CVPR25(19088-19097)
IEEE DOI Code:
WWW Link.
2508
Location awareness, Degradation, Noise, Diffusion models,
Anomaly detection, Image reconstruction, Standards
BibRef
Kashiani, H.[Hossein],
Talemi, N.A.[Niloufar Alipour],
Afghah, F.[Fatemeh],
ROADS: Robust Prompt-Driven Multi-Class Anomaly Detection Under
Domain Shift,
WACV25(7908-7917)
IEEE DOI
2505
Location awareness, Degradation, Roads, Interference, Detectors,
Robustness, Decoding, Anomaly detection, visual anomaly detection,
anomaly detection
BibRef
Zhang, J.[Jie],
Suganuma, M.[Masanori],
Okatani, T.[Takayuki],
Contextual Affinity Distillation for Image Anomaly Detection,
WACV24(148-157)
IEEE DOI
2404
Training, Representation learning, Correlation,
Image color analysis, Feature extraction, Vectors, Algorithms
BibRef
He, H.[Haitian],
Erfani, S.[Sarah],
Gong, M.M.[Ming-Ming],
Ke, Q.H.[Qiu-Hong],
Learning Transferable Representations for Image Anomaly Localization
Using Dense Pretraining,
WACV24(1102-1111)
IEEE DOI Code:
WWW Link.
2404
Training, Location awareness, Representation learning,
Fault detection, Self-supervised learning, Inspection, Algorithms
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
Jin, Y.[Ying],
Peng, J.L.[Jin-Long],
He, Q.D.[Qing-Dong],
Hu, T.[Teng],
Wu, J.[Jiafu],
Chen, H.[Hao],
Wang, H.X.[Hao-Xuan],
Zhu, W.B.[Wen-Bing],
Chi, M.M.[Ming-Min],
Liu, J.[Jun],
Wang, Y.B.[Ya-Biao],
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image
Generation,
CVPR25(30420-30429)
IEEE DOI
2508
Location awareness, Image synthesis, Shape, Computational modeling,
Fitting, Inspection, Diffusion models, Distortion, Data models, Manufacturing
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
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
Costanzino, A.[Alex],
Ramirez, P.Z.[Pierluigi Zama],
Lisanti, G.[Giuseppe],
di Stefano, L.[Luigi],
Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping,
CVPR24(17234-17243)
IEEE DOI
2410
Point cloud compression, Memory management, Feature extraction,
Manufacturing, Anomaly detection, anomaly, layer pruning
BibRef
Rolih, B.[Bla],
Ameln, D.[Dick],
Vaidya, A.[Ashwin],
Akcay, S.[Samet],
Divide and Conquer: High-Resolution Industrial Anomaly Detection via
Memory Efficient Tiled Ensemble,
VAND24(3866-3875)
IEEE DOI
2410
Training, Image resolution, Tiles, Memory management, Stacking,
Graphics processing units, Anomaly Detection,
High-resolution processing
BibRef
Costanzino, A.[Alex],
Ramirez, P.Z.[Pierluigi Zama],
del Moro, M.[Mirko],
Aiezzo, A.[Agostino],
Lisanti, G.[Giuseppe],
Salti, S.[Samuele],
di Stefano, L.[Luigi],
Test Time Training for Industrial Anomaly Segmentation,
VAND24(3910-3920)
IEEE DOI
2410
Training, Quality control, Feature extraction,
Standards, anomaly, anomaly detection, anomaly segmentation,
anomaly scores
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.Y.[Yan-Yun],
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],
Simões, 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
Liu, Z.K.[Zhi-Kang],
Zhou, Y.M.[Yi-Ming],
Xu, Y.S.[Yuan-Sheng],
Wang, Z.[Zilei],
SimpleNet: A Simple Network for Image Anomaly Detection and
Localization,
CVPR23(20402-20411)
IEEE DOI
2309
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
Fang, Z.[Zheng],
Wang, X.Y.[Xiao-Yang],
Li, H.C.[Hao-Cheng],
Liu, J.J.[Jie-Jie],
Hu, Q.[Qiugui],
Xiao, J.[Jimin],
FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature
Reconstruction,
ICCV23(17435-17444)
IEEE DOI Code:
WWW Link.
2401
BibRef
Rudolph, M.[Marco],
Wehrbein, T.[Tom],
Rosenhahn, B.[Bodo],
Wandt, B.[Bastian],
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection,
WACV23(2591-2601)
IEEE DOI
2302
Training, Location awareness, Neural networks, Estimation,
Algorithms: Image recognition and understanding, object detection
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
Yao, X.C.[Xin-Cheng],
Li, R.[Ruoqi],
Qian, Z.F.[Ze-Feng],
Luo, Y.[Yan],
Zhang, C.Y.[Chong-Yang],
Focus the Discrepancy: Intra- and Inter-Correlation Learning for
Image Anomaly Detection,
ICCV23(6780-6790)
IEEE DOI Code:
WWW Link.
2401
BibRef
Gula, T.[Tetiana],
Bertoldo, J.P.C.[Joćo P. C.],
Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection,
LXCV-ICCV23(4112-4120)
IEEE DOI
2401
BibRef
Chiu, L.L.[Li-Ling],
Lai, S.H.[Shang-Hong],
Self-Supervised Normalizing Flows for Image Anomaly Detection and
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VAND23(2927-2936)
IEEE DOI
2309
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Kurup, U.[Unmesh],
Bishop, T.[Tom],
No Shifted Augmentations (NSA):
Compact distributions for robust self-supervised Anomaly Detection,
WACV23(5500-5509)
IEEE DOI
2302
Representation learning, Measurement, Pollution, Costs,
Source coding, Training data, Feature extraction, visual reasoning
BibRef
Huang, C.Q.[Chao-Qin],
Guan, H.Y.[Hao-Yan],
Jiang, A.[Aofan],
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2211
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de Nardin, A.[Axel],
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Bringing Attention to Image Anomaly Detection,
PART22(115-126).
Springer DOI
2208
BibRef
Mishra, P.[Pankaj],
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Foresti, G.L.[Gian Luca],
Image Anomaly Detection by Aggregating Deep Pyramidal Representations,
IML20(705-718).
Springer DOI
2103
BibRef
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Self-supervised Augmented Patches Segmentation for Anomaly Detection,
ACCV22(II:93-107).
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2307
WWW Link.
BibRef
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Yacobi, R.[Ran],
Granoviter, O.[Omer],
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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
Tsai, C.C.[Chin-Chia],
Wu, T.H.[Tsung-Hsuan],
Lai, S.H.[Shang-Hong],
Multi-Scale Patch-Based Representation Learning for Image Anomaly
Detection and Segmentation,
WACV22(3065-3073)
IEEE DOI
2202
Representation learning, Training, Image segmentation,
Image representation, Benchmark testing,
Semi- and Un- supervised Learning
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
Ye, F.[Fei],
Zheng, H.J.[Huang-Jie],
Huang, C.Q.[Chao-Qin],
Zhang, Y.[Ya],
Deep Unsupervised Image Anomaly Detection:
An Information Theoretic Framework,
ICIP21(1609-1613)
IEEE DOI
2201
Image processing, Benchmark testing, Linear programming, Entropy,
Task analysis, Anomaly detection, Anomaly detection, entropy
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Kwon, G.[Gukyeong],
Prabhushankar, M.[Mohit],
Temel, D.[Dogancan],
Al Regib, G.[Ghassan],
Backpropagated Gradient Representations for Anomaly Detection,
ECCV20(XXI:206-226).
Springer DOI
2011
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Mishra, P.[Pankaj],
Piciarelli, C.[Claudio],
Foresti, G.L.[Gian Luca],
Image Anomaly Detection by Aggregating Deep Pyramidal Representations,
IML20(705-718).
Springer DOI
2103
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 .