CVPR 2024 Open Access Repository

VAND 2.0: Visual Anomaly and Novelty Detection

Blind Localization and Clustering of Anomalies in Textures: Andrei-Timotei Ardelean,

Tim Weyrich; [pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Ardelean_2024_CVPR, author = {Ardelean, Andrei-Timotei and Weyrich, Tim}, title = {Blind Localization and Clustering of Anomalies in Textures}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3900-3909} }
OmniCrack30k: A Benchmark for Crack Segmentation and the Reasonable Effectiveness of Transfer Learning: Christian Benz,

Volker Rodehorst; [pdf]
[bibtex]
@InProceedings{Benz_2024_CVPR, author = {Benz, Christian and Rodehorst, Volker}, title = {OmniCrack30k: A Benchmark for Crack Segmentation and the Reasonable Effectiveness of Transfer Learning}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3876-3886} }
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach: Ayush K. Rai,

Tarun Krishna,

Feiyan Hu,

Alexandru Drimbarean,

Kevin Mcguinness,

Alan F. Smeaton,

Noel E. O'connor; [pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Rai_2024_CVPR, author = {Rai, Ayush K. and Krishna, Tarun and Hu, Feiyan and Drimbarean, Alexandru and Mcguinness, Kevin and Smeaton, Alan F. and O'connor, Noel E.}, title = {Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3887-3899} }
SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection: Mathis Kruse,

Marco Rudolph,

Dominik Woiwode,

Bodo Rosenhahn; [pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Kruse_2024_CVPR, author = {Kruse, Mathis and Rudolph, Marco and Woiwode, Dominik and Rosenhahn, Bodo}, title = {SplatPose \& Detect: Pose-Agnostic 3D Anomaly Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3950-3960} }
Test Time Training for Industrial Anomaly Segmentation: Alex Costanzino,

Pierluigi Zama Ramirez,

Mirko Del Moro,

Agostino Aiezzo,

Giuseppe Lisanti,

Samuele Salti,

Luigi Di Stefano; [pdf] [arXiv]
[bibtex]
@InProceedings{Costanzino_2024_CVPR, author = {Costanzino, Alex and Ramirez, Pierluigi Zama and Del Moro, Mirko and Aiezzo, Agostino and Lisanti, Giuseppe and Salti, Samuele and Di Stefano, Luigi}, title = {Test Time Training for Industrial Anomaly Segmentation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3910-3920} }
LogicAL: Towards Logical Anomaly Synthesis for Unsupervised Anomaly Localization: Ying Zhao; [pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Zhao_2024_CVPR, author = {Zhao, Ying}, title = {LogicAL: Towards Logical Anomaly Synthesis for Unsupervised Anomaly Localization}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {4022-4031} }
Tracklet-based Explainable Video Anomaly Localization: Ashish Singh,

Michael J. Jones,

Erik G. Learned-Miller; [pdf] [supp]
[bibtex]
@InProceedings{Singh_2024_CVPR, author = {Singh, Ashish and Jones, Michael J. and Learned-Miller, Erik G.}, title = {Tracklet-based Explainable Video Anomaly Localization}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3992-4001} }
Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection: Justin Tebbe,

Jawad Tayyub; [pdf] [supp]
[bibtex]
@InProceedings{Tebbe_2024_CVPR, author = {Tebbe, Justin and Tayyub, Jawad}, title = {Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3940-3949} }
Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise: Fahimeh Fooladgar,

Minh Nguyen Nhat To,

Parvin Mousavi,

Purang Abolmaesumi; [pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Fooladgar_2024_CVPR, author = {Fooladgar, Fahimeh and To, Minh Nguyen Nhat and Mousavi, Parvin and Abolmaesumi, Purang}, title = {Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {4012-4021} }
COOD: Combined Out-of-distribution Detection Using Multiple Measures for Anomaly & Novel Class Detection in Large-scale Hierarchical Classification: Laurens E. Hogeweg,

Rajesh Gangireddy,

Django Brunink,

Vincent J. Kalkman,

Ludo Cornelissen,

Jacob W. Kamminga; [pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Hogeweg_2024_CVPR, author = {Hogeweg, Laurens E. and Gangireddy, Rajesh and Brunink, Django and Kalkman, Vincent J. and Cornelissen, Ludo and Kamminga, Jacob W.}, title = {COOD: Combined Out-of-distribution Detection Using Multiple Measures for Anomaly \& Novel Class Detection in Large-scale Hierarchical Classification}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3971-3980} }
Tri-VAE: Triplet Variational Autoencoder for Unsupervised Anomaly Detection in Brain Tumor MRI: Hansen Wijanarko,

Evelyne Calista,

Li-Fen Chen,

Yong-Sheng Chen; [pdf] [supp]
[bibtex]
@InProceedings{Wijanarko_2024_CVPR, author = {Wijanarko, Hansen and Calista, Evelyne and Chen, Li-Fen and Chen, Yong-Sheng}, title = {Tri-VAE: Triplet Variational Autoencoder for Unsupervised Anomaly Detection in Brain Tumor MRI}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3930-3939} }
Model-guided Contrastive Fine-tuning for Industrial Anomaly Detection: Aitor Artola,

Yannis Kolodziej,

Jean-Michel Morel,

Thibaud Ehret; [pdf] [supp]
[bibtex]
@InProceedings{Artola_2024_CVPR, author = {Artola, Aitor and Kolodziej, Yannis and Morel, Jean-Michel and Ehret, Thibaud}, title = {Model-guided Contrastive Fine-tuning for Industrial Anomaly Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3981-3991} }
Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection: Demetris Lappas,

Vasileios Argyriou,

Dimitrios Makris; [pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Lappas_2024_CVPR, author = {Lappas, Demetris and Argyriou, Vasileios and Makris, Dimitrios}, title = {Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3961-3970} }
DMR: Disentangling Marginal Representations for Out-of-Distribution Detection: Dasol Choi,

Dongbin Na; [pdf]
[bibtex]
@InProceedings{Choi_2024_CVPR, author = {Choi, Dasol and Na, Dongbin}, title = {DMR: Disentangling Marginal Representations for Out-of-Distribution Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {4032-4041} }
TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks: Ho-Weng Lee,

Shang-Hong Lai; [pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Lee_2024_CVPR, author = {Lee, Ho-Weng and Lai, Shang-Hong}, title = {TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3921-3929} }
BMAD: Benchmarks for Medical Anomaly Detection: Jinan Bao,

Hanshi Sun,

Hanqiu Deng,

Yinsheng He,

Zhaoxiang Zhang,

Xingyu Li; [pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Bao_2024_CVPR, author = {Bao, Jinan and Sun, Hanshi and Deng, Hanqiu and He, Yinsheng and Zhang, Zhaoxiang and Li, Xingyu}, title = {BMAD: Benchmarks for Medical Anomaly Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {4042-4053} }
Context-aware Video Anomaly Detection in Long-Term Datasets: Zhengye Yang,

Richard J. Radke; [pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Yang_2024_CVPR, author = {Yang, Zhengye and Radke, Richard J.}, title = {Context-aware Video Anomaly Detection in Long-Term Datasets}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {4002-4011} }
Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble: Blaž Rolih,

Dick Ameln,

Ashwin Vaidya,

Samet Akcay; [pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Rolih_2024_CVPR, author = {Rolih, Bla\v{z} and Ameln, Dick and Vaidya, Ashwin and Akcay, Samet}, title = {Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3866-3875} }