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This work studies a challenging and practical issue known as multi-class unsupervised anomaly detection (MUAD).
Imagenet: A large-scale hierarchical image database, in: CVPR
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009 · 2009
Earlier work this paper cites.
Describing textures in the wild, in: CVPR
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A., 2014 · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context, in: ECCV
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., 2014 · 2014
Earlier work this paper cites.
Deep residual learning for image recognition, in: CVPR
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Earlier work this paper cites.
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery, in: IPMI
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G., 2017 · 2017
Earlier work this paper cites.
Anomaly detection with robust deep autoencoders, in: KDD
Zhou, C., Paffenroth, R.C., 2017 · 2017
Earlier work this paper cites.
An unsupervised-learning-based approach for automated defect inspection on textured surfaces
Mei, S., Yang, H., Yin, Z., 2018 · 2018
Earlier work this paper cites.
Ganomaly: Semi-supervised anomaly detection via adversarial training, in: ACCV
Akcay, S., Atapour-Abarghouei, A., Breckon, T.P., 2019 · 2019
Earlier work this paper cites.
Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection, in: IJCNN
Akçay, S., Atapour-Abarghouei, A., Breckon, T.P., 2019 · 2019
Earlier work this paper cites.
Decoupled weight decay regularization, in: ICLR
Loshchilov, I., Hutter, F., 2019 · 2019
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks, in: ICML
Tan, M., Le, Q., 2019 · 2019
Earlier work this paper cites.
Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings, in: CVPR
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C., 2020 · 2020
Earlier work this paper cites.
Momentum contrast for unsupervised visual representation learning, in: CVPR
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R., 2020 · 2020
Earlier work this paper cites.
The mvtec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection
Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., Steger, C., 2021 · 2021
Earlier work this paper cites.
Emerging properties in self-supervised vision transformers, in: ICCV
Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., Joulin, A., 2021 · 2021
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale, in: ICLR
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., 2021 · 2021
Earlier work this paper cites.
Vilt: Vision-and-language transformer without convolution or region supervision, in: ICML
Kim, W., Son, B., Kim, I., 2021 · 2021
Earlier work this paper cites.
Cutpaste: Self-supervised learning for anomaly detection and localization, in: CVPR
Li, C.L., Sohn, K., Yoon, J., Pfister, T., 2021 · 2021
Earlier work this paper cites.
Swin transformer: Hierarchical vision transformer using shifted windows, in: ICCV
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B., 2021 · 2021
Earlier work this paper cites.
Vt-adl: A vision transformer network for image anomaly detection and localization, in: ISIE
Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L., 2021 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision, in: ICML
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al., 2021 · 2021
Cited alongside, same era.
Informative knowledge distillation for image anomaly segmentation
Cao, Y., Wan, Q., Shen, W., Gao, L., 2022 · 2022
Cited alongside, same era.
Utrad: Anomaly detection and localization with u-transformer
Chen, L., You, Z., Zhang, N., Xi, J., Le, X., 2022 · 2022
Cited alongside, same era.
Anomaly detection via reverse distillation from one-class embedding, in: CVPR
Deng, H., Li, X., 2022 · 2022
Cited alongside, same era.
Masked autoencoders are scalable vision learners, in: CVPR
Segment any anomaly without training via hybrid prompt regularization
Cao, Y., Xu, X., Sun, C., Cheng, Y., Du, Z., Gao, L., Shen, W., 2023 · 2023
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Chen, X., Han, Y., Zhang, J., 2023a · 2023
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Noise-to-norm reconstruction for industrial anomaly detection and localization
Deng, S., Sun, Z., Zhuang, R., Gong, J., 2023 · 2023
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Remembering normality: Memory-guided knowledge distillation for unsupervised anomaly detection, in: ICCV
Gu, Z., Liu, L., Chen, X., Yi, R., Zhang, J., Wang, Y., Wang, C., Shu, A., Jiang, G., Ma, L., 2023 · 2023
Closest in time.
Winclip: Zero-/few-shot anomaly classification and segmentation, in: CVPR
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He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R., 2022 · 2022
Cited alongside, same era.
Softpatch: Unsupervised anomaly detection with noisy data
Jiang, X., Liu, J., Wang, J., Nie, Q., Wu, K., Liu, Y., Wang, C., Zheng, F., 2022 · 2022
Cited alongside, same era.
Exploring plain vision transformer backbones for object detection, in: ECCV
Li, Y., Mao, H., Girshick, R., He, K., 2022 · 2022
Cited alongside, same era.
Haloae: An halonet based local transformer auto-encoder for anomaly detection and localization
Mathian, E., Liu, H., Fernandez-Cuesta, L., Samaras, D., Foll, M., Chen, L., 2022 · 2022
Cited alongside, same era.
Inpainting transformer for anomaly detection, in: ICIAP
Pirnay, J., Chai, K., 2022 · 2022
Cited alongside, same era.
Towards total recall in industrial anomaly detection, in: CVPR
Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P., 2022 · 2022
Cited alongside, same era.
Inception transformer
Si, C., Yu, W., Zhou, P., Zhou, Y., Wang, X., Yan, S., 2022 · 2022
Cited alongside, same era.
Jeong, J., Zou, Y., Kim, T., Zhang, D., Ravichandran, A., Dabeer, O., 2023 · 2023
Closest in time.
Segment anything, in: ICCV
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., Dollar, P., Girshick, R., 2023 · 2023
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Pyramidflow: High-resolution defect contrastive localization using pyramid normalizing flow, in: CVPR
Lei, J., Hu, X., Wang, Y., Liu, D., 2023 · 2023
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Omni-frequency channel-selection representations for unsupervised anomaly detection
Liang, Y., Zhang, J., Zhao, S., Wu, R., Liu, Y., Pan, S., 2023 · 2023
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Self-supervised masked convolutional transformer block for anomaly detection
Madan, N., Ristea, N.C., Ionescu, R.T., Nasrollahi, K., Khan, F.S., Moeslund, T.B., Shah, M., 2023 · 2023
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Revisiting reverse distillation for anomaly detection, in: CVPR
Tien, T.D., Nguyen, A.T., Tran, N.H., Huy, T.D., Duong, S., Nguyen, C.D.T., Truong, S.Q., 2023 · 2023
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Seggpt: Segmenting everything in context
Wang, X., Zhang, X., Cao, Y., Wang, W., Shen, C., Huang, T., 2023 · 2023
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Pushing the limits of fewshot anomaly detection in industry vision: Graphcore, in: ICLR
Xie, G., Wang, J., Liu, J., Jin, Y., Zheng, F., 2023 · 2023
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Omnial: A unified cnn framework for unsupervised anomaly localization, in: CVPR
Zhao, Y., 2023 · 2023
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Efficientad: Accurate visual anomaly detection at millisecond-level latencies, in: CACV
Batzner, K., Heckler, L., König, R., 2024 · 2024
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Diad: A diffusion-based framework for multi-class anomaly detection, in: AAAI
He, H., Zhang, J., Chen, H., Chen, X., Li, Z., Chen, X., Wang, Y., Wang, C., Xie, L., 2024 · 2024
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Anomalydiffusion: Few-shot anomaly image generation with diffusion model, in: AAAI
Hu, T., Zhang, J., Yi, R., Du, Y., Chen, X., Liu, L., Wang, Y., Wang, C., 2024 · 2024
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Deep industrial image anomaly detection: A survey
Liu, J., Xie, G., Wang, J., Li, S., Wang, C., Zheng, F., Jin, Y., 2024 · 2024
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DINOv2: Learning robust visual features without supervision
Oquab, M., Darcet, T., Moutakanni, T., Vo, H.V., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.Y., Li, S.W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., Bojanowski, P., 2024 · 2024
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