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Defect inspection is paramount within the closed-loop manufacturing system.
Mery, D., Filbert, D.: Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence. IEEE Transactions on Robotics and Automation 18
2002
Earlier work this paper cites.
Mery, D., Hahn, D., Hitschfeld, N.: Simulation of defects in aluminium castings using cad models of flaws and real x-ray images. Insight-Non-Destructive Testing and Condition Monitoring pp. 618–624 (2005)
2005
Earlier work this paper cites.
Wieler, M., Hahn, T.: Weakly supervised learning for industrial optical inspection. In: DAGM symposium in. vol. 6 (2007)
2007
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. pp. 248–255. IEEE (2009), https://ieeexplore.ieee.org/abstract/document/5206848/
2009
Earlier work this paper cites.
Huang, Q., Wu, Y., Baruch, J., Jiang, P., Peng, Y.: A template model for defect simulation for evaluating nondestructive testing in x-radiography. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 39
2009
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation (2015)
2015
Earlier work this paper cites.
Song, W., Chen, T., Gu, Z., Gai, W., Huang, W., Wang, B.: Wood materials defects detection using image block percentile color histogram and eigenvector texture feature. In: Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy. Atlantis Press (2015). https://doi.org/10.2991/icismme-15.2015.163, https://doi.org/10.2991/icismme-15.2015.163
2015
Earlier work this paper cites.
Vapnik, V.N., Chervonenkis, A.Y.: On the uniform convergence of relative frequencies of events to their probabilities. Measures of complexity: festschrift for alexey chervonenkis (2015)
2015
Earlier work this paper cites.
Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., De Schutter, B.: Deep convolutional neural networks for detection of rail surface defects. In: 2016 International joint conference on neural networks (IJCNN). pp. 2584–2589 (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2881–2890 (2017)
2017
Earlier work this paper cites.
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
2017
Earlier work this paper cites.
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Computer Vision (ICCV), 2017 IEEE International Conference on (2017)
2017
Earlier work this paper cites.
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV). pp. 801–818 (2018)
2018
Earlier work this paper cites.
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 9592–9600 (2019)
2019
Earlier work this paper cites.
Mundt, M., Majumder, S., Murali, S., Panetsos, P., Ramesh, V.: Meta-learning convolutional neural architectures for multi-target concrete defect classification with the concrete defect bridge image dataset. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11196–11205 (2019)
2019
Earlier work this paper cites.
Rott Shaham, T., Dekel, T., Michaeli, T.: Singan: Learning a generative model from a single natural image. In: Computer Vision (ICCV), IEEE International Conference on (2019)
2019
Earlier work this paper cites.
Silvestre-Blanes, J., Albero-Albero, T., Miralles, I., Pérez-Llorens, R., Moreno, J.: A public fabric database for defect detection methods and results. Autex Research Journal 19
2019
Earlier work this paper cites.
Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., Torralba, A.: Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision 127
2019
Cited alongside, same era.
Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization (2020)
2020
Cited alongside, same era.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.
Niu, S., Li, B., Wang, X., Lin, H.: Defect image sample generation with gan for improving defect recognition. IEEE Transactions on Automation Science and Engineering 17
2020
Cited alongside, same era.
Tabernik, D., Šela, S., Skvarč, J., Skočaj, D.: Segmentation-based deep-learning approach for surface-defect detection. Journal of Intelligent Manufacturing 31
Chen, X., Zhao, Z., Zhang, Y., Duan, M., Qi, D., Zhao, H.: Focalclick: Towards practical interactive image segmentation (2022)
2022
Later among the works it cites.
Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation (2022)
2022
Later among the works it cites.
Choi, J., Lee, J., Shin, C., Kim, S., Kim, H., Yoon, S.: Perception prioritized training of diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)
2022
Later among the works it cites.
Du, Z., Gao, L., Li, X.: A new contrastive gan with data augmentation for surface defect recognition under limited data. IEEE Transactions on Instrumentation and Measurement (2022)
2022
Later among the works it cites.
Ni, C., Yang, K., Xia, X., Lo, D., Chen, X., Yang, X.: Defect identification, categorization, and repair: Better together (2022)
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2020
Cited alongside, same era.
Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43
2020
Cited alongside, same era.
Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., Steger, C.: The mvtec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection. International Journal of Computer Vision 129
2021
Cited alongside, same era.
Chen, X., Zhao, Z., Yu, F., Zhang, Y., Duan, M.: Conditional diffusion for interactive segmentation. In: ICCV (2021)
2021
Cited alongside, same era.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in neural information processing systems 34
2021
Cited alongside, same era.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale (2021)
2021
Cited alongside, same era.
Guo, J., Wang, Q., Li, Y.: Semi-supervised learning based on convolutional neural network and uncertainty filter for façade defects classification. Computer-Aided Civil and Infrastructure Engineering pp. 302–317 (2021)
2021
Cited alongside, same era.
Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: VT-ADL: A vision transformer network for image anomaly detection and localization. In: 30th IEEE/IES International Symposium on Industrial Electronics (ISIE) (June 2021)
2021
Cited alongside, same era.
2022
Later among the works it cites.
Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Wei, J., Zhang, Z., Shen, F., Lv, C.: Mask-guided generation method for industrial defect images with non-uniform structures. Machines 10
2022
Later among the works it cites.
Zou, Y., Jeong, J., Pemula, L., Zhang, D., Dabeer, O.: Spot-the-difference self-supervised pre-training for anomaly detection and segmentation (2022)
2022
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2023
Closest in time.
Incorporated, C.: Standard fabric defect glossary (2023), uRL: https://www.cottoninc.com/quality-products/textile-resources/fabric-defect-glossary
2023
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2023
Closest in time.
Li, J., Li, D., Savarese, S., Hoi, S.: BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In: ICML (2023)
2023
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Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning (2023)
2023
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Lu, F., Yao, X., Fu, C.W., Jia, J.: Removing anomalies as noises for industrial defect localization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 16166–16175 (2023)
2023
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2023
Closest in time.
2023
Closest in time.
Tang, J., Lu, H., Xu, X., Wu, R., Hu, S., Zhang, T., Cheng, T.W., Ge, M., Chen, Y.C., Tsung, F.: An incremental unified framework for small defect inspection. In: 18th European Conference on Computer Vision (ECCV) (2024), https://github.com/jqtangust/IUF
2024
Closest in time.