Fetching the paper…
Reading the bibliography…
Recently, large-scale vision-language models such as CLIP have demonstrated immense potential in zero-shot anomaly segmentation (ZSAS) task, utilizing a unified model to directly detect anomalies on any unseen product with painstakingly crafted text prompts.
für Mustererkennung, D.A.: Weakly supervised learning for industrial optical inspection (2007)
2007
Earlier work this paper cites.
2014
Earlier work this paper cites.
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
2016
Earlier work this paper cites.
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). pp. 565–571. Ieee (2016)
2016
Earlier work this paper cites.
Shi, Y., Cui, L., Qi, Z., Meng, F., Chen, Z.: Automatic road crack detection using random structured forests. IEEE Transactions on Intelligent Transportation Systems 17
2016
Earlier work this paper cites.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
2017
Earlier work this paper cites.
Yu, H., Li, Q., Tan, Y., Gan, J., Wang, J., Geng, Y.a., Jia, L.: A coarse-to-fine model for rail surface defect detection. IEEE Transactions on Instrumentation and Measurement 68
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.
Lv, X., Duan, F., Jiang, J.j., Fu, X., Gan, L.: Deep metallic surface defect detection: The new benchmark and detection network. Sensors 20
2020
Earlier work this paper cites.
Božič, J., Tabernik, D., Skočaj, D.: Mixed supervision for surface-defect detection: From weakly to fully supervised learning. Computers in Industry 129
2021
Earlier work this paper cites.
Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. In: 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. IEEE (2021)
2021
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Cited alongside, same era.
Schlagenhauf, T., Landwehr, M.: Industrial machine tool component surface defect dataset. Data in Brief 39
2021
Cited alongside, same era.
Jia, M., Tang, L., Chen, B.C., Cardie, C., Belongie, S., Hariharan, B., Lim, S.N.: Visual prompt tuning. In: European Conference on Computer Vision. pp. 709–727. Springer (2022)
2022
Cited alongside, same era.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
Chiang, W.L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality (2023), https://lmsys.org/blog/2023-03-30-vicuna/
2023
Later among the works it cites.
2023
Later among the works it cites.
Jeong, J., Zou, Y., Kim, T., Zhang, D., Ravichandran, A., Dabeer, O.: Winclip: Zero-/few-shot anomaly classification and segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19606–19616 (2023)
2023
Later among the works it cites.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al.: Segment anything. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4015–4026 (2023)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rao, Y., Zhao, W., Chen, G., Tang, Y., Zhu, Z., Huang, G., Zhou, J., Lu, J.: Denseclip: Language-guided dense prediction with context-aware prompting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18082–18091 (2022)
2022
Cited alongside, same era.
Tao, X., Gong, X., Zhang, X., Yan, S., Adak, C.: Deep learning for unsupervised anomaly localization in industrial images: A survey. IEEE Transactions on Instrumentation and Measurement (2022)
2022
Cited alongside, same era.
Zhang, J., Ding, R., Ban, M., Guo, T.: Fdsnet: An accurate real-time surface defect segmentation network. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 3803–3807. IEEE (2022)
2022
Cited alongside, same era.
Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Conditional prompt learning for vision-language models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16816–16825 (2022)
2022
Cited alongside, same era.
Zou, Y., Jeong, J., Pemula, L., Zhang, D., Dabeer, O.: Spot-the-difference self-supervised pre-training for anomaly detection and segmentation. In: European Conference on Computer Vision. pp. 392–408. Springer (2022)
2022
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhou, Q., Pang, G., Tian, Y., He, S., Chen, J.: Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection. In: The Twelfth International Conference on Learning Representations (2023)
2023
Later among the works it cites.
Zhou, Z., Lei, Y., Zhang, B., Liu, L., Liu, Y.: Zegclip: Towards adapting clip for zero-shot semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11175–11185 (2023)
2023
Later among the works it cites.
Gu, Z., Zhu, B., Zhu, G., Chen, Y., Tang, M., Wang, J.: Anomalygpt: Detecting industrial anomalies using large vision-language models. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 1932–1940 (2024)
2024
Closest in time.