Foreground–background separation transformer for weakly supervised surface defect detection
Author:
Funder
National Natural Science Foundation of China
Nation Key Research and Development Program of China
Natural Science Foundation of Henan Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02446-8.pdf
Reference51 articles.
1. Božič, J., Tabernik, D., & Skočaj, D. (2021). Mixed supervision for surface-defect detection: From weakly to fully supervised learning. Computers in Industry, 129, 103459. https://doi.org/10.1016/j.compind.2021.103459
2. Chen, Q., Yang, L., Lai, J.-H., & Xie, X. (2022). Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4288–4298). https://doi.org/10.1109/CVPR52688.2022.00425
3. Chen, Z., Ding, J., Cao, L., Shen, Y., Zhang, S., Jiang, G., & Ji, R. (2023). Category-aware allocation transformer for weakly supervised object localization. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 6643–6652). https://doi.org/10.1109/ICCV51070.2023.00611
4. Choe, J., & Shim, H. (2019). Attention-based dropout layer for weakly supervised object localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2219–2228). https://doi.org/10.1109/CVPR.2019.00232
5. Cui, L., Jiang, X., Xu, M., Li, W., Lv, P., & Zhou, B. (2021). Sddnet: A fast and accurate network for surface defect detection. IEEE Transactions on Instrumentation and Measurement, 70, 1–13. https://doi.org/10.1109/TIM.2021.3056744
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