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Weakly-supervised object detection (WSOD) aims to train an object detector only requiring the image-level annotations.
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Chen, Z., Fu, Z., Jiang, R., wu Chen, Y., Hua, X.: Slv: Spatial likelihood voting for weakly supervised object detection. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 12992–13001 (2020)
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Arun, A., Jawahar, C., Kumar, M.P.: Dissimilarity coefficient based weakly supervised object detection. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Jun 2019). https://doi.org/10.1109/cvpr.2019.00966
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Jeong, J., Lee, S., Kim, J., Kwak, N.: Consistency-based semi-supervised learning for object detection. Advances in neural information processing systems 32
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Lee, S., Kwak, S., Cho, M.: Universal bounding box regression and its applications. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds.) Computer Vision – ACCV 2018. pp. 373–387. Springer International Publishing, Cham (2019)
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Li, J., Socher, R., Hoi, S.C.: Dividemix: Learning with noisy labels as semi-supervised learning. In: International Conference on Learning Representations (2019)
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Li, X., Kan, M., Shan, S., Chen, X.: Weakly supervised object detection with segmentation collaboration. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (October 2019)
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Shen, Y., Ji, R., Wang, Y., Wu, Y., Cao, L.: Cyclic guidance for weakly supervised joint detection and segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
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Wan, F., Liu, C., Ke, W., Ji, X., Jiao, J., Ye, Q.: C-mil: Continuation multiple instance learning for weakly supervised object detection. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Jun 2019)
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Ren, Z., Yu, Z., Yang, X., Liu, M.Y., Lee, Y.J., Schwing, A.G., Kautz, J.: Instance-aware, context-focused, and memory-efficient weakly supervised object detection. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
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Shen, Y., Ji, R., Wang, Y., Chen, Z., Zheng, F., Huang, F., Wu, Y.: Enabling deep residual networks for weakly supervised object detection. In: European Conference on Computer Vision (ECCV) (2020)
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Cao, T., Du, L., Zhang, X., Chen, S., Zhang, Y., Wang, Y.F.: Cat: Weakly supervised object detection with category transfer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 3070–3079 (October 2021)
2021
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Dong, B., Huang, Z., Guo, Y., Wang, Q., Niu, Z., Zuo, W.: Boosting weakly supervised object detection via learning bounding box adjusters. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2876–2885 (2021)
2021
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Jia, Q., Wei, S., Ruan, T., Zhao, Y., Zhao, Y.: Gradingnet: Towards providing reliable supervisions for weakly supervised object detection by grading the box candidates. Proceedings of the AAAI Conference on Artificial Intelligence 35
2021
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2021
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Sui, L., Zhang, C.L., Wu, J.: Salvage of supervision in weakly supervised detection (2021)
2021
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2021
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Yin, Y., Deng, J., Zhou, W., Li, H.: Instance mining with class feature banks for weakly supervised object detection. Proceedings of the AAAI Conference on Artificial Intelligence 35
2021
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