Abstract
Weakly-supervised semantic segmentation with image-level labels is a important task as it directly associates high-level semantic to low-level appearance, which can significantly reduce human efforts. Despite the remarkable progress, it is still not as good as fully supervised segmentation methods. To improve the accuracy, in this paper, we proposed a novel framework of weakly-supervised semantic segmentation with mean teacher (WSSS-MT) learning to advance the class estimation of image pixels. More specifically, our proposed framework includes a student network and a teacher network in the segmentation module, which aims to effectively utilize information of the training process. The student learns the semantic segmentation network with an updated supervision, while the teacher uses the exponential moving average of the student to achieve a more accurate estimation of supervision. WSSS-MT employs the trained teacher as final segmentation network. Experimental results on the PASCAL VOC 2012 dataset show that the performance of our framework is better than the competing methods.
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References
Kolesnikov, A., Lampert, C.H.: Seed, expand and constrain: three principles for weakly-supervised image segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 695–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_42
Wei, Y., Feng, J., Liang, X., Cheng, M.-M., Zhao, Y., Yan, S.: Object region mining with adversarial erasing: a simple classification to semantic segmentation approach (2017). arXiv preprint arXiv:1703.08448
Huang, Z., Wang, X., Wang, J., Liu, W., Wang, J.: Weakly-supervised semantic segmentation network with deep seeded region growing. In: CVPR, pp. 7014–7023 (2018)
Qi, X., Liu, Z., Shi, J., Zhao, H., Jia, J.: Augmented feedback in semantic segmentation under image level supervision. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 90–105. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_6
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1 (2017)
Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The Pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111(1), 98–136 (2015). https://doi.org/10.1007/s11263-014-0733-5
Hariharan, B., Arbelaez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: Proceedings of ICCV, pp. 991–998. IEEE (2011)
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs (2016). arXiv preprint arXiv:1606.00915
Papandreou, G., Chen, L.-C., Murphy, K., Yuille, A.L.: Weakly- and semi-supervised learning of a DCNN for semantic image segmentation. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1742–1750 (2015)
Ahn, J., Kwak, S.: Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation (2018). arXiv preprint arXiv:1803.10464
Khoreva, A., Benenson, R., Hosang, J., Hein, M., Schiele, B.: Simple does it: weakly supervised instance and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 876–885 (2017)
Vernaza, P., Chandraker, M.: Learning random-walk label propagation for weakly-supervised semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
Li, K., Wu, Z., Peng, K.-C., Ernst, J., Fu, Y.: Tell me where to look: guided attention inference network (2018). arXiv preprint arXiv:1802.10171
Bearman, A., Russakovsky, O., Ferrari, V., Fei-Fei, L.: What’s the point: semantic segmentation with point supervision. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 549–565. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_34
Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Proceedings of NIPS, vol. 2, no. 3, p. 4 (2011)
Adams, R., Bischof, L.: Seeded region growing. IEEE TPAMI 16(6), 641–647 (1994)
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of CVPR, pp. 2921–2929 (2016)
Wei, Y., et al.: STC: a simple to complex framework for weakly-supervised semantic segmentation. IEEE Trans. Pattern Recogn. Mach. Intell. 39(11), 2314–2320 (2017)
Hong, S., Oh, J., Han, B., Lee, H.: Learning transferrable knowledge for semantic segmentation with deep convolutional neural network. In: IEEE CVPR (2016)
Zheng, S., et al.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1529–1537 (2015)
Shimoda, W., Yanai, K.: Distinct class-specific saliency maps for weakly supervised semantic segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 218–234. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_14
Wang, X., You, S., Li, X., Ma, H.: Weakly-supervised semantic segmentation by iteratively mining common object features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1354–1362 (2018)
Pathak, D., Shelhamer, E., Long, J., Darrell, T.: Fully convolutional multi-class multiple instance learning (2014). arXiv preprint arXiv:1412.7144
Bachman, P., Alsharif, O., Precup, D.: Learning with Pseudo-Ensembles, December 2014. arXiv:1412.4864 [cs, stat]
Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In: Proceedings of CVPR, pp. 2083–2090 (2013)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of CVPR, pp. 3431–3440 (2015)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Acknowledgement
This work is partially supported by the National Natural Science Foundation of China (Grant no. 61772568), the Guangzhou Science and Technology Program (Grant no. 201804010288), and the Fundamental Research Funds for the Central Universities (Grant no. 18lgzd15).
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Tan, L., Luo, W., Yang, M. (2019). Weakly-Supervised Semantic Segmentation with Mean Teacher Learning. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_27
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