References
Zhan X, Liu Z, Luo P, et al. Mix-and-match tuning for self-supervised semantic segmentation. 2017. ArXiv: 1712.00661
Wang K, Lin L, Yan X, et al. Cost-effective object detection: active sample mining with switchable selection criteria. IEEE Trans Neural Netw Learn Syst, 2019, 30: 834–850
Wang K, Zhang D, Li Y, et al. Cost-effective active learning for deep image classification. IEEE Trans Circ Syst Video Technol, 2017, 27: 2591–2600
Acuna D, Ling H, Kar A, et al. Efficient interactive annotation of segmentation datasets with Polygon-RNN++. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. 859–868
Jain S D, Grauman K. Active image segmentation propagation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 2864–2873
Papandreou G, Chen L-C, Murphy K P, et al. Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, 2015. 1742–1750
Zhao C R, Chen K, Zang D, et al. Uncertainty-optimized deep learning model for small-scale person re-identification. Sci China Inf Sci, 2019, 62: 220102
Liu X, Kan M, Shan S, et al. Noisy face image sets refining collaborated with discriminant feature space learning. In: Proceedings of the 12th IEEE International Conference on Automatic Face & Gesture Recognition, 2017. 544–550
Huang T T, Xu Y C, Bai S, et al. Feature context learning for human parsing. Sci China Inf Sci, 2019, 62: 220101
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 61876084, 61876127, 61732011). The authors would like to greatly appreciate all the anonymous reviewers for their comments.
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Zhang, X., Wang, L., Xie, J. et al. Human-in-the-loop image segmentation and annotation. Sci. China Inf. Sci. 63, 219101 (2020). https://doi.org/10.1007/s11432-019-2759-y
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DOI: https://doi.org/10.1007/s11432-019-2759-y