Page 487 - 《软件学报》2025年第12期

5868 软件学报 2025 年第 36 卷第 12 期

Conf. on Computer vision. Seoul: IEEE, 2019. 5258–5269. [doi: 10.1109/ICCV.2019.00536]
[25] Berthelot D, Carlini N, Goodfellow I, Oliver A, Papernot N, Raffel C. MixMatch: A holistic approach to semi-supervised learning. In:
Proc. of the 33rd Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2019. 5049–5059.
[26] Arazo E, Ortego D, Albert P, O’Connor NE, McGuinness K. Pseudo-labeling and confirmation bias in deep semi-supervised learning. In:
Proc. of the 2020 Int’l Joint Conf. on Neural Networks (IJCNN). Glasgow: IEEE, 2020. 1–8. [doi: 10.1109/IJCNN48605.2020.9207304]
[27] Yang LH, Qi L, Feng LT, Zhang W, Shi YH. Revisiting weak-to-strong consistency in semi-supervised semantic segmentation. In: Proc.
of the 2023 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023. 7236–7246. [doi: 10.1109/
CVPR52729.2023.00699]
[28] Liu LM, Zong JX, Xiao ZJ, Lan H, Qu HC. Cross-consistent semantic segmentation algorithm based on manifold regularization. Journal
of Image and Graphics, 2022, 27(12): 3542–3552 (in Chinese with English abstract). [doi: 10.11834/jig.210571]
[29] Li YT, Zhang CL. Algorithm research of deep learning in semi-supervised semantic segmentation. Artificial Intelligence and Robotics
Research, 2023, 12(4): 328–339 (in Chinese with English abstract). [doi: 10.12677/AIRR.2023.124036]
[30] Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A. The pascal visual object classes (VOC) challenge. Int’l Journal of
Computer Vision, 2010, 88(2): 303–338. [doi: 10.1007/s11263-009-0275-4]
[31] Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B. The cityscapes dataset for semantic
urban scene understanding. In: Proc. of the 2016 IEEE Conf. on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016.
3213–3223. [doi: 10.1109/CVPR.2016.350]
[32] Zhao Z, Long SF, Pi JM, Wang JD, Zhou LP. Instance-specific and model-adaptive supervision for semi-supervised semantic
segmentation. In: Proc. of the 2023 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023.
23705–23714. [doi: 10.1109/CVPR52729.2023.02270]
[33] Chen SH, Chen YD, Zheng YH, Yang ZX, Wu EH. A transformer-based adaptive prototype matching network for few-shot semantic
segmentation. In: Proc. of the 33rd Int’l Joint Conf. on Artificial Intelligence (IJCAI). Jeju, 2024. 659–667.
[34] Kurakin A, Raffel C, Berthelot D. RemixMatch: Semi-supervised learning with distribution matching and augmentation anchoring. 2020.
[35] Sohn K, Berthelot D, Li CL, Zhang ZZ, Carlini N, Cubuk ED, Kurakin A, Zhang H, Raffel C. FixMatch: Simplifying semi-supervised
learning with consistency and confidence. In: Proc. of the 34th Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran
Associates Inc., 2020. 596–608.
[36] Liu YY, Tian Y, Chen YH, Liu FB, Belagiannis V, Carneiro G. Perturbed and strict mean teachers for semi-supervised semantic
segmentation. In: Proc. of the 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022.
4248–4257. [doi: 10.1109/CVPR52688.2022.00422]
[37] Olsson V, Tranheden W, Pinto J, Svensson L. ClassMix: Segmentation-based data augmentation for semi-supervised learning. In: Proc. of
the 2021 IEEE Winter Conf. on Applications of Computer Vision. Waikoloa: IEEE, 2021. 1368–1377. [doi: 10.1109/WACV48630.2021.
00141]
[38] Jiang F, Gu Q, Hao HZ, Li N, Guo YW, Chen DX. Survey on content-based image segmentation methods. Ruan Jian Xue Bao/Journal of
Software, 2017, 28(1): 160–183 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5136.htm [doi: 10.13328/j.cnki.jos.
005136]
[39] Wang ZC, Zhao Z, Xing XX, Xu D, Kong XY, Zhou LP. Conflict-based cross-view consistency for semi-supervised semantic
segmentation. In: Proc. of the 2023 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023.
19585–19595. [doi: 10.1109/CVPR52729.2023.01876]
[40] Xu HM, Liu LQ, Bian QC, Yang Z. Semi-supervised semantic segmentation with prototype-based consistency regularization. In: Proc. of
the 36th Int’l Conf. on Neural Information Processing Systems. New Orleans: Curran Associates Inc., 2022. 26007–26020.
[41] Wang YC, Wang HC, Shen YJ, Fei JJ, Li W, Jin GQ, Wu LW, Zhao R, Le XY. Semi-supervised semantic segmentation using unreliable
pseudo-labels. In: Proc. of the 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022.
4238–4247. [doi: 10.1109/CVPR52688.2022.00421]
[42] Yin JJ, Yan S, Chen T, Chen Y, Yao YZ. Class probability space regularization for semi-supervised semantic segmentation. Computer
Vision and Image Understanding, 2024, 249: 104146. [doi: 10.1016/j.cviu.2024.104146]
[43] Wu KB, Li WB, Xiao XF. IPixMatch: Boost semi-supervised semantic segmentation with inter-pixel relation. arXiv:2404.18891, 2024.
[44] Hou YZ, Gould S, Zheng L. View-coherent correlation consistency for semi-supervised semantic segmentation. Pattern Recognition,
2024, 147: 110089. [doi: 10.1016/j.patcog.2023.110089]
[45] Li YL, Gao Y, Yan JL, Zou BH, Wang JM. Image inpainting methods based on deep neural networks: A review. Chinese Journal of
Computes, 2021, 44(11): 2295–2316 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2021.02295]