[21] J. Li and Q. Dong. Open-set semantic segmentation for point clouds via adversarial prototype framework. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9425-9434, 2023.
[22] Z. Li and D. Hoiem. Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence, 40(12):2935-2947, 2017.
[23] Z. Liu, Z. Miao, X. Zhan, J. Wang, B. Gong, and S. X. Yu. Large-scale long-tailed recognition in an open world, 2019.
[24] U. Michieli and P. Zanuttigh. Incremental learning techniques for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision workshops, pages 0–0, 2019.
[25] U. Michieli and P. Zanuttigh. Incremental learning techniques for semantic segmentation. In ICCVWS, 2019.
[26] U. Michieli and P. Zanuttigh. Continual semantic segmentation via repulsion-attraction of sparse and disentangled latent representations. In Proc. Conf. Comp. Vision Pattern Rec., 2021.
[27] F. Ozdemir, P. Fuernstahl, and O. Goksel. Learn the new, keep the old: Extending pretrained models with new anatomy and images. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV 11, pages 361-369. Springer, 2018.
[28] F. Ozdemir and O. Goksel. Extending pretrained segmentation networks with additional anatomical structures. International journal of computer assisted radiology and surgery, 14:1187-1195, 2019.
[29] M. H. Phan, T.-A. Ta, S. L. Phung, L. Tran-Thanh, and A. Bouzerdoum. Class similarity weighted knowledge distillation for continual semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16866-16875, June 2022.
[30] Y. Qiu, Y. Shen, Z. Sun, Y. Zheng, X. Chang, W. Zheng, and R. Wang. Sats: Self-attention transfer for continual semantic segmentation. Pattern Recognition, 138:109383, 2023.
[31] S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert. icarl: Incremental classifier and representation learning. In Proc. Conf. Comp. Vision Pattern Rec., 2017.
[32] J. Ren, C. Yu, S. Sheng, X. Ma, H. Zhao, S. Yi, and H. Li. Balanced meta-softmax for long-tailed visual recognition, 2020.
[33] A. Robins. Catastrophic forgetting, rehearsal and pseudorehearsal. Connection Science, 1995.
[34] M. Rostami. Lifelong domain adaptation via consolidated internal distribution. In NeurIPS, 2021.
[35] A. Saporta, A. Douillard, T.-H. Vu, P. Pérez, and M. Cord. Multi-head distillation for continual unsupervised domain adaptation in semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, 2022.
[36] K. Shmelkov, C. Schmid, and K. Alahari. Incremental learning of object detectors without catastrophic forgetting. In Proceedings of the IEEE international conference on computer vision, pages 3400-3409, 2017.
[37] A. Szabó, H. Jamali-Rad, and S.-D. Mannava. Tilted cross-entropy (tce): Promoting fairness in semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2305–2310, 2021.
[38] S. Thrun. Lifelong learning algorithms. In Springer Learning to Learn, 1998.
[39] T.-D. Truong, C. N. Duong, N. Le, S. L. Phung, C. Rainwater, and K. Luu. Bimal: Bijective maximum likelihood approach to domain adaptation in semantic scene segmentation. In International Conference on Computer Vision, 2021.
[40] T.-D. Truong, N. Le, B. Raj, J. Cothren, and K. Luu. Freedom: Fairness domain adaptation approach to semantic scene understanding. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[41] R. Volpi, D. Larlus, and G. Rogez. Continual adaptation of visual representations via domain randomization and meta-learning. In Proc. Conf. Comp. Vision Pattern Rec., 2021.