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This study addresses the Domain-Class Incremental Learning problem, a realistic but challenging continual learning scenario where both the domain distribution and target classes vary across tasks.
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Prabhu, A., Torr, P.H., Dokania, P.K.: Gdumb: A simple approach that questions our progress in continual learning. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16. pp. 524–540. Springer (2020)
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He, C., Li, K., Zhang, Y., Tang, L., Zhang, Y., Guo, Z., Li, X.: Camouflaged object detection with feature decomposition and edge reconstruction. In: CVPR. pp. 22046–22055 (2023)
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Wang, R., Tang, D., Duan, N., Wei, Z., Huang, X., ji, J., Cao, G., Jiang, D., Zhou, M.: K-adapter: Infusing knowledge into pre-trained models with adapters (2020)
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Zhang, J., Zhang, J., Ghosh, S., Li, D., Tasci, S., Heck, L., Zhang, H., Kuo, C.C.J.: Class-incremental learning via deep model consolidation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1131–1140 (2020)
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De Lange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE transactions on pattern analysis and machine intelligence 44
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Lai, X., Tian, Z., Jiang, L., Liu, S., Zhao, H., Wang, L., Jia, J.: Semi-supervised semantic segmentation with directional context-aware consistency. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1205–1214 (2021)
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Hegde, D., Valanarasu, J.M.J., Patel, V.: Clip goes 3d: Leveraging prompt tuning for language grounded 3d recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2028–2038 (2023)
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Jie, S., Deng, Z.H.: Fact: Factor-tuning for lightweight adaptation on vision transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 37, pp. 1060–1068 (2023)
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Khan, M.G.Z.A., Naeem, M.F., Van Gool, L., Stricker, D., Tombari, F., Afzal, M.Z.: Introducing language guidance in prompt-based continual learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 11463–11473 (2023)
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Khattak, M.U., Rasheed, H., Maaz, M., Khan, S., Khan, F.S.: Maple: Multi-modal prompt learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19113–19122 (2023)
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Smith, J.S., Karlinsky, L., Gutta, V., Cascante-Bonilla, P., Kim, D., Arbelle, A., Panda, R., Feris, R., Kira, Z.: Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11909–11919 (2023)
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Sohn, K., Chang, H., Lezama, J., Polania, L., Zhang, H., Hao, Y., Essa, I., Jiang, L.: Visual prompt tuning for generative transfer learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19840–19851 (2023)
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Tang, L., Li, K., He, C., Zhang, Y., Li, X.: Consistency regularization for generalizable source-free domain adaptation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4323–4333 (2023)
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Tang, L., Li, K., He, C., Zhang, Y., Li, X.: Source-free domain adaptive fundus image segmentation with class-balanced mean teacher. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 684–694. Springer (2023)
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2024
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He, C., Li, K., Zhang, Y., Xu, G., Tang, L.: Weakly-supervised concealed object segmentation with sam-based pseudo labeling and multi-scale feature grouping. NeurIPS (2024)
2024
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He, C., Li, K., Zhang, Y., Zhang, Y., Guo, Z., Li, X.: Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects. In: ICLR (2024)
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Lai, X., Tian, Z., Chen, Y., Li, Y., Yuan, Y., Liu, S., Jia, J.: Lisa: Reasoning segmentation via large language model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9579–9589 (2024)
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Yang, S., Tian, Z., Jiang, L., Jia, J.: Unified language-driven zero-shot domain adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23407–23415 (2024)
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Yang, S., Wu, J., Liu, J., Li, X., Zhang, Q., Pan, M., Gan, Y., Chen, Z., Zhang, S.: Exploring sparse visual prompt for domain adaptive dense prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 16334–16342 (2024)
2024
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Zhou, H., Yang, R., Zhang, Y., Duan, H., Huang, Y., Hu, R., Li, X., Zheng, Y.: Unihead: unifying multi-perception for detection heads. IEEE Transactions on Neural Networks and Learning Systems (2024)
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