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Continual learning, also known as incremental learning or life-long learning, stands at the forefront of deep learning and AI systems.
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R. Polikar, L. Upda, S. Upda, and V. Honavar, “Learn++: an incremental learning algorithm for supervised neural networks,”
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2015
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G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,”
2015
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S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek, “On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,”
2015
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G. Ros, L. Sellart, J. Materzynska, D. Vázquez, and A. M. López, “The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,”
2016
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M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,”
2016
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R. R. Selvaraju, A. Das, R. Vedantam, M. Cogswell, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,”
2016
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B. Liu, “Lifelong machine learning: a paradigm for continuous learning,”
2017
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B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba, “Scene parsing through ade20k dataset,” in
2017
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S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert, “icarl: Incremental classifier and representation learning,”
2017
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J. Yoon, E. Yang, J. Lee, and S. J. Hwang, “Lifelong learning with dynamically expandable networks,”
2017
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J. Kirkpatrick, R. Pascanu, N. C. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell, “Overcoming catastrophic forgetting in neural networks,”
2017
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2017
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F. M. Castro, M. J. Marín-Jiménez, N. Guil, C. Schmid, and K. Alahari, “End-to-end incremental learning,” in
2018
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M. Schrimpf, J. Kubilius, H. Hong, N. J. Majaj, R. Rajalingham, E. B. Issa, K. Kar, P. Bashivan, J. Prescott-Roy, K. Schmidt, D. Yamins, and J. J. DiCarlo, “Brain-score: Which artificial neural network for object recognition is most brain-like?”
2018
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Z. Li and D. Hoiem, “Learning without forgetting,”
2018
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A. Chaudhry, P. K. Dokania, T. Ajanthan, and P. H. Torr, “Riemannian walk for incremental learning: Understanding forgetting and intransigence,” in
2018
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P. Dhar, R. V. Singh, K.-C. Peng, Z. Wu, and R. Chellappa, “Learning without memorizing,”
2018
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N. Dong and E. P. Xing, “Few-shot semantic segmentation with prototype learning,” in
2018
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2018
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Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, and Y. Fu, “Large scale incremental learning,” in
2019
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U. Michieli and P. Zanuttigh, “Incremental learning techniques for semantic segmentation,”
2019
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O. Tasar, Y. Tarabalka, and P. Alliez, “Incremental learning for semantic segmentation of large-scale remote sensing data,”
2019
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J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “Semantickitti: A dataset for semantic scene understanding of lidar sequences,”
2019
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R. Aljundi, M. Lin, B. Goujaud, and Y. Bengio, “Gradient based sample selection for online continual learning,”
2019
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C. Yang, L. Xie, C. Su, and A. L. Yuille, “Snapshot distillation: Teacher-student optimization in one generation,”
2019
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B. Heo, J. Kim, S. Yun, H. Park, N. Kwak, and J. Y. Choi, “A comprehensive overhaul of feature distillation,”
2019
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J. Chen, Y. K. Cho, and Z. Kira, “Multi-view incremental segmentation of 3-d point clouds for mobile robots,”
2019
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O. Tasar, Y. Tarabalka, and P. Alliez, “Incremental learning for semantic segmentation of large-scale remote sensing data,”
2019
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K. Wang, J. H. Liew, Y. Zou, D. Zhou, and J. Feng, “Panet: Few-shot image semantic segmentation with prototype alignment,”
2019
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G. M. van de Ven, H. T. Siegelmann, and A. S. Tolias, “Brain-inspired replay for continual learning with artificial neural networks,”
2020
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O. Tasar, A. Giros, Y. Tarabalka, P. Alliez, and S. Clerc, “Daugnet: Unsupervised, multisource, multitarget, and life-long domain adaptation for semantic segmentation of satellite images,”
2020
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M. Klingner, A. Bär, P. Donn, and T. Fingscheidt, “Class-incremental learning for semantic segmentation re-using neither old data nor old labels,”
2020
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F. Cermelli, M. Mancini, S. R. Bulò, E. Ricci, and B. Caputo, “Modeling the background for incremental learning in semantic segmentation,”
2020
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L. Liu, J. Cao, M. Liu, Y. Guo, Q. Chen, and M. Tan, “Dynamic extension nets for few-shot semantic segmentation,” in
2020
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M. Masana, X. Liu, B. Twardowski, M. Menta, A. D. Bagdanov, and J. van de Weijer, “Class-incremental learning: Survey and performance evaluation on image classification,”
2020
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T. Lesort, V. Lomonaco, A. Stoian, D. Maltoni, D. Filliat, and N. Díaz-Rodríguez, “Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges,”
2020
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M. Kanakis, D. Bruggemann, S. Saha, S. Georgoulis, A. Obukhov, and L. Van Gool, “Reparameterizing convolutions for incremental multi-task learning without task interference,” in
2020
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X. Li, L. Lei, Y. Sun, M. Li, and G. Kuang, “Multimodal bilinear fusion network with second-order attention-based channel selection for land cover classification,”
2020
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L. Yu, B. Twardowski, X. Liu, L. Herranz, K. Wang, Y. Cheng, S. Jui, and J. v. d. Weijer, “Semantic drift compensation for class-incremental learning,” in
2020
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,”
2020
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,”
2020
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L. Wang, B. Lei, Q. Li, H. Su, J. Zhu, and Y. Zhong, “Triple-memory networks: A brain-inspired method for continual learning,”
2020
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Y. Lin, G. Vosselman, Y. Cao, and M. Y. Yang, “Active and incremental learning for semantic als point cloud segmentation,”
2020
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W. Li, J. Gu, B. Chen, and J. Han, “Incremental instance-oriented 3d semantic mapping via rgb-d cameras for unknown indoor scene,”
2020
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2020
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2020
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C. Bian, C. Yuan, K. Ma, S. Yu, D. Wei, and Y. Zheng, “Domain adaptation meets zero-shot learning: an annotation-efficient approach to multi-modality medical image segmentation,”
2021
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Y. Feng, X. Sun, W. Diao, J. Li, X. Gao, and K. Fu, “Continual learning with structured inheritance for semantic segmentation in aerial imagery,”
2021
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X. Hu, K. Tang, C. Miao, X. Hua, and H. Zhang, “Distilling causal effect of data in class-incremental learning,”
2021
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2021
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S. Cha, Y. Yoo, T. Moon
2021
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U. Michieli and P. Zanuttigh, “Continual semantic segmentation via repulsion-attraction of sparse and disentangled latent representations,”
2021
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A. Maracani, U. Michieli, M. Toldo, and P. Zanuttigh, “Recall: Replay-based continual learning in semantic segmentation,”
2021
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A. Douillard, Y. Chen, A. Dapogny, and M. Cord, “Plop: Learning without forgetting for continual semantic segmentation,”
2021
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F. Cermelli, M. Mancini, Y. Xian, Z. Akata, and B. Caputo, “Prototype-based incremental few-shot segmentation,” in
2021
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S. Yan, J. Xie, and X. He, “Der: Dynamically expandable representation for class incremental learning,” in
2021
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E. Belouadah, A. Popescu, and I. Kanellos, “A comprehensive study of class incremental learning algorithms for visual tasks,”
2021
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M. De Lange, R. Aljundi, M. Masana, S. Parisot, X. Jia, A. Leonardis, G. Slabaugh, and T. Tuytelaars, “A continual learning survey: Defying forgetting in classification tasks,”
2021
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T. Kalb, M. Roschani, M. Ruf, and J. Beyerer, “Continual learning for class-and domain-incremental semantic segmentation,” in
2021
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W. Peng, X. Hong, G. Zhao, and E. Cambria, “Adaptive modality distillation for separable multimodal sentiment analysis,”
2021
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C. Sakaridis, D. Dai, and L. Van Gool, “Acdc: The adverse conditions dataset with correspondences for semantic driving scene understanding,” in
2021
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D. Hong, J. Hu, J. Yao, J. Chanussot, and X. Zhu, “Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model,”
2021
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J.-A. Termöhlen, M. Klingner, L. J. Brettin, N. M. Schmidt, and T. Fingscheidt, “Continual unsupervised domain adaptation for semantic segmentation by online frequency domain style transfer,” in
2021
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Y. Wang, G. Huang, S. Song, X. Pan, Y. Xia, and C. Wu, “Regularizing deep networks with semantic data augmentation,”
2021
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S. Yan, J. Zhou, J. Xie, S. Zhang, and X. He, “An em framework for online incremental learning of semantic segmentation,” in
2021
Cited alongside, same era.
F. Wiewel and B. Yang, “Entropy-based sample selection for online continual learning,” in
2021
Cited alongside, same era.
J. Bang, H. Kim, Y. J. Yoo, J.-W. Ha, and J. Choi, “Rainbow memory: Continual learning with a memory of diverse samples,”
2021
Cited alongside, same era.
2021
Cited alongside, same era.
J. Cen, P. Yun, J. Cai, M. Y. Wang, and M. Liu, “Deep metric learning for open world semantic segmentation,”
2021
Cited alongside, same era.
P. Liu, X. Wang, M. Fan, H. Pan, M. Yin, X. Zhu, D. Du, X. Zhao, L. Xiao, L. Ding
2022
Later among the works it cites.
J. Zhang, R. Gu, P. Xue, M. Liu, H. Zheng, Y. Zheng, L. Ma, G. Wang, and L. Gu, “S3r: Shape and semantics-based selective regularization for explainable continual segmentation across multiple sites,”
2023
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2023
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2023
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C. Yu, Q. feng Zhou, J. Li, J.-C. Yuan, Z. Wang, and F. Wang, “Foundation model drives weakly incremental learning for semantic segmentation,”
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C. Huynh, A. Tran, K. Luu, and M. Hoai, “Progressive semantic segmentation,”
2021
Cited alongside, same era.
C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. Le, Y.-H. Sung, Z. Li, and T. Duerig, “Scaling up visual and vision-language representation learning with noisy text supervision,” in
2021
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark
2021
Cited alongside, same era.
Y. Feng, X. Sun, W. Diao, J. Li, and X. Gao, “Double similarity distillation for semantic image segmentation,”
2021
Cited alongside, same era.
C. Shu, Y. Liu, J. Gao, Z. Yan, and C. Shen, “Channel-wise knowledge distillation for dense prediction,”
2021
Cited alongside, same era.
2021
Cited alongside, same era.
J. Zhang, R. Gu, G. Wang, and L. Gu, “Comprehensive importance-based selective regularization for continual segmentation across multiple sites,” in
2021
Cited alongside, same era.
2023
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L. Zhu, T. Chen, J. Yin, S. See, and J. Liu, “Continual semantic segmentation with automatic memory sample selection,” in
2023
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G. Wang, L. Bai, Y. Wu, T. Chen, and H. Ren, “Rethinking exemplars for continual semantic segmentation in endoscopy scenes: Entropy-based mini-batch pseudo-replay,”
2023
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2023
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G. Yang, E. Fini, D. Xu, P. Rota, M. Ding, M. Nabi, X. Alameda-Pineda, and E. Ricci, “Uncertainty-aware contrastive distillation for incremental semantic segmentation,”
2023
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D. Zhao, B. Yuan, and Z. Shi, “Inherit with distillation and evolve with contrast: Exploring class incremental semantic segmentation without exemplar memory,”
2023
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X. Rong, P. Wang, W. Diao, Y. Yang, W. Yin, X. Zeng, H. Wang, and X. Sun, “Micro: Modeling cross-image semantic relationship dependencies for class-incremental semantic segmentation in remote sensing images,”
2023
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D. Goswami, R. Schuster, J. van de Weijer, and D. Stricker, “Attribution-aware weight transfer: A warm-start initialization for class-incremental semantic segmentation,” in
2023
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J. Xiao, C. Zhang, J. Feng, X. Liu, J. van de Weijer, and M. Cheng, “Endpoints weight fusion for class incremental semantic segmentation,” in
2023
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Y. Qiu, Y. Shen, Z. Sun, Y. Zheng, X. Chang, W. Zheng, and R. Wang, “Sats: Self-attention transfer for continual semantic segmentation,”
2023
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C. Shang, H. Li, F. Meng, Q. Wu, H. Qiu, and L. Wang, “Incrementer: Transformer for class-incremental semantic segmentation with knowledge distillation focusing on old class,”
2023
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo
2023
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X. Wang, X. Zhang, Y. Cao, W. Wang, C. Shen, and T. Huang, “Seggpt: Towards segmenting everything in context,” in
2023
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2023
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H. Liu, Y. Zhou, B. Liu, J. Zhao, R. Yao, and Z. Shao, “Incremental learning with neural networks for computer vision: a survey,”
2023
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A. G. Menezes, G. de Moura, C. Alves, and A. C. de Carvalho, “Continual object detection: a review of definitions, strategies, and challenges,”
2023
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K. Roy, C. Simon, P. Moghadam, and M. Harandi, “Subspace distillation for continual learning,”
2023
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X. Zhang, F. Zhang, and C. Xu, “Vqacl: A novel visual question answering continual learning setting,” in
2023
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H. Cao, Y. Xu, J. Yang, P. Yin, S. Yuan, and L. Xie, “Multi-modal continual test-time adaptation for 3d semantic segmentation,” in
2023
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P. Testolina, F. Barbato, U. Michieli, M. Giordani, P. Zanuttigh, and M. Zorzi, “Selma: Semantic large-scale multimodal acquisitions in variable weather, daytime and viewpoints,”
2023
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K. Li, L. Yu, and P.-A. Heng, “Domain-incremental cardiac image segmentation with style-oriented replay and domain-sensitive feature whitening,”
2023
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B. Chen, K. Thandiackal, P. Pati, and O. Goksel, “Generative appearance replay for continual unsupervised domain adaptation,”
2023
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X. Li, S. Wang, J. Sun, and Z. Xu, “Variational data-free knowledge distillation for continual learning,”
2023
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Z. Lin, Z. Wang, and Y. Zhang, “Preparing the future for continual semantic segmentation,” in
2023
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Z. Zhang, G. Gao, J. Jiao, C. Liu, and Y. Wei, “Coinseg: Contrast inter- and intra- class representations for incremental segmentation,” in
2023
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T.-D. Truong, H.-Q. Nguyen, B. Raj, and K. Luu, “Fairness continual learning approach to semantic scene understanding in open-world environments,” in
2023
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E. Camuffo and S. Milani, “Continual learning for lidar semantic segmentation: Class-incremental and coarse-to-fine strategies on sparse data,” in
2023
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L. Riz, C. Saltori, E. Ricci, and F. Poiesi, “Novel class discovery for 3d point cloud semantic segmentation,” in
2023
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J. Li and Q. Dong, “Open-set semantic segmentation for point clouds via adversarial prototype framework,” in
2023
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Z. Yang, R. Li, E. Ling, C. Zhang, Y. Wang, D. Huang, K. T. Ma, M. Hur, and G. Lin, “Label-guided knowledge distillation for continual semantic segmentation on 2d images and 3d point clouds,” in
2023
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L. Yu, X. Liu, and J. van de Weijer, “Self-training for class-incremental semantic segmentation,”
2023
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C. Jiang, T. Wang, S. Li, J. Wang, S. Wang, and A. Antoniou, “Few-shot class-incremental semantic segmentation via pseudo-labeling and knowledge distillation,” in
2023
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Z. Zhou, Y. Lei, B. Zhang, L. Liu, and Y. Liu, “Zegclip: Towards adapting clip for zero-shot semantic segmentation,” in
2023
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2023
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X. Zou, Z.-Y. Dou, J. Yang, Z. Gan, L. Li, C. Li, X. Dai, H. Behl, J. Wang, L. Yuan
2023
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Z. Yang, R. Li, E. Ling, C. Zhang, Y. Wang, D. Huang, K. T. Ma, M. Hur, and G. Lin, “Label-guided knowledge distillation for continual semantic segmentation on 2d images and 3d point clouds,” in
2023
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F. Cermelli, M. Cord, and A. Douillard, “Comformer: Continual learning in semantic and panoptic segmentation,” in
2023
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Y.-H. Hsieh, G.-S. Chen, S.-X. Cai, T.-Y. Wei, H.-F. Yang, and C.-S. Chen, “Class-incremental continual learning for instance segmentation with image-level weak supervision,” in
2023
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T. Kalb, N. Ahuja, J. Zhou, and J. Beyerer, “Effects of architectures on continual semantic segmentation,” in
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C. Caucheteux, A. Gramfort, and J.-R. King, “Evidence of a predictive coding hierarchy in the human brain listening to speech,”
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T. Zhang, X. Cheng, S. Jia, C. T. Li, M.-m. Poo, and B. Xu, “A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost,”
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Y. Yang, M. Hayat, Z. Jin, C. Ren, and Y. Lei, “Geometry and uncertainty-aware 3d point cloud class-incremental semantic segmentation,” in
2023
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J. Dong, D. Zhang, Y. Cong, W. Cong, H. Ding, and D. Dai, “Federated incremental semantic segmentation,” in
2023
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Z. Zheng, M. Ma, K. Wang, Z. Qin, X. Yue, and Y. You, “Preventing zero-shot transfer degradation in continual learning of vision-language models,” in
2023
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G. Yang, E. Fini, D. Xu, P. Rota, M. Ding, T. Hao, X. Alameda-Pineda, and E. Ricci, “Continual attentive fusion for incremental learning in semantic segmentation,”
2023
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T. Kalb and J. Beyerer, “Principles of forgetting in domain-incremental semantic segmentation in adverse weather conditions,” in
2023
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V. Marsocci and S. Scardapane, “Continual barlow twins: continual self-supervised learning for remote sensing semantic segmentation,”
2023
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X. Rui, Z. Li, Y. Cao, Z. Li, and W. Song, “Dilrs: Domain-incremental learning for semantic segmentation in multi-source remote sensing data,”
2023
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Z. Ji, D. Guo, P. Wang, K. Yan, L. Lu, M. Xu, Q. Wang, J. Ge, M. Gao, X. Ye, and D. Jin, “Continual segment: Towards a single, unified and non-forgetting continual segmentation model of 143 whole-body organs in ct scans,” in
2023
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B. Yuan, D. Zhao, and Z. Shi, “Learning at a glance: Towards interpretable data-limited continual semantic segmentation via semantic-invariance modelling,”
2024
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Z. Yu, W. Yang, X. Xie, and Z. Shi, “Tikp: Text-to-image knowledge preservation for continual semantic segmentation,” in
2024
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L. Wang, X. Zhang, H. Su, and J. Zhu, “A comprehensive survey of continual learning: Theory, method and application,”
2024
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2024
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B. Yuan, D. Zhao, Z. Liu, W. Li, and T. Li, “Continual panoptic perception: Towards multi-modal incremental interpretation of remote sensing images,” 2024
2024
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D. Zhao, B. Yuan, Z. Chen, T. Li, Z. Liu, W. Li, and Y. Gao, “Panoptic perception: A novel task and fine-grained dataset for universal remote sensing image interpretation,”
2024
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J. Chen, R. Cong, Y. Luo, H. Ip, and S. Kwong, “Saving 100x storage: Prototype replay for reconstructing training sample distribution in class-incremental semantic segmentation,”
2024
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W. Cong, Y. Cong, J. Dong, G. Sun, and H. Ding, “Gradient-semantic compensation for incremental semantic segmentation,”
2024
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2024
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Y. Tan and X. Xiang, “Cross-domain few-shot incremental learning for point-cloud recognition,” in
2024
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Y. Zhou, X. Chen, Y. Guo, J. Yu, R. Hong, and Q. Tian, “Advancing incremental few-shot semantic segmentation via semantic-guided relation alignment and adaptation,” in
2024
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B. Kim, J. Yu, and S. J. Hwang, “Eclipse: Efficient continual learning in panoptic segmentation with visual prompt tuning,”
2024
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R. Zhang, Z. Jiang, Z. Guo, S. Yan, J. Pan, H. Dong, Y. Qiao, P. Gao, and H. Li, “Personalize segment anything model with one shot,” in
2024
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Y. Liu, M. Zhu, H. Li, H. Chen, X. Wang, and C. Shen, “Matcher: Segment anything with one shot using all-purpose feature matching,” in
2024
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W. Cong, Y. Cong, G. Sun, Y. Liu, and J. Dong, “Self-paced weight consolidation for continual learning,”
2024
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Q. Wang, Y. Wu, L. Yang, W. Zuo, and Q. Hu, “Layer-specific knowledge distillation for class incremental semantic segmentation,”
2024
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Y. Gong, S. Yu, X. Wang, and J. Xiao, “Continual segmentation with disentangled objectness learning and class recognition,”
2024
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W. Ji, J. Li, L. Cheng, Q. Bi, T. Liu, and W. Li, “Segment anything is not always perfect: An investigation of sam on different real-world applications,”
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M. Alfarra, Z. Cai, A. Bibi, B. Ghanem, and M. Müller, “Simcs: Simulation for domain incremental online continual segmentation,” in
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M. Xu, M. Islam, L. Bai, and H. Ren, “Privacy-preserving synthetic continual semantic segmentation for robotic surgery,”
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B. Zhang, L. Liu, M. H. Phan, Z. Tian, C. Shen, and Y. Liu, “Segvit v2: Exploring efficient and continual semantic segmentation with plain vision transformers,”
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J. Xie, B. Pan, X. Xu, and Z. Shi, “Missnet: Memory-inspired semantic segmentation augmentation network for class-incremental learning in remote sensing images,”
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L. Weng, W. Yang, B. Hu, P. Han, S. Xue, Y. Zhang, H. Li, J. Jin, and S. Bu, “Mdinet: Multidomain incremental network for change detection,”
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P. Garg, R. Saluja, V. N. Balasubramanian, C. Arora, A. Subramanian, and C. Jawahar, “Multi-domain incremental learning for semantic segmentation,” in
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