Fetching the paper…
Reading the bibliography…
Vision Language Models (VLMs), pre-trained on large-scale image-text datasets, enable zero-shot predictions for unseen data but may underperform on specific unseen tasks.
M. McCloskey and N. J. Cohen, “Catastrophic interference in connectionist networks: The sequential learning problem,” in Psychology of learning and motivation . Elsevier, 1989, vol. 24, pp. 109–165
1989
Earlier work this paper cites.
P. Bell, E. A. Davis, and M. C. Linn, “The knowledge integration environment: Theory and design,” 1995
1995
Earlier work this paper cites.
L. Fei-Fei, R. Fergus, and P. Perona, “Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops . IEEE, 2004, pp. 178–178
2004
Earlier work this paper cites.
M.-E. Nilsback and A. Zisserman, “Automated flower classification over a large number of classes,” in Proc. 6th Indian Conf. Comput. Vis. Graph. Image Process. IEEE, 2008, pp. 722–729
2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” Univ. Toronto, Toronto, ON, Canada, Tech. Rep. , 2009
2009
Earlier work this paper cites.
S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert, “icarl: Incremental classifier and representation learning,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2017, pp. 2001–2010
2010
Earlier work this paper cites.
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba, “Sun database: Large-scale scene recognition from abbey to zoo,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. IEEE, 2010, pp. 3485–3492
2010
Earlier work this paper cites.
L. Deng, “The mnist database of handwritten digit images for machine learning research [best of the web],” IEEE Signal Process. Mag. , vol. 29, no. 6, pp. 141–142, 2012
2012
Earlier work this paper cites.
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. Jawahar, “Cats and dogs,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) . IEEE, 2012, pp. 3498–3505
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
J. Krause, M. Stark, J. Deng, and L. Fei-Fei, “3d object representations for fine-grained categorization,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV) Workshops , 2013, pp. 554–561
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Proc. Int. Conf. Neural Inf. Process. Syst. (NeurIPS) , vol. 27, 2014
2014
Earlier work this paper cites.
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi, “Describing textures in the wild,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2014, pp. 3606–3613
2014
Earlier work this paper cites.
L. Bossard, M. Guillaumin, and L. Van Gool, “Food-101–mining discriminative components with random forests,” in Proc. Eur. Conf. Comput. Vis. (ECCV) . Springer, 2014, pp. 446–461
2014
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” Int. J. Comput. Vis. , vol. 115, pp. 211–252, 2015
2015
Earlier work this paper cites.
Z. Li and D. Hoiem, “Learning without forgetting,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 40, no. 12, pp. 2935–2947, 2017
2017
Earlier work this paper cites.
H. Shin, J. K. Lee, J. Kim, and J. Kim, “Continual learning with deep generative replay,” in Proc. Int. Conf. Neural Inf. Process. Syst. (NeurIPS) , vol. 30, 2017
2017
Earlier work this paper cites.
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska et al. , “Overcoming catastrophic forgetting in neural networks,” Proc. Nat. Acad. Sci. USA , vol. 114, no. 13, pp. 3521–3526, 2017
2017
Earlier work this paper cites.
S.-W. Lee, J.-H. Kim, J. Jun, J.-W. Ha, and B.-T. Zhang, “Overcoming catastrophic forgetting by incremental moment matching,” in Proc. Int. Conf. Neural Inf. Process. Syst. (NeurIPS) , vol. 30, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. Aljundi, P. Chakravarty, and T. Tuytelaars, “Expert gate: Lifelong learning with a network of experts,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2017, pp. 3366–3375
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
2018
Cited alongside, same era.
R. Aljundi, F. Babiloni, M. Elhoseiny, M. Rohrbach, and T. Tuytelaars, “Memory aware synapses: Learning what (not) to forget,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2018, pp. 139–154
2018
Cited alongside, same era.
F. M. Castro, M. J. Marín-Jiménez, N. Guil, C. Schmid, and K. Alahari, “End-to-end incremental learning,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2018, pp. 233–248
2018
Cited alongside, same era.
J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in Proc. Int. Conf. Mach. Learn. PMLR, 2022, pp. 12 888–12 900
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Wang, Z. Huang, and X. Hong, “S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning,” in Proc. Int. Conf. Neural Inf. Process. Syst. (NeurIPS) , vol. 35, 2022, pp. 5682–5695
2022
Later among the works it cites.
O. Ostapenko, T. Lesort, P. Rodríguez, M. R. Arefin, A. Douillard, I. Rish, and L. Charlin, “Continual learning with foundation models: An empirical study of latent replay,” in Proc. of the 1st Conf. on Lifelong Learning Agents (CoLLAs) . PMLR, 2022, pp. 60–91
2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
P. Dhar, R. V. Singh, K.-C. Peng, Z. Wu, and R. Chellappa, “Learning without memorizing,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2019, pp. 5138–5146
2019
Cited alongside, same era.
S. Hou, X. Pan, C. C. Loy, Z. Wang, and D. Lin, “Learning a unified classifier incrementally via rebalancing,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2019, pp. 831–839
2019
Cited alongside, same era.
Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, and Y. Fu, “Large scale incremental learning,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2019, pp. 374–382
2019
Cited alongside, same era.
2019
Cited alongside, same era.
P. Helber, B. Bischke, A. Dengel, and D. Borth, “Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. , vol. 12, no. 7, pp. 2217–2226, 2019
2019
Cited alongside, same era.
A. Prabhu, P. H. Torr, and P. K. Dokania, “Gdumb: A simple approach that questions our progress in continual learning,” in Proc. Eur. Conf. Comput. Vis. (ECCV) . Springer, 2020, pp. 524–540
2020
Cited alongside, same era.
P. Buzzega, M. Boschini, A. Porrello, D. Abati, and S. Calderara, “Dark experience for general continual learning: a strong, simple baseline,” in Proc. Int. Conf. Neural Inf. Process. Syst. (NeurIPS) , vol. 33, 2020, pp. 15 920–15 930
2020
Cited alongside, same era.
J. Ramapuram, M. Gregorova, and A. Kalousis, “Lifelong generative modeling,” Neurocomputing , vol. 404, pp. 381–400, 2020
2020
Cited alongside, same era.
Later among the works it cites.
K. Li, J. Wan, and S. Yu, “Ckdf: Cascaded knowledge distillation framework for robust incremental learning,” IEEE Trans. on Image Process. , vol. 31, pp. 3825–3837, 2022
2022
Later among the works it cites.
Z. Ji, J. Li, Q. Wang, and Z. Zhang, “Complementary calibration: Boosting general continual learning with collaborative distillation and self-supervision,” IEEE Trans. on Image Process. , vol. 32, pp. 657–667, 2022
2022
Later among the works it cites.
A. Douillard, A. Ramé, G. Couairon, and M. Cord, “Dytox: Transformers for continual learning with dynamic token expansion,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 9285–9295
2022
Later among the works it cites.
Z. Wang, Z. Zhang, C.-Y. Lee, H. Zhang, R. Sun, X. Ren, G. Su, V. Perot, J. Dy, and T. Pfister, “Learning to prompt for continual learning,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 139–149
2022
Later among the works it cites.
Z. Wang, Z. Zhang, S. Ebrahimi, R. Sun, H. Zhang, C.-Y. Lee, X. Ren, G. Su, V. Perot, J. Dy et al. , “Dualprompt: Complementary prompting for rehearsal-free continual learning,” in Proc. Eur. Conf. Comput. Vis. (ECCV) . Springer, 2022, pp. 631–648
2022
Later among the works it cites.
M. Wortsman, G. Ilharco, J. W. Kim, M. Li, S. Kornblith, R. Roelofs, R. G. Lopes, H. Hajishirzi, A. Farhadi, H. Namkoong et al. , “Robust fine-tuning of zero-shot models,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 7959–7971
2022
Later among the works it cites.
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 Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 19 125–19 136
2023
Later among the works it cites.
Y. Zhang, Z. Ji, D. Wang, Y. Pang, and X. Li, “User: Unified semantic enhancement with momentum contrast for image-text retrieval,” IEEE Trans. on Image Process. , 2024, doi: 10.1109/TIP.2023.3348297
2023
Later among the works it cites.
Z. Hu, Y. Li, J. Lyu, D. Gao, and N. Vasconcelos, “Dense network expansion for class incremental learning,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 11 858–11 867
2023
Later among the works it cites.
F. Ye and A. G. Bors, “Self-evolved dynamic expansion model for task-free continual learning,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 22 102–22 112
2023
Later among the works it cites.
J. S. Smith, L. Karlinsky, V. Gutta, P. Cascante-Bonilla, D. Kim, A. Arbelle, R. Panda, R. Feris, and Z. Kira, “Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 11 909–11 919
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Yu, Y. Zhuge, L. Zhang, P. Hu, D. Wang, H. Lu, and Y. He, “Boosting continual learning of vision-language models via mixture-of-experts adapters,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2024, pp. 23 219–23 230
2024
Closest in time.
2024
Closest in time.
J. Lu and S. Sun, “Pamk: Prototype augmented multi-teacher knowledge transfer network for continual zero-shot learning,” IEEE Trans. on Image Process. , 2024, doi: 10.1109/TIP.2024.3403053
2024
Closest in time.
C. Zhao, Y. Wang, X. Jiang, Y. Shen, K. Song, D. Li, and D. Miao, “Learning domain invariant prompt for vision-language models,” IEEE Trans. on Image Process. , 2024, doi: 10.1109/TIP.2024.3362062
2024
Closest in time.