CoChat
Sign Up
Cited by 220Domain Adaptation and Few-Shot LearningAdvanced Neural Network ApplicationsData Stream Mining Techniques

A survey on few-shot class-incremental learning

Open access
Tian, Songsong, Li, Lusi, Li, Weijun, Ran, Hang, Ning, Xin, Tiwari, Prayag·2024·Neural Networks
DOI 10.1016/j.neunet.2023.10.039
Open this paper in CoChat — free account

Abstract

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.neunet.2023.10.039

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 143

  1. Achituve, I., Navon, A., Yemini, Y., Chechik, G., and Fetaya, E. (2021). GP- Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning. In Proceedings of the 38th International Conference on Machine Learning, pages 54–65. PMLR.
  2. Agarwal, A., Banerjee, B., Cuzzolin, F., and Chaudhuri, S. (2022). Semantics- driven generative replay for few-shot class incremental learning. In Proceedings of the 30th ACM International Conference on Multimedia, pages 5246–5254.
  3. Ahmad, T., Dhamija, A. R., Cruz, S., Rabinowitz, R., Li, C., Jafarzadeh, M., and Boult, T. E. (2022a). Few-Shot Class Incremental Learning Leveraging Self-Supervised Features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3900–3910.
  4. Ahmad, T., Dhamija, A. R., Jafarzadeh, M., Cruz, S., Rabinowitz, R., Li, C., and Boult, T. E. (2022b). Variable few shot class incremental and open world learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 3688–3699.
  5. Aljundi, R., Chakravarty, P., and Tuytelaars, T. (2017). Expert gate: Lifelong learning with a network of experts. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3366–3375.
  6. Ayub, A. and Wagner, A. R. (2020a). Cognitively-inspired model for incre- mental learning using a few examples. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 222–223.
  7. Ayub, A. and Wagner, A. R. (2020b). Tell me what this is: few-shot incremental object learning by a robot. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 8344–8350. IEEE.
  8. Ayub, A. and Wagner, A. R. (2021). F-siol-310: A robotic dataset and bench- mark for few-shot incremental object learning. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 13496–13502.
  9. Azadi, S., Fisher, M., Kim, V. G., Wang, Z., Shechtman, E., and Darrell, T. (2018). Multi-content gan for few-shot font style transfer. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7564–7573.
  10. Bai, J., Yuan, A., Xiao, Z., Zhou, H., Wang, D., Jiang, H., and Jiao, L. (2020). Class incremental learning with few-shots based on linear programming for hyperspectral image classification. IEEE Transactions on Cybernetics.
  11. Bojchevski, A. and Günnemann, S. (2018). Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. In International Conference on Learning Representations.
  12. Caesar, H., Uijlings, J., and Ferrari, V. (2018). Coco-stuff: Thing and stuff classes in context. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1209–1218.
  13. Castro, F. M., Marı́n-Jiménez, M. J., Guil, N., Schmid, C., and Alahari, K. (2018). End-to-end incremental learning. In Proceedings of the European conference on computer vision (ECCV), pages 233–248.
  14. Cavalin, P., Ribeiro, V. H. A., Appel, A., and Pinhanez, C. (2020). Improving out-of-scope detection in intent classification by using embeddings of the word graph space of the classes. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3952– 3961.
  15. Cermelli, F., Mancini, M., Xian, Y., Akata, Z., and Caputo, B. (2021). Prototype-based incremental few-shot semantic segmentation. In British Machine Vision Conference.
  16. Chen, K. and Lee, C.-G. (2021). Incremental few-shot learning via vector quan- tization in deep embedded space. In International Conference on Learning Representations.
  17. Chen, L., Yu, C., and Chen, L. (2019). A new knowledge distillation for incre- mental object detection. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1–7. IEEE.
  18. Chen, P., Zhang, Y., Li, Z., and Sun, L. (2022). Few-Shot Incremen- tal Learning for Label-to-Image Translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3697–3707.
  19. Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., and Liu, Z. (2020). Dynamic convolution: Attention over convolution kernels. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11030–11039.
  20. Cheng, M., Wang, H., and Long, Y. (2022). Meta-Learning-Based Incremental Few-Shot Object Detection. IEEE Transactions on Circuits and Systems for Video Technology, 32(4):2158–2169.
  21. Cheraghian, A., Rahman, S., Fang, P., Roy, S. K., Petersson, L., and Ha- randi, M. (2021a). Semantic-Aware Knowledge Distillation for Few-Shot Class-Incremental Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2534–2543.
  22. Cheraghian, A., Rahman, S., Ramasinghe, S., Fang, P., Simon, C., Peters- son, L., and Harandi, M. (2021b). Synthesized feature based few-shot class-incremental learning on a mixture of subspaces. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8661– 8670.
  23. Cheraghian, A., Rahman, S., Ramasinghe, S., Fang, P., Simon, C., Peters- son, L., and Harandi, M. (2021c). Synthesized Feature Based Few-Shot Class-Incremental Learning on a Mixture of Subspaces. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8661– 8670.
  24. Chi, Z., Gu, L., Liu, H., Wang, Y., Yu, Y., and Tang, J. (2022). MetaFS- CIL: A Meta-Learning Approach for Few-Shot Class Incremental Learn- ing. In Proceedings of the IEEE/CVF Conference on Computer Vision and
  25. Cui, Y., Xiong, W., Tavakolian, M., and Liu, L. (2021). Semi-supervised few- shot class-incremental learning. In 2021 IEEE International Conference on Image Processing (ICIP), pages 1239–1243. IEEE.
  26. De Lange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., and Tuytelaars, T. (2021). A continual learning survey: Defy- ing forgetting in classification tasks. IEEE transactions on pattern analysis and machine intelligence, 44(7):3366–3385.
  27. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Ima- genet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee.
  28. Deng, Y., Luo, P., Loy, C. C., and Tang, X. (2014). Pedestrian attribute recognition at far distance. In Proceedings of the 22nd ACM international conference on Multimedia, pages 789–792.
  29. Dong, S., Hong, X., Tao, X., Chang, X., Wei, X., and Gong, Y. (2021). Few-Shot Class-Incremental Learning via Relation Knowledge Distillation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(2):1255– 1263.
  30. Elsken, T., Staffler, B., Metzen, J. H., and Hutter, F. (2020). Meta-learning of neural architectures for few-shot learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12365–12375.
  31. Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2):303–339.
  32. Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A. (2012). The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascal- network.org/challenges/VOC/voc2012/workshop/index.html.
  33. Fe-Fei, L. et al. (2003). A bayesian approach to unsupervised one-shot learning of object categories. In proceedings ninth IEEE international conference on computer vision, pages 1134–1141. IEEE.
  34. Fei-Fei, L., Fergus, R., and Perona, P. (2006). One-shot learning of object categories. IEEE transactions on pattern analysis and machine intelligence, 28(4):594–611.
  35. Feng, H., Zhang, L., Yang, X., and Liu, Z. (2022). Incremental few-shot object detection via knowledge transfer. Pattern Recognition Letters, 156:67–73.
  36. Finn, C., Abbeel, P., and Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning, pages 1126–1135. PMLR.
  37. Ganea, D. A., Boom, B., and Poppe, R. (2021). Incremental few-shot instance segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1185–1194.
  38. Gidaris, S. and Komodakis, N. (2018). Dynamic few-shot visual learning with- out forgetting. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4367–4375.
  39. Hamilton, W., Ying, Z., and Leskovec, J. (2017). Inductive representation learn- ing on large graphs. Advances in neural information processing systems, 30.
  40. Han, X., Zhu, H., Yu, P., Wang, Z., Yao, Y., Liu, Z., and Sun, M. (2018). Fewrel: A large-scale supervised few-shot relation classification dataset with state- of-the-art evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4803–4809, Brussels, Bel- gium. Association for Computational Linguistics.
  41. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961–2969.
  42. He, Y., Zhu, C., Wang, J., Savvides, M., and Zhang, X. (2019). Bounding box regression with uncertainty for accurate object detection. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pages
  43. Hemphill, C. T., Godfrey, J. J., and Doddington, G. R. (1990). The atis spoken language systems pilot corpus. In Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27, 1990.
  44. Hersche, M., Karunaratne, G., Cherubini, G., Benini, L., Sebastian, A., and Rahimi, A. (2022). Constrained Few-Shot Class-Incremental Learn- ing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9057–9067.
  45. Hochreiter, S. and Schmidhuber, J. (1997). Flat minima. Neural computation, 9(1):1–42.
  46. Hospedales, T., Antoniou, A., Micaelli, P., and Storkey, A. (2022). Meta- learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9):5149–5169.
  47. Hou, S., Pan, X., Loy, C. C., Wang, Z., and Lin, D. (2018). Lifelong learning via progressive distillation and retrospection. In Proceedings of the European Conference on Computer Vision (ECCV), pages 437–452.
  48. Hou, S., Pan, X., Loy, C. C., Wang, Z., and Lin, D. (2019a). Learning a unified classifier incrementally via rebalancing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
  49. Hou, S., Pan, X., Loy, C. C., Wang, Z., and Lin, D. (2019b). Learning a unified classifier incrementally via rebalancing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 831–839.
  50. Jadon, S. (2020). An overview of deep learning architectures in few-shot learn- ing domain. arXiv preprint arXiv:2008.06365. · arXiv
  51. Jamal, M. A. and Qi, G.-J. (2019). Task agnostic meta-learning for few-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11719–11727.
  52. Kalla, J. and Biswas, S. (2022). S3c: Self-supervised stochastic classifiers for few-shot class-incremental learning. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXV, pages 432–448. Springer.
  53. Kang, B., Liu, Z., Wang, X., Yu, F., Feng, J., and Darrell, T. (2019). Few-shot object detection via feature reweighting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8420–8429.
  54. Kim, D.-Y., Han, D.-J., Seo, J., and Moon, J. (2023). Warping the space: Weight space rotation for class-incremental few-shot learning. In International Conference on Learning Representations.
  55. Koch, G., Zemel, R., Salakhutdinov, R., et al. (2015). Siamese neural networks for one-shot image recognition. In ICML deep learning workshop, volume 2. Lille.
  56. Kong, C., Kim, J., Han, D., and Kwak, N. (2022). Few-shot image generation with mixup-based distance learning. In European Conference on Computer Vision, pages 563–580. Springer.
  57. Krause, J., Stark, M., Deng, J., and Fei-Fei, L. (2013). 3d object representations for fine-grained categorization. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops.
  58. Kukleva, A., Kuehne, H., and Schiele, B. (2021). Generalized and Incre- mental Few-Shot Learning by Explicit Learning and Calibration Without Forgetting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9020–9029.
  59. Li, D., Zhang, Z., Chen, X., Ling, H., and Huang, K. (2016). A richly annotated dataset for pedestrian attribute recognition. arXiv preprint arXiv:1603.07054. · arXiv
  60. Li, D.-W. and Huang, H. (2022). Few-shot class-incremental learning via compact and separable features for fine-grained vehicle recognition. IEEE Transactions on Intelligent Transportation Systems, 23(11):21418–21429.
  61. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014). Microsoft coco: Common objects in con- text. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740–755. Springer.
  62. Liu, B., Wang, X., Dixit, M., Kwitt, R., and Vasconcelos, N. (2018). Feature space transfer for data augmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 9090–9098.
  63. Liu, J., Song, L., and Qin, Y. (2020a). Prototype rectification for few-shot learning. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, pages 741–756. Springer.
  64. Liu, L., Kuang, Z., Chen, Y., Xue, J.-H., Yang, W., and Zhang, W. (2020b). Incdet: In defense of elastic weight consolidation for incremental object detection. IEEE transactions on neural networks and learning systems, 32(6):2306–2319.
  65. Liu, S., Davison, A., and Johns, E. (2019). Self-supervised generalisation with meta auxiliary learning. Advances in Neural Information Processing Systems, 32.
  66. Lu, B., Gan, X., Yang, L., Zhang, W., Fu, L., and Wang, X. (2022). Ge- ometer: Graph few-shot class-incremental learning via prototype represen- tation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1152–1161.
  67. Luo, J., Si, W., and Deng, Z. (2022). New classes inference, few-shot learning and continual learning for radar signal recognition. IET Radar, Sonar & Navigation, 16(10):1641–1655.
  68. Luo, Z., Zou, Y., Hoffman, J., and Fei-Fei, L. F. (2017). Label efficient learn- ing of transferable representations acrosss domains and tasks. Advances in neural information processing systems, 30.
  69. Mazumder, P., Singh, P., and Rai, P. (2021). Few-Shot Lifelong Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(3):2337– 2345.
  70. McAuley, J., Pandey, R., and Leskovec, J. (2015). Inferring networks of sub- stitutable and complementary products. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 785–794.
  71. Munkhdalai, T. and Yu, H. (2017). Meta networks. In International conference on machine learning, pages 2554–2563. PMLR.
  72. Nallapati, R., Zhou, B., Gulcehre, C., Xiang, B., et al. (2016). Abstractive text summarization using sequence-to-sequence rnns and beyond. arXiv preprint arXiv:1602.06023. · arXiv
  73. Nguyen, K. and Todorovic, S. (2022). ifs-rcnn: An incremental few-shot instance segmenter. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7010–7019.
  74. Nichol, A. and Schulman, J. (2018). Reptile: a scalable metalearning algorithm. arXiv preprint arXiv:1803.02999, 2(3):4. · arXiv
  75. Palatucci, M., Pomerleau, D., Hinton, G. E., and Mitchell, T. M. (2009). Zero- shot learning with semantic output codes. Advances in neural information processing systems, 22.
  76. Pan, Z., Yu, X., Zhang, M., and Gao, Y. (2023). Ssfe-net: Self-supervised feature enhancement for ultra-fine-grained few-shot class incremental learn- ing. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 6275–6284.
  77. Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., and Wermter, S. (2019). Con- tinual lifelong learning with neural networks: A review. Neural networks, 113:54–71.
  78. Park, W., Kim, D., Lu, Y., and Cho, M. (2019). Relational knowledge distilla- tion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3967–3976.
  79. Peng, C., Zhao, K., Wang, T., Li, M., and Lovell, B. C. (2022). Few- shot class-incremental learning from an open-set perspective. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXV, pages 382–397. Springer.
  80. Perez-Rua, J.-M., Zhu, X., Hospedales, T. M., and Xiang, T. (2020). Incremen- tal few-shot object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13846–13855.
  81. Qin, C. and Joty, S. (2021). Lfpt5: A unified framework for lifelong few- shot language learning based on prompt tuning of t5. arXiv preprint arXiv:2110.07298. · arXiv
  82. Qin, C. and Joty, S. (2022). Continual few-shot relation learning via embed- ding space regularization and data augmentation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2776–2789.
  83. Ren, M., Liao, R., Fetaya, E., and Zemel, R. (2019). Incremental Few- Shot Learning with Attention Attractor Networks. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc.
  84. Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., and Zemel, R. S. (2018). Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676. · arXiv
  85. Riemer, M., Cases, I., Ajemian, R., Liu, M., Rish, I., Tu, Y., , and Tesauro, G. (2019). Learning to learn without forgetting by maximizing trans- fer and minimizing interference. In International Conference on Learning Representations.
  86. Rosenfeld, A. and Tsotsos, J. K. (2018). Incremental learning through deep adaptation. IEEE transactions on pattern analysis and machine intelligence, 42(3):651–663.
  87. Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R. (2016). Progressive neu- ral networks. arXiv preprint arXiv:1606.04671. · arXiv
  88. Rusu, A. A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., and Hadsell, R. (2019). Meta-learning with latent embedding optimization. In International Conference on Learning Representations.
  89. Sabour, S., Frosst, N., and Hinton, G. E. (2017). Dynamic routing between capsules. Advances in neural information processing systems, 30.
  90. Sang, E. F. and De Meulder, F. (2003). Introduction to the conll-2003 shared task: Language-independent named entity recognition. arXiv preprint cs/0306050. · arXiv
  91. Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T. (2016). Meta-learning with memory-augmented neural networks. In International conference on machine learning, pages 1842–1850. PMLR.
  92. Shaban, A., Bansal, S., Liu, Z., Essa, I., and Boots, B. (2017). One-shot learn- ing for semantic segmentation. In British Machine Vision Conference 2017.
  93. SHI, G., CHEN, J., Zhang, W., Zhan, L.-M., and Wu, X.-M. (2021). Overcom- ing Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima. In Advances in Neural Information Processing Systems, vol- ume 34, pages 6747–6761. Curran Associates, Inc.
  94. Shi, G., Wu, Y., Liu, J., Wan, S., Wang, W., and Lu, T. (2022). Incremental few- shot semantic segmentation via embedding adaptive-update and hyper-class representation. In Proceedings of the 30th ACM International Conference on Multimedia, pages 5547–5556.
  95. Snell, J., Swersky, K., and Zemel, R. (2017). Prototypical networks for few- shot learning. Advances in neural information processing systems, 30.
  96. Song, Z., Zhao, Y., Shi, Y., Peng, P., Yuan, L., and Tian, Y. (2023b). Learn- ing with fantasy: Semantic-aware virtual contrastive constraint for few-shot class-incremental learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24183–24192.
  97. Sun, C., Gao, L., Li, X., and Gao, Y. (2022). A new knowledge distillation network for incremental few-shot surface defect detection. arXiv preprint arXiv:2209.00519. · arXiv
  98. Sun, L., Zhang, M., Wang, B., and Tiwari, P. (2023). Few-shot class- incremental learning for medical time series classification. IEEE Journal of Biomedical and Health Informatics.
  99. Tambwekar, A., Agrawal, K., Majee, A., and Subramanian, A. (2021). Few-Shot Batch Incremental Road Object Detection via Detector Fu- sion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3070–3077.
  100. Tan, Z., Ding, K., Guo, R., and Liu, H. (2022). Graph few-shot class- incremental learning. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pages 987–996.
  101. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., and Su, Z. (2008). Arnetminer: extraction and mining of academic social networks. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 990–998.
  102. Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., and Gong, Y. (2020). Few-Shot Class-Incremental Learning. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12180–12189, Seattle, WA, USA. IEEE.
  103. Van de Ven, G. M. and Tolias, A. S. (2019). Three scenarios for continual learning. arXiv preprint arXiv:1904.07734. · arXiv
  104. Varma, G., Subramanian, A., Namboodiri, A., Chandraker, M., and Jawahar, C. (2019). Idd: A dataset for exploring problems of autonomous naviga- tion in unconstrained environments. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1743–1751. IEEE.
  105. Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al. (2016). Matching networks for one shot learning. Advances in neural information processing systems, 29.
  106. Wang, F.-Y., Zhou, D.-W., Ye, H.-J., and Zhan, D.-C. (2022a). Foster: Fea- ture boosting and compression for class-incremental learning. In European conference on computer vision, pages 398–414. Springer.
  107. Wang, Q., Liu, X., Liu, W., Liu, A.-A., Liu, W., and Mei, T. (2020a). Metasearch: Incremental product search via deep meta-learning. IEEE Transactions on Image Processing, 29:7549–7564.
  108. Wang, R., Yu, T., Zhao, H., Kim, S., Mitra, S., Zhang, R., and Henao, R. (2022b). Few-Shot Class-Incremental Learning for Named Entity Recog- nition. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 571–582, Dublin, Ireland. Association for Computational Linguistics.
  109. Wang, T., Lv, Q., Hu, B., and Sun, D. (2021a). A few-shot class-incremental learning approach for intrusion detection. In 2021 International Conference on Computer Communications and Networks (ICCCN), pages 1–8. IEEE.
  110. Wang, Y., Bryan, N. J., Cartwright, M., Bello, J. P., and Salamon, J. (2021b). Few-shot continual learning for audio classification. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 321–325. IEEE.
  111. Wang, Y., Yao, Q., Kwok, J. T., and Ni, L. M. (2020b). Generalizing from a few examples: A survey on few-shot learning. ACM computing surveys (csur), 53(3):1–34.
  112. Wang, Z., Zhang, Z., Ebrahimi, S., Sun, R., Zhang, H., Lee, C.-Y., Ren, X., Su, G., Perot, V., Dy, J., et al. (2022c). Dualprompt: Complementary prompting for rehearsal-free continual learning. In European Conference on Computer Vision, pages 631–648. Springer.
  113. Wei, K., Deng, C., Yang, X., et al. (2020). Lifelong zero-shot learning. In IJCAI, pages 551–557.
  114. Wei, K., Deng, C., Yang, X., and Tao, D. (2021). Incremental Zero-Shot Learn- ing. IEEE Transactions on Cybernetics, pages 1–12.
  115. Wei, X.-S., Cui, Q., Yang, L., Wang, P., and Liu, L. (2019). Rpc: A large-scale retail product checkout dataset. arXiv preprint arXiv:1901.07249. · arXiv
  116. Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., and Fu, Y. (2019). Large scale incremental learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 374–382.
  117. Xiang, L., Jin, X., Ding, G., Han, J., and Li, L. (2019). Incremental few- shot learning for pedestrian attribute recognition. In International Joint Conference on Artificial Intelligence, pages 3912–3918.
  118. Xie, S., Li, Y., Lin, D., Lay Nwe, T., and Dong, S. (2019). Meta module generation for fast few-shot incremental learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pages 0–0.
  119. Yang, B., Lin, M., Zhang, Y., Liu, B., Liang, X., Ji, R., and Ye, Q. (2022). Dynamic support network for few-shot class incremental learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.
  120. Yang, L., Luo, P., Change Loy, C., and Tang, X. (2015). A large-scale car dataset for fine-grained categorization and verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  121. Yang, Y., Yuan, H., Li, X., Lin, Z., Torr, P., and Tao, D. (2023). Neural collapse inspired feature-classifier alignment for few-shot class-incremental learning. In International Conference on Learning Representations.
  122. Yao, G., Zhu, J., Zhou, W., and Li, J. (2022). Few-shot class-incremental learn- ing based on representation enhancement. Journal of Electronic Imaging, 31(4):043027.
  123. Ye, H.-J., Hu, H., and Zhan, D.-C. (2021). Learning adaptive classifiers syn- thesis for generalized few-shot learning. International Journal of Computer Vision, 129(6):1930–1953.
  124. Yin, L., Perez-Rua, J. M., and Liang, K. J. (2022). Sylph: A Hypernetwork Framework for Incremental Few-Shot Object Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9035–9045.
  125. Yoon, J., Madjid, S., Hwang, S. J., Yoo, C.-D., et al. (2023). On the soft-subnetwork for few-shot class incremental learning. In International Conference on Learning Representations (ICLR) 2023. International Con- ference on Learning Representations.
  126. Yoon, S. W., Kim, D.-Y., Seo, J., and Moon, J. (2020). XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning. In Proceedings of the 37th International Conference on Machine Learning, pages 10852–10860. PMLR.
  127. Yu, X., Zhao, Y., Gao, Y., Yuan, X., and Xiong, S. (2021). Benchmark platform for ultra-fine-grained visual categorization beyond human per- formance. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10285–10295.
  128. Zeng, H., Zhou, H., Srivastava, A., Kannan, R., and Prasanna, V. (2020). Graphsaint: Graph sampling based inductive learning method. In International Conference on Learning Representations.
  129. Zhang, C., Song, N., Lin, G., Zheng, Y., Pan, P., and Xu, Y. (2021). Few-shot incremental learning with continually evolved classifiers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12455–12464.
  130. Zhang, X., Sung, F., Qiang, Y., Yang, Y., and Hospedales, T. M. (2018). Deep comparison: Relation columns for few-shot learning. arXiv preprint arXiv:1811.07100. · arXiv
  131. Zhang, X., Zhao, J., and LeCun, Y. (2015). Character-level convolutional net- works for text classification. Advances in neural information processing systems, 28.
  132. Zhang, Y., Zhong, V., Chen, D., Angeli, G., and Manning, C. D. (2017). Position-aware attention and supervised data improve slot filling. In Conference on Empirical Methods in Natural Language Processing.
  133. Zhao, H., Fu, Y., Kang, M., Tian, Q., Wu, F., and Li, X. (2021). MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–1.
  134. Zheng, G. and Zhang, A. (2021). Few-Shot Class-Incremental Learning with Meta-Learned Class Structures. In 2021 International Conference on Data Mining Workshops (ICDMW), pages 421–430.
  135. Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., and Torralba, A. (2017). Scene parsing through ade20k dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 633–641.
  136. Zhou, D.-W., Wang, F.-Y., Ye, H.-J., Ma, L., Pu, S., and Zhan, D.-C. (2022a). Forward Compatible Few-Shot Class-Incremental Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9046–9056.
  137. Zhou, D.-W., Wang, Q.-W., Qi, Z.-H., Ye, H.-J., Zhan, D.-C., and Liu, Z. (2023). Deep class-incremental learning: A survey. arXiv preprint arXiv:2302.03648. · arXiv
  138. Zhou, D.-W., Ye, H.-J., Ma, L., Xie, D., Pu, S., and Zhan, D.-C. (2022b). Few- Shot Class-Incremental Learning by Sampling Multi-Phase Tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–16.
  139. Zhou, X., Wang, D., and Krähenbühl, P. (2019). Objects as points. arXiv preprint arXiv:1904.07850. · arXiv
  140. Zhu, K., Cao, Y., Zhai, W., Cheng, J., and Zha, Z.-J. (2021a). Self- promoted prototype refinement for few-shot class-incremental learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6801–6810.
  141. Zhu, K., Cao, Y., Zhai, W., Cheng, J., and Zha, Z.-J. (2021b). Self- Promoted Prototype Refinement for Few-Shot Class-Incremental Learn- ing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6801–6810.
  142. Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., and He, Q. (2020). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1):43–76.
  143. Zou, Y., Zhang, S., Li, Y., and Li, R. (2022). Margin-based few-shot class- incremental learning with class-level overfitting mitigation. In Oh, A. H., Agarwal, A., Belgrave, D., and Cho, K., editors, Advances in Neural Information Processing Systems.

Cited by

Sorted by citations

Authors
Tian, S., Li, L., Li, W., Ran, H., Ning, X., & Tiwari, P.
Affiliations
  • Chinese Academy of Sciences (CN)
  • Old Dominion University (US)
  • Halmstad University (SE)
Corresponding author
  • Ning, Xin
  • Tiwari, Prayag
ORCID
Venue
Neural Networks
Publisher
Elsevier BV
Published
2024-01
Volume
169
Pages
307-324
ISSN
0893-6080 (ISSN-L)
DOI
10.1016/j.neunet.2023.10.039
OpenAlex
W4388037272
Cited by
220
References
11
Citations by year
2026: 60 · 2025: 99 · 2024: 57 · 2023: 4
FWCI
4.60
Type
journal article
Language
en
Open access
Licence
Locations
  • Neural Networks (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • ODU Digital Commons (Old Dominion University) (submittedVersion, cc-by)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (62373343)
  • Natural Science Foundation of Beijing Municipality (L233036)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
Keywords
few-shot class-incremental learning, catastrophic forgetting, overfitting, meta-learning, dynamic network structure, few-shot learning, incremental learning, image classification, object detection, image segmentation, natural language processing, graph learning
MeSH terms
Machine Learning, Surveys and Questionnaires, Time, Neural Networks, Computer

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review