Abstract
Few-shot class incremental learning (FSCIL) poses a significant challenge in machine learning as it involves acquiring new knosstabwledge from limited samples while retaining previous knowledge. However, the scarcity of data for new classes not only leads to overfitting but also exacerbates catastrophic forgetting during incremental learning. To tackle these challenges, we propose the Grassmann Graph Embedding framework for Few-shot Class Incremental Learning (GGE-FSCIL), which effectively preserves the geometric properties and structural relationships of learned knowledge. Unlike most existing approaches that optimize network parameters in the Euclidean space, we leverage manifold space for optimizing the incremental learning network. Specifically, we embed the approximated characteristics of each class onto a Grassmann manifold, enabling the preservation of intra-class knowledge and enhancing adaptability to new tasks with few-shot samples. Recognizing that knowledge exists within interconnected relationships, we construct a neighborhood graph on the Grassmann manifold to maintain inter-class structure information, thereby alleviating catastrophic forgetting. We extensively evaluated our method on CIFAR100, miniImageNet, and CUB200 datasets, and the results demonstrate that our approach surpasses the state-of-the-art methods in few-shot class incremental learning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: CVPR (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR (2016)
Mazumder, P., Singh, P., Rai, P.: Few-shot lifelong learning. AAAI 35(3), 2337–2345 (2021)
Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., Gong, Y.: Few-shot class-incremental learning. In: CVPR, pp. 12 183–12 192 (2020)
Zhang, C., Song, N., Lin, G., Zheng, Y., Pan, P., Xu, Y.: Few-shot incremental learning with continually evolved classifiers. In: CVPR
Zhou, D.-W., Wang, F.-Y., Ye, H.-J., Ma, L., Pu, S., Zhan, D.-C.: Forward compatible few-shot class-incremental learning. In: CVPR (2022)
Chi, Z., Gu, L., Liu, H., Wang, Y., Yu, Y., Tang, J.: Metafscil: a meta-learning approach for few-shot class incremental learning. In: CVPR
Kalla, J., Biswas, S.: S3c: self-supervised stochastic classifiers for few-shot class-incremental learning. In: ECCV, pp. 432–448 (2022)
Zheng, L., Tse, D.N.C.: Communication on the grassmann manifold: a geometric approach to the noncoherent multiple-antenna channel. IEEE Trans. Inf. Theory 48(2), 359–383 (2002)
Eichenbaum, H.: How does the brain organize memories? Science 277(5324), 330–332 (1997)
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, "The caltech-ucsd birds-200-2011 dataset," 2011
Krizhevsky, A.: Learning multiple layers of features from tiny images (2009)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3) (2015)
Zhu, K., Cao, Y., Zhai, W., Cheng, J., Zha, Z.-J.: Self-promoted prototype refinement for few-shot class-incremental learning. In: CVPR
Hersche, M., Karunaratne, G., Cherubini, G., Benini, L., Sebastian, A., Rahimi, A.: Constrained few-shot class-incremental learning (2022)
Yan, S., Xu, D., Zhang, B., Zhang, H.-J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. TPAMI 29(1), 40–51 (2006)
Wang, R., Wu, X.-J., Liu, Z., Kittler, J.: Geometry-aware graph embedding projection metric learning for image set classification. TCDS 14(3), 957–970 (2021)
Harandi, M.T., Sanderson, C., Shirazi, S., Lovell, B.C.: Graph embedding discriminant analysis on grassmannian manifolds for improved image set matching. In: CVPR. IEEE 2011, pp. 2705–2712 (2011)
Bansal, N., Chen, X., Wang, Z.: Can we gain more from orthogonality regularizations in training deep networks? NeurIPS, vol. 31 (2018)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. NeurIPS, vol. 14 (2001)
He, X., Niyogi, P.: Locality preserving projections. In: NeurIPS, vol. 16 (2003)
Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019)
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017)
Castro, F.M., Marín-Jiménez, M.J., Guil, N., Schmid, C., Alahari, K.: End-to-end incremental learning. In: ECCV, pp. 233–248 (2018)
Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., Gong, Y.: Few-shot class-incremental learning. In: CVPR (2020)
Zhang, C., Cai, Y., Lin, G., Shen, C.: Deepemd: few-shot image classification with differentiable earth mover’s distance and structured classifiers. In: CVPR, pp. 12 203–12 213 (2020)
Liu, B., Cao, Y., Lin, Y., Li, Q., Zhang, Z., Long, M., Hu, H.: Negative margin matters: understanding margin in few-shot classification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 438–455. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_26
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. NeurIPS
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gu, Z., Xu, C., Cui, Z. (2024). Grassmann Graph Embedding for Few-Shot Class Incremental Learning. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14432. Springer, Singapore. https://doi.org/10.1007/978-981-99-8543-2_15
Download citation
DOI: https://doi.org/10.1007/978-981-99-8543-2_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8542-5
Online ISBN: 978-981-99-8543-2
eBook Packages: Computer ScienceComputer Science (R0)