Abstract
Few-shot fine-grained classification aims to recognize novel fine-grained categories with the help of a few examples. Under the impact of the low inter-class and high intra-class differences properties of fine-grained datasets, the prototype-based approach, which originally performed well in general FS classification, could not achieve the expected results. In this paper, we propose a transductive method consisting of a feature mapping module and a prototype rectification module. Specifically, the feature mapping module removes redundant attributes from the feature space to enhance the inter-class difference. The prototype rectification module assigns a pseudo-label for each query sample according to the membership-grade between the query samples and the prototypes and uses them to update the prototypes. Experiments on multiple popular fine-grained benchmark datasets and few-shot general classification datasets demonstrate the effectiveness of our approach.
S. Ning and R. Qi—Contributed equally to this work.
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
Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C.F., Huang, J.B.: A closer look at few-shot classification. arXiv preprint arXiv:1904.04232 (2019)
Chen, Y., Wang, X., Liu, Z., Xu, H., Darrell, T., et al.: A new meta-baseline for few-shot learning. arXiv preprint arXiv:2003.04390 2(3), 5 (2020)
Ding, Y., et al.: AP-CNN: weakly supervised attention pyramid convolutional neural network for fine-grained visual classification. IEEE Trans. Image Process. 30, 2826–2836 (2021)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126–1135. PMLR (2017)
Fu, Y., Fu, Y., Jiang, Y.G.: Meta-FDMixup: cross-domain few-shot learning guided by labeled target data. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 5326–5334 (2021)
Guo, J., Qi, G., Xie, S., Li, X.: Two-branch attention learning for fine-grained class incremental learning. Electronics 10(23), 2987 (2021)
Hou, R., Chang, H., Ma, B., Shan, S., Chen, X.: Cross attention network for few-shot classification. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Huang, H., Zhang, J., Yu, L., Zhang, J., Wu, Q., Xu, C.: TOAN: target-oriented alignment network for fine-grained image categorization with few labeled samples. IEEE Trans. Circuits Syst. Video Technol. 32(2), 853–866 (2021)
Khosla, A., Jayadevaprakash, N., Yao, B., Li, F.F.: Novel dataset for fine-grained image categorization: Stanford dogs. In: Proceedings of the CVPR Workshop on Fine-Grained Visual Categorization (FGVC), vol. 2. Citeseer (2011)
Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 554–561 (2013)
Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10657–10665 (2019)
Li, W., Wang, L., Xu, J., Huo, J., Gao, Y., Luo, J.: Revisiting local descriptor based image-to-class measure for few-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7260–7268 (2019)
Li, X., Wu, J., Sun, Z., Ma, Z., Cao, J., Xue, J.H.: BSNet: bi-similarity network for few-shot fine-grained image classification. IEEE Trans. Image Process. 30, 1318–1331 (2020)
Liu, J., Song, L., Qin, Y.: Prototype rectification for few-shot learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part I. LNCS, vol. 12346, pp. 741–756. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_43
Lu, Y., Wen, L., Liu, J., Liu, Y., Tian, X.: Self-supervision can be a good few-shot learner. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XIX. LNCS, vol. 13679, pp. 740–758. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19800-7_43 Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings
Maniparambil, M., McGuinness, K., O’Connor, N.: BaseTransformers: attention over base data-points for one shot learning. arXiv preprint arXiv:2210.02476 (2022)
Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676 (2018)
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199–1208 (2018)
Tang, H., Yuan, C., Li, Z., Tang, J.: Learning attention-guided pyramidal features for few-shot fine-grained recognition. Pattern Recogn. 130, 108792 (2022)
Tang, S., Chen, D., Bai, L., Liu, K., Ge, Y., Ouyang, W.: Mutual CRF-GNN for few-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2329–2339 (2021)
Tian, S., Tang, H., Dai, L.: Coupled patch similarity network for one-shot fine-grained image recognition. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 2478–2482. IEEE (2021)
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-UCSD birds-200-2011 dataset (2011)
Wei, X.S., Wang, P., Liu, L., Shen, C., Wu, J.: Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples. IEEE Trans. Image Process. 28(12), 6116–6125 (2019)
Wu, Y., et al.: Object-aware long-short-range spatial alignment for few-shot fine-grained image classification. arXiv preprint arXiv:2108.13098 (2021)
Zhang, F., Li, M., Zhai, G., Liu, Y.: Multi-branch and multi-scale attention learning for fine-grained visual categorization. In: Lokoč, J., Skopal, T., Schoeffmann, K., Mezaris, V., Li, X., Vrochidis, S., Patras, I. (eds.) MMM 2021, Part I. LNCS, vol. 12572, pp. 136–147. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67832-6_12
Zhu, H., Gao, Z., Wang, J., Zhou, Y., Li, C.: Few-shot fine-grained image classification via multi-frequency neighborhood and double-cross modulation. arXiv preprint arXiv:2207.08547 (2022)
Zhu, Y., Liu, C., Jiang, S.: Multi-attention meta learning for few-shot fine-grained image recognition. In: IJCAI, pp. 1090–1096 (2020)
Ziko, I., Dolz, J., Granger, E., Ayed, I.B.: Laplacian regularized few-shot learning. In: International Conference on Machine Learning, pp. 11660–11670. PMLR (2020)
Acknowledgment
This study is supported by the Sichuan Science and Technology Program (NO. 2021YFG0031).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ning, S., Qi, R., Jiang, Y. (2023). Membership-Grade Based Prototype Rectification for Fine-Grained Few-Shot Classification. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14262. Springer, Cham. https://doi.org/10.1007/978-3-031-44201-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-44201-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-44200-1
Online ISBN: 978-3-031-44201-8
eBook Packages: Computer ScienceComputer Science (R0)