Abstract
This paper is concerned with classifying geometric relationships in a few-shot learning (FSL) setting. Concept learning is proposed as a solution, combined with a new approach to use test samples during meta-testing. This new approach applies test images belonging to categories used during meta-training, but containing visually different content (e.g., circles, instead of rectangles, arranged in the same geometric relationship). The viability of the approach is demonstrated by training state-of-the-art FSL methods on datasets of synthetic images containing simple objects in various geometric relationships. The resulting models are able to generalise from images containing simple geometric shapes to images of more complex objects like artificial cracks and voids. The proposed method have application potential in many domains, where geometric relationships between, possibly complex, shapes are of importance, but only a handful of annotated samples are available per class. The FSL based approach may provide a useful alternative to presently used solutions using semi-supervised or self-supervised 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
Similar content being viewed by others
Notes
- 1.
The datasets and the code used to generate them is available at https://github.com/randoba/geoconshapes/.
- 2.
https://github.com/snap-stanford/comet.
- 3.
https://github.com/wyharveychen/CloserLookFewShot.
References
Cao, K., Brbić, M., Leskovec, J.: Concept learners for few-shot learning (07 2020). arXiv:2007.07375
Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C.F., Huang, J.B.: A closer look at few-shot classification (09 2018). https://openreview.net/forum?id=HkxLXnAcFQ
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. arXiv:1703.03400 (07 2017). arXiv:1703.03400
Han, C., Mao, J., Gan, C., Tenenbaum, J.B., Wu, J.: Visual concept-metaconcept learning. arXiv:2002.01464 (02 2020)
Hsu, J., Wu, J., Goodman, N.D.: Geoclidean: Few-shot generalization in euclidean geometry (11 2022). https://doi.org/10.48550/arXiv.2211.16663, arXiv:2211.16663
Hu, R., Andreas, J., Rohrbach, M., Darrell, T., Saenko, K.: Learning to reason: end-to-end module networks for visual question answering. arXiv:1704.05526pdf
Korchi, A.E., Ghanou, Y.: 2D geometric shapes dataset - for machine learning and pattern recognition. Data Brief 32, 106090 (10 2020). https://doi.org/10.1016/j.dib.2020.106090
Nie, W., Yu, Z., Mao, L., Patel, A., Zhu, Y., Anandkumar, A.: Bongard-logo: a new benchmark for human-level concept learning and reasoning (2021). arXiv:2010.00763v4.pdf
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning (2017). https://openreview.net/pdf?id=rJY0-Kcll
Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning (2017). arXiv:1703.05175
Sung, F., Yang, Y., Zhang, L., Xiang, T., H, P., Hospedales, T.M.: Learning to compare: relation network for few-shot learning (2017). arXiv:1711.06025
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. arXiv:1606.04080 (12 2017)
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: Caltech-ucsd birds-200-2011. Tech. Rep. CNS-TR-2011-001, California Institute of Technology (2011)
Acknowledgments
This paper is a result of Project C2279767 that has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the KDP-2023 funding scheme.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bodnár, A., Gulyás, L., Kárász, Z. (2024). Understanding Geometric Relationship Concepts in Few-Shot Learning. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2024. Lecture Notes in Computer Science(), vol 14811. Springer, Cham. https://doi.org/10.1007/978-3-031-70819-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-70819-0_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-70818-3
Online ISBN: 978-3-031-70819-0
eBook Packages: Computer ScienceComputer Science (R0)