Skip to main content
SpringerLink
Log in

A Proposal-Improved Few-Shot Embedding Model with Contrastive Learning

  • Conference paper
  • First Online:
MultiMedia Modeling (MMM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13834))

Included in the following conference series:

  • 1220 Accesses

Abstract

Few-shot learning is increasingly popular in image classification. The key is to learn the significant features from source classes to match the support and query pairs. In this paper, we redesign the contrastive learning scheme in a few-shot manner with selected proposal boxes generated by Navigator network. The main work of this paper includes: (i) We analyze the limitation of hard sample generating proposed by current few-shot learning methods with contrastive learning and find additional noise introduced in contrastive loss construction. (ii) We propose a novel embedding model with contrastive learning named infoPB which improves hard samples with proposal boxes to improve Noise Contrastive Estimation. (iii) We demonstrate infoPB is effective in few-shot image classification and benefited from Navigator network through the ablation study. (iv) The performance of our method is evaluated thoroughly on typical few-shot image classification tasks. It verifies a new state-of-the-art performance compared with outstanding competitors with their best results on miniImageNet in 5-way, 5-shot, and tieredImageNet in 5-way, 1-shot/5-way, 5-shot.

This work was supported by Support Scheme of Guangzhou for Leading Talents in Innovation and Entrepreneurship (No: 2020010).

F. Gong and Y. Xie—Equal contribution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Afrasiyabi, A., Lalonde, J.-F., Gagné, C.: Associative alignment for few-shot image classification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 18–35. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58558-7_2

    Chapter  Google Scholar 

  2. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  3. Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010, Physica-Verlag HD, pp. 177–186. Springer, Cham (2010). https://doi.org/10.1007/978-3-7908-2604-3_16

  4. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)

    Google Scholar 

  5. Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.: Big self-supervised models are strong semi-supervised learners. arXiv preprint arXiv:2006.10029 (2020)

  6. Chen, W., Si, C., Wang, W., Wang, L., Wang, Z., Tan, T.: Few-shot learning with part discovery and augmentation from unlabeled images. arXiv preprint arXiv:2105.11874 (2021)

  7. Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)

  8. Chen, Y., Wang, X., Liu, Z., Xu, H., Darrell, T.: A new meta-baseline for few-shot learning. arXiv preprint arXiv:2003.04390 (2020)

  9. Dhillon, G.S., Chaudhari, P., Ravichandran, A., Soatto, S.: A baseline for few-shot image classification. In: International Conference on Learning Representations (2019)

    Google Scholar 

  10. Fei, N., Lu, Z., Gao, Y., Tian, J., Xiang, T., Wen, J.R.: Meta-learning across meta-tasks for few-shot learning. arXiv preprint arXiv:2002.04274 (2020)

  11. Gidaris, S., Bursuc, A., Komodakis, N., Pérez, P., Cord, M.: Boosting few-shot visual learning with self-supervision. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8059–8068 (2019)

    Google Scholar 

  12. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  13. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. Li, H., Eigen, D., Dodge, S., Zeiler, M., Wang, X.: Finding task-relevant features for few-shot learning by category traversal. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1–10 (2019)

    Google Scholar 

  17. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

  18. Liu, C., et al.: Learning a few-shot embedding model with contrastive learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 8635–8643 (2021)

    Google Scholar 

  19. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  20. Luo, Q., Wang, L., Lv, J., Xiang, S., Pan, C.: Few-shot learning via feature hallucination with variational inference. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3963–3972 (2021)

    Google Scholar 

  21. Ma, J., Xie, H., Han, G., Chang, S.F., Galstyan, A., Abd-Almageed, W.: Partner-assisted learning for few-shot image classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10573–10582, October 2021

    Google Scholar 

  22. Mangla, P., Kumari, N., Sinha, A., Singh, M., Krishnamurthy, B., Balasubramanian, V.N.: Charting the right manifold: manifold mixup for few-shot learning. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2218–2227 (2020)

    Google Scholar 

  23. Oord, A.V.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)

  24. Oreshkin, B.N., Rodriguez, P., Lacoste, A.: Tadam: task dependent adaptive metric for improved few-shot learning. arXiv preprint arXiv:1805.10123 (2018)

  25. Qiao, L., Shi, Y., Li, J., Wang, Y., Huang, T., Tian, Y.: Transductive episodic-wise adaptive metric for few-shot learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3603–3612 (2019)

    Google Scholar 

  26. Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676 (2018)

  27. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)

    Article  Google Scholar 

  28. Rusu, A.A., et al.: Meta-learning with latent embedding optimization. arXiv preprint arXiv:1807.05960 (2018)

  29. Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4080–4090 (2017)

    Google Scholar 

  30. Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J.B., Isola, P.: Rethinking few-shot image classification: a good embedding is all you need? In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 266–282. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58568-6_16

    Chapter  Google Scholar 

  31. Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. Adv. Neural Inf. Process. Syst. 29, 3630–3638 (2016)

    Google Scholar 

  32. Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. (CSUR) 53(3), 1–34 (2020)

    Article  Google Scholar 

  33. Yang, F.S.Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: CVPR, vol. 1, p. 6 (2018)

    Google Scholar 

  34. Yang, L., Li, L., Zhang, Z., Zhou, X., Zhou, E., Liu, Y.: Dpgn: distribution propagation graph network for few-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13390–13399 (2020)

    Google Scholar 

  35. Zhang, C., Cai, Y., Lin, G., Shen, C.: Deepemd: few-shot image classification with differentiable earth mover’s distance and structured classifiers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12203–12213 (2020)

    Google Scholar 

  36. Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13001–13008 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. AsiaInfo Technologies, Beijing, China

    Fucai Gong, Yuchen Xie, Le Jiang, Keming Chen, Xiaozhou Ye & Ye Ouyang

  2. Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China

    Yunxin Liu

Authors
  1. Fucai Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuchen Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Le Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Keming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yunxin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaozhou Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ye Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuchen Xie .

Editor information

Editors and Affiliations

  1. University of Bergen, Bergen, Norway

    Duc-Tien Dang-Nguyen

  2. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  3. Radboud University Nijmegen, Nijmegen, The Netherlands

    Martha Larson

  4. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  5. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  6. National Institute of Information and Communications Technology, Tokyo, Japan

    Minh-Son Dao

  7. Department of Information Science and Media Studies, University of Bergen, Bergen, Norway

    Christoph Trattner

  8. La Trobe University, Melbourne, VIC, Australia

    Phoebe Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gong, F. et al. (2023). A Proposal-Improved Few-Shot Embedding Model with Contrastive Learning. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13834. Springer, Cham. https://doi.org/10.1007/978-3-031-27818-1_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-27818-1_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27817-4

  • Online ISBN: 978-3-031-27818-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation