Abstract
Few-Shot Object Detection (FSOD) task is widely used in various data-scarce scenarios, aiming to expand the object detector with a few novel class samples. The current mainstream FSOD models improve the accuracy by mining novel class instances in the training set and fine-tuning the detector with mined pseudo set. Substantial progress has been made using pseudo-label approaches, but the impact of pseudo-labels diversity on FSOD tasks has not been explored. In our work, for the purpose of fully utilizing the pseudo-label set and exploring their diversity, we propose a new framework mainly including Novel Instance Bank (NIB) and Correlation-Guided Loss Correction (CGLC). Dynamically updated NIB stores the novel class instances to increase the diversity of novel instances in each batch. Moreover, to better exploit the pseudo-label diversity, CGLC adaptively employs k-shot samples to guide correct and incorrect pseudo-labels to pull away from each other. Experimental results on the MS-COCO dataset demonstrate the effectiveness of our method, which does not require any additional training samples or parameters. Our code is available at: https://github.com/lotuser1/PDE.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779–788 (2016)
Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016. ECCV 2016, Lecture Notes in Computer Science, vol. 9905, pp. 21–37 Springer, Cham (2016).https://doi.org/10.1007/978-3-319-46448-0_2
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)
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)
Hu, H., Bai, S., Li, A., Cui, J., Wang, L.: Dense relation distillation with context-aware aggregation for few-shot object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10185–10194 (2021)
Wang, Y.X., Ramanan, D., Hebert, M.: Meta-learning to detect rare objects. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9925–9934 (2019)
Wang, X., Huang, T.E., Darrell, T., Gonzalez, J.E., Yu, F.: Frustratingly simple few-shot object detection. arXiv preprint arXiv:2003.06957 (2020)
Sun, B., Li, B., Cai, S., Yuan, Y., Zhang, C.: Fsce: few-shot object detection via contrastive proposal encoding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7352–7362 (2021)
Li, Y., et al.: Few-shot object detection via classification refinement and distractor retreatment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15395–15403 (2021)
Cao, Y., et al.: Few-shot object detection via association and discrimination. Adv. Neural. Inf. Process. Syst. 34, 16570–16581 (2021)
Wang, Z., Li, Y., Guo, Y., Fang, L., Wang, S.: Data-uncertainty guided multi-phase learning for semi-supervised object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4568–4577 (2021)
Liu, W., Wang, C., Yu, S., Tao, C., Wang, J., Wu, J.: Novel Instance Mining with Pseudo-Margin Evaluation for Few-Shot Object Detection. In: ICASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2250–2254. IEEE (2022)
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)
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)
Liu, J., Song, L., Qin, Y.: Prototype rectification for few-shot learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds.) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol. 12346, pp. 741–756. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_43
Khodadadeh, S., Boloni, L., Shah, M.: Unsupervised meta-learning for few-shot image classification. In: Advances in Neural Information Processing Systems 32, pp. 10132–10142. Curran Associates, Inc. (2019)
Sun, Q., Liu, Y., Chua, T. S., Schiele, B.: Meta-transfer learning for few-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.403–412 (2019)
Yan, X., Chen, Z., Xu, A., Wang, X., Liang, X., Lin, L.: Meta r-cnn: towards general solver for instance-level low-shot learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9577–9586 (2019)
Xiao, Y., Marlet, R.: Few-shot object detection and viewpoint estimation for objects in the wild. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds.) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol. 12362, pp. 192–210. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58520-4_12
Köhler, M., Eisenbach, M., & Gross, H. M.: Few-Shot Object Detection: A Survey. arXiv preprint arXiv:2112.11699 (2021)
Cao, Y., Wang, J., Lin, Y., Lin, D.: MINI: mining implicit novel instances for few-shot object detection. arXiv preprint arXiv:2205.03381 (2022)
Kaul, P., Xie, W., Zisserman, A.: Label, verify, correct: a simple few-shot object detection method. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14237–14247 (2022)
Khosla, P., et al.: Supervised contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 18661–18673 (2020)
Kang, B., Liu, Z., Wang, X., Yu, F., Feng, J., Darrell, T.: Few-shot object detection via feature reweighting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8420–8429 (2019)
Han, G., He, Y., Huang, S., Ma, J., Chang, S. F.: Query adaptive few-shot object detection with heterogeneous graph convolutional networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3263–3272 (2021)
Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Acknowledgments
This work was supported by the Zhejiang Provincial Natural Science Foundation of China (No. LY20F030005) and National Natural Science Foundation of China (No. 61603202). (Corresponding Author: Chong Wang).
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
Chen, S., Wang, C., Liu, W., Ye, Z., Deng, J. (2023). Pseudo-label Diversity Exploitation for Few-Shot Object Detection. 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_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-27818-1_24
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)