Abstract
One-shot person re-identification (Re-ID) is a hot spot nowadays, where there is only one labeled image along with many unlabeled images for each identity. Due to the short of labeled training images, it’s hard to catch up with performance under full supervision. In this paper, we propose a progressive method with identity-based data augmentation to improve lack of supervision information, which takes advantage of information of each identity to generate high-quality images. Specifically, with a certain image-to-image translation model, images are decoupled into content and style codes, where the images holding the features of identity well and injected in the style codes exclusive to the identity can be obtained by labeled images through the process of recombination. A progressive data augmentation method for one-shot labeled samples is also designed to optimize the sampling accuracy of pseudo labeled images, which contributes to our identity-based data augmentation process. The experimental results show that our method represents new state-of-the-art one-shot Re-ID work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8789–8797 (2018)
Choi, Y., Uh, Y., Yoo, J., Ha, J.W.: Stargan v2: diverse image synthesis for multiple domains. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8188–8197 (2020)
Chung, D., Delp, E.J.: Camera-aware image-to-image translation using similarity preserving stargan for person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)
Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., Jiao, J.: Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 994–1003 (2018)
Hu, M., Zeng, K., Wang, Y., Guo, Y.: Threshold-based hierarchical clustering for person re-identification. Entropy 23(5), 522 (2021). WOS:000653870600001
Huang, X., Liu, M.Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 172–189 (2018)
Li, H., Xiao, J., Sun, M., Lim, E.G., Zhao, Y.: Progressive sample mining and representation learning for one-shot person re-identification. Pattern Recogn. 110, 107614 (2021)
Li, M., Zhu, X., Gong, S.: Unsupervised tracklet person re-identification. CoRR abs/1903.00535 (2019)
Ma, L., Zhang, X., Lan, L., Huang, X., Luo, Z.: Ranking-embedded transfer canonical correlation analysis for person re-identification. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018). WOS:000585967403027
Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3960–3969 (2017)
Wang, J., Zhu, X., Gong, S., Li, W.: Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2275–2284 (2018)
Wu, Y., Lin, Y., Dong, X., Yan, Y., Ouyang, W., Yang, Y.: Exploit the unknown gradually: one-shot video-based person re-identification by stepwise learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5177–5186 (2018)
Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: past, present and future. CoRR abs/1610.02984 (2016)
Zhong, Z., Zheng, L., Luo, Z., Li, S., Yang, Y.: Invariance matters: exemplar memory for domain adaptive person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 598–607 (2019)
Zhong, Z., Zheng, L., Zheng, Z., Li, S., Yang, Y.: Camera style adaptation for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5157–5166 (2018)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (No. 61803375 and 91948303), and the National Key Research and Program of China (No. 2017YFB1001900 and 2017YFB1301104).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Si, R., Yang, S., Zhao, J., Chi, H., Tang, Y. (2021). Identity-Based Data Augmentation via Progressive Sampling for One-Shot Person Re-identification. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13111. Springer, Cham. https://doi.org/10.1007/978-3-030-92273-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-92273-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92272-6
Online ISBN: 978-3-030-92273-3
eBook Packages: Computer ScienceComputer Science (R0)