Abstract
Existing person re-identification methods often suffer significant performance degradation on unseen domains, which fuels interest in domain generalizable person re-identification (DG-PReID). As an effective technology to alleviate domain variance, the Instance Normalization (IN) has been widely employed in many existing works. However, IN also suffers from the limitation of eliminating discriminative patterns that might be useful for a particular domain or instance. In this work, we propose a new normalization scheme called Dynamically Transformed Instance Normalization (DTIN) to alleviate the drawback of IN. Our idea is to employ dynamic convolution to allow the unnormalized feature to control the transformation of the normalized features into new representations. In this way, we can ensure the network has sufficient flexibility to strike the right balance between eliminating irrelevant domain-specific features and adapting to individual domains or instances. We further utilize a multi-task learning strategy to train the model, ensuring it can adaptively produce discriminative feature representations for an arbitrary domain. Our results show a great domain generalization capability and achieve state-of-the-art performance on three mainstream DG-PReID settings.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akula, A., Jampani, V., Changpinyo, S., Zhu, S.C.: Robust visual reasoning via language guided neural module networks. In: Proceedings of the Advances in Neural Information Processing Systems (2021)
Bai, Y., et al.: Person30k: a dual-meta generalization network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp. 2123–2132 (2021)
Chen, L., et al.: SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp. 5659–5667 (2017)
Choi, S., Kim, T., Jeong, M., Park, H., Kim, C.: Meta batch-instance normalization for generalizable person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp. 3425–3435 (2021)
Dai, Y., Li, X., Liu, J., Tong, Z., Duan, L.Y.: Generalizable person re-identification with relevance-aware mixture of experts. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp. 16145–16154 (2021)
Deng, J., Dong, W., Socher, R., Li, L., Li, K., FeiFei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp. 248–255 (2009)
Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 262–275. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88682-2_21
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)
Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Scandinavian Conference on Image Analysis, pp. 91–102 (2011)
Jia, J., Ruan, Q., Hospedales, T.M.: Frustratingly easy person re-identification: generalizing person re-id in practice. arXiv preprint arXiv:1905.03422 (2019)
Jia, X., De Brabandere, B., Tuytelaars, T., Gool, L.V.: Dynamic filter networks. Proc. Adv. Neural Inf. Process. Syst. 29, 667–675 (2016)
Jin, X., Lan, C., Zeng, W., Chen, Z., Zhang, L.: Style normalization and restitution for generalizable person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp. 3143–3152 (2020)
Li, W., Wang, X.: Locally aligned feature transforms across views. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp. 3594–3601 (2013)
Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 152–159 (2014)
Liao, S., Shao, L.: Interpretable and generalizable person re-identification with query-adaptive convolution and temporal lifting. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 456–474. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_27
Lin, S., Li, C.T., Kot, A.C.: Multi-domain adversarial feature generalization for person re-identification. IEEE Trans. Image Process. 30, 1596–1607 (2020)
Liu, J., Zha, Z.J., Chen, D., Hong, R., Wang, M.: Adaptive transfer network for cross-domain person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp. 7202–7211 (2019)
Loy, C.C., Xiang, T., Gong, S.: Multi-camera activity correlation analysis. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp. 1988–1995. IEEE (2009)
Pan, X., Luo, P., Shi, J., Tang, X.: Two at once: enhancing learning and generalization capacities via IBN-Net. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 484–500. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_29
Shankar, S., Piratla, V., Chakrabarti, S., Chaudhuri, S., Jyothi, P., Sarawagi, S.: Generalizing across domains via cross-gradient training. arXiv preprint arXiv:1804.10745 (2018)
Song, J., Yang, Y., Song, Y.Z., Xiang, T., Hospedales, T.M.: Generalizable person re-identification by domain-invariant mapping network. In: Proceedings of the IEEE conference on computer vision and pattern Recognition (2019)
Tang, Y., Yang, X., Wang, N., Song, B., Gao, X.: CGAN-TM: a novel domain-to-domain transferring method for person re-identification. IEEE Trans. Image Process. 29, 5641–5651 (2020)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)
Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer GAN to bridge domain gap for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp. 79–88 (2018)
Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: End-to-end deep learning for person search. arXiv preprint arXiv:1604.01850 (2016)
Zhai, Y., et al.: AD-Cluster: augmented discriminative clustering for domain adaptive person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern Recognition (2020)
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp. 1116–1124 (2015)
Zheng, W.S., Gong, S., Xiang, T.: Associating groups of people. In: Proceedings of the British Machine Vision Conference, No. 6, pp. 1–11 (2009)
Zheng, Y., et al.: Online pseudo label generation by hierarchical cluster dynamics for adaptive person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp. 8371–8381 (2021)
Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3754–3762 (2017)
Zhou, K., Liu, Z., Qiao, Y., Xiang, T., Loy, C.C.: Domain generalization in vision: a survey. arXiv preprint arXiv:2103.02503 (2021)
Zhou, K., Yang, Y., Cavallaro, A., Xiang, T.: Learning generalisable omni-scale representations for person re-identification. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)
Zhou, K., Yang, Y., Hospedales, T., Xiang, T.: Learning to generate novel domains for domain generalization. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 561–578. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58517-4_33
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 (2017)
Acknowledgments
This work is supported by National Key R &D Program of China (No.2020AAA0106900), the National Natural Science Foundation of China (No. U19B2037), Shaanxi Provincial Key R &D Program (No.2021KWZ–03), Natural Science Basic Research Program of Shaanxi (No.2021JCW–03). This work is also supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG–RP–2018–003), and the MOE AcRF Tier-1 research grant: RG95/20.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jiao, B. et al. (2022). Dynamically Transformed Instance Normalization Network for Generalizable Person Re-Identification. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13674. Springer, Cham. https://doi.org/10.1007/978-3-031-19781-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-19781-9_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19780-2
Online ISBN: 978-3-031-19781-9
eBook Packages: Computer ScienceComputer Science (R0)