Abstract
Visible-infrared person re-identification (VI-ReID) is a crucial part of open world ReID task, targeting at cross-modality pedestrian retrieval between visible and infrared images. Its large intra-class variation and cross-modality discrepancy lead to difficulty in discriminated representation learning. In this paper, we investigate adaptive neurons deactivation technique to improve VI-ReID model performance and propose two auxiliary training schemes. First, we present a one-stream module (gated module, GM) and its corresponding training scheme (gated learning, GL), to assist model training by adaptive neuron deactivation. Based on GM and GL, we design two-stream module (dual gated module, DGM) and its corresponding training scheme (dual gated learning, DGL) for further utilizing deactivated neurons in GL. During inference, GL and DGL are abandoned, resulting in no extra computation cost. Extensive experiments are performed on SYSU-MM01 and RegDB dataset to demonstrate the superiority of GL and DGL approach. Experimental results show that our proposed methods achieve significant improvement.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, Y.C., Zhu, X., Zheng, W.S., Lai, J.H.: Person re-identification by camera correlation aware feature augmentation. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 392–408 (2017)
Dai, P., Ji, R., Wang, H., Wu, Q., Huang, Y.: Cross-modality person re-identification with generative adversarial training. In: IJCAI. vol. 1, p. 2 (2018)
Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: a benchmark. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 304–311. IEEE (2009)
Feng, Z., Lai, J., Xie, X.: Learning modality-specific representations for visible-infrared person re-identification. IEEE Trans. Image Process. 29, 579–590 (2019)
Gheissari, N., Sebastian, T.B., Hartley, R.: Person reidentification using spatiotemporal appearance. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1528–1535. IEEE (2006)
Hao, Y., Wang, N., Li, J., Gao, X.: Hsme: hypersphere manifold embedding for visible thermal person re-identification. Proc. AAAI Conf. Artif. Intell. 33, 8385–8392 (2019)
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)
Hou, R., Ma, B., Chang, H., Gu, X., Shan, S., Chen, X.: Interaction-and-aggregation network for person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9317–9326 (2019)
Hou, R., Ma, B., Chang, H., Gu, X., Shan, S., Chen, X.: Vrstc: occlusion-free video person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7183–7192 (2019)
Jia, M., Zhai, Y., Lu, S., Ma, S., Zhang, J.: A similarity inference metric for rgb-infrared cross-modality person re-identification. arXiv preprint arXiv:2007.01504 (2020)
Leng, Q., Ye, M., Tian, Q.: A survey of open-world person re-identification. IEEE Trans. Circ. Syst. Video Technol. 30(4), 1092–1108 (2019)
Li, D., Wei, X., Hong, X., Gong, Y.: Infrared-visible cross-modal person re-identification with an x modality. Proc. AAAI Conf. Artif. Intell. 34, 4610–4617 (2020)
Li, Y., Xu, H.: Deep attention network for rgb-infrared cross-modality person re-identification. In: Journal of Physics: Conference Series, vol. 1642, p. 012015. IOP Publishing (2020)
Lu, Y., et al.: Cross-modality person re-identification with shared-specific feature transfer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13379–13389 (2020)
Luo, H., et al.: A strong baseline and batch normalization neck for deep person re-identification. IEEE Trans. Multimedia 22(10), 2597–2609 (2019)
Mudunuri, S.P., Venkataramanan, S., Biswas, S.: Dictionary alignment with re-ranking for low-resolution nir-vis face recognition. IEEE Trans. Inform. Forensics Secur. 14(4), 886–896 (2018)
Nguyen, D.T., Hong, H.G., Kim, K.W., Park, K.R.: Person recognition system based on a combination of body images from visible light and thermal cameras. Sensors 17(3), 605 (2017)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Wang, G., Zhang, T., Cheng, J., Liu, S., Yang, Y., Hou, Z.: Rgb-infrared cross-modality person re-identification via joint pixel and feature alignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3623–3632 (2019)
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)
Wang, Z., Wang, Z., Zheng, Y., Chuang, Y.Y., Satoh, S.: Learning to reduce dual-level discrepancy for infrared-visible person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 618–626 (2019)
Wu, A., Zheng, W.S., Yu, H.X., Gong, S., Lai, J.: Rgb-infrared cross-modality person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5380–5389 (2017)
Ye, M., Lan, X., Li, J., Yuen, P.: Hierarchical discriminative learning for visible thermal person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Ye, M., Lan, X., Wang, Z., Yuen, P.C.: Bi-directional center-constrained top-ranking for visible thermal person re-identification. IEEE Trans. Inform. Forensics Secur. 15, 407–419 (2019)
Ye, M., Shen, J., Crandall, D.J., Shao, L., Luo, J.: Dynamic dual-attentive aggregation learning for visible-infrared person re-identification. In: European Conference on Computer Vision (ECCV) (2020)
Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., Hoi, S.C.: Deep learning for person re-identification: a survey and outlook. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)
Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1318–1327 (2017)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, Y., Xian, J., Wei, D., Jin, X., Xu, T. (2021). Dual Gated Learning for Visible-Infrared Person Re-identification. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-87358-5_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87357-8
Online ISBN: 978-3-030-87358-5
eBook Packages: Computer ScienceComputer Science (R0)