Abstract
Most of the existing person re-identification (ReID) methods use a classification network pre-trained on external data as the backbone and then fine-tune it, which results in a network architecture that is fixed and dependent on pre-training of external data. There are also some methods that are specifically designed by human experts for ReID, but manual network design becomes more difficult as network requirements increase and often fails to achieve optimal settings. In this paper, we consider using emerging neural architecture search (NAS) technology as a tool to solve above problems. However, most of NAS methods deal with classification tasks, which causes NAS to not be directly extended to ReID. In order to coordinate the inconsistency between the two optimization goals, we propose to establish an objective function with the assistant of the triplet loss to guide the direction of architecture search. Finally, it is no longer dependent on external data to automatically generate a ReID network with excellent performance using NAS directly on the target dataset. The experimental results on three public datasets validate that our method can automatically and efficiently find the network architecture suitable for ReID.
S. Zhang and R. Cao—Denotes co-first authors. This work is partifically supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2019JQ158), China Postdoctoral Science Foundation funded project under Grant No. 2018M633577, the Fundamental Research Funds for Central Universities of China under Grant No. G2018KY0303, and National Science Foundation of China under Grant No. 61801395, 61801393 and 61801391.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: CVPR (2015)
Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: NETVLAD: CNN architecture for weakly supervised place recognition. In: CVPR, pp. 5297–5307 (2016)
Baker, B., Gupta, O., Naik, N., Raskar, R.: Designing neural network architectures using reinforcement learning. In: ICLR (2017)
Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: CVPR (2016)
Elsken, T., Metzen, J.H., Hutter, F.: Simple and efficient architecture search for convolutional neural networks. In: ICLR (2018)
Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey. J. Mach. Learn. Res. 20, 55:1–55:21 (2019)
Geng, M., Wang, Y., Xiang, T., Tian, Y.: Deep transfer learning for person re-identification. CoRR (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. CoRR (2017)
Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR (2016)
Kandasamy, K., Neiswanger, W., Schneider, J., Póczos, B., Xing, E.P.: Neural architecture search with Bayesian optimisation and optimal transport. In: NeurIPS, pp. 2020–2029 (2018)
Kim, E., Hannan, D., Kenyon, G.: Deep sparse coding for invariant multimodal halle berry neurons. In: CVPR (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)
Lavi, B., Serj, M.F., Ullah, I.: Survey on deep learning techniques for person re-identification task. CoRR (2018)
Li, D., Chen, X., Zhang, Z., Huang, K.: Learning deep context-aware features over body and latent parts for person re-identification. In: CVPR, pp. 7398–7407 (2017)
Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: CVPR (2014)
Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: CVPR, pp. 2197–2206 (2015)
Lin, J., Ren, L., Lu, J., Feng, J., Zhou, J.: Consistent-aware deep learning for person re-identification in a camera network. In: CVPR, pp. 3396–3405 (2017)
Lin, Y., Zheng, L., Zheng, Z., Wu, Y., Yang, Y.: Improving person re-identification by attribute and identity learning. CoRR (2017)
Liu, C., et al.: Progressive neural architecture search. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 19–35. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01246-5_2
Liu, H., Simonyan, K., Yang, Y.: DARTS: differentiable architecture search. CoRR (2018)
Luo, R., Tian, F., Qin, T., Liu, T.: Neural architecture optimization. CoRR (2018)
Negrinho, R., Gordon, G.J.: DeepArchitect: automatically designing and training deep architectures. CoRR (2017)
Paisitkriangkrai, S., Shen, C., van den Hengel, A.: Learning to rank in person re-identification with metric ensembles. In: CVPR (2015)
Pham, H., Guan, M.Y., Zoph, B., Le, Q.V., Dean, J.: Efficient neural architecture search via parameter sharing. In: The International Conference on Machine Learning (ICML), pp. 4092–4101 (2018)
Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. CoRR (2018)
Real, E., et al.: Large-scale evolution of image classifiers. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6–11 August 2017, pp. 2902–2911 (2017)
Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17–35. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_2
Shen, Y., Xiao, T., Li, H., Yi, S., Wang, X.: End-to-end deep Kronecker-product matching for person re-identification. In: CVPR (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: ICCV (2017)
Szegedy, C., et al.: Going deeper with convolutions. In: CVPR (2015)
Varior, R.R., Haloi, M., Wang, G.: Gated siamese convolutional neural network architecture for human re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 791–808. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_48
Wei, X., Zhang, Y., Gong, Y., Zhang, J., Zheng, N.: Grassmann pooling as compact homogeneous bilinear pooling for fine-grained visual classification. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 365–380. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_22
Wei, X., Zhang, Y., Gong, Y., Zheng, N.: Kernelized subspace pooling for deep local descriptors. In: CVPR, pp. 1867–1875 (2018)
Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: Joint detection and identification feature learning for person search. In: CVPR, pp. 3376–3385 (2017)
Xie, S., Zheng, H., Liu, C., Lin, L.: SNAS: stochastic neural architecture search. CoRR (2018)
Yi, D., Lei, Z., Liao, S., Li, S.Z.: Deep metric learning for person re-identification. In: ICPR, pp. 34–39 (2014)
Zhang, H., Kiranyaz, S., Gabbouj, M.: Finding better topologies for deep convolutional neural networks by evolution. CoRR (2018)
Zhang, S., Zhang, Q., Wei, X., Zhang, Y., Xia, Y.: Person re-identification with triplet focal loss. IEEE Access 6, 78092–78099 (2018)
Zhao, H., eet al.: Spindle net: Person re-identification with human body region guided feature decomposition and fusion. In: CVPR, pp. 907–915 (2017)
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: ICCV, pp. 1116–1124 (2015)
Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: past, present and future. CoRR (2016)
Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: ICCV, pp. 3774–3782 (2017)
Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: CVPR, pp. 3652–3661 (2017)
Zhou, J., Yu, P., Tang, W., Wu, Y.: Efficient online local metric adaptation via negative samples for person re-identification. In: ICCV, pp. 2439–2447 (2017)
Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. In: ICLR (2017)
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: CVPR, pp. 8697–8710 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, S., Cao, R., Wei, X., Wang, P., Zhang, Y. (2019). Person Re-identification with Neural Architecture Search. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11857. Springer, Cham. https://doi.org/10.1007/978-3-030-31654-9_46
Download citation
DOI: https://doi.org/10.1007/978-3-030-31654-9_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31653-2
Online ISBN: 978-3-030-31654-9
eBook Packages: Computer ScienceComputer Science (R0)