Abstract
Recently, center loss and triplet loss have proved their effectiveness for person re-identification. However, they have difficulties in making optimizations of the intra/inter-class distance and the cost of computing and mining hard training samples simultaneously. To solve these problems, in this paper, we propose a hard mining center-triplet loss, a novel improved strategy of triplet loss. For one thing, it combines the advantages of center loss and triplet loss aiming at minimizing the intra-class distance and maximizing the inter-class distance. For another thing, it employs hard sample mining strategy on the level of center of class instead of individual sample to mine hard triplets with the purpose to reducing the number of hard triplets for training and further reducing the cost of computing. Finally, the results on two large-scale datasets Market1501 and DukeMTMC-reID show the robustness and efficiency of our method in making optimizations of these problems simultaneously and learning robust feature representation, which also demonstrate that our method outperforms most of existing loss function and achieves better performance for person re-identification.
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References
Zheng, L., Shen, L., Tian, L., et al.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116–1124 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Li, W., Zhao, R., Xiao, T., et al.: 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)
Li, S., Liu, X., Liu, W., et al.: A discriminative null space based deep learning approach for person re-identification. In: 2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 480–484. IEEE (2016)
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
Ding, S., Lin, L., Wang, G., et al.: Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn. 48(10), 2993–3003 (2015)
Cheng, D., Gong, Y., Zhou, S., et al.: Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1335–1344 (2016)
Chen, W., Chen, X., Zhang, J., et al.: Beyond triplet loss: a deep quadruplet network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 403–412 (2017)
Zhang, X., Luo, H., Fan, X., et al.: AlignedReID: surpassing human-level performance in person re-identification. arXiv preprint arXiv:1711.08184 (2017)
Wu, S., Chen, Y.C., Li, X., et al.: An enhanced deep feature representation for person re-identification. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–8. IEEE (2016)
Zhao, H., Tian, M., Sun, S., et al.: Spindle Net: person re-identification with human body region guided feature decomposition and fusion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1077–1085 (2017)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31
Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)
Xiao, Q., Luo, H., Zhang, C.: Margin sample mining loss: a deep learning based method for person re-identification. arXiv preprint arXiv:1710.00478 (2017)
Alex, D., Sami, Z., Banerjee, S., et al.: Cluster Loss for Person Re-Identification. arXiv preprint arXiv:1812.10325 (2018)
Zhang, X., Fang, Z., Wen, Y., et al.: Range loss for deep face recognition with long-tailed training data. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5409–5418 (2017)
Xiao, T., Li, S., Wang, B., et al.: Joint detection and identification feature learning for person search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3415–3424 (2017)
Zheng, Y., Pal, D.K., Savvides, M.: Ring loss: convex feature normalization for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5089–5097 (2018)
Ming, Z., Chazalon, J., Luqman, M.M., et al.: Simple triplet loss based on intra/inter-class metric learning for face verification. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 1656–1664. IEEE (2017)
He, X., Zhou, Y., Zhou, Z., et al.: Triplet-center loss for multi-view 3D object retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1945–1954 (2018)
Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
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)
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
Reddi, S.J., Kale, S., Kumar, S.: On the convergence of adam and beyond (2018)
Acknowledgement
The authors would like to thank the anonymous reviewers for their critical and constructive comments and suggestions. This work was supported by the China National Natural Science Foundation under Grant No. 61673299, 61203247, 61573259, 61573255.
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Lv, X., Zhao, C., Chen, W. (2019). A Novel Hard Mining Center-Triplet Loss for Person Re-identification. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_17
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