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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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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