Abstract
Triplet loss has been proven to be useful in the task of person re-identification (ReID). However, it has limitations due to the influence of large intra-pair variations and unreasonable gradients. In this paper, we propose a novel loss to reduce the influence of large intra-pair variations and improve optimization gradients via optimizing the ratio of intra-identity distance to inter-identity distance. As it also requires a triplet of pedestrian images, we call this new loss as triplet ratio loss. Experimental results on four widely used ReID benchmarks, i.e., Market-1501, DukeMTMC-ReID, CUHK03, and MSMT17, demonstrate that the triplet ratio loss outperforms the previous triplet loss.
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Notes
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Repelling gradient denotes the gradient that pushes the features away from each other, while attracting gradient indicates the gradient that pulls the features closer.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant U2013601, and the Program of Guangdong Provincial Key Laboratory of Robot Localization and Navigation Technology, under Grant 2020B121202011 and Key-Area Research and Development Program of Guangdong Province, China, under Grant 2019B010154003.
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Hu, S., Wang, K., Cheng, J., Tan, H., Pang, J. (2022). Triplet Ratio Loss for Robust Person Re-identification. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_4
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DOI: https://doi.org/10.1007/978-3-031-18907-4_4
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