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Triplet penalty matters: penalty metric space triplet loss for person re-identification

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Abstract

This paper provided a unified perspective to improve triplet loss function and proposed a substantial triplet loss named Penalty Metric Space Triplet Loss (Tri-PMS) based on this perspective for person re-identification. Specifically, we performed two kinds of reversed penalty on the positive and negative distance of triplet loss with hard mining (Tri-hard) and adaptively weakened the original margin based on penalty intensity. We have divided penalty metric space into two categories, including consistency and autonomy space. In consistency space, all batches shared the same penalty intensity without extra samples' information. Tri-PMS with consistency space is a tiny, friendly, and relatively efficient method for person re-identification tasks. In autonomy space, each batch holds their penalty based on triplet distribution. Tri-PMS with autonomy space grasps more information on each batch sample and learns a more perfect embedded space than consistency space. Our proposed method has obtained excellent performance on Market1501 with 94.8% mAP and 96.2% Rank-1, on DukeMTMC-reID with 89.7% mAP and 91.8% Rank-1. For CUHK03 and MSMT17, Tri-PMS also shows strong performance compared to other loss functions. More notably, Tri-PMS with autonomy space has stood the state-of-the-art performance on Occluded-DukeMTMC and Partial-iLIDS, two mainstream challenging datasets for person re-identification.

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Acknowledgements

This work is supported by National Key R&D Program of China (No. 2021YFC2009200).

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Authors and Affiliations

  1. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, People’s Republic of China

    Wen-cheng Qin, Zhi-yong Huang, Tian-hui Guan, Fang Xie, Fen Luo & Pin-zhong Qin

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  1. Wen-cheng Qin
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  2. Zhi-yong Huang
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Correspondence to Zhi-yong Huang.

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Qin, Wc., Huang, Zy., Guan, Th. et al. Triplet penalty matters: penalty metric space triplet loss for person re-identification. J Ambient Intell Human Comput 14, 14029–14043 (2023). https://doi.org/10.1007/s12652-022-04109-z

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