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Long-Tailed Contrastive Loss for Video-Based Person Re-identification

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Image and Graphics Technologies and Applications (IGTA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1043))

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Abstract

Contrastive loss based deep metric learning has been generally used in video-based person re-identification, which learns a metric by preserving the distance between positive sample pairs close and negative sample pairs far on the embedding space. Yet contrastive loss still suffers not only from “hard” negative examples loosely defined by a hard margin, but also from severe sampling imbalance caused by equal sampling technique. To address these defeats, this paper presents a novel loss called Long-Tailed Contrastive Loss (LTCL). A Gaussian kernel function is used as the negative loss term, which takes into account the effect of long-range negative sample pairs. Meanwhile, a focusing factor is introduced for adaptive hard negative data mining and a rebalancing factor is used to compensate the sampling imbalance. Experiments conducted on two classic datasets demonstrate the effectiveness of the proposed method.

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China

    Liqiang Bao

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  1. Liqiang Bao
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Correspondence to Liqiang Bao .

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Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Yongtian Wang

  2. University of Chinese Academy of Science, Beijing, China

    Qingmin Huang

  3. Institute of Computer Science and Technology, Peking University, Beijing, China

    Yuxin Peng

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Bao, L. (2019). Long-Tailed Contrastive Loss for Video-Based Person Re-identification. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_51

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  • DOI: https://doi.org/10.1007/978-981-13-9917-6_51

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9916-9

  • Online ISBN: 978-981-13-9917-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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