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International Conference on Knowledge Science, Engineering and Management

KSEM 2020: Knowledge Science, Engineering and Management pp 388–400Cite as

Relationship-Aware Hard Negative Generation in Deep Metric Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12275))

Abstract

Data relationships and the impact of synthetic loss have not been concerned by previous sample generation methods, which lead to bias in model training. To address above problem, in this paper, we propose a relationship-aware hard negative generation (RHNG) method. First, we build a global minimum spanning tree for all categories to measure the data distribution, which is used to constrain hard sample generation. Second, we construct a dynamic weight parameter which reflects the convergence of the model to guide the synthetic loss to train the model. Experimental results show that the proposed method outperforms the state-of-the-art methods in terms of retrieval and clustering tasks.

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Acknowledgments

Supported by National Key R&D Program of China (No. 2017YFB1402400), National Nature Science Foundation of China (No. 61762025), Guangxi Key Laboratory of Trusted Software (No. kx202006), Guangxi Key Laboratory of Optoelectroric Information Processing (No. GD18202), and Natural Science Foundation of Guangxi Province, China (No. 2019GXNSFDA185007).

Author information

Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400030, China

    Jiaqi Huang & Yong Feng

  2. Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, 400030, China

    Jiaqi Huang, Yong Feng & Mingliang Zhou

  3. State Key Lab of Internet of Things for Smart City, University of Macau, Taipa, 999078, Macau, China

    Mingliang Zhou

  4. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Baohua Qiang

  5. Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology, Guilin, 541004, China

    Baohua Qiang

Authors
  1. Jiaqi Huang
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  2. Yong Feng
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  3. Mingliang Zhou
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  4. Baohua Qiang
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Corresponding author

Correspondence to Yong Feng .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, VIC, Australia

    Gang Li

  2. University of Electronic Science and Technology of China, Chengdu, China

    Heng Tao Shen

  3. Beijing Institute of Technology, Beijing, China

    Ye Yuan

  4. Zhejiang Gongshang University, Hangzhou, China

    Xiaoyang Wang

  5. Zhejiang Normal University, Jinhua, China

    Huawen Liu

  6. National University of Defense Technology, Changsha, China

    Xiang Zhao

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Huang, J., Feng, Y., Zhou, M., Qiang, B. (2020). Relationship-Aware Hard Negative Generation in Deep Metric Learning. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12275. Springer, Cham. https://doi.org/10.1007/978-3-030-55393-7_35

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  • DOI: https://doi.org/10.1007/978-3-030-55393-7_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-55392-0

  • Online ISBN: 978-3-030-55393-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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