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Data-Efficient Ranking Distillation for Image Retrieval

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Computer Vision – ACCV 2020 (ACCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12622))

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Abstract

Recent advances in deep learning has lead to rapid developments in the field of image retrieval. However, the best performing architectures incur significant computational cost. The paper addresses this issue using knowledge distillation for metric learning problems. Unlike previous approaches, our proposed method jointly addresses the following constraints: i) limited queries to teacher model, ii) black box teacher model with access to the final output representation, and iii) small fraction of original training data without any ground-truth labels. In addition, the distillation method does not require the student and teacher to have same dimensionality. The key idea is to augment the original training set with additional samples by performing linear interpolation in the final output representation space. In low training sample settings, our approach outperforms the fully supervised baseline approach on ROxford5k and RParis6k with the least possible teacher supervision.

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Author information

Authors and Affiliations

  1. Aalto University, Espoo, Finland

    Zakaria Laskar & Juho Kannala

Authors
  1. Zakaria Laskar
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  2. Juho Kannala
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Corresponding author

Correspondence to Zakaria Laskar .

Editor information

Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

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Laskar, Z., Kannala, J. (2021). Data-Efficient Ranking Distillation for Image Retrieval. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12622. Springer, Cham. https://doi.org/10.1007/978-3-030-69525-5_28

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  • DOI: https://doi.org/10.1007/978-3-030-69525-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69524-8

  • Online ISBN: 978-3-030-69525-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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