Self-supervised Image Retrieval

Self-Supervised Image Retrieval is a machine learning approach within image retrieval that leverages self-supervised pretraining to learn discriminative visual representations from unlabeled image data by generating pseudo-labels through intrinsic data properties, thereby eliminating the need for human-annotated supervision.

25

Publications

916

Citations

112

Authors

55

Institutions

Publications per year

2017–2026

25

Authors

112

  1. DK
    Dìmosthenis Karatzas

    Universitat Autònoma de Barcelona, Spain

    2 publications · 118 citations

    2

    h-index

  2. ZL
    Zhe Liu

    Shanghai Jiao Tong University, China

    2 publications · 57 citations

    2

    h-index

  3. HH
    Hanjiang Hu

    Carnegie Mellon University, United States

    2 publications · 57 citations

    2

    h-index

  4. MR
    Marçal Rusiñol

    Universitat Autònoma de Barcelona, Spain

    2 publications · 118 citations

    2

    h-index

  5. LG
    Lluís Gómez

    Universitat Autònoma de Barcelona, Spain

    2 publications · 118 citations

    2

    h-index

1–5 of 112

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 80 citations

    3

    h-index

  2. IEEE Transactions on Image Processing

    3 publications · 67 citations

    3

    h-index

  3. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 2 publications · 146 citations

    2

    h-index

  4. Information Sciences

    1 publications · 32 citations

    1

    h-index

  5. 1

    h-index

1–5