Siamese Retrieval Models

Siamese Retrieval Models is a retrieval framework that employs a twin neural network architecture to jointly encode query and document representations into a shared semantic space, enabling similarity-based ranking through symmetric parameter sharing and distance metric learning.

22

Publications

1.5K

Citations

85

Authors

48

Institutions

Publications per year

2017–2026

20

Authors

85

  1. CX
    Chenyan Xiong

    Carnegie Mellon University, United States

    3 publications · 611 citations

    3

    h-index

  2. PN
    Paul N. Bennett

    Microsoft (United States), United States

    3 publications · 611 citations

    3

    h-index

  3. JL
    Jialin Liu

    Southern University of Science and Technology, China

    2 publications · 565 citations

    2

    h-index

  4. AO
    Arnold Overwijk

    Microsoft Research (United Kingdom), United Kingdom

    2 publications · 276 citations

    2

    h-index

  5. YL
    Ye Li

    Harbin Institute of Technology, China

    2 publications · 565 citations

    2

    h-index

1–5 of 85

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 299 citations

    3

    h-index

  2. IEEE Transactions on Multimedia

    1 publications · 48 citations

    1

    h-index

  3. Multimedia Tools and Applications

    1 publications · 13 citations

    1

    h-index

  4. IEEE Transactions on Cybernetics

    1 publications · 11 citations

    1

    h-index

  5. Expert Systems with Applications

    1 publications · 30 citations

    1

    h-index

1–5