Deep Template Matching

Deep Template Matching is a template matching paradigm that integrates deep learning architectures to learn discriminative feature representations for matching templates in complex visual data, distinguishing itself from traditional methods by leveraging hierarchical neural network models to capture semantic and contextual patterns.

69

Publications

2.7K

Citations

320

Authors

152

Institutions

Publications per year

2017–2026

56

Authors

320

  1. PD
    Peng Dai

    China Academy of Railway Sciences, China

    2 publications · 230 citations

    2

    h-index

  2. SW
    Shengchun Wang

    China Academy of Railway Sciences, China

    2 publications · 230 citations

    2

    h-index

  3. DR
    Dhananjay Ram

    Idiap Research Institute, Switzerland

    2 publications · 50 citations

    2

    h-index

  4. QC
    Qian Chen

    Nanjing University of Science and Technology, China

    2 publications · 41 citations

    2

    h-index

  5. KR
    Kan Ren

    Nanjing University of Science and Technology, China

    2 publications · 41 citations

    2

    h-index

1–5 of 320

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 32 citations

    3

    h-index

  2. Remote Sensing

    2 publications · 105 citations

    2

    h-index

  3. IEEE Access

    2 publications · 37 citations

    2

    h-index

  4. IEEE Geoscience and Remote Sensing Letters

    GRSL · 2 publications · 84 citations

    2

    h-index

  5. Measurement

    2 publications · 192 citations

    2

    h-index

1–5