Bounding Box Regression

Bounding Box Regression is a machine learning technique in object detection that refines the localization of detected objects by predicting the parameters of a bounding box around each object, differentiating itself from classification tasks through its focus on spatial coordinate adjustment rather than categorical labeling.

292

Publications

40.9K

Citations

1.2K

Authors

330

Institutions

Publications per year

2017–2026

283

Authors

1.2K

  1. QW
    Qi Wu

    The University of Adelaide, Australia

    4 publications · 295 citations

    4

    h-index

  2. XB
    Xiang Bai

    Huazhong University of Science and Technology, China

    4 publications · 2.1K citations

    4

    h-index

  3. XY
    Xue Yang

    Shanghai Jiao Tong University, China

    4 publications · 548 citations

    4

    h-index

  4. ZZ
    Zhiqiang Zhou

    Beijing Institute of Technology, China

    4 publications · 298 citations

    4

    h-index

  5. RY
    Rongguang Ye

    Tianjin University, China

    4 publications · 6.4K citations

    4

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 42 publications · 5.2K citations

    30

    h-index

  2. Sensors

    19 publications · 1.6K citations

    16

    h-index

  3. IEEE Access

    18 publications · 1.2K citations

    14

    h-index

  4. Remote Sensing

    11 publications · 513 citations

    11

    h-index

  5. Electronics

    7 publications · 319 citations

    7

    h-index

1–5