Confidence Maps

Confidence Maps is a visualization framework that represents the spatial distribution of model certainty across input regions by assigning a confidence score to each location, differentiating it from general uncertainty quantification methods through its explicit emphasis on spatially explicit, pixel-level confidence representation in tasks such as image segmentation and object detection.

100

Publications

13.5K

Citations

363

Authors

184

Institutions

Publications per year

2017–2026

60

Authors

363

  1. JM
    James M. Keller

    University of Missouri, United States

    4 publications · 101 citations

    4

    h-index

  2. DX
    Donglai Xiang

    Carnegie Mellon University, United States

    4 publications · 583 citations

    4

    h-index

  3. SY
    Shuicheng Yan

    National University of Singapore, Singapore

    4 publications · 385 citations

    4

    h-index

  4. JF
    Jiashi Feng

    National University of Singapore, Singapore

    3 publications · 361 citations

    3

    h-index

  5. XL
    Xiaodan Liang

    Sun Yat-sen University, China

    3 publications · 236 citations

    3

    h-index

1–5 of 363

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 254 citations

    9

    h-index

  2. 6

    h-index

  3. IEEE Transactions on Medical Imaging

    4 publications · 243 citations

    4

    h-index

  4. IEEE Transactions on Image Processing

    4 publications · 107 citations

    4

    h-index

  5. 3

    h-index

1–5