Model Confidence Maps

Model Confidence Maps is a visualization technique that represents the spatial distribution of a predictive model's confidence in its outputs across an input domain, distinguishing itself by mapping localized uncertainty estimates to provide interpretable, pixel-level confidence assessments.

32

Publications

1.2K

Citations

131

Authors

60

Institutions

Publications per year

2017–2026

28

Authors

131

  1. XH
    Xiaodong He

    Microsoft (United States), United States

    2 publications · 147 citations

    2

    h-index

  2. LZ
    Lei Zhang

    Hong Kong Polytechnic University, Hong Kong SAR China

    2 publications · 147 citations

    2

    h-index

  3. ML
    Miaopeng Li

    Zhejiang University, China

    2 publications · 126 citations

    2

    h-index

  4. ZZ
    Zimeng Zhou

    Kunming Institute of Botany, China

    2 publications · 126 citations

    2

    h-index

  5. KT
    Kenneth Tran

    University of Auckland, New Zealand

    2 publications · 147 citations

    2

    h-index

1–5 of 131

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 83 citations

    4

    h-index

  2. 2

    h-index

  3. 1

    h-index

  4. EURASIP Journal on Image and Video Processing

    1 publications · 65 citations

    1

    h-index

  5. Publications Office of the European Union

    Repository · 1 publications · 85 citations

    1

    h-index

1–5