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
Parents
100
Gated Fusion Network for Single Image Dehazing
Wenqi Ren, Lin Ma, Jiawei Zhang et al. · 2018 · 934 citations
Monocular Total Capture: Posing Face, Body, and Hands in the Wild
Donglai Xiang, Hanbyul Joo, Yaser Sheikh · 2019 · 346 citations
1–5 of 100
363
4
h-index
4
h-index
4
h-index
3
h-index
3
h-index
1–5 of 363
184
Tsinghua University
Beijing, China
4 h-index
6 publications
329 citations
University of Missouri
Columbia, United States
4 h-index
12 publications
303 citations
National University of Singapore
Singapore, Singapore
4 h-index
14 publications
1.4K citations

Zhejiang University
Hangzhou, China
4 h-index
15 publications
535 citations

Johns Hopkins University
Baltimore, United States
4 h-index
10 publications
287 citations

City University of Hong Kong
Hong Kong, Hong Kong
3 h-index
6 publications
1.3K citations
Adobe Systems (United States)
San Jose, United States
3 h-index
3 publications
236 citations
Tencent (China)
Shenzhen, China
3 h-index
12 publications
3.7K citations
1–8 of 184
Repository · 9 publications · 254 citations
9
h-index
6 publications · 101 citations
6
h-index
4 publications · 243 citations
4
h-index
4 publications · 107 citations
4
h-index
3 publications · 68 citations
3
h-index
1–5