Demo Data from SIGIR 2026 / reviewers in the wild / expert
Amy L. Bearman
dblp:164/5978
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Segmentation and scene understanding · 87% Learning paradigms · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Segmentation and scene understanding › semantic segmentation › weakly supervised semantic segmentation
point supervision |
0.2 | 1 | 2016 | What's the Point: Semantic Segmentation with Point Supervision · ECCV (7) 2016 |
Computer vision › Segmentation and scene understanding
semantic segmentation |
0.2 | 1 | 2016 | What's the Point: Semantic Segmentation with Point Supervision · ECCV (7) 2016 |
Machine learning › Learning paradigms
weakly supervised learning |
0.1 | 1 | 2016 | What's the Point: Semantic Segmentation with Point Supervision · ECCV (7) 2016 |
Methods — techniques the papers use, named apart from their topics
point supervision · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | What's the Point: Semantic Segmentation with Point Supervision
Amy L. Bearman, Olga Russakovsky, Vittorio Ferrari, Li Fei-Fei 0001 |
ECCV (7) | 1 |