Demo Data from EDBT 2026 / reviewers in the wild / expert
Philipp Scholze
dblp:317/0962
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0003-0776-0144ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
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 |
3D vision · 56% Trustworthy machine learning · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning › robustness
adversarial robustness |
0.6 | 1 | 2022 | A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow · ECCV (22) 2022 |
Computer vision › 3D vision › motion estimation › optical flow
robust optical flow |
0.6 | 1 | 2022 | A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow · ECCV (22) 2022 |
Computer vision › 3D vision › motion estimation
optical flow |
0.2 | 1 | 2022 | A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow · ECCV (22) 2022 |
Methods — techniques the papers use, named apart from their topics
perturbation constraint · 0.6adversarial attack · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow
Jenny Schmalfuss, Philipp Scholze, Andrés Bruhn |
ECCV (22) | 2 |