Demo Data from EDBT 2026 / reviewers in the wild / expert
Jae Hong Yang
dblp:425/3716
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0007-6448-1690ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, 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 · 75% Video understanding and tracking · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › neural rendering
3d gaussian splatting |
0.9 | 1 | 2025 | See Through the Occlusions: Few-Shot Gaussian Splatting with Layered Amodal Supervision · ACM Multimedia 2025 |
Computer vision › 3D vision › 3d scene understanding
amodal perception |
0.9 | 1 | 2025 | See Through the Occlusions: Few-Shot Gaussian Splatting with Layered Amodal Supervision · ACM Multimedia 2025 |
Computer vision › 3D vision
novel view synthesis |
0.9 | 1 | 2025 | See Through the Occlusions: Few-Shot Gaussian Splatting with Layered Amodal Supervision · ACM Multimedia 2025 |
Computer vision › Video understanding and tracking › object tracking
occlusion handling |
0.9 | 1 | 2025 | See Through the Occlusions: Few-Shot Gaussian Splatting with Layered Amodal Supervision · ACM Multimedia 2025 |
Methods — techniques the papers use, named apart from their topics
layered amodal supervision · 0.9few-shot learning · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | See Through the Occlusions: Few-Shot Gaussian Splatting with Layered Amodal Supervision
Gwon-Jung Kim, Du Yeol Lee, Jae Hong Yang, Chae-Eun Rhee |
ACM Multimedia | 3 |