EDBT 2026 Demo / reviewers in the wild / expert
Jinwon Ko
dblp:376/2962
· DBLP profile ↗
3ranked-venue papers
2as first author
3since 2021 · last 2026
0009-0008-9832-5036ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 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 |
Segmentation and scene understanding · 62% 3D vision · 19% Image recognition and object detection · 19% |
Topics — the 2 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 line detection |
0.8 | 1 | 2024 | Semantic Line Combination Detector · CVPR 2024 |
Computer vision › 3D vision › camera calibration
vanishing point estimation |
0.2 | 1 | 2024 | Semantic Line Combination Detector · CVPR 2024 |
Methods — techniques the papers use, named apart from their topics
line combination scoring · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | LUTFormer: Lookup table transformer for image enhancement
Jinwon Ko, Keunsoo Ko, Chang-Su Kim 0001 |
Neurocomputing | 1 |
| 2024 | Semantic Line Combination DetectorabstractA novel algorithm, called semantic line combination detector (SLCD), to find an optimal combination of semantic lines is proposed in this paper. It processes all lines in each line combination at once to assess the overall harmony of the lines. First, we generate various line combinations from reliable lines. Second, we estimate the score of each line combination and determine the best one. Experimental results demonstrate that the proposed SLCD outperforms existing semantic line detectors on various datasets. More-over, it is shown that SLCD can be applied effectively to three vision tasks of vanishing point detection, symmetry axis detection, and composition-based image retrieval. Our codes are available at https://github.com/Jinwon-Ko/SLCD. Jinwon Ko, Dongkwon Jin, Chang-Su Kim 0001 |
CVPR | 1 |
| 2024 | Image cropping based on order learning
Nyeong-Ho Shin, Seon-Ho Lee, Jinwon Ko, Chang-Su Kim 0001 |
J. Vis. Commun. Image Represent. | 3 |