Demo Data from VLDB 2026 / reviewers in the wild / expert
Shousheng Luo
dblp:243/3731
· DBLP profile ↗
3ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0002-3898-5365ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorArtificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 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 |
Segmentation and scene understanding · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Segmentation and scene understanding › image segmentation
level set segmentation |
0.4 | 1 | 2019 | Convex Shape Prior for Multi-Object Segmentation Using a Single Level Set Function · ICCV 2019 |
Computer vision › Segmentation and scene understanding › object segmentation
multi-object segmentation |
0.4 | 1 | 2019 | Convex Shape Prior for Multi-Object Segmentation Using a Single Level Set Function · ICCV 2019 |
Methods — techniques the papers use, named apart from their topics
level set method · 0.4gaussian mixture model · 0.4ADMM · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Coupled block diagonal regularization for multi-view subspace clustering
Huazhu Chen, Weiwei Wang 0005, Shousheng Luo |
Data Min. Knowl. Discov. | 3 |
| 2020 | Image denoising based on the adaptive weighted TVp regularization
Zhi-Feng Pang, Hui-Li Zhang, Shousheng Luo, Tieyong Zeng |
Signal Process. | 3 |
| 2019 | Convex Shape Prior for Multi-Object Segmentation Using a Single Level Set FunctionabstractMany objects in real world have convex shapes. It is a difficult task to have representations for convex shapes with good and fast numerical solutions. This paper proposes a method to incorporate convex shape prior for multi-object segmentation using level set method. The relationship between the convexity of the segmented objects and the signed distance function corresponding to their union is analyzed theoretically. This result is combined with Gaussian mixture method for the multiple objects segmentation with convexity shape prior. Alternating direction method of multiplier (ADMM) is adopted to solve the proposed model. Special boundary conditions are also imposed to obtain efficient algorithms for 4th order partial differential equations in one step of ADMM algorithm. In addition, our method only needs one level set function regardless of the number of objects. So the increase in the number of objects does not result in the increase of model and algorithm complexity. Various numerical experiments are illustrated to show the performance and advantages of the proposed method. Shousheng Luo, Xue-Cheng Tai, Limei Huo, Yang Wang 0020, Roland Glowinski |
ICCV | 1 |