VLDB 2026 Research / reviewers in the wild / expert
Sean M. Culhane
dblp:40/19
· DBLP profile ↗
3ranked-venue papers
2as first author
0since 2021 · last 1995
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 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
2 papers |
Deep learning architectures and training · 42% Image recognition and object detection · 35% 3D vision · 23% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Deep learning architectures and training › attention mechanism
visual attention |
0.0 | 1 | 1995 | Modeling Visual Attention via Selective Tuning · Artif. Intell. 1995 |
Computer vision › Image recognition and object detection
visual attention modeling |
0.0 | 1 | 1995 | Modeling Visual Attention via Selective Tuning · Artif. Intell. 1995 |
Computer vision › 3D vision
low-level vision |
0.0 | 1 | 1992 | An Attentional Prototype for Early Vision · ECCV 1992 |
Machine learning › Deep learning architectures and training
attention mechanism |
0.0 | 1 | 1992 | An Attentional Prototype for Early Vision · ECCV 1992 |
Methods — techniques the papers use, named apart from their topics
selective tuning · 0.0attentional prototype · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1995 | Modeling Visual Attention via Selective Tuning
John K. Tsotsos, Sean M. Culhane, Winky Yan Kei Wai, Yuzhong Lai, Neal Davis, Fernando Nuflo |
Artif. Intell. | 2 |
| 1992 | An Attentional Prototype for Early Vision
Sean M. Culhane, John K. Tsotsos |
ECCV | 1 |
| 1992 | A prototype for data-driven visual attentionabstractMounting evidence suggests that attentional mechanisms may be required to successfully perform many vision tasks. The paper presents an attentional prototype for early visual processing. The model is composed of a processing hierarchy and an attention beam that traverses the hierarchy, passing through the regions of greatest interest and inhibiting the regions that are not relevant. The type of input to the prototype is not limited to visual stimuli. Aspects of attention such as localizing spatial regions of interest and ordering their importance are addressed; other aspects of attention such as the role of task guidance are encompassed by the model but are not detailed here. Simulations using high-resolution digitized images were conducted, with oriented edge information as the input to the model. The results confirm that this prototype is both robust and fast, and promises to be essential to any real-time vision system.> Sean M. Culhane, John K. Tsotsos |
ICPR (1) | 1 |