VLDB 2026 Research / reviewers in the wild / expert
Lvkeshen Shen
dblp:309/8201
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
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.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
User interface design and tools · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › visual encoding
glyph-based visualization |
0.6 | 1 | 2022 | GlyphCreator: Towards Example-based Automatic Generation of Circular Glyphs · IEEE Trans. Vis. Comput. Graph. 2022 |
Methods — techniques the papers use, named apart from their topics
glyph parsing · 1.1deep learning · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | GlyphCreator: Towards Example-based Automatic Generation of Circular GlyphsabstractCircular glyphs are used across disparate fields to represent multidimensional data. However, although these glyphs are extremely effective, creating them is often laborious, even for those with professional design skills. This paper presents GlyphCreator, an interactive tool for the example-based generation of circular glyphs. Given an example circular glyph and multidimensional input data, GlyphCreator promptly generates a list of design candidates, any of which can be edited to satisfy the requirements of a particular representation. To develop GlyphCreator, we first derive a design space of circular glyphs by summarizing relationships between different visual elements. With this design space, we build a circular glyph dataset and develop a deep learning model for glyph parsing. The model can deconstruct a circular glyph bitmap into a series of visual elements. Next, we introduce an interface that helps users bind the input data attributes to visual elements and customize visual styles. We evaluate the parsing model through a quantitative experiment, demonstrate the use of GlyphCreator through two use scenarios, and validate its effectiveness through user interviews. Lu Ying, Tan Tang, Yuzhe Luo, Lvkeshen Shen, Xiao Xie, Lingyun Yu 0001, Yingcai Wu |
IEEE Trans. Vis. Comput. Graph. | 4 |