Demo Data from EDBT 2026 / reviewers in the wild / expert
Shang-Yin Tsai
dblp:77/10793
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
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 |
Multimedia analysis and retrieval · 56% Audio and music processing · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Audio and music processing › music information retrieval
beat tracking |
0.1 | 1 | 2012 | Rhythm of Motion Extraction and Rhythm-Based Cross-Media Alignment for Dance Videos · IEEE Trans. Multim. 2012 |
Multimedia analysis and retrieval › audio-visual learning
music-dance alignment |
0.1 | 1 | 2012 | Rhythm of Motion Extraction and Rhythm-Based Cross-Media Alignment for Dance Videos · IEEE Trans. Multim. 2012 |
Multimedia analysis and retrieval › video content analysis › human behavior analysis
motion trajectory analysis |
0.0 | 1 | 2012 | Rhythm of Motion Extraction and Rhythm-Based Cross-Media Alignment for Dance Videos · IEEE Trans. Multim. 2012 |
Methods — techniques the papers use, named apart from their topics
turn and stop detection · 0.1motion trajectory analysis · 0.1beat detection · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Rhythm of Motion Extraction and Rhythm-Based Cross-Media Alignment for Dance VideosabstractWe present how to extract rhythm information in dance videos and music, and accordingly correlate them based on rhythmic representation. From dancer's movement, we construct motion trajectories, detect turnings, and stops of trajectories, and then estimate rhythm of motion (ROM). For music, beats are detected to describe rhythm of music. Two modalities are therefore represented as sequences of rhythm information to facilitate finding cross-media correspondence. Two applications, i.e., background music replacement and music video generation, are developed to demonstrate the practicality of cross-media correspondence. We evaluate performance of ROM extraction, and conduct subjective/objective evaluation to show that rich browsing experience can be provided by the proposed applications. Wei-Ta Chu, Shang-Yin Tsai |
IEEE Trans. Multim. | 2 |