Demo Data from SIGIR 2026 / reviewers in the wild / expert
Kazutaka Hirata
dblp:35/3356
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2
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 · 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 |
|---|---|---|---|---|
User interface design and tools › visual programming
visual query formulation |
0.0 | 1 | 2004 | A visuospatial memory cue system for meeting video retrieval · ACM Multimedia 2004 |
Methods — techniques the papers use, named apart from their topics
memory-cue paradigm · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | A visuospatial memory cue system for meeting video retrievalabstractWe present a system based on a new, memory-cue paradigm for retrieving meeting video scenes. The system graically represents important memory retrieval cues such as room layout, participant's faces and sitting positions, etc.. Queries are formulated dynamically: as the user graically manipulates the cues, the query results are shown. Our system (1) helps users easily express the cues they recall about a particular meeting; (2) helps users remember new cues for meeting video retrieval. We discuss the experiments that motivate this new approach, implementation, and future work. Takeshi Nagamine, Alejandro Jaimes, Kengo Omura, Kazutaka Hirata |
ACM Multimedia | 4 |
| 2004 | Interactive visualization of multi-stream meeting videos based on automatic visual content analysisabstractWe present a new approach to segment and visualize informally captured multi-stream meeting videos. We process the visual content in each stream individually by analyzing the differences between frames in each sequence to find change areas. These results are combined with face detection to determine visual activity in each of the streams. We then combine the activity scores from multiple streams and automatically generate a 3D representation of the video. Our representation allows the user to obtain an at-a-glance view of the video at different granularities of activity, view multiple streams simultaneously, and select particular points in time for viewing. We present experiments that suggest that low-level visual analysis can be effective for finding highlights that can be used for browsing multi-stream meeting videos. Alejandro Jaimes, Naofumi Yoshida, Kazumasa Murai, Kazutaka Hirata, Jun Miyazaki |
MMSP | 4 |