Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yoichi Natori
dblp:276/3187
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—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.
| Artificial intelligence
1 paper |
Face, body and person analysis · 61% Transfer learning and domain adaptation · 30% Robot manipulation · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Face, body and person analysis › person re-identification
cross-domain person re-identification |
0.4 | 1 | 2020 | Progressive Domain Adaptation for Robot Vision Person Re-identification · ACM Multimedia 2020 |
Computer vision › Face, body and person analysis
person re-identification |
0.4 | 1 | 2020 | Progressive Domain Adaptation for Robot Vision Person Re-identification · ACM Multimedia 2020 |
Machine learning › Transfer learning and domain adaptation › domain adaptation
progressive domain adaptation |
0.4 | 1 | 2020 | Progressive Domain Adaptation for Robot Vision Person Re-identification · ACM Multimedia 2020 |
Robotics › Robot manipulation
robot vision |
0.1 | 1 | 2020 | Progressive Domain Adaptation for Robot Vision Person Re-identification · ACM Multimedia 2020 |
Methods — techniques the papers use, named apart from their topics
person tracking · 0.4face recognition · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Progressive Domain Adaptation for Robot Vision Person Re-identificationabstractPerson re-identification has received much attention in the last few years, as it enhances the retrieval effectiveness in the video surveillance networks and video archive management. In this paper, we demonstrate a guiding robot with person followers system, which recognizes the follower using a person re-identification technology. It first adopts existing face recognition and person tracking methods to generate person tracklets with different IDs. Then, a classic person re-identification model, pre-trained on the surveillance dataset, is adapted to the new robot vision condition incrementally. The demonstration showcases the quality of robot follower focusing. Zijun Sha, Zelong Zeng, Zheng Wang 0007, Yoichi Natori, Yasuhiro Taniguchi, Shin'ichi Satoh 0001 |
ACM Multimedia | 4 |