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Yoichi Natori

dblp:276/3187 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Computer vision › Face, body and person analysis › person re-identification
cross-domain person re-identification
0.412020
Progressive Domain Adaptation for Robot Vision Person Re-identification · ACM Multimedia 2020
Computer vision › Face, body and person analysis
person re-identification
0.412020
Progressive Domain Adaptation for Robot Vision Person Re-identification · ACM Multimedia 2020
Machine learning › Transfer learning and domain adaptation › domain adaptation
progressive domain adaptation
0.412020
Progressive Domain Adaptation for Robot Vision Person Re-identification · ACM Multimedia 2020
Robotics › Robot manipulation
robot vision
0.112020
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
YearPublicationVenuePosition
2020 Progressive Domain Adaptation for Robot Vision Person Re-identification
abstract
Person 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 Multimedia4