Demo Data from VLDB 2026 / reviewers in the wild / expert
Chengli Xie
dblp:123/2900
· DBLP profile ↗
1ranked-venue papers
1as 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 · 1 first-author
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 |
Image recognition and object detection · 67% Trustworthy machine learning · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Image recognition and object detection › object detection
instance detection |
0.1 | 1 | 2012 | Real-time multiple object instances detection · ACM Multimedia 2012 |
Machine learning › Trustworthy machine learning
pairwise classification |
0.1 | 1 | 2012 | Real-time multiple object instances detection · ACM Multimedia 2012 |
Computer vision › Image recognition and object detection
template matching |
0.1 | 1 | 2012 | Real-time multiple object instances detection · ACM Multimedia 2012 |
Methods — techniques the papers use, named apart from their topics
template matching · 0.1pairwise classification · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Real-time multiple object instances detectionabstractIn this paper, we present a novel, real-time multiple object instance detection system via template matching and pairwise classification. Instance detection aims to find and locate exactly the same object instances as specified. Our system is composed of two heterogeneous stages. The first stage adopts instance-specific detection to generate candidates. And the second stage makes use of a pairwise-based classifier across instance categories to test and verify these candidates with respect to templates. Experiments show the superiority of our approach. Chengli Xie, Jinqiao Wang, Yifan Zhang 0001, Hanqing Lu |
ACM Multimedia | 1 |