Demo Data from VLDB 2026 / reviewers in the wild / expert
Luis Martinell
dblp:19/9818
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 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 |
Image recognition and object detection · 77% Efficient and distributed learning · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Image recognition and object detection › image retrieval
large-scale image retrieval |
0.2 | 1 | 2014 | High volume offline image recognition · ISMAR 2014 |
Machine learning › Efficient and distributed learning
on-device inference |
0.1 | 1 | 2014 | High volume offline image recognition · ISMAR 2014 |
Methods — techniques the papers use, named apart from their topics
image recognition engine · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | High volume offline image recognitionabstractWe show a prototype of an offline image recognition engine, running on a tablet with Intel®Atom™ processor, searching within less than 250ms through thousands (5000+) of images. Moreover, the prototype still offers the advanced capabilities of recognising real world 3D objects, until now reserved only for cloud solutions. Until now image search within large collections of images could be performed only in the cloud, requiring mobile devices to have Internet connectivity. However, for many use cases the connectivity requirement is impractical, e.g. many museums have no network coverage, or do not want their visitors incurring expensive roaming charges. Existing commercial solutions are very limited in terms of searched collections sizes, often imposing a maximum limit of <100 reference images. Moreover, adding images typically affects the recognition speed and increases RAM requirements. Tomasz Adamek, Luis Martinell, Miquel Ferrarons, Alex Torrents, David Marimon |
ISMAR | 2 |
| 2011 | Productive Cluster Programming with OmpSs
Javier Bueno, Luis Martinell, Alejandro Duran, Montse Farreras, Xavier Martorell, Rosa M. Badia, Eduard Ayguadé, Jesús Labarta |
Euro-Par (1) | 2 |