Demo Data from VLDB 2026 / reviewers in the wild / expert
Benjamín Earle
dblp:331/6988
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0003-4391-6767ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
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 |
Vision and language · 50% Language models and text generation · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › instruction tuning
instruction generation |
0.6 | 1 | 2022 | Bridging the Visual Semantic Gap in VLN via Semantically Richer Instructions · ECCV (37) 2022 |
Computer vision › Vision and language
vision-and-language navigation |
0.6 | 1 | 2022 | Bridging the Visual Semantic Gap in VLN via Semantically Richer Instructions · ECCV (37) 2022 |
Methods — techniques the papers use, named apart from their topics
semantic instruction enrichment · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Bridging the Visual Semantic Gap in VLN via Semantically Richer Instructions
Joaquín Ossandón, Benjamín Earle, Alvaro Soto |
ECCV (37) | 2 |