VLDB 2026 Research / reviewers in the wild / expert
Shengao Wang Boston University
dblp:435/3527
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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% Efficient and distributed learning · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning › data-efficient learning
data-efficient pretraining |
0.9 | 1 | 2025 | BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning · ICCV 2025 |
Computer vision › Vision and language
vision-language pretraining |
0.9 | 1 | 2025 | BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning · ICCV 2025 |
Methods — techniques the papers use, named apart from their topics
infant-inspired learning · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning
Shengao Wang Boston University, Arjun Chandra, Aoming Liu, Venkatesh Saligrama, Boqing Gong |
ICCV | 1 |