VLDB 2026 Research / reviewers in the wild / expert
Zhuoyao Liu
dblp:402/0803
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 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
2 papers |
Efficient and distributed learning · 35% Trustworthy machine learning · 20% Segmentation and scene understanding · 20% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning › learning from noisy data
noisy correspondence learning |
1.0 | 1 | 2026 | PCSR: Pseudo-label Consistency-Guided Sample Refinement for Noisy Correspondence Learning · AAAI 2026 |
Computer vision › Segmentation and scene understanding › pseudo-label learning
pseudo-label refinement |
1.0 | 1 | 2026 | PCSR: Pseudo-label Consistency-Guided Sample Refinement for Noisy Correspondence Learning · AAAI 2026 |
Machine learning › Efficient and distributed learning › model compression › knowledge distillation
feature distillation |
0.9 | 1 | 2025 | Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching · ICCV 2025 |
Computer vision › Vision and language › cross-modal matching
image-text matching |
0.9 | 1 | 2025 | Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching · ICCV 2025 |
Machine learning › Efficient and distributed learning › model compression
knowledge distillation |
0.9 | 1 | 2025 | Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching · ICCV 2025 |
Machine learning › Learning paradigms › semi-supervised learning
consistency regularization |
0.3 | 1 | 2026 | PCSR: Pseudo-label Consistency-Guided Sample Refinement for Noisy Correspondence Learning · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
sample refinement · 1.0pseudo label consistency · 1.0feature distillation · 0.9dense-to-sparse alignment · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | PCSR: Pseudo-label Consistency-Guided Sample Refinement for Noisy Correspondence Learning
Zhuoyao Liu, Wentao Feng, Shudong Huang |
AAAI | 1 |
| 2025 | Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching
Yang Liu 0264, Wentao Feng, Zhuoyao Liu, Shudong Huang, Jiancheng Lv 0001 |
ICCV | 3 |