VLDB 2026 Research / reviewers in the wild / expert
Saikat Ghosh
dblp:308/0820
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0002-0526-1870ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 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 |
Image recognition and object detection · 67% Learning paradigms · 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 › detector training
active learning for object detection |
0.6 | 1 | 2022 | Talisman: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information · ECCV (38) 2022 |
Computer vision › Image recognition and object detection
object detection |
0.6 | 1 | 2022 | Talisman: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information · ECCV (38) 2022 |
Machine learning › Learning paradigms › class imbalance
rare class detection |
0.6 | 1 | 2022 | Talisman: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information · ECCV (38) 2022 |
Methods — techniques the papers use, named apart from their topics
submodular mutual information · 0.6active learning · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | A Data-Driven Approach for Fibres Recognition via SpectrophotometryabstractThe increasing volume of textile waste presents significant environmental and economic challenges, necessitating the development of efficient automated sorting techniques to support a more effective textile waste recycling. Automated sorting is a notoriously complex task, due to deployment constraints and to the variability of textiles. To advance the work on automated textile sorting, this study investigates the use of data-driven approaches on spectrophotometer-based reflectance measurements for recognising fibres. Spectrophotometry offers significant advantages in terms of operational simplicity and reliability, making it a promising choice for use in textile sorting facilities where environmental conditions are difficult to control. Considering an extensive dataset of specifically acquired pure textile samples, in this work we leverage on AutoML solutions to determine the best architecture to discriminate between cotton and polyethylene terephthalate (PET) fibres. Megan Robinson, Saikat Ghosh, Chenyu Du, Parikshit Goswami, Mauro Vallati |
ICAART (3) | 2 |
| 2022 | Talisman: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information
Suraj Kothawade, Saikat Ghosh, Rishabh Iyer 0001 |
ECCV (38) | 2 |