EDBT 2026 Demo / reviewers in the wild / expert
C. C. Laan
dblp:190/7217
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 |
Segmentation and scene understanding · 50% Image recognition and object detection · 25% Generative modeling · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Segmentation and scene understanding › image segmentation › document image segmentation
character segmentation |
0.2 | 1 | 2016 | Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data · NIPS 2016 |
Computer vision › Segmentation and scene understanding
instance segmentation |
0.2 | 1 | 2016 | Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data · NIPS 2016 |
Machine learning › Generative modeling › 3d generative model
mesh generative model |
0.2 | 1 | 2016 | Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data · NIPS 2016 |
Computer vision › Image recognition and object detection
scene text recognition |
0.2 | 1 | 2016 | Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data · NIPS 2016 |
Methods — techniques the papers use, named apart from their topics
generative modeling · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training DataabstractWe demonstrate that a generative model for object shapes can achieve state of the art results on challenging scene text recognition tasks, and with orders of magnitude fewer training images than required for competing discriminative methods. In addition to transcribing text from challenging images, our method performs fine-grained instance segmentation of characters. We show that our model is more robust to both affine transformations and non-affine deformations compared to previous approaches. Xinghua Lou, Ken Kansky, Wolfgang Lehrach, C. C. Laan, Bhaskara Marthi, D. Scott Phoenix, Dileep George |
NIPS | 4 |