Demo Data from SIGIR 2026 / reviewers in the wild / expert
Dmitry Panteleev
dblp:218/3833
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 since 2021Databases, data management, data science and information retrieval · 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 |
Image recognition and object detection · 44% Segmentation and scene understanding · 44% Information extraction and text analysis · 13% |
Topics — the 1 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
scene text detection |
0.6 | 1 | 2022 | Towards End-to-End Unified Scene Text Detection and Layout Analysis · CVPR 2022 |
Methods — techniques the papers use, named apart from their topics
end-to-end unified model · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | ICDAR 2023 Competition on Hierarchical Text Detection and Recognition
Shangbang Long, Siyang Qin, Dmitry Panteleev, Alessandro Bissacco, Yasuhisa Fujii, Michalis Raptis |
ICDAR (2) | 3 |
| 2022 | Towards End-to-End Unified Scene Text Detection and Layout AnalysisabstractScene text detection and document layout analysis have long been treated as two separate tasks in different image domains. In this paper, we bring them together and introduce the task of unified scene text detection and layout analysis. The first hierarchical scene text dataset is introduced to enable this novel research task. We also propose a novel method that is able to simultaneously detect scene text and form text clusters in a unified way. Comprehensive experiments show that our unified model achieves better performance than multiple well-designed baseline methods. Additionally, this model achieves state-of-the-art results on multiple scene text detection datasets without the need of complex post-processing. Dataset and code: https://github.com/google-research-datasets/hiertext. Shangbang Long, Siyang Qin, Dmitry Panteleev, Alessandro Bissacco, Yasuhisa Fujii, Michalis Raptis |
CVPR | 3 |