EDBT 2026 Demo / reviewers in the wild / expert

Hao Wang 0052

dblp:w/HaoWang52 · DBLP profile ↗
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3ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0003-3123-6043ORCID · verified

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 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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
Segmentation and scene understanding · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Segmentation and scene understanding
foundation model segmentation		 0.912025
Segment Anything in Context with Vision Foundation Models · Int. J. Comput. Vis. 2025
Computer vision › Segmentation and scene understanding
image segmentation		 0.912025
Segment Anything in Context with Vision Foundation Models · Int. J. Comput. Vis. 2025
Computer vision › Segmentation and scene understanding
open-world segmentation		 0.912025
Unified Open-World Segmentation with Multi-Modal Prompts · ICCV 2025

Methods — techniques the papers use, named apart from their topics

vision foundation model · 0.9multimodal prompting · 0.9
YearPublicationVenuePosition
2025 Unified Open-World Segmentation with Multi-Modal Prompts
Yang Liu 0357, Yufei Yin, Chenchen Jing, Muzhi Zhu, Hao Chen 0041, Yuling Xi, Hao Wang 0052, Chunhua Shen
 ICCV8
2025 Segment Anything in Context with Vision Foundation Models
Yang Liu 0357, Muzhi Zhu, Hao Chen 0041, Hao Wang 0052, Raviteja Vemulapalli, Chunhua Shen
 Int. J. Comput. Vis.6
2015 HArCo: Hierarchical Fiducial Markers for Pose Estimation in Helicopter Landing Tasks
abstract
A reliable pose estimation is crucial in the landing tasks of helicopters. This paper mainly focuses on presenting a smooth and reliable pose estimation method during helicopter landing using Augmented Reality (AR) markers. Based on the proposed hierarchical fiducial marker system called Hierarchical Augmented Reality Code (HArCo), pose estimation can be performed within a much longer range. The design of the system, generation of the marker dictionary and the application in helicopter landing are thoroughly described in this paper. The performance of the pose estimation algorithm based on HArCo is tested in a landing task. As pose information is accessible throughout the landing procedure, a safe auto-landing based on HArCo can also be conducted.
Hao Wang 0052, Xiongfeng Wang, Geng Lu, Yisheng Zhong
 SMC1