Open Set Object Detection
Open Set Object Detection is an object detection framework that extends conventional detection paradigms by enabling the identification of both known-class objects and previously unseen object categories during inference, thereby explicitly accounting for unlabeled or unknown instances in the detection process.
27
Publications
2.2K
Citations
126
Authors
42
Institutions
Publications per year
2017–2026
23
Parents
27
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer
Haipeng Xiong, Hao Lü, Chengxin Liu et al. · 2019 · 187 citations
The Overlooked Elephant of Object Detection: Open Set
Akshay Raj Dhamija, Manuel Günther, Jonathan Ventura et al. · 2020 · 174 citations
Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
Tianhe Ren, Shilong Liu, Ailing Zeng et al. · arXiv (Cornell University) · 2024 · 92 citations · Full text
1–5 of 27
126
3
h-index
University of Colorado Colorado Springs, United States
3 publications · 853 citations
3
h-index
3
h-index
3
h-index
3
h-index
1–5 of 126
42
Eskişehir Osmangazi University
Eskişehir, Türkiye
3 h-index
5 publications
181 citations
Wuhan University
Wuhan, China
3 h-index
5 publications
298 citations

Huazhong University of Science and Technology
Wuhan, China
3 h-index
12 publications
915 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
2 h-index
5 publications
238 citations
Xiamen University
Xiamen, China
2 h-index
7 publications
350 citations

University of Colorado Colorado Springs
Colorado Springs, United States
2 h-index
5 publications
1.8K citations

United States Department of the Army
Arlington, United States
1 h-index
1 publications
42 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
1 h-index
5 publications
150 citations
1–8 of 42
Repository · 5 publications · 452 citations
5
h-index
TPAMI · 4 publications · 779 citations
4
h-index
2 publications · 47 citations
2
h-index
MSSP · 1 publications · 24 citations
1
h-index
Conference · 1 publications · 56 citations
1
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1–5