Open-vocabulary Instance Segmentation
Open-Vocabulary Instance Segmentation is an instance segmentation framework that extends conventional instance segmentation by enabling the detection and delineation of object instances across a broad, potentially unseen vocabulary of categories at test time, leveraging external knowledge sources such as language models to generalize beyond the fixed set of classes observed during training.
13
Publications
1.2K
Citations
76
Authors
21
Institutions
Publications per year
2017–2026
13
Parents
13
YOLO-World: Real-Time Open-Vocabulary Object Detection
Tianheng Cheng, Lin Song, Yixiao Ge et al. · 2024 · 561 citations
Generalized Decoding for Pixel, Image, and Language
Xueyan Zou, Zi-Yi Dou, Jianwei Yang et al. · 2023 · 164 citations
A Simple Framework for Open-Vocabulary Segmentation and Detection
Hao Zhang, Feng Li, Xueyan Zou et al. · 2023 · 107 citations
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation
Jie Oin, Jie Wu, Pengxiang Yan et al. · 2023 · 74 citations
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Microsoft Research (United Kingdom)
Cambridge, United Kingdom
2 h-index
6 publications
870 citations
University of Wisconsin–Madison
Madison, United States
2 h-index
3 publications
435 citations

Peking University
Beijing, China
2 h-index
3 publications
69 citations
Tsinghua University
Beijing, China
2 h-index
2 publications
138 citations

Chinese Academy of Sciences
Beijing, China
1 h-index
2 publications
148 citations

Huazhong University of Science and Technology
Wuhan, China
1 h-index
3 publications
1.7K citations
Nanyang Technological University
Singapore, Singapore
1 h-index
4 publications
76 citations
Tencent (China)
Shenzhen, China
1 h-index
3 publications
1.7K citations
1–8 of 21
Repository · 4 publications · 86 citations
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Conference · 1 publications · 78 citations
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1 publications · 18 citations
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