Objectness Scoring
Objectness Scoring is an evaluative mechanism in object detection that quantifies the likelihood of a region proposal containing a meaningful object instance, distinguishing itself from general classification or localization methods by focusing on the presence or absence of salient visual entities independent of specific category labels.
68
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78.2K
Citations
250
Authors
103
Institutions
Publications per year
2017–2026
39
Parents
68
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren, Kaiming He, Ross Girshick et al. · arXiv (Cornell University) · 2015 · 18.2K citations · Full text
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
Ming‐Ming Cheng, Ziming Zhang, Wen-Yan Lin et al. · 2014 · 1.1K citations · Full text
Bogdan Alexe, Thomas Deselaers, Vittorio Ferrari · 2010 · 804 citations
Relation Distillation Networks for Video Object Detection
Jiajun Deng, Yingwei Pan, Ting Yao et al. · 2019 · 236 citations
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103

Chinese Academy of Sciences
Beijing, China
5 h-index
13 publications
449 citations

Australian National University
Canberra, Australia
4 h-index
10 publications
969 citations
University of Oxford
Oxford, United Kingdom
4 h-index
5 publications
2.5K citations
ETH Zurich
Zurich, Switzerland
3 h-index
11 publications
3.3K citations

Zhejiang University
Hangzhou, China
3 h-index
5 publications
105 citations

Nankai University
Tianjin, China
3 h-index
10 publications
1.1K citations
Tsinghua University
Beijing, China
3 h-index
10 publications
916 citations
Institute of Automation
Beijing, China
3 h-index
7 publications
316 citations
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Repository · 10 publications · 18.5K citations
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4 publications · 349 citations
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2 publications · 117 citations
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Conference · 2 publications · 273 citations
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TPAMI · 2 publications · 54.2K citations
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