Bounding Box Regression
Bounding Box Regression is a machine learning technique in object detection that refines the localization of detected objects by predicting the parameters of a bounding box around each object, differentiating itself from classification tasks through its focus on spatial coordinate adjustment rather than categorical labeling.
292
Publications
40.9K
Citations
1.2K
Authors
330
Institutions
Publications per year
2017–2026
283
Parents
292
Focal and efficient IOU loss for accurate bounding box regression
Yifan Zhang, Weiqiang Ren, Zhang Zhang et al. · Neurocomputing · 2022 · 1.9K citations
Joint 3D Proposal Generation and Object Detection from View Aggregation
Jason S. Ku, Melissa Mozifian, Jungwook Lee et al. · 2018 · 1.7K citations
Synthetic Data for Text Localisation in Natural Images
Ankush Gupta, Andrea Vedaldi, Andrew Zisserman · 2016 · 1.5K citations
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Chinese Academy of Sciences
Beijing, China
15 h-index
60 publications
12.9K citations
University of Chinese Academy of Sciences
Beijing, China
11 h-index
29 publications
9K citations

Shanghai Jiao Tong University
Shanghai, China
10 h-index
27 publications
2.5K citations

Beijing Institute of Technology
Beijing, China
10 h-index
36 publications
2.5K citations
Wuhan University
Wuhan, China
8 h-index
19 publications
4.8K citations

Zhejiang University
Hangzhou, China
7 h-index
19 publications
2.7K citations

The University of Adelaide
Adelaide, Australia
7 h-index
10 publications
2K citations

Xidian University
Xi'an, China
6 h-index
26 publications
2.5K citations
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Repository · 42 publications · 5.2K citations
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19 publications · 1.6K citations
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18 publications · 1.2K citations
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11 publications · 513 citations
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7 publications · 319 citations
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