Anchor-free Detection
Anchor-Free Detection is a category of object detection methodology that localizes and classifies objects directly from feature map positions without relying on predefined anchor boxes to guide the prediction process.
234
Publications
27K
Citations
934
Authors
229
Institutions
Publications per year
2017–2026
233
Parents
234
FCOS: Fully Convolutional One-Stage Object Detection
Zhi Tian, Chunhua Shen, Hao Chen et al. · 2019 · 6.1K citations
YOLOX: Exceeding YOLO Series in 2021
Zheng Ge, Songtao Liu, Feng Wang et al. · arXiv (Cornell University) · 2021 · 3K citations · Full text
RepPoints: Point Set Representation for Object Detection
Ze Yang, Shaohui Liu, Han Hu et al. · 2019 · 1.2K citations
FoveaBox: Beyound Anchor-Based Object Detection
IEEE Transactions on Image Processing · 2020 · 907 citations · Full text
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229

Chinese Academy of Sciences
Beijing, China
20 h-index
78 publications
4.2K citations
University of Chinese Academy of Sciences
Beijing, China
17 h-index
52 publications
4.6K citations

Xidian University
Xi'an, China
13 h-index
57 publications
2.8K citations

Beihang University
Beijing, China
12 h-index
33 publications
1.4K citations

Beijing Institute of Technology
Beijing, China
9 h-index
33 publications
2K citations

University of Electronic Science and Technology of China
Chengdu, China
7 h-index
34 publications
2K citations
Tsinghua University
Beijing, China
7 h-index
14 publications
4.2K citations

Shanghai Jiao Tong University
Shanghai, China
6 h-index
17 publications
350 citations
1–8 of 229
Repository · 37 publications · 5.7K citations
24
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23 publications · 2.1K citations
21
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18 publications · 630 citations
16
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GRSL · 10 publications · 227 citations
10
h-index
11 publications · 374 citations
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