Few-shot Object Detection
Few-Shot Object Detection is a subfield of object detection in computer vision that focuses on recognizing and localizing novel object categories from a limited number of annotated training examples, typically ranging from one to few instances per class.
159
Publications
11.2K
Citations
621
Authors
158
Institutions
Publications per year
2017–2026
156
159
Few-Shot Object Detection via Feature Reweighting
Bingyi Kang, Zhuang Liu, Xin Wang et al. · 2019 · 798 citations
Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector
Qi Fan, Wei Zhuo, Chi–Keung Tang et al. · 2020 · 622 citations
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
Bo Sun, Banghuai Li, Shengcai Cai et al. · 2021 · 520 citations
Learning Deep Object Detectors from 3D Models
Xingchao Peng, Baochen Sun, Karim Ali et al. · 2015 · 350 citations
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158
University of Chinese Academy of Sciences
Beijing, China
14 h-index
59 publications
4.2K citations

Chinese Academy of Sciences
Beijing, China
13 h-index
50 publications
3K citations
Tsinghua University
Beijing, China
7 h-index
11 publications
369 citations
National University of Singapore
Singapore, Singapore
7 h-index
30 publications
4.2K citations

Beihang University
Beijing, China
6 h-index
24 publications
676 citations
Tongji University
Shanghai, China
6 h-index
12 publications
616 citations
Aerospace Information Research Institute
Beijing, China
5 h-index
27 publications
1.4K citations

Xidian University
Xi'an, China
5 h-index
9 publications
784 citations
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Repository · 26 publications · 560 citations
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11 publications · 641 citations
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Conference · 8 publications · 647 citations
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Conference · 6 publications · 592 citations
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5 publications · 169 citations
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