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Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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The pascal visual object classes challenge: A retrospective
Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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Yolo9000: better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Few-shot object detection via feature reweighting
Bingyi Kang, Zhuang Liu, Xin Wang, Fisher Yu, Jiashi Feng, and Trevor Darrell · 2019
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Generative modeling for small-data object detection
Lanlan Liu, Michael Muelly, Jia Deng, Tomas Pfister, and Li-Jia Li · 2019
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Meta-learning to detect rare objects
Yu-Xiong Wang, Deva Ramanan, and Martial Hebert · 2019
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Meta r-cnn: Towards general solver for instance-level low-shot learning
Xiaopeng Yan, Ziliang Chen, Anni Xu, Xiaoxi Wang, Xiaodan Liang, and Liang Lin · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Cited alongside, same era.
Yolov4: Optimal speed and accuracy of object detection
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao · 2020
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
Cited alongside, same era.
Few-shot object detection with attention-rpn and multi-relation detector
Qi Fan, Wei Zhuo, Chi-Keung Tang, and Yu-Wing Tai · 2020
Cited alongside, same era.
Transformation invariant few-shot object detection
Aoxue Li and Zhenguo Li · 2021
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Beyond max-margin: Class margin equilibrium for few-shot object detection
Bohao Li, Boyu Yang, Chang Liu, Feng Liu, Rongrong Ji, and Qixiang Ye · 2021
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Few-shot object detection via classification refinement and distractor retreatment
Yiting Li, Haiyue Zhu, Yu Cheng, Wenxin Wang, Chek Sing Teo, Cheng Xiang, Prahlad Vadakkepat, and Tong Heng Lee · 2021
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Trivialaugment: Tuning-free yet state-of-the-art data augmentation
Samuel G Müller and Frank Hutter · 2021
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Defrcn: Decoupled faster r-cnn for few-shot object detection
Limeng Qiao, Yuxuan Zhao, Zhiyuan Li, Xi Qiu, Jianan Wu, and Chi Zhang · 2021
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Learning transferable visual models from natural language supervision
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
Cited alongside, same era.
Frustratingly simple few-shot object detection
Xin Wang, Thomas E Huang, Trevor Darrell, Joseph E Gonzalez, and Fisher Yu · 2020
Cited alongside, same era.
Multi-scale positive sample refinement for few-shot object detection
Jiaxi Wu, Songtao Liu, Di Huang, and Yunhong Wang · 2020
Cited alongside, same era.
Few-shot object detection via association and discrimination
Yuhang Cao, Jiaqi Wang, Ying Jin, Tong Wu, Kai Chen, Ziwei Liu, and Dahua Lin · 2021
Cited alongside, same era.
Dynamic head: Unifying object detection heads with attentions
Xiyang Dai, Yinpeng Chen, Bin Xiao, Dongdong Chen, Mengchen Liu, Lu Yuan, and Lei Zhang · 2021
Cited alongside, same era.
Simple copy-paste is a strong data augmentation method for instance segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph · 2021
Cited alongside, same era.
Query adaptive few-shot object detection with heterogeneous graph convolutional networks
Guangxing Han, Yicheng He, Shiyuan Huang, Jiawei Ma, and Shih-Fu Chang · 2021
Cited alongside, same era.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Fsce: Few-shot object detection via contrastive proposal encoding
Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, and Chi Zhang · 2021
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Universal-prototype enhancing for few-shot object detection
Aming Wu, Yahong Han, Linchao Zhu, and Yi Yang · 2021
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Hallucination improves few-shot object detection
Weilin Zhang and Yu-Xiong Wang · 2021
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Few-shot object detection with fully cross-transformer
Guangxing Han, Jiawei Ma, Shiyuan Huang, Long Chen, and Shih-Fu Chang · 2022
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Label, verify, correct: A simple few shot object detection method
Prannay Kaul, Weidi Xie, and Andrew Zisserman · 2022
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The majority can help the minority: Context-rich minority oversampling for long-tailed classification
Seulki Park, Youngkyu Hong, Byeongho Heo, Sangdoo Yun, and Jin Young Choi · 2022
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Kernelized few-shot object detection with efficient integral aggregation
Shan Zhang, Lei Wang, Naila Murray, and Piotr Koniusz · 2022
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