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The generalization power of the pre-trained model is the key for few-shot deep learning.
“The pascal visual object classes (voc) challenge,”
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman, · 2010
Earlier work this paper cites.
“Dropout: a simple way to prevent neural networks from overfitting,”
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov, · 2014
Earlier work this paper cites.
“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
Earlier work this paper cites.
“Faster r-cnn: Towards real-time object detection with region proposal networks,”
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, · 2016
Earlier work this paper cites.
“R-fcn: Object detection via region-based fully convolutional networks,”
Jifeng Dai, Yi Li, Kaiming He, and Jian Sun, · 2016
Earlier work this paper cites.
“Matching networks for one shot learning,”
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al., · 2016
Earlier work this paper cites.
“Feature pyramid networks for object detection,”
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie, · 2017
Earlier work this paper cites.
“Model-agnostic meta-learning for fast adaptation of deep networks,”
Chelsea Finn, Pieter Abbeel, and Sergey Levine, · 2017
Earlier work this paper cites.
“Prototypical networks for few-shot learning,”
Jake Snell, Kevin Swersky, and Richard Zemel, · 2017
Cited alongside, same era.
“Dropblock: A regularization method for convolutional networks,”
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le, · 2018
Cited alongside, same era.
“Few-shot learning with metric-agnostic conditional embeddings,”
Nathan Hilliard, Lawrence Phillips, Scott Howland, Artëm Yankov, Courtney D Corley, and Nathan O Hodas, · 2018
Cited alongside, same era.
“Learning to compare: Relation network for few-shot learning,”
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales, · 2018
Cited alongside, same era.
“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.
“Frustratingly simple few-shot object detection,”
Xin Wang, Thomas E Huang, Trevor Darrell, Joseph E Gonzalez, and Fisher Yu, · 2020
Later among the works it cites.
“Multi-scale positive sample refinement for few-shot object detection,”
Jiaxi Wu, Songtao Liu, Di Huang, and Yunhong Wang, · 2020
Later among the works it cites.
“Fsce: Few-shot object detection via contrastive proposal encoding,”
Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, and Chi Zhang, · 2021
Later among the works it cites.
“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
Later among the works it cites.
“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
Later among the works it cites.
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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
Cited alongside, same era.
“A closer look at few-shot classification,”
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang, · 2019
Cited alongside, same era.
“Hallucination improves few-shot object detection,”
Weilin Zhang and Yu-Xiong Wang, · 2021
Later among the works it cites.
“Free lunch for few-shot learning: Distribution calibration,”
Shuo Yang, Lu Liu, and Min Xu, · 2021
Later among the works it cites.