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Emerging interests have been brought to recognize previously unseen objects given very few training examples, known as few-shot object detection (FSOD).
Five Points to Check when Comparing Visual Perception in Humans and Machines
Christina M. Funke, Judy Borowski, Karolina Stosio, Wieland Brendel, Thomas S. A. Wallis, and Matthias Bethge · 2004
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Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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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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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Deep learning face representation by joint identification-verification
Yi Sun, Yuheng Chen, Xiaogang Wang, and Xiaoou Tang · 2014
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Deep learning face representation by joint identification-verification
Yi Sun, Yuheng Chen, Xiaogang Wang, and Xiaoou Tang · 2014
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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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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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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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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNING
Sachin Ravi and Hugo Larochelle · 2017
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Soft-nms — improving object detection with one line of code
N. Bodla, B. Singh, R. Chellappa, and L. S. Davis · 2017
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Feature pyramid networks for object detection
T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Cited alongside, same era.
Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
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Low-shot learning from imaginary data
Y. Wang, R. Girshick, M. Hebert, and B. Hariharan · 2018
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Meta-learning for semi-supervised few-shot classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, and Richard S. Zemel · 2018
Cited alongside, same era.
Cosface: Large margin cosine loss for deep face recognition
H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu · 2018
Cited alongside, same era.
Large margin deep networks for classification
Gamaleldin F. Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio · 2018
A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Wang, and Jia-Bin Huang · 2019
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Few-shot object detection via feature reweighting
B. Kang, Z. Liu, X. Wang, F. Yu, J. Feng, and T. Darrell · 2019
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Repmet: Representative-based metric learning for classification and few-shot object detection
L. Karlinsky, J. Shtok, S. Harary, E. Schwartz, A. Aides, R. Feris, R. Giryes, and A. M. Bronstein · 2019
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Focal loss for dense object detection
T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2020
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Efficientdet: Scalable and efficient object detection
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Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, X Yu Stella, and Dahua Lin · 2018
Cited alongside, same era.
Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
Cited alongside, same era.
Few-shot learning with graph neural networks
Victor Garcia Satorras and Joan Bruna Estrach · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Oriol Vinyals Aaron van den Oord, Yazhe Li · 2018
Cited alongside, same era.
Lstd: A low-shot transfer detector for object detection
Hao Chen, Yali Wang, Guoyou Wang, and Yu Qiao · 2018
Cited alongside, same era.
Deformable convnets v2: More deformable, better results
Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai · 2019
Cited alongside, same era.
Mingxing Tan, Ruoming Pang, and Quoc V. Le · 2020
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Cspnet: A new backbone that can enhance learning capability of cnn
Chien-Yao Wang, Hong-yuan Liao, Yuen-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh, and I-Hau Yeh · 2020
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Few-shot object detection and viewpoint estimation for objects in the wild
Yang Xiao and Renaud Marlet · 2020
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Frustratingly simple few-shot object detection
Xin Wang, Thomas E. Huang, Trevor Darrell, Joseph E Gonzalez, and Fisher Yu · 2020
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Multi-scale positive sample refinement for few-shot object detection
Jiaxi Wu, Songtao Liu, Di Huang, and Yunhong Wang · 2020
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Few-shot object detection with attention-rpn and multi-relation detector
Qi Fan, Wei Zhuo, Chi-Keung Tang, and Yu-Wing Tai · 2020
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Delving into inter-image invariance for unsupervised visual representations
Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, and Chen Change Loy · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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A simple framework for contrastive learning of visual representations
Chen Ting, Kornblith Simon, Norouzi Mohammad, and Hinton Geoffrey · 2020
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Debiased contrastive learning
Ching-Yao Chuang, Joshua Robinson, Lin Yen-Chen, Antonio Torralba, and Stefanie Jegelka · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Big self-supervised models are strong semi-supervised learners
Kevin Swersky Mohammad Norouzi Geoffrey Hinton Ting Chen, Simon Kornblith · 2020
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