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There has been a remarkable progress in learning a model which could recognise novel classes with only a few labeled examples in the last few years.
Unitary triangularization of a nonsymmetric matrix
A. Householder · 1958
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C. Bischof and X. Sun · 1996
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Density estimation by dual ascent of the log-likelihood
Esteban Tabak and Eric Vanden-Eijnden · 2010
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Hmdb: A large video database for human motion recognition
Hilde Kuehne, Hueihan Jhuang, Estíbaliz Garrote, T. Poggio, and Thomas Serre · 2011
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Action recognition by dense trajectories
Heng Wang, Alexander Kläser, C. Schmid, and C. Liu · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Ucf101: A dataset of 101 human actions classes from videos in the wild
K. Soomro, A. Zamir, and M. Shah · 2012
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A family of nonparametric density estimation algorithms
E. Tabak and C. Turner · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and M. Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, S. Mohamed, and Daan Wierstra · 2014
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Two-stream convolutional networks for action recognition in videos
K. Simonyan and Andrew Zisserman · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Siamese neural networks for one-shot image recognition
Gregory R. Koch · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and S. Mohamed · 2015
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Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir D. Bourdev, R. Fergus, L. Torresani, and Manohar Paluri · 2015
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Low-shot visual object recognition
Bharath Hariharan and Ross B. Girshick · 2016
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Improving variational auto-encoders using householder flow
Jakub M. Tomczak and M. Welling · 2016
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Matching networks for one shot learning
Oriol Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and Daan Wierstra · 2016
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Quo vadis, action recognition? a new model and the kinetics dataset
A generative approach to zero-shot and few-shot action recognition
Ashish Mishra, V. Verma, M. K. Reddy, Arulkumar Subramaniam, Piyush Rai, and Anurag Mittal · 2018
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A generative approach to zero-shot and few-shot action recognition
Ashish Mishra, Vinay Kumar Verma, M Shiva Krishna Reddy, S Arulkumar, Piyush Rai, and Anurag Mittal · 2018
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and P. Abbeel · 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
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Low-shot learning from imaginary data
Yu-Xiong Wang, Ross B. Girshick, M. Hebert, and Bharath Hariharan · 2018
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João Carreira and Andrew Zisserman · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, P. Abbeel, and Sergey Levine · 2017
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The “something something” video database for learning and evaluating visual common sense
R. Goyal, S. Kahou, Vincent Michalski, Joanna Materzynska, S. Westphal, Heuna Kim, Valentin Haenel, Ingo Fründ, P. Yianilos, Moritz Mueller-Freitag, F. Hoppe, Christian Thurau, I. Bax, and R. Memisevic · 2017
Cited alongside, same era.
Low-shot visual recognition by shrinking and hallucinating features
Bharath Hariharan and Ross B. Girshick · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
J. Snell, Kevin Swersky, and R. Zemel · 2017
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, and Timothy M. Hospedales · 2017
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Baohan Xu, Hao Ye, Yingbin Zheng, Heng Wang, Tianyu Luwang, and Yu-Gang Jiang · 2018
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Visual data synthesis via gan for zero-shot video classification
Chenrui Zhang and Y. Peng · 2018
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Metagan: An adversarial approach to few-shot learning
R. Zhang, Tong Che, Zoubin Ghahramani, Yoshua Bengio, and Y. Song · 2018
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Compound memory networks for few-shot video classification
Linchao Zhu and Y. Yang · 2018
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Protogan: Towards few shot learning for action recognition
Sai Kumar Dwivedi, Vikram Gupta, Rahul Mitra, Shuaib Ahmed, and Arjun Jain · 2019
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Generating classification weights with gnn denoising autoencoders for few-shot learning
Spyros Gidaris and Nikos Komodakis · 2019
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Edge-labeling graph neural network for few-shot learning
Jongmin Kim, Taesup Kim, Sungwoong Kim, and Chang D. Yoo · 2019
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A simple dynamic learning rate tuning algorithm for automated training of dnns
K. Mukherjee, Alind Khare, and A. Verma · 2019
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Few-shot video classification via temporal alignment
Kaidi Cao, Jingwei Ji, Zhangjie Cao, C. Chang, and Juan Carlos Niebles · 2020
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Few-shot action recognition with permutation-invariant attention
Hongguang Zhang, Liyong Zhang, Xiaojuan Qi, Hongdong Li, Philip H. S. Torr, and Piotr Koniusz · 2020
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