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Few-shot learning (FSL) for action recognition is a challenging task of recognizing novel action categories which are represented by few instances in the training data.
Modeling temporal structure of decomposable motion segments for activity classification
J. C. Niebles, C.-W. Chen, and L. Fei-Fei · 2010
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Ucf101: A dataset of 101 human actions classes from videos in the wild
K. Soomro, A. R. Zamir, and M. Shah · 2012
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A large video database for human motion recognition
H. Kuehne, H. Jhuang, R. Stiefelhagen, and T. Serre · 2013
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Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
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Adam: a method for stochastic optimization
D. Kingma and J. Ba · 2015
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Learning spatiotemporal features with 3d convolutional networks
D. Tran, L. D. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 2015
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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2016
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al · 2016
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Quo vadis, action recognition? a new model and the kinetics dataset
J. Carreira and A. Zisserman · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
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Low-shot visual object recognition
B. Hariharan and R. B. Girshick · 2017
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2017
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Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
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Few-shot object recognition from machine-labeled web images
Z. Xu, L. Zhu, and Y. Yang · 2017
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A simple neural attentive meta-learner
N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel · 2018
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Meta-learning for semi-supervised few-shot classification
M. Ren, S. Ravi, E. Triantafillou, J. Snell, K. Swersky, J. B. Tenenbaum, H. Larochelle, and R. S. Zemel · 2018
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Learning to compare: Relation network for few-shot learning
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales · 2018
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Temporal segment networks: Towards good practices for deep action recognition
L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. Van Gool · 2018
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Dense dilated network for few shot action recognition
B. Xu, H. Ye, Y. Zheng, H. Wang, T. Luwang, and Y.-G. Jiang · 2018
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One-shot action localization by learning sequence matching network
H. Yang, X. He, and F. Porikli · 2018
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A. Antoniou, A. Storkey, and H. Edwards · 2018
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Multi-modal cycle-consistent generalized zero-shot learning
R. Felix, B. V. Kumar, I. Reid, and G. Carneiro · 2018
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Deepncm: Deep nearest class mean classifiers
S. Guerriero, B. Caputo, and T. Mensink · 2018
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A generative approach to zero-shot and few-shot action recognition
A. Mishra, V. K. Verma, M. S. K. Reddy, A. Subramaniam, P. Rai, and A. Mittal · 2018
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Visual data synthesis via gan for zero-shot video classification
C. Zhang and Y. Peng · 2018
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Metagan: An adversarial approach to few-shot learning
R. Zhang, T. Che, Z. Ghahramani, Y. Bengio, and Y. Song · 2018
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Compound memory networks for few-shot video classification
L. Zhu and Y. Yang · 2018
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A closer look at few-shot classification
W.-Y. Chen, Y.-C. Liu, Z. Kira, Y.-C. F. Wang, and J.-B. Huang · 2019
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