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There is a growing interest in learning a model which could recognize novel classes with only a few labeled examples.
Gapped blast and psi-blast: a new generation of protein database search programs
S. F. Altschul, T. L. Madden, A. A. Schäffer, J. Zhang, Z. Zhang, W. Miller, and D. J. Lipman · 1997
Earlier work this paper cites.
Dynamic time warping
M. Müller · 2007
Earlier work this paper cites.
A 3-dimensional sift descriptor and its application to action recognition
P. Scovanner, S. Ali, and M. Shah · 2007
Earlier work this paper cites.
A spatio-temporal descriptor based on 3d-gradients
A. Klaser, M. Marszałek, and C. Schmid · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Large-scale machine learning with stochastic gradient descent
L. Bottou · 2010
Earlier work this paper cites.
One shot similarity metric learning for action recognition
O. Kliper-Gross, T. Hassner, and L. Wolf · 2011
Earlier work this paper cites.
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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Action recognition with improved trajectories
H. Wang and C. Schmid · 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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Siamese neural networks for one-shot image recognition
G. Koch, R. Zemel, and R. Salakhutdinov · 2015
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Learning spatiotemporal features with 3d convolutional networks
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 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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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2016
Earlier work this paper cites.
Hollywood in homes: Crowdsourcing data collection for activity understanding
G. A. Sigurdsson, G. Varol, X. Wang, A. Farhadi, I. Laptev, and A. Gupta · 2016
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al · 2016
Earlier work this paper cites.
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 · 2016
Cited alongside, same era.
Quo vadis, action recognition? a new model and the kinetics dataset
J. Carreira and A. Zisserman · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
Cited alongside, same era.
Few-shot learning with graph neural networks
V. Garcia and J. Bruna · 2017
Cited alongside, same era.
The” something something” video database for learning and evaluating visual common sense
R. Goyal, S. E. Kahou, V. Michalski, J. Materzynska, S. Westphal, H. Kim, V. Haenel, I. Fruend, P. Yianilos, M. Mueller-Freitag, et al · 2017
Cited alongside, same era.
Low-shot visual recognition by shrinking and hallucinating features
A generative approach to zero-shot and few-shot action recognition
A. Mishra, V. K. Verma, M. S. K. Reddy, S. Arulkumar, P. Rai, and A. Mittal · 2018
Later among the works it cites.
Reptile: a scalable metalearning algorithm
A. Nichol and J. Schulman · 2018
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Low-shot learning with imprinted weights
H. Qi, M. Brown, and D. G. Lowe · 2018
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Neuralnetwork-viterbi: A framework for weakly supervised video learning
A. Richard, H. Kuehne, A. Iqbal, and J. Gall · 2018
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Meta-learning with latent embedding optimization
A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell · 2018
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B. Hariharan and R. Girshick · 2017
Cited alongside, same era.
Learning to remember rare events
Ł. Kaiser, O. Nachum, A. Roy, and S. Bengio · 2017
Cited alongside, same era.
The kinetics human action video dataset
W. Kay, J. Carreira, K. Simonyan, B. Zhang, C. Hillier, S. Vijayanarasimhan, F. Viola, T. Green, T. Back, P. Natsev, et al · 2017
Cited alongside, same era.
Meta networks
T. Munkhdalai and H. Yu · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Cited alongside, same era.
Learning spatio-temporal representation with pseudo-3d residual networks
Z. Qiu, T. Yao, and T. Mei · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
Cited alongside, same era.
A closer look at spatiotemporal convolutions for action recognition
D. Tran, H. Wang, L. Torresani, J. Ray, Y. LeCun, and M. Paluri · 2018
Later among the works it cites.
Non-local neural networks
X. Wang, R. Girshick, A. Gupta, and K. He · 2018
Later among the works it cites.
Videos as space-time region graphs
X. Wang and A. Gupta · 2018
Later among the works it cites.
Low-shot learning from imaginary data
Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan · 2018
Later among the works it cites.
Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification
S. Xie, C. Sun, J. Huang, Z. Tu, and K. Murphy · 2018
Later among the works it cites.
Temporal relational reasoning in videos
B. Zhou, A. Andonian, A. Oliva, and A. Torralba · 2018
Later among the works it cites.
Compound memory networks for few-shot video classification
L. Zhu and Y. Yang · 2018
Later among the works it cites.
Eco: Efficient convolutional network for online video understanding
M. Zolfaghari, K. Singh, and T. Brox · 2018
Later among the works it cites.
C.-Y. Chang, D.-A. Huang, Y. Sui, L. Fei-Fei, and J. C. Niebles · 2019
Closest in time.
A closer look at few-shot classification
W.-Y. Chen, Y.-C. Liu, Z. Kira, Y.-C. Wang, and J.-B. Huang · 2019
Closest in time.