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We describe the DeepMind Kinetics human action video dataset.
Caltech-256 object category dataset
G. Griffin, A. Holub, and P. Perona · 2007
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Learning realistic human actions from movies
I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld · 2008
Earlier work this paper cites.
Unsupervised learning of human action categories using spatial-temporal words
J. C. Niebles, H. Wang, and L. Fei-Fei · 2008
Earlier work this paper cites.
Convolutional learning of spatio-temporal features
G. W. Taylor, R. Fergus, Y. LeCun, and C. Bregler · 2010
Earlier work this paper cites.
HMDB: a large video database for human motion recognition
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre · 2011
Earlier work this paper cites.
Unbiased look at dataset bias
A. Torralba and A. A. Efros · 2011
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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
Earlier work this paper cites.
3d convolutional neural networks for human action recognition
S. Ji, W. Xu, M. Yang, and K. Yu · 2013
Earlier work this paper cites.
Action recognition with improved trajectories
H. Wang and C. Schmid · 2013
Earlier work this paper cites.
2d human pose estimation: New benchmark and state of the art analysis
M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele · 2014
Cited alongside, same era.
Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
Cited alongside, same era.
Two-stream convolutional networks for action recognition in videos
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Activitynet: A large-scale video benchmark for human activity understanding
F. Caba Heilbron, V. Escorcia, B. Ghanem, and J. C. Niebles · 2015
Cited alongside, same era.
Long-term recurrent convolutional networks for visual recognition and description
J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell · 2015
Cited alongside, same era.
The pascal visual object classes challenge: A retrospective
Beyond short snippets: Deep networks for video classification
J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al · 2016
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T. Cooijmans, N. Ballas, C. Laurent, and A. Courville · 2016
Later among the works it cites.
Convolutional two-stream network fusion for video action recognition
C. Feichtenhofer, A. Pinz, and A. Zisserman · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, S. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. Berg, and F. Li · 2015
Cited alongside, same era.
Learning spatiotemporal features with 3d convolutional networks
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 2015
Cited alongside, same era.
Actions ~ transformations
X. Wang, A. Farhadi, and A. Gupta · 2016
Later among the works it cites.
Semantics derived automatically from language corpora contain human-like biases
A. Caliskan, J. J. Bryson, and A. Narayanan · 2017
Closest in time.
Quo vadis, action recognition? new models and the kinetics dataset
J. Carreira and A. Zisserman · 2017
Closest in time.