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Recently, convolutional neural networks with 3D kernels (3D CNNs) have been very popular in computer vision community as a result of their superior ability of extracting spatio-temporal features within video frames compared to 2D CNNs.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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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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cudnn: Efficient primitives for deep learning
S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, and E. Shelhamer · 2014
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Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
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Flattened convolutional neural networks for feedforward acceleration
J. Jin, A. Dundar, and E. Culurciello · 2014
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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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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
V. Lebedev, Y. Ganin, M. Rakhuba, I. Oseledets, and V. Lempitsky · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights
D. Soudry, I. Hubara, and R. Meir · 2014
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S. Han, H. Mao, and W. J. Dally · 2015
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Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 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.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer · 2016
Cited alongside, same era.
Pruning convolutional neural networks for resource efficient inference
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2016
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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Learning spatio-temporal representation with pseudo-3d residual networks
Z. Qiu, T. Yao, and T. Mei · 2017
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Convnet architecture search for spatiotemporal feature learning
D. Tran, J. Ray, Z. Shou, S.-F. Chang, and M. Paluri · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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A short note about kinetics-600
J. Carreira, E. Noland, A. Banki-Horvath, C. Hillier, and A. Zisserman · 2018
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Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
Cited alongside, same era.
Quantized convolutional neural networks for mobile devices
J. Wu, C. Leng, Y. Wang, Q. Hu, and J. Cheng · 2016
Cited alongside, same era.
S. Zagoruyko and N. Komodakis · 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.
Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
C. Feichtenhofer, H. Fan, J. Malik, and K. He · 2018
Later among the works it cites.
Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?
K. Hara, H. Kataoka, and Y. Satoh · 2018
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Analysis on temporal dimension of inputs for 3d convolutional neural networks
O. Köpüklü and G. Rigoll · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
N. Ma, X. Zhang, H.-T. Zheng, and J. Sun · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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A closer look at spatiotemporal convolutions for action recognition
D. Tran, H. Wang, L. Torresani, J. Ray, Y. LeCun, and M. Paluri · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2018
Later among the works it cites.