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Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information.
High accuracy optical flow estimation based on a theory for warping
T. Brox, A. Bruhn, N. Papenberg, and J. Weickert · 2004
Earlier work this paper cites.
A duality based approach for realtime TV-L1 optical flow
C. Zach, T. Pock, and H. Bischof · 2007
Earlier work this paper cites.
Learning realistic human actions from movies
I. Laptev, M. Marszałek, C. Schmid, and B. Rozenfeld · 2008
Earlier work this paper cites.
Improving the Fisher kernel for large-scale image classification
F. Perronnin, J. Sánchez, and T. Mensink · 2010
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.
Semantic segmentation with second-order pooling
J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu · 2012
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
UCF101: A dataset of 101 human actions calsses 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.
Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
Earlier work this paper cites.
Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
Earlier work this paper cites.
Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice
X. Peng, L. Wang, X. Wang, and Y. Qiao · 2014
Cited alongside, same era.
Two-stream convolutional networks for action recognition in videos
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Cited alongside, same era.
P-CNN: Pose-based CNN features for action recognition
G. Chéron, I. Laptev, and C. Schmid · 2015
Cited alongside, same era.
Deep filter banks for texture recognition and segmentation
M. Cimpoi, S. Maji, and A. Vedaldi · 2015
Action-conditional video prediction using deep networks in atari game
J. Oh, X. Guo, H. Lee, S. Singh, and R. Lewis · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Unsupervised learning of video representations using LSTMs
N. Srivastava, E. Mansimov, and R. Salakhutdinov · 2015
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Human action recognition using factorized spatio-temporal convolutional networks
L. Sun, K. Jia, D.-Y. Yeung, and B. Shi · 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
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Efficient object localization using convolutional networks
J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler · 2015
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Cited alongside, same era.
Long-term recurrent convolutional networks for visual recognition and description
J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell · 2015
Cited alongside, same era.
Finding action tubes
G. Gkioxari and J. Malik · 2015
Cited alongside, same era.
Thumos challenge: Action recognition with a large number of classes
A. Gorban, H. Indrees, Y. Jiang, A. R. Zamir, I. Laptev, M. Shah, and R. Sukthankar · 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.
Bilinear CNN models for fine-grained visual recognition
T.-Y. Lin, A. RoyChowdhury, and S. Maji · 2015
Cited alongside, same era.
Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
Cited alongside, same era.
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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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MatConvNet – convolutional neural networks for MATLAB
A. Vedaldi and K. Lenc · 2015
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Sequence to sequence video to text
S. Venugopalan, M. Rohrbach, R. Mooney, T. Darrell, and K. Saenko · 2015
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Action recognition with trajectory-pooled deep-convolutional descriptors
L. Wang, Y. Qiao, and X. Tang · 2015
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Towards good practices for very deep two-stream convnets
L. Wang, Y. Xiong, Z. Wang, and Y. Qiao · 2015
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Learning to track for spatio-temporal action localization
P. Weinzaepfel, Z. Harchaoui, and C. Schmid · 2015
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