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We propose a novel method for temporally pooling frames in a video for the task of human action recognition.
Behavior recognition via sparse spatio-temporal features
P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie · 2005
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A 3-dimensional sift descriptor and its application to action recognition
P. Scovanner, S. Ali, and M. Shah · 2007
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Learning realistic human actions from movies
I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld · 2008
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VLFeat: An open and portable library of computer vision algorithms
A. Vedaldi and B. Fulkerson · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Evaluation of local spatio-temporal features for action recognition
H. Wang, M. M. Ullah, A. Klaser, I. Laptev, and C. Schmid · 2009
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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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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Improving the fisher kernel for large-scale image classification
F. Perronnin, J. Sánchez, and T. Mensink · 2010
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Modeling the temporal extent of actions
S. Satkin and M. Hebert · 2010
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HMDB: a large video database for human motion recognition
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre · 2011
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Trajectory-based modeling of human actions with motion reference points
Y.-G. Jiang, Q. Dai, X. Xue, W. Liu, and C.-W. Ngo · 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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Learning latent temporal structure for complex event detection
K. Tang, L. Fei-Fei, and D. Koller · 2012
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A comparative study of encoding, pooling and normalization methods for action recognition
X. Wang, L. Wang, and Y. Qiao · 2012
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Temporal Localization of Actions with Actoms
A. Gaidon, Z. Harchaoui, and C. Schmid · 2013
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3d convolutional neural networks for human action recognition
S. Ji, W. Xu, M. Yang, and K. Yu · 2013
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Dynamic pooling for complex event recognition
W. Li, Q. Yu, A. Divakaran, and N. Vasconcelos · 2013
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Poselet key-framing: A model for human activity recognition
M. Raptis and L. Sigal · 2013
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Large-scale web video event classification by use of fisher vectors
C. Sun and R. Nevatia · 2013
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Dense trajectories and motion boundary descriptors for action recognition
H. Wang, A. Kläser, C. Schmid, and C.-L. Liu · 2013
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Mining motion atoms and phrases for complex action recognition
L. Wang, Y. Qiao, and X. Tang · 2013
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Action recognition with actons
J. Zhu, B. Wang, X. Yang, W. Zhang, and Z. Tu · 2013
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Unsupervised learning of video representations using lstms
N. Srivastava, E. Mansimov, 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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A robust and efficient video representation for action recognition
H. Wang, D. Oneata, J. Verbeek, and C. Schmid · 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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Activity representation with motion hierarchies
A. Gaidon, Z. Harchaoui, and C. Schmid · 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
Cited alongside, same era.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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Action recognition with stacked fisher vectors
X. Peng, C. Zou, Y. Qiao, and Q. Peng · 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. Carlos Niebles · 2015
Cited alongside, same era.
Describing videos by exploiting temporal structure
L. Yao, A. Torabi, K. Cho, N. Ballas, C. Pal, H. Larochelle, and A. Courville · 2015
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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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Dynamic image networks for action recognition
H. Bilen, B. Fernando, E. Gavves, A. Vedaldi, and S. Gould · 2016
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White paper: Cisco vni forecast and methodology, 2015-2020
CISCO · 2016
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Rank pooling for action recognition
B. Fernando, E. Gavves, J. Oramas, A. Ghodrati, and T. Tuytelaars · 2016
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Videolstm convolves, attends and flows for action recognition
Z. Li, E. Gavves, M. Jain, and C. G. Snoek · 2016
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Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice
X. Peng, L. Wang, X. Wang, and Y. Qiao · 2016
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Discriminatively trained latent ordinal model for video classification
K. Sikka and G. Sharma · 2016
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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 · 2016
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Actions transformations
X. Wang, A. Farhadi, and A. Gupta · 2016
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Improving human action recognition by non-action classification
Y. Wang and M. Hoai · 2016
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A key volume mining deep framework for action recognition
W. Zhu, J. Hu, G. Sun, X. Cao, and Y. Qiao · 2016
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