Fetching the paper…
Reading the bibliography…
Recent two-stream deep Convolutional Neural Networks (ConvNets) have made significant progress in recognizing human actions in videos.
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-l 1 optical flow
C. Zach, T. Pock, and H. Bischof · 2007
Earlier work this paper cites.
Evaluation of local spatio-temporal features for action recognition
H. Wang, M. M. Ullah, A. Klaser, I. Laptev, and C. Schmid · 2009
Earlier work this paper cites.
Torch7: A matlab-like environment for machine learning
R. Collobert, K. Kavukcuoglu, and C. Farabet · 2011
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.
Action recognition by dense trajectories
H. Wang, A. Kläser, C. Schmid, and C.-L. Liu · 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
Earlier work this paper cites.
Action recognition with improved trajectories
H. Wang and C. Schmid · 2013
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.
Two-stream convolutional networks for action recognition in videos
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
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.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 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.
Jointly modeling embedding and translation to bridge video and language
Y. Pan, T. Mei, T. Yao, H. Li, and Y. Rui · 2015
Cited alongside, same era.
Human action recognition using factorized spatio-temporal convolutional networks
L. Sun, K. Jia, D.-Y. Yeung, and B. E. Shi · 2015
Cited alongside, same era.
Exploiting image-trained cnn architectures for unconstrained video classification
S. Zha, F. Luisier, W. Andrews, N. Srivastava, and R. Salakhutdinov · 2015
Later among the works it cites.
Youtube-8m: A large-scale video classification benchmark
S. Abu-El-Haija, N. Kothari, J. Lee, P. Natsev, G. Toderici, B. Varadarajan, and S. Vijayanarasimhan · 2016
Later among the works it cites.
Spatiotemporal residual networks for video action recognition
C. Feichtenhofer, A. Pinz, and R. Wildes · 2016
Later among the works it cites.
Convolutional two-stream network fusion for video action recognition
C. Feichtenhofer, A. Pinz, and A. Zisserman · 2016
Later among the works it 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 · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 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.
Towards good practices for very deep two-stream convnets
L. Wang, Y. Xiong, Z. Wang, and Y. Qiao · 2015
Cited alongside, same era.
Beyond short snippets: Deep networks for video classification
J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici · 2015
Cited alongside, same era.
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi · 2016
Later among the works it 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
Later among the works it cites.
Actions transformations
X. Wang, A. Farhadi, and A. Gupta · 2016
Later among the works it cites.
Two-stream sr-cnns for action recognition in videos
Y. Wang, J. Song, L. Wang, L. Van Gool, and O. Hilliges · 2016
Later among the works it cites.
A key volume mining deep framework for action recognition
W. Zhu, J. Hu, G. Sun, X. Cao, and Y. Qiao · 2016
Later among the works it cites.